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StatPearls [Internet]. Treasure Island (FL): StatPearls Publishing; 2024 Jan-.

StatPearls [Internet].

Obesity and type 2 diabetes.

Kanica Yashi ; Sharon F. Daley .

Affiliations

Last Update: June 19, 2023 .

Continuing Education Activity

Obesity is a common risk factor for type 2 diabetes. Screening for diabetes is indicated in all patients with obesity. Treating obesity is the cornerstone in the prevention and management of type 2 diabetes. Weight reduction leads to prevention, control, and in some cases, remission of diabetes. This activity reviews the management of patients with obesity and diabetes, including lifestyle interventions, pharmacologic treatment, and metabolic and bariatric surgery.

Determine lifestyle risk factors and plan aggressive modification strategies to initiate treatment.
Select appropriate pharmacologic treatments for patients with coexisting obesity and type 2 diabetes.
Identify the indications for surgical intervention for coexisting obesity and type 2 diabetes.
Collaborate with the interprofessional team to educate, treat, and monitor patients with obesity and type 2 diabetes to improve patient outcomes.
Introduction

Excess body weight and obesity are significant risk factors for type 2 diabetes mellitus (T2DM). Obesity management in patients with T2DM should be implemented according to Guidelines from the American Diabetes Association and the American Obesity Association, including lifestyle interventions, pharmacologic treatments, and surgical indications. Leaders at an international consensus conference, the Second Diabetes Surgery Summit (2016), developed a treatment algorithm for metabolic and bariatric surgery in patients with obesity and diabetes. [1] [2]

Issues of Concern

The lifetime diabetes risk in men older than 18 years increases from 7% to 70% when BMI increases from less than 18.5 kg/m to more than 35 kg/m. Similarly, the lifetime diabetes risk in females increases from 12% to 74% with the same BMI values. [3] Therefore, screening for diabetes is indicated in all patients with obesity. Treating obesity is the cornerstone in the prevention and management of T2DM. Weight loss leads to a significant reduction in the incidence of diabetes in at-risk populations. In one study, lifestyle modifications such as modest weight reduction (5-10% of baseline weight) and increased physical activity to at least 150 minutes per week reduced the incidence of diabetes by more than 50%. [4]

Similarly, bariatric surgery led to a 5-fold reduction in T2DM incidence over 7 years. [5] Weight reduction is also effective in the treatment of T2DM. Glycemic control improves proportionally with weight loss, sometimes leading to remission. [6] Treatment of T2DM begins with lifestyle management, followed by pharmacologic treatment and surgical therapy, if indicated.

The most common metric used to evaluate body weight is body mass index (BMI). BMI is calculated by dividing the patient's weight by the square of the height (kg/m). BMI stratifies patients into the following categories:

Underweight: less than 18.5 kg/m
Healthy weight: 18.5 to 24.9 kg/m (18.5 to 22.9 kg/m in the Asian population)
Overweight: 25 to 29.9 kg/m (23 to 27.4 kg/m in the Asian population)
Obese, class 1: 30 to 34.9 kg/m (27.5 to 32.4 kg/m in the Asian population)
Obese, class 2: 35 to 39.9 kg/m (32.5 to 37.4 kg/m in the Asian population)
Obese, class 3: greater than 40 kg/m (greater than 37.5 kg/m in the Asian population)

The upper value in each category is reduced for individuals of Asian descent because they tend to have a higher body fat content and increased risk of T2DM at a lower BMI. Many international societies have adopted these lower ranges to define obesity in individuals of Asian descent. [7]

Lifestyle Management

Obesity is a chronic medical condition and a known risk factor for the development of T2DM. In patients diagnosed with overweight or obesity and T2DM, treatment begins with aggressive modification of lifestyle risk factors. [8] These modifications include the following:

Self-management education
Nutritional counseling
Increasing physical activity
Psychosocial care, if indicated
Smoking cessation for smokers

Patient self-management education promotes a better understanding of coexisting chronic conditions and improves knowledge about self-testing and compliance with medical therapy. Patient education is first recommended when T2DM is diagnosed, then annually, and again if complications develop. [9] Nurses, registered dietitians, primary care providers, and other specialists contribute to patient education. Motivational interviewing, visual aids, handouts, and electronic resources are valuable teaching tools.

The goal of lifestyle modification is a loss of at least 5% of body weight to achieve beneficial effects. [10] The Look AHEAD trial provides the most data on the effect of intensive lifestyle intervention (ILI). This trial demonstrated sustained weight loss greater than 5% in at least half of the participants, and 27% of participants had greater than 10% weight loss at 8 years. [11]

Participants assigned to the ILI group required fewer medications for diabetes, hypertension, and lipid management. A calorie deficit of 500 to 750 kcal/day is generally advised to achieve this. Typically, this results in a calorie goal of 1200 to 1500 kcal/day for women and 1500-1800 kcal/day for men. Meal replacement plans can be helpful for some individuals in reaching a set calorie deficit, but their use is unlikely to be sustainable over the long term. Other successful dietary interventions include the DASH diet (Dietary Approach to Stop Hypertension) and the Mediterranean diet. [12] [13]

Modified alternate-day fasting and the 5:2 diet are the only types of intermittent fasting diets that have demonstrated a statistically significant weight loss of more than 5%. [14]

The diet chosen should be tailored to the patient's cultural and dietary patterns, food availability, and other factors such as hunger and access to healthy foods. Counseling sessions for nutrition, physical activity, and behavioral goals to achieve weight loss should be available to all patients. More than 16 such sessions in 6 months are included in standard ILI, and monthly sessions continue for patients who achieve the target weight loss after one year. However, this degree of intensive intervention may not be readily available or financially feasible in primary care settings.

Individuals with T2DM and obesity should be encouraged to increase their physical activity gradually. A minimum target of 150 minutes per week of moderate-intensity exercise is recommended. The weekly exercise plan should include aerobic exercise and two to three resistance training sessions. Exercising daily, or at least avoiding more than 2 days between exercise sessions, promotes decreased insulin resistance. [15]

Screening for mood disorders and other psychosocial factors related to diabetes and obesity should be done regularly. [16] [17] Treatment of coexisting mental health conditions can improve compliance and outcomes for patients with obesity and T2DM.

Teens and adults with obesity and diabetes should be screened for smoking and other tobacco use, including e-cigarettes. Smoking is associated with increased diabetes risk, possibly by increasing insulin resistance. [18] Counseling and appropriate pharmacological measures for smoking cessation should be offered to smokers.

Medical Evaluation and Treatment

When lifestyle modifications do not achieve weight loss goals, clinicians should review the patient's medical history for other contributing factors. This includes obesogenic medications such as thiazolidinediones, beta-blockers, sulphonylureas, insulin, risperidone, other antipsychotics, antidepressants, steroids, and gabapentin.

Pharmacotherapy is recommended as an adjunct to ILI in patients with a BMI of greater than 30 kg/m (greater than 25 kg/m in Asians) or greater than 27 kg/m (greater than 23 kg/m in Asians) with a weight-related complication such as hypertension, dyslipidemia, T2DM, or sleep apnea. [19]

After initiating therapy, patients should be monitored closely for medication efficacy and adverse effects. Early responders with a weight reduction of at least 5% in the first 12 weeks typically continue to achieve sustained and significant weight loss. Most weight loss medications are intended for long-term use. Initially, medical therapy for weight loss was studied only for brief use before the evidence supporting long-term use emerged in the last 2 decades. However, the FDA has approved phentermine only for short-term use of fewer than 12 weeks due to concerns about possible abuse.

FDA-approved weight loss medications for long-term use are detailed below.

(1) Phentermine-topiramate, extended-release

This combination was approved in 2012. The SEQUEL trial investigated this medication and found an average of 10% weight loss compared to less than 2% in the placebo arm. [20] It is efficacious in patients who are overweight up to class 3 obesity with a BMI of greater than 45 kg/m. The initial dose is 3.75 mg of phentermine and 23 mg of topiramate ER (3.75/23). This can be titrated up to 15 mg/92 mg in 2-week intervals as tolerated. Common adverse effects include insomnia, increased blood pressure, dry mouth, and paresthesias. It should not be used with a monoamine oxidase inhibitor. Phentermine-topiramate is associated with congenital malformations such as cleft lip or cleft palate. Pregnancy should be ruled out before prescribing, and contraception is essential when treating women of childbearing age with this agent.

(2) Liraglutide, semaglutide, and tirzepatide

These medications are glucagon-like peptide-1 receptor (GLP-1) agonists. They were initially approved for the treatment of T2DM but were found to be beneficial for weight loss. Since then, the FDA has approved liraglutide and semaglutide. FDA approval for tirzepatide to treat obesity is expected in 2023. GLP-1 agonists promote weight loss by multiple mechanisms, including insulin release stimulation, insulin sensitization, glucagon suppression, slower gastric emptying, and centrally acting early satiety. These medications are expensive, and availability can be limited based on insurance coverage.

Liraglutide was studied in the SCALE trial and led to about 5% additional weight loss compared to the placebo. [21] Dosing starts at 0.6 mg daily, administered subcutaneously (SC), and titrated at weekly intervals of up to 3 mg daily SC. Gastrointestinal disturbance and nausea are the most common adverse effects.

Semaglutide was examined in the STEP clinical trial. [22] Semaglutide administration led to a 15 to 16% weight loss by week 68. This medication leads to the most robust weight loss compared to other GLP-1 agonists. The initial dose is 0.25 mg SC weekly and can be increased every 4 weeks to 2.4 mg SC weekly.

The SURMOUNT-1 trial investigated the use of tirzepatide for weight loss in people with a BMI of more than 30 kg/m or 27 kg/m with diabetes mellitus. [23] Tirzepatide has a dual mechanism as a GLP-1 agonist and a glucose-dependent insulinotropic peptide. Approximately 20% weight loss was seen with 15 mg/week SC, the highest dose.

(3) Naltrexone-bupropion sustained release (SR)

This combination works by multiple mechanisms to reduce food intake and promote weight loss. Naltrexone is an opiate antagonist, and bupropion is an antidepressant. The medication must be titrated slowly to avoid intolerance. The starting dose for naltrexone/bupropion is one 8/90 mg tablet daily. Then it is increased to 2 tablets (16mg/180 mg) twice a day if necessary. Use is contraindicated in patients with uncontrolled hypertension, seizure disorder, or long-term opioid therapy.

(4) Orlistat

Orlistat is a pancreatic lipase inhibitor that prevents fat absorption. Orlistat is dosed as 60 mg tablets, 3 times daily with meals. The XENDOS trial showed an approximate 5% weight loss with orlistat. [24] Adverse events include flatulence, abdominal pain, and fecal urgency, limiting the usefulness of this medication. Orlistat can also lead to a deficiency of fat-soluble vitamins and predispose patients to cholelithiasis and nephrolithiasis.

Surgical Treatment

Surgical obesity treatment is indicated in patients with suboptimal weight loss or uncontrolled hyperglycemia. Surgery is recommended for patients with a BMI greater than 40 kg/m or a BMI of 35 to 39.9 kg/m with hyperglycemia, comorbid weight-related conditions, or inability to achieve sustainable weight loss. The Second Diabetes Surgery Summit evaluated the evidence for surgical treatment in patients with a BMI of 30 to 34.9 kg/m.

Metabolic and bariatric surgery is safe and effective and should also be considered in diabetic patients with a BMI of 30 to 34.9 kg/m with uncontrolled hyperglycemia despite optimal medical therapy for T2DM. Surgery for obesity includes several options. The most common procedures are Roux-en-Y gastric bypass (RYGB), vertical sleeve gastrectomy (VSG), laparoscopic adjustable gastric banding (LAGB), and biliopancreatic diversion (BPD). [2]

RYGB and VSG are the most frequently used techniques as they have better long-term outcomes and safety data. BPD is very effective but carries a higher risk of complications. LAGB is the safest procedure but carries the highest risk of revision and re-intervention.

Surgery achieves results by altering gastrointestinal (GI) anatomy, producing early satiety, decreasing the absorptive surface area, and modifying hormones responsible for glucose homeostasis. [25] The positive effects on intestinal glucose metabolism, changes in pancreatic islet hormonal activity, nutrient sensing, and bile acid metabolism are now being increasingly recognized. [26] [27] [28]

RYGB has been shown to improve insulin sensitivity in many human trials. Furthermore, increased levels of adiponectin (insulin-sensitizing hormone) and insulin receptors in muscles are observed. This promotes muscle fatty acid metabolism, which decreases lipid accumulation in muscle and liver and improves insulin sensitization. [25] Another study demonstrated that RYGB increases insulin secretion by both glucose-dependent and glucose-independent mechanisms. [26] Bariatric surgery is often referred to as metabolic surgery because of these effects.

Extensive data exists that bariatric procedures can control and even prevent T2DM. [29] [30] [31] [32] [33] Most randomized controlled trials compared surgical treatments and ILI for only one to 2 years of follow-up, but a few collected data for five years. [34] The mean hemoglobin A1c (HbA1c) reduction in the surgical group was about 2% compared to 0.5% in the conventional arm. Most of the surgical patients reached an HbA1c of nearly 6%. Remission of T2DM, defined as a non-diabetic HbA1c without medication, was also achieved in most patients. Sustained remission of T2DM has been documented in 30 to 60% of patients in multiple studies with follow-ups ranging from 1 to 5 years. The benefits of surgery may decrease with time, especially in individuals with poor control of T2DM in the preoperative period, longer duration of T2DM, and insulin use. [35]

A relapse of T2DM was observed in 35 to 50% of the patients. In the same study, RYGB was associated with a median disease-free period of 8.3 years. Surgical treatment is associated with improved glycemic control and clinical outcome, even when remission does not occur. An observational study assessed diabetes remission and complications for 10 to 20 years and noted a significant reduction in complications and higher remission rates in the surgical group. [36]

RYGB and BPD patients experience the greatest reduction in HbA1c and BMI. Patients with a BMI of 30-35 kg/m and uncontrolled T2DM have also been evaluated by many studies. [32] [37] [38] [31] The data consistently show improved HbA1c and higher remission of T2DM in surgical patients. LAGB led to even better outcomes of T2DM in patients with a BMI of 25 to 30 kg/m. [39]

The economic impact of bariatric procedures has been studied in patients with T2DM. The cost per quality-adjusted life-year (QALY) for metabolic surgery is generally $3,200 to $6,300, below the $50,000 estimate for nonsurgical care. [2] A 15-year follow-up of the Swedish Obese Subjects (SOS) study demonstrated no difference in total healthcare costs in patients with T2DM. [40]

Metabolic surgery has become safer and more effective in the past two decades. Results are highly dependent upon the operating surgeon. Mortality is low at 0.1 to 0.5%, but discussing potential risks and complications is part of informed consent and shared decision-making with patients. [2] [41] Reoperation and readmission rates are 2.5% and 5.1% for RYGB, 0.6% and 5.5% for VSG, and 0.6% and 2% for LAGB. [42]

Long-term follow-up demonstrates that LAGB has the highest rate of removal or revision. VSG is a newer procedure that has increased in popularity as surgeons gain additional experience. BPD is the most complex surgery, resulting in increased mortality, morbidity, and complications. Clinicians should educate all surgical patients about the risk of postoperative nutritional deficiencies such as iron deficiency anemia, hypoglycemia, and bone demineralization and monitor them closely for short and long-term complications.

Clinical Significance

Many quality metrics in healthcare evaluate the effectiveness of diabetes prevention and treatment. Obesity is a significant risk factor for T2DM and contributes to its severity. Early screening of patients with obesity and intensive treatment can result in long-term improvement or remission of the disease. Strictly controlling diabetes decreases complications such as ketoacidosis, diabetic ulcers, amputations, soft tissue infections, and osteomyelitis. Furthermore, aggressively managing HbA1c levels reduces the risk of coronary artery disease and chronic kidney disease.

Enhancing Healthcare Team Outcomes

Diabetes is a disabling chronic illness, and the obesity epidemic is increasing the incidence of this disease. Treatment of obesity is the cornerstone in the prevention and management of T2DM. This requires the combined efforts of every member of the healthcare team. An interdisciplinary approach will achieve the best clinical outcomes. The first step is the early recognition of patients with obesity in primary care practices. Nurses, the initial medical contact, must accurately measure body weight and height and record the BMI. Nursing staff should review diet and physical activity at baseline and each follow-up visit. Patients with elevated BMI are at higher risk for diabetes. All patients should know their BMI category when discussing treatment options. Clinicians should help them set a personal weight loss goal, with a target of losing at least 10% of body weight, optimal for a positive metabolic effect on diabetes. Intensive lifestyle interventions and pharmacotherapy can be prescribed following recommended guidelines.

Suboptimal glycemic control should be addressed at every visit. Inadequate weight loss (less than 10%) and persistent hyperglycemia should prompt a referral to medical obesity specialists or bariatric surgeons. Physicians, surgeons, and other clinicians work as a team in many weight loss programs. Obesity medicine physicians can help patients achieve the target weights necessary to become eligible for a surgical procedure. They also play a crucial role in the postoperative period, identifying complications and reinforcing healthy behaviors for ongoing weight loss. Many patients are ineligible for metabolic surgery due to potential operative risks from advanced cardiac or pulmonary disease. Obesity medicine teams can still achieve positive outcomes for this population.

Registered dieticians, nutritionists, and therapists are core members of these teams and schedule frequent sessions to promote optimal diet and exercise. These teams can be led by trained dieticians, therapists, nurses, physicians, or surgeons. Endocrinology consultations provide additional treatment strategies for patients with persistent hyperglycemia. Preoperative assessment by a cardiologist or a pulmonologist may be required. High-risk patients should undergo procedures in centers with on-site cardiac and pulmonary critical care. Pharmacists are invaluable in managing medication dosages, especially in the perioperative setting. Many drugs are weight-based and require careful dose adjustments after surgery.

