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Data and case studies

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World Obesity have collated some of the recent data and case studies available looking pertaining to obesity and the current outbreak of COVID-19. 

Researchers at Johns Hopkins University in the US examined 265 patients to determine if younger patients hospitalised with COVID-19 were more likely to be living with overweight and obesity. They found a correlation, which they hypothesise may be due to physiologic changes from obesity. Other comorbidities these patients may have had were not reported. Read the full study here .

Chinese researchers identified 66 patients with COVID-19 and fatty liver disease and compared the outcomes for those with and without obesity. They found obesity was a significant risk factor for severe illness in this population after accounting for other factors (age, gender, smoking, diabetes, high blood pressure, and dyslipidaemia). Read the full study here . 

The global rise in the prevalence of obesity and type 2 diabetes can be partially explained by a rise in diets high in fats, sugars and refined carbohydrates. Diets high in saturated fatty acids cause inflammation and immune disfunction, which may explain why minority groups (who experience disproportionate rates of diseases linked to nutrition, such as obesity and diabetes) are also hospitalised with COVID-19 at higher rates. Read the full study here .

MicroRNAs (abbreviated miRNAs) are produced in human cells to regulate gene expression. Some research has suggested that these may also defend against viruses. These researchers identified 848 miRNAs that are may be effective against SARS and 873 that could target COVID-19 using genome sequences of each of these viruses. Previous studies have suggested that the elderly and those with underlying conditions (including obesity) may produce less of these miRNAs, possibly explaining why these groups are at increased risk of severe illness from COVID-19. However, trials in human and animal subjects are needed to verify these theoretical results. Read the full study here .  

Given the importance of determining the risk factors for morbidity and mortality related to COVID-19, this retrospective study analysed the frequency and outcomes of COVID-19 patients in critical care who are living with overweight or obesity. “Of the 3,615 individuals who tested positive for COVID-19, 775 (21%) had a body mass index (BMI) 30-34, and 595 (16% of the total cohort) had a BMI >35.” While patients were separated into elderly (over 60) and younger (under 60) groups, it was not reported if the study controlled for other variables that may affect the course of COVID-19. Read the full study here .

This piece describes two patients with obesity that experienced damage to their airways while being intubated due to severe illness from COVID-19. The authors recommend videolaryngoscopy for intubation to protect both patients and healthcare workers. Read the full study here .

These researchers chose to specifically examine how many COVID-19 patients living with obesity or overweight were placed on ventilators. Based in Lille, France, the study included 124 patients, 68.8% of whom ultimately required ventilation. They established a dose-response relationship- increasing body max index (BMI) increased the risk of needing ventilation. This study found that BMI seemed to be associated with ventilator treatments independently of age, diabetes or high blood pressure. However, further research must be conducted before this relationship is proven. Read the full study here .

Researchers obtained medical records of 16,749 people hospitalised for COVID-19 to determine what were some of the factors that made patients more likely to experience severe cases of the illness. Slightly over half of patients had at least one underlying condition (including obesity) and these patients were more likely to die from COVID-19. The study found that obesity is linked to mortality, independently of age, gender and other associated conditions. Read the full study here .

Using a very large sample size of 17,425,455, this cohort study aimed to identify risk factors associated with mortality due to COVID-19 across the general population. Among the comorbidities, most of them were associated with increased risk, including obesity. Furthermore, deprivation was also identified as a major risk factor. Specifically, for patients with overweight and obesity, as their body mass index increased, so did their risk of dying from COVID-19. Read the full study here .

This study included 48 critically ill patients with COVID-19 treated with invasive ventilation in Spain. Of this population, 48% were living with obesity, 44% with hypertension, and 38% with chronic lung disease. Symptoms and patient outcomes were also described. Read the full study here .

This study examined the correlation between severe disease and body mass index (BMI) among 357 patients in France. People diagnosed with severe COVID-19 were 1.35 times more likely to also be living with obesity and people in critical care with COVID-19 were 1.89 times more likely to be living with obesity than the general public. This study adjusted for age and gender of patients but no other cofounding factors. Read the full study here .

Previous research has demonstrated that children tend to gain weight during when school is not in session, so experts have been concerned about the impact of lockdowns due to coronavirus on childhood obesity rates. This study observed lifestyle behaviours in 41 children living with obesity at baseline and then three weeks into quarantine. Scientists found that children reported eating more meals, as well as more potato chips, red meat, and sugar-sweetened beverages. They slept more, exercised less and spent much more time looking at screens. As a result, researchers recommend that lifestyle interventions be delivered through telemedicine while the lockdown lasts. Read the full study here .

A recent study from France examined 1317 COVID-19 patients living with diabetes. Of these, more than 10% passed away and almost 33% needed to be placed on a ventilator within a week of admission to the hospital. Obesity was found to be an independent risk factor for poor outcomes when other cofounding factors were accounted for. Read the full study here .

This study found that, of 5700 patients admitted to 12 selected New York hospitals with COVID-19, 56.6% had hypertension (high blood pressure), 41.7% were living with obesity and 33.8% had diabetes. It also reported data on patient outcomes. Read the full study here .  

Wuhan city, the capital of Hubei province in China, was for a long time the epicentre of the COVID-19 outbreak. This study presents information of patients admitted to two Wuhan hospitals with laboratory-confirmed COVID-19. 191 patients were included in order to determine what risk factors lead to fatalities, describe Covid-19 symptoms over time, determine how long patients are infectious after they recover and record what treatments were tried. It should be noted that almost half of patients had underlying health conditions such as hypertension or heart disease, although obesity was not measured. Read the full study here . 

This study examined 24 adults to determine which populations in the Seattle area were hospitalised with severe illness from COVID-19, what underlying conditions they had, the results of medical imaging tests and whether they recovered. Patients had an average body mass index of 33.2 (give or take 7.2 units) and over half (58%) of patients were diagnosed with diabetes. Scientists concluded that “patients with coexisting conditions and older age are at risk for severe disease and poor outcomes after ICU [intensive care unit] admission.” Read the full study here .

Looking at 383 patients in Shenzen, China, this study was the first to directly examine the correlation between obesity and severe illness from coronavirus. For this study, a person with a body mass index (BMI) between 24.0 - 27.9 was considered overweight and a person with a BMI greater than 28 was considered to be living with obesity. While people living with obesity generally experienced the same length of illness, they were significantly more likely to develop severe pneumonia, even when accounting for other risk factors. Read the full study here .

Based on a sample of 4,103 New York City residents, this paper evaluates what characteristics make people more likely to be admitted to the hospital and critical care.  Overall, it was observed that 39.8% of people living with obesity were hospitalised, compared to 14.5% without. Scientists found “particularly strong associations of older age, obesity, heart failure and chronic kidney disease with hospitalization risk, with much less influence of race, smoking status, chronic pulmonary disease and other forms of heart disease.” Read the full study here .

In order to ensure the proper monitoring of COVID-19-related hospitalisations across the United States, the COVID-19-Associated Hospitalization Surveillance Network (COVID-NET) was developed. This report “presents age-stratified COVID-19-associated hospitalisation rates for patients admitted during March 1-28, 2020, and clinical data on patients admitted during March 1-30, 2020.” Among the 1,482 patients diagnosed and hospitalised with COVID-19, 90% had at least one comorbidity and 42% were living with obesity, with African Americans and the elderly disproportionately affected. Read the full study here .

This report examined demographic information of patients hospitalised with COVID-19 in China. Of these, older patients, diabetics and those living with obesity were significantly more likely to be considered “severely ill.” The study also looked at symptoms during admission at admission and treatment options. Read the full study here .

In this study, researchers used data from 103 consecutive patients hospitalized in the USA. There were two major findings- a correlation between critical care admissions due to COVID-19 and a body mass index greater than 35 in general, and a correlation between needing invasive mechanical ventilation and having both heart disease and obesity. These findings were adjusted for age, sex, and race. Read the full study here .

This article examined how SARS- CoV-2 impacts pregnancy using 46 patients in the USA. Almost all patients who developed severe disease were living with overweight and obesity. After diagnosis, 16% of patients were admitted to the hospital and 2% were placed in intensive care. Researchers believe this, along with the need to induce labour prematurely in some patients to improve breathing, may suggest that pregnant women should be classified as a vulnerable group. Read the full study here .

School and recreational space closures due to COVID-19 have reduced physical activity among children. Researchers used modeling software to simulate the following scenarios: 

• No school closures (control) 
• Schools closed for two months 
• Schools closed for two months and 10% reduction in physical activity over the summer break  
• Schools closed for four months (April through May and September through October) and 10% reduction in physical activity over the summer break 
• Schools closed for six months (April through May and September through December) and 10% reduction in physical activity over the summer break 

Overall, the pandemic is projected to increase mean standardised body mass index (BMI) between 0.056 (two-month closure) and 0.198 (six-month closure) units. It may also increase the percentage of children living with obesity in the USA by up to 2.373 percentage points. Read the full study here .

This study was conducted to examine the characteristics and course of disease in 50 New York children (under 21 years of age) hospitalised with COVID-19. Of the study population, 11 patients had obesity and 8 had overweight.  Obesity was found to be a significant risk factor for both severe disease and mechanical ventilation while immunosuppression was not.  Read the full study here .

Researchers at the University of Chicago Medical Center found that patients hospitalized with COVID-19 were more likely to die if they were also living with obesity, even when accounting for age, sex, and underlying conditions. 238 patients were included within the study. These researchers did not find a significant connection with admission to critical care units or mechanical ventilation in patients with obesity. Limitations included the makeup of the study population, as the sample size was small and the vast majority were African American, so the results may not be representative of all people. Read the full study here.  

This meta-analysis and systematic review found nine separate articles regarding the link between COVID-19, obesity and more severe diseases. Between all studies, 1817 patients were examined. Researchers found an odds ratio of 1.89 for poor outcomes in patients with obesity, especially among younger patients, which indicates that obesity increases the risk of severe diseases. Read the full study here . 

A meta-analysis concluded that people living with obesity were more likely to have worse outcomes if they also contracted COVID-19. Researchers identified nine articles (six of which were retrospective case-control studies, four of which were retrospective cohort studies, and one of which used both methods) and extracted data from each. Limitations included heterogeneity in study design (particularly regarding the definition of obesity), lack of comorbidity reporting, and low quantity of studies used. Read the full study here .

As almost 75% of American adults over the age of 20 are living with overweight or obesity, this disease should be considered a public health priority, especially given the increased likelihood of poor outcomes in COVID-19 patients with obesity. The paper outlines several mechanisms explaining why obesity may lead to more severe disease, including having more of the receptor the virus uses to enter cells, reduced lung function, chronic inflammation, endothelial disfunction, changes in blood clotting, and physiological changes related to common comorbidities of obesity. Finally, several compelling studies linking obesity to increased risk of complications are included. Read the full study here .

Evidence shows that the impact of COVID-19 tends to be more serious in specific vulnerable groups, including people living with obesity. Furthermore, the pandemic also seems to have a number of indirect repercussions including on eating behaviour patterns among people with obesity. The objective of this study was “to examine the impact of the COVID-19 pandemic on patronage to unhealthy eating establishments in populations with obesity.”   

These researchers combined GPS data with known obesity rates to determine how many people with obesity entered unhealthy restaurants during the COVID-19 pandemic (December 2019- April 2020). Prior to lockdowns, more people in areas with high obesity rates entered fast food restaurants; in March, fewer people did across all areas; however, the numbers of patrons steadily increased during April, at a faster rate in areas with higher obesity rates. While informative, a number of limitations were observed, including the fact that not all consumers exactly match the demographics of the area they live in and that more variables may contribute to restaurant traffic than accounted for here. Read the full study here . 

Various studies over the past few months have linked obesity to a more serious course of illness from COVID-19. It is therefore essential that we improve our understanding of the possible reasons for the link and what it means for those living with obesity. This systematic review looks at the influence of obesity on COVID-19 outcomes and proposes biological mechanisms as to why a more severe courseof illness can occur. It also discusses the implications of COVID-19 for those living with obesity. Read the full study here .

Both COVID-19 and childhood obesity are pandemics raging across America today. Obesity is an independent risk factor for the severity of COVID-19, suggesting that children with obesity could see a more severe course of illness due to COVID-19. The stay-at-home mandates and physical distancing preventative measures have resulted in a lack of access to nutritious foods, physical activity, routines and social interactions, all of which could negatively impact children -especially those living with obesity. Read the full study here .

Obesity has been suggested as a risk factor for poor outcome in those with COVID-19. Studies show that patients with obesity are more likely to require mechanical ventilation. In fact, multiorgan failure in patients with COVID-19 and obesity could be dueto the chronic metabolic inflammation and predisposition to the “enhanced release of cytokines-pathophysiology accompanying severe obesity”. However, the association between body mass index (BMI) and COVID-19 outcomes has yet to be fully explored. This study intends to address that gap. Read the full study here .

Emerging evidence suggests that the severity of COVID-19 in a patient is associated with overweight and obesity. Patients with obesity are at risk for a number of other non-communicable diseases, including cardiovascular dysfunction and hypertension and diabetes. In individuals living with overweight and obesity, macronutrient excess in adipose tissue stimulates adipocytes “to release tumour necrosis factor α(TNF-α), interleukin-6 (IL-6) and other pro-inflammatory mediators and to reduce production of the anti-inflammatory adiponectin, thus predisposing to a proinflammatory state and oxidative stress”. Obesity also impairs immune responses; it has a negative impact on pathogen defences within the body. Therefore, the acceleration of viral inflammatory responses in COVID-19 and more unfavourable prognoses are associated with individuals living with obesity. Read the full study here .

Obesity has been identified as a comorbidity for severe outcomes in patients with COVID-19. In this study, comorbidities associated with increased risk of COVID-19 were determined in a population-based analysis of Mexicans with at least one comorbidity. Data was obtained from the COVID-19 database of the publicly available Mexican Ministry of Health “Dirección General de Epidemiología”. Variables of the patients’ heath were all noted, such as age, gender, smoking status, history of COVID-19 contact, comorbidities, etc. Patients with missing information were excluded in the analysis. To determine the independent effect of each comorbidityon COVID-19 and separate the effect of two or more, “analysis was limited to patients reporting only one comorbidity." Read the full study here .

Obesity has arisen as a major complication for the COVID-19 pandemic, which has been caused by the novel SARS-CoV-2 virus. The former is a major health concern due to its side-effects on human health and association with morbidity and mortality. Evidence points out that obesity can worsen patient prognosis due to COVID-19 infection. There may be a “pathophysiological link that could explain the fact that obese patients are prone to present with SARS-CoV-2 complications”. The authors present mechanistic obesity-related issues that aggravate the SARS-CoV-2 infection in patients living with obesity and the possible molecular links between obesity and SARS-CoV-2 infection. Read the full study here .

The highly infectious serious acute respiratory syndrome COVID-19 has caused high morbidity and mortality all over the world. It has been suggested that SARS-CoV-2, the pathogen of COVID-19, uses angiotensin-converting enzyme 2 (ACE2) as a cell receptor. This receptor is found in the lungs but also many other organs, including the adipose tissue, heart, and oral epithelium. Previous studies have identified obesity as a critical factor in the prognoses of COVID-19 patients, and that, in patients with COVID-19, non-survivors had a higher body mass index (BMI) than survivors. This study intended to “investigate the association between obesity and poor outcomes of COVID-19 patients." Read the full study here .

Approximately 45% of individuals worldwide have overweight or obesity. Obesity is characterized by its pro-inflammatory condition. The excess visceral and omental adiposity seen in individuals with obesity are linked with an increase in pro-inflammatory cytokines that affect systemic cellular processes. Importantly, they “change the nature and frequency of immune cells infiltration”. When a high percentage of a population have obesity, more virulent viral strains tend to develop, and the reach of a virus is wider. Furthermore, the state of obesity is correlated to the presence of comorbidities that are dangerous to human health, such as type 2 diabetes and hypertension. This systematic review includes articles from a myriad of databases in order to address how living with obesity impacts one’s reaction to the SARS-CoV-2 virus and course of COVID-19. Read the full study here .

The psychological impact of COVID-19 lockdown and quarantine on children has been documented to cause “anxiety, worrying, irritability, depressive symptoms, and even post-traumatic stress disorder symptoms”. In particular, children living with severe obesity may struggle with anxieties about the possibility of obesogenic issues that can arise during the course of illness due to COVID-19. In this study, 75 families (one child interviewed per family) were interviewed on anxiety that their child with severe obesity may have, and on what specific type anxieties they are. 24 of 75 children reported having COVID-19 related anxieties. Read the full study here . 

In this multi-centre study focused on retrospective observational data from eight hospitals throughout Greece, the data on 90 critically ill patients positive for COVID-19 is analysed. Those hospitalised due to COVID-19 reflect critically ill patients whodeveloped extremely severe acute respiratory syndrome (SARS) in elderly patients with COVID-19-related pneumonia and/or underlying chronic diseases. Many underlying chronic diseases have been identified as risk factors for developing more severe COVID-19. These include type-2 diabetes, cardiovascular diseases, and hypertension. Obesity has also been associated with disease severity. In this study the relation of comorbidities such as obesity and type-2 diabetes and COVID-19 disease severity is explored. Read the full study here .

According to the World Health Organisation, physical inactivity is the fourth leading cause of death, and increases the risk of a person contracting a “metabolic disease, including obesity and type 2 diabetes (T2D).” This article points out that those seeking treatment for obesity or T2D may find difficulty in doing so during the COVID-19 pandemic due to lockdowns. As it has been found that sedentary behaviour increases one's risk for many chronic diseases, the authors wished to explore hypothetical immunopathologyof COVID-19 in patients living with obesity and how the immune defences against COVID-19 may be related to the “immuno-metabolic dysregulations'' characterised by it. Furthermore, they explore the possibility of exercise as a counteractive measure due to its anti-inflammatory properties. Read the full study here .

Obesity has been linked to a less-efficient immune response in the human body as well as poorer outcomes for respiratory diseases. In this article, researchers hypothesised that a higher Body Mass Index is a risk factor for a more severe course of illness for COVID-19. They followed all patients hospitalised from 11 January to 16 February 2020 until March 26 2020 at the Third People’s Hospital of Shenzhen (China), which was dedicated to COVID-19 treatment. Read the full study here .

As reported by the World Health Organization, the global prevalence of obesity is still on the rise both across high-income as well as low-and middle-income countries. Obesity has been associated with an increase in mortality for patients fighting COVID-19. The authors suggest that the inflammatory profile associated with patients with obesity is conducive to a more severe course of illness in patients with COVID-19. Read the full study here .

Researchers studying COVID-19, the severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2), have concluded that obesity, diabetes, hypertension or cardiovascular disease is correlated to an increased severity of illness due to COVID-19. Obesity has been associated with SARS-CoV-2 due to the “cytokine storm” of the latter; a number of the pro-inflammatory cytokines released in the “storm” which are detrimental to organ function are also found contributing to the chronic low-grade inflammation in patients with obesity. The authors wished to study a Middle Eastern population and assess the outcome of COVID-19 in relation to obesity. They observed clinical data from patients in the Al Kuwait Hospital in Dubai, UAE, to study the correlation between obesity and poor clinical outcomes of COVID-19. Read the full study here .

In many previous studies, underlying conditions such as obesity, hypertension and diabetes have been found to be correlated with an increased rate of hospitalisation and death due to SARS-CoV-2. Obesity is a non-communicable disease marked by an imbalanced energy state due to hypertrophy and hyperplasia of adipose tissue. Increased secretion of various cytokines and hormones, such as interleukin-6, tumour necrosis factor alpha and leptin, establishes a low-grade inflammatory state in patients with obesity. These pro-inflammatory cytokines predispose individuals “to increased risk for infection and adverse outcomes”. The metabolic disorders that are associated with obesity are numerous, including diabetes, hypertension and cardiovascular diseases. Most are associated with an increased risk of severe COVID-19. Due to this link, obesity is “an important factor in determining the morbidity and mortality risk in SARS CoV 2 patients” as well as the need for mechanical ventilation. Read the full study here .

Pulmonary consolidation is the most common complication of COVID-19. A high percentageof COVID-19 related pulmonary consolidationis due to extensive pulmonary fibrosis (PF). Viral infections have been shown to be a risk factor for PF, and both viral infections and aging were“strongly associated cofactors” for PF in this study. Infection with SARS-CoV-2, the virus responsible for COVID-19,suppresses the angiotensin-converting enzyme 1 (ACE2), which is a receptor exploited by the virus for cell entry; this receptor is “a negative regulator of” PF, which therefore links the virus to the progression of PF. Read the full study here .

Elevated body mass index has been marked as a risk factor for COVID-19 severity, hospital admissions and mortality. Diabetes and hypertension have also been associated with severe and fatal cases of COVID-19. Mendelian randomisation (MR) analyses the causal effect of an exposure risk factor on an outcome using genetic variants as instruments of estimation. In this study, the causal relationship between obesity traits (such as elevated BMI and metabolic disorders) and quantitative cardiometabolic biomarkers and COVID-19 susceptibility was examined by MR. Data was obtained from the UK Biobank. 1,211 individuals who had tested positive for COVID-19 and 387,079 individuals who were negativeor untestedwere analysed. Read the full study here .

Obesity and diabetes have both been identified in epidemiological reports as comorbidities “frequently associated with severe forms of COVID-19”. Both have also been identified as an independent risk factor for the severity of COVID-19 in a patient. The presence of these diseases is associated with each other; therefore, they could “confer a particularly high risk of severe COVID-19”. In previous analysis of the CORONAvirus-SARS-CoV-2 and Diabetes Outcomes (CORONADO) Study, it was shown “that body mass index (BMI) was positively and independently associated with severe COVID-19-related outcomes ... in patients with diabetes hospitalised for COVID-19”. In this analysis of the CORONADO data, the course of COVID-19 and its relationship to obesity in patients with type 2 diabetes hospitalised for this disease is explored. The influence of age on BMI and COVID-19 prognosis is also addressed due to the heightened impact of COVID-19 on the elderly population. Read the full study here .

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• Open access
• Published: 21 June 2021

The lived experience of people with obesity: study protocol for a systematic review and synthesis of qualitative studies

• Emma Farrell   ORCID: orcid.org/0000-0002-7780-9428 1 ,
• Marta Bustillo 2 ,
• Carel W. le Roux 3 ,
• Joe Nadglowski 4 ,
• Eva Hollmann 1 &
• Deirdre McGillicuddy 1  

Systematic Reviews volume  10 , Article number:  181 ( 2021 ) Cite this article

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Obesity is a prevalent, complex, progressive and relapsing chronic disease characterised by abnormal or excessive body fat that impairs health and quality of life. It affects more than 650 million adults worldwide and is associated with a range of health complications. Qualitative research plays a key role in understanding patient experiences and the factors that facilitate or hinder the effectiveness of health interventions. This review aims to systematically locate, assess and synthesise qualitative studies in order to develop a more comprehensive understanding of the lived experience of people with obesity.

This is a protocol for a qualitative evidence synthesis of the lived experience of people with obesity. A defined search strategy will be employed in conducting a comprehensive literature search of the following databases: PubMed, Embase, PsycInfo, PsycArticles and Dimensions (from 2011 onwards). Qualitative studies focusing on the lived experience of adults with obesity (BMI >30) will be included. Two reviewers will independently screen all citations, abstracts and full-text articles and abstract data. The quality of included studies will be appraised using the critical appraisal skills programme (CASP) criteria. Thematic synthesis will be conducted on all of the included studies. Confidence in the review findings will be assessed using GRADE CERQual.

The findings from this synthesis will be used to inform the EU Innovative Medicines Initiative (IMI)-funded SOPHIA (Stratification of Obesity Phenotypes to Optimize Future Obesity Therapy) study. The objective of SOPHIA is to optimise future obesity treatment and stimulate a new narrative, understanding and vocabulary around obesity as a set of complex and chronic diseases. The findings will also be useful to health care providers and policy makers who seek to understand the experience of those with obesity.

Systematic review registration

PROSPERO CRD42020214560 .

