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| Title | Type | --> | |||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | journal | 10.247 Q1 | 173 | 194 | 575 | 4605 | 7030 | 222 | 12.48 | 23.74 | 41.98 | ||
| 2 | journal | 7.922 Q1 | 307 | 40 | 95 | 7025 | 2339 | 95 | 22.37 | 175.63 | 43.13 | ||
| 3 | journal | 7.274 Q1 | 203 | 148 | 480 | 7209 | 5908 | 227 | 10.85 | 48.71 | 43.22 | ||
| 4 | journal | 7.192 Q1 | 75 | 226 | 515 | 10360 | 6320 | 395 | 10.84 | 45.84 | 40.83 | ||
| 5 | journal | 5.694 Q1 | 418 | 403 | 1520 | 13962 | 16386 | 1188 | 11.21 | 34.65 | 45.33 | ||
| 6 | journal | 3.378 Q1 | 65 | 63 | 151 | 7185 | 1971 | 149 | 14.22 | 114.05 | 39.50 | ||
| 7 | journal | 3.355 Q1 | 261 | 219 | 715 | 9606 | 5618 | 647 | 7.12 | 43.86 | 51.56 | ||
| 8 | journal | 2.957 Q1 | 103 | 142 | 433 | 4325 | 2292 | 391 | 5.54 | 30.46 | 51.65 | ||
| 9 | journal | 2.927 Q1 | 197 | 82 | 267 | 6532 | 2502 | 256 | 8.88 | 79.66 | 40.86 | ||
| 10 | journal | 2.818 Q1 | 199 | 118 | 512 | 11158 | 4567 | 472 | 8.44 | 94.56 | 59.38 | ||
| 11 | journal | 2.792 Q1 | 166 | 137 | 469 | 9685 | 4347 | 424 | 10.09 | 70.69 | 43.83 | ||
| 12 | journal | 2.621 Q1 | 108 | 342 | 734 | 16314 | 5974 | 716 | 8.02 | 47.70 | 39.83 | ||
| 13 | journal | 2.606 Q1 | 110 | 96 | 288 | 10080 | 2990 | 288 | 8.54 | 105.00 | 40.92 | ||
| 14 | journal | 2.566 Q1 | 60 | 53 | 138 | 2328 | 1123 | 127 | 10.48 | 43.92 | 46.53 | ||
| 15 | journal | 2.541 Q1 | 370 | 178 | 784 | 7230 | 4347 | 706 | 4.89 | 40.62 | 44.15 | ||
| 16 | journal | 2.460 Q1 | 182 | 50 | 180 | 6983 | 1929 | 168 | 9.11 | 139.66 | 43.90 | ||
| 17 | journal | 2.079 Q1 | 153 | 415 | 912 | 16065 | 4956 | 812 | 5.23 | 38.71 | 38.43 | ||
| 18 | journal | 2.075 Q1 | 99 | 94 | 221 | 10959 | 1783 | 220 | 7.32 | 116.59 | 47.84 | ||
| 19 | journal | 1.991 Q1 | 135 | 101 | 346 | 5097 | 2518 | 311 | 5.14 | 50.47 | 46.69 | ||
| 20 | journal | 1.899 Q1 | 391 | 555 | 2715 | 14591 | 12014 | 2262 | 4.25 | 26.29 | 48.26 | ||
| 21 | journal | 1.892 Q1 | 127 | 72 | 180 | 5559 | 1175 | 164 | 6.15 | 77.21 | 45.55 | ||
| 22 | journal | 1.889 Q1 | 164 | 190 | 598 | 6321 | 3227 | 529 | 5.34 | 33.27 | 45.30 | ||
| 23 | journal | 1.868 Q1 | 268 | 178 | 736 | 8546 | 3588 | 639 | 4.57 | 48.01 | 45.53 | ||
| 24 | journal | 1.758 Q1 | 101 | 31 | 202 | 1946 | 1122 | 202 | 5.79 | 62.77 | 63.51 | ||
| 25 | journal | 1.652 Q1 | 66 | 88 | 302 | 3915 | 1329 | 227 | 5.19 | 44.49 | 36.52 | ||
| 26 | journal | 1.604 Q1 | 173 | 176 | 616 | 7033 | 2972 | 571 | 4.71 | 39.96 | 53.10 | ||
| 27 | journal | 1.586 Q1 | 227 | 305 | 850 | 12241 | 3779 | 742 | 3.69 | 40.13 | 51.75 | ||
| 28 | journal | 1.557 Q1 | 104 | 61 | 263 | 2091 | 1080 | 191 | 3.95 | 34.28 | 43.90 | ||
| 29 | journal | 1.504 Q1 | 251 | 168 | 803 | 8228 | 3604 | 763 | 4.13 | 48.98 | 48.22 | ||
| 30 | journal | 1.479 Q1 | 224 | 178 | 473 | 8588 | 2164 | 444 | 4.25 | 48.25 | 41.79 | ||
| 31 | journal | 1.479 Q1 | 51 | 103 | 750 | 4208 | 3199 | 738 | 3.88 | 40.85 | 46.08 | ||
| 32 | journal | 1.432 Q1 | 120 | 62 | 187 | 3344 | 816 | 151 | 4.09 | 53.94 | 55.07 | ||
| 33 | journal | 1.373 Q1 | 201 | 200 | 836 | 11781 | 3258 | 812 | 3.54 | 58.91 | 58.29 | ||
| 34 | journal | 1.340 Q1 | 140 | 403 | 1260 | 15929 | 5314 | 1120 | 3.87 | 39.53 | 45.34 | ||
| 35 | journal | 1.331 Q1 | 146 | 108 | 282 | 6365 | 1135 | 275 | 4.11 | 58.94 | 44.27 | ||
| 36 | journal | 1.313 Q1 | 83 | 145 | 987 | 6390 | 5953 | 866 | 3.99 | 44.07 | 40.87 | ||
| 37 | journal | 1.312 Q1 | 27 | 35 | 138 | 1816 | 561 | 111 | 3.96 | 51.89 | 45.99 | ||
| 38 | journal | 1.303 Q1 | 165 | 248 | 808 | 7425 | 2489 | 650 | 2.82 | 29.94 | 56.83 | ||
| 39 | journal | 1.301 Q1 | 44 | 39 | 176 | 2630 | 754 | 168 | 4.04 | 67.44 | 47.27 | ||
| 40 | journal | 1.246 Q1 | 89 | 142 | 618 | 5320 | 2001 | 566 | 2.85 | 37.46 | 31.63 | ||
| 41 | journal | 1.245 Q1 | 96 | 62 | 479 | 2444 | 1702 | 450 | 3.65 | 39.42 | 60.18 | ||
| 42 | journal | 1.240 Q1 | 120 | 3542 | 6449 | 199781 | 28163 | 6174 | 3.82 | 56.40 | 48.51 | ||
| 43 | journal | 1.232 Q1 | 135 | 44 | 180 | 2072 | 593 | 139 | 3.05 | 47.09 | 31.25 | ||
| 44 | journal | 1.231 Q1 | 77 | 76 | 198 | 7006 | 985 | 198 | 4.19 | 92.18 | 52.63 | ||
| 45 | journal | 1.224 Q1 | 58 | 26 | 122 | 1097 | 592 | 119 | 4.11 | 42.19 | 53.48 | ||
| 46 | journal | 1.208 Q1 | 103 | 143 | 577 | 5756 | 1927 | 479 | 3.09 | 40.25 | 49.13 | ||
| 47 | journal | 1.197 Q1 | 15 | 30 | 84 | 1195 | 320 | 81 | 4.14 | 39.83 | 29.25 | ||
| 48 | journal | 1.179 Q1 | 224 | 218 | 1089 | 11706 | 4280 | 1044 | 3.59 | 53.70 | 44.56 | ||
| 49 | journal | 1.161 Q1 | 38 | 13 | 106 | 712 | 563 | 106 | 4.12 | 54.77 | 30.68 | ||
| 50 | journal | 1.159 Q1 | 167 | 79 | 291 | 2715 | 1078 | 287 | 3.45 | 34.37 | 51.08 |
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- ISSN: 2476-1435
- NLM ID: 101706830
- LCCN: 2017200622
- OCoLC: 972910312
- Impact Factor: 1.760 (2023-24) 2.034 (2023-24) -->
Pubmed Indexed Articles
- Global Journal of Addiction & Rehabilitation Medicine --> Glia Maturation Factor in the Pathogenesis of Alzheimers disease
PMID: 32775957
- Global Journal of Addiction & Rehabilitation Medicine --> Current Trends in Biomarkers for Traumatic Brain Injury
PMID: 32775958
- Global Journal of Addiction & Rehabilitation Medicine --> Inter-scan Reproducibility of Cardiovascular Magnetic Resonance Imaging-Derived Myocardial Perfusion Reserve Index in Women with no Obstructive Coronary Artery Disease.
PMID: 30976755
- Global Journal of Addiction & Rehabilitation Medicine --> What is the Role of Race and Ethnicity in the Development Of Thionamide-Induced Neutropenia?
PMID: 30828700
- Global Journal of Addiction & Rehabilitation Medicine --> Increased Fluoroquinolone-Susceptibility and Preserved Nitrofurantoin-Susceptibility among Escherichia coli Urine Isolates from Women Long-Term Care Residents: A Brief Report.
PMID: 30465048
- Global Journal of Addiction & Rehabilitation Medicine --> New Method Application for Marker-Trait Association Studies in Plants: Partial Least Square Regression Aids Detection of Simultaneous Correlations.
PMID: 30345411
- Global Journal of Addiction & Rehabilitation Medicine --> Health facilities readiness to provide friendly reproductive health services to young people aged 10-24 years in Wakiso district, Uganda.
PMID: 30148262
- Global Journal of Addiction & Rehabilitation Medicine --> Blood Serum Affects Polysaccharide Production and Surface Protein Expression in S. Aureus.
PMID: 29863159
- Global Journal of Addiction & Rehabilitation Medicine --> Intervertebral Disc Aging, Degeneration, and Associated Potential Molecular Mechanisms.
PMID: 29911686
- Global Journal of Addiction & Rehabilitation Medicine --> Statistical Methods for Clinical Trial Designs in the New Era of Cancer Treatment.
PMID: 29645007
- Global Journal of Addiction & Rehabilitation Medicine --> Critical Analysis of White House Anti-Drug Plan
PMID: 29057394
- Global Journal of Addiction & Rehabilitation Medicine --> Impaired Cerebral Autoregulation-A Common Neurovascular Pathway in Diabetes may Play a Critical Role in Diabetes-Related Alzheimers Disease.
PMID: 28825056
- Global Journal of Addiction & Rehabilitation Medicine --> Opioid Prescription Drug Use and Expenditures in US Outpatient Physician Offices: Evidence from Two Nationally Representative Surveys.
PMID: 28845476
- Global Journal of Addiction & Rehabilitation Medicine --> Psychological Well-Being and Type 2 Diabetes.
PMID: 29276801
- Global Journal of Addiction & Rehabilitation Medicine --> The Role of Txnip in Mitophagy Dysregulation and Inflammasome Activation in Diabetic Retinopathy: A New Perspective.
PMID: 29376145
- Global Journal of Addiction & Rehabilitation Medicine --> Can Diabetes Be Controlled by Lifestyle Activities?
PMID: 29399663
- Global Journal of Addiction & Rehabilitation Medicine --> Effect of Arginase-1 Inhibition on the Incidence of Autoimmune Diabetes in NOD Mice.
PMID: 29450408
- Global Journal of Addiction & Rehabilitation Medicine --> Coupling Genetic Addiction Risk Score (GARS) and Pro Dopamine Regulation (KB220) to Combat Substance Use Disorder (SUD).
PMID: 29399668
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Back to Journals » Diabetes, Metabolic Syndrome and Obesity » Volume 16 » default
Diabetes, Metabolic Syndrome and Obesity
Issn: 1178-7007.