The anesthesia team evaluates patients preoperatively and treats anesthesia-related events during and after metabolic procedures. Physical therapists help patients with muscle strengthening in the postoperative period. Because obesity is often associated with mental health conditions such as anxiety, depression, and body image disorders, psychiatry or psychology consultations should be considered if indicated.

Obesity and T2DM are chronic, systemic conditions requiring an interprofessional approach to prevention and management. Patients should be supported during their evaluation and treatment and provided with ample resources by the multidisciplinary team to make informed decisions about their care.

Nursing, Allied Health, and Interprofessional Team Interventions

Interprofessional interventions target identifying T2DM in patients with obesity. Providers should be knowledgeable about medications that promote weight gain and consider alternatives. Nurses check the BMI at each visit and alert the treating provider when the value exceeds 25 kg/m. These patients are screened for T2DM if that has not been done previously. Primary care clinicians recommend lifestyle interventions for all patients with a BMI greater than 25 kg/m. Standard written instructions about diet and exercise are reviewed at each visit.

Clinicians help patients identify measurable weight loss goals to reach a BMI less than 25 kg/m or a 5 to 10% weight loss. A BMI greater than 30 kg/m alerts providers to consider ILI and pharmacological treatments. Registered dieticians and nutritionists provide information for patients to make healthy dietary choices. A multidisciplinary approach with closed-loop communication between primary care providers and endocrinologists can improve outcomes. If ILI and pharmacotherapy fail to achieve adequate diabetes control and weight loss, timely referral to bariatric and metabolic surgeons is essential for optimal patient care.

Nursing, Allied Health, and Interprofessional Team Monitoring

Members of the interprofessional team caring for patients with diabetes and obesity monitor hbA1c, body weight, BMI, and lifestyle changes at each visit. When clinicians prescribe medications, they educate patients about side effects and adjust doses when indicated. Pharmacists can reinforce and augment this counseling and perform medication reconciliation. After metabolic surgery, patients are closely monitored by the interprofessional team.

Frequently, these patients require fewer medications since metabolic parameters improve, and there is a higher risk of hypoglycemia. Nurses should note any postoperative complications, including wound dehiscence, perforation, and signs of infection, and alert the treating providers. Clinicians, dieticians, and nutritionists monitor for signs of metabolic derangements such as vitamin and mineral deficiencies or malabsorption syndromes. Avoiding continuous use of nonsteroidal anti-inflammatory medications in the first month helps prevent erosion of anastomosis sites. Internal quality metrics aid in assessing remission rates, the safety of procedures, the postoperative course, and long-term outcomes after surgery.

Obesity is a risk factor for T2DM, cardiovascular disease, and other chronic health conditions. Effective healthcare teams actively screen for obesity and diabetes and use the BMI to classify the stages of obesity. ILI and pharmacologic therapy are utilized to achieve a 10% weight loss. Patients with suboptimal weight loss and poor glycemic control despite maximal medical treatment are referred for consideration of bariatric and metabolic surgery. Over the past two decades, bariatric surgery has become safer and more effective. Ongoing surveillance following surgery identifies potential complications and monitors results to improve clinical outcomes.

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Treatment options for type 2 diabetes mellitus and obesity Contributed by Kanica Yashi

Disclosure: Kanica Yashi declares no relevant financial relationships with ineligible companies.

Disclosure: Sharon Daley declares no relevant financial relationships with ineligible companies.

This book is distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0) ( http://creativecommons.org/licenses/by-nc-nd/4.0/ ), which permits others to distribute the work, provided that the article is not altered or used commercially. You are not required to obtain permission to distribute this article, provided that you credit the author and journal.

Cite this Page Yashi K, Daley SF. Obesity and Type 2 Diabetes. [Updated 2023 Jun 19]. In: StatPearls [Internet]. Treasure Island (FL): StatPearls Publishing; 2024 Jan-.

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New research directions on disparities in obesity and type 2 diabetes

Affiliations.

1 Division of Diabetes, Endocrinology, and Metabolic Diseases, National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK), National Institutes of Health, Bethesda, Maryland.
2 Drexel University Dornsife School of Public Health, Philadelphia, Pennsylvania.
3 Epidemiology and Statistics Branch, Division of Diabetes Translation, Centers for Disease Control and Prevention (CDC), Atlanta, Georgia.
4 Department of Family Medicine and Public Health, University of California, San Diego, La Jolla, California.
5 Division of Preventive Medicine, Department of Medicine, University of Alabama at Birmingham, Birmingham, Alabama.
6 University of Chicago Medicine, Chicago, Illinois.
7 Oregon Health and Science University and Portland State University Joint School of Public Health, Portland, Oregon.
8 Indiana University School of Medicine, Indianapolis, Indiana.
9 Department of Health Promotion Sciences, University of Arizona Mel and Enid Zuckerman College of Public Health, Tucson, Arizona.
10 Washington University in St. Louis, School of Medicine and the Brown School, St. Louis, Missouri.
11 University of Michigan Medical School, Ann Arbor, Michigan.
12 Johns Hopkins School of Medicine and Welch Center for Prevention, Epidemiology & Clinical Research, Baltimore, Maryland.
13 David Geffen School of Medicine at the University of California, Los Angeles, Los Angeles, California.
14 Kaiser Permanente Center for Health Research, Portland, Oregon.
15 Colorado School of Public Health, Aurora, Colorado.
16 University of Arizona Health Sciences, Phoenix, Arizona.
17 Harvard/MGH Center on Genomics, Vulnerable Populations, and Health Disparities, Mongan Institute, Massachusetts General Hospital and Department of Medicine, Harvard Medical School, Boston, Massachusetts.
18 University of North Carolina Gillings School of Global Public Health, Chapel Hill, North Carolina.
19 David Geffen School of Medicine at the University of California, and UCLA Fielding School of Public Health, Los Angeles, Los Angeles, California.
PMID: 31793006
PMCID: PMC7159314
DOI: 10.1111/nyas.14270

Obesity and type 2 diabetes disproportionately impact U.S. racial and ethnic minority communities and low-income populations. Improvements in implementing efficacious interventions to reduce the incidence of type 2 diabetes are underway (i.e., the National Diabetes Prevention Program), but challenges in effectively scaling-up successful interventions and reaching at-risk populations remain. In October 2017, the National Institutes of Health convened a workshop to understand how to (1) address socioeconomic and other environmental conditions that perpetuate disparities in the burden of obesity and type 2 diabetes; (2) design effective prevention and treatment strategies that are accessible, feasible, culturally relevant, and acceptable to diverse population groups; and (3) achieve sustainable health improvement approaches in communities with the greatest burden of these diseases. Common features of guiding frameworks to understand and address disparities and promote health equity were described. Promising research directions were identified in numerous areas, including study design, methodology, and core metrics; program implementation and scalability; the integration of medical care and social services; strategies to enhance patient empowerment; and understanding and addressing the impact of psychosocial stress on disease onset and progression in addition to factors that support resiliency and health.

Keywords: NIDDK; NIH; diabetes; disparities; obesity; social determinants.

Publication types

Research Support, N.I.H., Extramural
Diabetes Mellitus, Type 2 / diagnosis
Diabetes Mellitus, Type 2 / epidemiology*
Diabetes Mellitus, Type 2 / psychology
Healthcare Disparities*
Obesity / epidemiology*
Obesity / psychology
Residence Characteristics
Translational Research, Biomedical*

Grants and funding

P30 DK092926/DK/NIDDK NIH HHS/United States
P30 DK111022/DK/NIDDK NIH HHS/United States
The National Cancer Institute (NCI)/International
R01 DK121475/DK/NIDDK NIH HHS/United States
P30 DK092949/DK/NIDDK NIH HHS/United States
Z99 DK999999/ImNIH/Intramural NIH HHS/United States
NIH Office of Disease Prevention (ODP)/International
P30 DK092950/DK/NIDDK NIH HHS/United States
P30 DK048520/DK/NIDDK NIH HHS/United States
P30 DK079626/DK/NIDDK NIH HHS/United States
R18 DK092765/DK/NIDDK NIH HHS/United States

Obesity and Type 2 Diabetes

Reference work entry
First Online: 08 January 2019
Cite this reference work entry

Sviatlana Zhyzhneuskaya 5 &
Roy Taylor 5

Part of the book series: Endocrinology ((ENDOCR))

1788 Accesses

This chapter separately describes the pathophysiology of type 2 diabetes and that of obesity, and identifies the relationship between these states. The important concept of long-term reversibility of type 2 diabetes is discussed along with the beta-cell dedifferentiation, which explains the insulin secretory defect.

Obesity brings about distinct pathophysiological changes as a consequence of individuals’ pattern of food intake and levels of activity. The practical issue of clinical management is considered with particular reference to the weight management goals for type 2 diabetes.

Following therapeutic weight loss in both conditions, long-term avoidance of weight regain is vital and is optimally achieved by a combination of ongoing food energy restriction and daily physical activity.

This chapter separately describes the pathophysiology of type 2 diabetes and that of obesity. The relationship between these two conditions is then discussed, together with practical issues of clinical management.

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Zhyzhneuskaya, S., Taylor, R. (2019). Obesity and Type 2 Diabetes. In: Sbraccia, P., Finer, N. (eds) Obesity. Endocrinology. Springer, Cham. https://doi.org/10.1007/978-3-319-46933-1_21

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ORIGINAL RESEARCH article

Forecasting obesity and type 2 diabetes incidence and burden: the vila-obesity simulation model.

$\nRoch A. Nianogo,$

1 Department of Epidemiology, Fielding School of Public Health, University of California, Los Angeles (UCLA), Los Angeles, CA, United States
2 California Center for Population Research (CCPR), Los Angeles, CA, United States
3 Department of Statistics, Division of Physical Sciences, UCLA College, Los Angeles, CA, United States
4 Research Unit for Epidemiology, Department of Public Health, Aarhus University, Aarhus, Denmark

Background: Obesity is a major public health problem affecting millions of Americans and is considered one of the most potent risk factors for type 2 diabetes. Assessing future disease burden is important for informing policy-decision making for population health and healthcare.

Objective: The aim of this study was to develop a computer model of a cohort of children born in Los Angeles County to study the life course incidence and trends of obesity and its effect on type 2 diabetes mellitus.

Methods: We built the Virtual Los Angeles cohort—ViLA, an agent-based model calibrated to the population of Los Angeles County. In particular, we developed the ViLA-Obesity model, a simulation suite within our ViLA platform that integrated trends in the causes and consequences of obesity, focusing on diabetes as a key obesity consequence during the life course. Each agent within the model exhibited obesity- and diabetes-related healthy and unhealthy behaviors such as sugar-sweetened beverage consumption, physical activity, fast-food consumption, fresh fruits, and vegetable consumption. In addition, agents could gain or lose weight and develop type 2 diabetes mellitus with a certain probability dependent on the agent's socio-demographics, past behaviors and past weight or type 2 diabetes status. We simulated 98,230 inhabitants from birth to age 65 years, living in 235 neighborhoods.

Results: The age-specific incidence of obesity generally increased from 10 to 30% across the life span with two notable peaks at age 6–12 and 30–39 years, while that of type 2 diabetes mellitus generally increased from <2% at age 18–24 to reach a peak of 25% at age 40–49. The 16-year risks of obesity were 32.1% (95% CI: 31.8%, 32.4%) for children aged 2–17 and 81% (95% CI: 80.8%, 81.3%) for adults aged 18–65. The 48-year risk of type 2 diabetes mellitus was 53.4% (95% CI: 53.1%, 53.7%) for adults aged 18–65.

Conclusion: This ViLA-Obesity model provides an insight into the future burden of obesity and type 2 diabetes mellitus in Los Angeles County, one of the most diverse places in the United States. It serves as a platform for conducting experiments for informing evidence-based policy-making.

Introduction

Obesity is a major public health problem affecting millions of Americans with two in three adults and one in three children considered overweight or obese ( 1 ). This condition disproportionately affects lower-income minority and disadvantaged groups ( 1 ) giving rise to health disparities. Obesity has been on the rise for the past few decades ( 1 , 2 ) despite ongoing prevention efforts warranting its description as a pervasive and complex phenomenon ( 3 , 4 ). As a result, the obesity epidemic has been suggested to result from the complex interplay between individual and environmental factors and behaviors ( 3 , 4 ). This complexity is clearly seen when considering the socio-ecological framework ( 5 ) and exemplified by the fact that our individual behaviors can be influenced by our past behaviors ( 6 ), the neighborhood we live in ( 7 ) and the people around us ( 8 ).

Obesity (and overweight) is considered one of the most potent risk factors for type 2 diabetes ( 9 ). Almost 80–90% of type 2 diabetes patients are overweight or obese. This is alarming as type 2 diabetes is a disabling disease that imposes considerable burden on individuals, families, communities and the health system. The total direct medical and indirect expenditures attributable to diabetes in the U.S. amounted to ~$245 billion in 2012 ( 10 ).

To model obesity and forecast its future, researchers have suggested using complex methods ( 3 , 4 ). One such method is an agent-based model—a computer representation of the real world ( 11 , 12 ) where researchers and policymakers can run experiments in silico to evaluate the impact of potential interventions by simulating counterfactual scenarios ( 13 ). An example of such a virtual world is represented by the Coronary Heart Disease Policy Model developed to forecast and address coronary heart disease incidence, mortality and cost ( 14 ). Another prominent model is the Archimedes diabetes model ( 15 ), which was built to address clinical problems and questions around diabetes and modeled after several randomized controlled trials. In the present study, we chose to model our virtual world after that of Los Angeles County, California, for its high population density, its ethnic diversity ( 16 ), its rising rates of obesity and its marked racial/ethnic disparities in obesity ( 17 ).

In addition, modeling approaches that provide different and complementary insights on how changes in individual and environmental risk factors could affect disease rates in the future in a recent birth cohort are needed. Therefore, we set up a discrete-time modeling approach that will incorporate trends in individual and environmental risk factors in the hopes of evaluating their joint effects, at critical life stages, on future obesity or diabetes status in a recent birth cohort ( 13 ).

The overarching goal of this study was to develop an agent-based simulation model of a cohort of children born in Los Angeles County and followed into adulthood to study the life-course development of obesity and of its effects on diabetes mellitus. Specifically, we aimed to forecast and study the life course incidence and trends of obesity and its effect on type 2 diabetes mellitus risk. This synthetic cohort could serve as a platform for conducting in silico experiments and testing hypothetical public health interventions to inform evidence-based clinical and population-health decision- and policy-making ( 13 , 18 ).

We developed the ViLA–Obesity model, a stochastic, dynamic, discrete-time, agent-based model informed by various data sources and calibrated to the population of Los Angeles County in California to explore the incidence and trends in obesity and type 2 diabetes.

Description of the ViLA Simulated Population and Overview of the ViLA-Obesity Simulation Model

According to the 2010 US Census, Los Angeles County was inhabited by 9,818,605 individuals who lived in 2,346 census tracts ( 19 ). In this model, as it is the case in some other studies ( 20 ), we considered a census tract to represent a neighborhood. We simulated 235 neighborhoods with 418 inhabitants per neighborhood for a total simulated population of 98,230, which represented a 100th of the Los Angeles County (LAC) total population ( Supplementary Table 1 ). Simulated individuals in the model are referred to as agents. In this closed cohort, each agent was born in a specific neighborhood and was simulated from birth (aged 0–1 year, i.e., time = 0 ) to middle adulthood (aged 60–65 years, i.e., time = 9 ) in 10 discrete time steps representing critical life stages ( Supplementary Table 2 ). At each time step the agent's age is simulated using a uniform distribution bounded within the specific critical life stages ( Supplementary Table 2 ).

ViLA-Obesity represents a simulation model or suite within our ViLA platform. It integrates trends in the causes and consequences of obesity, focusing on diabetes as a key obesity consequence during the life course. During the simulation, each agent exhibited obesity- and diabetes-related healthy and unhealthy behaviors [e.g., sugar-sweetened beverage consumption (SSB), physical activity, smoking], gained/lost weight and developed type 2 diabetes with a certain probability dependent on the agent's current state ( Figures 1 – 3 ). We calculated and reported age-specific incidence, cumulative incidence, prevalence and average incidence rate of obesity and diabetes. To calculate the incidence measures, we considered the first-time diagnosis of obesity or type 2 diabetes among at-risk individuals. All data preparation and analysis and Monte Carlo simulation were also done in SAS 9.4 software (Cary, NC).

Figure 1 . Conceptual directed acyclic diagram underlying the data-generating process. SSB, sugar-sweetened beverage consumption; BMI, body mass index; FFV, Fresh fruit and vegetable consumption; T2DM, type 2 diabetes; Ado, Adolescence. T is an index of time. The smaller dotted square represents the neighborhood variables and the larger dotted square represents the individual level variables.

Figure 2 . Model initialization diagram of the ViLA-Obesity model. The neighborhood attributes are first initialized at time t=0 and subsequently followed by the initialization of the agent. The neighborhood food and physical activity attributes are predicted as a function of neighborhood socio-demographics. The individual time-invariant variables are set to their baseline values or predicted from socio-demographic variables.

Figure 3 . Model execution diagram of the ViLA-Obesity model. BMI, body mass index; T2DM, type 2 diabetes; SES, Socio-economic status. Individual behaviors are predicted based on the agent's socio-demographics, previous behavior and depending on the behavior neighborhood characteristics. The body mass index and type 2 diabetes are predicted based on the agent's socio-demographics, previous behaviors, and previous body mass index in the case of body mass index.