Peer Review reports

Obesity is a complex chronic disease in which abnormal or excess body fat (adiposity) impairs health and quality of life, increases the risk of long-term medical complications and reduces lifespan [ 1 ]. Operationally defined in epidemiological and population studies as a body mass index (BMI) greater than or equal to 30, obesity affects more than 650 million adults worldwide [ 2 ]. Its prevalence has almost tripled between 1975 and 2016, and, globally, there are now more people with obesity than people classified as underweight [ 2 ].

Obesity is caused by the complex interplay of multiple genetic, metabolic, behavioural and environmental factors, with the latter thought to be the proximate factor which enabled the substantial rise in the prevalence of obesity in recent decades [ 3 , 4 ]. This increased prevalence has resulted in obesity becoming a major public health issue with a resulting growth in health care and economic costs [ 5 , 6 ]. At a population level, health complications from excess body fat increase as BMI increases [ 7 ]. At the individual level, health complications occur due to a variety of factors such as distribution of adiposity, environment, genetic, biologic and socioeconomic factors [ 8 ]. These health complications include type 2 diabetes [ 9 ], gallbladder disease [ 10 ] and non-alcoholic fatty liver disease [ 11 ]. Excess body fat can also place an individual at increased cardiometabolic and cancer risk [ 12 , 13 , 14 ] with an estimated 20% of all cancers attributed to obesity [ 15 ].

Although first recognised as a disease by the American Medical Association in 2013 [ 16 ], the dominant cultural narrative continues to present obesity as a failure of willpower. People with obesity are positioned as personally responsible for their weight. This, combined with the moralisation of health behaviours and the widespread association between thinness, self-control and success, has resulted in those who fail to live up to this cultural ideal being subject to weight bias, stigma and discrimination [ 17 , 18 , 19 ]. Weight bias, stigma and discrimination have been found to contribute, independent of weight or BMI, to increased morbidity or mortality [ 20 ].

Thomas et al. [ 21 ] highlighted, more than a decade ago, the need to rethink how we approach obesity so as not to perpetuate damaging stereotypes at a societal level. Obesity research then, as now, largely focused on measurable outcomes and quantifiable terms such as body mass index [ 22 , 23 ]. Qualitative research approaches play a key role in understanding patient experiences, how factors facilitate or hinder the effectiveness of interventions and how the processes of interventions are perceived and implemented by users [ 24 ]. Studies adopting qualitative approaches have been shown to deliver a greater depth of understanding of complex and socially mediated diseases such as obesity [ 25 ]. In spite of an increasing recognition of the integral role of patient experience in health research [ 25 , 26 ], the voices of patients remain largely underrepresented in obesity research [ 27 , 28 ].

Systematic reviews and syntheses of qualitative studies are recognised as a useful contribution to evidence and policy development [ 29 ]. To the best of the authors’ knowledge, this will be the first systematic review and synthesis of qualitative studies focusing on the lived experience of people with obesity. While systematic reviews have been carried out on patient experiences of treatments such as behavioural management [ 30 ] and bariatric surgery [ 31 ], this review and synthesis will be the first to focus on the experience of living with obesity rather than patient experiences of particular treatments or interventions. This focus represents a growing awareness that ‘patients have a specific expertise and knowledge derived from lived experience’ and that understanding lived experience can help ‘make healthcare both effective and more efficient’ [ 32 ].

This paper outlines a protocol for the systematic review of qualitative studies based on the lived experience of people with obesity. The findings of this review will be synthesised in order to develop an overview of the lived experience of patients with obesity. It will look, in particular, at patient concerns around the risks of obesity and their aspirations for response to obesity treatment.

The review protocol has been registered within the PROSPERO database (registration number: CRD42020214560) and is being reported in accordance with the reporting guidance provided in the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Protocols (PRISMA-P) statement [ 33 , 34 ] (see checklist in Additional file  1 ).

Information sources and search strategy

The primary source of literature will be a structured search of the following electronic databases (from January 2011 onwards—to encompass the increase in research focused on patient experience observed over the last 10 years): PubMed, Embase, PsycInfo, PsycArticles and Dimensions. There is no methodological agreement as to how many search terms or databases out to be searched as part of a ‘good’ qualitative synthesis (Toye et al. [ 35 ]). However, the breadth and depth of the search terms, the inclusion of clinical and personal language and the variety within the selected databases, which cover areas such as medicine, nursing, psychology and sociology, will position this qualitative synthesis as comprehensive. Grey literature will not be included in this study as its purpose is to conduct a comprehensive review of peer-reviewed primary research. The study’s patient advisory board will be consulted at each stage of the review process, and content experts and authors who are prolific in the field will be contacted. The literature searches will be designed and conducted by the review team which includes an experienced university librarian (MB) following the methodological guidance of chapter two of the JBI Manual for Evidence Synthesis [ 36 ]. The search will include a broad range of terms and keywords related to obesity and qualitative research. A full draft search strategy for PubMed is provided in Additional file  2 .

Eligibility criteria

Studies based on primary data generated with adults with obesity (operationally defined as BMI >30) and focusing on their lived experience will be eligible for inclusion in this synthesis (Table  1 ). The context can include any country and all three levels of care provision (primary, secondary and tertiary). Only peer-reviewed, English language, articles will be included. Studies adopting a qualitative design, such as phenomenology, grounded theory or ethnography, and employing qualitative methods of data collection and analysis, such as interviews, focus groups, life histories and thematic analysis, will be included. Publications with a specific focus, for example, patient’s experience of bariatric surgery, will be included, as well as studies adopting a more general view of the experience of obesity.

Screening and study selection process

Search results will be imported to Endnote X9, and duplicate entries will be removed. Covidence [ 38 ] will be used to screen references with two reviewers (EF and EH) removing entries that are clearly unrelated to the research question. Titles and abstracts will then be independently screened by two reviewers (EF and EH) according to the inclusion criteria (Table  1 ). Any disagreements will be resolved through a third reviewer (DMcG). This layer of screening will determine which publications will be eligible for independent full-text review by two reviewers (EF and EH) with disagreements again being resolved by a third reviewer (DMcG).

Data extraction

Data will be extracted independently by two researchers (EF and EH) and combined in table format using the following headings: author, year, title, country, research aims, participant characteristics, method of data collection, method of data analysis, author conclusions and qualitative themes. In the case of insufficient or unclear information in a potentially eligible article, the authors will be contacted by email to obtain or confirm data, and a timeframe of 3 weeks to reply will be offered before article exclusion.

Quality appraisal of included studies

This qualitative synthesis will facilitate the development of a conceptual understanding of obesity and will be used to inform the development of policy and practice. As such, it is important that the studies included are themselves of suitable quality. The methodological quality of all included studies will be assessed using the critical appraisal skills programme (CASP) checklist, and studies that are deemed of insufficient quality will be excluded. The CASP checklist for qualitative research comprises ten questions that cover three main issues: Are the results of the study under review valid? What are the results? Will the results help locally? Two reviewers (EF and EH) will independently evaluate each study using the checklist with a third and fourth reviewer (DMcG and MB) available for consultation in the event of disagreement.

Data synthesis

The data generated through the systematic review outlined above will be synthesised using thematic synthesis as described by Thomas and Harden [ 39 ]. Thematic synthesis enables researchers to stay ‘close’ to the data of primary studies, synthesise them in a transparent way and produce new concepts and hypotheses. This inductive approach is useful for drawing inference based on common themes from studies with different designs and perspectives. Thematic synthesis is made up of a three-step process. Step one consists of line by line coding of the findings of primary studies. The second step involves organising these ‘free codes’ into related areas to construct ‘descriptive’ themes. In step three, the descriptive themes that emerged will be iteratively examined and compared to ‘go beyond’ the descriptive themes and the content of the initial studies. This step will generate analytical themes that will provide new insights related to the topic under review.

Data will be coded using NVivo 12. In order to increase the confirmability of the analysis, studies will be reviewed independently by two reviewers (EF and EH) following the three-step process outlined above. This process will be overseen by a third reviewer (DMcG). In order to increase the credibility of the findings, an overview of the results will be brought to a panel of patient representatives for discussion. Direct quotations from participants in the primary studies will be italicised and indented to distinguish them from author interpretations.

Assessment of confidence in the review findings

Confidence in the evidence generated as a result of this qualitative synthesis will be assessed using the Grading of Recommendations Assessment, Development and Evaluation Confidence in Evidence from Reviews of Qualitative Research (GRADE CERQual) [ 40 ] approach. Four components contribute to the assessment of confidence in the evidence: methodological limitations, relevance, coherence and adequacy of data. The methodological limitations of included studies will be examined using the CASP tool. Relevance assesses the degree to which the evidence from the primary studies applies to the synthesis question while coherence assesses how well the findings are supported by the primary studies. Adequacy of data assesses how much data supports a finding and how rich this data is. Confidence in the evidence will be independently assessed by two reviewers (EF and EH), graded as high, moderate or low, and discussed collectively amongst the research team.

Reflexivity

For the purposes of transparency and reflexivity, it will be important to consider the findings of the qualitative synthesis and how these are reached, in the context of researchers’ worldviews and experiences (Larkin et al, 2019). Authors have backgrounds in health science (EF and EH), education (DMcG and EF), nursing (EH), sociology (DMcG), philosophy (EF) and information science (MB). Prior to conducting the qualitative synthesis, the authors will examine and discuss their preconceptions and beliefs surrounding the subject under study and consider the relevance of these preconceptions during each stage of analysis.

Dissemination of findings

Findings from the qualitative synthesis will be disseminated through publications in peer-reviewed journals, a comprehensive and in-depth project report and presentation at peer-reviewed academic conferences (such as EASO) within the field of obesity research. It is also envisaged that the qualitative synthesis will contribute to the shared value analysis to be undertaken with key stakeholders (including patients, clinicians, payers, policy makers, regulators and industry) within the broader study which seeks to create a new narrative around obesity diagnosis and treatment by foregrounding patient experiences and voice(s). This synthesis will be disseminated to the 29 project partners through oral presentations at management board meetings and at the general assembly. It will also be presented as an educational resource for clinicians to contribute to an improved understanding of patient experience of living with obesity.

Obesity is a complex chronic disease which increases the risk of long-term medical complications and a reduced quality of life. It affects a significant proportion of the world’s population and is a major public health concern. Obesity is the result of a complex interplay of multiple factors including genetic, metabolic, behavioural and environmental factors. In spite of this complexity, obesity is often construed in simple terms as a failure of willpower. People with obesity are subject to weight bias, stigma and discrimination which in themselves result in increased risk of mobility or mortality. Research in the area of obesity has tended towards measurable outcomes and quantitative variables that fail to capture the complexity associated with the experience of obesity. A need to rethink how we approach obesity has been identified—one that represents the voices and experiences of people living with obesity. This paper outlines a protocol for the systematic review of available literature on the lived experience of people with obesity and the synthesis of these findings in order to develop an understanding of patient experiences, their concerns regarding the risks associated with obesity and their aspirations for response to obesity treatment. Its main strengths will be the breadth of its search remit—focusing on the experiences of people with obesity rather than their experience of a particular treatment or intervention. It will also involve people living with obesity and its findings disseminated amongst the 29 international partners SOPHIA research consortium, in peer reviewed journals and at academic conferences. Just as the study’s broad remit is its strength, it is also a potential challenge as it is anticipated that searchers will generate many thousands of results owing to the breadth of the search terms. However, to the best of the authors’ knowledge, this will be the first systematic review and synthesis of its kind, and its findings will contribute to shaping the optimisation of future obesity understanding and treatment.

Availability of data and materials

Not applicable.

Abbreviations

Body mass index

Critical appraisal skills programme

Grading of Recommendations Assessment, Development and Evaluation Confidence in Evidence from Reviews of Qualitative Research

Innovative Medicines Initiative

Medical Subject Headings

Population, phenomenon of interest, context, study type

Stratification of Obesity Phenotypes to Optimize Future Obesity Therapy

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Acknowledgements

Any amendments made to this protocol when conducting the study will be outlined in PROSPERO and reported in the final manuscript.

This project has received funding from the Innovative Medicines Initiative 2 Joint Undertaking (JU) under grant agreement No 875534. The JU receives support from the European Union’s Horizon 2020 research and innovation programme and EFPIA and T1D Exchange, JDRF and Obesity Action Coalition. The funding body had no role in the design of the study and will not have a role in collection, analysis and interpretation of data or in writing the manuscript.

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EF conceptualised and designed the protocol with input from DMcG and MB. EF drafted the initial manuscript. EF and MB defined the concepts and search items with input from DmcG, CleR and JN. MB and EF designed and executed the search strategy. DMcG, CleR, JN and EH provided critical insights and reviewed and revised the protocol. All authors have approved and contributed to the final written manuscript.

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Additional file 1:..

PRISMA-P (Preferred Reporting Items for Systematic review and Meta-Analysis Protocols) 2015 checklist: recommended items to address in a systematic review protocol*.

Additional file 2: Table 1

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Farrell, E., Bustillo, M., le Roux, C.W. et al. The lived experience of people with obesity: study protocol for a systematic review and synthesis of qualitative studies. Syst Rev 10 , 181 (2021). https://doi.org/10.1186/s13643-021-01706-5

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CASE REPORT article

Clinical challenge: patient with severe obesity bmi 46 kg/m 2.

• Section of Endocrinology, Diabetes, Nutrition and Weight Management, Department of Medicine, Boston Medical Center, Boston University School of Medicine, Boston, MA, United States

Obesity causes and exacerbates many disease processes and affects every organ system. Thus it is not surprising that clinical providers are often overwhelmed with the multitude of symptomatology upon initial presentation in patients with obesity. However, despite a “complicated medical history,” a systematic, organized approach in obesity medicine utilizes a personalized-tailored treatment strategy coupled with understanding of the disease state, presence of comorbidities, contraindications, side effects, and patient preferences. Here, we present the case of a young patient with Class 3b severe obesity, several obesity-related complications, and extensive psychological history. Through synergistic and additive treatments (behavioral/nutritional therapy combined with anti-obesity pharmacotherapy and concurrent enrollment in our bariatric surgery program), the patient was able to achieve significant −30.5% total body weight loss with improvement of metabolic parameters. Though these results are not typical of all patients, we must emphasize the need to encompass all available anti-obesity therapies (lifestyle, pharmacotherapy, medical devices, bariatric surgery in monotherapy or combination) in cases of refractory or severe obesity, as we do similarly for other disease modalities such as refractory hypertension or poorly controlled Type 2 diabetes that requires robust escalation in therapy.

Clinical Challenge

A 31 year old patient with a past medical history of Class 3 obesity BMI 46 kg/m 2 , Type 2 diabetes mellitus (A1c <5.7%, well controlled on metformin), polycystic ovarian syndrome, non-alcoholic steatosis of the liver, pulmonary and neurosarcoidosis on infliximab and methotrexate, and chronic worsening pain presents for weight management evaluation. She had a history of opioid use disorder due to the chronic pain, though in remission. She had been on several weight-promoting pain medications for symptom control, including gabapentin, duloxetine and nortriptyline. Contributing factors over the years to her weight gain also included her diagnosis of Bipolar Disorder with antipsychotic medication-induced weight gain (previously trialed aripiprazole, responded to lurasidone with decreasing efficiency, and now finally stable on paliperidone though weight gain promoting). Her highest adult weight was her current weight of 295 pounds with a lowest adult weight of 140 lbs. that pre-dated her Bipolar and sarcoidosis diagnoses several years ago. She had stable eating patterns, and often chose healthy meals such as hummus, vegetables, Greek salads, and lean meats, though had a weakness for sweet cravings. She engaged in structured gym exercise for 30 minutes three times per week despite the chronic pain. Recent stressors included her close aunt who had been diagnosed with cancer. She also suffered from insomnia and had been evaluated closely with sleep therapists and sleep hygiene specialists. Her polysomnogram was negative for sleep apnea.

What Would You Do Next?

A. Offer more aggressive intensive lifestyle therapy intervention

B. Trial of anti-obesity medication if option A above becomes ineffective

C. Metabolic and bariatric surgery only as anti-obesity medication would be contraindicated given her history of opioid use

D. Trial of anti-obesity medication for 3 months with concurrent referral to bariatric surgery

The patient depicted in the case has chronic, debilitating severe obesity classification with several inflammatory obesity-related comorbidities and other contributing etiology to her weight gain.

In regards to lifestyle intervention, the patient was started on a healthy low fat high fiber diet with increased consumption of vegetables, while minimizing intake of processed foods, added sugar, trans fats, and refined flours ( 1 ). Nutrient-dense whole foods prepared at home were encouraged. Acceptable macronutrient distribution range is 45–65% carbohydrates, 20–35% total fat of which <10% should be polyunsaturated fats, and 10–35% protein and amino acids 1 . However, obesity-related comorbidities such as type 2 diabetes mellitus, polycystic ovarian syndrome, and non-alcoholic steatosis of the liver suggesting features of insulin resistance need to be taken into consideration when implementing dietary modifications specific to this case. The patient's daily carbohydrate intake should be reduced to 40–50% to combat insulin resistance. Several studies have shown improvement in metabolic parameters and more rapid weight loss when a low carbohydrate diet was implemented initially in the first 3–6 months ( 2 , 3 ). At presentation, the patient's calculated daily protein intake was <20% of total daily intake and increasing her protein intake to 30% reduced her sweet cravings and increased satiety. In addition, she would benefit from at least 150 min per week of structured moderately intensive exercise as tolerated as recommended by The American College of Sports Medicine ( 4 ). Of note, the patient is also under significant stressors. Stress has been very strongly linked to hyperphagia, binging, and obesity ( 5 , 6 ). Stress management would also provide long-term strategies for emotional/stress eating should they arise. Her sleep has been adequately addressed by a specialist multidisciplinary team. Further, the patient was already under intense behavioral therapy given her underlying psychiatric illness. Early behavioral therapy intervention should be strongly considered in patients with adverse psychological factors, eating disorders and underlying psychiatric conditions that would otherwise impede their overall progress toward health goals. However, it may be difficult to promote more aggressive lifestyle intervention alone, especially in a patient with an advanced obesity disease staging who is already making strides to eat healthy and undergoing behavioral therapy.

Furthermore, the patient also meets criteria for initiation of anti-obesity pharmacotherapy (AOM): BMI >27 kg/m 2 plus the presence of one obesity-related comorbidity and/or BMI >30 kg/m 2 in conjunction with lifestyle intervention ( 7 , 8 ). Though the patient has a history of opioid use disorder, it is in remission and there is no active contraindication to AOM. The patient also does not have underlying heart disease, end-stage-renal disease, or acute angle glaucoma that would negate use of several AOM such as phentermine/topiramate, lorcaserin, and naltrexone/bupropion. Liraglutide 3.0 mg would be a first option given its double benefits in patients with severe obesity and diabetes ( 7 ) and other obesity-related comorbidities such as fatty liver ( 9 ) and polycystic ovarian disease ( 10 ). The medication is also generally well-tolerated and safe. Because anti-obesity medications can exert central effects in a patient with Bipolar Disorder, close monitoring and communication with the patient's psychiatrist would be critical. Because her BMI is already very elevated, clinically, both lifestyle changes and pharmacological treatment would be implemented together, rather than separately. Moreover, based on her current body mass index alone of 40 kg/m 2 , the patient meets National Institutes of Health consensus criteria for metabolic and bariatric surgery ( 11 ): BMI 35 kg/m 2 in the presence of at least one obesity-related comorbidity or BMI 40 kg/m 2 . Therefore, it would be prudent to discuss bariatric surgery in this patient given her disease severity.

The correct answer is D. The patient was actually started on AOM with concurrent referral to the institution's bariatric surgery program. Since the patient's insurance did not provide coverage for liraglutide 3.0 mg, she was alternatively prescribed a combination anti-obesity medication therapy (phentermine/topiramate) after discussion with her psychiatrist and other specialists. AOM were instrumental in improving the patient's overall hunger drive, cravings, and satiety. Despite being the best option for her at presentation, the patient was unwilling to undergo the bariatric procedure. Oftentimes, this may be the case in many patients until they consent to surgical intervention or have weight regain on non-surgical therapy. Future guidelines may need to be more definitive about earlier referral to bariatric surgery.

The patient continued AOM long-term, having lost 90 pounds over a 2 year time period ( Figure 1 ). Her BMI now is 28.7 kg/m 2 , weight 205 lbs. (reversed from Class 3 obesity, BMI 46 kg/m 2 , weight 295 lbs.) with improvement in quality of life and obesity-related comorbidities. Liver transaminases that were previously elevated in the context of fatty liver disease normalized along with return of regular menstrual cycles. In the process of losing weight with related attenuation in disease comorbidity and metabolic profile improvement, the patient's neurosarcoidosis continued to show remarkable recovery with stabilization of her mental health conditions and disability. Her specialists reported that this was the best she had been in many years. The patient lost −30.5% of her total body weight, which is typical weight loss achieved by metabolic and bariatric surgery means, through non-surgical intervention.

Figure 1 . Patient's weight graph derived from the electronic health record. The patient lost a total of 90 lbs. over a 2 year time period with adjunctive anti-obesity pharmacotherapy (phentermine/topiramate) in combination with behavioral and lifestyle intervention.

Though these results may not be usual for all patients, it is important to note that all treatment modalities (behavioral, lifestyle, pharmacological, and/or surgical whether as monotherapy or in combination) must be utilized for patients suffering with severe obesity and its devastating consequences on overall health and quality of life. Many of these patients present with complicated disease states and multiple comorbidities. Thus, important health targets include not only weight loss but treatment-enhanced double benefits leading to improvement of comorbidities.

Data Availability Statement

All datasets for this study were directly generated from the patient's electronic health record and are available upon request.

Informed Consent

Written informed consent to publish this case report was obtained from the patient.

Author Contributions

GS and CA contributed and edited the contents of this manuscript.

No external funding was provided for the creation of this manuscript.

Conflict of Interest

GS served as a consultant for Johnson and Johnson and advisor for Rhythm Pharmaceuticals. CA reports grants from Aspire Bariatrics, Myos, the Vela Foundation, the Dr. Robert C. and Veronica Atkins Foundation, Coherence Lab, Energesis, NIH, and PCORI, grants and personal fees from Orexigen, GI Dynamics, Takeda, personal fees from Nutrisystem, Zafgen, Sanofi-Aventis, NovoNordisk, Scientific Intake, Xeno Biosciences, Rhythm Pharmaceuticals, Eisai, EnteroMedics, Bariatrix Nutrition, and other from Science-Smart LLC, outside the submitted work.

Acknowledgments

We would like to thank the patient for permission to publish.

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Keywords: anti-obesity medications, weight loss drugs, combination therapy, bariatric surgery, lifestyle intervention

Citation: Srivastava G and Apovian CM (2019) Clinical Challenge: Patient With Severe Obesity BMI 46 kg/m 2 . Front. Endocrinol. 10:635. doi: 10.3389/fendo.2019.00635

Received: 30 April 2019; Accepted: 03 September 2019; Published: 02 October 2019.

Reviewed by:

Copyright © 2019 Srivastava and Apovian. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Gitanjali Srivastava, geet5sri@gmail.com

Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.

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Enhancing knowledge and coordination in obesity treatment: a case study of an innovative educational program

Affiliations.

• 1 NTNU Social Research, Dragvoll Allé 38b, N-7491, Trondheim, Norway. [email protected].
• 2 Norwegian Hospital Construction Agency, Klæbuveien 118, 7031, Trondheim, Norway.
• 3 Centre for Obesity Research (ObeCe), Clinic of Surgery, St. Olavs University Hospital, 7006, Trondheim, Norway.
• 4 Department of Cancer Research and Molecular Medicine, Norwegian University of Science and Technology, N-7489, Trondheim, Norway.
• PMID: 31046766
• PMCID: PMC6498688
• DOI: 10.1186/s12913-019-4119-9

Background: Currently, there is a lack of knowledge, organisation and structure in modern health care systems to counter the global trend of obesity, which has become a major risk factor for noncommunicable diseases. Obesity increases the risk of diabetes, cardiovascular diseases, musculoskeletal disorders and cancer. There is a need to strengthen integrated care between primary and secondary health care and to enhance care delivery suited for patients with complex, long-term problems such as obesity. This study aimed to explore how an educational program for General Practitioners (GPs) can contribute to increased knowledge and improved coordination between primary and secondary care in obesity treatment, and reports on these impacts as perceived by the informants.