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- Volume 17, 2024 (246)
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Archive: Volume 16, 2023
- default (373)
- Managing obesity with pharmacotherapy (6)
Application of Metabolomics and Traditional Chinese Medicine for Type 2 Diabetes Mellitus Treatment
Li J, Zhu N, Wang Y, Bao Y, Xu F, Liu F, Zhou X
Diabetes, Metabolic Syndrome and Obesity 2023 , 16:4269-4282
Published Date: 28 December 2023
Randomized, Double-Blind, Placebo-Controlled, Phase I, Dose- Escalation Study to Evaluate the Tolerance, Pharmacokinetics, Pharmacodynamics and Immunogenicity of PEGylated Urate Oxidase for Injection in Healthy Adults and Hyperuricemia Volunteers: Study Protocol
Sun F, Wang C, Xu Y, Lin P, Cao Y, Zhang J, Li X, Jiang X, Fu Y, Cao Y
Diabetes, Metabolic Syndrome and Obesity 2023 , 16:4263-4268
Diabetic Retinopathy and Cardiovascular Disease: A Literature Review
Yu W, Yang B, Xu S, Gao Y, Huang Y, Wang Z
Diabetes, Metabolic Syndrome and Obesity 2023 , 16:4247-4261
The Metabolic Score for Insulin Resistance Index is Superior to the Triglyceride and Glucose Index in Identifying Nonalcoholic Fatty Liver Disease in Hyperuricemia Subjects
Shu Y, Huang G, Mao Y
Diabetes, Metabolic Syndrome and Obesity 2023 , 16:4235-4246
Antibacterial Activity of Surfactin and Synergistic Effect with Conventional Antibiotics Against Methicillin-Resistant Staphylococcus Aureus Isolated from Patients with Diabetic Foot Ulcers [Response to Letter]
Diabetes, Metabolic Syndrome and Obesity 2023 , 16:4233-4234
Urinary PART1 and PLA2R1 Could Potentially Serve as Diagnostic Markers for Diabetic Kidney Disease Patients
Ye Q, Xu G, Yuan H, Mi J, Xie Y, Li H, Li Z, Huang G, Chen X, Li W, Yang R
Diabetes, Metabolic Syndrome and Obesity 2023 , 16:4215-4231
Published Date: 27 December 2023
Effect of Chiglitazar and Sitagliptin on Bone Mineral Density and Body Composition in Untreated Patients with Type 2 Diabetes
Wang Y, Zhou Y, Zhou X, Su X, Xu X, Li H, Ma J
Diabetes, Metabolic Syndrome and Obesity 2023 , 16:4205-4214
Tryptophan Prevents the Development of Non-Alcoholic Fatty Liver Disease
Yanko R, Levashov M, Chaka OG, Nosar V, Khasabov SG, Khasabova I
Diabetes, Metabolic Syndrome and Obesity 2023 , 16:4195-4204
Published Date: 23 December 2023
Advances in Psychological and Social Aetiology of Patients with Diabetes
Diabetes, Metabolic Syndrome and Obesity 2023 , 16:4187-4194
Correlation Between Neck Circumference and Gestational Diabetes
Li X, Bai L, Niu Z, Lu Q
Diabetes, Metabolic Syndrome and Obesity 2023 , 16:4179-4185
The Correlation Between Leg Muscle Mass Index and Non-Alcoholic Fatty Liver Disease in Patients with Type 2 Diabetes Mellitus
Liu M, Zhang Q, Liu J, Bai H, Yang P, Ye X, Yuan X
Diabetes, Metabolic Syndrome and Obesity 2023 , 16:4169-4177
Published Date: 20 December 2023
The Association Between Short Sleep Duration and Metabolic Syndrome: A Case-Control Study
Elshoeibi AM, Akomolafe A, Al-Khulaifi A, Metwally O, Al-Khayarin R, Al Abiad AR, Al-Naemi L, Chivese T, Farooqui H
Diabetes, Metabolic Syndrome and Obesity 2023 , 16:4157-4167
Obesity and COVID-19 Pandemics: Epidemiology, Mechanisms, and Management
Yang Y, Song Y, Hou D
Diabetes, Metabolic Syndrome and Obesity 2023 , 16:4147-4156
Response to Association of Triglyceride-Glucose Index with Risk of Large for Gestational Age: A Prospective Cohort Study [Letter]
Wei R, Nong Y
Diabetes, Metabolic Syndrome and Obesity 2023 , 16:4145-4146
Published Date: 19 December 2023
Risk Factors and Prediction Nomogram of Cognitive Frailty with Diabetes in the Elderly [Letter]
Xu G, Nong Y
Diabetes, Metabolic Syndrome and Obesity 2023 , 16:4143-4144
Published Date: 18 December 2023
Association Between Physical Activity and Arterial Stiffness in Patients with Type 2 Diabetes in Ningbo, China: A Cross-Sectional Study
Li X, Chattopadhyay K, Chen X, Li J, Xu M, Chen X, Li L
Diabetes, Metabolic Syndrome and Obesity 2023 , 16:4133-4141
Analysis of Bulk Transcriptome Sequencing Data and in vitro Experiments Reveal SIN3A as a Potential Target for Diabetic Foot Ulcer
Chen R, Deng H, Zou L
Diabetes, Metabolic Syndrome and Obesity 2023 , 16:4119-4132
The Implications of the Pandemic with Covid-19 in the Glycemic Control of People with Type 2 Diabetes
Gherbon A, Frandes M, Nicula-Neagu M, Timar R, Timar B
Diabetes, Metabolic Syndrome and Obesity 2023 , 16:4109-4118
Published Date: 15 December 2023
Expert Opinion on Addressing the Gap in Injection Technique and Needle Reuse for People with Diabetes in Indonesia
Soewondo P, Suastika K, Kshanti IA, Mardianto M, Mudjanarko SW, Pramono RB, Mustikawati DE
Diabetes, Metabolic Syndrome and Obesity 2023 , 16:4101-4107
Published Date: 13 December 2023
Willingness to Receive mHealth Services Among Patients with Diabetes on Chronic Follow-up in Public Hospitals in Eastern Ethiopia: Multicenter Mixed-Method Study
Firdisa D, Abera A, Dereje J, Asefa F
Diabetes, Metabolic Syndrome and Obesity 2023 , 16:4081-4099
Arginine Biosynthesis Pathway Found to Play a Key Role in the Neuroprotective Effect of Liu-Wei-Luo-Bi (LWLB) Granules in Diabetic db/db Mice with Peripheral Neuropathy Using an Untargeted Metabolomics Strategy
Liu Q, Chen Y, Wang B, Chen Y, Li B, Guan S, Du K, Liu X, Yu Y, Liu J, Wang Z
Diabetes, Metabolic Syndrome and Obesity 2023 , 16:4065-4080
Published Date: 11 December 2023
Biological Role and Related Natural Products of SIRT1 in Nonalcoholic Fatty Liver
Meng D, Zhang F, Yu W, Zhang X, Yin G, Liang P, Feng Y, Chen S, Liu H
Diabetes, Metabolic Syndrome and Obesity 2023 , 16:4043-4064
Published Date: 8 December 2023
Electroacupuncture Improves Insulin Resistance in Type 2 Diabetes Mice by Regulating Intestinal Flora and Bile Acid
Pan T, Li X, Guo X, Wang H, Zhou X, Shang R, Xie D, Qian X, Dai M, Fan E, Chen X, Chen C
Diabetes, Metabolic Syndrome and Obesity 2023 , 16:4025-4042
Published Date: 7 December 2023
Oxymatrine Alleviates High-Fat-High-Fructose-Induced Fatty Liver in Rats: Understanding the Molecular Mechanism Through an Untargeted Metabonomics Study
Li H, Wang C, Wang Q, Liu X, Zhang J, Zhang H, Fei W, Zhao H, Ren L
Diabetes, Metabolic Syndrome and Obesity 2023 , 16:4013-4024
Obesity Patterns, Metabolic Abnormality, and Diabetic Kidney Disease in Patients with Type 2 Diabetes
Zhang K, Zhang W, Xia F, Wang N, Lu Y, Sui C, Wang B
Diabetes, Metabolic Syndrome and Obesity 2023 , 16:3999-4011
Association of Triglyceride-Glucose Index with Risk of Large for Gestational Age: A Prospective Cohort Study [Letter]
Wardoyo S, Khayan K, Anwar T
Diabetes, Metabolic Syndrome and Obesity 2023 , 16:3997-3998
Published Date: 6 December 2023
Antibacterial Activity of Surfactin and Synergistic Effect with Conventional Antibiotics Against Methicillin-Resistant Staphylococcus aureus Isolated from Patients with Diabetic Foot Ulcers [Letter]
Wardoyo S, Anwar T
Diabetes, Metabolic Syndrome and Obesity 2023 , 16:3995-3996
Correlation Between the Levels of ANGPTL3, ANGPTL4, ANGPTL8 and Postprandial Triglyceride-Rich Lipoprotein (TRL)
Zhang T, Hou Y, Liu M, Hou X, Tang Y, Ren L, Song G
Diabetes, Metabolic Syndrome and Obesity 2023 , 16:3979-3993
Association Between Different Metabolic Markers and Metabolic Syndrome in an Outpatient Hyperuricemia Population: A Cross-Sectional Study
Li X, Huang B, Liu Y, Wang M, Wang YX, Feng KL, Cui JQ
Diabetes, Metabolic Syndrome and Obesity 2023 , 16:3967-3978
Published Date: 4 December 2023
Association of Novel Insulin Resistance Indices with Blood Pressure in New-Onset Hypertension and Elevated Blood Pressure Individuals: A Cross-Sectional Study
Liu H, Meng C, Wang Z, Liu C, Yu C, Li Z
Diabetes, Metabolic Syndrome and Obesity 2023 , 16:3953-3965
Published Date: 5 December 2023
Microarray Expression Profile of Exosomal circRNAs from High Glucose Stimulated Human Renal Tubular Epithelial Cells
Sha YH, Cao SL, Zhang L, Lai LS, Ke PF, Yu KW, Fang XZ, Deng RT, Wan ZM, Wu XB, Han G, Jie YB, Song LL, Huang XZ, Fu WJ
Diabetes, Metabolic Syndrome and Obesity 2023 , 16:3937-3951
More Physical Exercise is Required for Overweight or Obese Women with Gestational Diabetes Mellitus to Achieve Good Plasma Glucose Control During Pregnancy: Finding from a Prospective Cohort in Shanghai
Gao X, Zhang X, Kuai L, Yang Q, Li H, Zhang R, Li B, Wang R
Diabetes, Metabolic Syndrome and Obesity 2023 , 16:3925-3935
Associations Between Serum TNF-α, IL-6, hs-CRP and GLMD in Obese Children and Adolescents: A Cross-Sectional Study
Zhao X, Niu Y, Zhao XL, Ruan HJ, Xiang Y, Wang LY, Feng Y, Tang QY
Diabetes, Metabolic Syndrome and Obesity 2023 , 16:3915-3923
Published Date: 2 December 2023
Application of Interpretable Machine Learning Models Based on Ultrasonic Radiomics for Predicting the Risk of Fibrosis Progression in Diabetic Patients with Nonalcoholic Fatty Liver Disease
Meng F, Wu Q, Zhang W, Hou S
Diabetes, Metabolic Syndrome and Obesity 2023 , 16:3901-3913
Suyanto E, Fajar I, Hariyanto T
Diabetes, Metabolic Syndrome and Obesity 2023 , 16:3899-3900
Published Date: 1 December 2023
Lived Experience of People with Type 1 Diabetes in North East Ethiopia; Psycho-Social and Economical Perspective, a Phenomenological Study
Ketema Bogale E, Getachew Desalew E, Tesfa H, Endeshaw D, Guadie Tiruneh M, Ketema Bogale S
Diabetes, Metabolic Syndrome and Obesity 2023 , 16:3885-3898
Published Date: 30 November 2023
Sex-Specific Contribution of Cardiometabolic Index in Predicting Metabolic Dysfunction-Associated Fatty Liver Disease: Insights from a General Population
Gu D, Lu Y, Xu B, Tang X
Diabetes, Metabolic Syndrome and Obesity 2023 , 16:3871-3883
Diabetes Distress and Illness Perceptions in Tunisian Type 2 Diabetes Patients [Letter]
Kusmiwiyati A, Hakimah N, Aryani HR, Wulandari LP
Diabetes, Metabolic Syndrome and Obesity 2023 , 16:3869-3870
Published Date: 28 November 2023
Prevalence of Obesity and Lifestyle Risk Factors Following Two Years’ COVID-19 Related Service Closure at Wellness Center, Primary Health Care
Musa S, Al-Dahshan A, Singh R
Diabetes, Metabolic Syndrome and Obesity 2023 , 16:3851-3868
Published Date: 29 November 2023
Loss of Muscle Mass in Delayed Diagnosis of Renal Cysts and Diabetes Syndrome: A Case Report
Li X, Cao Y, Gu N, Yuan Z
Diabetes, Metabolic Syndrome and Obesity 2023 , 16:3847-3850
Published Date: 27 November 2023
Association of Triglyceride-Glucose Index with Risk of Large for Gestational Age: A Prospective Cohort Study
Lin L, Lin J, Yang F, Chen S, Liu Z
Diabetes, Metabolic Syndrome and Obesity 2023 , 16:3837-3846
Exploring the Correlation Between the Systemic Immune Inflammation Index (SII), Systemic Inflammatory Response Index (SIRI), and Type 2 Diabetic Retinopathy
Wang S, Pan X, Jia B, Chen S
Diabetes, Metabolic Syndrome and Obesity 2023 , 16:3827-3836
Published Date: 24 November 2023
Association Between Weight Change During Adulthood and Chronic Kidney Disease: Results from a National Survey 2011–2018
Gong X, Zeng X, Fu P
Diabetes, Metabolic Syndrome and Obesity 2023 , 16:3817-3826
Published Date: 23 November 2023
Progress in Research on Biomarkers of Gestational Diabetes Mellitus and Preeclampsia
Liu Y, Li DY, Bolatai A, Wu N
Diabetes, Metabolic Syndrome and Obesity 2023 , 16:3807-3815
Published Date: 22 November 2023
Stronger Associations of TyG Index with Diabetes Than TyG-Obesity-Related Parameters: More Pronounced in Young, Middle-Aged, and Women
Xing Y, Liu J, Gao Y, Zhu Y, Zhang Y, Ma H
Diabetes, Metabolic Syndrome and Obesity 2023 , 16:3795-3805
Integrated Analysis of Ferroptosis and Immunity-Related Genes Associated with Diabetic Kidney Disease