Data Sources and Parameters

• Proportions, means and standard deviations:

The parameters for the individual-level socio-demographics and those of the neighborhood-level socio-demographics were obtained from the American Community Survey (ACS) ( Supplementary Table 4 ). The individual-level race and income group were derived respectively from the neighborhood-specific race percentage and percent below federal poverty level (FPL). The proportions, means and standard deviations of the individual-level exposures and outcomes [breastfeeding, SSB, physical activity, fast-food consumption and fruit and vegetable consumption, smoking, alcohol consumption, body mass index (BMI), type 2 diabetes] were obtained from the California Health Interview Survey (CHIS) ( 21 ), the Centers for Disease Control and Prevention (CDC) ( 22 ), the World Health Organization (WHO) ( Supplementary Table 5 ).

• Parameters for effect and association measures:

These regression coefficients were taken from various sources detailed in the Supplementary Table 3 . For clarity, we defined three levels of evidence. “Evidence level 1” parameters are directly taken, in this order of preference, from published systematic reviews and meta-analyses, randomized control trial studies or cohort studies. “Evidence level 2” parameters are directly taken from cross-sectional studies from the peer-reviewed literature. “Evidence level 3” parameters are computed (indirectly obtained) by our research team using merged publicly and privately available data [e.g., American Community Survey, National Establishment Time-Series (NETS), Walkscore.com , WHO, National Health and Nutrition Examination Survey (NHANES) and the Los Angeles County Health and Nutrition Examination Survey (LAHANES) ( 21 – 25 ). Ideally, all parameters would be coming from “evidence level 1” but because most studies do not report on the relationships between covariates such as age, sex, race, socio-economic status [SES], and the outcome and between the covariates and the exposures, we identified other sources of evidence ( Supplementary Tables 6–10 ).

Model Specification

Each simulated agent had three domains of attributes. The first domain was the agent's socio-demographics [i.e., age, sex, socio-economic status (SES), race/ethnicity and marital status] representing the individual's inherent susceptibility which was not allowed to change (i.e., time-invariant variables) with the exception of age. We assumed that individuals born in a certain SES group will remain in that group until the end of the simulation (i.e., inherit their parents' SES) and that agents could only get married after their 18th birthday ( Supplementary Table 4 ). The second domain was the agent's behaviors and was divided into: (i) dietary behaviors (breastfeeding, fast-food consumption, SSB, fresh fruit, and vegetable consumption); (ii) physical activity behaviors (moderate-to-vigorous physical activity) and (iii) other behaviors (smoking, alcohol consumption) ( Supplementary Table 5 ). The last domain was the agent's outcomes (BMI, and type 2 diabetes status).

Agents were only allowed to engage in smoking, alcohol consumption and develop type 2 diabetes after their 18th birthday. Both behavior and outcome domains were considered time-varying variables. For children aged 0–19, we defined overweight and obesity using the WHO BMI Z-score international child cutoffs ( 26 ). We calculated BMI Z-scores using CDC's SAS codes ( 27 ). Based on the WHO growth charts, a child with a BMI Z-score (BMIz) < -2 was classified as underweight; a BMIz ≥ −2 but <1 was classified as normal-weight; a BMIz ≥ 1 but < + 2 was classified as overweight and a BMIz ≥ 2 was classified as obese ( 28 ).

Similarly, an adult with a BMI <18.5 was classified as underweight; a BMI ≥ 18.5 but <25 was classified as normal-weight; a BMI ≥ 25 but <30 was classified as overweight and a BMI ≥ 30 was classified as obese ( 29 ).

Neighborhood Environment

The neighborhood where the agents dwelled had three domains. The first domain was the neighborhood socio-demographics encompassing the proportion of individuals who self-identified as non-White, the proportion of individuals living below the federal poverty level (FPL) and the proportion of individuals who had a bachelor's degree or higher. The data for this domain were obtained from the American Community Survey [ACS] ( 19 ). The second domain was the neighborhood physical activity opportunities that comprise the neighborhood walkability and access to parks. The data for the second domain were obtained from Walkscore.com ( 30 ), the National Establishment Time-Series (NETS) ( 31 ) and Wolch et al. ( 30 ). The third domain was the neighborhood food environment comprising the supermarket and the fast-food density. The data for the third domain were obtained from NETS ( 31 ) (see Supplementary Table 4 for more details).

Conceptual Model, Equations, and Decision Rules

The decision rules underlying this model were mainly based on mathematical equations. Completely exogenous variables in this model were few and limited to individual- and neighborhood-level socio-demographics. Except at birth (time t = 0), all behavior equations (e.g., SSB, physical activity) had a common form whereby the dependent variable would be a function of the following: intercept, lagged version of the dependent variables and socio-demographics. Likewise, the outcome equations (e.g., BMI, type 2 diabetes) had in addition to the previous ones listed all age-specific behaviors (e.g., SSB, physical activity, and smoking). Linear and logistic regressions were used for modeling continuous and binary dependent variables, respectively. Accordingly, the inverse of the link functions used in the regression modeling were used for simulation (i.e., identity and expit functions respectively). The neighborhood environment and its attributes are first simulated, then agents with their attributes by time period are simulated within neighborhoods. These will engender a change in BMI and will subsequently affect diabetes risk. Most endogenous variables allow for time-dependency (i.e., previous behavior affecting future behavior). Features of feedback were also allowed. For instance, when BMI changed, it affected subsequent ability to exercise which subsequently affected future BMI and so on ( 32 ). A detailed description of the equation structure are presented in the Supplementary Table 11 .

Model Calibration, Verification, and Validation

We undertook several iterative steps to build the ViLA-Obesity model. These included calibration, validation, and verification. Of note, calibration is the process through which we assign input parameters within the model and ensure that the predicted model output is close to that of the observed data (ideally using training data if available or the entire data if not). Evaluating whether calibration worked within one's own data could also be seen as an internal validation procedure. Validation ( or sometimes external validation), on the other hand, strives to ensure that the predicted model output (ideally using a training data if available or using the entire data if not) is close that of the observed data (ideally using a test data or using observed data from a different period if not). Verification is a process that involves different techniques such as structured code walk-throughs to check for model consistency and errors and makes sure that the model does what it is intended to do ( 33 , 34 ).

Model Verification

In the model verification step, we used structured code walk-through to check for model consistency and errors throughout the modeling process in an iterative fashion.

Model Calibration and Internal Validation

We first obtained parameters (i.e., proportions, means, standard deviations of each variable, and the regression coefficients relating any two variables) from multiple studies and datasets. Many commonly used external validation techniques ( 35 ) could not be used here because we did not have a base cohort in Los Angeles that followed individuals from birth to adulthood and which studied our exposures and outcomes of interests. In other words, we could not externally validate our model. Nevertheless, we used a “calibration-in-the-large” technique to calibrate and internally validate our model ( 35 ). In brief, the “calibration-in-the-large” is a calibration whereby one ensures that the mean predicted outcome equals the mean observed outcome [i.e., mean( Y predicted ) = mean( Y observed )] through the fine tuning of the intercept ( 35 ), or other coefficient. The finding of the equality mean( Y predicted ) = mean( Y observed ) ensured the internal validity of the model testifying that there was agreement between the observed data and our model predictions (i.e., internal validation). From a practical standpoint, after we have assigned the parameters in our equation models (see Supplementary Table 11 , e.g., relative risks obtained from the three levels of evidence, etc.), we sought to find and finetune the remaining parameters, that is, those that could not be otherwise obtained directly from the literature. There were two such parameters: intercepts and feedback parameters (i.e., coefficients reflecting the relationship between current behavior or outcome to previous behavior and outcome, all else equal). As such, we defined a calibration objective function as the Mean Absolute Error (MAE) between the predicted and observed variable mean or prevalence. Once the objective function has been defined, we used a grid search strategy to find the appropriate parameters of interest. Parameter values that minimized the objective function were selected to parametrize the model. This was done sequentially starting from birth (aged 0–1 year, i.e., time = 0 ) to middle adulthood (aged 60–65 years, i.e., time = 9 ) in 10 discrete time steps. Furthermore, after the whole model parametrization, we evaluated whether our calibration (internal validation) was successful by (1) plotting our simulated and observed outcome means and proportions over time for each behavior [e.g., sugar-sweetened beverage (SSB)] and outcome (e.g., body mass index) and (2) computing the variance explained, R 2 , between the simulated and observed data for each behavior and outcome over time. As such, we internally validated our model on the basis of its ability to the predict observed outcomes. To extend the model to other populations, we could adjust our intercepts to match the site-specific observed prevalence ( 35 ).

Calibration and Internal Validation

Figure 4 shows the simulated and observed means and proportions by age groups. Our simulation results broadly matched the age-specific means and proportions from CHIS 2009. However, there were some small but notable departures from the observed data for physical activity, fresh fruit and vegetable consumption, smoking and diabetes prevalence. This can also be seen with the computed R 2 which was high (>0.9) for body mass index, sugar sweetened beverage, fresh fruits and fast-food consumption and moderate (>0.6) for fresh fruits and vegetables and physical activity. The R 2 for exclusive breastfeeding, smoking, alcohol and type 2 diabetes could not be computed because of the low number of data points available (see Supplementary Table 12 for details).

Figure 4 . Calibration of the ViLA-Obesity model. This figure depicts the results of the model calibration. It compares the observed (plain lines) to the simulated data (dotted lines).

Trends in Obesity and Type 2 Diabetes

Figure 5 depicts the overall and racial subgroup trends (incidence and prevalence) in obesity and type 2 diabetes over time in the ViLA-Obesity model.

Figure 5 . Obesity and type 2 diabetes prevalence (A) , cumulative incidence (B) , age-specific incidence proportion (C) , and annual incidence rates (D) in the ViLA-Obesity model. The incidence measures were calculated for first-time diagnosis of obesity or type 2 diabetes among at-risk individuals (i.e., without the diagnosis).

We found that the obesity age-specific incidence proportion was generally increasing from about 10% to about 30% across the individual life span with two notable peaks at age 6–12 and 30–39. Likewise, the age-specific incidence proportion of type 2 diabetes increases from <2% at age 18–24 to reach a peak of about 25% at age 40–49.

The prevalence of obesity was highest in childhood with about 25% of children considered obese between the age of 6 and 12 years. During adulthood, the prevalence of obesity rose to reach a maximum of 40% at the end of follow-up at age 60–65 years.

Compared to Whites, the incidence and prevalence of obesity and type 2 diabetes were generally higher among the non-White subpopulation. There were marked disparities in the prevalence of type 2 diabetes compared to that of obesity. The racial disparity gap in the prevalence of type 2 diabetes was greatest during middle adulthood but that in the prevalence of obesity was small but more uniform across ages.

Trends in Drivers of Health Behaviors

Figure 6 shows the overall and racial subgroup trends in key health behaviors. The consumption of fast-food was generally high and decreasing with age. It was highest during childhood and adolescence with ~75–85% of children and adolescents consuming fast-foods more than one time per week. The consumption of sugar-sweetened beverage was also generally high and decreasing with age. It was highest during childhood and adolescence with ~60–70% of children and adolescents consuming more than one 12-oz drink of SSB per day. Engaging in moderate-to-vigorous physical activity was generally low and decreasing with age. It was lowest during adolescence with only about 20% of adolescents engaging in moderate-to-vigorous physical activity. The consumption of fresh fruits and vegetables was fairly constant over time. It was lowest during childhood with only about 40–50% of children aged 6–12 consuming more than five servings of fruit and vegetables per day. About one out of five individuals were breastfed for 6 months or longer during their 1st year of life.

Figure 6 . Proportion of obesity- and type 2 diabetes-related health behaviors over time in the ViLA-Obesity model. This figure highlights the health behaviors prevalence across the population.

Cumulative Incidence and Average Incidence Rate of Obesity and Type 2 Diabetes in the ViLA-Obesity Model

Table 1 presents the cumulative incidence and average incidence rates of obesity and type 2 diabetes in the ViLA-Obesity model.

Table 1 . Incidence rates and cumulative incidence of obesity and type 2 diabetes in the ViLA-Obesity model ( n = 98,230).

Type 2 Diabetes

The 48-year risk or cumulative incidence of type 2 diabetes in the ViLA-Obesity model was 53.4% (95% CI: 0.53.1%, 0.53.7%) and the average incidence rate of type 2 diabetes was about 13 cases per 1,000 person-years (95% CI: 12.7, 12.9) for adults aged 18–65 years.

The 16-year risk or cumulative incidence of obesity was 32.1% (95% CI: 31.8%, 32.4%) and the average incidence rate of obesity was about 22 cases per 1,000 person-years (95% CI: 22.0, 22.5) for children aged 2–17 years. The 48-year risk or cumulative incidence of obesity was 81% (95% CI: 80.8%, 81.3%) and the average incidence rate of obesity was about 28 cases per 1,000 person-years (95% CI: 27.8, 28.2) for adults aged 18–65 years.

The purpose of this study was to build an agent-based model of a cohort of children born in Los Angeles County and followed from birth into adulthood in order to study the life course development of obesity and of its effects on diabetes mellitus. This virtual cohort would then serve as a platform for conducting in silico experiments and testing hypothetical public health interventions to inform evidence-based clinical decision- and policy-making ( 13 , 18 ).

Our findings suggest that the incidence and prevalence of obesity and type 2 diabetes within the ViLA-Obesity model were generally high and increasing during the life span. The prevalence of obesity was highest during childhood and among individuals in their 30's while the prevalence of type 2 diabetes started rising among individuals in their 40's. In addition, one in three children and adolescents and four in five adults will become obese before age 65 and one in two adults will develop type 2 diabetes before age 65 in the simulated cohort. There were some racial differences in the prevalence and incidence of obesity and type 2 diabetes. The non-White subpopulation experienced higher proportions of individuals who became obese or developed type 2 diabetes at any point in time throughout the 64-year follow-up compared their White counterparts. The presence of such racial disparities in obesity and type 2 diabetes has been well-documented in Los Angeles ( 17 , 36 ).

Furthermore, our results also suggested that the proportion of individuals engaging in moderate-to-vigorous physical activity and consuming at least five servings of fresh fruit and vegetables was generally low while the proportion of individuals consuming fast-food and drinking sugar-sweetened beverages was generally high within the simulated cohort. There were also some racial differences among these obesity-related health behaviors. Among the non-White subpopulation, there was a lower proportion of individuals who engaged in moderate-to-physical activity, and a higher proportion of individuals who drank more than one sugar-sweetened beverage a day compared to their White counterparts. In contrast, among the White subpopulation, there was a lower proportion of individuals who ate fresh fruit and vegetables and a higher proportion of individuals who ate fast-food more than once per week compared to their non-White counterparts.

This study provided a unique perspective of the development of obesity and type 2 diabetes among individuals who would have been followed from birth into adulthood in Los Angeles. This approach allowed us to simultaneously appreciate the aging effect on and forecast the future burden of obesity and type 2 diabetes within a birth cohort between 2009 and 2074 (i.e., 2009+65), something that has seldom been done in the literature. In addition, our modeling approach provides different and complementary insights on how disease rates will change in the future in a recent birth cohort. Specifically, our discrete-time modeling approach will allow researchers to see how current or future obesity or diabetes burden could reflect the joint and cumulative effects of prior and current environmental and individual exposures at critical life stages. In other words, as individual and environmental risk factors change over time, so will the trends in obesity and diabetes be expected to change.

Importantly, unless done for calibration purposes, one should be cautious when comparing our estimates to past and projected prevalence and incidence of obesity and diabetes. In fact, many trend estimates are based on cross-sectional data which typically reflect a given period effect and averaged across several age-groups and birth cohorts ( 37 , 38 ). Nevertheless, these past and projected trends remain important for gauging the current and potential future state of obesity and diabetes in Los Angeles and the US. For instance, in 2011, the prevalence of obesity was 22.4% among children and 23.6% among adults ( 17 ) and the prevalence of diabetes was 9.9% ( 36 ) among adults in Los Angeles County. In the absence of projection studies in Los Angeles County, one can look to regional and national projection data to better appreciate the burden of disease attributable to obesity and type 2 diabetes. In fact, the UCLA Health forecasting tool, a simulation model that simulated individual life course among California's adult population, predicted that the obesity and type 2 diabetes prevalence will reach 30.8 and 9.93% respectively by 2020 in their baseline scenario ( 39 ). In addition, other projection studies based on nationally representative data found that the prevalence of impaired glucose tolerance could reach 15% by 2048 ( 40 ) and that the prevalence of obesity could reach 51.1% by the year 2030 ( 41 ). The latter study also predicted that 80, 90, and 100% of Americans will become obese by the year 2072, 2087, and 2102, respectively and that the non-White subpopulation may reach those levels sooner compared to Whites ( 41 ). Interestingly, when using the linear annual rate of increase reported in that study and the prevalence of obesity among adults in Los Angeles in 2011, we estimated that the projected prevalence of obesity in 2074 would be ~67%. A study of the growth trajectory, which used a simulation model, also found that about 57.3% could become obese by the age of 35 ( 42 ). Lastly, the predicted life-time risk of diagnosed diabetes from age 20 was estimated to be about 40% for men and women in a nationally representative sample ( 43 ). All of these projections reflect similar alarming trends as suggested by our model and their insights warrant immediate action to reverse or slow the epidemic in the US and in Los Angeles County in particular.