Methods: In 2010, an educational program for the specialist training of GPs was launched at three hospitals in Central Norway opting for improved care delivery for patients with obesity. In contrast to the usual programs, this educational program was tailored to the needs of GPs by offering practice and training with a large number of patients with obesity and type 2 diabetes for an extended period of time. In order to investigate the outcomes of the program, a qualitative design was applied involving interviews with 13 GPs, head physicians and staff at the hospitals and in one municipality.

Results: Through the program, participants strengthened care delivery by building knowledge and competence. They developed relations between primary and secondary care providers and established shared understanding and practices. The program also demonstrated improvement opportunities, especially concerning the involvement of municipalities.

Conclusions: The educational program promoted integrated care between primary and secondary care by establishing formal and informal relations, by improving service delivery through increased competence and by fostering shared understanding and practices between care levels. The educational program illustrates the combination of advanced high-quality training with the development of integrated care.

Keywords: Case study; Diabetes; Education; General practitioners; Integrated care; Obesity; Specialist training.

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Conflict of interest statement

Ethics approval and consent to participate.

The project received approval from the Ombudsman for Research and Social Science Data Service in Norway, which serves as an ethics committee for Norwegian Research Institutes. We received informed consent for the interviews and for recording of the interviews by e-mail. This information was repeated verbally to the informants before the interviews started. All data has been treated and presented to preserve anonymity and confidentiality.

Consent for publication

The informants involved in this study gave consent for direct quotes from their interviews to be used in this manuscript.

Competing interests

The authors declare that they have no competing interests.

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

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Case Examples

The role of psychologists and other behavioral health providers

Multicomponent behavioral treatment for obesity and overweight is often best provided by a team of healthcare professionals. A team may include a psychologist, physician, dietician, exercise specialist, nurse practitioner, or other professional. Which types of professionals should ideally be involved, and to what degree, depends on the needs and characteristics of the child or adolescent.

The following case examples focus on the role of psychologists (or other behavioral health providers), particularly at the early stages of treatment, rather than illustrating all aspects or stages of multicomponent behavioral treatment. These cases point to the need to consider such factors as the patient’s age, gender, socioeconomic status, ethnicity, and culture. Further, they demonstrate the relevance of psychosocial factors – such as the patient’s motivation, social support, family situation, and psychological symptoms (e.g., depression, anxiety, and executive function difficulties) – for understanding and addressing obesity and overweight.

These case examples were developed by Eleanor Mackey, PhD and Laura Kurzius, PhD of Children’s National Health System in Washington, DC. Each example describes an amalgamation of several patient presentations. None of these cases represents a specific patient.

Carmen, 6-year-old Latina girl

Carmen lived with her parents and grandmother. She was referred to a multidisciplinary weight management program due to concerns about her body mass index (BMI), which was at the 99th percentile for her age and gender.

Jason, 15-year-old white male

Jason lived with his mother and niece. He expressed a desire to be a healthier weight, but was having difficulty with managing his weight and had not been successful in a general weight management program.

Marcus, 11-year-old African-American boy

Marcus lived with his mother and two younger brothers and attended middle school. His body mass index (BMI) was at the 99th percentile for his age and gender. He had a very close relationship with his mother and did well in school, but he also needed additional support in completing tasks because of his diagnosis of ADHD.

June, 18-year-old biracial female

June, who lived with her parents, was a senior in high school with sporadic attendance. June was referred for psychotherapy and multicomponent behavioral weight treatment, with specific concerns focused on her difficulty making healthy eating choices and her low motivation to engage in physical activity.

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11 Case Study: Can We Reduce Obesity by Encouraging People to Eat Healthy Food?

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In the United States, an estimated 17% of children age 2 to 19 years are considered obese; 32% are overweight. Worldwide, 12.9% to 23.8% of children are obese, and the prevalence is increasing. Preventing the onset of obesity remains a critical public health goal of the next decade. Population health science approaches to reducing the prevalence of obesity are presented: one that focuses on coaching individuals to change their behaviors related to food and exercise, and another that focuses on changing the food environment (ubiquitous exposure). An illustration is provided of how to conceptualize the limits of individual-level behavioral interventions on the population distributions of obesity incidence using basic assumptions and data simulation. The effect of individual motivation to prevent obesity is bounded by the prevalence of unhealthy environments in which children are living, which affects the number of incident obesity cases observed and the proportion attributable to individual determinants.

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Case Study on obesity and type 2 diabetes

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Case Presentation

Case study: a patient with diabetes and weight-loss surgery.

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Sue Cummings; Case Study: A Patient With Diabetes and Weight-Loss Surgery. Diabetes Spectr 1 July 2007; 20 (3): 173–176. https://doi.org/10.2337/diaspect.20.3.173

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A.W. is a 65-year-old man with type 2 diabetes who was referred by his primary care physician to the weight center for an evaluation of his obesity and recommendations for treatment options, including weight-loss surgery. The weight center has a team of obesity specialists, including an internist, a registered dietitian (RD), and a psychologist, who perform a comprehensive initial evaluation and make recommendations for obesity treatment. A.W. presented to the weight center team reluctant to consider weight-loss surgery;he is a radiologist and has seen patients who have had complications from bariatric surgery.

Pertinent medical history. A.W.'s current medications include 30 and 70 units of NPH insulin before breakfast and before or after dinner, respectively, 850 mg of metformin twice daily, atorvastatin,lisinopril, nifedipine, allopurinol, aspirin, and an over-the-counter vitamin B 12 supplement. He has sleep apnea but is not using his continuous positive airway pressure machine. He reports that his morning blood glucose levels are 100–130 mg/dl, his hemoglobin A 1c (A1C) level is 6.1%, which is within normal limits, his triglyceride level is 201 mg/dl, and serum insulin is 19 ulU/ml. He weighs 343 lb and is 72 inches tall, giving him a BMI of 46.6 kg/m 2 .

Weight history. A.W. developed obesity as a child and reports having gained weight every decade. He is at his highest adult weight with no indication that medications or medical complications contributed to his obesity. His family history is positive for obesity; his father and one sister are also obese.

Dieting history. A.W. has participated in both commercial and medical weight-loss programs but has regained any weight lost within months of discontinuing the programs. He has seen an RD for weight loss in the past and has also participated in a hospital-based, dietitian-led, group weight-loss program in which he lost some weight but regained it all. He has tried many self-directed diets, but has had no significant weight losses with these.

Food intake. A.W. eats three meals a day. Dinner, his largest meal of the day, is at 7:30 p . m . He usually does not plan a mid-afternoon snack but will eat food if it is left over from work meetings. He also eats an evening snack to avoid hypoglycemia. He reports eating in restaurants two or three times a week but says his fast-food consumption is limited to an occasional breakfast sandwich from Dunkin'Donuts. His alcohol intake consists of only an occasional glass of wine. He reports binge eating (described as eating an entire large package of cookies or a large amount of food at work lunches even if he is not hungry) about once a month, and says it is triggered by stress.

Social history. Recently divorced, A.W. is feeling depressed about his life situation and has financial problems and stressful changes occurring at work. He recently started living with his girlfriend, who does all of the cooking and grocery shopping for their household.

Motivation for weight loss. A.W. says he is concerned about his health and wants to get his life back under control. His girlfriend, who is thin and a healthy eater, has also been concerned about his weight. His primary care physician has been encouraging him to explore weight-loss surgery; he is now willing to learn more about surgical options. He says that if the weight center team's primary recommendation is for weight-loss surgery,he will consider it.

Does A.W. have contraindications to weight-loss surgery, and, if not, does he meet the criteria for weight-loss surgery?

What type of weight-loss surgery would be best for A.W.?

Roles of the obesity specialist team members

The role of the physician as an obesity specialist is to identify and evaluate obesity-related comorbidities and to exclude medically treatable causes of obesity. The physician assesses any need to adjust medications and,if possible, determines if the patient is on a weight-promoting medication that may be switched to a less weight-promoting medication.

The psychologist evaluates weight-loss surgery candidates for a multitude of factors, including the impact of weight on functioning, current psychological symptoms and stressors, psychosocial history, eating disorders,patients' treatment preferences and expectations, motivation, interpersonal consequences of weight loss, and issues of adherence to medical therapies.

The RD conducts a nutritional evaluation, which incorporates anthropometric measurements including height (every 5 years), weight (using standardized techniques and involving scales in a private location that can measure patients who weigh > 350 lb), neck circumference (a screening tool for sleep apnea), and waist circumference for patients with a BMI < 35 kg/m 2 . Other assessments include family weight history,environmental influences, eating patterns, and the nutritional quality of the diet. A thorough weight and dieting history is taken, including age of onset of overweight or obesity, highest and lowest adult weight, usual weight, types of diets and/or previous weight-loss medications, and the amount of weight lost and regained with each attempt. 1  

Importance of type of obesity

Childhood- and adolescent-onset obesity lead to hyperplasic obesity (large numbers of fat cells); patients presenting with hyperplasic and hypertrophic obesity (large-sized fat cells), as opposed to patients with hypertrophic obesity alone, are less likely to be able to maintain a BMI < 25 kg/m 2 , because fat cells can only be shrunk and not eliminated. This is true even after weight-loss surgery and may contribute to the variability in weight loss outcomes after weight loss surgery. Less than 5% of patients lose 100% of their excess body weight. 2 , 3  

Criteria and contraindications for weight-loss surgery

In 1998, the “Clinical Guidelines on the Identification, Evaluation,and Treatment of Overweight and Obesity in Adults: The Evidence Report” 4   recommended that weight-loss surgery be considered an option for carefully selected patients:

with clinically severe obesity (BMI ≥ 40 kg/m 2 or ≥ 35 kg/m 2 with comorbid conditions);

when less invasive methods of weight loss have failed; and

the patient is at high risk for obesity-associated morbidity or mortality.

Contraindications for weight-loss surgery include end-stage lung disease,unstable cardiovascular disease, multi-organ failure, gastric verices,uncontrolled psychiatric disorders, ongoing substance abuse, and noncompliance with current regimens.

A.W. had no contraindications for surgery and met the criteria for surgery,with a BMI of 46.6 kg/m 2 . He had made numerous previous attempts at weight loss, and he had obesity-related comorbidities, including diabetes,sleep apnea, hypertension, and hypercholesterolemia.

Types of procedures

The roux-en-Y gastric bypass (RYGB) surgery is the most common weight-loss procedure performed in the United States. However, the laparoscopic adjustable gastric band (LAGB) procedure has been gaining popularity among surgeons. Both procedures are restrictive, with no malabsorption of macronutrients. There is,however, malabsorption of micronutrients with the RYGB resulting from the bypassing of a major portion of the stomach and duodenum. The bypassed portion of the stomach produces the intrinsic factor needed for the absorption of vitamin B 12 . The duodenum is where many of the fat-soluble vitamins, B vitamins, calcium, and iron are absorbed. Patients undergoing RYGB must agree to take daily vitamin and mineral supplementation and to have yearly monitoring of nutritional status for life.

Weight loss after RYGB and LAGB

The goal of weight-loss surgery is to achieve and maintain a healthier body weight. Mean weight loss 2 years after gastric bypass is ∼ 65% of excess weight loss (EWL), which is defined as the number of pounds lost divided by the pounds of overweight before surgery. 5   When reviewing studies of weight-loss procedures, it is important to know whether EWL or total body weight loss is being measured. EWL is about double the percentage of total body weight loss; a 65% EWL represents about 32% loss of total body weight.

Most of the weight loss occurs in the first 6 months after surgery, with a continuation of gradual loss throughout the first 18–24 months. Many patients will regain 10–15% of the lost weight; a small number of patients regain a significant portion of their lost weight. 6   Data on long-term weight maintenance after surgery indicate that if weight loss has been maintained for 5 years, there is a > 95% likelihood that the patient will keep the weight off over the long term.

The mean percentage of EWL for LAGB is 47.5%. 3   Although the LAGB is considered a lower-risk surgery, initial weight loss and health benefits from the procedure are also lower than those of RYGB.

Weight-loss surgery and diabetes

After gastric bypass surgery, there is evidence of resolution of type 2 diabetes in some individuals, which has led some to suggest that surgery is a cure. 7   Two published studies by Schauer et al. 8   and Sugarman et al. 9   reported resolution in 83 and 86% of patients, respectively. Sjoström et al. 10   published 2-and 10-year data from the Swedish Obese Subjects (SOS) study of 4,047 morbidly obese subjects who underwent bariatric surgery and matched control subjects. At the end of 2 years, the incidence of diabetes in subjects who underwent bariatric surgery was 1.0%, compared to 8.0% in the control subjects. At 10 years, the incidence was 7.0 and 24.0%, respectively.

The resolution of diabetes often occurs before marked weight loss is achieved, often days after the surgery. Resolution of diabetes is more prevalent after gastric bypass than after gastric banding (83.7% for gastric bypass and 47.9% for gastric banding). 5   The LAGB requires adjusting (filling the band through a port placed under the skin),usually five to six times per year. Meta-analysis of available data shows slower weight loss and less improvement in comorbidities including diabetes compared to RYGB. 5  

A.W. had diabetes; therefore, the weight center team recommended the RYGB procedure.

Case study follow-up

A.W. had strong medical indications for surgery and met all other criteria outlined in current guidelines. 4   He attended a surgical orientation session that described his surgical options,reviewed the procedures (including their risks and possible complications),and provided him the opportunity to ask questions. This orientation was led by an RD, with surgeons and post–weight-loss surgical patients available to answer questions. After attending the orientation, A.W. felt better informed about the surgery and motivated to pursue this treatment.

The weight center evaluation team referred him to the surgeon for surgical evaluation. The surgeon agreed with the recommendation for RYGB surgery, and presurgical appointments and the surgery date were set. The surgeon encouraged A.W. to try to lose weight before surgery. 11  

Immediately post-surgery. The surgery went well. A.W.'s blood glucose levels on postoperative day 2 were 156 mg/dl at 9:15 a . m . and 147 mg/dl at 11:15 a . m . He was discharged from the hospital on that day on no diabetes medications and encouraged to follow a Stage II clear and full liquid diet( Table 1 ). 12  

Diet Stages After RYBG Surgery

On postoperative day 10, he returned to the weight center. He reported consuming 16 oz of Lactaid milk mixed with sugar-free Carnation Instant Breakfast and 8 oz of light yogurt, spread out over three to six meals per day. In addition, he was consuming 24 oz per day of clear liquids containing no sugar, calories, or carbonation. A.W.'s diet was advanced to Stage III,which included soft foods consisting primarily of protein sources (diced,ground, moist meat, fish, or poultry; beans; and/or dairy) and well-cooked vegetables. He also attended a nutrition group every 3 weeks, at which the RD assisted him in advancing his diet.

Two months post-surgery. A.W. was recovering well; he denied nausea, vomiting, diarrhea, or constipation. He was eating without difficulty and reported feeling no hunger. His fasting and pre-dinner blood glucose levels were consistently < 120 mg/dl, with no diabetes medications. He continued on allopurinol and atorvastatin and was taking a chewable daily multivitamin and chewable calcium citrate (1,000 mg/day in divided doses) with vitamin D (400 units). His weight was 293 lb, down 50 lb since the surgery. A pathology report from a liver biopsy showed mild to moderate steatatosis without hepatitis.

One year post-surgery. A.W.'s weight was 265 lb, down 78 lb since the surgery, and his weight loss had significantly slowed, as expected. He was no longer taking nifedipine or lisinipril but was restarted at 5 mg daily to achieve a systolic blood pressure < 120 mmHg. His atorvastatin was stopped because his blood lipid levels were appropriate (total cholesterol 117 mg/dl, triglycerides 77 mg/dl, HDL cholesterol 55 mg/dl, and LDL cholesterol 47 mg/dl). His gastroesophageal reflux disease has been resolved, and he continued on allopurinol for gout but had had no flare-ups since surgery. Knee pain caused by osteoarthritis was well controlled without anti-inflammatory medications, and he had no evidence of sleep apnea. Annual medical follow-up and nutritional laboratory measurements will include electrolytes, glucose,A1C, albumin, total protein, complete blood count, ferritin, iron, total iron binding capacity, calcium, parathyroid hormone, vitamin D, magnesium, vitamins B 1 and B 12 , and folate, as well as thyroid, liver, and kidney function tests and lipid measurements.

In summary, A.W. significantly benefited from undergoing RYBP surgery. By 1 year post-surgery, his BMI had decreased from 46.6 to 35.8 kg/m 2 ,and he continues to lose weight at a rate of ∼ 2 lb per month. His diabetes, sleep apnea, and hypercholesterolemia were resolved and he was able to control his blood pressure with one medication.

Clinical Pearls

Individuals considering weight loss surgery require rigorous presurgical evaluation, education, and preparation, as well as a comprehensive long-term postoperative program of surgical, medical, nutritional, and psychological follow-up.

Individuals with diabetes should consider the RYBP procedure because the data on resolution or significant improvement of diabetes after this procedure are very strong, and such improvements occur immediately. Resolution in or improvement of diabetes with the LAGB procedure are more likely to occur only after excess weight has been lost.

Individuals with diabetes undergoing weight loss surgery should be closely monitored; an inpatient protocol should be written regarding insulin regimens and sliding-scale use of insulin if needed. Patients should be educated regarding self-monitoring of blood glucose and the signs and symptoms of hypoglycemia. They should be given instructions on stopping or reducing medications as blood glucose levels normalize.

Patient undergoing RYGB must have lifetime multivitamin supplementation,including vitamins B 1 , B 12 , and D, biotin, and iron, as well as a calcium citrate supplement containing vitamin D (1,000–1,500 mg calcium per day). Nutritional laboratory measurements should be conducted yearly and deficiencies repleted as indicated for the duration of the patient's life.

Sue Cummings, MS, RD, LDN, is the clinical programs coordinator at the MGH Weight Center in Boston, Mass.

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• Published: 11 September 2024

The dual nature of DNA damage response in obesity and bariatric surgery-induced weight loss

• David Israel Escobar Marcillo   ORCID: orcid.org/0000-0001-7807-5190 1   na1 ,
• Valeria Guglielmi 2   na1 ,
• Grete Francesca Privitera 3   na1 ,
• Michele Signore   ORCID: orcid.org/0000-0002-0262-842X 4 ,
• Valeria Simonelli 1 ,
• Federico Manganello 1 ,
• Ambra Dell’Orso 1 ,
• Serena Laterza 2 ,
• Eleonora Parlanti 1 ,
• Alfredo Pulvirenti 3 ,
• Francesca Marcon 1 ,
• Ester Siniscalchi 1 ,
• Veronica Fertitta 1 ,
• Egidio Iorio 5 ,
• Rosaria Varì 6 ,
• Lorenza Nisticò 7 ,
• Mahara Valverde   ORCID: orcid.org/0000-0001-6975-1185 8 ,
• Paolo Sbraccia 2 ,
• Eugenia Dogliotti 1 &
• Paola Fortini   ORCID: orcid.org/0000-0001-6206-8498 1  

Cell Death & Disease volume  15 , Article number:  664 ( 2024 ) Cite this article

155 Accesses

Metrics details

• Predictive markers

This novel study applies targeted functional proteomics to examine tissues and cells obtained from a cohort of individuals with severe obesity who underwent bariatric surgery (BS), using a Reverse-Phase Protein Array (RPPA). In obese individuals, visceral adipose tissue (VAT), but not subcutaneous adipose tissue (SAT), shows activation of DNA damage response (DDR) markers including ATM, ATR, histone H2AX, KAP1, Chk1, and Chk2, alongside senescence markers p16 and p21. Additionally, stress-responsive metabolic markers, such as survivin, mTOR, and PFKFB3, are specifically elevated in VAT, suggesting both cellular stress and metabolic dysregulation. Conversely, peripheral blood mononuclear cells (PBMCs), while exhibiting elevated mTOR and JNK levels, did not present significant changes in DDR or senescence markers. Following BS, unexpected increases in phosphorylated ATM, ATR, and KAP1 levels, but not in Chk1 and Chk2 nor in senescence markers, were observed. This was accompanied by heightened levels of survivin and mTOR, along with improvement in markers of mitochondrial quality and health. This suggests that, following BS, pro-survival pathways involved in cellular adaptation to various stressors and metabolic alterations are activated in circulating PBMCs. Moreover, our findings demonstrate that the DDR has a dual nature. In the case of VAT from individuals with obesity, chronic DDR proves to be harmful, as it is associated with senescence and chronic inflammation. Conversely, after BS, the activation of DDR proteins in PBMCs is associated with a beneficial survival response. This response is characterized by metabolic redesign and improved mitochondrial biogenesis and functionality. This study reveals physiological changes associated with obesity and BS that may aid theragnostic approaches.

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Introduction.

Obesity is a multifactorial disease resulting from the interaction between genetic and environmental factors and nowadays has reached epidemic proportions [ 1 ].

The obese phenotype increases the risk of developing metabolic syndrome (MS) [ 2 ], which in turn raises the susceptibility to various non-communicable diseases, including certain cancers [ 3 ]. Additionally, obesity is associated with the onset of several complications such as type 2 diabetes (T2DM), cardiovascular disease (CVD), and stroke [ 4 ].

Currently, bariatric surgery (BS) is the most effective treatment for severe obesity, ensuring a considerable and long-term reduction of body weight, as well as the remission of the most common obesity-related morbidities [ 5 , 6 ]. However, negative outcomes related to BS have also been identified [ 7 , 8 ].

Numerous studies have addressed the impact of obesity and therapeutic surgery on health outcomes by examining adipose tissue and circulating peripheral blood mononuclear cells (PBMCs). These studies have consistently documented the presence in these tissues of inflammation, metabolic derangements, and oxidative stress, all of which are recognized as contributors to DNA damage. For example, an increase of both nuclear phosphorylated histone H2AX foci and micronuclei in PBMCs of obese/overweight individuals, compared to normal weight (NW) controls, was reported in an Italian cohort of children in association with increased levels of circulating pro-inflammatory cytokines [ 9 ]. An increase of DNA breaks and micronuclei frequency was reported in blood and lymphocytes of patients with metabolic syndrome versus healthy controls [ 10 ]. Additionally, in line with the beneficial health effects of BS-induced weight loss, more recent studies have reported a significant reduction in DNA breaks in PBMCs from patients with obesity one year after surgery. Unexpectedly, no reduction in oxidative damage to DNA was found [ 11 , 12 ]. While the connection between inflammation, oxidative stress and DNA damage in the context of obesity starts to emerge, there still remains a substantial knowledge gap concerning their association and the underlying mechanisms.

Another unanswered question pertains to the connection between severe obesity, physiological aging and DNA damage accumulation. A growing body of evidence indicates that obesity and aging share several phenotypic features, such as progressive white adipose tissue dysfunction, systemic chronic inflammation, and multi-organs alterations (adipaging) [ 13 , 14 ]. DNA damage accumulation and chronic activation of DDR lead to cellular senescence characterized by the release of matrix metalloproteases, growth factors, pro-inflammatory cytokines and chemokines, a process known as senescence-associated secretory phenotype (SASP) [ 15 , 16 ].

In recent years, there has been an increasing amount of evidence highlighting the relationship between excessive nutrient intake and mitochondrial dysfunction. Indeed, an overload of calories induces oxidative stress, leading to mitochondrial dysfunction that, in turn, intensifies the production of ROS, creating a harmful cycle that contributes to the development of chronic inflammation [ 17 ]. Furthermore, a functional association between telomeres, oxidative stress and mitochondria is increasingly recognized [ 18 ] highlighting their pivotal role in aging and metabolic syndrome [ 19 ].