Wang J, Wang L, Pang Z, Ge Q, Wu Y, Qi X
Diabetes, Metabolic Syndrome and Obesity 2023 , 16:3773-3793
The Correlation Between Thyroid Parameters and the Ratios of Neutrophil/Lymphocyte and Platelet/Lymphocyte in Euthyroid Type 2 Diabetic Patients
Chen H, Ju JQ, Qian XW, Zhu ZT, Zhao CZ, Liu Z
Diabetes, Metabolic Syndrome and Obesity 2023 , 16:3763-3771
Correlation Between Abdominal Fat Distribution and Serum Uric Acid in Patients Recently Diagnosed with Type 2 Diabetes
Wang HP, Xu YY, Xu BL, Lu J, Xia J, Shen T, Fang J, Lei T
Diabetes, Metabolic Syndrome and Obesity 2023 , 16:3751-3762
Published Date: 21 November 2023
Can Antidiabetic Medications Affect Telomere Length in Patients with Type 2 Diabetes? A Mini-Review
Diabetes, Metabolic Syndrome and Obesity 2023 , 16:3739-3750
Antibacterial Activity of Surfactin and Synergistic Effect with Conventional Antibiotics Against Methicillin-Resistant Staphylococcus aureus Isolated from Patients with Diabetic Foot Ulcers
Li Z, Li T, Tang J, Huang L, Ding Y, Zeng Z, Liu J
Diabetes, Metabolic Syndrome and Obesity 2023 , 16:3727-3737
Published Date: 20 November 2023
Jiangtang Decoction Ameliorates Diabetic Kidney Disease Through the Modulation of the Gut Microbiota
Hong J, Fu T, Liu W, Du Y, Bu J, Wei G, Yu M, Lin Y, Min C, Lin D
Diabetes, Metabolic Syndrome and Obesity 2023 , 16:3707-3725
The Characteristics, Mechanisms and Therapeutics: Exploring the Role of Gut Microbiota in Obesity
Zhuang Z, Zhou P, Wang J, Lu X, Chen Y
Diabetes, Metabolic Syndrome and Obesity 2023 , 16:3691-3705
Precision Medicine in Type 2 Diabetes Mellitus: Utility and Limitations
Galiero R, Caturano A, Vetrano E, Monda M, Marfella R, Sardu C, Salvatore T, Rinaldi L, Sasso FC
Diabetes, Metabolic Syndrome and Obesity 2023 , 16:3669-3689
Published Date: 16 November 2023
From Metabolically Healthy Obesity to Metabolically Unhealthy Obesity Populations: Decreased Bone Turnover Bioactivity
Zhang N, Wang G
Diabetes, Metabolic Syndrome and Obesity 2023 , 16:3657-3667
Female is Associated with Left Ventricular Diastolic Dysfunction in Patients with Type 2 Diabetes [Response to Letter]
Wang Y, Zhou Y, Zhang Y, Ren Q, Wang Y, Su H
Diabetes, Metabolic Syndrome and Obesity 2023 , 16:3655-3656
Published Date: 14 November 2023
Latent Profile Analysis of Fear of Hypoglycemia in Middle-Aged and Elderly Hospitalized Patients with Type 2 Diabetes and Its Relationship with Sleep Quality
Xu J, Xiao S, Xie X, Kang Y, Peng D, Lu Y
Diabetes, Metabolic Syndrome and Obesity 2023 , 16:3641-3654
Published Date: 13 November 2023
Sex Difference in the Association Between Serum Versican and Albuminuria in Patients with Type 2 Diabetes Mellitus
Li S, Li N, Li L, Zhan J
Diabetes, Metabolic Syndrome and Obesity 2023 , 16:3631-3639
Published Date: 11 November 2023
Roux-en-Y Gastric Bypass Improves Insulin Sensitivity in Obese Rats with Type 2 Diabetes Mellitus by Regulating the Grin3a/AMPK Signal Axis in Hypothalamic Arcuate Nucleus
Zhang LH, Wang J, Tan BH, Yin YB, Kang YM
Diabetes, Metabolic Syndrome and Obesity 2023 , 16:3617-3629
Published Date: 10 November 2023
Prevalence and Risk Factors of Diabetic Nephropathy Among Saudi Type-1 Diabetic Patients in Taif City, Saudi Arabia
Al-Zahrani N, AlSwat HK, AlQarni AM, Alzahrani SS, Boubshait LA, Alassaf LA, Alsalman Z
Diabetes, Metabolic Syndrome and Obesity 2023 , 16:3609-3616
Published Date: 9 November 2023
Association Between Interleukin-6 Gene Polymorphism and Severity of Coronary Artery Disease in Patients with Diabetes
Yao H, Pang Y, Chen Y, Si N, Wu C, Wang Z, Ren Y
Diabetes, Metabolic Syndrome and Obesity 2023 , 16:3599-3608
SGLT2 Inhibitors in the Management of Type 1 Diabetes (T1D): An Update on Current Evidence and Recommendations
Maffei P, Bettini S, Busetto L, Dassie F
Diabetes, Metabolic Syndrome and Obesity 2023 , 16:3579-3598
Evaluating Glycemic Control Efficacy and Safety of the Oral Small Molecule Glucagon-Like Peptide 1 Receptor Agonist Danuglipron in Type 2 Diabetes Patients: A Systemic Review and Meta-Analysis
Fatima H, Rangwala HS, Mustafa MS, Shafique MA, Abbas SR, Rizwan A, Fadlalla Ahmed TK, Arshad A
Diabetes, Metabolic Syndrome and Obesity 2023 , 16:3567-3578
Published Date: 7 November 2023
The Role of PPARγ Gene Polymorphisms, Gut Microbiota in Type 2 Diabetes: Current Progress and Future Prospects
Zhao YK, Zhu XD, Liu R, Yang X, Liang YL, Wang Y
Diabetes, Metabolic Syndrome and Obesity 2023 , 16:3557-3566
Diabetes Distress and Illness Perceptions in Tunisian Type 2 Diabetes Patients
Masmoudi R, Hadj Kacem F, Bouattour M, Guermazi F, Sellami R, Feki I, Mnif M, Masmoudi J, Baati I, Abid M
Diabetes, Metabolic Syndrome and Obesity 2023 , 16:3547-3556
Published Date: 6 November 2023
Sex-Based Differences and Risk Factors for Comorbid Nonalcoholic Fatty Liver Disease in Patients with Bipolar Disorder: A Cross-Sectional Retrospective Study
Wang Y, Liu Y, Zhang X, Wu Q
Diabetes, Metabolic Syndrome and Obesity 2023 , 16:3533-3545
Saudi Adults’ Understanding and Views of Weight Management Practices and Obesity
Alshehri FS, Alorfi NM
Diabetes, Metabolic Syndrome and Obesity 2023 , 16:3513-3531
Elevated ApoB/apoA-1 is Associated with in-Hospital Mortality in Elderly Patients with Acute Myocardial Infarction
Chen Y, Chen S, Han Y, Xu Q, Zhao X
Diabetes, Metabolic Syndrome and Obesity 2023 , 16:3501-3512
Published Date: 3 November 2023
Criteria in the Diagnosis of Metabolic Syndrome in Children: A Scoping Review
Díaz-Ortega JL, Yupari-Azabache IL, Caballero Vidal JA, Conde-Parada NE, Rojas Gamboa AF
Diabetes, Metabolic Syndrome and Obesity 2023 , 16:3489-3500
Metformin Reduces the Risk of Total Hip Arthroplasty in Elderly Patients with Hip Osteoarthritis and Type 2 Diabetes Mellitus
Diabetes, Metabolic Syndrome and Obesity 2023 , 16:3481-3487
Published Date: 2 November 2023
Outcome of Diabetic Keto Acidosis Treatment and Associated Factors Among Adult Patients Admitted to Emergency and Medical Wards at St. Paul’s Hospital, Addis Ababa Ethiopia, 2023: A Cross-Sectional Study
Derse TK, Haile MT, Chamiso TM
Diabetes, Metabolic Syndrome and Obesity 2023 , 16:3471-3480
The Combination of Remnant Cholesterol and Waist Circumference Offers Predictive Value for Nonalcoholic Fatty Liver Disease: A Cross-Sectional Study from a Medical Examination Center
Jia Z, Pan X, Chen S
Diabetes, Metabolic Syndrome and Obesity 2023 , 16:3459-3469
Multi-Tiered Assessment of Gene Expression Provides Evidence for Mechanisms That Underlie Risk for Type 2 Diabetes
Asam K, Lewis KA, Kober K, Gong X, Kanaya AM, Aouizerat BE, Flowers E
Diabetes, Metabolic Syndrome and Obesity 2023 , 16:3445-3457
Published Date: 31 October 2023
Ultrasound Assessment of the Gastrocnemius Muscle as a Potential Tool for Identifying Sarcopenia in Patients with Type 2 Diabetes
Wei W, Xie C, Cao R, Que Y, Zhong X, Chen Z, Lv F, Kang Q, Lin R, Cao B, Lai X, Tu M
Diabetes, Metabolic Syndrome and Obesity 2023 , 16:3435-3444
Published Date: 30 October 2023
Association Between Serum Thyroid Measurements and Hyperhomocysteinemia in Euthyroid Subjects: A Retrospective Cross-Sectional Study
Zou J, Wang Y
Diabetes, Metabolic Syndrome and Obesity 2023 , 16:3425-3433
Knowledge, Attitude, and Practice of Insulin Pump Among Non-Endocrinology Nurses in South China
Mo C, Huang X, Lu X, Zhou Y, Su Y, Zhang H
Diabetes, Metabolic Syndrome and Obesity 2023 , 16:3417-3423
Evaluation of Trace Elements Levels and Construction of Auxiliary Prediction Model in Patients with Diabetes Ketoacidosis in Type 1 Diabetes
Chai J, Sun Z, Zhou Q, Xu J
Diabetes, Metabolic Syndrome and Obesity 2023 , 16:3403-3415
Body Mass Index z Scores Correlate with Epidermal Function in Chinese Children
Yang B, Lai Q, Chen A, Ye L, Wang X, Lai Y, Liu D, Man MQ
Diabetes, Metabolic Syndrome and Obesity 2023 , 16:3393-3401
Effect of Liraglutide on Fat Mass Percentage Among Overweight and Obese Adults with Type 2 Diabetes: A Systematic Review
Aladel A, Aljaouni J, Basaad R
Diabetes, Metabolic Syndrome and Obesity 2023 , 16:3381-3391
Published Date: 27 October 2023
Association of Subclinical Hypothyroidism with Type 2 Diabetes Mellitus in Qatar: A Cross-Sectional Study
Fakhroo A, Elhadary MR, Elsayed B, Al-Kuwari A, Aly R, Mesilhy R, Bakalaf A, Al-Maadhadi M, Al-Dehaimi AA, Chivese T, Rathnaiah Babu G
Diabetes, Metabolic Syndrome and Obesity 2023 , 16:3373-3379
Published Date: 28 October 2023
Associations of the MIND Diet with Cardiometabolic Diseases and Their Risk Factors: A Systematic Review
Akbar Z, Fituri S, Ouagueni A, Alalwani J, Sukik A, Al-Jayyousi GF, Bassil M, Tayyem R
Diabetes, Metabolic Syndrome and Obesity 2023 , 16:3353-3371
Published Date: 26 October 2023
Application of Health Belief Model to Assess Predictors of Weight Management Behaviour Intention Among Civil Servants in Ethiopia: A Mixed Method Study
Kebede N, Mohammed Y, Kasaye MD, Zewdie A
Diabetes, Metabolic Syndrome and Obesity 2023 , 16:3339-3352
The Relationship Between Bone Metabolism and Peripheral Artery Disease in Patients on Hemodialysis: The Potential Role of Osteocalcin
Chen ZY, Yang J, Tian CY, Jia W
Diabetes, Metabolic Syndrome and Obesity 2023 , 16:3331-3337
Clinical Classification of Obesity and Implications for Metabolic Dysfunction-Associated Fatty Liver Disease and Treatment
Ding Y, Deng Q, Yang M, Niu H, Wang Z, Xia S
Diabetes, Metabolic Syndrome and Obesity 2023 , 16:3303-3329
Published Date: 25 October 2023
Association Between Visceral Fat Area and Glycated Hemoglobin in Type 2 Diabetics: A Retrospective Study
Luo B, Xu W, Feng L, Chen J, Shi R, Cao H
Diabetes, Metabolic Syndrome and Obesity 2023 , 16:3295-3301
Published Date: 23 October 2023
Genetic Polymorphism of Lipoprotein-Associated Phospholipase A2 Influences Susceptibility to Gestational Diabetes Mellitus in Chinese Population
Qin L, Ma Q, Zhang C, Lu Z, Liu L, Huang Z
Diabetes, Metabolic Syndrome and Obesity 2023 , 16:3285-3294
Published Date: 20 October 2023
The Association Between the Imbalance of Single-Carbon Nutrients in Early Pregnancy and Gestational Diabetes Mellitus Risk is Influenced by Serum Selenium Status: A Cohort Study
Liu PJ, Ma L, Li R, Liu Y
Diabetes, Metabolic Syndrome and Obesity 2023 , 16:3275-3283
Leptin Mediates Prostate Stromal Cell Proliferation, Smooth Muscle Contraction, and Mitochondrial Function in Benign Prostate Hyperplasia
Wang X, Guo L, Liu G, Liu T
Diabetes, Metabolic Syndrome and Obesity 2023 , 16:3261-3273
Published Date: 19 October 2023
Influence of Fibrinogen/Albumin Ratio and Fibrinogen/Pre-Albumin Ratio on Cardiac Autonomic Neuropathy in Type 2 Diabetes
Zhao S, Yang Z, Yu M, Xiang L, Lv Y, Tian C, Li R
Diabetes, Metabolic Syndrome and Obesity 2023 , 16:3249-3259
Published Date: 18 October 2023
The Role of Iron Overload in Diabetic Cognitive Impairment: A Review
An JR, Wang QF, Sun GY, Su JN, Liu JT, Zhang C, Wang L, Teng D, Yang YF, Shi Y
Diabetes, Metabolic Syndrome and Obesity 2023 , 16:3235-3247
Ginsenoside Rb1 Promotes Hepatic Glycogen Synthesis to Ameliorate T2DM Through 15-PGDH/PGE2/EP4 Signaling Pathway
Liang M, Zhan W, Wang L, Bei W, Wang W
Diabetes, Metabolic Syndrome and Obesity 2023 , 16:3223-3234
Published Date: 17 October 2023
Prediabetes is Associated with Worse Long-Term Outcomes in Young Patients with Acute Coronary Syndrome
Xu R, Wang C, Lang J, Wu J, Hu Y, Wang T, Zhang J, Cong H, Wang L
Diabetes, Metabolic Syndrome and Obesity 2023 , 16:3213-3222
The Metabolic Characteristics of Patients at the Risk for Diabetic Foot Ulcer: A Comparative Study of Diabetic Patients with and without Diabetic Foot