This study has several limitations. First, the calibration and validation of the ViLA-Obesity model were suboptimal in the absence of a base cohort in Los Angeles that followed individuals from birth to adulthood and studied our exposures and outcomes of interests. Nevertheless, we used age-group-specific means and proportions from publicly available data (i.e., CHIS) representing whenever available the population of Los Angeles County in 2009. This has some limitations since it does not allow one to disentangle the cohort/secular trend effects from the age effects. As such, we have assumed that the cohort/secular trend effect would be smaller relative to that of the age effect since we are simulating each individual as they age over time within the simulated cohort. Our results may reflect at the very least the age effect but could also reflect age and cohort effects. In addition, as cross-sectional data typically include people who are more likely to have chronic conditions such as diabetes, the use of such data for our calibration could result in the overestimation of the measures of occurrence within our simulation. Nevertheless, in the absence of longitudinal data, using age-group specific data in a specific year appears to be a better alternative than using repeated cross-sectional data to calibrate our model since the latter would not allow one to disentangle age and period effects. Second, while we have incorporated relevant obesity-related environmental exposures, we did not account for the possibility of residual social network effect in this iteration of the model. While there have been some suggestions that obesity can spread through social networks (i.e., induction or person-to-person spread) ( 8 ), other authors have demonstrated that such effects may be the result of confounding by contextual exposures (e.g., food environment, built-environment) ( 44 ). These authors concluded that after properly accounting for environmental exposures, the social network effects in obesity almost vanished ( 44 ). This finding, however, did not mean that peer support could not enhance the effectiveness of certain prevention efforts ( 45 ). We hope to explore the added insights gained from incorporating social network effects in the next iteration of the model. Third, the ViLA-Obesity model represented a simplified version of the Los Angeles County population in that the simulated cohort was closed (that is agents could not drop out, die, experience a competing risk, beget children, move in and out of the cohort). This will likely result in an overestimation of the incidence and prevalence measures. Future iteration of the model will incorporate competing risk in the data generating process. Fourth, using larger age categorization for calibration could result in suboptimal model calibration. We chose this approach since the regression parameters obtained from internal data analysis and to some extent from the literature was generally obtained for similar larger age categorization (most likely because of sample size consideration). Fifth, it is possible that the inclusion of large number of parameters and predictors in the model could add some additional uncertainty in the estimates produced by the model. We have included information on both individual factors as well as environmental factors because we intended to evaluate the impact of several interventions including single and combined interventions at the individual level and at environmental level at different critical life stages. Nevertheless, although the model has recently been used to evaluate impacts of obesity related-interventions ( 13 ), we believe such models should continue to undergo refinement through continuous validation and calibration as data and methods improve and new applications are found. In addition, the model was built to represent a 100th of the actual population of Los Angeles and agents were only allowed to engage in certain behaviors (e.g., smoking, alcohol consumption, and develop type 2 diabetes) after their 18th birthday.

Uses of the ViLA Modeling Suite

The current model will be kept up to date to reflect current trends and changes in trends in individual and environmental factors over time. In addition, we hope to incorporate additional outcomes including but not limited to cardiovascular diseases and cancer. The ViLA-Obesity suite has been used to evaluate single and combined (i.e., joint and cumulative) impact several known and hypothetical interventions that target individual and or environmental factors ( 13 ). For instance, the Los Angeles County Department of Public Health (LAC/DPH) in collaboration with the Center for Disease Control and Prevention (CDC) implemented from 2010 to 2012 several interventions to curb the obesity epidemic such as the “Community Putting Prevention to Work (CPPW)” with the RENEW project (Renew Environments for Nutrition, Exercise, and Wellness). The project “sought to implement policy, systems, and environmental changes to improve nutrition, increase physical activity, and reduce obesity, especially in disadvantaged communities” ( 46 ). As an initial modeling endeavor, we proposed to evaluate the long-term effects of individual-level dietary interventions (e.g., breastfeeding promotion, and reduction of sugar-sweetened beverages) and environmental physical activity-related interventions (e.g., increasing access to parks and recreations and designing pedestrian-friendly communities) on obesity and diabetes incidence in the ViLA cohort ( 13 ). Generally, to evaluate the effectiveness of an intervention, we would contrast the projected incidence and prevalence under say a hypothetical scenario where we would “alter” the exposure status to the desired level (intervention course) to the projected incidence and prevalence under the natural course (no interventions) ( 13 ).

We developed and validated a virtual cohort representing Los Angeles County wherein we explored the development of obesity and diabetes from birth to adulthood. Our findings suggest that the incidence and prevalence of obesity and type 2 diabetes within the ViLA-Obesity model were generally high and increasing with age during the individual life span. In this virtual Los Angeles, one in three children and adolescents and four in five adults will become obese before age 17 and age 65 respectively and one in two adults will develop type 2 diabetes before age 65. We also noted the presence of racial disparities in obesity, type 2 diabetes, and obesity-related behaviors. This virtual cohort serves as a platform for conducting in silico experiments and testing hypothetical public health interventions to inform evidence-based clinical decision and policymaking. This study illustrates the usefulness of simulations like agent-based models in forecasting the burden of disease within a population over time to support the need for effective interventions.

Data Availability Statement

The relevant data used in this study are included in the article/ Supplementary Material , further inquiries about the full list of data citations can be directed to the corresponding author/s.

Author Contributions

RN participated in the study conception, design, analysis, and wrote the first draft of the article. OA supervised and participated in the study conception, design, analysis, reviewed, and revised the manuscript. All authors provided critical input and insights into the development, writing of the article, and approved the final manuscript as submitted.

RN was supported by a Burroughs Wellcome Fellowship and the Dissertation Year Fellowship from UCLA. OA was partly supported by grant R01-HD072296-01A1 from the Eunice Kennedy Shriver National Institute of Child Health and Human Development. In addition, RN benefited from facilities and resources provided by the California Center for Population Research at UCLA (CCPR), which receives core support (R24-HD041022) from the Eunice Kennedy Shriver National Institute of Child Health and Human Development (NICHD).

Conflict of Interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Publisher's Note

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.

Acknowledgments

RN wishes to thank his doctoral dissertation committee for providing constructive feedback that helped improved this article. In addition, we would like thank the UCLA Lewis Center and Norman Wong for providing the WalkScore data. RN would also like to thank the Division of Chronic Disease and Injury Prevention and the Office of Health Assessment and Epidemiology in the Los Angeles County Department of Public Health for providing us with the LAHANES data and for their inputs in this project. In addition, RN would like to thank Darren Ho for his help in preparing the NHANES data and Mekdes Gebremariam for providing feedback on an earlier draft of this manuscript. In addition, an oral presentation of an earlier version of this manuscript was presented at AcademyHealth's Annual Research Meeting, New Orleans, Louisiana, June 25–27th, 2017. This abstract was selected as one the Best of AcademyHealth's Annual Research Meeting abstracts that year.

Supplementary Material

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fpubh.2022.818816/full#supplementary-material

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Keywords: agent-based model, obesity, type 2 diabetes, simulation, prediction

Citation: Nianogo RA and Arah OA (2022) Forecasting Obesity and Type 2 Diabetes Incidence and Burden: The ViLA-Obesity Simulation Model. Front. Public Health 10:818816. doi: 10.3389/fpubh.2022.818816

Received: 20 November 2021; Accepted: 01 March 2022; Published: 05 April 2022.

Reviewed by:

Copyright © 2022 Nianogo and Arah. 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: Roch A. Nianogo, rnianogo@gmail.com ; niaroch@ucla.edu

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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How People with Type 2 Diabetes Can Live Longer

Life expectancy is known as the number of years a person is expected to live.
At age 50, life expectancy is 6 years shorter for people with type 2 diabetes than for people without diabetes.
By meeting type 2 diabetes treatment goals, life expectancy can increase by 3 years, or for some, as much as 10 years.

Shot of a elderly man holding a grocery bag in the kitchen

What did this study examine?

This study measured average increases in life expectancy years for people with type 2 diabetes who met four different treatment goals. Researchers divided study participants into six groups based on sex and three age ranges. People with diabetes can use results to estimate their own likely life expectancy increase.

Terms to know

Body mass index (BMI) is a measure of weight based on height.

Hemoglobin A1C , or A1C , is a blood test that measures average blood sugar over the past 3 months.

Low-density lipoprotein (LDL) cholesterol can build up in the blood vessels, causing damage to vessel walls.

Systolic blood pressure (SBP) measures the force of blood pushing against artery walls as it moves through the body. Blood pressure is measured with a top and bottom number, and SBP refers to the top number.

Study results

Managing weight, blood sugar, blood pressure, and cholesterol can increase life expectancy by 3 years for the average person with type 2 diabetes. For people with the highest levels of BMI, A1C, LDL, and SBP, reducing these levels can potentially increase life expectancy by more than 10 years.

The benefits in life expectancy from meeting treatment goals in this study were highest in adults ages 51 to 60, compared to those 61 and older.

Of the four treatment goals studied, reduced BMI on average was associated with the greatest gain in life expectancy, followed by reduced A1C.

The benefit of weight loss may have been underestimated since it is often connected with other treatment goals in this study. Weight loss must be maintained in the long term to potentially increase life expectancy.

What's important about this study?

Living well with diabetes requires more than blood sugar management. Diabetes management is also connected to weight, blood pressure, and cholesterol. This study shows how people with type 2 diabetes can reduce their risk of complications and extend their lives.

These findings can help people with diabetes and their doctors determine treatment goals with the most impact on life expectancy. Decision makers can use this study to support diabetes programs in the United States.

Diabetes is a chronic disease that affects how your body turns food into energy. About 1 in 10 Americans has diabetes.

For Everyone

Health care providers, public health.

News Releases

Semaglutide can produce clinically meaningful weight loss and reduce waist size for at least 4 years in adults with overweight or obesity who don’t have diabetes, and delivers cardiovascular benefits irrespective of weight lost

European Association for the Study of Obesity

Two important studies based on the largest and longest clinical trial of the effects of semaglutide on weight in over 17,000 adults with overweight and obesity but not diabetes find patients lost on average 10% of their body weight and over 7cm from their waistline after 4 years.

Clinically meaningful weight loss was achieved by men and women of all races, ages, and body sizes, across all regions, with a lower rate of serious adverse events compared with placebo.

Over half of adults taking semaglutide moved down at least one BMI category after 2 years compared to 16% receiving placebo; and 12% reached a healthy BMI (25 kg/m² or less) compared with 1% in the placebo group.

Importantly, the findings also indicate that semaglutide delivers cardiovascular benefits irrespective of starting weight and the amount of weight lost—suggesting that even patients with mild obesity or those not losing weight are likely to gain some advantage.

Two important studies are being presented at this year’s European Congress on Obesity (ECO) in Venice, Italy (12-15 May), based on the landmark Semaglutide and Cardiovascular Outcomes (SELECT) trial from the same international author group. The first new study, led by Professor Donna Ryan from Pennington Biomedical Research Centre, New Orleans, USA, and being published simultaneously in Nature Medicine , examines the long-term weight effects of semaglutide. The second study led by led by Professor John Deanfield from University College London, UK, investigates whether the cardiovascular benefits are related to starting weight or the amount of weight lost.

Semaglutide is a GLP-1 medication primarily prescribed for adults with type 2 diabetes but is also approved for weight loss in people with obesity or overweight who have at least one other health issue. This class of medications simulate the functions of the body’s natural incretin hormones, which help to lower blood sugar levels after a meal. Adjusting these hormone levels can also make people feel full, and in doing so, helps lower their daily calorie intake.

In 2023, the SELECT trial reported that adults with overweight or obesity but not diabetes taking semaglutide for more than 3 years had a 20% lower risk of heart attack, stroke, or death due to cardiovascular disease, and lost an average 9.4% of their bodyweight [1].

Between October 2018 and June 2023, 17,604 adults (aged 45 or older; 72% male) from 804 sites in 41 countries with overweight or obesity (BMI of 27 kg/m² or higher) were enrolled and treated with Semaglutide (2.4mg) or placebo for an average of 40 months. They had previously experienced a heart attack, stroke and/or had peripheral artery disease, but did not have type 1 or type 2 diabetes when they joined the study.

The researchers examined markers of obesity that include body composition and fat distribution (waist circumference and waist circumference-to-height ratio [WHtR]), rather than just BMI alone, to help clarify the effect of semaglutide on central abdominal fat which has been proven to cause greater cardiovascular risk than general obesity.

Clinically meaningful weight loss in all sexes, races, body sizes, and regions

The first new study shows that once-weekly treatment with semaglutide can produce clinically meaningful and sustained weight loss and decrease waist size for at least 4 years in adults with overweight or obesity who do not have diabetes, with a lower rate of serious adverse events compared with placebo.

Importantly, men and women of all races, ages, and body sizes, across all geographical regions were able to achieve sustained, clinically meaningful weight loss.

“Our long-term analysis of semaglutide establishes that clinically relevant weight loss can be sustained for up to 4 years in a geographically and racially diverse population of adults with overweight and obesity but not diabetes,” says Professor Ryan. “This degree of weight loss in such a large and diverse population suggests that it may be possible to impact the public health burden of multiple obesity-related illnesses. While our trial focused on cardiovascular events, many other chronic diseases including several types of cancer, osteoarthritis, and anxiety and depression would benefit from effective weight management.”

In the semaglutide group, weight loss continued to week 65 and was sustained for 4 years, with participants’ losing on average 10.2% of their body weight and 7.7cm from their waistline, compared with 1.5% and 1.3cm respectively in the placebo group.

Similarly, in the semaglutide group, average WHtR fell by 6.9% compared with 1% in the placebo group.

These improvements were seen across both sexes and all categories of race and age, irrespective of starting blood sugar (glycaemic) status or metabolically unhealthy body fat. However, women taking semaglutide tended to lose more weight on average than men, and Asian patients lost less weight on average than other races.

Interestingly, after 2 years over half (52%) of participants treated with semaglutide had transitioned to a lower BMI category compared with 16% of those given placebo. For example, the proportion of participants with obesity (BMI 30kg/m² or higher) declined from 71% to 43% in the semaglutide group, and from 72% to 68% in the placebo group. Moreover, 12% of adults in the semaglutide group achieved a healthy weight (BMI 25kg/m² or less) compared with 1.2% in the placebo group

For each BMI category (<30, ≤30-<35, ≤35-<40, and ≥40 kg/m2) there were lower rates (events per 100 years of observation) of SAEs with semaglutide (43.23, 43.54, 51.0, 47.06) than with placebo (50.48, 49.66, 52.73, 60.85) respectively.

There were no unexpected safety issues with semaglutide in the SELECT trial. The proportion of participants with serious adverse events (SAEs) was lower in the semaglutide group than the placebo group (33% vs 36%), mainly driven by differences in cardiac disorders (11.5% vs 13.5%). More patients receiving semaglutide discontinued the trial due to gastrointestinal symptoms, including nausea and diarrhoea, mainly during the 20-week dose escalation phase. Importantly, semaglutide did not lead to an increased rate of pancreatitis, but rates of cholelithiasis (stones in gallbladder) were higher in the semaglutide group.

Cardiovascular benefits irrespective of weight loss

The second study examined the relationship between weight measures at baseline, and change in weight during the study with cardiovascular outcomes. These included time to first major adverse cardiovascular event (MACE) and heart failure measures.

The findings showed that treatment with semaglutide delivered cardiovascular benefits, irrespective of the starting weight and the amount of weight lost. This suggests that even patients with relatively mild levels of obesity, or those who only lose modest amount of weight, may have improved cardiovascular outcome.

“These findings have important clinical implications”, says Professor Deanfield. “Around half of the patients that I see in my cardiovascular practice have levels of weight equivalent to those in the SELECT trial and are likely to derive benefit from taking Semaglutide on top of their usual level of guideline directed care.”

He adds, “Our findings show that the magnitude of this treatment effect with semaglutide is independent of the amount of weight lost, suggesting that the drug has other actions which lower cardiovascular risk beyond reducing unhealthy body fat. These alternative mechanisms may include positive impacts on blood sugar, blood pressure, or inflammation, as well as direct effects on the heart muscle and blood vessels, or a combination of one or more of these.”

Despite these important findings, the authors caution that SELECT is not a primary prevention trial so that the data cannot be extrapolated to all adults with overweight and obesity to prevent MACE; and despite being large and diverse, it does not include enough individuals from different racial groups to understand different potential effects.

Nature Medicine

COI Statement

DR is an Advisor/consultant: Altimmune, Amgen, Biohaven, Calibrate, Carmot, CINRx, Currax, Epitomee, Gila, Ifa Celtic, Lilly, Nestle, Novo Nordisk, Scientific Intake, Structure Therapeutics, Wondr Health, Xeno Bioscience, Zealand. Speaker’s Bureau: Novo Nordisk, Lilly. Stock Options: Epitomee, Calibrate, Roman, Scientific Intake, Xeno. Research: SELECT Steering Committee (Novo Nordisk). DSMB: IQVIA setmelanotide (2); Lilly(1). JD received CME honoraria and/or consulting fees from Amgen, Boehringer Ingelheim, Merck, Pfizer, Aegerion, Novartis, Sanofi, Takeda, Novo Nordisk, Bayer. Research grants from British Heart Foundation, MRC(UK), NIHR, PHE, MSD, Pfizer, Aegerion, Colgate, Roche. Member of Study Steering Committees for Novo Nordisk (SOUL and SELECT) Editorial support was provided by Richard Ogilvy-Stewart of Titan, OPEN Health Communications, and funded by Novo Nordisk A/S, in accordance with Good Publication Practice guidelines (www.ismpp.org/gpp-2022). Funding Research relating to this abstract was funded by Novo Nordisk.