In this study, we tested the hypothesis that increased production of metabolic byproducts, generated by chronic excessive caloric intake, causes irreparable DNA damage accumulation and chronic DDR activation, leading to cellular senescence, local and systemic chronic inflammation and metabolic derangement in target tissues. Additionally, we aimed to examine whether therapeutic surgery could potentially reverse these effects. To this end, we enrolled a cohort of subjects affected by severe obesity who underwent BS and collected both pre-surgery visceral (VAT) and subcutaneous adipose tissue (SAT) biopsies, as well as PBMCs samples, before and after BS, with one-year follow-up assessment. Proteomics represents a valuable approach to identify possible biomarkers for diagnosis and targeted therapy. Untargeted, mass spectrometry analysis allows broad proteomic analysis but requires high amounts of input material for targeted enrichment of specific sub-fractions of the proteome such as post-translational modifications. Therefore, we opted for a targeted, antibody-based approach and used the Reverse Phase Protein microArrays (RPPA) technology, that allowed us to quantify relative levels of key phosphorylated, i.e. activated, DDR players starting from a few tens of micrograms of protein extracts. The RPPA was performed in both VAT and SAT biopsies to evaluate the expression pattern of markers of DDR, senescence, and obesity-related metabolic changes. Furthermore, to gain insights into the functional implications of the proteomics profile, mitochondrial health and telomere dynamics were characterized.

RPPA-based proteomics on preselected proteins was conducted in both adipose tissue and PBMCs from 36 subjects affected by severe obesity who had well-documented clinical and biochemical profiles and were eligible for BS (Supplementary information table S1 ). To obtain reference values for our biomarkers, samples from 16 NW subjects were used [ 8 ]. In the case of PBMCs, the analysis was extended to include samples collected during the post-BS time, up to a 1-year follow-up period.

Chronic DDR is associated with senescence in VAT of subjects with obesity

The RPPA profile of DDR and senescence markers in VAT and SAT biopsies from our group of subjects with obesity was compared with that of NW individuals. As shown in Fig. 1 , the phosphorylation levels of ATM (Fig. 1a ) and ATR (Fig. 1b ), as well as those of their recognized downstream effectors, histone H2AX (Fig. 1c ) and KAP1 (Fig. 1d ), were significantly higher in the VAT biopsies of subjects affected by obesity compared to NW individuals. Additional investigations into the DDR cascade revealed that the cell cycle checkpoint kinases, Chk1 (Fig. 1e ) and Chk2 (Fig. 1f ), were also activated. Phosphorylation levels of p15-16 (Fig. 1g ) and p21 (Fig. 1h ), two readouts of senescence, were found to be higher in the VAT tissue samples from individuals with obesity compared to controls. In contrast, no significant differences in the levels of these biomarkers were found when comparing SAT specimens from obese subjects to control individuals (Fig. 1a–h ). In addition, in individuals with obesity, the activation level of most of these proteins was higher in VAT than in SAT specimens (Fig. 1 ).

Box plots resulting from comparison of RPPA profiles of biopsies of subcutaneous adipose tissue (SAT) and visceral adipose tissue (VAT) of severely obese patients (OB-SAT and OB-VAT) and normal-weight (NW) controls ( a – h ). The plots represent the distribution of RPPA intensity values (median ± SD). Statistical comparisons were performed with Wilcoxon rank sum test as non-parametric test and t-test as parametric test for not-paired samples (black brackets). Paired samples were compared using Wilcoxon signed-rank test as non-parametric test and t-test as parametric test (blue brackets). Statistical significance is coded with an asterisk according to the level of significance ( *p  < 0.05, ** p  < 0.01, *** p  < 0.001, **** p  < 0.0001).

Our findings demonstrate the concurrent activation of DDR and senescence markers in the VAT, but not in the SAT, in our cohort of individuals with obesity. It is worth noting that our previous findings have shown elevated levels of inflammatory and oxidative markers in the plasma of these subjects (Fig S1 ) [ 8 ].

This aligns with the activation of SASP in the omental tissue of individuals with obesity who exhibit chronic low-grade inflammation.

Stress-responsive metabolic markers are specifically increased in omental adipose tissue of individuals with severe obesity

The levels of obesity-related metabolic markers, for which there is growing evidence of an association with DDR, were also monitored (Fig. 2 ). A significant increase in the levels of the anti-apoptotic protein survivin was observed in VAT, but not in SAT biopsies, compared to controls. VAT biopsies exhibited higher survivin expression levels compared to SAT biopsies (Fig. 2a ). mTOR, a major nutritional and stress sensor, exerts its activity in two major complexes, mTORC1 and mTORC2. The first complex contains mTOR mainly phosphorylated at Ser2448, while mTORC2 is characterized by phosphorylation at Ser2481 [ 20 ]. Both mTOR Ser2448 (Fig. 2b ) and mTOR Ser2481 (Fig. 2c ) levels were significantly increased only in VAT samples of subjects affected by obesity when compared to controls, and higher activation of mTOR was also observed in VAT versus SAT biopsies of individuals with obesity. Although JNK and obesity-related inflammation are closely connected, the levels of JNK (Fig. 2d ) in both VAT and SAT were not significantly different from controls. Nonetheless, a few VAT samples showed relatively high levels of JNK. The levels of PFKFB3 (Fig. 2e ), the major regulator of glycolysis, showed a significant increase in VAT but not in SAT from patients with obesity versus adipose tissues from NW individuals.

Box plots resulting from comparison of the RPPA profiles of biopsies of subcutaneous adipose tissue (SAT) and visceral adipose tissue (VAT) of severely obese patients (OB-SAT and OB-VAT) and normal-weight (NW) controls ( a-e ). The plots represent the distribution of RPPA intensity values (median ± SD). The mtDNA/nDNA ratio was measured by digital droplet PCR (copy/µl) ( f ). Statistical comparisons were performed with Wilcoxon rank sum test as non-parametric test and t-test as parametric test for not-paired samples (black brackets). Paired samples were compared using Wilcoxon signed-rank test as non-parametric test and t-test as parametric test (blue brackets). Statistical significance is coded with an asterisk according to the level of significance (* p  < 0.05, ** p  < 0.01, *** p  < 0.001, **** p  < 0.0001).

Consistent with the elevated stress and metabolic demand of VAT in individuals with obesity, a higher mitochondrial content, as quantified by the mitochondrial-to-nuclear DNA (mtDNA/nDNA) ratio, was measured in VAT versus SAT (Fig. 2f ).

Overall, these findings indicate that VAT is the critical target of metabolic changes associated with obesity-related stressors.

A specific cell response is activated in PBMCs of individuals affected by severe obesity after bariatric surgery

To determine if the proteomics profile of VAT in individuals with obesity could be reflected systemically in the bloodstream, we examined the same markers in PBMCs. The convenient accessibility and non-invasive nature of this biological matrix enabled us to conduct RPPA profiling not only at the enrolment time but also at various time points up to one year after BS. As shown in Fig. 3 , unlike the observations in adipose tissue, there were no significant differences in the levels of phosphorylated DDR and senescence markers at baseline (T0) when comparing PBMCs from obese subjects with NW individuals. However, there was a tendency towards elevated levels of phosphorylated ATM (pATM) (Fig. 3a ), ATR (pATR) (Fig. 3b ), H2AX (γ-H2AX) (Fig. 3c ) and KAP1 (pKAP1) (Fig. 3d ) in subjects with obesity. Following surgery (T6 and T12), an unexpected increase of the levels of pATM (Fig. 3a ), pATR (Fig. 3b ) and pKAP1 (Fig. 3d ) was observed showing an upward trend as a function of the post-surgery time while the levels of γ-H2AX (Fig. 3c ) showed a significant increase only at T12. It is worth noting that no activation of Chk1 (Fig. 3e ) nor of Chk2 (Fig. 3f ) was detected. At baseline, PBMCs showed increased levels of p15-p16 (Fig. 3g ) compared to NW controls while no changes were observed in post-surgery time points. The levels of p21 (Fig. 3h ) remained unchanged both at baseline when compared to NW controls as well as at post-surgery time points.

Box plots resulting from the comparison of the RPPA profiles of peripheral blood mononuclear cells (PBMCs) of severely obese patients before (T0) and six (T6), and 12 (T12) months after bariatric surgery and normal-weight (NW) controls. a – h The plots represent the distribution of RPPA intensity values (mean ± SD), and statistical comparisons between OB and NW subjects were performed using the Wilcoxon rank sum test for non-normal data and t-test for normal distributed data. For the comparison at the different times, we used two tests for paired samples: Wilcoxon signed-rank test as non-parametric test and t-test as parametric test. Statistical significance is coded with an asterisk according to the level of significance (* p  < 0.05, ** p  < 0.01, *** p  < 0.001, **** p  < 0.0001).

As shown in Fig. 4 , a trend towards increased levels of survivin (Fig. 4a ) and a significant increase of phosphorylated mTOR (Fig. 4b, c ) and JNK (Fig. 4d ) were observed in pre-BS PBMCs of subjects with obesity versus the control counterpart. A further significant increase of survivin, mTOR Ser2448 and mTOR Ser2481 was observed at post-surgery times (Fig. 4a–c ). Notably, in contrast to the findings in VAT, a significant decrease of PFKFB3 levels (Fig. 4e ) was detected in PBMCs of individuals with obesity compared to the NW controls. In addition, PFKFB3 levels showed a decreasing trend in the post-surgery period, suggesting that a metabolic reconfiguration of glycolysis is occurring in post-BS PBMCs.

Box plots resulting from the comparison of RPPA profiles of peripheral blood mononuclear cells (PBMCs) of severely obese patients before (T0) and six (T6), and 12 (T12) months after bariatric surgery and their normal-weight (NW) controls. a – e The plots represent the distribution of RPPA intensity values (mean ± SD), and statistical comparisons were performed using the Wilcoxon rank sum test for the comparison between OB and NW subjects. For the comparison at the different times, we used two tests for paired samples: Wilcoxon signed-rank test as non-parametric test and t-test as parametric test. Statistical significance is coded with an asterisk according to the level of significance ( *p  ≤ 0.05, ** p  < 0.01, ** *p  < 0.001, **** p  < 0.0001).

In conclusion, we show that pre-surgery PBMCs from individuals with obesity exhibit a tissue-specific RPPA profile with no alterations in the levels of DDR and senescence markers but changes in stress-response and metabolism markers when compared to NW controls. Conversely, a partial engagement of DDR and senescence pathways, together with the activation of proteins involved in cell adaptation to various stressors and metabolic changes, characterizes post-BS PBMCs.

Mitochondrial health is restored in PBMCs after bariatric surgery

To gain a deeper understanding of the physiological consequences of this cellular response, we examined the expression levels of markers associated with mitochondrial health and function in the blood of obese patients, before and after undergoing BS. As shown in Fig. 5 , we observed a significant increase in the mRNA levels of PGC1-α (Fig. 5a ), Drp1 (Fig. 5b ) and SIRT3 (Fig. 5c ) in whole blood of BS patients at both the six- and twelve-months post-weight loss time points, suggesting an improvement in terms of mitochondrial quality and health. Moreover, a significant increase of mtDNA/nDNA ratio (Fig. 5d ) was found in PBMCs of patients affected by obesity when compared to NW individuals. Interestingly, a gradual reduction in the mtDNA/nDNA ratio was observed after surgery. Remarkably, the mean value of this ratio, one year after surgery, was comparable to that observed in controls.

Scatter plots resulting from the analysis of mitochondrial markers in whole blood, PBMCs and plasma samples by RT-PCR and ddPCR. Whole blood PGC1-α ( a ), DRP-1 ( b ) and SIRT3 ( c ) expression levels (mean ∓ SD) of severely obese patients before (T0) and six (T6) and twelve (T12) months after BS. Ratio between mitochondrial and nuclear DNA (mtDNA/nDNA) (mean ± SD) in PBMCs ( d ). Circulating cell-free mtDNA (ccf-mtDNA) levels in plasma samples before and after bariatric surgery ( e ). Telomere length (TL) by RT-PCR in PBMCs expressed in arbitrary units (A.U.) ( f ). Statistics were calculated using a two-tailed Student’s t-test (α = 0.05) if the requirements of normal distribution (Shapiro-Wilk test) were met. Otherwise, the Mann-Whitney test was performed. Non-parametric statistics (Wilcoxon signed-rank test) or paired t-tests were used to compare the levels of repeated measurements T0, T6, and T12. Statistical significance is coded with an asterisk according to the level of significance (* p  ≤ 0.05, ** p  < 0.01, *** p  < 0.001, **** p  < 0.0001).

The presence of circulating cell free mitochondrial DNA (ccf-mtDNA) in the bloodstream is indicative of damaged mitochondria and inflammatory response [ 21 ]. We noticed a significant increase in ccf-mtDNA (Fig. 5e ) levels in the plasma of subjects with obesity compared to NW individuals and, in addition, a progressive decrease in ccf-mtDNA levels was observed after BS, reaching the levels of control individuals. The trend of ccf-mtDNA in obesity and after BS reflects that of inflammatory markers, as previously reported [ 8 ] (Supplementary information Fig S1 ).

In summary, these findings suggest that the cellular response activated in PBMCs after BS is associated with the recovery of mitochondrial health. Ccf-mtDNA may serve as a promising marker for detecting mitochondrial dysfunction and systemic inflammation linked to obesity, both of which are mitigated following weight loss.

Telomere length reverts to healthy range in PBMCs after bariatric surgery

Telomere dynamics and mitochondrial health are both susceptible to the impact of oxidative stress and inflammation, which are prominent characteristics of obesity. The analysis of telomere length (TL) (Fig. 5f ) was performed in PBMCs from subjects with severe obesity before and after BS. Statistically significant lengthening of telomeres was observed in individuals with obesity at T0 compared to NW controls. To investigate whether this phenotype could be reversed when inflammation and oxidative stress were lowered, as occurs post BS [ 8 ], samples collected during the post-surgery time were analyzed. Statistically significant differences between patients affected by obesity and control individuals were still detected at T6, while at T12 after surgery, TL was comparable to that of NW individuals. Interestingly, these results are also paralleled by the recovery of mitochondrial health and the slowdown of inflammation, in agreement with the well-known functional relationship between telomeres and mitochondria.

In this study, we present the first evidence that chronic DDR and related stress response pathways are specifically activated in VAT but not in SAT in individuals with obesity. In obesity, VAT is characterized by hypertrophy, a key factor contributing to the release of inflammatory cytokines [ 22 ]. Consequently, VAT is more susceptible to inflammation and oxidative stress compared to the less metabolically active SAT. Our findings consistently reveal a VAT-specific activation of ATM and ATR along with downstream targets (i.e. H2AX, KAP1, Chk1, Chk2) suggesting that the accumulation of oxidative damage in this tissue triggers DDR activation. The pronounced activation of mTOR observed specifically in VAT is likely to play a significant role in influencing adipocyte function and metabolic health. Prolonged mTOR activation has indeed been shown to induce the conversion of brown adipocytes into a “white” state, which is linked to metabolic disturbances and obesity [ 23 ]. Distinct markers of VAT also include elevated levels of survivin and PFKFB3. Survivin, which is known to increase with obesity, acts as a protective mechanism, preventing apoptosis in adipose tissue stem cells and contributing to adipose tissue expansion [ 24 ]. mTOR signaling drives the increased expression of survivin in mature adipocytes from mice exposed to a high fat diet [ 25 ]. These findings align with our discovery of concomitant mTOR hyperactivation in VAT samples. Elevated expression of PFKFB3 in obesity has been previously reported [ 26 ]. We show increased expression of PFKFB3 in VAT, possibly supporting energy demands of DDR through increased glycolysis. Moreover, its contribution to oxidative stress induced double strand breaks repair has been recently documented [ 27 ]. Senescence serves as a protective mechanism triggered by DDR in the presence of prolonged or irreparable DNA damage [ 15 ]. Studies utilizing animal models have provided evidence supporting the causal involvement of senescence cells within adipose tissue in the development of metabolic dysfunctions associated with obesity [ 28 ]. In this study, we present evidence demonstrating that this phenomenon extends to adipose tissue in obese individuals. Concurrent with the activation of DDR, senescence markers p21 and p15-p16 are specifically activated in VAT. Furthermore, the mtDNA/nDNA ratio in VAT is higher than SAT, suggesting either an increase in mitochondrial biogenesis in response to increased energy demands or ongoing cellular stress potentially leading to mitochondrial dysfunction.

All these elements, coupled with the disruption of systemic adipokines and elevated levels of several pro-inflammatory cytokines observed in our study cohort [ 8 ], provide an example of how prolonged obesity in humans can lead to the development of a SASP within the primary target tissue.

A clear tissue-specificity emerged when the same markers were analyzed in the PBMCs. At the enrolment time, no significant differences in the levels of DDR and senescence markers were observed between obese and NW subjects, though a trend toward increased levels in obesity was noted. It should be considered that while adipocytes may accumulate DNA damage and senescent cells over time due to their relatively long lifespan (up to many years), PBMCs, because of their much shorter half-life, may show more dynamic and rapid responses. Increased levels of phosphorylated mTOR and JNK were observed in PBMCs of subjects with obesity as compared to NW subjects (Fig. 4a–d ). Both are involved in the pro-inflammatory state and insulin resistance associated with obesity [ 29 , 30 ]. Notably, in contrast to VAT, a significant decrease in PFKFB3 levels was detected in PBMCs of obese subjects, indicating a tissue-specific variation in the expression of this enzyme.

When the RPPA profile of our markers was evaluated post-surgery, a robust activation of ATM and ATR, along with increased level of KAP1, mTOR, JNK and survivin, were observed, persisting for up to 12 months. Some activation of H2AX and p15-p16 was also observed, while the levels of Chk1 and Chk2, as well as p21, did not change throughout the post-surgery period. ATM and ATR activation is a multifaceted process playing a role not only in DNA repair but also in regulating the cellular survival response to new challenges [ 31 ]. Based on our findings, we propose that, following BS-induced weight loss, PBMCs experience a set of adaptive mechanisms and processes to cope with the new metabolic conditions resulting from the weight loss, with the primary goal being to maintain cell viability. Essential components of the cellular survival response encompass recognition of stress, DNA repair, control of the cell cycle, anti-apoptotic processes, metabolic adaptation, activation of stress-responsive genes, immune reactions, epigenetic alterations, and autophagy, all working together to maintain cellular homeostasis. In the context of post-BS induced weight loss, these elements are all observable in PBMCs. Epigenetic changes and immune reactions are not addressed here but have been widely reported in several studies including our own conducted on the same cohort [ 32 ]. Several studies suggest ongoing DNA repair in PBMCs post-surgery, as indicated by the reduction of chromosomal damage [ 11 , 12 , 33 ]. The activation of ATM, ATR, and KAP1 that we observe over time may reflect activation of DDR and chromatin remodeling at DNA damaged sites. We did not observe a reduction of γ-H2AX levels post-BS as confirmed by two methodologies, RPPA and WB. This appears to contrast with the observed reduction of γ-H2AX foci in post-BS PBMCs. However, it is important to note that we measured the average levels of γ-H2AX in bulk samples rather than γ-H2AX foci, which specifically identify cells with double strand breaks. Additionally, cells can phosphorylate H2AX also in response to cellular stress beyond DNA damage [ 34 ]. Despite their usual quiescence, PBMCs have the capacity to transition between quiescent and active states in response to various signals. Such a transition has also been documented in PBMCs of bariatric patients following weight loss, showing a significant increase in their proliferation index and mitosis, coupled with a reduction in apoptosis that persists for up to 12 months after surgery [ 11 ]. This observed activation aligns with the increased levels in our post-surgery PBMCs of the stress-responsive proteins mTOR complexes and JNK as well as of survivin. Importantly, these elevated levels, known to promote cell proliferation, are sustained for up to 12 months post-surgery.

Conflicting results have been reported in the literature concerning TL changes in lymphocytes from obese subjects as well as after BS [ 35 ]. Our study revealed longer TL in lymphocytes from obese subjects compared to NW individuals. Post-surgery, TL returned to the control range. Telomere shortening may be linked to increased PBMCs proliferation after surgery [ 11 ]. Alternatively, the accumulation of oxidative base damage within telomeric sequences might be responsible for destabilization of telomeric G-quadruplex structures resulting in telomere lengthening [ 36 , 37 ]. This process could explain both increased TL in obese subjects and the restoration of telomere structure post-surgery concomitantly with reduced systemic oxidative stress and inflammation as previously reported [ 8 ].

The adaptive changes are expected to impact energy metabolism to cope with the rapid weight loss. We show a reduction in PFKFB3 as a function of the post-surgery time, suggesting that a restructuring of glycolysis is occurring. Suppression of glycolysis has previously been observed in the presence of extensive DNA damage in association with an enhancement of the antioxidant response [ 38 ]. This occurs as cells prioritize DNA repair and reduce energy-demanding processes such as glycolysis.

To investigate whether this shift in metabolism is associated with an improvement of mitochondrial health, we conducted an analysis of indicators of mitochondrial function. PGC1-α, SIRT3, and Drp1 are interconnected components of the complex regulatory network that governs mitochondrial dynamics and energy metabolism. Their role in the development of metabolic syndromes has been suggested by findings in both knockout mice and humans [ 39 ]. A significant increase in the expression levels of proteins associated with mitochondrial dynamics and PGC1-α was found in the leukocytes of patients with obesity who underwent Roux-en-Y gastric bypass surgery at a one-year follow up [ 40 ]. Similarly, a significant increase in pgc1 -α, sirt3 and drp1 gene expression levels was measured in whole blood of our bariatric patients after surgery.

Current evidence shows that the mitochondrial dysfunction in tissues and organs can be responsible for the release into the blood stream of several endogenous DAMPs able to trigger innate immunity [ 41 ]. Oxidized mt-DNA fragments escape mitochondria and are specifically responsible for the activation of the inflammasome [ 42 ]. The increase in ccf-mtDNA that we observed in the plasma of subjects with obesity and its trend toward decrease after BS testifies not only to the recovery of mitochondrial health and the prompt reversibility of this inflammatory index after weight loss, but also to its value as a predictive biomarker for metabolic syndrome development.

The main strength of this work is the longitudinal design of the study, allowing for repeated observations at an individual level. This approach effectively mitigates inter-individual variability, a potent confounding factor in human studies, thereby compensating for the relatively limited number of patients analyzed. Our results unveil distinctive metabolic patterns in specific tissues of obese subjects, such as adipose tissue and PBMCs. This suggests caution in utilizing markers from surrogate tissues. We identify a dual role of DDR and related stress response pathways, dependent on the context and cell type. These pathways may either be associated with a SASP in adipose tissue, contributing to metabolic syndrome in obese subjects, or they may support cell survival and DNA repair, promoting a healthier profile in circulating lymphocytes post- BS (Fig. 6 ). Further exploration is necessary to establish mechanistic connections among the analyzed proteins and to correlate proteomic profiles with clinical outcomes.

Under excessive caloric intake, hypertrophic expansion of white adipose tissue leads to adipocyte dysfunction, necrosis and fibrosis leading to low-grade systemic inflammation. At the molecular level, we speculate that DNA damage accumulation, chronic DDR activation, cellular senescence and mitochondrial dysfunction in visceral adipocytes concur to the release of autocrine and paracrine signals. These signals contribute to the development of a senescence-associated secretory phenotype (SASP), which is a systemic maladaptive homeostasis. Within the bloodstream, PBMCs in their quiescent state accumulate DNA damage without DDR activation. Following massive weight loss post-bariatric surgery, PBMCs shift toward an active state, characterized by cell proliferation, DDR activation associated with DNA repair and recovery of mitochondrial homeostasis. This adaptive cell survival response potentially contributes to the beneficial health effects of bariatric surgery. M1, M2: macrophages 1 and 2.

In summary, our findings support the notion that post-BS, an adaptive cellular response is triggered to accommodate the metabolic changes stemming from weight loss. This response may, at least in part, contribute to the positive health outcomes associated with BS.

Materials and Methods

Study design.

A cohort of 36 subjects with severe obesity (33 females; mean age 47.1 ± 10.8 years; mean BMI 44.3 ± 6.9 kg/m 2 ) attending the outpatient service of the Obesity Center of the University Hospital “Policlinico Tor Vergata” (Rome, Italy) for clinical evaluation before BS were enrolled and 32 patients (89%) completed the study. Eligible patients met the criteria for BS and had a stable body weight in the last 3 months preceding the evaluation. All patients are of Caucasian origin and lived in Italy. Exclusion criteria were: chronic liver or kidney disease; infections; malignancy; other acute or chronic systemic diseases; use of glucocorticoids, nonsteroidal anti-inflammatory medications, antibiotics, prebiotics, and probiotics intake in the last 3 months prior to BS. All patients underwent laparoscopic BS, mostly restrictive procedures (sleeve gastrectomy 17 [47.2%]; banded sleeve gastrectomy 13 [36.1%]; adjustable gastric banding 1 [2.8%]; banded Roux-en Y gastric bypass 3 [8.3%]; mini-gastric bypass 2 [5.6%]). At enrolment (T0) and 6 (T6) and 12 (T12) months after surgery, all the participants underwent a comprehensive medical evaluation, including anamnestic interview, physical examination, and the collection of blood samples for biomarkers measurements. After BS, patients received periodic counseling about dietary and lifestyle modifications as recommended [ 43 ]. At the end of the follow up, the BMI mean was 29.8 ± 5.1 kg/m 2 .