Li X, Wen S, Dong M, Yuan Y, Gong M, Wang C, Yuan X, Jin J, Zhou M, Zhou L
Diabetes, Metabolic Syndrome and Obesity 2023 , 16:3197-3211
The Association of the Triglyceride and Muscle to Fat Ratio During Early Pregnancy with the Development of Gestational Diabetes Mellitus
Wang F, Bao YY, Yu K
Diabetes, Metabolic Syndrome and Obesity 2023 , 16:3187-3196
Published Date: 16 October 2023
Risk Factors and Prediction Nomogram of Cognitive Frailty with Diabetes in the Elderly
Deng Y, Li N, Wang Y, Xiong C, Zou X
Diabetes, Metabolic Syndrome and Obesity 2023 , 16:3175-3185
Female is Associated with Left Ventricular Diastolic Dysfunction in Patients with Type 2 Diabetes [Letter]
Ponath Sukumaran G, Kurian N, Kurian NM, Rasheed R
Diabetes, Metabolic Syndrome and Obesity 2023 , 16:3173-3174
Published Date: 12 October 2023
Association Between Thyroid Parameters and Subclinical Atherosclerosis in Hospitalised Euthyroid Patients with Type 2 Diabetes Mellitus
Du J, Zhao X, Xu X, Zhang Z, Zhang X
Diabetes, Metabolic Syndrome and Obesity 2023 , 16:3163-3171
Analysis of Risk Factors for Revitrectomy in Eyes with Diabetic Vitreous Hemorrhage [Corrigendum]
Guo H, Li W, Wang K, Nie Z, Zhang X, Bai S, Duan N, Li X, Hu B
Diabetes, Metabolic Syndrome and Obesity 2023 , 16:3161-3162
Published Date: 11 October 2023
Formate Might Be a Novel Potential Serum Metabolic Biomarker for Type 2 Diabetic Peripheral Neuropathy
Xu W, Xue W, Zhou Z, Wang J, Qi H, Sun S, Jin T, Yao P, Zhao JY, Lin F
Diabetes, Metabolic Syndrome and Obesity 2023 , 16:3147-3160
Published Date: 10 October 2023
Pancreatic Cancer-Derived Exosomal miR-Let-7b-5p Stimulates Insulin Resistance in Skeletal Muscle Cells Through RNF20/STAT3/FOXO1 Axis Regulation
Wang L, Li X, Wu J, Tang Q
Diabetes, Metabolic Syndrome and Obesity 2023 , 16:3133-3145
Published Date: 9 October 2023
Comparative Analysis of Red Blood Cells, White Blood Cells, Platelet Count, and Indices in Type 2 Diabetes Mellitus Patients and Normal Controls: Association and Clinical Implications
Essawi K, Dobie G, Shaabi MF, Hakami W, Saboor M, Madkhali AM, Hamami AA, Allallah WH, Akhter MS, Mobarki AA, Hamali HA
Diabetes, Metabolic Syndrome and Obesity 2023 , 16:3123-3132
Published Date: 6 October 2023
The Impact of Intermittent Fasting on Non-Alcoholic Fatty Liver Disease in Older Adults: A Review of Clinicaltrials.gov Registry
Alorfi NM, Ashour AM
Diabetes, Metabolic Syndrome and Obesity 2023 , 16:3115-3121
Effect of Ramadan Fasting on Blood Glucose Level in Pregnant Women with Gestational and Type 2 Diabetes
Alsulami SS, Ghamri KA
Diabetes, Metabolic Syndrome and Obesity 2023 , 16:3105-3113
Trapa Bispinosa Roxb. Inhibits the Insulin-Dependent AKT/WNK1 Pathway to Induce Autophagy in Mice with Type 2 Diabetes
Suzuki T, Sato T, Masuhara K, Tokusanai M, Akatsuka H, Kashikawa T, Suzuki Y
Diabetes, Metabolic Syndrome and Obesity 2023 , 16:3095-3104
Published Date: 5 October 2023
High Atherogenic Risk in Ketosis-Prone Type 2 Diabetic Individuals with Ketosis Episodes: A Cross-Sectional Study
He X, Luo Y, Hao J, Hu R, Yang X, Ren L
Diabetes, Metabolic Syndrome and Obesity 2023 , 16:3085-3094
Blood Lipid Disorders in Post-Operative Craniopharyngioma Children and Adolescents and the Improvement with Recombinant Human Growth Hormone Replacement
Du HZ, Chen K, Zhang LY, Zhang YL, Wu DN, Guo XY, Duan L, Wang LJ, Yang HB, Chen S, Pan H, Zhu HJ
Diabetes, Metabolic Syndrome and Obesity 2023 , 16:3075-3084
Published Date: 3 October 2023
Circulating MicroRNA-30a, Beclin1 and Their Association with Different Variables in Females with Metabolically Healthy /Unhealthy Obesity
Naguib M, Magdy M, Yousef OAE, Ibrahim W, Gharib DM
Diabetes, Metabolic Syndrome and Obesity 2023 , 16:3065-3074
Diabetic Ketoacidosis in Northern Jordan: Seasonal Morbidity and Characteristics of Patients
Saadeh NA, Hammouri HM, Zahran DJ
Diabetes, Metabolic Syndrome and Obesity 2023 , 16:3057-3064
Advanced Glycation End Products Downregulate Connexin 43 and Connexin 40 in Diabetic Atrial Myocytes via the AMPK Pathway
Yang F, Liu HH, Zhang L, Zhang XL, Zhang J, Li F, Zhao N, Zhang ZY, Kong Q, Liu XY, Wu Y, Yu ZM, Qian LL, Wang RX
Diabetes, Metabolic Syndrome and Obesity 2023 , 16:3045-3056
Published Date: 2 October 2023
Effect of Intestinal Flora on Hyperuricemia-Induced Chronic Kidney Injury in Type 2 Diabetic Patients and the Therapeutic Mechanism of New Anti-Diabetic Prescription Medications
Yan W, Wen S, Zhou L
Diabetes, Metabolic Syndrome and Obesity 2023 , 16:3029-3044
Published Date: 29 September 2023
Correlation Between Hemoglobin Levels and Polycystic Ovary Syndrome Metabolic Disorder
Ha LX, Du YD, Qu XX, Wang JJ
Diabetes, Metabolic Syndrome and Obesity 2023 , 16:3019-3027
Published Date: 28 September 2023
Serum Uric Acid to Creatinine Ratio and Risk of Metabolic Syndrome in Patients with Overweight/Obesity
She D, Xu W, Liu J, Zhang Z, Fang P, Li R, Kong D, Xuan M, Liu Q, Pan MY, Wang Y, Xue Y
Diabetes, Metabolic Syndrome and Obesity 2023 , 16:3007-3017
Correlation Between Fibrinogen/Albumin and Diabetic Peripheral Neuropathy
Ban J, Pan X, Yang L, Jia Z, Zhen R, Zhang X, Chen S
Diabetes, Metabolic Syndrome and Obesity 2023 , 16:2991-3005
Published Date: 27 September 2023
The Incidence of Metabolic Syndrome in People Over 40 Years Old with Normal Fasting Plasma Glucose and the Predictive Cutoff Value of Effective Fasting Plasma Glucose for Metabolic Syndrome in Guiyang
Liu Y, Zhuang H, Han N, Yang Y, Zhang Q, Peng N, Hu Y, Shi L, Li H
Diabetes, Metabolic Syndrome and Obesity 2023 , 16:2979-2989
Published Date: 25 September 2023
Saudi Arabian Physical Therapists’ Knowledge, Attitudes, and Clinical Practice in Diabetes Prevention and Management
Alhowimel AS, Alshahrani AA, Abulaban AA, Althobeit AM, Alenazi AM, Alshehri MM, Alqahtani BA, Alodaibi F
Diabetes, Metabolic Syndrome and Obesity 2023 , 16:2967-2977
Published Date: 22 September 2023
Causal Paradigm Between Common Comorbidities of Cardiovascular and Metabolism-Related Diseases in Elderly: Evidence from Cross-Sectional and Mendelian Randomization Studies
Gu J, Wang Q, Wu X, Zhang H, Wu C, Qiu W
Diabetes, Metabolic Syndrome and Obesity 2023 , 16:2953-2966
Driving Pressure-Guided Ventilation in Obese Patients Undergoing Laparoscopic Sleeve Gastrectomy: A Randomized Controlled Trial [Corrigendum]
Yang G, Zhang P, Li L, Wang J, Jiao P, Wang J, Chu Q
Diabetes, Metabolic Syndrome and Obesity 2023 , 16:2951-2952
Novel Associations of Dyslipidaemia with Vitamin D and Bone Metabolism in Elderly Patients with Diabetes: A Cross-Sectional Study
Zha K, Wang N, Zhou Y, Ying R, Gu T, Zhao Y, Guo H, An Z, Lu Y
Diabetes, Metabolic Syndrome and Obesity 2023 , 16:2939-2950
Splenic Infarction with Myocardial Injury in a Diabetic Patient: A Case Report
Wang C, Wen S, Zhou L
Diabetes, Metabolic Syndrome and Obesity 2023 , 16:2929-2937
Development of Patient-Reported Outcome Scale for Patients with Diabetic Foot and Its Reliability and Validity Test
Wang X, Hu X, Que H
Diabetes, Metabolic Syndrome and Obesity 2023 , 16:2921-2927
Published Date: 20 September 2023
Impact of Fixed Combination of Metformin and Pioglitazone on Insulin Resistance of Patients with Type 2 Diabetes: Results of a Randomized Open-Label Study
Sun R, Yuan L, Shen Y, Shen Z, Ding B, Ma J
Diabetes, Metabolic Syndrome and Obesity 2023 , 16:2911-2919
Amplitude of Low-Frequency Fluctuation to Determine Disturbed Spontaneous Brain-Activity Pattern in Patients with Diabetic Optic Neuropathy
Wang XY, Xu SH, Wei H, Kang M, Zou J, Ling Q, Huang H, Chen X, Wang YX, Shao Y, Yu Y
Diabetes, Metabolic Syndrome and Obesity 2023 , 16:2899-2909
Prediction of the Risk of Bone Mineral Density Decrease in Type 2 Diabetes Mellitus Patients Based on Traditional Multivariate Logistic Regression and Machine Learning: A Preliminary Study
Zhang J, Xu Z, Fu Y, Chen L
Diabetes, Metabolic Syndrome and Obesity 2023 , 16:2885-2898
Published Date: 19 September 2023
Effect of Aerobic Exercise in Chinese Adult Individuals at Risk for Type 2 Diabetes Mellitus (T2DM) with Low Salivary Amylase Gene (AMY1) Copy Number Variation
Zhang X, Moran C, Wang R, Zhou Y
Diabetes, Metabolic Syndrome and Obesity 2023 , 16:2875-2883
Analysis of Risk Factors for Revitrectomy in Eyes with Diabetic Vitreous Hemorrhage
Diabetes, Metabolic Syndrome and Obesity 2023 , 16:2865-2874
Effects of NKT Cells on Metabolic Disorders Caused by High-Fat Diet Using CD1d-Knockout Mice
Ishikawa H, Nagashima R, Kuno Y, Sasaki H, Kohda C, Iyoda M
Diabetes, Metabolic Syndrome and Obesity 2023 , 16:2855-2864
Insulin Resistance-Induced Platelet Hyperactivity and a Potential Biomarker Role of Platelet Parameters: A Narrative Review
Kelem A, Adane T, Shiferaw E
Diabetes, Metabolic Syndrome and Obesity 2023 , 16:2843-2853
Published Date: 18 September 2023
Incidence of Metabolic Syndrome and Its Risk Factors in Elderly with Nonalcoholic Fatty Liver Disease
Lin FA, Hwang LC, Tsou MT, Huang WH
Diabetes, Metabolic Syndrome and Obesity 2023 , 16:2835-2842
Published Date: 15 September 2023
Metabolic and Hepatic Effects of Empagliflozin on Nonalcoholic Fatty Liver Mice [Corrigendum]
Niu S, Ren Q, Chen S, Pan X, Yue L, Chen X, Li Z, Zhen R
Diabetes, Metabolic Syndrome and Obesity 2023 , 16:2833-2834
Published Date: 16 September 2023
Integrative Korean Medicine Treatment for Obesity and Overweight in Children and Adolescents: A Multicenter Retrospective Chart Review Study
Seo HS, Lee SH, Yim MH, Jeong YK, Chang GT, Ahn YJ, Lee JD, Chung WS, Ahn SY, Ahn YM, Lee BC, Noh JW, Lee B
Diabetes, Metabolic Syndrome and Obesity 2023 , 16:2821-2832
A Cross-Sectional Study Exploring the Relationship Between BMI and Nursing Students’ Emotional Well-Being During the COVID-19 Pandemic
Almadni NA, Alsenany SA, Abusabeib ZA, Ibrahim HK
Diabetes, Metabolic Syndrome and Obesity 2023 , 16:2813-2820
Published Date: 14 September 2023
Sleep Quality Among People with Type 2 Diabetes Mellitus During COVID-19 Pandemic: Evidence from Qatar’s National Diabetes Center
Abdu Y, Naja S, Mohamed Ibrahim MI, Abdou M, Ahmed R, Elhag S, Saleh AO, Yassin M, Bougmiza I
Diabetes, Metabolic Syndrome and Obesity 2023 , 16:2803-2812
Correlation Between the Variability of Different Obesity Indices and Diabetic Kidney Disease: A Retrospective Cohort Study Based on Populations in Taiwan
Sun Z, Wang K, Yun C, Bai F, Yuan X, Lee Y, Lou Q
Diabetes, Metabolic Syndrome and Obesity 2023 , 16:2791-2802
Published Date: 12 September 2023
The Ratio of Serum Uric Acid to Glycosylated Haemoglobin as a Predictor of All-Mortality in Elderly Patients with Diabetic Foot Ulcers: A Longitudinal Cohort Study
Huang X, Deng L, Huang J, Sun J, Wang Q, Mo J, Zhai Z, Nong Y, Lu W
Diabetes, Metabolic Syndrome and Obesity 2023 , 16:2779-2790
MicroRNA-221-3p Targets THBS1 to Promote Wound Healing in Diabetes
Hu K, Liu X, Chang H, Zhang Y, Zhou H, Liu L, Zhang X, Jiao Z, Shen B, Zhang Q
Diabetes, Metabolic Syndrome and Obesity 2023 , 16:2765-2777
Published Date: 11 September 2023
Analysis of Methylome, Transcriptome, and Lipid Metabolites to Understand the Molecular Abnormalities in Polycystic Ovary Syndrome
Zhang F, Ding Y, Zhang B, He M, Wang Z, Lu C, Kang Y
Diabetes, Metabolic Syndrome and Obesity 2023 , 16:2745-2763
The Importance of Clinical Pharmacists in Improving Blood Glucose and Lipid Levels in Patients with Diabetes and Myocardial Infarction
Shi FH, Yu BB, Shen L, Xu L, Jiang YH, Gu ZC, Lin HW, Li H
Diabetes, Metabolic Syndrome and Obesity 2023 , 16:2733-2744
Published Date: 8 September 2023