Disclaimer: AAAS and EurekAlert! are not responsible for the accuracy of news releases posted to EurekAlert! by contributing institutions or for the use of any information through the EurekAlert system.

Acknowledgments

Obesity and type 2 diabetes: what can be unified and what needs to be individualized.

R.H.E. and S.E.K. contributed equally to this report.

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Robert H. Eckel , Steven E. Kahn , Ele Ferrannini , Allison B. Goldfine , David M. Nathan , Michael W. Schwartz , Robert J. Smith , Steven R. Smith; Obesity and Type 2 Diabetes: What Can Be Unified and What Needs to Be Individualized?. Diabetes Care 1 June 2011; 34 (6): 1424–1430. https://doi.org/10.2337/dc11-0447

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This report examines what is known about the relationship between obesity and type 2 diabetes and how future research in these areas might be directed to benefit prevention, interventions, and overall patient care.

An international working group of 32 experts in the pathophysiology, genetics, clinical trials, and clinical care of obesity and/or type 2 diabetes participated in a conference held on 6–7 January 2011 and cosponsored by The Endocrine Society, the American Diabetes Association, and the European Association for the Study of Diabetes. A writing group comprising eight participants subsequently prepared this summary and recommendations. Participants reviewed and discussed published literature and their own unpublished data.

The writing group unanimously supported the summary and recommendations as representing the working group's majority or unanimous opinions.

The major questions linking obesity to type 2 diabetes that need to be addressed by combined basic, clinical, and population-based scientific approaches include the following: 1 ) Why do not all patients with obesity develop type 2 diabetes? 2 ) Through what mechanisms do obesity and insulin resistance contribute to β-cell decompensation, and if/when obesity prevention ensues, how much reduction in type 2 diabetes incidence will follow? 3 ) How does the duration of type 2 diabetes relate to the benefits of weight reduction by lifestyle, weight-loss drugs, and/or bariatric surgery on β-cell function and glycemia? 4 ) What is necessary for regulatory approval of medications and possibly surgical approaches for preventing type 2 diabetes in patients with obesity? Improved understanding of how obesity relates to type 2 diabetes may help advance effective and cost-effective interventions for both conditions, including more tailored therapy. To expedite this process, we recommend further investigation into the pathogenesis of these coexistent conditions and innovative approaches to their pharmacological and surgical management.

Most patients with type 2 diabetes are obese, and the global epidemic of obesity largely explains the dramatic increase in the incidence and prevalence of type 2 diabetes over the past 20 years. Currently, over a third (34%) of U.S. adults are obese (defined as BMI >30 kg/m 2 ), and over 11% of people aged ≥20 years have diabetes ( 1 ), a prevalence projected to increase to 21% by 2050 ( 2 ). However, the precise mechanisms linking the two conditions remain unclear, as does our understanding of interindividual differences. Improved understanding will help advance identification and development of effective treatment options.

Excess weight is an established risk factor for type 2 diabetes, yet most obese individuals do not develop type 2 diabetes. Recent studies have identified “links” between obesity and type 2 diabetes involving proinflammatory cytokines (tumor necrosis factor and interleukin-6), insulin resistance, deranged fatty acid metabolism, and cellular processes such as mitochondrial dysfunction and endoplasmic reticulum stress. These interactions are complex, with the relative importance of each unclearly defined. Further genetic studies may elucidate additional common pathophysiological pathways for obesity and diabetes and identify promising new treatment targets. As physicians frequently prescribe glucose-lowering medications associated with weight gain, trade-offs between glycemic control and body weight with current therapeutic options need more consideration. This issue is particularly pressing given accumulating evidence that even modest weight reduction—whether through lifestyle/behavioral interventions, obesity medications, or bariatric surgery—can improve glycemic control and reduce diabetes risk.

These intriguing, but still largely unexplored, connections between obesity and type 2 diabetes suggested the timely need to convene a group of scientific experts in the fields to more closely examine underlying pathophysiology and treatment options for patients with type 2 diabetes addressing issues of excess weight and glycemic control simultaneously. Participants in the January 2011 conference (Supplementary Data) were tasked with examining what is known about the relationship between obesity and type 2 diabetes and the heterogeneity of these conditions, what needs to be learned, and how to direct future research in these areas to advance effective interventions and improve patient care. What follows summarizes the major issues addressed and the outcomes of the discussion.

Mechanisms of obesity-associated insulin resistance

The influence of obesity on type 2 diabetes risk is determined not only by the degree of obesity but also by where fat accumulates. Increased upper body fat including visceral adiposity, as reflected in increased abdominal girth or waist-to-hip ratio, is associated with the metabolic syndrome, type 2 diabetes, and cardiovascular disease ( 3 ), although underlying mechanisms remain uncertain. Whether subcutaneous fat lacks the pathological effects of visceral fat or is simply a more neutral storage location, for example, requires further study. Beyond differences in body fat distribution, emerging evidence suggests that different subtypes of adipose tissue may be functionally distinct and affect glucose homeostasis differentially. Adult humans have limited and variable numbers of brown fat cells ( 4 ), which play a role in thermogenesis and potentially influence energy expenditure and obesity susceptibility ( 5 ). Improved understanding of the function of different fat cell types and depots and their roles in metabolic homeostasis is a priority for investigation into the pathogenesis and complications of obesity. Likewise, adipose tissue is composed of heterogeneous cell types. Immune cells within adipose tissue also likely contribute to systemic metabolic processes. As the study of adipose biology progresses, it will be important to consider whether additional subtypes of adipocytes or other cell types can be identified to refine our understanding of obesity complications and generate novel approaches to prevention.

At least three distinct mechanisms have been proposed to link obesity to insulin resistance and predispose to type 2 diabetes: 1 ) increased production of adipokines/cytokines, including tumor necrosis factor-α, resistin, and retinol-binding protein 4, that contribute to insulin resistance as well as reduced levels of adiponectin ( 6 ); 2 ) ectopic fat deposition, particularly in the liver and perhaps also in skeletal muscle, and the dysmetabolic sequelae ( 7 ); and 3 ) mitochondrial dysfunction, evident by decreased mitochondrial mass and/or function ( 8 ). Mitochondrial dysfunction could be one of many important underlying defects linking obesity to diabetes, both by decreasing insulin sensitivity and by compromising β-cell function.

Mechanisms of progressive β-cell dysfunction in obese individuals

The link between obesity and hyperinsulinemia, first identified ∼50 years ago ( 9 ), reflects compensation by insulin-secreting β-cells to systemic insulin resistance. Although mechanisms underlying this coupling (e.g., mild hyperglycemia and raised levels of circulating free fatty acids) remain elusive, obese normoglycemic individuals have both increased β-cell mass and function ( 9 – 12 ). Obesity-induced glucose intolerance reflects failure to mount one or more of these compensatory responses ( 13 ).

Factors predisposing to β-cell decompensation could also be primarily genetic or epigenetic. A clear, mechanistic basis for this decompensation has remained elusive. Genetic studies have helped identify the role of some key molecules in β-cell biology that may be important in this regard. For example, recent rodent studies have demonstrated diabetogenic effects of reduced pancreatic expression of the Pdx1 gene ( 14 , 15 ). While these animal studies have demonstrated that PDX1 deficiency relates mechanistically to diabetes through β-cell apoptosis, and PDX1 deficiency is linked to MODY4 ( 16 ), it is not clear yet that PDX1 deficiency has a role in common forms of type 2 diabetes in humans. This example illustrates how a growing understanding of genetics and cellular function of the β-cell can identify potential mediators predisposing obese individuals to type 2 diabetes and further may provide insights for the development of new therapeutic agents.

Genetic factors linking obesity and diabetes

Genome-wide association scans (GWAS) and candidate gene approaches now have identified ∼40 genes associated with type 2 diabetes ( 17 , 18 ) and a similar number, albeit largely different, with obesity. Most type 2 diabetes genes appear to be related to β-cell dysfunction, with many fewer involved in pathways related to insulin resistance independent of obesity ( 19 , 20 ). Not surprisingly, many obesity gene variants appear to be involved in pathways affecting energy homeostasis. Although numerous diabetes- and obesity-associated genes have been identified, the known genes are estimated to predict only 15% of type 2 diabetes and 5% of obesity risk ( 21 ). Although additional genes with important roles will undoubtedly be discovered, this low predictive power may reflect the importance of environmental factors, less frequent genetic variants with stronger effects, or gene-environment, gene-gene, and epigenetic interactions that are not readily identified through methods based on population genetics. Methods for detecting gene-gene interactions exist, but the population size needed to detect them is substantially greater than is required for detection of single genes of relatively small effect. Alternatively, pathway analyses or a systems biology approach combining information from DNA variations with transcript, protein, and metabolite profiles may better capture the genetic influences on metabolism than studying single genes. One should also keep in mind that the missing heritability could be an illusion of inferring additive genetic effects from epidemiological data ( 22 ).

Does a shared pathogenesis underlie both obesity and type 2 diabetes?

Although the link between obesity and type 2 diabetes is widely held to involve two discrete lesions—obesity-induced insulin resistance and β-cell failure—both disorders may share an underlying defect. This “unified field theory” raises questions about whether defects favoring progressive weight gain and metabolic impairment also contribute to β-cell decompensation.

One potential link could be sustained cell exposure to nutrient concentrations exceeding energy requirements. Deleterious cellular effects of nutrient excess can include impaired inflammatory signaling, endoplasmic reticulum stress, excess production of reactive oxygen species, mitochondrial dysfunction, accumulation of triglycerides and/or fatty acyl intermediates, and activation of serine-threonine kinases ( 23 ). These responses are not mutually exclusive, and induction of one may trigger another, leading to a cascade of damage. Obesity-associated cellular injury can in turn recruit and activate macrophages and other immune cells that exacerbate tissue inflammation ( 23 , 24 ). Collectively, these responses contribute to the pathogenesis of insulin resistance in the liver, skeletal muscle, and adipose tissue, and some (e.g., acquired mitochondrial dysfunction and inflammation) may occur in β-cells as well via mechanisms discussed above. In susceptible individuals, therefore, obesity-induced metabolic impairment can favor insulin resistance on the one hand and progressive β-cell dysfunction on the other. Reduced insulin secretion can in turn worsen the nutrient excess problem by raising circulating concentrations of glucose, free fatty acids, and other nutrients. In this way, a vicious cycle arises whereby obesity-induced nutrient excess triggers inflammatory responses that cause insulin resistance, placing a greater demand on the β-cell, and as β-cell function declines the cellular toll taken by nutrient excess increases. Since not all obese individuals develop hyperglycemia, however, an underlying abnormality of the β-cell must coexist with nutrient excess to promote type 2 diabetes ( 13 ).

Brain neurocircuits governing energy homeostasis also affect insulin sensitivity in the liver and perhaps other peripheral tissues ( 25 ), and inflammation similar to that induced by obesity in peripheral insulin-sensitive tissues also occurs in these areas of the brain ( 26 ). If obesity is associated with impairment of neurocircuits regulating both energy balance and insulin action, obesity-induced insulin resistance may arise not only as a direct consequence of excessive adipose mass but via neuronal mechanisms as well. Whether disturbed neurocircuits also contribute to deteriorating β-cell dysfunction as obesity and its sequelae progress is an active area of investigation ( 27 ).

Managing body weight by behavioral change and medications

The dramatic increase in incidence and prevalence of obesity over the past 50 years, associated in part with major worldwide changes in caloric intake and dietary composition, has focused attention on lifestyle intervention to reverse or ameliorate caloric imbalance. In general, programs including individual or group counseling to modify behavior result in 5–10% weight loss and are effective for 6–12 months, after which weight regain is the rule. Some longer-term lifestyle intervention studies with sustained interventions demonstrate more durable weight loss ( 28 , 29 ), with extent of weight loss in the first 3–6 months generally predicting longer-term success. Successful lifestyle intervention programs typically involve self-monitoring of weight, dietary intake, and activity; behavioral modification; frequent contact; and caloric balance through diet, with or without exercise. For example, short-term intervention studies suggest that dietary changes, which emphasize less fat and refined carbohydrates, make it easier to reduce total caloric intake in obese adults and overweight children ( 30 , 31 ).

Medications have been used to assist in weight loss for almost 80 years, but adverse effects frequently restrict utility. Medications have been developed based on physiological insights, more recently targeting central nervous system control of appetite and metabolism, or opportunistically when weight loss was noted as a side effect of approved medications. Table 1 lists medications that have been available and others under development. In general, weight loss achieved with these medications ranges from 2 to 8% greater than placebo, with some suggestion that combination therapy may either increase weight loss or ameliorate side effects and increase tolerability. However, most drug trials last only 6–12 months, and thus there are few long-term data that weight loss can be sustained. Moreover, high drop-out rates, which approach 50%, are characteristic of many weight-loss trials and result in survivor effects in efficacy analyses, thereby potentially amplifying drug benefits and limiting generalizability. Furthermore, concern regarding adverse effects, including cardiovascular disease risk and central effects (e.g., depression) in drugs crossing the blood-brain barrier, continue to limit approval and application.

Weight-loss medications: past, current, and future

EMA, European Medicines Agency; FDA, U.S. Food and Drug Administration; GLP-1, glucagon-like peptide 1.

*Phenylpropanolamine is still available in some European countries and sibutramine in some South American countries.

#Phentermine is one of a class of sympathomimetic drugs that also includes benzphetamine, diethylpropion, and phendimetrazine.

Managing body weight by bariatric surgery

Health benefits of bariatric surgery, determined largely from nonrandomized studies, are being increasingly recognized. These benefits include substantial and sustained weight loss ( 32 ), resolution of comorbidities such as diabetes, hypertension, and dyslipidemia ( 33 , 34 ), and reduced myocardial infarction, cancers, and associated mortality ( 35 ). For extreme obesity, surgery is now the preferred and currently only effective treatment modality. Acute morbidity and mortality of surgical approaches have been dramatically reduced, enabling widespread use of these procedures. Furthermore, over the long term, bariatric surgery might reduce aggregate health care expenditures ( 36 ). There is also a growing movement toward using surgery to control diabetes, independent of severe excess weight, but there are currently few scientifically valid data to support this clinical path.

Bariatric surgery falls into two general categories: purely restrictive procedures such as the laparoscopic adjustable gastric band devices, which appear to improve diabetes via weight loss, and procedures bypassing the proximal gut, such as the Roux-en-Y gastric bypass (RYGB) or newer gastric sleeve procedures. The latter approaches (“metabolic” surgery) appear to produce unique effects on enteroendocrine hormones and neuronal signaling pathways and produce more weight loss and diabetes remission than banding alone ( 34 , 37 ). Metabolic surgeries are associated with increases in anorexigenic and decreases in orexigenic hormones, changes largely absent in band or restrictive procedures, and may explain the differential outcomes ( 38 ). Although mechanisms leading to weight loss and diabetes remission are only beginning to be understood, the above endocrine, peptide, and neural effects may mediate these benefits because of structural changes including isolation of the gastric cardia; exclusion of the distal stomach, duodenum, and proximal jejunum; exposure of the distal intestine to undigested nutrients; and partial vagotomy. Longer duration of diabetes and insulin use, both typically associated with decreased β-cell function and possibly surrogates for reduced β-cell mass, are associated with reduced postsurgical remission rates, thus suggesting that residual β-cell function may be a critical factor for metabolic benefits ( 39 ).

Known differences in mechanism and efficacy, along with risks and patient priorities (e.g., weight loss vs. metabolic/diabetes goals) already inform the choice of surgical procedure. However, many questions remain, including the following: How much weight loss is required for health benefits? What is the effect of different interventional methods on long-term outcomes? What mechanisms underlie the heterogeneous responses? Further, regarding diabetes, Is the optimal timing for treatment the same or different from obesity? Are β-cells preserved or do they even grow? Why do not we see the same efficacy and durability of response for other obesity-related pathologies (e.g., hypertension) as for glycemic control? Ongoing randomized clinical trials ( 40 ) promise to answer many questions regarding patient selection, optimal procedure, when to intervene, and where initial and chronic care should be delivered.

Barriers to effective management

A vast array of barriers—ranging from deficits in basic research to socioeconomic and individual psychological factors beyond the scope of the conference—undermines current efforts to manage obesity, particularly in individuals with type 2 diabetes. Lessons learned from efforts such as those applied to tobacco cessation may be quite relevant ( 41 ).

Lifestyle programs (especially long-term) are often plagued by inadequate reimbursement. Further, there is a lack of evidence-based individualized goals and strategies combining lifestyle and medications, or appreciation of sequential (stepped) therapy. As mechanisms leading to obesity and its maintenance are not fully understood, questions remain about which interventions, be they lifestyle or pharmacological, might be most effective during various stages of weight gain, loss, and regain. In addition, medications under development may carry indeterminate risk. Likewise, surgery is an imperfect remedy due in part to perceived risks and high cost. With laparoscopic banding now approved for BMI >30 kg/m 2 with a comorbidity such as diabetes or hypertension, 27 million Americans would be eligible for surgery. However, the large-scale feasibility of such an approach is uncertain and compounded by issues related to reimbursement. Thus, the search must continue for how to implement optimal lifestyle interventions and to find effective drugs and/or minimally invasive devices.