Anthropometric (weight, height, BMI, waist and hip circumference), clinical (blood pressure, heart rate) and hematological and biochemical parameters were assessed as previously described [ 44 , 45 ]. Before BS, SAT and VAT biopsies and PBMCs were collected both before and after surgery (see Samples collection).

To obtain reference values for our biomarkers, we also enrolled 15 NW subjects (69% female; mean age 36.6 ± 11 years; mean BMI 23.5 ± 2 kg/m 2 ) who underwent the same clinical assessment as bariatric patients only once. Among these, 15 NW subjects provided blood samples and 6 NW individuals who underwent elective laparoscopic surgery (cholecystectomy), provided samples of subcutaneous (NW-SAT) and omental adipose tissue (NW-VAT). All NW subjects met the same exclusion criteria adopted for bariatric patients. All the analysis has been carried out in a blinded manner. The study was approved by the ethical committees of Istituto Superiore di Sanità (prot. PRE 173116 of 15 March 2016; PRE-BIO-CE 10938 of 6 April 2018) and of the University Hospital ‘Policlinico Tor Vergata’ (protocol of the study connecting DNA 169/15) and all patients signed an informed consent. A partial set of data obtained in these two cohorts has been described in our recent publication [ 8 ].

Samples collection

Blood samples were collected in the morning from fasting subjects and analyzed immediately (hematology and clinical biochemistry assays) or processed and stored in aliquots at −80 °C (whole blood, serum and plasma) until use.

PBMCs were isolated from whole blood within 2 h after collection using a standardized protocol designed to minimize sample processing time. Briefly, BD Vacutainers CPT™ (8 ml draw volume each), containing sodium citrate as anti-coagulant, were used and PBMCs and plasma were separated accordingly to the manufacturer instruction (on average 5 to 8 × 10 6 cells/vacutainer were isolated). Moreover, for total RNA purification, 2.5 ml of whole blood was also collected into PAXgene® Blood RNA Tubes (PreAnalytix QIAGEN, Inc., Germantown, MD, USA) and cryopreserved at −80 °C. This methodology guarantees the stability of intracellular RNA for years. Furthermore, BD Vacutainer™ SST™ II Advance Tubes were used for the separation of the serum from the cellular components. Urine samples were also collected and cryopreserved. The SAT and VAT biopsies were cut in aliquots, immediately frozen and cryopreserved at −80 °C. Obtaining adipose tissue samples from NW subjects was limited (5-6 samples) due to difficulties in identifying subjects who met the exclusion criteria (see M&M). Nevertheless, the variability between individuals enabled us to achieve statistical significance.

Reverse-phase protein microarrays

Reverse-Phase Protein microArrays (RPPA) analysis was performed following established protocols [ 46 , 47 ]. Briefly, PBMCs lysis was performed using T-PER buffer (Thermo-Fisher Scientific) added with 60 μL/mL 5 M NaCl, 1X Protease Inhibitor cocktail, 1X Phosphatase Inhibitor Cocktail II and 1X Phosphatase Inhibitor Cocktail III (Sigma-Aldrich) for a maximum of 30’ on ice, followed by refrigerated centrifugation 10’ at 13.000 rpm. Supernatants (i.e. protein extracts) were collected, quantified using Bradford method (Bio-Rad Laboratories) [ 48 ] and stored at −80 °C. Lysis of adipose tissue was performed using the same protocol but samples underwent mechanical dissociation. Protein lysates were resuspended in Laemmli sample buffer [ 49 ] at a final concentration of 0.5 mg/mL with 2.5% TCEP reducing agent (Thermo-Fisher Scientific) and boiled for 3’ prior to printing with an Aushon 2470 (Quanterix) microarrayer equipped with 185 μm pins. RPPA samples were printed in technical triplicates onto nitrocellulose-coated slides (Grace Bio-labs). Series of control cell extracts (HeLa ± Pervanadate, Jurkat ± Etoposide, Jurkat ± Calyculin A, A431 ± Pervanadate and A431 ± EGF) were printed as 10%-fold-decrease mixtures of treated and untreated samples in a ten-point dilution curve format. Printed slides were promptly collected and stored at -20 °C for later use. Total protein content of printed slides was measured using Sypro Ruby (ThermoFisher Scientific). Immunostaining was performed by means of an automated system (DAKO AutostainerLink 48) using selected antibodies that had been pre-validated for RPPA and a commercially available signal amplification kit (Agilent/DAKO GenPoint). Prior to immunostaining, slides underwent an antigen retrieval (reblot) step (Millipore) followed by 2 hours blocking with 0.2% I-Block™ Protein-Based Blocking Reagent (Thermo-Fisher Scientific) in PBS. The tertiary reagent used for signal detection was streptavidin-conjugated IRDye680LT (LI-COR Biosciences). Stained slides were scanned by a Power Scanner (TECAN) and 16-bit images were analyzed via MicroVigene v5.2 software (VigeneTech) to detect spots, normalize signal and output RPPA data tables. The primary antibodies are listed: ATM (phospho S1981), Abcam ab81292; ATR (Ser428), Cell Signaling Technology 2853; Chk1 (Ser317), Cell Signaling Technology 12302; Chk2 (Thr68), Cell Signaling Technology 2661; Histone H2A.X (Ser139), Cell Signaling Technology 9718; KAP1 pS284, Bethyl Labs A300-767A; mTOR (Ser2448) (D9C2), Cell Signaling Technology 5536; mTOR (Ser2481), Cell Signaling Technology 2974; p15/p16 (C-7), Santa Cruz Biotechnology sc-377412; p21 Waf1/Cip1, Cell Signaling 2947; PFKFB3, Cell Signaling Technology 13123; SAPK/JNK (Thr183/Tyr185), Cell Signaling Technology 9251; Survivin (71G4B7E), Cell Signaling 2808. The original data of RPPA analysis are available in the section of supplementary materials (Table S2 and Table S3 ). Furthermore, we compared the results obtained in two independent experiments to check the reproducibility; linear regressions and Pearson’s correlation coefficient have been analyzed for pATM and γ-H2AX in 31 patients at the baseline (Supplementary Table S4 ). For a subset of patients, we performed a validation test of RPPA data by western blot analysis (Supplementary information Fig S2 and Fig S3 ).

Droplet digital PCR and Real time PCR

Norgen genomic DNA isolation kit (NorgenBiotek Corp. cat. n. 24700) was used to isolate total DNA from PBMCs according to the manufacturer instructions. Relative mitochondrial DNA copy number was analyzed by digital droplet PCR (Biorad QX 200). Mitochondrial and nuclear DNA were detected by using ND2 and Rplp0 single tube Taqman real-time PCR assay, respectively (cat. n. 4331182; Life Technologies, Austin, TX, USA). Ccf-mtDNA levels were analyzed by digital droplet PCR in plasma samples. This analysis was carried out directly in 0.5 μl of plasma using the same PCR assays used in the real time PCR, without previous DNA isolation [ 50 ].

Total RNA from whole blood samples was extracted according to the manufacturer instructions (PAXgene blood RNA kit, PreAnalytix, Qiagen/BD company). Complementary DNA was retro-transcribed by the high-capacity cDNA reverse transcription kit (cat. no. 4368813; Life Technologies) and the gene expression analysis was carried out by quantitative real-time PCR (qRT-PCR). The gene expression levels of sirt3 , drp1 and pgc1 -α, were calculated using specific Taqman assays (FAM labeled) by comparative method (2 –ΔΔCt ). rRNA 18 S assay (VIC labeled) was used as housekeeping reference gene. The original data are shown in supplementary materials (Tables S5 , S6 and S7 ).

Telomere length

Telomere length was measured by qRT-PCR using DNA samples isolated from PBMCs as described above. This method is based on the rationale that the amount of telomere signal per genome measured by qRT-PCR represents the average telomere length in a given DNA sample [ 51 ]. Telomere length was quantified as the relative ratio of telomere (T) repeat copy number to a single copy gene (S), called the T/S ratio, in experimental samples using standard curves. The Rplp0, encoding acidic ribosomal phosphoprotein P0, was used as the control single copy gene needed to quantify input genomic DNA and to normalize the signal from the telomere reaction. The primer sequences for telomere amplification were: TelF 5’-GGTTTTTGAGGGTGAGGGTGAGGGTGAGGGTGAGGGT-3’, TelR 5’-TCCCGACTATCCCTATC CCTATCCCTA TCCCTATCCCTA-3’. The primer sequences for the amplification of the reference gene Rplp0 were: Rplp0F 5’CAGCAAGTGGGAAGGTGTAATCC3’; Rplp0R 5’CCCATTCTATCATCAACGGGTACAA3’. The reaction was carried out in triplicate. The PCR master mix included 10 µl of SYBR Green PCR master mix (Bioline), 2 µl of forward primer and 2 µl of reverse primer (final concentration 100 nM), 5 µl (20 ng) of stock DNA (4 ng/µl) and purified water to a total volume of 20 µl. A standard curve and a negative control (no DNA template) were included in each experiment. For the standard curve, the reference DNA sample was diluted serially to produce six final concentrations (20, 10, 5, 2.5, 1.25 and 0.625 ng/µl). The PCR cycling condition for both amplicons were 95 °C for 20 s, followed by 40 cycles at 95 °C for 3 seconds and 60 °C for 30 s. The specificity of the PCR reaction was checked through the analysis of melting curves, obtained at the end of each PCR. The resulting T/S ratio represented the average telomere length per genome. To determine equal copy numbers per cell, the beta-globin gene was used as the housekeeping gene and amplified in all DNA samples. Experimental variability was corrected applying the Pfaffl model [ 52 ]. All PCRs were performed using the ABI Prism 7000 Sequence Detection System (Applied Biosystems). Fluorescence was analyzed with the ABI Prism 7000 SDS software (v2.0.5) to quantify PCR products for each sample based on the standard curve. The original data are shown in supplementary materials (Table S8 ).

Statistical analysis

All analyses were conducted using R (v.4.3.0). Both parametric and non-parametric tests were performed to compare several DDR and metabolic markers between controls and patients with obesity before and after BS. Using the Shapiro-Wilk test we opted for either the t-test or the Wilcoxon test. Moreover, to compare patients before and after BS we used paired analysis, while to compare controls and patients with obesity we used unpaired analysis. Only the differences between groups with p value < 0.05 have been considered significant. Finally, to plot the results we used two R packages: ggplot2 (v.3.4.3) ( https://ggplot2.tidyverse.org ) and ggpubr (v0.6.0) ( https://rpkgs.datanovia.com/ggpubr/ ). Statistical significance was coded using asterisk according to the level of significance (ns= not significative, * p  < 0.05, ** p  < 0.01, *** p  < 0.001, **** p  < 0.0001). For gene expression and ccf-mtDNA, we used GraphPad Prism 8.0.2 for statistical analyses and graphs. When comparing two groups, a two-tailed Student’s t-test (α = 0.05) was performed if the requirements for normal distribution (Shapiro-Wilk test) and homogeneity of variances (F-test) were met. Otherwise, the Mann-Whitney U test was performed. Non-parametric statistics (Wilcoxon signed-rank test) or paired t-tests were used to compare the levels of repeated measurements (T0, T6, and T12). As previously described in [ 8 ], where we employed the same cohort of patients, we powered our study based on the expected change in micronuclei frequency in the blood mononuclear cells of patients upon BS, as DNA damage was the primary aim of the grants that funded the original research. The calculated sample size for obese was 22.3 patients per group; we instead recruited 50% more patients to obtain accurate results. Our sample size was calculated through a paired t-test power calculation with the R package pwr (pwr: Basic Functions for Power Analysis. R package version 1.3-0, https://cran.r-project.org/package=pwr ) using a power of 0.95 and an effect size of 0.8. The longitudinal nature of the analysis (before and after bariatric surgery) can provide high accuracy when observing changes. For the control group, we estimated the need of at least 13 patients to perform a comparison with 36 obese patients to achieve a good power of 0.7. Considering that the number of control patients analyzed was relatively small, weaker associations could be missed. To this regard, it should be considered that this study had a main explorative nature aimed to detect interesting signals that may be worth of further investigation in subsequent better-powered studies.

Data availability

The data that support the findings of this study are available from the corresponding author upon reasonable request.

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Acknowledgements

We are grateful to Dr. Lucia Conti for the helpful discussion and the critical reading of the manuscript. We are also grateful to Paola Di Matteo and Roberto Ricci for their technical support.

This work was funded by Italian Ministry of Health (Ricerca Finalizzata 2013-02357791 “Connecting DNA repair and metabolic alterations of obesity in search for predictive markers” to ED, by Italian Ministry of Instruction and Research, prot. 2017L8Z2EM (PRIN) to VG and by the Collaborative project ISS/IARC: “Obesity and cancer”- E.F. 2018 to PF and by prot.2020:2020SH2ZZA_004(PRIN) to PS.

Author information

These authors contributed equally: David Israel Escobar Marcillo, Valeria Guglielmi, Grete Francesca Privitera.

Authors and Affiliations

Dept of Environment and Health, ISS, Viale Regina Elena 299, 00161, Roma, Italy

David Israel Escobar Marcillo, Valeria Simonelli, Federico Manganello, Ambra Dell’Orso, Eleonora Parlanti, Francesca Marcon, Ester Siniscalchi, Veronica Fertitta, Eugenia Dogliotti & Paola Fortini

Internal Medicine Unit and Obesity Center, University Hospital Policlinico Tor Vergata, Rome, Italy

Valeria Guglielmi, Serena Laterza & Paolo Sbraccia

Department of Clinical and Experimental Medicine, Bioinformatics Unit, University of Catania, Catania, Italy

Grete Francesca Privitera & Alfredo Pulvirenti

Core Facilities, ISS, Viale Regina Elena 299, 00161, Roma, Italy

Michele Signore

High Resolution NMR Unit-Core Facilities, ISS, Viale Regina Elena, 299, 00161, Roma, Italy

Egidio Iorio

Center for Gender-Specific Medicine, ISS, Viale Regina Elena 299, 00161, Rome, Italy

Rosaria Varì

Centre for Behavioral Sciences and Mental Health, ISS, Viale Regina Elena 299, 00161, Roma, Italy

Lorenza Nisticò

Instituto de Investigaciones Biomédicas, Universidad Nacional Autónoma de México, C.U. C.P, 04510, CDMX, México

Mahara Valverde

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Contributions

Conceptualization was done by PF, ED, DIEM and VG; Methodology was done by MS, VS, FM, AD, SL, EP, ES, VF, RV and MV; Investigation—Sample collection were done by VG, VS, SL, EI and PS; Investigation—Coordination of sample collection, sample preparation, profiling and data preparation were done by PF, VG, VS, FM, AD, EP, FM, ES, LN, PS and DIEM; Bioinformatics, statistical analyses and interpretation were done by DIEM, GFP, AP, PF, ED and FM; Writing—Original Draft were done by DIEM, ED, EP and PF; Writing—Review and Editing were done by all authors; Funding acquisition was done by PF, ED, VG and PS; Supervision was done PF, VG and ED. All authors reviewed the manuscript. All authors read and approved the final manuscript.

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Correspondence to Eugenia Dogliotti or Paola Fortini .

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The study was approved by the ethical committees of Istituto Superiore di Sanità (prot. PRE 173116 of 15 March 2016; PRE-BIO-CE 10938 of 6 April 2018) and of the University Hospital ‘Policlinico Tor Vergata’ (protocol of the study connecting DNA 169/15) accordingly with the declaration of Helsinki and all patients signed an informed consent.

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Escobar Marcillo, D.I., Guglielmi, V., Privitera, G.F. et al. The dual nature of DNA damage response in obesity and bariatric surgery-induced weight loss. Cell Death Dis 15 , 664 (2024). https://doi.org/10.1038/s41419-024-06922-0

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• Published: 13 September 2024

Adiposity and mortality among intensive care patients with COVID-19 and non-COVID-19 respiratory conditions: a cross-context comparison study in the UK

• Joshua A. Bell 1 , 2   na1 ,
• David Carslake 1 , 2   na1 ,
• Amanda Hughes 1 , 2 ,
• Kate Tilling 1 , 2 ,
• James W. Dodd 1 , 3 ,
• James C. Doidge 4 ,
• David A. Harrison 4   na2 ,
• Kathryn M. Rowan 4   na2 &
• George Davey Smith 1 , 2   na2  

BMC Medicine volume  22 , Article number:  391 ( 2024 ) Cite this article

Metrics details

Adiposity shows opposing associations with mortality within COVID-19 versus non-COVID-19 respiratory conditions. We assessed the likely causality of adiposity for mortality among intensive care patients with COVID-19 versus non-COVID-19 by examining the consistency of associations across temporal and geographical contexts where biases vary.

We used data from 297 intensive care units (ICUs) in England, Wales, and Northern Ireland (Intensive Care National Audit and Research Centre Case Mix Programme). We examined associations of body mass index (BMI) with 30-day mortality, overall and by date and region of ICU admission, among patients admitted with COVID-19 ( N  = 34,701; February 2020–August 2021) and non-COVID-19 respiratory conditions ( N  = 25,205; February 2018–August 2019).

Compared with non-COVID-19 patients, COVID-19 patients were younger, less often of a white ethnic group, and more often with extreme obesity. COVID-19 patients had fewer comorbidities but higher mortality. Socio-demographic and comorbidity factors and their associations with BMI and mortality varied more by date than region of ICU admission. Among COVID-19 patients, higher BMI was associated with excess mortality (hazard ratio (HR) per standard deviation (SD) = 1.05; 95% CI = 1.03–1.07). This was evident only for extreme obesity and only during February–April 2020 (HR = 1.52, 95% CI = 1.30–1.77 vs. recommended weight); this weakened thereafter. Among non-COVID-19 patients, higher BMI was associated with lower mortality (HR per SD = 0.83; 95% CI = 0.81–0.86), seen across all overweight/obesity groups and across dates and regions, albeit with a magnitude that varied over time.

Conclusions

Obesity is associated with higher mortality among COVID-19 patients, but lower mortality among non-COVID-19 respiratory patients. These associations appear vulnerable to confounding/selection bias in both patient groups, questioning the existence or stability of causal effects.

Peer Review reports

The global spread of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), and the resultant coronavirus disease 2019 (COVID-19), continues to threaten public health [ 1 ]. Identifying modifiable causes of mortality among patients severely ill with COVID-19 remains a priority. In the post-2022 era, this task coincides with the need to manage possible dual surges of severe COVID-19 and influenza-related respiratory diseases, and studies must now consider the impact of risk factors within both conditions to guide appropriate messaging.

Higher adiposity likely causes numerous non-infectious diseases [ 2 ] and large-scale evidence, including from Mendelian randomisation (MR) studies, now supports adiposity as a likely cause of SARS-CoV-2 infection and hospitalisation with severe COVID-19, with the available (non-MR) studies suggesting higher adiposity is associated with higher mortality with COVID-19 [ 3 , 4 , 5 , 6 , 7 , 8 , 9 , 10 , 11 ]. This contrasts sharply with evidence on patients with non-COVID-19 respiratory conditions, with studies suggesting that higher adiposity, including extreme obesity, is associated with lower mortality—although the one available MR study suggests that the causal effect might be positive [ 12 , 13 , 14 ]. Nearly all studies estimating the potential effects of adiposity on mortality among patients hospitalised with severe respiratory disease (COVID-19 and non-COVID-19) have been estimated via conventional observational studies which use multivariable adjustments to address confounding and other biases. Such adjustments rarely fully remove bias because they rely on implausible assumptions of no unmeasured confounding and no measurement error—issues which could explain modest effect sizes commonly observed [ 15 , 16 ]. MR is often unfeasible for assessing mortality in hospital settings given the lack of genetic data at scale in clinically selected samples [ 5 , 7 , 8 , 10 , 11 , 14 ]. As a result, the potential for unmeasured/residual confounding, reverse causation, and selection bias makes causality difficult to infer.

Cross-context comparison is an underutilised tool for causal inference in observational studies. This approach involves directly comparing associations between exposures and outcomes across temporal or geographical contexts where confounding or selection pressures vary. Consistency in the direction and magnitude of exposure-outcome associations across contexts, despite variation in the impact of confounding/selection, builds confidence in the causality of those exposure-outcome associations [ 17 , 18 ]. Previously, comparisons across geographical contexts where socioeconomic gradients in exposures differ helped to affirm the likely effects of gestational blood glucose on offspring birthweight and of breastfeeding on offspring intelligence, and reduce confidence in the suggested benefits of breastfeeding for offspring adiposity and blood pressure [ 17 , 19 ]. Cross-context comparisons have not been formally applied to assess the causality of adiposity for mortality among patients severely ill with COVID-19 and non-COVID-19 respiratory conditions, but this is now feasible within the United Kingdom (UK) setting given that the prevalence and impact of confounding/selection factors among patients may differ by time and geography [ 20 , 21 , 22 ].

Cross-context comparison requires variation in bias across contexts (time/place), i.e. it can assess the impact of context-varying bias, but not the impact of context-stable bias. Given uniquely rapid changes in COVID-19 management over time and geography, we may expect context-varying bias to influence adiposity-mortality associations more among COVID-19 patients than non-COVID-19 respiratory patients. Moreover, the influence of context-stable bias such as reverse causation, which can be expected to be very stable in situations where external confounders have differing effects, may influence adiposity-mortality associations more among non-COVID-19 respiratory patients. This may be assessable by comparing characteristics of COVID-19 and non-COVID-19 respiratory patients and examining how adiposity-mortality associations differ between them.

The Intensive Care National Audit and Research Centre (ICNARC) has coordinated the collection of data on nearly all patients admitted to intensive care units (ICUs) in England, Wales, and Northern Ireland since 2010 [ 20 , 21 , 23 , 24 ]. In this study, we aimed to assess the causality of adiposity for mortality among people hospitalised with severe COVID-19 and non-COVID-19 respiratory conditions using a cross-context comparison approach. We used nationally representative data from the ICNARC Case Mix Programme on patients admitted to ICU with COVID-19 (~ 33,000 patients between February 2020 and August 2021) and with non-COVID-19 respiratory conditions (~ 25,000 patients between February 2018 and August 2019 (pre-pandemic)). Within each patient group, we estimated the overall association between adiposity and mortality. We then examined whether socio-demographic and comorbidity indicators (potential confounding/selection factors) and their associations with adiposity and mortality vary by date and geographical region of ICU admission. Lastly, we examined whether adiposity-mortality associations are consistent across dates and regions with varying confounding/selection pressures.

Study population

We included patients aged ≥ 16 years admitted to any of 280 ICUs across England, Wales, and Northern Ireland with COVID-19 confirmed at or after admission between 5 February 2020 and 1 August 2021, plus adult patients admitted to any of 266 ICUs with respiratory diseases which were not COVID-19, including viral and bacterial pneumonia, bronchitis, bronchiolitis, or laryngotracheobronchitis (encompassing suspected/confirmed influenza) between 1 February 2018 and 31 August 2019 (Fig.  1 ). These start/end dates for COVID-19 admissions were based on data availability when this study commenced; the start/end dates for non-COVID-19 admissions were chosen to match the first/last months of COVID-19 admissions in a similarly long pre-pandemic period. Non-overlapping periods were used for primary analyses because these were expected to involve less disease misclassification and less estimate imprecision for non-COVID-19 respiratory conditions given their relative rarity during COVID-19 waves (e.g. given lockdowns). Data were from ICUs participating in the Case Mix Programme: the national clinical audit covering all National Health Service (NHS) adult, general intensive care, and combined intensive care/high dependency units, plus some additional specialist ICUs and standalone high dependency units, coordinated by ICNARC [ 23 , 24 , 25 ]. This audit excludes all paediatric and neonatal ICUs and ICUs in Scotland. The same individual patient could have one COVID-19 record and one or more non-COVID-19 records. Approval for the collection and use of patient-identifiable data without consent in the Case Mix Programme was obtained from the Confidentiality Advisory Group of the Health Research Authority under Sect. 251 of the NHS Act 2006 (approval number PIAG2–10[f]/2005). All data were pseudonymised (patient identifiers removed) prior to extraction for this research.