Effect of Hypoglycemic Drugs on Kisspeptin Expression in the Hypothalamic Arcuate Nucleus of PCOS Rats
Zheng P, Lu Y, Tang Y, Xu F, Wang L, Qian R, Hui C, Xu Q, He J, Deng D
Diabetes, Metabolic Syndrome and Obesity 2023 , 16:2717-2732
Published Date: 7 September 2023
Shear-Wave Elastography-Based Radiomics Nomogram for the Prediction of Cardiovascular Disease in Patients with Diabetic Kidney Disease
Diabetes, Metabolic Syndrome and Obesity 2023 , 16:2705-2716
Serum Uric Acid is a Better Indicator of Kidney Impairment Than Serum Uric Acid-to-Creatinine Ratio and Serum Uric Acid-to-High-Density Lipoprotein Ratio: A Cross-Sectional Study of Type 2 Diabetes Mellitus Patients
Han R, Duan L, Zhang Y, Jiang X
Diabetes, Metabolic Syndrome and Obesity 2023 , 16:2695-2703
Published Date: 5 September 2023
Fructosamine is Not a Reliable Test for the Detection of Hyperglycemia: Insight from the Africans in America Study
Gatete JDD, Worthy CC, Jagannathan R, DuBose CW, Sacks DB, Sumner AE
Diabetes, Metabolic Syndrome and Obesity 2023 , 16:2689-2693
Association Between Sensitivity to Thyroid Hormones and Metabolic Dysfunction-Associated Fatty Liver Disease in Euthyroid Subjects: A Cross-Sectional Study [Response to Letter]
Liu H, Xing Y, Nie Q, Li Z, Meng C, Ma H
Diabetes, Metabolic Syndrome and Obesity 2023 , 16:2687-2688
Published Date: 4 September 2023
Nursing Intervention and Summary of Evidence Pertaining to Neonatal Recurrent Hypoglycemia Induced by Terbutaline
Wang MQ, Zhuang Y, Zheng YN
Diabetes, Metabolic Syndrome and Obesity 2023 , 16:2677-2685
Association Analysis of Triglyceride Glucose-Body Mass Index and Bone Turnover Markers in Patients with Type 2 Diabetes Mellitus [Letter]
Mo J, Zhang L
Diabetes, Metabolic Syndrome and Obesity 2023 , 16:2675-2676
The Relationship Between Non-HDL-C /HDL-C Ratio (NHHR) and Vitamin D in Type 2 Diabetes Mellitus
Zhen R, Ban J, Jia Z, Liu Y, Li Z, Chen S
Diabetes, Metabolic Syndrome and Obesity 2023 , 16:2661-2673
Published Date: 31 August 2023
The Triglyceride-Glucose Index is Associated with Vitamin D Status in Metabolic-Associated Fatty Liver Disease
Liu Z, Zhang W, Zhao Z, Li W, Zhang J
Diabetes, Metabolic Syndrome and Obesity 2023 , 16:2651-2660
Novel Curcumin Analogue L6H4 in Treating Liver Fibrosis and Type 2 Diabetes
Ma J, Vaishnani DK, Mansi, Zeng J, Xie Z, Jin X, Zhang H, Wut Yi Hla K, Ying F
Diabetes, Metabolic Syndrome and Obesity 2023 , 16:2639-2650
Published Date: 30 August 2023
Liver Fibrosis Scores and Coronary Artery Disease: Novel Findings in Patients with Metabolic Dysfunction-Associated Fatty Liver Disease
Lu C, Chen Y, Zhang Y, Zhao X
Diabetes, Metabolic Syndrome and Obesity 2023 , 16:2627-2637
Published Date: 29 August 2023
The Correlation Between C-Peptide and Severity of Peripheral Atherosclerosis in Type 2 Diabetes Mellitus
Wahab MA, Alhabibi AM, Sakr AK, Zakaria MY, Saleh OI, Ahmad IH, Abdelrahman EK, Taha RSE, Ahmed FKAA, Ismail B, Azel LH, Hassan AS, El Sayed HME, Kasim SA, Elsharkawy SA
Diabetes, Metabolic Syndrome and Obesity 2023 , 16:2617-2625
Is Metabolic Score for Visceral Fat (METS-VF) a Better Index Than Other Adiposity Indices for the Prediction of Visceral Adiposity
Torun C, Ankaralı H, Caştur L, Uzunlulu M, Erbakan AN, Akbaş MM, Gündüz N, Doğan MB, Oğuz A
Diabetes, Metabolic Syndrome and Obesity 2023 , 16:2605-2615
Stachydrine Relieved the Inflammation and Promoted the Autophagy in Diabetes Retinopathy Through Activating the AMPK/SIRT1 Signaling Pathway
Yu J, Ke L, Zhou J, Ding C, Yang H, Yan D, Yu C
Diabetes, Metabolic Syndrome and Obesity 2023 , 16:2593-2604
Published Date: 25 August 2023
Clopidogrel as a Distinctive Cause of Insulin Autoimmune Syndrome: A Systematic Case Review
Chen S, Qiang J, Zhao B, Tian R, Yuan T, Li M, Li M, Gu Z, Yang L, Zhu H, Pan H, Tang Y, Li Y
Diabetes, Metabolic Syndrome and Obesity 2023 , 16:2583-2592
Effects of Metformin on COVID-19 Patients with Type 2 Diabetes: A Retrospective Study
Guo Z, Gao Y, Xie E, Ye Z, Li Y, Zhao X, Shen N, Zheng J
Diabetes, Metabolic Syndrome and Obesity 2023 , 16:2573-2582
Published Date: 24 August 2023
Triglyceride-Glucose Index is Related to Carotid Artery Plaque in Railway Workers: A Cross-Sectional Study
Pan J, Yang B, Wang Z, Tang L, Jia P, Yang S, Zeng H
Diabetes, Metabolic Syndrome and Obesity 2023 , 16:2561-2571
Metabolic and Hepatic Effects of Empagliflozin on Nonalcoholic Fatty Liver Mice
Diabetes, Metabolic Syndrome and Obesity 2023 , 16:2549-2560
Retrospective Study on the Impact of COVID-19 Lockdown on Patients with Type 2 Diabetes in Northern Taiwan
Huang H, Su HL, Huang CH, Lin YH
Diabetes, Metabolic Syndrome and Obesity 2023 , 16:2539-2547
Efficacy of Emerging Technologies to Manage Childhood Obesity [Corrigendum]
Alotaibi M, Alnajjar F, Cappuccio M, Khalid S, Alhmiedat T, Mubin O
Diabetes, Metabolic Syndrome and Obesity 2023 , 16:2537-2538
Published Date: 23 August 2023
Association of Met420del Variant of Metformin Transporter Gene SLC22A1 with Metformin Treatment Response in Ethiopian Patients with Type 2 Diabetes
Degaga A, Sirgu S, Huri HZ, Sim MS, Kebede T, Tegene B, Loganadan NK, Engidawork E, Shibeshi W
Diabetes, Metabolic Syndrome and Obesity 2023 , 16:2523-2535
Association Between Plasma Asprosin Levels and Gestational Diabetes Mellitus
Boz İB, Aytürk Salt S, Salt Ö, Sayın NC, Dibirdik İ
Diabetes, Metabolic Syndrome and Obesity 2023 , 16:2515-2521
Prognostic Nutritional Index (PNI) as a Predictor in Patients with Metabolic Syndrome and Heart Failure
Zhang X, Zhang J, Liu F, Li W, Zhang T, Fang B, Zhang Z, Xie Q, Yang Y, Li X
Diabetes, Metabolic Syndrome and Obesity 2023 , 16:2503-2514
Published Date: 18 August 2023
Development and Validation of a Risk Prediction Model for Ketosis-Prone Type 2 Diabetes Mellitus Among Patients Newly Diagnosed with Type 2 Diabetes Mellitus in China
Jiang Y, Zhu J, Lai X
Diabetes, Metabolic Syndrome and Obesity 2023 , 16:2491-2502
The Association Between Lipid Profile and Subfoveal Choroidal Thickness in Chinese Patients with Proliferative Diabetic Retinopathy Secondary to Type 2 Diabetes
Lei C, Ran Q, Duan J, Zhang M
Diabetes, Metabolic Syndrome and Obesity 2023 , 16:2477-2489
Published Date: 17 August 2023
Examining the Links Between Physical Activity, Sitting Time, and Renal Function in T2DM Patients
Wang X, Yan N, Pan R, Dang Y, Wang L
Diabetes, Metabolic Syndrome and Obesity 2023 , 16:2469-2476
Published Date: 16 August 2023
The Obesity Paradox: Effect of Body Mass Index and Waist Circumference on Post-Stroke Cognitive Impairment
Liu Q, Liao X, Pan Y, Xiang X, Zhang Y
Diabetes, Metabolic Syndrome and Obesity 2023 , 16:2457-2467
Correlation Between Blood Glucose Indexes Generated by the Flash Glucose Monitoring System and Diabetic Vascular Complications
Sheng X, Li T, Hu Y, Xiong CS, Hu L
Diabetes, Metabolic Syndrome and Obesity 2023 , 16:2447-2456
GLP-1RA Liraglutide and Semaglutide Improves Obesity-Induced Muscle Atrophy via SIRT1 Pathway
Xiang J, Qin L, Zhong J, Xia N, Liang Y
Diabetes, Metabolic Syndrome and Obesity 2023 , 16:2433-2446
Published Date: 15 August 2023
Role of Increased miR-222-3p Expression in Peripheral Blood and Wound Marginal Tissues of Type 2 Diabetes Mellitus Patients with Diabetic Foot Ulcer
Jie R, Qian J, Tang Y, Li Y, Xu M, Zhao X, Chen M
Diabetes, Metabolic Syndrome and Obesity 2023 , 16:2419-2432
Abdominal Massage Ameliorates Inguinal Fat Accumulation via Augmentation of PPARγ Signaling in High-Fat Diet-Induced Obese Mice
Zhang J, Wang T, Shi Y, Liu Y, Lu T
Diabetes, Metabolic Syndrome and Obesity 2023 , 16:2409-2418
Association Between Sensitivity to Thyroid Hormones and Metabolic Dysfunction-Associated Fatty Liver Disease in Euthyroid Subjects: A Cross-Sectional Study [Letter]
Han J, Lu W
Diabetes, Metabolic Syndrome and Obesity 2023 , 16:2407-2408
Optimizing Type 2 Diabetes Management in a Medically Complex Patient: A Case Report of a Patient with Type 2 Diabetes and HIV
Kabakambira JD, Kong JM
Diabetes, Metabolic Syndrome and Obesity 2023 , 16:2401-2406
Published Date: 11 August 2023
Relationships Between Body Composition and Cognitive Impairment in Hospitalised Middle-Aged Type 2 Diabetic Patients
Li YS, Liu YL, Wang JJ, Haghbin N, Wang XH, Jiang WR, Qiu HN, Xia LF, Wu F, Lin CY, Li JB, Lin JN
Diabetes, Metabolic Syndrome and Obesity 2023 , 16:2389-2400
Published Date: 9 August 2023
Associations of Relative Fat Mass, a Novel Adiposity Indicator, with Non-Alcoholic Fatty Liver Disease and Cardiovascular Disease: Data from SPECT-China
Shen W, Cai L, Wang B, Wang Y, Wang N, Lu Y
Diabetes, Metabolic Syndrome and Obesity 2023 , 16:2377-2387
Published Date: 8 August 2023
Effects of Glycemic Variability on Regulatory T Cells in Patients with Type 2 Diabetes and Kidney Disease
Gu QW, Sun Q, Wang J, Gu WS, Wang W, Mao XM
Diabetes, Metabolic Syndrome and Obesity 2023 , 16:2365-2375
Female is Associated with Left Ventricular Diastolic Dysfunction in Patients with Type 2 Diabetes
Diabetes, Metabolic Syndrome and Obesity 2023 , 16:2355-2364
Published Date: 7 August 2023
Non-Traditional Blood Lipid Indices for Metabolism Dysfunction-Associated Fatty Liver Disease Prediction in Non-Obese Type 2 Diabetes Mellitus
Gao Q, Feng L, Zhou W, Li X, Yin L, Wang Y
Diabetes, Metabolic Syndrome and Obesity 2023 , 16:2345-2354
Effect of Benaglutide on Gut Microbiota and Fecal Metabolites in Patients with Type 2 Diabetes Mellitus
Han CY, Ye XM, Lu JP, Jin HY, Wang P, Xu WW, Zhang M
Diabetes, Metabolic Syndrome and Obesity 2023 , 16:2329-2344
Study on the Association Between Dietary Quality and Overweight/Obesity of Han Nationality with Cold in Yunnan Plateau by DBI-16 – A Study Based on a Multi-Ethnic Cohort in China
Qian Y, Che Z, Fu C, Chen X, Zeng J, Zhang X, Zeng Y, Yin J
Diabetes, Metabolic Syndrome and Obesity 2023 , 16:2311-2327
Published Date: 3 August 2023
Racial Disparities in Diabetes Technology Adoption and Their Association with HbA1c and Diabetic Ketoacidosis
Conway RB, Gerard Gonzalez A, Shah VN, Geno Rasmussen C, Akturk HK, Pyle L, Forlenza G, Alonso GT, Snell-Bergeon J
Diabetes, Metabolic Syndrome and Obesity 2023 , 16:2295-2310
Published Date: 2 August 2023
Survival of Patients with Hepatitis B-Related Hepatocellular Carcinoma with Concomitant Metabolic Associated Fatty Liver Disease
You Y, Yang T, Wei S, Liu Z, Liu C, Shen Z, Yang Y, Feng Y, Yao P, Zhu Q
Diabetes, Metabolic Syndrome and Obesity 2023 , 16:2283-2293
Is Chemerin Associated with Gestational Diabetes Mellitus? A Case-Control Study
Ma Z, Chu L, Zhang Y, Lu F, Zhu Y, Wu F, Zhang Z
Diabetes, Metabolic Syndrome and Obesity 2023 , 16:2271-2281
Prevalence and Associated Factors of Urinary Tract Infection in Patients with Diabetic Neuropathy [Letter]
Wang Q, Huang X, Lu W
Diabetes, Metabolic Syndrome and Obesity 2023 , 16:2269-2270
Published Date: 1 August 2023
Association of Non-Invasive Markers with Significant Fibrosis in Patients with Nonalcoholic Fatty Liver Disease: A Cross-Sectional Study
Zhang F, Han Y, Zheng L, Liu J, Wu Y, Bao Z, Liu L, Li W
Diabetes, Metabolic Syndrome and Obesity 2023 , 16:2255-2268
Published Date: 31 July 2023
Global Prevalence of Anemia Among Type 2 Diabetic Adult Patients: A Systematic Review and Meta-Analysis
Arkew M, Asmerom H, Gemechu K, Tesfa T
Diabetes, Metabolic Syndrome and Obesity 2023 , 16:2243-2254
Association of Hyper-Triglyceridemic Waist Phenotype and Diabetic Vascular Complication in the Chinese Population
Li K, Cao B, Ke J, Yang L, Zhao D
Diabetes, Metabolic Syndrome and Obesity 2023 , 16:2233-2241
Published Date: 26 July 2023