These barriers are further complicated in the context of type 2 diabetes. Obese patients with hyperglycemia are often poorly characterized not only in terms of their history of obesity but also in the duration of their glucose intolerance. Further, interventions are typically started late in the disease, with minimal preventive efforts. In addition, as initial weight loss is the main determinant of longer-term weight loss, the typical initial goal of ∼5–10% weight loss may be inadequate to produce glycemic control ( 42 ). Furthermore, although controlling body weight (either by reduction or by prevention of further rise) improves glycemic control by ameliorating both insulin resistance and β-cell dysfunction, the impact of pharmacologically induced improved glycemic control on body weight varies by individual drug. Glucose-lowering medications can be broadly categorized into those associated with weight gain and those essentially weight neutral or promoting weight loss ( Table 2 ). Whether weight gain offsets any benefit of reduced glycemia on cardiovascular risk needs to be determined. Further, weight changes do not necessarily predict changes in glycemic control ( 43 ), and while specific therapies may work in certain diabetes subtypes, the response to glucose-lowering medications varies considerably. This latter topic was the focus of a similar workshop in 2009 on individualizing therapies in type 2 diabetes ( 44 ).

Weight effects of glucose-lowering medications

DPP-4, dipeptidyl peptidase-4; GLP-1, glucagon-like peptide 1.

Equally challenging is the problem of weight regain, which usually follows any degree of weight loss, however achieved ( Fig. 1 ). Well studied and viewed as a normal response in lean individuals, this phenomenon is equally robust among the obese. It involves complex, highly integrated physiological responses that are similar to those invoked in weight-reduced, nonobese individuals. The biologic basis appears to be the tendency to defend attained weight, whether normal or excessive, which seems to be wired in multiple central nervous system defenses against weight loss. Current models of energy homeostasis predict genetic or acquired defects in key neurocircuits that undermine the normal response to adiposity-related humoral signals. Much of the basic science in this area has been performed in animal models of obesity (genetic or overfeeding); extrapolation to the pathophysiology of human obesity remains uncertain.

Figure 1. Schematic representation of the natural history of obesity. Primary (excess) weight gain occurs usually over years against the typical background of mild age-related increase in weight in the general population. Intentional weight loss frequently is at least partially successful, but in the vast majority of cases, is followed by weight regain. Weight loss and its maintenance is the therapeutic goal; prevention of primary weight gain is a societal endeavor.

Schematic representation of the natural history of obesity. Primary (excess) weight gain occurs usually over years against the typical background of mild age-related increase in weight in the general population. Intentional weight loss frequently is at least partially successful, but in the vast majority of cases, is followed by weight regain. Weight loss and its maintenance is the therapeutic goal; prevention of primary weight gain is a societal endeavor.

The panoply of potential mechanisms defending body weight helps explain why the field is moving toward targeting multiple pathways by harnessing additive effects of current drugs, which individually produce ∼5% weight loss ( 45 ). A number of compounds, old and new, alone or in combination, are being developed. It is hoped that they may safely achieve the magnitude of change in body weight, as well as other beneficial effects such as glucose control, that has been obtained with some of the surgical approaches.

Recommendations

Elucidate the pathogenesis linking obesity and type 2 diabetes.

A better understanding of mechanisms linking obesity, insulin resistance, and type 2 diabetes may ultimately facilitate more individualized treatment. One future research priority is to clarify how identified gene variants affect glucose, fatty acid, and energy metabolism at both cellular and whole-body levels. Rather than searching for a single factor or theory explaining the predisposition to β-cell decompensation in obese individuals, a multifactorial, synergistic explanation seems more compatible with current knowledge. Multiple mechanisms may link β-cell dysfunction to systemic insulin resistance, including differing cellular responses to nutrient excess and impaired brain neurocircuits governing energy homeostasis. One way to approach this complex pathophysiology is to examine glucose-tolerant obese patients and study the association with and progression to β-cell decompensation.

Expand research on heterogeneity

So far, genetic studies have been limited by a lack of accurate assessments of phenotype. Additional large-scale population-based analyses addressing more complex disease determinants of obesity and diabetes (beyond single genetic polymorphisms) might improve understanding of the relative impact of genetic and environmental factors linking them. Other priorities include clarifying the genetic basis for differences in fat distribution across ethnic groups ( 46 ); identifying factors that control homing of adipose tissue to the different—visceral versus subcutaneous—fat depots ( 47 ) and adipose tissue angiogenesis ( 48 ); and understanding the time course and extent of transdifferentiation of brown and white adipocytes in humans ( 5 ).

Human β-cells, including those from patients with type 2 diabetes, need to be made more widely available for investigational use. An additional approach would be the creation of patient-specific stem cell–derived β-cells. Moreover, longitudinal studies of β-cell dysfunction in humans should address differences in the amount of weight loss required to durably improve β-cell function. Finally, research to elucidate the intrauterine environment's impact on β-cell development and function may provide further strategic approaches to protecting progressive β-cell dysfunction.

Develop innovative approaches to pharmacological and surgical management

Innovative approaches to managing obesity may lower certain barriers undermining treatment of both obesity and type 2 diabetes. For example, modulating the incretin axis may benefit both energy balance and glycemia. Novel pharmacological development may depend on information gained from more efficient use of genomic, proteomic, and metabolomic approaches and from information learned from studying weight-loss mechanisms in bariatric surgery. In addition, co-opting less traditional organs such as the brain and gut into the core pathophysiology of type 2 diabetes may reveal new biomarkers and/or targets for therapeutic intervention. Finally, safe and effective centrally acting drugs that decrease appetite or increase satiety are urgently needed. However, as regulatory agencies increase the need for safety testing, fewer new and innovative approaches for weight loss are being developed because of the prolonged time and immense expense involved.

Emphasize primary prevention of obesity and type 2 diabetes

Current clinical approaches to obesity continue to focus on secondary and tertiary intervention. Physicians often introduce secondary interventions when patients surpass some dichotomous BMI threshold or when patients self-identify, for cosmetic or health reasons. They introduce tertiary intervention when obesity-related complications responsive to weight loss, such as diabetes, hypertension, or sleep apnea, develop. Because weight problems develop over the entire life span, however, emphasizing obesity prevention is urgent and must include cooperation of public health institutions, the school systems, and the private (e.g., food industry) sector. The likelihood of sustained benefits of weight reduction on β-cell function and glycemia in patients with early-onset versus more prolonged durations of type 2 diabetes needs to be determined.

Although intensive lifestyle modifications and medications have been conclusively demonstrated to slow the development of type 2 diabetes in those with impaired glucose metabolism ( 28 , 49 ), regulatory authorities have still not approved medications for preventing type 2 diabetes, nor have they provided a regulatory framework to do so. Guidance on what would be required to approve medications for treating high-risk individuals would foster more scientific investment in this area and subsequent availability of additional preventive options.

Adopt a chronic disease model linking obesity to diabetes care

Current understanding of both pathophysiology and management suggests the need to adopt a chronic disease model of care linking obesity and diabetes care management systems. Besides including stepped-care approaches similar to those used for other chronic diseases, this model involves basing interventional (pharmacological and surgical) approaches on severity, duration, and individual risk/benefit. The common perception that the obesity problem is insurmountable leads to some degree of clinical inertia. What is needed is similar to what occurred with tobacco—a comprehensive social, economic, and workplace approach to prevention and intervention. In addition, community-setting approaches supplemented by physician involvement can work when combining treatment modalities ( 50 ). Furthermore, multidisciplinary teams including nutritionists, exercise physiologists, and behavioral/mental health professionals can achieve both initial and sustained weight management and glucose control ( 28 , 29 ). This approach to attaining and maintaining weight reduction is critically important both in alleviating the intensive defense of body weight by multiple biological systems and in reducing risk of β-cell decompensation and, over the long term, diabetes complications.

Summary and conclusions

Improved understanding of obesity's heterogeneity, including interindividual differences in pathogenesis, propensity to regain lost weight, development of obesity-related complications including diabetes, and response to therapy, is critical to advance the development of effective and cost-effective interventions. The insights that improve obesity prevention and treatment will almost certainly benefit the incidence and care of type 2 diabetes. The converse may not be true since current treatments of diabetes can have differential effects on weight. Even so, we have reached a point when we can begin to consider innovative and potentially more effective approaches to managing both obesity and type 2 diabetes. Increased understanding of the pathogenesis of obesity and type 2 diabetes, for example, should not only help differentiate responders from nonresponders but also make tailored therapy a reality. Equally beneficial will be incorporating these ideas into a chronic disease model of care linking obesity management to diabetes care systems, including multidisciplinary approaches to patient care designed to prevent weight regain that is almost universal when therapy is stopped.

Presently, some of the major questions linking obesity to type 2 diabetes that need to be urgently addressed include the following:

Why do not all patients with obesity develop type 2 diabetes?

Through what mechanisms do obesity and insulin resistance contribute to β-cell decompensation, and if/when obesity prevention ensues, how much reduction in type 2 diabetes incidence will follow?

How does the duration of type 2 diabetes relate to the benefits of weight reduction by lifestyle, weight-loss drugs, and/or bariatric surgery on β-cell function and glycemia?

What is necessary for regulatory approval of medications and possibly surgical approaches for preventing type 2 diabetes in patients with obesity?

This article is based on a conference jointly sponsored by The Endocrine Society, the American Diabetes Association, and the European Association for the Study of Diabetes, with the financial support of an unrestricted educational grant from Novo Nordisk.

R.H.E. received grant/research support from Sanofi Research Grant (fellowship educational grant), diaDexus, and GlaxoSmithKline; compensation for working as a consultant for Amylin, GTC Nutrition, Genfit, Eli Lilly, Pfizer, Johnson & Johnson, and Esperion; financial or material support from Cardiometabolic Health Congress and Metabolic Syndrome Institute; and honoraria from Vindico, CME Incite, and Voxmedia. S.E.K. received consulting fees from Eli Lilly, GlaxoSmithKline, Intarcia Therapeutics, and Novo Nordisk for acting as an advisory board member; consulting fees and honoraria from Boehringer Ingelheim and Merck for acting as an advisory board member and speaker; and grant support from Daiichi Sankyo. E.F. received consulting fees from Merck, Boehringer Ingelheim, Bristol-Myers Squibb/AstraZeneca, sanofi-aventis, Novartis, GlaxoSmithKline, and Daiichi Sankyo and grant support from Merck and Eli Lilly. A.B.G. acted as Site Principal Investigator for a clinical trial funded by Eli Lilly that is now complete with results published. M.W.S. received consulting fees from Merck, Pfizer, and Orexigen. R.J.S. received consulting fees from GI Dynamics and royalties from Baxter and Fresenius Kabi. S.R.S. received consulting fees from Amylin, Bristol-Myers Squibb, Eli Lilly, and Novartis and consulting fees as an advisory board member for Arena. No other potential conflicts of interest relevant to this article were reported.

The authors are grateful for the contributions of the speakers and participants in the January 2011 conference, who are listed, together with affiliations, in the Supplementary Data. In addition, the authors acknowledge the editorial assistance of Dr. Terra Ziporyn, medical editor, in writing the manuscript.

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Review Article
Published: 23 February 2022

Association of weight status and the risks of diabetes in adults: a systematic review and meta-analysis of prospective cohort studies

Hong-jie Yu 1 ,
Mandy Ho ORCID: orcid.org/0000-0002-4460-7969 1 ,
Xiangxiang Liu 2 ,
Jundi Yang 1 ,
Pui Hing Chau 1 &
Daniel Yee Tak Fong 1

International Journal of Obesity volume 46 , pages 1101–1113 ( 2022 ) Cite this article

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Type 2 diabetes

Obesity is a known risk factor for type 2 diabetes mellitus (T2DM); however, the associations between underweight and T2DM and between weight status and prediabetes have not been systematically reviewed. We aimed to estimate the relative risks (RRs) of prediabetes/T2DM in underweight/overweight/obesity relative to normal weight. PubMed, Embase, Web of Science, and Cochrane Library were searched from inception to December 8, 2021. Prospective cohort studies with a minimum 12-month follow-up period reporting the association between baseline body mass index (BMI) categories and risk of prediabetes/T2DM in adults were included. Study quality was assessed using the Newcastle-Ottawa Scale. The main analyses of T2DM risk were performed using the ethnic-specific (Asian/non-Asian) BMI classification and additional analyses of prediabetes/T2DM risk by including all eligible studies. Random-effects models with inverse variance weighting were used. Subgroup analyses and meta-regression were conducted to explore the potential effects of pre-specified modifiers. The study protocol was registered with PROSPERO (CRD42020215957). Eighty-four articles involving over 2.69 million participants from 20 countries were included. The pooled RR of prediabetes risk was 1.24 (95% CI: 1.19–1.28, I 2 = 9.7%, n = 5 studies) for overweight/obesity vs. normal weight. The pooled RRs of T2DM based on the ethnic-specific BMI categories were 0.93 (95% CI: 0.75–1.15, I 2 = 55.5%, n = 12) for underweight, 2.24 (95% CI: 1.95–2.56, I 2 = 92.0%, n = 47) for overweight, 4.56 (95% CI: 3.69–5.64, I 2 = 96%, n = 43) for obesity, and 22.97 (95% CI: 13.58–38.86, I 2 = 92.1%, n = 6) for severe obesity vs. normal weight. Subgroup analyses indicated that underweight is a protective factor against T2DM in non-Asians (RR = 0.68, 95% CI: 0.40–0.99, I 2 = 56.1%, n = 6). The magnitude of the RR of T2DM in overweight/obesity decreased with age and varied by region and the assessment methods for weight and T2DM. Overweight/obesity was associated with an increased prediabetes/T2DM risk. Further studies are required to confirm the association between underweight and prediabetes/T2DM, particularly in Asian populations.

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Adult weight gain and the risk of cardiovascular disease: a systematic review and dose–response meta-analysis of prospective cohort studies

Associations of obesity indices change with cardiovascular outcomes: a dose-response meta-analysis

Body mass index and cancer risk in patients with type 2 diabetes: a dose–response meta-analysis of cohort studies

Data availability.

Application for datasets generated during and/or analyzed during the current study may be considered by the corresponding author on reasonable request.

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Acknowledgements

The authors would like to thank the consultant service provided by the medical librarian of the University of Hong Kong when designing the search strategy for different databases.

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Hong-jie Yu, Mandy Ho, Jundi Yang, Pui Hing Chau & Daniel Yee Tak Fong

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YHJ and HM conceived and designed this study. YHJ, LXX, and YJD conducted the database searches, screened titles, abstract, and full text. YHJ and LXX extracted data. YHJ and YJD assessed the study quality. YHJ did statistical analysis and data visualization. YHJ and HM drafted the manuscript. All Authors contributed data interpretation and review and revision of manuscript. HM was responsible for overall supervision to this study.

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Yu, Hj., Ho, M., Liu, X. et al. Association of weight status and the risks of diabetes in adults: a systematic review and meta-analysis of prospective cohort studies. Int J Obes 46 , 1101–1113 (2022). https://doi.org/10.1038/s41366-022-01096-1

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Published: 14 May 2024

Estimating effects of whole grain consumption on type 2 diabetes, colorectal cancer and cardiovascular disease: a burden of proof study

Houpu Liu 1 ,
Jiahao Zhu 1 ,
Rui Gao 1 ,
Lilu Ding 1 ,
Ye Yang 1 ,
Wenxia Zhao 1 ,
Xiaonan Cui 2 ,
Wenli Lu 3 ,
Jing Wang 1 na1 &
Yingjun Li 1 na1

Nutrition Journal volume 23 , Article number: 49 ( 2024 ) Cite this article

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Metrics details

Previous studies on whole grain consumption had inconsistent findings and lacked quantitative assessments of evidence quality. Therefore, we aimed to summarize updated findings using the Burden of Proof analysis (BPRF) to investigate the relationship of whole grain consumption on type 2 diabetes (T2D), colorectal cancer (CRC), stroke, and ischemic heart disease (IHD).

We conducted a literature search in the Medline and Web of Science up to June 12, 2023, to identify related cohort studies and systematic reviews. The mean RR (relative risk) curve and uncertainty intervals (UIs), BPRF function, risk-outcome score (ROS), and the theoretical minimum risk exposure level (TMREL) were estimated to evaluate the level of four risk-outcome pairs.

In total, 27 prospective cohorts were included in our analysis. Consuming whole grain at the range of TMREL (118.5–148.1 g per day) was associated with lower risks: T2D (declined by 37.3%, 95% UI: 5.8 to 59.5), CRC (declined by 17.3%, 6.5 to 27.7), stroke (declined by 21.8%, 7.3 to 35.1), and IHD (declined by 36.9%, 7.1 to 58.0). For all outcomes except stroke, we observed a non-linear, monotonic decrease as whole grain consumption increased; For stroke, it followed a J -shaped curve (the greatest decline in the risk of stroke at consuming 100 g whole grain for a day). The relationships between whole grain consumption and four diseases are all two-star pairs (ROS: 0.087, 0.068, 0.062, 0.095 for T2D, CRC, stroke, and IHD, respectively).

Consuming 100 g of whole grains per day offers broad protective benefits. However, exceeding this threshold may diminish the protective effects against stroke. Our findings endorse replacing refined grains with whole grains as the main source of daily carbohydrates.

Registry and registry number for systematic reviews or meta-analyses

We have registered our research in PROSPERO, and the identifier of our meta-analyses is CRD42023447345 .

Peer Review reports

Introduction

Whole grains have been widely endorsed as a superior substitute for primary energy and carbohydrate sources in daily dietary guidelines because of their high dietary fiber content and numerous bioactive compounds [ 1 ]. The Global Burden of Disease Study 2019 (GBD 2019) has reported that lower intake of whole grain accounted for 1,844,836 (95% uncertainty interval [UI]: 2,338,609–921,291) deaths and 42.5 million (53.2–17.5) disability-adjusted life years (DALYs) [ 2 ]. The large estimated burden demonstrated the importance of fully appreciating the relationship between whole grain consumption and potentially related health outcomes and of further improving the strength of evidence supporting the understanding of those relationships.