Numbers of patients admitted to any of 280 ICUs in England, Wales, and Northern Ireland with COVID-19 (5 Feb 2020 to 1 Aug 2021) and to any of 266 ICUs with non-COVID-19 respiratory conditions (1 Feb 2018 to 31 Aug 2019 and 1 Feb 2020 to 30 June 2021) participating in the ICNARC Case Mix Programme (297 ICUs overall). E/M East/Midlands, N North, S South. N  = 34,701 COVID-19 patients, 25,205 non-COVID-19 patients before the pandemic and 8241 non-COVID-19 patients during the pandemic

Adiposity (exposure)

Body mass index (BMI, as kg/m 2 ) was calculated from height and weight which were recorded or estimated by clinicians on admission to ICU. Patient severity and clinician workload often make directly measuring height and weight in the ICU infeasible [ 26 ]. About half of the height and weight recordings were visually estimated by clinical staff rather than directly measured for both COVID-19 and non-COVID-19 respiratory patients (53% of COVID-19 patients and 54% of non-COVID-19 respiratory patients had at least one of the height and weight estimated, and 38% of COVID-19 patients and 37% of non-COVID-19 respiratory patients had both height and weight estimated). Previous analyses of ICNARC ICU data supported similar associations of BMI with mortality based on measured vs. estimated values of height and weight [ 27 ], and we therefore expected little impact of this measurement error in BMI on BMI-mortality associations aside from potential bias towards the null. We first examined BMI as a continuous variable (per standard deviation (SD) based on z-scores derived separately within COVID-19 and non-COVID-19 groups using all available data). These z-scores were analysed linearly and as cubic splines with five knots at the recommended percentiles [ 28 ]. Secondly, we analysed BMI as a categorical variable based on World Health Organization classifications of underweight (< 18.5 kg/m 2 ), recommended weight (18.5 to < 25 kg/m 2 ), overweight (25.0 to < 30 kg/m 2 ), obesity class 1 (30.0 to < 35 kg/m 2 ), obesity class 2 (35.0 to < 40 kg/m 2 ), and obesity class 3 + (≥ 40.0 kg/m 2 ).

Thirty-day all-cause mortality (outcome)

We included deaths in ICU from any cause (reported directly to ICNARC by hospital staff). Within any continuous hospital stay, a patient’s time in ICU was considered to run from their first ICU admission until their last ICU discharge. We censored analysis time at 30 days after (first) admission to ICU and assumed that patients discharged from ICU (for the last time within a hospital stay) lived to be censored at 30 days post-admission [ 20 ]. This was because censoring discharged patients at discharge would have selectively removed the healthiest patients from follow-up (‘informative’ censoring). On the other hand, discharged patients who were subsequently re-admitted to ICU within the same hospital stay were treated as if they had remained in ICU because assuming their survival to 30 days from the first admission would have included many who in fact died by this time. Deaths occurring more than 30 days post-admission were excluded due to the generally high short-term mortality rate within ICU settings; later deaths are rare and risk introducing unrelated causes of death, while the assumption of survival in discharged patients also becomes less plausible with time [ 23 ]. Patients with unknown outcomes were assumed to have remained alive in the ICU at the end of their follow-up and were censored at the end of their follow-up (or at 30 days if their ICU stay already exceeded this). Follow-up which ended on the date of admission was considered to have lasted 0.5 days.

Socio-demographic and comorbidity indicators (confounding and selection factors)

Measured confounders (hypothesised to cause adiposity and mortality) included patient age, sex, ethnic group, socioeconomic deprivation, and the geographical region and date of ICU admission. Age was used as a continuous variable with a potentially non-linear effect modelled with cubic splines with five knots at the recommended percentiles [ 28 ]. Ethnic groups included ‘White’, ‘Black’, ‘Asian’ (specifically South Asian), and ‘mixed/other’ (including East Asian), based on 2011 census categories used for NHS data entry. Deprivation was based on quintiles of the Index of Multiple Deprivation (IMD) which summarises several area-level deprivation indicators including income, education, and employment for neighbourhood-level areas within each UK country, derived from patients’ postcodes. Geographic region was grouped as (1) London; (2) East England and Midlands; (3) North-East England, North-West England, and Yorkshire; (4) South-East England and South-West England; (5) Wales; and (6) Northern Ireland. Admission date was considered as six periods for COVID-19 patients: (1) 5 February–30 April 2020; (2) 1 May–31 July 2020; (3) 1 August–31 October 2020; (4) 1 November–31 January 2021; (5) 1 February–30 April 2021; and (6) 1 May–1 August 2021; and six corresponding periods 2 years earlier for non-COVID-19 respiratory patients: (1) 1 February–30 April 2018; (2) 1 May–31 July 2018; (3) 1 August–31 October 2018; (4) 1 November 2018–31 January 2019; (5) 1 February–30 April 2019; and (6) 1 May–31 August 2019.

Several comorbidity indicators were recorded and considered here as selection factors. These are factors thought likely to influence selection into the study (through admission to ICU with COVID-19 or non-COVID-19 respiratory disease) and which may also influence mortality. If adiposity also affects the probability of selection, an induced association between adiposity and the selection factor will bias the estimated effect of adiposity on mortality. However, these selection factors are very plausibly caused by adiposity (whereas adjusted-for covariates are either causal of adiposity or share a common cause with it). Adjustment for selection factors would cause bias by blocking a mediated effect of adiposity on mortality. Additionally, if the selection factor has other causes which also influence mortality, adjustment for the selection factor may induce an association between adiposity and these other causes, further biasing estimates, a mechanism referred to as collider bias [ 22 , 29 ]. Rather than adjusting for these selection factors, we therefore only measured their associations with adiposity and mortality, as an indication of the potential for bias in the sample. The comorbidity variables examined as selection factors included severe comorbid diseases documented in the patient notes as being present within the 6 months prior to ICU admission: respiratory disease (shortness of breath with light activity or home ventilation), cardiovascular disease (symptoms at rest), end-stage renal disease requiring chronic renal replacement therapy, liver disease (biopsy-proven cirrhosis, portal hypertension or hepatic encephalopathy), metastatic disease, haematological disease (acute or chronic leukaemia, multiple myeloma, or lymphoma) and an immunocompromised state (chemotherapy, radiotherapy, daily high-dose steroids, AIDS, or acquired immunohumoral or cellular immune deficiency). As indicators of acute severity within ICU, we included (1) the acute physiology and chronic health evaluation-II (APACHE-II) score and the ICNARC extreme physiology score which summarise patient physiology within the first 24 h in ICU (higher values being adverse), (2) the ratio of arterial oxygen partial pressure (PaO 2 ) to fractional inspired oxygen (FiO 2 ) (kPa units) calculated from arterial blood gas with the lowest PaO 2 in the first 24 h, and (3) the number of days on which advanced respiratory support was received (including ventilation). Additionally, we examined two indicators of pre-ICU health status: whether the patient was physically dependent on others for activities of daily living prior to admission (some dependency; total dependency; or independent), and whether the patient had any past severe illness (defined as having a zero value of the APACHE-II past medical history weighting, grouped as yes/no).

Statistical approach

Analyses informing cross-context comparisons.

We described the socio-demographic and comorbidity indicators (confounding/selection factors) of patients admitted to ICU with COVID-19 and non-COVID-19 respiratory conditions via proportions or means and standard deviations. Patient groups were first described overall, and then with separate stratification by time period and region of ICU admission to examine temporal/geographic trends in patient characteristics. These temporal trend descriptions excluded COVID-19 patients admitted during February 2020 and August 2021 due to low case counts (5 and 18 cases, respectively); these patients were not excluded from subsequent regression models because those models use aggregated cases/months only.

We examined associations of socio-demographic and comorbidity indicators (independent variables in separate models) with BMI using linear regression models, and with mortality using Gompertz proportional hazards models, each adjusted for age (cubic splines) and sex. Time since ICU admission was used as the time axis in the survival analyses. The patient group (COVID-19 vs. non-COVID-19) was interacted with all terms allowing separate estimates for each group and a test for heterogeneity between the estimates (using Stata’s post-estimation ‘test’ command). Gompertz models were used as a parametric alternative to semi-parametric Cox models to relax the unrealistic assumption of identical baseline hazards in each patient group and a Gompertz distribution was preferred to the more commonly used Weibull distribution because it gave a closer fit to the observed hazard and survivor functions. These models were then additionally stratified by ICU admission period or region, separately, each in 6 strata as described above. Interactions and independent baseline hazards were used such that separate estimates were made in each combination of stratum and patient condition. Heterogeneity tests were carried out within each patient group, comparing estimates in the six strata.

Analyses for overall adiposity-mortality associations and cross-context comparisons

We used Gompertz parametric survival models to estimate the overall association of BMI with 30-day all-cause mortality among ICU patients with COVID-19 and non-COVID-19 respiratory conditions. BMI was first modelled linearly in SD units, then categorically relative to recommended weight and finally, for plotting, as cubic splines of the z -scores with five knots at the recommended percentiles [ 28 ]. We adjusted for age (cubic splines), sex, ethnic group (4 categories), deprivation (quintile categories), admission period (6 categories), and admission region (6 categories). The time axis, entry, and censoring for these Gompertz models were as described above. The proportional hazards assumption was tested for the linear Gompertz models by splitting follow-up time at the median time to death (10 days for COVID-19 patients and 4 days for non-COVID-19 patients) and comparing hazard ratios (HRs) between these 2 periods. As sensitivity analyses for non-COVID-19 respiratory patients we (1) additionally excluded patients admitted to ICU with bacterial pneumonia (thus considering only viral respiratory conditions for comparison with COVID-19) and (2) considered non-COVID-19 respiratory patients who were admitted during the same months as COVID-19 patients (1 February 2020 to 30 June 2021, since data on non-COVID-19 respiratory patients in July 2021 were not available). In two further sensitivity analyses, we repeated the analyses described above including only those patients (i) with measured (not estimated) height and weight or (ii) who were not dependent on others for the activities of daily living prior to admission.

We then repeated Gompertz models of BMI with mortality among COVID-19 and non-COVID-19 respiratory patients with the same confounding adjustments but with separate stratification by admission date and region, using the groupings noted above. The stratifying variable was omitted from the adjustment set in each analysis. Interactions and independent baseline hazards were used to make estimates equivalent to those from completely separate models in each stratum. Stata’s post-estimation ‘test’ command was used to test for interaction between BMI and each stratifying variable in relation to mortality (smaller P -values indicate stronger evidence that associations vary by date/region). Analyses were conducted using Stata 17 and code is available at https://github.com/Carslake/ICU_BMI_Covid/ .

Characteristics of ICU patients with COVID-19 and non-COVID-19 respiratory conditions

Of 39,426 adult patients admitted to ICU with COVID-19 between 5 February 2020 and 1 August 2021, 34,701 (88%) were eligible for the main analyses based on having data on BMI (exposure), sociodemographic adjustment variables, and 30-day ICU mortality (outcome) (Fig.  2 ). Of 27,328 adult patients admitted to ICU with non-COVID-19 respiratory conditions between 1 February 2018 and 31 August 2019, 25,205 (92%) were eligible for the main analyses based on the same criteria. For both patient groups, data were most often missing for BMI or ethnicity. For the descriptive analysis of comorbidities, patients were further excluded if they were missing the variable in question; this excluded up to 7% of COVID-19 patients and 6% of non-COVID-19 patients.

Flow of patients admitted to ICU with COVID-19 and non-COVID-19 respiratory conditions. ICU Intensive care unit, BMI body mass index

Compared with non-COVID-19 respiratory patients who were admitted to ICU before the COVID-19 pandemic (the main comparison group for analyses), COVID-19 patients were younger, more often male, more deprived and substantially less often of a white ethnic group (Table  1 ). COVID-19 patients had a more than twofold higher proportion of Black, and a threefold higher proportion of Asian and mixed, ethnic groups. All of these sociodemographic variables were stable over time among non-COVID-19 patients (Additional files 1 – 2 : Figs. S1–S2), but there were clear temporal differences among COVID-19 patients. The average age declined, particularly after December 2020 (when UK vaccination programmes began). The proportion of males also declined. Deprivation fluctuated considerably but rose overall. The ethnic composition of COVID-19 patients fluctuated, particularly the relative proportions of white and Asian patients, without showing a clear overall trend. Regional variation in age and sex was minimal in both patient groups, but regions differed in levels of deprivation and ethnic composition (Additional files 3 – 5 : Figs. S3–S5). Regional patterns in deprivation were similar for COVID-19 and non-COVID-19 patients, but regional differences in ethnic composition (non-white ethnic groups being most common in London and least common in Northern Ireland) were more pronounced among COVID-19 patients.

COVID-19 patients had a higher mean BMI, at 30.9 kg/m 2 vs. 27.6 kg/m 2 in non-COVID-19 patients (Table  1 ). They presented as underweight one-sixth as often and as recommended weight nearly half as often, yet presented with obesity, particularly classes 2 and 3 + obesity, about twice as often. Mean BMI was relatively stable over time among non-COVID-19 patients but showed a modest increase among COVID-19 patients, starting at 29.8 kg/m 2 in March 2020 and ending at 31.7 kg/m 2 in July 2021 (Additional file 2 : Fig. S2). This corresponded to a fall in the proportion of recommended weight and overweight patients while the proportion of patients with obesity rose, e.g. from 7.6% in March 2020 to 13.2% in July 2021 for class 3 + obesity (Additional file 6 : Fig. S6). There was regional heterogeneity in BMI, with a lower mean in London reflected in corresponding differences across the BMI categories. This heterogeneity was apparent among both patient groups but was more pronounced among COVID-19 patients (Additional file 5 : Fig. S5, Additional file 7 : Fig. S7). The proportion of BMI which was estimated rather than measured was similar overall for COVID-19 and non-COVID-19 patients but was a little more variable over time in the former group, with the most estimation in spring 2020 and winter 2020–2021, when admissions were highest (Fig.  1 and Additional file 8 : Fig. S8). Regional variation was similar in COVID-19 and non-COVID-19 patients, with weight most frequently estimated in Northern Ireland (Additional file 9 : Fig. S9).

COVID-19 patients had less prior severe illness than non-COVID-19 patients (9.0% vs. 21.4%) and less pre-ICU dependency on others for activities of daily living (11.3% vs. 33.9%; Table  1 ). They also presented less often with severe comorbidities including cardiovascular, respiratory, liver, renal, metastatic, haematological, respiratory, and immunocompromising diseases, compared to non-COVID-19 patients. These pre-existing conditions showed limited variation over time and between regions, to a similar extent for COVID-19 and non-COVID-19 patients (Additional files 6 and 10 : Figs. S6 and S10). COVID-19 patients had a lower PaO 2 /FiO 2 ratio and more days of advanced respiratory support, indicating worse respiratory function than non-COVID-19 patients, but lower overall severity of illness as indicated by mean APACHE-II and ICNARC scores (Table  1 ). While these severity indicators remained relatively constant over time among non-COVID-19 patients, they all declined over time among COVID-19 patients (indicating less severe illness except for the PaO 2 /FiO 2 ratio which suggests the opposite; Additional file 8 : Fig. S8, Additional file 11 : Fig. S11). The ICNARC physiological severity score and the days of advanced respiratory support both showed more variability between regions in COVID-19 patients than among non-COVID-19 patients (Additional files 9 and 12 : Figs. S9 and S12). In contrast, the PaO 2 /FiO 2 ratio was more variable between regions among non-COVID-19 patients.

Despite being younger with fewer comorbidities, COVID-19 patients experienced higher 30-day ICU mortality at 34.3% vs. 22.4% for non-COVID-19 patients (Table  1 ). This difference declined substantially over time, as mortality among COVID-19 patients fell from 41.4% in March 2020 to 14.5% in July 2021 (Additional file 8 : Fig. S8). In contrast, mortality among non-COVID-19 respiratory patients was relatively stable over time, at 23.9% in March 2018 and 21.0% in July 2019. Regional variation in mortality was similar for both conditions, being highest in Wales (39.8% of COVID-19 patients and 27.5% of non-COVID-19 patients; Additional file 13 : Fig. S13).

The characteristics of non-COVID-19 patients admitted to ICU during the COVID-19 pandemic (a smaller comparison group for sensitivity analyses) were not substantially different from those of non-COVID-19 patients admitted pre-pandemic (Table  1 ). They were a little more similar to COVID-19 patients, e.g. younger age, higher proportions of non-white ethnic groups, higher mean BMI, and less historical illness, indicating a greater potential for misclassification with COVID-19 in this overlapping admissions period.

Associations of confounding/selection factors with BMI and mortality among ICU patients with COVID-19 and non-COVID-19 respiratory conditions

Among COVID-19 patients, non-white ethnic groups were associated with lower BMI, particularly the Asian group at − 2.77 kg/m 2 (95% confidence interval (CI) = − 2.99, − 2.56) (Additional file 14 : Table S1). There was considerable temporal and regional heterogeneity in this association, e.g. Asian ethnic group was most associated with lower BMI in South England (excluding London) and Northern Ireland (Additional files 15 – 16 : Tables S2–S3). Among non-COVID-19 patients, an association between BMI and ethnic group was also present but the association, and its temporal and regional heterogeneity, were weaker (Additional files 17 – 18 : Tables S4–S5). While the temporal and regional heterogeneity among COVID-19 patients was strongest for the association between Asian ethnicity and BMI, heterogeneity among non-COVID-19 patients was greatest for the association between Black ethnicity and BMI.

Asian ethnicity was associated with higher mortality in both patient groups but more so in COVID-19 patients (Additional file 14 : Table S1). There was no evidence of temporal or regional heterogeneity in this association (Additional files 19 – 22 : Tables S6–S9). Black ethnicity was associated with slightly higher mortality among COVID-19 patients (HR = 1.04, 95% CI = 0.97, 1.12) and lower mortality among non-COVID-19 patients (HR = 0.67, 95% CI = 0.54, 0.82) but the reverse was found among white patients (HR = 0.85, 95% CI = 0.82, 0.89 and HR = 1.12, 95% CI = 1.02, 1.23, respectively). Higher mortality among Black COVID-19 patients was apparent only in the first admissions period (February–April 2020, HR = 1.22, 95% CI = 1.09, 1.37); the association was null or slightly protective during each period thereafter.

Higher deprivation was similarly associated with higher BMI in COVID-19 and non-COVID-19 patients (Additional file 14 : Table S1). There was strong evidence of regional heterogeneity in this association among non-COVID-19 patients (Additional file 18 : Table S5) and a suggestion of temporal heterogeneity among COVID-19 patients (Additional file 15 : Table S2).

Deprivation was associated with higher mortality among COVID-19 patients but not among non-COVID-19 patients. These associations were largely consistent across regions and admission dates (Additional files 14 , 19 – 22 : Tables S1, S6–S9).

Most comorbidities were associated with lower BMI in both patient groups, but magnitudes were often higher among COVID-19 patients (Additional file 14 : Table S1). In contrast, pre-ICU dependency and comorbid respiratory disease (the definition of which includes home ventilation due to obesity-related sleep disorders) were associated with higher BMI in both groups. Heterogeneity tests indicated that the magnitude of many of these associations between BMI and comorbidities varied over time among COVID-19 patients (Additional file 15 : Table S2) but there was much less evidence of temporal heterogeneity among non-COVID-19 patients (Additional file 17 : Table S4). There was little evidence of regional heterogeneity in associations between BMI and comorbidities in either patient group (Additional files 16 and 18 : Tables S3 and S5).

Comorbidities were almost always associated with higher mortality (Additional file 14 : Table S1). The magnitude of these associations often differed between COVID-19 and non-COVID-19 patients, being usually stronger in non-COVID-19 patients. An interesting exception was renal disease, which was consistently associated with higher mortality among COVID-19 patients but lower mortality among non-COVID-19 patients. Other associations between comorbidities and mortality showed considerable temporal heterogeneity among COVID-19 patients, tending to increase over the study period (Additional file 19 : Table S6) but there was little temporal heterogeneity in the comorbidity-mortality association among non-COVID-19 patients (Additional file 21 : Table S8). There was some evidence of regional heterogeneity in the associations between comorbidities and mortality, particularly among non-COVID-19 patients (Additional files 20 and 22 : Tables S7 and S9).

Overall associations of BMI with mortality among ICU patients with COVID-19 and non-COVID-19 respiratory conditions

Among COVID-19 patients admitted to ICU, higher BMI (per SD, or 7.6 kg/m 2 ) was associated with a small excess ICU mortality (HR = 1.05; 95% CI = 1.03, 1.07; Table  2 ). Compared with recommended weight patients, underweight was associated with excess mortality at HR = 1.27 (95% CI = 1.04, 1.55), whereas there was little evidence that overweight, class 1 obesity, or class 2 obesity were associated with ICU mortality. Mortality was elevated with class 3 + obesity vs. recommended weight at HR = 1.22 (95% CI = 1.14, 1.32). Cubic spline models (Fig.  3 ) also indicated that the positive overall association between BMI and mortality derived mainly from those with a BMI over 35–40 kg/m 2 .

Association between BMI and mortality in COVID-19 and non-COVID-19 respiratory patients. BMI body mass index, HR hazard ratio, CI confidence interval. Results from parametric survival analyses with Gompertz baseline hazard functions. BMI was modelled as a cubic spline with five knots and converted back from condition-specific standard deviations to kg/m 2 for display. Survival time was censored at 30 days with patients discharged earlier assumed to survive to 30 days. Adjusted for sex, age (cubic spline), ethnic group, deprivation, admission period and admission region. Crosses indicate the truncation of plots at the 1st and 99th percentiles of condition-specific BMI z -scores

Among non-COVID-19 patients admitted to ICU, higher BMI (per SD, or 7.5 kg/m 2 ) was associated with lower ICU mortality (HR = 0.83; 95% CI = 0.81, 0.86). Compared with recommended weight patients, those who were underweight had substantial excess mortality at HR = 1.52 (95% CI = 1.36, 1.69), whereas patients who were overweight or with obesity had substantially lower mortality (e.g. HR = 0.72; 95% CI = 0.63, 0.82 for class 3 + obesity). The cubic spline models suggested progressively lower mortality with BMI up to BMI in excess of 35–40 kg/m 2 , above which mortality remained consistently low.

The pattern and magnitude of these associations were similar when (i) considering non-COVID-19 patients admitted during the same months as COVID-19 patients (Table  2 ), (ii) excluding non-COVID-19 patients admitted to ICU with bacterial pneumonia (thus considering only viral respiratory conditions; Table  2 ), (iii) considering only those patients with measured BMI (Additional file 23 : Table S10) or excluding patients who were dependent on others for the activities of daily living prior to admission (Additional file 24 : Table S11). The first and third of these suggested weakly that mortality in non-COVID-19 patients with class 3 + obesity might not be reduced to the degree suggested by the main analysis (but was still reduced relative to recommended weight). However, these estimates were less precise owing to smaller sample size and non-COVID-19 patients admitted during the pandemic may have been more prone to disease misclassification.

Proportional hazards tests splitting follow-up at the median time to death suggested that both the positive BMI-mortality association in COVID-19 patients and the negative one in non-COVID-19 patients attenuated towards the null with increasing time spent in ICU. This was rather more pronounced in non-COVID-19 patients (HR = 0.76, 95% CI = 0.72, 0.80 in the first 4 days in ICU; HR = 0.90, 95% CI = 0.86, 0.94 subsequently; P difference  < 0.0001) than in COVID-19 patients (HR = 1.07, 95% CI = 1.04, 1.10 in the first 10 days in ICU; HR = 1.03, 95% CI = 1.00, 1.06 subsequently; P difference  = 0.07). In the presence of non-proportional hazards, average HR can be sensitive to the censoring distribution [ 30 , 31 ]. In our main analyses, 13.3% of COVID-19 patients and 4.9% of non-COVID-19 patients were administratively censored at 30 days, with 7.6% and 4.0% of all recorded deaths, respectively, occurring after this censoring. Censoring before 30 days was very rare (1.2% of COVID-19 patients and 0.5% of non-COVID-19 patients). Importantly for our interest in heterogeneity, both forms of censoring were similar across regions and dates of admission, except for COVID-19 patients in the final period (May–August 2021), when there was less administrative censoring (4.4%) and more censoring before 30 days (20.2%).