Partial Thyroid Hormone-Binding Globulin Deficiency: A Case Report and Literature Review
Liu X, Li S, Xiong J, Chen D, Jiang C, Zeng L, Qiu Y, Xia BW
Diabetes, Metabolic Syndrome and Obesity 2023 , 16:2225-2232
Hypoglycaemic Molecules for the Management of Diabetes Mellitus from Marine Sources
Chellappan DK, Chellian J, Rahmah NSN, Gan WJ, Banerjee P, Sanyal S, Banerjee P, Ghosh N, Guith T, Das A, Gupta G, Singh SK, Dua K, Kunnath AP, Norhashim NA, Ong KH, Palaniveloo K
Diabetes, Metabolic Syndrome and Obesity 2023 , 16:2187-2223
Published Date: 25 July 2023
Application of MR Imaging Characteristics in the Differentiation of Renal Changes Between Patients with Stage III Type 2 Diabetic Kidney Disease and Healthy People
Zhang H, Yu B, Yang H, Ying H, Qu X, Zhu L, Wang C, Ding J
Diabetes, Metabolic Syndrome and Obesity 2023 , 16:2177-2186
Published Date: 24 July 2023
Clinical Efficacy of Dapagliflozin in the Treatment of Patients with Diabetic Nephropathy and Its Effect on Proteinuria Level
Jin ZJ, Wang GZ
Diabetes, Metabolic Syndrome and Obesity 2023 , 16:2167-2175
Published Date: 22 July 2023
The Ratio of Fibrinogen to Albumin is Related to the Occurrence of Retinopathy in Type 2 Diabetic Patients [Letter]
Yang H, Zhang B, Huang S
Diabetes, Metabolic Syndrome and Obesity 2023 , 16:2165-2166
Published Date: 21 July 2023
Association Between Sensitivity to Thyroid Hormones and Metabolic Dysfunction-Associated Fatty Liver Disease in Euthyroid Subjects: A Cross-Sectional Study
Diabetes, Metabolic Syndrome and Obesity 2023 , 16:2153-2163
Published Date: 19 July 2023
Identifying Metabolic Syndrome Easily and Cost Effectively Using Non-Invasive Methods with Machine Learning Models
Xu W, Zhang Z, Hu K, Fang P, Li R, Kong D, Xuan M, Yue Y, She D, Xue Y
Diabetes, Metabolic Syndrome and Obesity 2023 , 16:2141-2151
Published Date: 17 July 2023
Association of Serum Leptin and Adiponectin Concentrations with Type 2 Diabetes Biomarkers and Complications Among Saudi Women
Mohammed Saeed W, Nasser Binjawhar D
Diabetes, Metabolic Syndrome and Obesity 2023 , 16:2129-2140
Published Date: 13 July 2023
Association of Serum Bile Acid and Unsaturated Fatty Acid Profiles with the Risk of Diabetic Retinopathy in Type 2 Diabetic Patients
Feng S, Guo L, Wang S, Chen L, Chang H, Hang B, Mao J, Snijders AM, Lu Y, Ding D
Diabetes, Metabolic Syndrome and Obesity 2023 , 16:2117-2128
A Novel Circ_Arf3/miR-452-5p/Mbnl1 Axis Regulates Proliferation and Expression of Fibrosis-Related Proteins of Mouse Mesangial Cells Under High Glucose
Wang Q, Zhu Y, Dong Q, Zhang L, Zhang W
Diabetes, Metabolic Syndrome and Obesity 2023 , 16:2105-2116
Published Date: 11 July 2023
Prevalence of Microalbuminuria Among Diabetes Patients in Africa: A Systematic Review and Meta-Analysis
Mohammed O, Alemayehu E, Bisetegn H, Debash H, Gedefie A, Ebrahim H, Tilahun M, Fiseha T
Diabetes, Metabolic Syndrome and Obesity 2023 , 16:2089-2103
Increased Left Atrial Stiffness is Significantly Associated with Paroxysmal Atrial Fibrillation in Diabetic Patients
Arnautu DA, Arnautu SF, Tomescu MC, Luca S, Luca CT
Diabetes, Metabolic Syndrome and Obesity 2023 , 16:2077-2087
Prediction of Diabetic Kidney Disease in Newly Diagnosed Type 2 Diabetes Mellitus
Mu X, Wu A, Hu H, Zhou H, Yang M
Diabetes, Metabolic Syndrome and Obesity 2023 , 16:2061-2075
Published Date: 8 July 2023
Prior Diagnosis of Diabetes but Not Its Control is Associated with Higher Depression Score Among Older Individuals
Kozela M, Matras A, Koziara K, Małecki MT, Pająk A
Diabetes, Metabolic Syndrome and Obesity 2023 , 16:2051-2059
Published Date: 7 July 2023
Real-World Effectiveness and Safety of Hydrogen Inhalation in Chinese Patients with Type 2 Diabetes: A Single-Arm, Retrospective Study
Ji H, Zhao Z, Liu Z, Sun R, Li Y, Ding X, Ni T
Diabetes, Metabolic Syndrome and Obesity 2023 , 16:2039-2050
Published Date: 5 July 2023
Glycemic Control and Associated Factors Among Children and Adolescents with Type 1 Diabetes Mellitus, Southwest Ethiopia
Abrahim AM, Tilahun T, Gelana B
Diabetes, Metabolic Syndrome and Obesity 2023 , 16:2025-2037
Published Date: 4 July 2023
Serum Isthmin-1 Was Increased in Type 2 Diabetic Patients but Not in Diabetic Sensorimotor Peripheral Neuropathy
Liao J, Li Y, Gui X, Zhang Y, Hu X, Cheng L, Hu W, Bai F
Diabetes, Metabolic Syndrome and Obesity 2023 , 16:2013-2024
Published Date: 3 July 2023
Transverse Tibial Bone Transfer in the Treatment of Diabetes Foot Ulcer: A Pilot Study
Wen R, Cheng X, Cao H, Zhang L, Luo F, Shang W
Diabetes, Metabolic Syndrome and Obesity 2023 , 16:2005-2012
Development of Machine Learning Models for Predicting Osteoporosis in Patients with Type 2 Diabetes Mellitus—A Preliminary Study
Wu X, Zhai F, Chang A, Wei J, Guo Y, Zhang J
Diabetes, Metabolic Syndrome and Obesity 2023 , 16:1987-2003
Published Date: 30 June 2023
The Association Between Health-Related Behaviors and Traditional Chinese Medicine Syndromes in Type 2 Diabetes Mellitus Patients
Wang L, Li J, Dang Y, Pan R, Niu Y
Diabetes, Metabolic Syndrome and Obesity 2023 , 16:1977-1985
Comparison of Clinical Profiles, Demographics, and Surgical Outcomes of 25-Gauge Vitrectomy for Proliferative Diabetic Retinopathy in Young Adults with Type 1 or Type 2 Diabetes
Zhang M, Zhang J, Xu G, Ruan L, Huang X
Diabetes, Metabolic Syndrome and Obesity 2023 , 16:1967-1975
Published Date: 29 June 2023
Perirenal Fat Thickness is Associated with Metabolic Dysfunction-Associated Fatty Liver Disease in Type 2 Diabetes Mellitus
Yang J, Li CW, Zhang JR, Qiu H, Guo XL, Wang W
Diabetes, Metabolic Syndrome and Obesity 2023 , 16:1953-1965
Published Date: 28 June 2023
Mechanisms of Small Intestine Involvement in Obesity-Induced Atherosclerosis
Pan X, Jia Z, Zhen R, Yue L, Niu S, Ban J, Chen S
Diabetes, Metabolic Syndrome and Obesity 2023 , 16:1941-1952
Evaluation of Social Platform-Based Continuity of Care in Improving Cognitive and Prognostic Effects of Young Patients with Diabetic Retinopathy
Cao GL, Chen KJ
Diabetes, Metabolic Syndrome and Obesity 2023 , 16:1931-1939
Published Date: 27 June 2023
Ferroptosis and Traditional Chinese Medicine for Type 2 Diabetes Mellitus
Xie D, Li K, Feng R, Xiao M, Sheng Z, Xie Y
Diabetes, Metabolic Syndrome and Obesity 2023 , 16:1915-1930
Published Date: 26 June 2023
Combined Exercise Training Promotes More Benefits on Cardiovascular Autonomic Modulation in Ovariectomized Rats Than Isolated Aerobic or Resistance Training
Da Costa-Santos N, Minguta Santos Costa G, Dos-Santos A, Nascimento-Carvalho B, Ribeiro TF, Freitas SCF, Caperuto E, Irigoyen MC, De Angelis K, Scapini KB, Sanches IC
Diabetes, Metabolic Syndrome and Obesity 2023 , 16:1903-1913
Exogenous Insulin Antibody Syndrome in Patients with Type 2 Diabetes
Han CY, Ye XM, Lu JP, Jin HY, Xu WW, Wang P, Zhang M
Diabetes, Metabolic Syndrome and Obesity 2023 , 16:1895-1902
Insulin Stimulates IL-23 Expression in Human Adipocytes: A Possible Explanation for the Higher Prevalence of Psoriasis in Obesity
Di Vincenzo A, Granzotto M, Crescenzi M, Costa C, Piaserico S, Vindigni V, Vettor R, Rossato M
Diabetes, Metabolic Syndrome and Obesity 2023 , 16:1885-1893
Published Date: 23 June 2023
Haematological Parameters in People with Atherosclerotic Cardiovascular Disease versus Those Who are Only at Risk for Cardiovascular Disease: A Comparative Cross-Sectional Study
Abate E, Degef M, Melkie A, Gnanasekeran N, Mehdi M, Tolcha Y, Chala D
Diabetes, Metabolic Syndrome and Obesity 2023 , 16:1869-1883
The Ratio of Fibrinogen to Albumin is Related to the Occurrence of Retinopathy in Type 2 Diabetic Patients
Chen X, Zhao J, You Y, Li Z, Chen S
Diabetes, Metabolic Syndrome and Obesity 2023 , 16:1859-1867
Serum Lipoprotein(a) and High-Density Lipoprotein Cholesterol Associate with Diabetic Nephropathy: Evidence from Machine Learning Perspectives
Gao RH, Liu B, Yang Y, Ran R, Zhou Y, Liu SM
Diabetes, Metabolic Syndrome and Obesity 2023 , 16:1847-1858
Published Date: 22 June 2023
Prevalence of Type 2 Diabetes in Mongolia: Results from Population-Based Survey Compared with 1999 Study
Dayan A, Erkhembayar R, Luvsandavaajav O, Mukhtar Y, Enkhtuvshin B, Tumenbayar B
Diabetes, Metabolic Syndrome and Obesity 2023 , 16:1833-1846
Published Date: 21 June 2023
Retinal Microvascular Diameters are Associated with Diabetic Kidney Disease in Patients with Type 2 Diabetes Mellitus
Feng J, Xie X, Teng Z, Fei W, Zhen Y, Liu J, Yang L, Chen S
Diabetes, Metabolic Syndrome and Obesity 2023 , 16:1821-1831
Therapeutic Approaches for Nonalcoholic Fatty Liver Disease: Established Targets and Drugs
Huang X, Chen H, Wen S, Dong M, Zhou L, Yuan X
Diabetes, Metabolic Syndrome and Obesity 2023 , 16:1809-1819
Dapagliflozin Improves Erythropoiesis and Iron Metabolism in Type 2 Diabetic Patients with Renal Anemia
Osonoi T, Shirabe S, Saito M, Hosoya M, Watahiki N, Douguchi S, Ofuchi K, Katoh M
Diabetes, Metabolic Syndrome and Obesity 2023 , 16:1799-1808
Published Date: 20 June 2023
Fasting Plasma Glucose and Glycated Hemoglobin Levels as Risk Factors for the Development of Hypertension: A Retrospective Cohort Study
Luo B, Feng L, Bi Q, Shi R, Cao H, Zhang Y
Diabetes, Metabolic Syndrome and Obesity 2023 , 16:1791-1798
Published Date: 17 June 2023
Potential Role and Expression Level of Urinary CXCL8 in Different Stages of Incipient Diabetic Nephropathy with Undiminished Creatinine Clearance: A Pilot Study
He Y, Li H, Wang R, Ma N, Liu L, Shi R, Zhang B, Lin N, Tian Y
Diabetes, Metabolic Syndrome and Obesity 2023 , 16:1783-1790
The Influence of SLC22A3 Genetic Polymorphisms on Susceptibility to Type 2 Diabetes Mellitus in Chinese Population
Li Z, Yuan X, Liu X, Yang Y, Huang L, Tan Q, Li C
Diabetes, Metabolic Syndrome and Obesity 2023 , 16:1775-1781
Published Date: 15 June 2023
Novel Anti-obesity Therapies and their Different Effects and Safety Profiles: A Critical Overview
Telci Caklili O, Cesur M, Mikhailidis DP, Rizzo M
Diabetes, Metabolic Syndrome and Obesity 2023 , 16:1767-1774
Published Date: 14 June 2023
Predictive Factors of Menstrual Recovery After Laparoscopic Sleeve Gastrectomy in Polycystic Ovary Syndrome Women with Obesity
Cai M, Zhang Y, Gao J, Dilimulati D, Bu L, Cheng X, Du L, Zhou D, Zhu J, Qu S, Zhang M
Diabetes, Metabolic Syndrome and Obesity 2023 , 16:1755-1766
Published Date: 13 June 2023
Hyperinsulinemia Influences the Short-Term Efficiency of Laparoscopic Sleeve Gastrectomy for Patients with Obesity and Insulin Resistance
Yue Z, Qian L, Jin Y, Xia Y, Sha H, Wu Q, Hu K
Diabetes, Metabolic Syndrome and Obesity 2023 , 16:1745-1753
Profilin1 Promotes Renal Tubular Epithelial Cell Apoptosis in Diabetic Nephropathy Through the Hedgehog Signaling Pathway
Mai L, He G, Chen J, Zhu J, Chen S, Yang H, Zhang M, Hou X, Ke M, Li X
Diabetes, Metabolic Syndrome and Obesity 2023 , 16:1731-1743
Published Date: 9 June 2023
Comparison of Pathway Referrals for Liver Fibrosis Risk Stratification Performed in Diabetology and Nutrition Clinics
Caussy C, Telliam C, Al-Nuaimi B, Maynard-Muet M, Dumortier J, Zoulim F, Disse E, Colin C, Levrero M, Moulin P
Diabetes, Metabolic Syndrome and Obesity 2023 , 16:1721-1729
Published Date: 8 June 2023
A Peptide in a Pill – Oral Semaglutide in the Management of Type 2 Diabetes
Selvarajan R, Subramanian R
Diabetes, Metabolic Syndrome and Obesity 2023 , 16:1709-1720
Detection of Thyroid Nodule Prevalence and Associated Risk Factors in Southwest China: A Study of 45,023 Individuals Undergoing Physical Examinations
Liang Y, Li X, Wang F, Yan Z, Sang Y, Yuan Y, Qin Y, Zhang X, Ju M
Diabetes, Metabolic Syndrome and Obesity 2023 , 16:1697-1707