Increasing evidence has found that a high intake of whole grains is related to a reduction in the risk of type 2 diabetes (T2D), colorectal cancer (CRC), ischemic heart disease (IHD), and stroke [ 2 , 3 ]. However, regarding CRC, T2D and IHD, previous studies, including dose-response meta-analysis or cohort studies, exhibit variations in their consumption ranges. This complicates the comparability and consolidation of evidence [ 3 , 4 , 5 , 6 ]. Besides, in relation to stroke, recent meta-analyses have presented inconsistent findings [ 7 , 8 ]. Although there is an increasing body of evidence supporting the positive impact of consuming whole grains on health, the challenge lies in accurately estimating RR associated with varying levels of consumption. This limitation hinders the ability of decision-makers to fully comprehend the strength of the connection between consuming whole grains and various health outcomes.

Burden of proof risk function (BPRF) is a new meta-analysis method that can quantitatively estimate the level of risk closest to the null hypothesis [ 9 ]. Hitherto, most of meta-regression studies applied given fixed knots to fit the spline models or forced a log-linear assumption to simplify statistical analysis. However, such a method may limit their ability to capture the effects of whole grain consumption on health outcomes, as the relationship between increasing whole grain intake and its impact on health might not be straightforward: it could lead to slight decreases in positive effects, or it could even become harmful if the consumption of whole grains goes beyond a certain point [ 3 , 10 ]. Unlike existing methods, BPRF relaxed the conventional assumption of a log-linear shape in risk functions, and instead applied a data-driven approach to determine the relationship of risk-outcome pairs using a quadratic spline. Thus, BPRF can help to identify the ‘true’ shape of the risk function [ 11 ]. In addition, existing methods, such as Grading of Recommendations, Assessment, Development and Evaluations (GRADE) or NutriGRADE, are commonly applied to assess the quality of the underlying evidence [ 12 ]. However, such methods are unable to extend to quantify variation in true effect size caused by bias from covariates or other limitations of the evidence [ 11 ]. Nevertheless, BPRF can synthesize available evidence in algorithm to calculate uncertainty inclusive of between-study heterogeneity.

To precisely quantify the health effects of whole grain consumption, a meta-regression analysis was conducted on the evidence from prospective cohort studies. This study focused specifically on four health outcomes (T2D, CRC, IHD, and stroke) linked to whole grain consumption, as reported by the GBD study [ 13 ].

Our protocol has been registered in International Prospective Register of Systematic Reviews (PROSPERO, identifier: CRD42023447345 ). We followed a standard framework of Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guideline to report our results [ 14 ].

Search strategy and selection criteria

We searched data published in English in the MEDLINE and Web of Science for systematic review, and cohort studies from 1 January, 2000 to 12 June, 2023, using standard search strings (Supplementary Table 1 ). A reference list of included publications was also manually screened to identify additional cohort studies. Titles and abstracts were screened by two reviewers (H Liu and J Wang), with discrepancies being reconciled through consulting a third author (Y Li).

Only prospective observational studies (for both incidence and mortality) published in English were included. Studies should report a relative risk ratio (RR), odds ratio (OR) or hazard ratio (HR) of the associations between whole grain consumption and at least one of the four outcomes. Additionally, they should specify the amount of whole grain consumption in both the reference group and the alternate group for comparison.

Retrospective studies, conference abstracts, ecological studies, case reports, case-series, letters to the editor, conference proceedings, umbrella reviews, systematic reviews or meta-analyses as well as studies conducted in animals, children, or adolescents were excluded. Besides, we excluded studies that failed to report whole grain consumption without grams or servings equivalent, such as studies that used aggregated “diet scores” as a measure of consumption, and those that only reported specific subtypes of grains were also excluded. And studies reporting outcomes outside the scope of interest, such as all-cause mortality, or lacking specificity such as cardiovascular disease or diabetes mellitus, have been excluded.

Data extraction

For each study, we collected the information of the eligible studies including the first author’s name, location, population characteristics (age, sex, race, and sample size), follow-up period, exposure definition, exposure assessment method, outcome definition, outcome ascertainment method, and covariates used in the study. Data were extracted by one author (H Liu) and checked by another author (J Wang) for accuracy. Besides, we also collected data on the range of exposure, sample size, person-years, number of events and risk estimate (RRs, HRs or ORs) and its corresponding uncertainty to conduct BPRF analysis. The uniform extraction procedures are shown in Supplementary Table 2 .

We used a framework of BPRF methodology developed by Zheng et al. to assess the risk of bias in included studies [ 11 , 15 , 16 ]. For each included study, we extracted information concerning aspects of study design that could potentially bias the reported effect size and coded this information into study-level covariates [ 11 ]. These study-level covariates are followed as: follow-up time (≤ 10 months and > 10 months), exposure definitions, outcome definitions, effect size measures (HRs, RRs or ORs), the endpoint of outcome events (incidence or mortality), frequency of exposure measurements (single or repeat), outcome ascertainment methods (administrative records or self-reports), and the level of adjustment for relevant confounders (creating cascading dummy variables standing for the number of confounders adjusted in risk regression model from selected studies, and the minimum threshold for confounder adjustment for age and sex) [ 11 ]. These covariates would be further adjusted in our BPRF analysis if they significantly biased our estimated risk functions.

In addition to these covariates, we selected four common study characteristics that are highly relevant and likely to introduce bias, in order to evaluate the study quality [ 9 , 17 , 18 ]. These characteristics include the representativeness of the study population (whether it represents the general population or specific sub-groups such as high-risk populations), outcome confirmation, exposure mesurement and assessment, and control for confounding factors [ 11 ]. The quality score for each selected study was calculated by summing the scores across these four domains.

Statistical methods

The estimates for our primary indicators of this work are mean RRs across a range of exposures, BRPFs, ROSs and star ratings for each risk-outcome pair. And the exposure unit was standardized to grams of consumption per day before synthesis. For each study that reported means or quantiles consumption rather than ranges of whole grain consumption, midpoint of defined quantile as the cutoff for intake intervals was used [ 10 , 19 , 20 , 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 ]. When the quantile dose range didn’t have a specific endpoint, and mean and standard deviation weren’t available, we assumed a consumption level of 0 g per day as the lowest amount [ 22 , 24 , 25 , 28 , 30 , 31 , 32 , 33 ]. For the upper limit of consumption, we used the range from the closest quartile or tertile within the cohort. In addition, we used 30 g to evaluate one serving of whole grain consumption if the value of a serving was not stated [ 34 ].

Estimating the shape of relationships between whole grain consumption and four health outcomes

We firstly modeled the mean log-RR (a measure of effect size) curve with MR-BRT (a Bayesian meta-regression tool) developed at the Institute for Health Metrics and Evaluation (IHME) [ 13 , 35 ], and followed a uniform analysis procedure to select model specifications for all dietary risks, which is described by Zheng et al. [ 15 ]. For protective risk factors with hypothesis of monotonic deceresing, the final models were run applying quadratic splines with two internal knots and a linearity prior on the right tail [ 11 , 15 ]. However, for the J-shaped risk curve, we employed quadratic splines with three internal knots and a linearity prior on both the right and left tails, without a monotonic prior [ 11 ]. Besides, to avoid the influence of extreme data and reduce publication bias, we trimmed 10% of data for each outcome as outliers [ 11 ].

Following the GRADE approach, we created binary covariates based on the the extracted information about specific study characteristics to identify potential sources of systematic bias within our included datasets. A step-wise Lasso approach were applied to assess the significance of these bias covariates at a threshold of 0.05. If the bias covariates were found to be significant, they were selected for adjustment in the final log-RR model.

To evaluate and adjust between-study heterogeneity, we quantified common sources of bias across the selected covariates that were likely to cause bias. And we calculated 95% UIs for each mean risk curve both with between-study heterogeneity incorporated (a ‘conservative’ UI) and without between-study heterogeneity incorporated (a ‘conventional’ UI) based on the selected biased covariates. Only the UIs that include between-study heterogeneity are presented in our main results unless specified.

Based on the aforementioned models, we then adjusted the selected bias covariates to decrease the variation in model residuals arising from differences in study quality and analysis. However, we primarily applied empirical evidence to choose bias covariate related to potential publication or reporting biases, which may ignore some confounders. Thus, Egger’s regression was conducted to detect publication bias, which estimated correlation between the study residuals and standard deviation of the corresponding data points. Funnel plots of the residuals of the risk function and standard deviations were generated to inspect reporting bias visually. And P value was used to assess the statistical significance of a risk for publication and/or reporting bias.

Estimating the TMREL/minimum risk exposure level

To draw robust conclusions about health benefits of whole grain consumption, we calculated the theoretical minimum risk exposure level (TMREL) of all potential outcomes linked to consuming whole grains. TMREL aligns with real-world consumption patterns supported by the included data, enabling an estimation of the average risk associated with whole grain intake. For protective risk factors, the lower bound of TMREL is defined as the 85th percentile of the lower limit within the highest consumption range across all studies. whereas the upper bound of TMREL is determined as the 85th percentile of the midpoint within the highest consumption range across all studies [ 15 ].

Estimating BPRF value, risk-outcome score (ROS) and star rating

Using the mean RR curves that incorporated between-study heterogeneity into uncertainty estimate, we estimated the BPRF from a conservative risk function. The BPRF was defined as the 5th (for harmful) or 95th percentile risk curve that is closest to the null. Afterwards, we calculated the ROS, which was equivalent to the mean log-BPRF averaged value over the 15th and 85th percentiles of the distribution of whole grain consumption. This value can give conservative interpretations regarding the association between whole grain consumption and four health outcomes [ 15 , 36 ]. Then, the ROSs of risk-outcome pairs were converted into a comparison across risk-outcome pairs and a star rating (from one to five) was assigned based on the quantitative assessment of the association, where a one-star rating indicating a non-significant relationship based on the conservative interpretation, two-star through five-star ratings implying a decrease in risk with average exposure (compared to no exposure). And the ranges of ROS in 0–0.1398 stands for two-star pairs, > 0.1398–0.4055 for three-star pairs, > 0.4055–0.6152 for four-star pairs and greater than 0.6152 for five-star pairs for protective risks [ 11 ].

Sensitivity analyses

To strengthen our estimates on the association between whole grain intake and four health outcomes and reduce the impact of outliers, we used trimming analysis with the Least Trimmed Squares (LTS) method. This method automatically identifies and removes outliers within the model’s likelihood. In our study, we trimmed the top 10% of data points that deviated the most from the expected dose-response curve as part of sensitivity analysis [ 11 , 35 ].

Dose-response analysis on whole grain consumption and four health outcomes was conducted by applying MR-BRT tool which included several Python packages (limetr 0.0.5, mrtool 0.0.1, IPOPT 1.2.0). And we executed BPRF analysis in Visual Studio Code with extensions of R version 4.2.1 and Python 3.9.0.

Study identification

A total of 3118 articles were found using search strings, and of those we identified 28 population-based prospective cohort studies, presenting a total of 184 estimates of effect sizes for associations between whole grain consumption and the four included health outcomes [ 10 , 25 , 26 , 28 , 29 , 32 , 33 ]. Details of the literature search are shown in Supplementary Fig. 1 . Eleven studies were from the United States, including data mainly from the HPFS, NHS, NHSII, the ATBC cohort, and the Cancer Prevention Study‑II Nutrition Cohort [ 10 , 20 , 21 , 27 , 28 , 29 , 32 , 38 , 39 , 41 , 42 ]. Fifteen of 28 studies were from the European population [ 19 , 22 , 23 , 24 , 25 , 26 , 30 , 33 , 25 , 43 , 44 , 45 , 46 , 47 ]. Besides, one study (the PURE cohort) collected information covering 21 countries (including the regions of North America and Europe, South America, Africa, the Middle East, South Asia, South East Asia, and China) [ 48 ] and one study reported the role of whole grain consumption on IHD in Chinese population [ 40 ]. Detailed information about the included cohorts is displayed in Supplementary Table 3 .

Characteristics of included studies

Of 28 included publications in the BPRF analysis, a total of eight studies investigated the association between whole grains and T2D [ 10 , 28 , 29 , 32 , 33 , 37 , 46 , 47 ], seven for CRC [ 19 , 20 , 21 , 30 , 42 , 43 , 44 ], six for both IHD and stroke [ 23 , 26 , 39 , 40 , 41 , 48 ], three for IHD [ 25 , 38 , 45 ], three for stroke [ 24 , 27 , 49 ], and one for IHD, stroke and CRC [ 22 ]. The median follow-up time of all included studies was 13.5 years (range: 6–25.8 years).

All included publications used dietary records or recalls, or food frequency questionnaires to collect data regarding whole grain intake. In total, seventeen publications used baseline data of whole grain intake in their analysis (single measurement) [ 19 , 23 , 24 , 25 , 26 , 30 , 33 , 39 , 25 , 43 , 44 , 45 , 48 ], whereas ten considered the average whole grain intake throughout the follow-up (i.e., based on multiple measurements) as the main exposure [ 10 , 20 , 21 , 22 , 27 , 37 , 38 , 40 , 41 , 42 ]. Three studies took self-report records to assess outcomes [ 28 , 29 , 32 ], and 25 studies used administrative medical records [ 10 , 19 , 21 , 22 , 25 , 26 , 27 , 30 , 33 , 37 , 41 , 43 , 44 , 49 ]. Three studies used mortality as the endpoint [ 26 , 39 , 43 ], and the rest studies considered incidence as the endpoint. 8 studies reported effect sizes with RRs [ 19 , 20 , 24 , 26 , 30 , 32 , 33 , 49 ], seventeen studies reported HRs [ 22 , 23 , 25 , 27 , 37 , 38 , 39 , 40 , 41 , 48 ], one study reported ORs [ 46 ], and one study reported incidence rate ratios (IRRs) [ 44 ]. The detailed information is presented in Supplementary Tables 4 – 7 .

Estimation of the shape of whole grains with T2D, CRC, IHD and stroke

Using BPRF methodology, our analyses revealed a correlation between higher whole grain intake and a reduced risk across all the outcomes considered. Figures 1 , 2 , 3 and 4 depict the BPRF curves for each risk-outcome pair, while Table 1 presents the results of the dose-response analysis.

BPRF analysis on the association between whole grain consumption and T2D. a, log RR function. b, RR function. c, modified funnel plot showing the residuals (relative to zero) on the x-axis and the estimated s.d. that includes reported s.d. and between-study heterogeneity on the y-axis

BPRF analysis on the association between whole grain consumption and CRC. a, log RR function. b, RR function. c, modified funnel plot showing the residuals (relative to zero) on the x-axis and the estimated s.d. that includes reported s.d. and between-study heterogeneity on the y-axis

BPRF analysis on the association between whole grain consumption and IHD. a, log RR function. b, RR function. c, modified funnel plot showing the residuals (relative to zero) on the x-axis and the estimated s.d. that includes reported s.d. and between-study heterogeneity on the y-axis

BPRF analysis on the association between whole grain consumption and stroke. a, log RR function. b, RR function. c, modified funnel plot showing the residuals (relative to zero) on the x-axis and the estimated s.d. that includes reported s.d. and between-study heterogeneity on the y-axis

Specifically, our analysis revealed that the associations between whole grain consumption and the risk of T2D, CRC and IHD all exhibited non-linear, monotonically decreasing trends (Figs. 1 , 2 and 3 ). In regard to T2D (Fig. 1 a and b), the sharpest decline in risk was noted at daily consumption of 50 g, with a reduction of 34.3% (95% UI including between-study heterogeneity: 5.3 to 55.7), compared to no whole grain consumption (at 0 g per day). Nonetheless, the reduction in risk tapered off to a mere 1.2% (0.2–1.6) when comparing a consumption level of 90 g per day to 50 g per day. With respect to CRC (Fig. 2 a and b), the largest reduction in CRC risk was identified when comparing the risk between an intake of 0 g per day and of 80 g per day, showcasing a noteworthy decline of 17.3%. we observed only marginal additional reductions in risk when consumption is beyond 80 g per day. As for IHD, the steepest decline of 32.1% (95% UI inclusive of between-study heterogeneity of 6.0 to 51.8) in IHD risk was observed when comparing risk between an intake of 0 g per day and of 30 g per day, with more modest marginal declines in IHD risk when consumption levels greater than 30 g per day (Fig. 3 a and b).

Different from the aforementioned results, a J -shape association was found between whole grain consumption and the risk of stroke (Fig. 4 a and b). The greatest reduction in stroke risk, observed at an intake of 100 g per day, was 24.6% (95% UI including between heterogeneity: 8.8 to 38.8). The mean risk of stroke at 60 g per day was 14.1% (7.0 to 21.3 including between-study heterogeneity) higher than at 100 g per day. And it was 1.5% (0.3 to 2.0) higher at 120 g per day compared to 100 g per day.

Additionally, the BPRF estimated ROSs for IHD, T2D, CRC and stroke of 0.095, 0.087, 0.068 and 0.062, respectively, which were applied to explore the average health benefits across the universe of whole grain consumption. Such estimates indicated that the consuming whole grains, on average, was related to a 9.9% decreased risk of IHD, a 9.1.% lower risk of T2D, a 7.0% lower risk of CRC and a 6.4% lower risk of stroke compared to a 0 g of whole grain intake. The star ratings of the four risk-outcome pairs all correspond to a two-star rating. After adjusting for between-study heterogeneity, the relationships still achieved statistical significance.