Temporal cross-context comparison: associations of BMI with mortality among ICU patients with COVID-19 and non-COVID-19 respiratory conditions, by date of ICU admission

Among COVID-19 patients admitted to ICU, higher BMI (per SD) was associated with higher mortality (HR = 1.11, 95% CI = 1.06, 1.15) during February–April 2020 (Table  3 ); this association weakened in May–July 2020 and thereafter with a small positive association during the most populous period of November 2020–January 2021 (HR = 1.04, 95% CI = 1.01, 1.07; BMI-date interaction P  = 0.015). Compared with recommended weight patients, mortality was highest for patients with class 3 + obesity in February–April 2020 (HR = 1.52, 95% CI = 1.30, 1.77), reducing thereafter. A small positive gradient in risk associated with overweight and classes 1 and 2 obesity (vs. recommended weight) was seen only during February–April 2020, with HRs for these BMI groups (versus recommended weight) < 1 or null across subsequent periods. Being underweight was associated with higher mortality in each period except May–July 2020, but estimates were imprecise given the rarity of underweight COVID-19 patients. The cubic spline plots of continuous BMI (Additional file 25 : Fig. S14) support the patterns observed for categories of BMI and mortality among COVID-19 patients.

Among non-COVID-19 respiratory patients admitted to ICU, higher BMI (per SD) was associated with lower mortality across admission dates, e.g. HR = 0.81 (95% CI = 0.75, 0.87) in February–April 2018, but with varying strength of association (BMI-date interaction P  = 0.001, indicating stronger evidence of heterogeneity in association magnitude than for COVID-19). Mortality was highest among underweight patients across all periods, e.g. HR = 1.60 (95% CI = 1.26, 2.04) during February–April 2018. In the same period, mortality was lower with overweight and each obesity group vs. recommended weight, e.g. HR = 0.60 (95% CI = 0.43, 0.85) for class 3 + obesity. This lower mortality with class 3 + obesity was seen in most periods except for May–July 2018 and November 2018–January 2019 when mortality did not differ from recommended weight. The cubic spline plots of continuous BMI reiterate both the variability and the imprecision of the BMI-mortality association among non-COVID-19 patients with BMI above 40 kg/m 2 .

Geographical cross-context comparison: associations of BMI with mortality among ICU patients with COVID-19 and non-COVID-19 respiratory conditions, by region of ICU admission

Among COVID-19 patients admitted to ICU, the association of BMI (per SD higher) with higher mortality did not vary across regions (BMI-region interaction P  = 0.703; Table  4 ). Compared with recommended weight, mortality was consistently elevated with class 3 + obesity across regions, with HR varying between 1.14 (95% CI = 0.94, 1.38) in southern England and an imprecise 1.29 (95% CI = 0.74, 2.25) in Northern Ireland. Neither overweight nor class 1 or 2 obesity were clearly associated with mortality in any region though there was a strong but imprecisely estimated association with class 2 obesity in Northern Ireland (HR = 1.49, 95% CI = 0.90, 2.46). The cubic spline plots (Additional file 26 : Fig. S15) also do not show clear differences between regions in the association between BMI and mortality.

Among non-COVID-19 patients admitted to ICU, the association of BMI (per SD higher) with lower mortality did not vary across regions (BMI-region interaction P  = 0.385; Table  4 ). Compared with recommended weight, underweight was associated with higher mortality in all regions except Wales (HR = 0.90, 95% CI = 0.53, 1.52). The magnitude of these positive associations varied between East England and Midlands (HR = 1.32, 95% CI = 1.05, 1.65) and London (HR = 1.89, 95% CI = 1.50, 2.38) or the less precise Northern Ireland (HR = 1.92, 95% CI = 0.95, 3.89). In most regions, mortality continued to decline across higher BMI groups above recommended weight, with class 3 + obesity often associated with the lowest risk, e.g. at HR = 0.60 (95% CI = 0.46, 0.78) in East England and Midlands and HR = 0.48 (95% CI = 0.27, 0.87) in Wales. The cubic spline plots (Additional file 26 : Fig. S15) also suggest regional variation in the degree of elevated mortality at low BMI and show no evidence (albeit with low precision) of elevated mortality at very high BMI, relative to mean BMI.

The numbers of deaths and total sample sizes for each BMI category, admission period, and admission region contributing to the main survival models described above are shown in Additional files 27 – 29 : Tables S12–S14.

We aimed in this study to assess the likely causality of adiposity for mortality among patients severely ill with COVID-19 and non-COVID-19 respiratory conditions using a cross-context comparison approach with nationally representative ICU data in the UK. Consistent adiposity-mortality associations despite varying confounding/selection would increase confidence in causality. Our results suggest that higher adiposity, primarily extreme obesity, is associated with higher mortality among patients admitted to ICU with COVID-19, but lower mortality among patients admitted with non-COVID-19 respiratory conditions both before and during the COVID-19 pandemic. These associations appear vulnerable to confounding/selection bias in both patient groups, questioning the existence or stability of causal effects. Among COVID-19 patients, unfavourable obesity-mortality associations differ substantially by ICU admission date, perhaps reflecting high levels of temporal heterogeneity in potential confounding and selection bias. Among non-COVID-19 respiratory patients, obesity-mortality associations were consistently favourable but varied in magnitude, despite apparently stable circumstances of the measured potential bias. The strong associations of comorbidities with both BMI and mortality (whether stable or unstable) suggest that comorbidity-induced weight loss may bias BMI-mortality associations in both conditions, but particularly among non-COVID-19 patients due to their higher prevalence of comorbidities.

The two contexts examined here were the date and geographical region of ICU admission. These were chosen because the prevalence/level of confounding and selection factors relevant to adiposity-mortality effects differ by time and geography among ICU patients with COVID-19 [ 25 ], and thus the effects of those confounders/selectors on adiposity/mortality might also differ among them, although this had not been previously examined. Such context-varying bias is expected to be most influential for COVID-19 given rapid changes in its viral biology and clinical/public management, whereas context-stable bias is expected to be most influential for non-COVID-19 respiratory conditions which was assessed here by comparing characteristics between patient groups. The ability of COVID-19 to cause severe disease in otherwise healthy people, especially prior to vaccination, at a time of restricted social contact also suggests that COVID-19 patients in ICU include a potentially varying proportion of occupationally exposed patients, with a distinct covariate profile. Our results suggest that the associations of confounding/selection factors with BMI and mortality varied mostly by date (not region) of ICU admission, and particularly for COVID-19. For example, among COVID-19 patients, the Black ethnic group was associated with slightly different degrees of mostly lower BMI across admission dates, and with higher mortality only during February–April 2020. This diminishing association with mortality over time has also been seen in the UK general population outside of ICU settings [ 32 ]. Deprivation showed little variation in its association with BMI or mortality by date but several comorbidity indicators (e.g. liver disease and APACHE score) had varying associations with BMI and/or mortality.

Notably, higher BMI was not consistently associated with higher mortality across admission dates among COVID-19 patients, with ~ 50% higher mortality for extreme obesity relative to recommended weight seen only within the earliest period of February–April 2020. The negative obesity-mortality associations among non-COVID-19 patients were a little more consistent over time in direction and perhaps magnitude but still displayed heterogeneity well beyond that expected by chance. This consistency among non-COVID-19 patients does not necessarily support causality in that group, however, as this could still reflect stable forms of bias such as reverse causation. This was supported by the high proportion (compared to COVID-19 patients) of non-COVID-19 patients admitted to ICU who were underweight and/or had comorbidities. Such patients might be expected to experience both higher mortality and comorbidity-induced weight loss/cachexia.

Adiposity-mortality associations may differ over time because of changing bias or because of genuine changes in causality, e.g. due to changes in viral variants, immunological naivety, vaccines, and therapeutics. Obesity may have increased COVID-19 mortality mostly during the early pandemic stage (February–April 2020) because this was when all individuals were immunologically naïve to SARS-CoV-2, and excess adiposity may have weakened immune system responses to this new virus; whereas later admission periods will have included more patients who have experienced repeat infections [ 33 , 34 , 35 ]. COVID-19 vaccines were introduced in the UK in December 2020 and uptake was earliest among the oldest and most clinically vulnerable (including both extreme obesity and underweight)—the same populations who present most often to ICU. Vaccines substantially reduce mortality from SARS-CoV-2 infection and COVID-19 [ 36 ], and along with improved therapeutics likely explain overall declines in mortality, acute severity, and mean age of patients admitted to ICU with COVID-19 post-2020. Notably, however, our results suggest that adiposity-mortality associations among COVID-19 patients started diminishing in May–July 2020, before vaccines were introduced in December 2020. This suggests that vaccines/therapeutics are not the sole reason for variation over time in adiposity-mortality associations among COVID-19 patients and that, in addition to changes in natural immunological naivety of the population, confounding/selection bias, which also varied over time, played a role. For example, collider (selection) bias [ 22 , 29 , 37 ] could have manifested in COVID-19 patients being admitted to ICU at higher BMIs yet with fewer comorbidities over the course of the pandemic, following early evidence on obesity-mortality associations [ 4 , 20 ]. Our descriptions of patient characteristics over time do suggest increased mean/median BMI of patients admitted to ICU with COVID-19 from July 2020, while the prevalence of past severe illness declined/fluctuated over the same period. These negative BMI-comorbidity associations among COVID-19 patients selected into ICU could have biased obesity-mortality associations in February–April 2020 when obesity was associated with higher mortality, and/or in later time periods where obesity appears unrelated to mortality.

Our results and the results of earlier descriptive reports by ICNARC [ 25 ] indicate that obesity, including extreme obesity, is more common within COVID-19 than non-COVID-19 respiratory conditions. It is well known that severe COVID-19 occurs more frequently in patients with more comorbidities in the general population [ 38 ], but our results suggest that, within ICU, patients with COVID-19 tend to have fewer comorbidities than patients with non-COVID-19 respiratory conditions. This is despite obesity being more common among COVID-19 patients. This same pattern was also seen in the Netherlands, based on one study using ICU data which compared the adiposity and comorbidity profile of ~ 2600 ICU patients with COVID-19 vs. ~ 2900 with non-COVID-19 viral pneumonia [ 39 ]. That Dutch study reported the same contrasting pattern of BMI-mortality associations between patient groups: positive among COVID-19 patients and negative among non-COVID-19 respiratory patients. MR studies of adiposity and COVID-19 mortality do not exist for comparison, including for clinically selected patient samples, except for one MR study which examined ‘critical respiratory illness’ as a composite outcome of death, intubation, or advanced oxygen support, which supported a detrimental effect of BMI [ 5 ]. This obesity paradox in ICU is striking and may help reveal the potential for patient selection and reverse causation to bias BMI-mortality associations within severe respiratory disease more broadly—with COVID-19 vs. non-COVID-19 offering another type of cross-context comparison to assess context-stable bias. Pre-existing disease may reduce BMI and raise mortality, and this may explain long-standing observations of higher mortality with underweight and lower mortality with obesity (vs. recommended weight) among respiratory disease patients [ 12 , 13 ]. Indeed, MR estimates, which should be less prone to confounding by pre-existing disease, suggest that higher adiposity raises pneumonia mortality [ 14 ], although MR analyses are lacking for hospitalised patients specifically given the lack of genetic data at scale. Given the relatively healthy comorbidity profile of COVID-19 patients, associations between BMI and mortality among them may be less subject to reverse causation and thus inform on the likely causality of BMI for mortality among both COVID-19 and non-COVID-19 patients (if these conditions are clinically similar). This is important given the need for appropriate clinical messaging around obesity during potential dual burdens of COVID-19 and influenza in future. Our results suggest unfavourable obesity-mortality associations among COVID-19 patients (who have fewer comorbidities), in contrast to favourable obesity-mortality associations among non-COVID-19 patients (who have more comorbidities). If comorbidity-induced weight loss is expected to bias associations, then these results suggest that obesity may not be protective in either group, and that weight loss/maintenance advice applies to both groups.

Over a dozen previous studies examined associations of BMI with mortality within severe COVID-19; all used conventional observational designs and most were small scale ( N  < 500), with larger studies suggesting that excess mortality is driven by extreme obesity, where this was examined [ 40 , 41 , 42 , 43 , 44 , 45 ]. Given the global spread of COVID-19, the totality of studies examining adiposity-mortality associations naturally provides a comparison of these associations across temporal and geographical contexts, but the study designs and methods used differ in many other respects and no previous study directly compared dates or regions using the same analytical strategy. One UK study examined how associations between ethnicity and mortality among COVID-19 patients changed over calendar time, but did not examine BMI [ 32 ]. Importantly, none of these past studies directly compared the associations of confounding/selection factors with adiposity and mortality across contexts to appraise their impact. Previous studies also tended to statistically adjust for comorbidities in main effect models, which may be an overadjustment and could induce collider bias given that comorbidities can result from adiposity. With cross-context comparisons, however, it is difficult to identify specific confounding or selection factors which underpin any differences in exposure-outcome associations; multiple factors are likely influential, many of which are likely unmeasured and are only proxied by factors which are stratified on. Interpreting results necessarily relies on critical judgement and assessing causality is inherently qualitative.

Limitations

This study is observational and associations are subject to confounding, selection bias, reverse causation, and measurement error. Cross-context comparisons are intended to interrogate the extent and impact of such biases on exposure-outcome associations but offer incomplete and qualitative assessments. Measurement error may be problematic for adiposity given that this was measured indirectly using BMI which correlates less well with more objective measures of fat mass in severely ill vs. young healthy populations, although the correlation between BMI and abdominal fat area is ~ 0.7 among severely ill adults [ 46 , 47 ]. The relationship between BMI and percentage body fat may also differ between ethnicity groups, although the extent of this is variable [ 48 , 49 ]. Data are also collected within ‘real world’ ICU settings and are often recorded less accurately than in research-grade clinics. BMI measures here were a mixture of directly measured and visually estimated values upon ICU admission, and these proportions varied more over time for COVID-19 vs. non-COVID-19 patients (reflecting changing staff workloads/resources during virus waves). The extent of estimated values was similar between patient groups, however, and these different methods of BMI recording have previously shown consistent associations with ICU mortality [ 27 ]. Furthermore, the exclusion of estimated BMI values in a sensitivity analysis did not change the overall BMI-mortality associations beyond their confidence limits. The estimation of BMI appeared to be most common when COVID-19 admissions were highest, which probably reflects staff workloads at the time. Staff workload and ICU capacity (including seasonal variation in non-COVID-19 admissions) might cause temporal variation in the potential for bias in BMI-mortality associations if they affect admission criteria and/or clinical practice.

Our study is limited to data which are routinely collected in ICU settings nationally, and thus data on other adiposity measures such as waist circumference and lifestyle factors such as smoking, diet, and physical activity were not available. Smoking history data would be particularly useful for assessing confounding, e.g. where excess mortality with underweight in non-COVID-19 may have been partly confounded by the effects of smoking on weight loss and mortality. Comorbid disease indicators were also limited to very severe forms of disease and excluded less severe diseases which are still relevant to mortality, such as type 2 diabetes and other cardiovascular diseases. One Dutch ICU study did, however, record diabetes history in patients with COVID-19 and non-COVID-19 pneumonia and found no difference between groups (~ 20% in each) [ 39 ].

Proportional hazards tests found that both the positive BMI-mortality associations among COVID-19 patients and the negative ones among non-COVID-19 patients attenuated towards the null with increasing time in ICU. This could be because mortality disproportionately removes frailer patients, causing a decline in the hazard over time, but more so at those levels of BMI where mortality is highest [ 50 ]. A marginal hazard ratio under non-proportional hazards can be interpreted as an average effect over the support of the data, but its value can be sensitive to the censoring distribution [ 30 , 31 ]. Had we chosen to administratively censor at more than 30 days, it is therefore likely that HR would have been a little closer to the null, particularly for COVID-19 patients. Because of our interest in heterogeneity between regions or dates of admission, heterogeneity in the censoring distribution is of concern to us. This was only apparent in the final period for COVID-19 patients (May to August 2021), when there was less administrative censoring (i.e. at 30 days) and much more earlier censoring (people who were still in ICU when data collection ended). It is therefore reassuring that results for this period were not particularly different from the others (with the possible exception of the very high HR for underweight people, which should be interpreted with caution). Rather, it is the first period for COVID-19 patients which was distinctive from the others.

Lastly, the ICU data used here for COVID-19 patients were representative of adult ICU patients in England, Wales, and Northern Ireland, but likely excluded individuals who were most extremely clinically vulnerable as they would have been shielding during peak stages of the pandemic and thus presenting less than usual to ICU, whereas equivalently extremely vulnerable non-COVID-19 respiratory patients in 2018–2019 would have presented more readily. Hospital practices were also atypical during COVID-19 surges and many patients severely ill with COVID-19 were likely managed outside of ICU on regular wards due to limited capacity. Such practices would have likely varied more by time than by geography given that national clinical guidance and protocols were rapidly shared across regions of the UK via the NHS during the pandemic. However, BMI-mortality associations among non-COVID-19 patients during the pandemic resembled closely those from 2018 to 2019 and were similarly different from contemporary BMI-mortality associations among COVID-19 patients, suggesting that patient selection affects BMI-mortality associations much less than the reason for admission does. We did not account for potential pseudoreplication due to the same individuals having multiple non-COVID-19 admissions (or both COVID-19 and non-COVID-19 admissions) but this would have been rare in the short study period.

Our results based on a cross-context comparison approach with nationally representative ICU data in the UK suggest that higher adiposity, primarily extreme obesity, is associated with higher mortality among patients admitted to ICU with COVID-19, but lower mortality among patients admitted with non-COVID-19 respiratory conditions. If these associations among COVID-19 patients had remained consistent despite the observed temporal heterogeneity in potential confounding/selection bias, it would have increased our willingness to interpret them as causal. However, BMI-mortality associations among COVID-19 patients differed by admission date, questioning the existence or stability of causal effects. Among non-COVID-19 respiratory patients, there was less temporal or regional heterogeneity in potential bias, diminishing the power of this approach to test causation. However, the relatively stable and strong associations of comorbidities with both BMI and mortality in this patient group, coupled with their high prevalence of comorbidity, suggest that favourable obesity-mortality associations among non-COVID-19 respiratory patients may reflect comorbidity-induced weight loss.

Availability of data and materials

Individual-level data are available via application to ICNARC (managed access).

Abbreviations

Acute physiology and chronic health evaluation-II

Body mass index

Confidence interval

Coronavirus disease 2019

Fractional inspired oxygen

Hazard ratio

Intensive Care National Audit and Research Centre

Intensive care unit

Index of Multiple Deprivation

Mendelian randomisation

National Health Service

Arterial oxygen partial pressure

Severe acute respiratory syndrome coronavirus 2

Standard deviation

United Kingdom

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Acknowledgements

We thank and respect all those working in critical care units across England, Wales, and Northern Ireland and contributing to the care of patients and, particularly, those responsible for submitting data rapidly and regularly during the COVID-19 epidemic. Additional Intensive Care National Audit & Research Centre Coronavirus Disease 2019 Team Members: Yemi Banjo, Kasia Borowczak, Tom Cousins, Peter Cummins, Keji Dalemo, Robert Darnell, Hanna Demissie, Laura Drikite, Andrew Fleming, Ditte Frederiksen, Sarah Furnell, Abdo Hussein, Abby Koelewyn, Tim Matthews, Izabella Orzechowska, Sam Peters, Alvin Richards-Belle, Tyrone Samuels, and Michelle Saull.

JAB, DC, and AH are supported by the Elizabeth Blackwell Institute for Health Research and the Development and Alumni Relations Office, University of Bristol. JAB, DC, AH, KT, and GDS work in a unit funded by the UK MRC (MC_UU_00011/1; MC_UU_00011/3) and the University of Bristol. This publication is the work of the authors and JAB is the guarantor for its contents. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Author information

Joshua A. Bell and David Carslake contributed equally to this work.

David A. Harrison, Kathryn M. Rowan and George Davey Smith contributed equally to this work.

Authors and Affiliations

MRC Integrative Epidemiology Unit at the University of Bristol, Bristol, UK

Joshua A. Bell, David Carslake, Amanda Hughes, Kate Tilling, James W. Dodd & George Davey Smith

Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK

Joshua A. Bell, David Carslake, Amanda Hughes, Kate Tilling & George Davey Smith

Academic Respiratory Unit, Southmead Hospital, University of Bristol, Bristol, UK

James W. Dodd

Intensive Care National Audit & Research Centre (ICNARC), London, UK

James C. Doidge, David A. Harrison & Kathryn M. Rowan

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Contributions

JAB, DC, AH, and GDS planned the study. JAB and DC conducted analyses presented here and JAB wrote the first draft. JCD, DAH, and KMR contributed to the curation of ICNARC data. JAB, DC, AH, KT, JWD, JCD, DAH, KMR, and GDS critically reviewed the intellectual content of manuscript drafts and read and approved the final manuscript. JAB (corresponding author) attests that all listed authors meet authorship criteria and that no others meeting the criteria have been omitted.

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Correspondence to Joshua A. Bell .

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Approval for the collection and use of patient-identifiable data without consent in the Case Mix Programme was obtained from the Confidentiality Advisory Group of the Health Research Authority under Sect. 251 of the NHS Act 2006 (approval number PIAG2–10[f]/2005). All data were pseudonymised (patient identifiers removed) prior to extraction for this research.

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Supplementary Information

12916_2024_3598_moesm1_esm.docx.

Additional file 1: Figure S1 Age and sex profiles of ICU patients with COVID-19 (1 March 2020 to 31 July 2021) and non-COVID-19 respiratory conditions (1 Feb 2018 to 31 Aug 2019), by admission date.

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Additional file 2: Figure S2 Ethnic group, deprivation and adiposity profiles of ICU patients with COVID-19 (1 March 2020 to 31 July 2021) and non-COVID-19 respiratory conditions (1 Feb 2018 to 31 Aug 2019), by admission date.

12916_2024_3598_MOESM3_ESM.docx

Additional file 3: Figure S3 Age profiles of ICU patients with COVID-19 (5 Feb 2020 to 1 Aug 2021) and non-COVID-19 respiratory conditions (1 Feb 2018 to 31 Aug 2019), by geographical region.

12916_2024_3598_MOESM4_ESM.docx

Additional file 4: Figure S4 Sex and ethnic group profiles of ICU patients with COVID-19 (5 Feb 2020 to 1 Aug 2021) and non-COVID-19 respiratory conditions (1 Feb 2018 to 31 Aug 2019), by geographical region.

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Additional file 5: Figure S5 Deprivation and adiposity profiles of ICU patients with COVID-19 (5 Feb 2020 to 1 Aug 2021) and non-COVID-19 respiratory conditions (1 Feb 2018 to 31 Aug 2019), by geographical region.

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Additional file 6: Figure S6 Adiposity, dependency and comorbidity profiles of ICU patients with COVID-19 (1 March 2020 to 31 July 2021) and non-COVID-19 respiratory conditions (1 Feb 2018 to 31 Aug 2019), by admission date.

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Additional file 7: Figure S7 Adiposity and prior dependency profiles of ICU patients with COVID-19 (5 Feb 2020 to 1 Aug 2021) and non-COVID-19 respiratory conditions (1 Feb 2018 to 31 Aug 2019), by geographical region.

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Additional file 8: Figure S8 Respiratory support, BMI reporting and mortality profiles of ICU patients with COVID-19 (1 March 2020 to 31 July 2021) and non-COVID-19 respiratory conditions (1 Feb 2018 to 31 Aug 2019), by date of admission.