Relationship Between Six Insulin Resistance Surrogates and Nonalcoholic Fatty Liver Disease Among Older Adults: A Cross-Sectional Study
Li H, Shi Z, Chen X, Wang J, Ding J, Geng S, Sheng X, Shi S
Diabetes, Metabolic Syndrome and Obesity 2023 , 16:1685-1696
Published Date: 7 June 2023
Integrated Analysis of Single-Cell RNA-Seq and Bulk RNA-Seq Combined with Multiple Machine Learning Identified a Novel Immune Signature in Diabetic Nephropathy
Peng YL, Zhang Y, Pang L, Dong YF, Li MY, Liao H, Li RS
Diabetes, Metabolic Syndrome and Obesity 2023 , 16:1669-1684
Accelerated Wound Healing in Diabetic Rat by miRNA-185-5p and Its Anti-Inflammatory Activity
Wang KX, Zhao LL, Zheng LT, Meng LB, Jin L, Zhang LJ, Kong FL, Liang F
Diabetes, Metabolic Syndrome and Obesity 2023 , 16:1657-1667
Association Between Plasma Levels of ANGPTL3, 4, 8 and the Most Common Additional Cardiovascular Risk Factors in Patients with Hypertension
Xu F, Shen L, Yang Y, Kong L, Zu W, Tian D, Cao X, Huang G
Diabetes, Metabolic Syndrome and Obesity 2023 , 16:1647-1655
Relationship Between Time in Range and Dusk Phenomenon in Outpatients with Type 2 Diabetes Mellitus
Gao X, Li H, Yu Y, Huai X, Feng B, Song J
Diabetes, Metabolic Syndrome and Obesity 2023 , 16:1637-1646
Published Date: 6 June 2023
Relationship Between Plasma Aldosterone Concentrations and Non-Alcoholic Fatty Liver Disease Diagnosis in Patients with Hypertension: A Retrospective Cohort Study
Hu J, Cai X, Zhu Q, Heizhati M, Wen W, Luo Q, Hong J, Dang Y, Yang W, Li N
Diabetes, Metabolic Syndrome and Obesity 2023 , 16:1625-1636
Hyperinsulinemia and Poor Coronary Collateral Circulation in Coronary Artery Occlusion: A Novel Finding, Yet of Therapeutic Significance or Not [Letter]
Asif M, Zehra SS, Siddiqui T
Diabetes, Metabolic Syndrome and Obesity 2023 , 16:1623-1624
Published Date: 5 June 2023
Association Between Intestinal Prevotella copri Abundance and Glycemic Fluctuation in Patients with Brittle Diabetes
Liu J, Zhou L, Sun L, Ye X, Ma M, Dou M, Shi L
Diabetes, Metabolic Syndrome and Obesity 2023 , 16:1613-1621
Published Date: 3 June 2023
Painful Diabetic Peripheral Neuropathy: Practical Guidance and Challenges for Clinical Management
Preston FG, Riley DR, Azmi S, Alam U
Diabetes, Metabolic Syndrome and Obesity 2023 , 16:1595-1612
Published Date: 2 June 2023
Irisin Ameliorates Renal Tubulointerstitial Fibrosis by Regulating the Smad4/β-Catenin Pathway in Diabetic Mice
Yang Z, Wei J, Wang Y, Du Y, Song S, Li J, Su Z, Shi Y, Wu H
Diabetes, Metabolic Syndrome and Obesity 2023 , 16:1577-1593
Serum Fibroblast Growth Factor 21 Level After an Oral Fat Tolerance Test is Related to Postprandial Free Fatty Acid Level
Li X, Zheng K, Gu W, Hou X, Guan Y, Liu L, Hou L, Geng J, Song G
Diabetes, Metabolic Syndrome and Obesity 2023 , 16:1567-1576
Published Date: 1 June 2023
The Waist–Hip Ratio is a Mediator Between Serum Levels of Brain-Derived Neurotrophic Factor and Its Val66Met Polymorphism in Adolescents
Zhang JC, Wang JH, Lin J, Su GM, Shen YL, Guo QW, Cai JJ, Liu JY, Jia KX, Fang DZ
Diabetes, Metabolic Syndrome and Obesity 2023 , 16:1555-1565
Published Date: 30 May 2023
The Association Between Sarcopenia and Diabetes: From Pathophysiology Mechanism to Therapeutic Strategy
Chen H, Huang X, Dong M, Wen S, Zhou L, Yuan X
Diabetes, Metabolic Syndrome and Obesity 2023 , 16:1541-1554
Serum Proteomic Signatures in Umbilical Cord Blood of Preterm Neonates Delivered by Women with Gestational Diabetes
Li X, Zhang B, Ding W, Jia X, Han Z, Zhang L, Hu Y, Shen B, Wang H
Diabetes, Metabolic Syndrome and Obesity 2023 , 16:1525-1539
Published Date: 26 May 2023
Driving Pressure-Guided Ventilation in Obese Patients Undergoing Laparoscopic Sleeve Gastrectomy: A Randomized Controlled Trial
Diabetes, Metabolic Syndrome and Obesity 2023 , 16:1515-1523
Published Date: 24 May 2023
Relationship Between Hyperuricemia-Waist Phenotype and Obstructive Sleep Apnea in Type 2 Diabetes Mellitus
Lu N, Yin F
Diabetes, Metabolic Syndrome and Obesity 2023 , 16:1505-1513
Published Date: 23 May 2023
Early Gestational Blood Markers to Predict Preeclampsia Complicating Gestational Diabetes Mellitus
Xie Y, Zhou W, Tao X, Lv H, Cheng Z
Diabetes, Metabolic Syndrome and Obesity 2023 , 16:1493-1503
Published Date: 22 May 2023
Associations Between Serum Folate Level and HOMA-IR in Chinese Patients with Type 2 Diabetes Mellitus
Yang X, Hu R, Wang Z, Hou Y, Song G
Diabetes, Metabolic Syndrome and Obesity 2023 , 16:1481-1491
Published Date: 19 May 2023
Association Between Hemoglobin Glycation Index and Metabolic Syndrome in Middle-Aged and Older People
Xie SS, Luo XT, Dong MH, Wang Q, Li J, Wu QF
Diabetes, Metabolic Syndrome and Obesity 2023 , 16:1471-1479
Prevalence and Risk Factors of Comorbid Obesity in Chinese Patients with Bipolar Disorder
Wu Q, Zhang X, Liu Y, Wang Y
Diabetes, Metabolic Syndrome and Obesity 2023 , 16:1459-1469
Published Date: 18 May 2023
Predictors for the Longevity of People with Diabetes in Buno Bedele and Illubabor Zones, South-west Ethiopia
Tadege M, Misganaw A, Truneh Z, Tegegne AS
Diabetes, Metabolic Syndrome and Obesity 2023 , 16:1449-1457
Association Analysis of Triglyceride Glucose-Body Mass Index and Bone Turnover Markers in Patients with Type 2 Diabetes Mellitus
Sun W, Xing Y, Zhou F, Ma Y, Wan X, Ma H
Diabetes, Metabolic Syndrome and Obesity 2023 , 16:1435-1447
Hyperinsulinemia Impaired Coronary Collateral Circulation in Patients with Chronic Total Coronary Occlusion
Zou X, Chen M, Sun L, Tan Q
Diabetes, Metabolic Syndrome and Obesity 2023 , 16:1425-1433
Renoprotection by Inhibiting Connexin 43 Expression in a Mouse Model of Obesity-Related Renal Injury
An X, Li G, Wang S, Xie T, Ren X, Zhao Y
Diabetes, Metabolic Syndrome and Obesity 2023 , 16:1415-1424
Published Date: 17 May 2023
Relationship Between Serum 25-Hydroxyvitamin D Deficiency and Thyroid Disease in Postmenopausal Women with Type 2 Diabetes Mellitus
Diabetes, Metabolic Syndrome and Obesity 2023 , 16:1407-1414
Published Date: 16 May 2023
Diabetes Mellitus Knowledge, Attitudes, Preventive Practices and Associated Factors Among a Sample of Adult Non-Diabetic Saudi Residents
Baig M, Alzahrani S, Abualhamael S, Alotaibi A, Alharbi M, Almohammadi T, Alkaabi T
Diabetes, Metabolic Syndrome and Obesity 2023 , 16:1393-1406
Published Date: 15 May 2023
Treatment of Painful Diabetic Neuropathy Using Frequency Rhythmic Electro Magnetic Neural Stimulation (FREMS); Effectiveness in Daily Practice
Imholz B, Heijster J, Tahrani A, Kooy A
Diabetes, Metabolic Syndrome and Obesity 2023 , 16:1383-1391
Published Date: 11 May 2023
Comprehensive Study of Untargeted Metabolomics and 16S rRNA Reveals the Mechanism of Fecal Microbiota Transplantation in Improving a Mouse Model of T2D
Yang W, Xia Z, Zhu Y, Tang H, Xu H, Hu X, Lin C, Jiang T, He P, Shen J
Diabetes, Metabolic Syndrome and Obesity 2023 , 16:1367-1381
Health Provider’s Feedback on Physical Activity Surveillance Using Wearable Device-Smartphone Application for Adults with Metabolic Syndrome; a 12-Week Randomized Control Study
Jang M, Park JH, Kim GM, Song S, Huh U, Kim DR, Sung M, Tak YJ
Diabetes, Metabolic Syndrome and Obesity 2023 , 16:1357-1366
Published Date: 10 May 2023
Correlation of White Blood Cell, Neutrophils, and Hemoglobin with Metabolic Syndrome and Its Components
Li N, Liu C, Luo Q, Zhang F, Sheng D, Liu Z
Diabetes, Metabolic Syndrome and Obesity 2023 , 16:1347-1355
Establishment of a Nomogram Based on Inflammatory Response-Related Methylation Sites in Intraoperative Visceral Adipose Tissue to Predict EWL% at One Year After LSG
Chen G, Li Z, Sang Q, Wang L, Wuyun Q, Wang Z, Chen W, Yu C, Lian D, Zhang N
Diabetes, Metabolic Syndrome and Obesity 2023 , 16:1335-1345
Published Date: 8 May 2023
Triglyceride-Glucose Index is Significantly Associated with the Risk of Hyperuricemia in Patients with Nonalcoholic Fatty Liver Disease
Qi J, Ren X, Hou Y, Zhang Y, Zhang Y, Tan E, Wang L
Diabetes, Metabolic Syndrome and Obesity 2023 , 16:1323-1334
Translation Animal Models of Diabetic Kidney Disease: Biochemical and Histological Phenotypes, Advantages and Limitations
Luo W, Tang S, Xiao X, Luo S, Yang Z, Huang W, Tang S
Diabetes, Metabolic Syndrome and Obesity 2023 , 16:1297-1321
Published Date: 5 May 2023
Metabolic Score for Insulin Resistance (METS-IR) Predicts Adverse Cardiovascular Events in Patients with Type 2 Diabetes and Ischemic Cardiomyopathy
Zhang X, Liu F, Li W, Zhang J, Zhang T, Yu X, Luo J, Zhao Q, Zhang J, Fang B, Yang Y, Li X
Diabetes, Metabolic Syndrome and Obesity 2023 , 16:1283-1295
Development and External Validation of Nomogram to Identify Risk Factors for CHD in T2DM in the Population of Northwestern China
Meng Q, Yang J, Wang F, Li C, Sang G, Liu H, Shen D, Zhang J, Jiang S, Yusufu A, Du G
Diabetes, Metabolic Syndrome and Obesity 2023 , 16:1271-1282
Published Date: 4 May 2023
Prevalence and Associated Factors of Urinary Tract Infection in Patients with Diabetic Neuropathy: A Hospital-Based Cross-Sectional Study
Wang X, Wang Y, Luo L, Tan L, Cai W, Chen L, Ren W
Diabetes, Metabolic Syndrome and Obesity 2023 , 16:1261-1270
Published Date: 3 May 2023
The Correlation Between Health Risk Factors and Diabesity and Lipid Profile Indicators: The Role Mediator of TSH
Zhang Y, Zhang Y, Zhu L, Yu Z, Lu F, Wang Z, Zhang Q
Diabetes, Metabolic Syndrome and Obesity 2023 , 16:1247-1259
Published Date: 2 May 2023
Liraglutide in Combination with Insulin Has a Superior Therapeutic Effect to Either Alone on Fracture Healing in Diabetic Rats
Liu H, Tian F, Hu Y, Ping S, Zhang L
Diabetes, Metabolic Syndrome and Obesity 2023 , 16:1235-1245
Published Date: 29 April 2023
Prevalence and Risk Factors of Metabolic-Associated Fatty Liver Disease Among Hospital Staff
Zhang D, Zhang L, Chen S, Chen R, Zhang X, Bai F
Diabetes, Metabolic Syndrome and Obesity 2023 , 16:1221-1234
Published Date: 27 April 2023
Assessment of Electrolyte Imbalance and Associated Factors Among Adult Diabetic Patients Attending the University of Gondar Comprehensive Specialized Hospital, Ethiopia: A Comparative Cross-Sectional Study
Eshetu B, Worede A, Fentie A, Chane E, Fetene G, Wondifraw H, Shimelis M, Girma M, Hadgu R, Demeke K, Fasil A
Diabetes, Metabolic Syndrome and Obesity 2023 , 16:1207-1220
Published Date: 26 April 2023
Baicalin Exerts a Protective Effect in Diabetic Nephropathy by Repressing Inflammation and Oxidative Stress Through the SphK1/S1P/NF-κB Signaling Pathway
Ren G, Jiao P, Yan Y, Ma X, Qin G
Diabetes, Metabolic Syndrome and Obesity 2023 , 16:1193-1205
Association of Bone Turnover Markers with Type 2 Diabetes Mellitus and Microvascular Complications: A Matched Case-Control Study
Hou Y, Hou X, Nie Q, Xia Q, Hu R, Yang X, Song G, Ren L
Diabetes, Metabolic Syndrome and Obesity 2023 , 16:1177-1192
Risk Factors Associated with Bone Marrow Adiposity Deposition in Postmenopausal Women in the CASH China Study
Xu L, Gong Y, Zhao Q, Blake GM, Li K, Zhang Y, Liu Q, Li C, Cheng X
Diabetes, Metabolic Syndrome and Obesity 2023 , 16:1167-1176
Published Date: 24 April 2023
The Rate of Postoperative Mortality and Renal and Respiratory Complications are Increased in Patients with Extreme Obesity Undergoing Cardiac Surgery – A Retrospective Observational Cohort Study of 8848 Patients
Pacholewicz J, Kuligowska E, Szylińska A, Walerowicz P, Biskupski A, Sielicki P, Kotfis K, Listewnik M
Diabetes, Metabolic Syndrome and Obesity 2023 , 16:1155-1166
Hypoglycemic Coma Caused by Methimazole-Induced Insulin Autoimmune Syndrome, a Case Study
He Y, Xu A, Yu D, Lv S, Xu H