TMREL level of whole grain consumption

Based on observed exposure levels reported in the included studies, a TMREL of 118.5 g to 148.1 g per day, corresponding to approximately 4–5 servings per day, was estimated (detailed input information presented in Supplementary Table 8 ). Compared to TMREL (118.5–148.1 g), consuming no whole grains was associated with a 37.3% (5.8 to 59.5, inclusive of between-study heterogeneity) greater mean risk of T2D, a 17.3% (95% UI inclusive of between-study heterogeneity of 6.5 to 27.7) greater risk of CRC, a 36.9% (95% UI inclusive of between-study heterogeneity of 7.1 to 58.0) greater mean risk of IHD, and a 21.8% greater mean risk of stroke (95% UI inclusive of between-study heterogeneity of 7.3 to 35.1).

Sensitivity analysis and publication bias

The sensitivity analyses showed that trimming had significant effects on the ROS and reporting bias of the association between whole grain consumption and T2D and IHD. Without trimming, the ROS of T2D is -0.136, and a significant publication bias was detected using Egger’s regression ( P = 0.023, as shown in Supplementary Fig. 2 c). With respect to IHD, the results without trimming have reported a ROS of -0.273 and statistically significant evidence of small-study bias ( P for Egger’s regression = 0.028, Supplementary Fig. 3 c). However, both of the two health outcomes were found two significant study-level bias covariates (T2D: age of the population and outcome ascertainment methods; IHD: exposure measurement and outcome ascertainment methods). after removing outliers (T2D: 4 [ 31 , 50 ], IHD: 5 [ 24 , 50 ]) and adjusted for the selected bias covariates, no evidence of publication bias was observed (Figs. 1 c and 3 c). On the other hand, for CRC and stroke, trimming had a minor impact on the results. In both our analyses, with and without trimming, no significant evidence of publication bias was detected and no bias covariates were identified (Figs. 2 c and 4 c; and Supplementary Fig. 4 c, 5 c).

In this analysis, we applied a BPRF framework, which takes into account between-study heterogeneity, to quantify the association between whole grain consumption and four health outcomes. Our results suggested that increasing the intake of whole grains was significantly related to a reduction in the risk of CRC, T2D, IHD and stroke. When comparing TMREL (118.5–148.1 g per day) with a daily intake of 0 g of whole grains, the risk reductions for four diseases (T2D, CRC, IHD, and stroke) were 37.2%, 27.3%, 26.9%, and 21.8%, respectively. For all outcomes except stroke, we observed that mean risk exhibited a non-linear, monotonic decrease as whole grain consumption increased. However, the relationship between whole grains and stroke is like a J -shaped as the risk increased with exposure levels above or below a global minimum. Based on a conservative interpretation of available data (the averaged BPRF value), we found a slight decline in the risk of stroke, CRC, T2D, and IHD compared to no whole grain intake (by at least 6.4%, 7.0%, 9.1%, and 9.9%, respectively). The converted grade ratings of our evidence were all two-star ratings.

The protective role of whole grain consumption on CRC risk is well-documented. The World Cancer Research Fund/American Institute for Cancer Research (WCRF/AICR) has provided strong evidence on the protective role of whole grain consumption at 90 g/day on the risk of CRC (RR: 0.83; 95% CI: 0.79 to 0.89) [ 51 ]. Similarly, Schwingshackl et al. also reported an RR of 0.84 (95% CI: 0.78 to 0.90) of whole grain consumption at 90 g per day, and such evidence was rated as moderate according to the NutriGrade recommendation [ 5 ]. In consistent with previous studies, we found that consuming 90 g whole grains per day was also related with a similar reduction in the risk of CRC (RR: 0.83; 95% UI: 0.73 to 0.95), although the range of confidence interval was relatively wide (due to the consideration of between-study heterogeneity). Furthermore, as computing the mean across the universe of studies is appropriate to estimate the relationship between risk and outcome [ 52 ], we calculated the averaged BPRF value of 0.932, which is corresponding to a decline in the mean risk of CRC by 7.0%. Additionally, the star rating considering between-study heterogeneity of the whole grains-CRC pair is two-star. This estimate implied that the strength of the association evidence was relatively weak, in contrast to the WCRF/ARIC assessment, which categorized the evidence as “convincing” [ 11 ]. In fact, the extent of biases in nutritional epidemiological studies, including substantial residual confounding and selective reporting, can significantly impact the accuracy of health risks estimates related to the studied nutrients. Furthermore, the observational findings from prospective cohort studies exhibit considerable variation across different research endeavors [ 53 ]. Therefore, it is important to consider the strength of the association and employ a quantitative approach to assess consistency (that is, between-study heterogeneity) when evaluating evidence. Additionally, it is advisable to adopt a more conservative interpretation [ 54 ]. Our risk assessment indicated that increasing whole grain consumption can slightly reduce the risk of CRC, after correcting for biases due to factors such as study design, the representativeness of the study population, control for confounding, and so on.

With respect to IHD, the evidence stemming from previous meta-analyses has displayed a lack of consistency. For instance, Hu H et al. only found a linear association with 3 knots percentiles (25th, 50th, and 75th) selected [ 8 ]. However, Bechthold A, et al. provided evidence of a non-linear dose-response association ( P non-linearity <0.001) for IHD using three fixed knots at 10%, 50%, and 90% through the total distribution of the reported intake [ 7 ]. The disparities in their findings could be potentially due to variations in the selection of different knot placements along the estimated risk function curve, which might influence on the resulting accuracy of a spline approximation of a curve [ 55 ]. On the other hand, BPRF analysis, according to the given degree and number of knots, automatically sampled a set of knot placements for a feasible knot distribution, evaluated each resulting model by computing its fit and curvature, and then aggregated the final model as a weighted combination of the ensemble to mitigate the effect of spline parameter selection results and draw a robust conclusion. With this methodology, we found a non-linear, monotonic decline association between whole grain consumption and IHD.

In the case of stroke, previous meta-analyses generated mixed results. Bechthold et al. observed no association between whole grain intake and the risk of stroke in the non-linear dose-response analysis [ 7 ]. Conversely, Aune et al. observed a protective role of whole grain consumption on stroke risk, but this role was only significant in their non-linear dose-response analysis, and the risk curve exhibited a J -shaped pattern [ 6 ]. The difference between these studies might be partially attributable to different included studies [ 6 , 7 ]. Our analysis, including the results of newly published studies (the PURE study, China Kadoorie Biobank study and UK Biobank study) and applying BPRF methodology (free of log-linear hypothesis), found a J -shaped relationship between whole grain consumption and stroke, and we observed the greatest reduction in stroke risk observed at an intake of 100 g per day (RR: 0.75, 95%UI: 0.62 to 0.92). Unlike our analysis, both of the aforementioned studies assumed the association between whole grains and stroke to be log-linear [ 6 , 7 ], which might be inappropriate. A log-linear association implies that a fixed increment of health roles of whole grain consumption (for example, 30 g/day) remains constant across all levels of intake; however, an increase in consumption from 0 to 120 g/day would not have the same impact as an increase from 240 to 360 g/day, especially considering that excessive consumption may cause health issues such as overweight [ 56 ].

Our analyses support the need for stronger efforts and policies to encourage increased whole grain consumption as a means to reduce the risk of chronic diseases. Whole grains are well-known for their abundance of dietary fiber and nutrients. However, they can also be a notable source of food-borne contaminants. Nonetheless, current evidence suggests that increasing whole grain consumption could improve public health [ 57 ]. We estimated a TMREL of 118.1–148.5 g per day as the high consumption levels of whole grain intake in the real world, and such estimates are in line with the recommended intake of whole grains promoted by the GBD and the World Health Organization (WHO) [ 58 ], which is at least 125 g per day [ 59 ]. To address both individual and environmental health, the Lancet EAT Commission recommends a primarily plant-based diet, including 232 g of whole grains per day to reduce the carbon footprint of animal-based foods [ 60 ]. Nevertheless, our analysis solely took the individual-level health benefits into consideration, and the potential environmental benefits of increased whole grain consumption were not evaluated. Based on our analyses, particularly the notable protective roles observed with daily consumption of 100 g of whole grains against the risk of stroke, it seems that incorporating a minimum of three servings of whole grains per day has the potential to lower the risk of chronic diseases.

Our study employed BPRF methodology to estimate the association between whole grain consumption and four health outcomes. Compared to traditional meta-analysis methods, this method could quantify between-study heterogeneity, and infer flexible risk functions. It does so without imposing a log-linear hypothesis, which may exaggerate risks at higher exposure levels and overlook crucial details at lower exposure levels. With this methodology, we have found that the risk curves for whole grain consumption and IHD, CRC and T2D displayed decreasing marginal returns, indicating that as whole grain intake increases, the incremental health benefits of whole grains decrease. In addition, quantifications of between-study heterogeneity and corrections for biases due to study design in the methods can contribute to a conservative interpretation and a better understanding of the protective role of whole grain consumption in real-world settings. Thirdly, by estimating RRs associated with consuming whole grains at the TMREL (in correspondence to high real-world consumption levels), we were able to provide sufficient evidence to justify more robust efforts and policies promoting increased whole grain consumption to reduce chronic disease risk, especially with regard to CRC, T2D, IHD and stroke. In general, our analysis results indicate that improving whole grain consumption is beneficial toward enhancing public health.

Although the methodological framework addressed by Zheng et al. overcame many of the limitations in existing meta-analysis approaches, this study still has several limitations. Firstly, all studies included in our analysis were observational, and we were unable to definitively assess causality. Besides, this study mainly focused on total or whole grain consumption, and the impacts of different specific subtypes of whole grains on health outcomes may vary. For example, previous reviews have indicated that increasing whole-grain breakfast cereals, other than whole-grain bread, may decrease the risk of stroke [ 6 ]; furthermore, oats or oatmeal are linked to lower all-cause mortality but show no impact on T2D and CVD incidence [ 6 , 61 ]. Thus, further prospective cohort studies and randomized clinical trials focusing on different subtypes of whole grains and their associations with specific chronic diseases are required. Besides, most of the studies included were from the US and Europe, which limited the ability to make evidence-based recommendations, as dietary patterns can vary significantly between Asian and Western populations [ 62 ]. With respect to the Asian population, rather than whole grain consumption, most of studies investigated the role of refined grain consumption in the form of white rice and noodles [ 63 ], and further studies are needed to explore the association between whole grains and health outcomes on populations in Asia. Thirdly, the associations between whole grains and risks of different stroke types may be heterogeneous [ 24 ]. Unfortunately, we couldn’t investigate these associations separately due to a lack of reported data on stroke types in available studies.

In conclusion, the present study demonstrates that the consumption of whole grains plays a protective role in the risks of CRC, T2D, IHD and stroke, and the BPRF analysis, which did not rely on log-linear assumptions, revealed non-linear associations between whole grain intake and the four diseases of interests. The star ratings converted by ROSs for all four outcomes are all two stars, indicating that the associations between whole grain intake and CRC, T2D, IHD and stroke remain significant. The current body of evidence justifies the need for increased efforts and policies to promote higher whole grain consumption for the betterment of public health.

Data availability

No datasets were generated or analysed during the current study.

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This study was supported by the Key Discipline of Zhejiang Province in Public Health and Preventative Medicine (First Class, Category A), Hangzhou Medical College.

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Jing Wang and Yingjun Li contributed equally to this work.

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Department of Epidemiology and Health Statistics, School of Public Health, Hangzhou Medical College, 481 Binwen Road, Hangzhou, 310053, China

Houpu Liu, Jiahao Zhu, Rui Gao, Lilu Ding, Ye Yang, Wenxia Zhao, Jing Wang & Yingjun Li

Department of Radiology, Key Laboratory of Cancer Prevention and Therapy, Tianjin Medical University Cancer Institute & Hospital, National Clinical Research Center for Cancer, Tianjin, China

Xiaonan Cui

Department of Epidemiology and Health Statistics, School of Public health, Tianjin Medical University, Tianjin, China

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All authors contributed to the study’s conception and design. Liu Houpu, Wang, Jing, and Li, Yingjun conceived the study, searched the literature and performed data extraction. Liu, Houpu and Zhu, Jiahao had the idea for the article, performed the main analysis and written the first manuscript. Gao Rui, Yang Ye and Zhao Wenxia conducted the sensitivity analysis and inspected the analysis results. Ding Lilu, Cui, Xiaonan, and Lu, Wenli provided statistical expertise. Wang, Jing and Li, Yingjun reviewed the original manuscript and critically revised the work. All authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

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Liu, H., Zhu, J., Gao, R. et al. Estimating effects of whole grain consumption on type 2 diabetes, colorectal cancer and cardiovascular disease: a burden of proof study. Nutr J 23 , 49 (2024). https://doi.org/10.1186/s12937-024-00957-x

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The prevalence of obesity is expected to increase from 14% in 2020 to 24% in 2035, as the number of obese individuals is nearing 2 billion. 1 Obesity is accompanied by a range of additional health problems, including insulin resistance (IR), type 2 diabetes mellitus (T2DM), fatty liver disease, cardiovascular disease (CVD), degenerative disease ...
Managing obesity in people with type 2 diabetes
Table 2. Type 2 diabetes mellitus mediations and their overall weight effect. Orlistat is licensed in the management of obesity and NICE recommends its prescription as part of an overall plan for managing obesity in those with BMI >30 kg/m 2 or >28 kg/m 2 with other associated risk factors. Side effects are often poorly tolerated, however.
How People with Type 2 Diabetes Can Live Longer
Study results. Managing weight, blood sugar, blood pressure, and cholesterol can increase life expectancy by 3 years for the average person with type 2 diabetes. For people with the highest levels of BMI, A1C, LDL, and SBP, reducing these levels can potentially increase life expectancy by more than 10 years.
Semaglutide can produce clinically meaningful
Two important studies based on the largest and longest clinical trial of the effects of semaglutide on weight in over 17,000 adults with overweight and obesity but not diabetes find patients lost ...
Cells
Obesity is an urgent public health issue worldwide. According to the World Health Organization, in 2022, one in eight people were living with obesity [].Obesity is associated with multiple health problems, such as type 2 diabetes mellitus, hypertension, cerebrovascular disease, kidney disease, many types of cancers, and a variety of musculoskeletal illnesses [2,3,4].
Obesity and Type 2 Diabetes: What Can Be Unified and What Needs to Be
Most patients with type 2 diabetes are obese, and the global epidemic of obesity largely explains the dramatic increase in the incidence and prevalence of type 2 diabetes over the past 20 years. Currently, over a third (34%) of U.S. adults are obese (defined as BMI >30 kg/m 2 ), and over 11% of people aged ≥20 years have diabetes ( 1 ), a ...
Association of weight status and the risks of diabetes in ...
Obesity is a known risk factor for type 2 diabetes mellitus (T2DM); however, the associations between underweight and T2DM and between weight status and prediabetes have not been systematically ...
Estimating effects of whole grain consumption on type 2 diabetes
Specifically, our analysis revealed that the associations between whole grain consumption and the risk of T2D, CRC and IHD all exhibited non-linear, monotonically decreasing trends (Figs. 1, 2 and 3).In regard to T2D (Fig. 1a and b), the sharpest decline in risk was noted at daily consumption of 50 g, with a reduction of 34.3% (95% UI including between-study heterogeneity: 5.3 to 55.7 ...
Oral glucagon-like peptide-1 receptor agonists and combinations of
Multiple other dual and triple agonists are currently in phase 3 trials for T2D and/or obesity including the combination of the amylin agonist cagrilintide 2.4mg with the GLP-1 receptor agonist (RA) semaglutide 2.4 mg (CagriSema), the dual GLP-1/glucagon RAs survodutide and mazdutide as well as the triple agonist (GLP-1/GIP/glucagon) retatrutide.

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ORIGINAL RESEARCH article

Introduction

Description of the ViLA Simulated Population and Overview of the ViLA-Obesity Simulation Model

Data Sources and Parameters

Model Specification

Neighborhood Environment

Conceptual Model, Equations, and Decision Rules

Model Calibration, Verification, and Validation

Model Verification

Model Calibration and Internal Validation

Calibration and Internal Validation

Trends in Obesity and Type 2 Diabetes

Trends in Drivers of Health Behaviors

Cumulative Incidence and Average Incidence Rate of Obesity and Type 2 Diabetes in the ViLA-Obesity Model

Type 2 Diabetes

Uses of the ViLA Modeling Suite

Data Availability Statement

Author Contributions

Conflict of Interest

Publisher's Note

Acknowledgments

Supplementary Material

How People with Type 2 Diabetes Can Live Longer

What did this study examine?

Terms to know

Study results

What's important about this study?

For Everyone

Semaglutide can produce clinically meaningful weight loss and reduce waist size for at least 4 years in adults with overweight or obesity who don’t have diabetes, and delivers cardiovascular benefits irrespective of weight lost

COI Statement

Acknowledgments

Mechanisms of obesity-associated insulin resistance

Mechanisms of progressive β-cell dysfunction in obese individuals

Genetic factors linking obesity and diabetes

Does a shared pathogenesis underlie both obesity and type 2 diabetes?

Managing body weight by behavioral change and medications

Managing body weight by bariatric surgery

Barriers to effective management

Recommendations

Expand research on heterogeneity

Develop innovative approaches to pharmacological and surgical management

Emphasize primary prevention of obesity and type 2 diabetes

Adopt a chronic disease model linking obesity to diabetes care

Summary and conclusions

Supplementary data

Clinical Compendia

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Association of weight status and the risks of diabetes in adults: a systematic review and meta-analysis of prospective cohort studies

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Adult weight gain and the risk of cardiovascular disease: a systematic review and dose–response meta-analysis of prospective cohort studies

Associations of obesity indices change with cardiovascular outcomes: a dose-response meta-analysis

Body mass index and cancer risk in patients with type 2 diabetes: a dose–response meta-analysis of cohort studies

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