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Additional file 9: Figure S9 Respiratory support and BMI reporting profiles of ICU patients with COVID-19 (1 March 2020 to 31 July 2021) and non-COVID-19 respiratory conditions (1 Feb 2018 to 31 Aug 2019), by geographical region.

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Additional file 10: Figure S10 Comorbidity and acute severity profiles of ICU patients with COVID-19 (5 Feb 2020 to 1 Aug 2021) or non-COVID-19 respiratory conditions (1 Feb 2018 to 31 Aug 2019), by geographical region.

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Additional file 11: Figure S11 Acute severity, physiological severity and respiratory severity profiles of ICU patients with COVID-19 (1 March 2020 to 31 July 2021) and non-COVID-19 respiratory conditions (1 Feb 2018 to 31 Aug 2019), by admission date.

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Additional file 12: Figure S12 Physiological and respiratory severity profiles of ICU patients with COVID-19 (5 Feb 2020 to 1 Aug 2021) or non-COVID-19 respiratory conditions (1 Feb 2018 to 31 Aug 2019), by geographical region.

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Additional file 13: Figure S13 Mortality profiles of ICU patients with COVID-19 (1 March 2020 to 31 July 2021) and non-COVID-19 respiratory conditions (1 Feb 2018 to 31 Aug 2019), by geographical region.

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Additional file 14: Figure S14 Association between BMI and mortality in COVID-19 and non-COVID-19 respiratory patients, by admission date.

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Additional file 15: Figure S15 Association between BMI and mortality in COVID-19 and non-COVID-19 respiratory patients, by geographical region.

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Additional file 16: Table S1 Associations of confounding and selection factors with BMI and 30-day all-cause mortality among ICU patients with COVID-19 (5 Feb 2020 to 1 Aug 2021) and non-COVID-19 respiratory conditions (1 Feb 2018 to 31 Aug 2019).

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Additional file 17: Table S2 Associations of confounding/selection factors with BMI among ICU patients with COVID-19, by admission date

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Additional file 18: Table S3 Associations of confounding/selection factors with BMI among ICU patients with COVID-19, by admission region

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Additional file 19: Table S4 Associations of confounding/selection factors with BMI among ICU patients with non-COVID-19 respiratory conditions, by admission date

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Additional file 20: Table S5 Associations of confounding/selection factors with BMI among ICU patients with non-COVID-19 respiratory conditions, by admission region

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Additional file 21: Table S6 Associations of confounding/selection factors with all-cause mortality among ICU patients with COVID-19, by admission date

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Additional file 22: Table S7 Associations of confounding/selection factors with all-cause mortality among ICU patients with COVID-19, by admission region

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Additional file 23: Table S8 Associations of confounding/selection factors with all-cause mortality among ICU patients with non-COVID-19 respiratory conditions, by admission date

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Additional file 24: Table S9 Associations of confounding/selection factors with all-cause mortality among ICU patients with non-COVID-19 respiratory conditions, by admission region

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Additional file 25: Supplementary Table S10 Main analyses of all-cause mortality and BMI, restricted to ICU patients whose BMI was measured, not estimated.

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Additional file 26: Supplementary Table S11 Main analyses of all-cause mortality and BMI, restricted to ICU patients who were not physically dependent on others for the activities of daily living prior to admission.

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Additional file 27: Table S12 Number of deaths and total sample size for each BMI category in Table  2 .

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Additional file 28: Table S13 Number of deaths and total sample size for each BMI category and admission period in Table  3 .

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Additional file 29: Table S14 Number of deaths and total sample size for each BMI category and admission region in Table  4 .

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Bell, J.A., Carslake, D., Hughes, A. et al. Adiposity and mortality among intensive care patients with COVID-19 and non-COVID-19 respiratory conditions: a cross-context comparison study in the UK. BMC Med 22 , 391 (2024). https://doi.org/10.1186/s12916-024-03598-3

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Case Study: Behavior changes in the Family-Focused, Obesity-Prevention HOME Plus Program

The purpose of this case study is to describe two successful HOME Plus participants and highlight how an intervention with individual and group components can help families make lifestyle changes that result in improvements in child weight status.

One hundred sixty families participated in the HOME Plus study, and were randomized to either a control or intervention group.

Two successful HOME Plus participants were chosen because of their healthful changes in weight status and behavior and high engagement in the program.

Measurements

Data were collected at baseline and post-intervention, one year later. Data included height, weight, home food inventory, dietary recalls, and psychosocial surveys.

Intervention

Families in the intervention group participated in cooking and nutrition education sessions, goal-setting activities, and motivational interviewing telephone calls to promote behavioral goals associated with meal planning, family meal frequency and healthfulness of meals and snacks.

Analysis of the families’ behaviors showed Oliver (fictitious name) experienced changes in nutritional knowledge and cooking skill development while Sophia’s (fictitious name) changes were associated with healthful food availability and increased family meal frequency.

These cases show that offering a multi-component, family-focused program allows participants to select behavior strategies to fit their unique family needs.

Childhood obesity is a prevalent public health concern, resulting in negative effects on children’s health ( Ogden, Carroll, Kit, & Flegal, 2014 ). Therefore, it is important to develop public health strategies to address and prevent the epidemic of childhood obesity. Both individual and group interventions have been developed to prevent childhood obesity ( Sobol-Goldberg, Rabinowitz, & Gross, 2013 ; Wang et al., 2015 ). Primary prevention interventions show the most promise in healthful lifestyle change when they incorporate multiple components and levels that influence children’s eating and exercise habits in the home, school, community and peer environment ( Foltz et al., 2012 ; Hoelscher, Kirk, Ritchie, & Cunningham-Sabo, 2013 ). Public health programs incorporating group settings provide a cost-effective way to reach large numbers of people while providing a setting for peer support and reinforcement of learned skills ( Goldfield, Epstein, Kilanowski, Paluch, & Kogut-Bossler, 2001 ). However, group-based interventions often have a blanket approach to obesity prevention, which can allow some participants, especially the most vulnerable (e.g.. low income), to “slip through the cracks” ( Hoelscher et al., 2013 ). Group interventions may fall short of offering the appropriate amount of support needed by disadvantaged populations.

Secondary and tertiary approaches to obesity prevention take a more individualized approach, often involving the child’s family ( Hoelscher et al., 2013 ). Successful interventions have used motivational interviewing (MI), goal setting, cognitive restructuring, positive reinforcement and parent involvement ( Davis et al., 2007 ). These approaches provide participants the opportunity to self-identify unhealthful behaviors and develop strategies for improvement ( Resnicow, Davis, & Rollnick, 2006 ). In particular, MI promotes self-efficacy and encourages change in participants by exploring ambivalence they may feel towards behavioral changes while providing opportunities to make healthful decisions, goals and lifestyle changes. This active participation is a critical component of adherence; participants are more likely to maintain motivation and exhibit long-term healthful behaviors ( Tripp, Perry, Romney, & Blood-Siegfried, 2011 ).

Various community-based lifestyle intervention programs have shown moderate success in primary outcomes such as decreasing standardized BMI scores (i.e., BMI z-score), waist circumferences and fat mass while increasing cardiovascular health and self-esteem ( Ho et al., 2012 ; Summerbell et al., 2005 ). However, many of these programs have published solely on the main outcomes of their studies, without examining how participants specifically use the interventions and education offered. Success is greatest when health behaviors are integrated into an everyday routine. Understanding how this dynamic process of behavior change occurs can help guide the development of public health programing to effectively reduce childhood obesity.

The purpose of this study is to describe two participants in the Healthy Home Offerings via the Mealtime Environment (HOME) Plus study who were successful in reducing their BMI z-score during the intervention program. The HOME Plus program offered parents and their 8–12 year old child cooking and nutrition education in the form of group cooking and nutrition education sessions, family goal-setting activities, and individualized telephone calls using an MI approach. Using this multi-component methodology, the HOME Plus program aimed to prevent childhood obesity by increasing healthful meal and snack habits while decreasing sedentary behaviors such as screen time ( Flattum et al., 2015 ; Fulkerson et al., 2014 ; Fulkerson et al., 2015 ). Although the main outcomes of the overall HOME Plus program have been evaluated ( Fulkerson et al., 2015 , Fulkerson et al., 2018 ), less is known about what approach works best to help families apply the information they learn to achieve the desired behavioral changes and decrease children’s weight. The two children and their families described, Oliver Jorgenson and Sophia Lee (whose names have been changed to maintain confidentiality), both experienced a significant enough reduction in BMI to move from either being obese to overweight or overweight to normal weight over the course of the program. Their health behavior changes during the intervention program will be examined and compared to the changes reported for the intervention group as a whole to provide context and a greater understanding of the magnitude of the participants’ health related behavior changes.

Design and Sample

The HOME Plus study used a randomized controlled trial design to test the effects of a community-based, family-focused childhood obesity prevention program. A description of the methodology can be found elsewhere ( Fulkerson et al., 2014 ). Primary meal preparing parents (n=160) and one 8–12 year old child (n=160) per family were recruited from the Minneapolis/St. Paul metropolitan area. Families were randomized into the intervention or control group after baseline assessments. Control group families (n=79) received monthly newsletters, while intervention families (n=81) attended monthly family-focused, group-taught sessions and received five individual telephone calls over the 10-month program ( Flattum et al., 2015 ). Parent and child participants provided written consent or assent, respectively. The University of Minnesota’s Institutional Review Board approved all study methods and procedures.

Social Cognitive Theory and an ecological framework were used to develop the HOME Plus intervention ( Bandura, 1986 ; Bronfenbrenner, 1994 ). These theories incorporate the role of families in the initiation, support and reinforcement of behavior change (e.g., healthful dietary intake, reduction of sedentary behaviors). The HOME Plus study’s purpose was to reduce childhood obesity by increasing family meal frequency and the availability of healthful food in the home, while improving children’s dietary intake and decreasing sedentary behavior ( Fulkerson et al., 2014 ). Three behavioral objectives guided intervention behavioral messages and family goals: 1) plan healthy meals and snacks with your family more often, 2) have meals with your family at home more often , and 3) improve the healthfulness of the food available at home ( Draxten, Flattum, & Fulkerson, 2016 ; Flattum et al., 2015 ).

Each group session incorporated nutrition education, cooking skills and family meal goal setting ( Flattum et al., 2015 ). In addition to group sessions, the primary meal preparer was contacted five times by a registered dietitian trained in MI. MI phone calls complimented group sessions by providing extra support, motivation and opportunity for families to explore and individualize goals for their families in relation to the behavioral objectives ( Draxten, Flattum, & Fulkerson, 2016 ).

Data were collected at baseline (2011 for Oliver and 2012 for Sophia) and one year later at post-intervention (2012 for Oliver and 2013 for Sophia).

Motivational Interview Phone Call Data

Interventionists entered call notes, including goal selection, ongoing progress and goal attainment from each MI call into a secure REDCap database. These notes were reviewed for themes to identify which of the three behavioral objectives each family chose to work on during their calls.

Child Anthropometry Data

Trained staff measured child height and weight using standardized protocols ( Lohman, Roche, & Martorell, 1988 ). BMI values and percentiles were calculated and adjusted for age and gender ( Centers for Disease Control, 1999 ). Three weight categories were created: normal weight (i.e., <85 th percentile), overweight (i.e., ≥85 th percentile and <95 th percentile) and obese (i.e., ≥ 95 th percentile) according to the CDC definitions.

Home Food Inventory Survey

Parents completed a Home Food Inventory (HFI) shown to have high construct and criterion validity (kappa range = 0.61 to 0.83; sensitivity range = 0.69–0.89; specificity = 0.86–0.95) ( Fulkerson et al., 2008 ). The HFI obesogenic score was used to measure the availability of high-fat, sugar and/or processed foods in the home. Higher scores (i.e., more obesogenic) indicated a less healthful home food environment.

Child 24-Hour Dietary Recall

Trained staff conducted three (two weekdays and one weekend day) 24-hour dietary recall interviews. Dietary intake data were collected using Nutrition Data System for Research software versions 2011 and 2012. Final calculations were completed using version 2012 (Nutrition Coordinating Center, University of Minnesota, Minneapolis, MN). Results were averaged across the three days to obtain the child’s average daily servings of fruit and vegetables.

Psychosocial Surveys

Parents and children each independently completed psychosocial surveys to assess personal and behavioral variables targeted in the intervention. Parents reported on their own self-efficacy to cook healthy meals and food restriction practices . Parents also reported on their perception of the frequency that their child helps choose and prepare meals and snacks , their perception of their child’s cooking skills , their perception of the frequency of family meals , and their perception of family meal expectations and discussions . Children reported on their own perceptions of family connectedness and dinner enjoyment and their food neophobia (fear of trying new foods). See Table 1 for a description of these personal and behavioral measures.

Scales from the Parent and Child Psychosocial Survey used in the HOME Plus Study

Analytic Strategies

Two child participants and their family’s primary meal preparer were chosen from the intervention group: Oliver Jorgenson, a ten-year-old male, and Sophia Lee, a nine-year-old female. Both identified from underrepresented minority populations. Selection criteria included high program participation (≥70% attendance) and a reduction in BMI z-scores over the course of the program. These two cases were chosen subjectively, but represent a wide range of health challenges encountered by many families in the study. MI phone call notes were reviewed to evaluate participant’s ongoing progress and goal attainment. To provide context for the magnitude of the cases’ data, behavioral changes were compared to average changes in the entire HOME Plus intervention group.

Oliver Jorgenson

Oliver Jorgenson’s anthropometric data placed him in the “obese” weight category at baseline (BMI percentile = 97.3%). After the intervention, his BMI decreased and he was considered “overweight” ( Table 2 ). Oliver and his primary meal-preparing parent attended 90% of the HOME Plus group sessions and participated in all of the MI phone calls.

Participant Change in BMI and Weight Status Category

Note. Normal weight = child age and gender adjusted BMI < 85%; Overweight = child age and gender adjusted BMI 85% ≤ but < 95%; Obese = child age and gender adjusted BMI ≥ 95%

Many of the topics brought up by Oliver’s parent during the phone calls aligned with behavioral objectives one ( plan healthful meals with family) and three ( improve healthfulness of foods available at home). During the first few calls, Oliver’s parent expressed surprise upon learning many of the foods the family was eating were considered unhealthful. Throughout the calls, Oliver’s parent showed progressive learning in regards to nutrition and healthy meal planning. The family incorporated several changes in their meal routine including decreased portion sizes, having fruit for snacks, adding vegetables to pizza, and exchanging some white rice for brown rice at meals. During the last call, Oliver’s parent conveyed more confidence in using strategies learned through the HOME Plus program.

Aligned with their focus on behavioral objectives one ( plan healthful meals with family) and three ( improve healthfulness of foods available at home,) during MI phone calls, Oliver and his family also showed improvement in these behavioral objectives as measured by their psychosocial survey scale scores ( Table 3 ). Compared to the average scores of the HOME Plus intervention group, Oliver experienced greater improvement in all aspects under behavioral objective one ( plan healthful meals with family) , including parent’s self-efficacy to cook healthful meals, parent’s perception of the frequency child helps choose and prepare meals and snacks, and parent’s perception of child cooking skills. Oliver and his family also showed development under behavioral objective three ( improve the healthfulness of foods available at home) . Although the family’s obesogenic home food availability (HFI) score increased, Oliver’s food neophobia score improved when compared to the intervention group’s average change ( Table 3 ). In addition, compared to his baseline intake, Oliver reported eating more servings of fruit and vegetables after the intervention ( Table 3 ).

Behavioral Objectives and Dietary Outcome

Note. BL= baseline, P=post-intervention, Δ=difference between baseline and post-intervention scores

In comparison to behavioral objectives one and three, survey responses from Oliver and his parent showed less change in behavioral objective two ( have meals with your family at home more often). Parental perception of frequency of family meals increased while child’s perception of family connectedness and dinner enjoyment decreased, no data were available on parental perceptions of family meal expectations and discussions.

Baseline anthropometric data indicated Sophia Lee was “overweight” (BMI percentile = 86.3%). At post intervention, Sophia’s BMI decreased and was categorized as “normal” weight ( Table 2 ). Sophia and her primary meal-preparing parent attended 70% of the HOME Plus intervention sessions and participated in all of the MI calls.

Throughout MI phone calls, Sophia’s parent focused on topics related to behavioral objectives two ( have meals with your family at home more often) and three ( improve healthfulness of foods available at home) . During the initial MI call, Sophia’s parent expressed feeling “busy and overwhelmed,” and stated healthful eating “was not a priority.” During the last two calls, Sophia’s parent reported the family was eating together more often, trying new foods, and having a greater variety of food choices in their home.

Aligned with the focus on behavioral objectives two ( have meals with your family at home more often) and three ( improve healthfulness of foods available at home) in the MI phone calls, analysis of psychosocial survey data showed Sophia and her family improved in these areas ( Table 3 ). Specifically, Sophia showed consistent improvement in measures under behavioral objective number two ( have meals with your family at home more often) , showing increases in parent’s perception of frequency of family meals, parent’s perception of family meal expectations and discussions, and child’s perception of family connectedness and dinner enjoyment ( Table 3 ). The family also showed changes under behavioral objective three ( improves the healthfulness of foods available at home) . Sophia’s family showed a decrease in their HFI obesogenic score while their parental food restriction practices increased, especially when compared with the intervention group’s average change ( Table 3 ). In addition, compared to baseline, Sophia reported eating more servings of vegetables at post-intervention. Sophia’s family showed less improvement in behavioral objective one ( plan healthful meals and snack with family more often). Data showed decreases in parental perception of both frequency in which child helps choose and prepare meals and snacks and in child’s cooking skills.

The HOME Plus program is a family-focused, multi-component, childhood obesity prevention program incorporating group and individualized approaches to healthful lifestyle modifications, particularly related to frequent and healthful family meals. The program offered participants the opportunity to learn about and employ nutritional knowledge, hands-on cooking skills and meal planning strategies. This multi-component approach provided participants with consistent healthful lifestyle messages and tools and strategies to promote and attain better health while allowing for individualization. Currently, most of the research published on similar programs only analyzes and reports on program effectiveness as a whole without consideration of individual participant differences. Therefore, these two case studies are unique, illustrating how families choose to adopt/incorporate lifestyle changes into their daily routines.

MI phone calls with Oliver’s family revealed the benefit of nutrition education obtained from the group sessions, which provided the initial building blocks needed to make healthful lifestyle changes. This finding is consistent with studies that have shown the positive effects of nutritional education on children’s dietary intake ( Evans, Christian, Cleghorn, Greenwood, & Cade, 2012 ; Howerton et al., 2007 ). Oliver’s family was also able to increase cooking self-efficacy, child cooking skills and child frequency of helping with meal preparation. Cooking instruction emphasizing healthier ways to prepare meals to have a positive impact on dietary intake and behaviors and nutrition education incorporating hands-on cooking skills increases participants’ confidence, knowledge and attitudes towards cooking, while improving overall healthy eating behaviors such as vegetable and fruit intake ( Fulkerson et al., 2015 ; Hersch, Perdue, Ambroz, & Boucher, 2014 ; Reicks, Trofholz, Stang, & Laska, 2014 ). In addition, children who develop cooking skills are also more likely to try new foods and less likely to depict food neophobic traits, a change seen in Oliver’s case. Oliver and his family are an example of how developing practical cooking skills can empower families. Families gain more control over what and how their food is prepared by participating in hands-on programming that includes cooking skills.

Sophia and her family showed substantive changes in behavioral outcome two ( having meals at home with family more often) . Despite their busy schedule, Sophia’s family increased their family meal frequency and improved the quality of their family mealtime dynamics. Numerous studies have shown significant associations between family meal frequency and physical and psychological benefits for children and adolescents ( Fulkerson, Larson, Horning, & Neumark-Sztainer, 2014 ; Hammons & Fiese, 2011 ). Family meals provide an informal “check-in” time for children and parents to connect; children are able to express emotions or concerns, while parents can validate their child’s feelings and provide support. By creating a consistent, supportive mealtime environment, Sophia’s family may be providing her with the foundation needed to make healthful lifestyle changes.

Sophia’s parent reported lower scores at post-intervention measurements compared to baseline measurement for parent’s perception of the frequency their child helps choose and prepare meals and snacks and also parent’s perception of their child’s cooking skills. This decrease was inconsistent with the rest of the intervention families. It could be that Sophia was helping with cooking and meal preparation less at the time of the post-intervention measurement. However, the decrease may have occurred as Sophia’s parent gained a more realistic perception of Sophia’s cooking skills and abilities after participating in the HOME Plus program. Sophia’s family did not focus on behavioral outcome one ( plan healthful meals and snack with family more often) during their MI calls, suggesting this was not a focus for their family.

Oliver and Sophia both experienced an overall improvement in behavioral outcome number three ( improving the healthfulness in food availability at home) . They both decreased their food neophobia scores, indicating more willingness to try new foods. In addition, both reported higher parental food restriction practices. Although severely restrictive parenting styles have been associated with an increase in eating impulsivity and high BMI in children, discrete restriction tactics such as limiting unhealthful home food availability have been shown to be beneficial for controlling adolescent intake ( Loth, MacLehose, Larson, Berge, & Neumark-Sztainer, 2016 ). By limiting the availability of unhealthful foods in the home, as indicated by the decreased HFI obesogenic score, Sophia’s parent may have been exercising the appropriate level of restriction needed to encourage Sophia’s healthier dietary intake.

Looking only at anthropometric data, both Oliver and Sophia experienced a decrease in BMI after participating in the HOME Plus program ( Table 1 ). However, a more refined story emerges when looking at the different paths each family took to become healthier. Although both families showed healthier home food availability, Oliver’s family (but not Sophia’s family) showed clear improvements in planning healthful meals. In contrast, Sophia’s family (but not Oliver’s family) showed greater improvement in having family meals together. Childhood obesity continues to be a major concern in public health today and finding effective yet efficient interventions for lowering community obesity rates can be challenging. The cases of Oliver and Sophia are examples of how multi-component programing can be used in the public health arena to deliver effective interventions to a large community, while still recognizing and capitalizing on participants’ differences, strengths and needs. When working at the community level of practice, using a multi-component approach to prevent childhood obesity may benefit future obesity prevention program effectiveness, as it can allow for some customization for participants.

This case study description has limitations. As a case study, it merely aims to describe changes and not predict nor denote statistical significance between groups. In addition, all survey data were self-reported and may be influenced by social desirability. In-person interviews may have provided this study with a greater, more in-depth understanding of families’ perspectives. Nonetheless, this case review used data from a variety of well-validated scales to help paint a multi-dimensional picture of two HOME Plus study participants who managed success in weight change but in different ways. Future direction for multi-component obesity prevention programs should examine the value of providing group and individual support across systems. Intervention programs, such as HOME Plus, should consider collaboration with schools and other community programs, such as park and recreation, in order to reach populations within and across different settings.

Analysis of the study cases shows how the HOME Plus program meet needs of different families, providing them with choice and support to make healthful behavior change. Traditionally, group-based obesity prevention programs have offered families a broad, blanket approach to encourage behavior change, but families are vastly different, varying in lifestyle habits, cultural preferences, daily routines and values. Ultimately, one lifestyle change may be helpful for one family, but not for another. By taking the time to understand how components are integrated into families’ lives by analyzing individual cases, public health nurses may be better able to tailor obesity prevention programs to better meet participants’ needs and leverage their strengths. In addition, offering individualized components in a multi-faceted intervention may be important for maximal participant engagement as well as support and follow-up during behavior change. Healthful living is a lifestyle change requiring active participation and dedication from the participants. For this reason, it is imperative public health nurses help families feel empowered to take charge and play an active role in their health.

Acknowledgments

This publication was completed as a capstone project for a Master’s of Nursing degree at the University of Minnesota. This study and publication was supported by Grant R01 DK08400 by the National Institute of Diabetes and Digestive and Kidney Diseases at the National Institutes of Health (NIH). Its contents are solely the responsibility of the authors and do not necessarily represent the views of the NIH. Software support was also provided by the University of Minnesota’s Clinical and Translational Science Institute (Grant 1UL1RR033183 from the National Center for Research Resources of the NIH). The HOME Plus trial is registered with ClinicalTrials.gov Identifier: NCT01538615.

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