Diabetes, Metabolic Syndrome and Obesity 2023 , 16:1149-1154
Plasminogen Activator Inhibitor-1 4G/5G (rs1799889) Polymorphism in Chinese Patients with Diabetes Mellitus and Hypertension
Wang Z, Chen J, Song J, Kong L, Wang X, Xu T, Lin S, Dou Y
Diabetes, Metabolic Syndrome and Obesity 2023 , 16:1133-1147
Published Date: 22 April 2023
The Application of the Insulin to C-Peptide Molar Ratio (ICPR) in Primary Screening for Insulin Antibodies in Type 2 Diabetes Mellitus Patients: A Further Quantitative Study on the Relationship Between ICPR and Insulin Antibodies
Hua KF, Jing BY, Wu YH
Diabetes, Metabolic Syndrome and Obesity 2023 , 16:1121-1132
Development and Validation of a Risk Nomogram Model for Predicting Constipation in Patients with Type 2 Diabetes Mellitus
Yuan HL, Zhang X, Peng DZ, Lin GB, Li HH, Li FX, Lu JJ, Chu WW
Diabetes, Metabolic Syndrome and Obesity 2023 , 16:1109-1120
Published Date: 20 April 2023
Predictive Effect of Triglyceride-Glucose Index on Adverse Prognostic Events in Patients with Type 2 Diabetes Mellitus and Ischemic Cardiomyopathy
Abuduaini B, Yang L, Jiamali N, Seyiti Z, Shan XF, Gao XM
Diabetes, Metabolic Syndrome and Obesity 2023 , 16:1093-1107
Differences and Clinical Significance of Serum 25-Hydroxyvitamin D3 and Vasohibin-1 (VASH-1) Levels in Patients with Diabetic Nephropathy and Different Renal Injuries
Liu H, Wang D, Tang J, Yu L, Su S
Diabetes, Metabolic Syndrome and Obesity 2023 , 16:1085-1091
Published Date: 19 April 2023
Predictive Value of Noninvasive Peripheral Atherosclerosis Measurement for Coronary Artery Disease in Patients with Long T2DM Duration
Chen W, Wang L, Hu B, Zheng Y, Zhang S, Zhou Z, Mai Y
Diabetes, Metabolic Syndrome and Obesity 2023 , 16:1075-1083
Published Date: 18 April 2023
Resveratrol Regulates Glucose and Lipid Metabolism in Diabetic Rats by Inhibition of PDK1/AKT Phosphorylation and HIF-1α Expression
Li S, Feng F, Deng Y
Diabetes, Metabolic Syndrome and Obesity 2023 , 16:1063-1074
Published Date: 15 April 2023
Continuous Nursing Intervention Based on Network Interactive in Early Diabetic Kidney Disease Patients
Wang Q, Luo L
Diabetes, Metabolic Syndrome and Obesity 2023 , 16:1055-1062
Published Date: 14 April 2023
Teneligliptin, a DPP-4 Inhibitor, Improves Vascular Endothelial Function via Divergent Actions Including Changes in Circulating Endothelial Progenitor Cells
Akashi N, Umemoto T, Yamada H, Fujiwara T, Yamamoto K, Taniguchi Y, Sakakura K, Wada H, Momomura SI, Fujita H
Diabetes, Metabolic Syndrome and Obesity 2023 , 16:1043-1054
Published Date: 13 April 2023
An Assessment of the Effect of Bariatric Surgery on Cardiovascular Disease Risk in the Chinese Population Using Multiple Cardiovascular Risk Models
Xu G, Wang Z, Yu C, Amin B, Du D, Li T, Chen G, Wang L, Li Z, Chen W, Tian C, Wuyun Q, Sang Q, Shang M, Lian D, Zhang N
Diabetes, Metabolic Syndrome and Obesity 2023 , 16:1029-1042
Published Date: 12 April 2023
Pre-Procedure Blood Sugar Levels in Diabetic and High-Risk Patients Visiting King Abdulaziz University Dental Hospital: The Role of the Dentist in Diabetes Screening
Al-Sebaei MO, Bamashmous M, Bassyoni L, Alsubaie R, Alnahdi DO
Diabetes, Metabolic Syndrome and Obesity 2023 , 16:1021-1027
Serum MOTS-C Levels are Decreased in Obese Children and Associated with Vascular Endothelial Function
Luo YH, Xie L, Li JY, Xie Y, Li MQ, Zhou L
Diabetes, Metabolic Syndrome and Obesity 2023 , 16:1013-1020
Effects of Metabolic Syndrome on Cardiovascular Outcomes of Psoriatic Patients with Coronary Artery Disease: A Single Center Retrospective Cohort Study
Zhao L, Sun L, Yang K, Li Z, Wang Y, Wang T, Wang M, Zeng Y, Zhou X, Yang W
Diabetes, Metabolic Syndrome and Obesity 2023 , 16:1003-1012
Published Date: 10 April 2023
Ethnic Variability in Glucose and Insulin Response to Rice Among Healthy Overweight Adults: A Randomized Cross-Over Study
Sadiya A, Jakapure V, Kumar V
Diabetes, Metabolic Syndrome and Obesity 2023 , 16:993-1002
Published Date: 9 April 2023
Chemical Constituents and Antidiabetic Activity of Dichloromethane Extract from Ficus carica Leaves
Lin L, Zhang Y
Diabetes, Metabolic Syndrome and Obesity 2023 , 16:979-991
Published Date: 5 April 2023
Association Between Serum Bicarbonate Levels and Prediabetes and Subclinical Inflammation in Young Healthy Adults: A Cross-sectional Study
Omar AA, Radwani KJ, Al Thani MK, Abouzeid NH, Qabeel YE, Al Shamari M, Chivese T, Zughaier SM
Diabetes, Metabolic Syndrome and Obesity 2023 , 16:969-977
Published Date: 4 April 2023
Serum Uric Acid to High‑density Lipoprotein Cholesterol Ratio is Associated with Visceral Fat in Patients with Type 2 Diabetes
Sun H, Su H, Zheng R, Liu C, Wang Y, Fang C
Diabetes, Metabolic Syndrome and Obesity 2023 , 16:959-967
Study on the Relationship and Predictive Value of First-Trimester Pregnancy-Associated Plasma Protein-A, Maternal Factors, and Biochemical Parameters in Gestational Diabetes Mellitus: A Large Case-Control Study in Southern China Mothers
Cui J, Li P, Chen X, Li L, Ouyang L, Meng Z, Fan J
Diabetes, Metabolic Syndrome and Obesity 2023 , 16:947-957
Published Date: 1 April 2023
Cost-Effectiveness of Bariatric Surgery in Tunisia
Galvain T, Bosut MP, Jamous N, Ben Mansour N
Diabetes, Metabolic Syndrome and Obesity 2023 , 16:935-945
Obesity and Its Associations with Gender, Smoking, Consumption of Sugary Drinks, and Hour of Sleep Among King Abdulaziz University Students in Saudi Arabia
Alsulami S, Althagafi N, Hazazi E, Alsayed R, Alghamdi M, Almohammadi T, Almurashi S, Baig M
Diabetes, Metabolic Syndrome and Obesity 2023 , 16:925-934
Gender-Specific Association of Handgrip Strength with Type 2 Diabetes Mellitus in Chinese Han Older Adults
Huang Q, Chen X, Shen HY, Zhou JM, Zhang HQ, Wang L, Chen R, Cheng J, Zhang Y, Zhang DM, Chen GM
Diabetes, Metabolic Syndrome and Obesity 2023 , 16:913-923
YQBS Improves Cognitive Dysfunction in Diabetic Rats: Possible Association with Tyrosine and Tryptophan Metabolism
Liu Y, Cai J, Wang Y, Zhao X, Qiao Y, Liu CJ
Diabetes, Metabolic Syndrome and Obesity 2023 , 16:901-912
Published Date: 30 March 2023
Associations of Colonoscopy and Risk of Hypoglycemia in Patients with Type 2 Diabetes
Yang H, Liu D, Zeng L, Peng S, Liu H
Diabetes, Metabolic Syndrome and Obesity 2023 , 16:893-900
Published Date: 29 March 2023
Lower Limb Arterial Ischemia: An Independent Risk Factor of Sudomotor Dysfunction in Type 2 Diabetes
Lv Y, Yang Z, Xiang L, Yu M, Zhao S, Zhang X, Li R
Diabetes, Metabolic Syndrome and Obesity 2023 , 16:883-891
Published Date: 28 March 2023
Effects of High-Fat Diet on Cardiovascular Protein Expression in Mice Based on Proteomics
Pan X, Zhang X, Ban J, Yue L, Ren L, Chen S
Diabetes, Metabolic Syndrome and Obesity 2023 , 16:873-882
Relationship Between Index of Cardiac Electrophysiological Balance, Frontal QRS-T Angle and Retinopathy in People with Type 2 Diabetes
Zhang YT, Li HY, Sun XT, Tong XW, Shan YY, Xu YX, Pu SD, Gao XY
Diabetes, Metabolic Syndrome and Obesity 2023 , 16:861-871
Published Date: 21 March 2023
Association Between Metabolic Syndrome and Decline in Cognitive Function: A Cross-Sectional Study
Alsuwaidi HN, Ahmed AI, Alkorbi HA, Ali SM, Altarawneh LN, Uddin SI, Roueentan SR, Alhitmi AA, Djouhri L, Chivese T
Diabetes, Metabolic Syndrome and Obesity 2023 , 16:849-859
Association of Different Obesity Phenotypes with Sarcopenia in Han Chinese Middle-Aged and Elderly with Type 2 Diabetes Individuals
Lu L, Liu B, Ma Y
Diabetes, Metabolic Syndrome and Obesity 2023 , 16:841-848
The Beneficial Effects of a Multispecies Probiotic Supplement on Glycaemic Control and Metabolic Profile in Adults with Type 1 Diabetes: A Randomised, Double-Blinded, Placebo-Controlled Pilot-Study
Zhang X, Zhang Y, Luo L, Le Y, Li Y, Yuan F, Wu Y, Xu P
Diabetes, Metabolic Syndrome and Obesity 2023 , 16:829-840
Published Date: 18 March 2023
Effect of Urinary Albumin Creatinine Ratio on Cardiovascular Morbidity and Mortality in Diabetes Patients with Atherosclerotic Disease
Gao F, Zhou Y, Yan X, Huang H, Liang G, Xie Y, Zhu Q, Chen Z, Wang B, Li H, Mai Z, Ying M, Liu J, Chen S, Chen J
Diabetes, Metabolic Syndrome and Obesity 2023 , 16:819-828
Published Date: 17 March 2023
Relationship Between Estimated Glucose Disposal Rate and Type 2 Diabetic Retinopathy
Meng C, Xing Y, Huo L, Ma H
Diabetes, Metabolic Syndrome and Obesity 2023 , 16:807-818
Published Date: 16 March 2023
Combining Fat-to-Muscle Ratio and Alanine Aminotransferase/Aspartate Aminotransferase Ratio in the Prediction of Cardiometabolic Risk: A Cross-Sectional Study
Yan F, Nie G, Zhou N, Zhang M, Peng W
Diabetes, Metabolic Syndrome and Obesity 2023 , 16:795-806
Published Date: 15 March 2023
Ginsenoside Rg1 Ameliorates Pancreatic Injuries via the AMPK/mTOR Pathway in vivo and in vitro
Chen J, Zhu G, Xiao W, Huang X, Wang K, Zong Y
Diabetes, Metabolic Syndrome and Obesity 2023 , 16:779-794
The Safety and Efficacy of Mesenchymal Stem Cells in the Treatment of Type 2 Diabetes- A Literature Review
Mathur A, Taurin S, Alshammary S
Diabetes, Metabolic Syndrome and Obesity 2023 , 16:769-777
Published Date: 14 March 2023
Complex Association Among Diet Styles, Sleep Patterns, and Obesity in Patients with Diabetes
Zhang Y, Wang Y, Zhang S, Zhang Y, Zhang Q
Diabetes, Metabolic Syndrome and Obesity 2023 , 16:749-767
Published Date: 11 March 2023
Brain-Imaging Mechanisms on Female Abdominal Obesity Treated by “Shu-Mu” Acupoint Catgut Embedding and Compatibility Relation: Study Protocol for a 12-Week Randomized Controlled Trial
Li Q, Lu Y, Zhang X, Chen Z, Feng J, Zeng X, Zhao S, Huang G, Li L, Xing C, Liang F, Guo T
Diabetes, Metabolic Syndrome and Obesity 2023 , 16:733-747
Gender Differences in Relation to Body Composition, Insulin Resistance, and Islet Beta Cell Function in Newly Diagnosed Diabetic or Pre-Diabetic Patients
Ma M, Jiang T, Wen Z, Zhang D, Xiu L
Diabetes, Metabolic Syndrome and Obesity 2023 , 16:723-732
Published Date: 10 March 2023
Clinicopathological Characteristics and Risk Factors for Rapid eGFR Decline in Chinese Patients with Biopsy-Proven Obesity-Related Glomerulopathy
Yang J, Yang Q, Fang Y, Liu F
Diabetes, Metabolic Syndrome and Obesity 2023 , 16:713-721
Adolescents with Type 2 Diabetes: Overcoming Barriers to Effective Weight Management
Salama M, Biggs BK, Creo A, Prissel R, Al Nofal A, Kumar S
Diabetes, Metabolic Syndrome and Obesity 2023 , 16:693-711
Published Date: 9 March 2023
A Scoping Review of Trials Designed to Achieve Remission of Type 2 Diabetes with Lifestyle Intervention Alone: Implications for Sub-Saharan Africa
Duhuze Karera MG, Wentzel A, Ishimwe MCS, Gatete JDD, Jagannathan R, Horlyck-Romanovsky MF, Sumner AE
Diabetes, Metabolic Syndrome and Obesity 2023 , 16:677-692
Validity and Reliability of the Chinese Version of the Diabetes Treatment Burden Questionnaire (DTBQ) Among Patients with Type 2 Diabetes
Mei Y, Yang X, Li Y, Zhang X, Gui J, Wang Y, Chen W, Chen M, Liu C, Zhang L
Diabetes, Metabolic Syndrome and Obesity 2023 , 16:663-675
Published Date: 8 March 2023
Association Between Visceral Fat, Blood Pressure and Arterial Stiffness in Patients with HFpEF: A Mediation Analysis
Sun M, Gao L, Bai H, Hu W, Zhang X, Xiao J, Deng X, Tao H, Ge P, Qin Y, Zhang D
Diabetes, Metabolic Syndrome and Obesity 2023 , 16:653-662
Prevalence of Prediabetes and Related Modifiable Cardiovascular Risk Factors Among Employees of Ayder Comprehensive Specialized Hospital, Tigray, Northern Ethiopia
Gidey G, Hiruy M, Teklu D, Ramanathan K, Amare H
Diabetes, Metabolic Syndrome and Obesity 2023 , 16:643-652
Comparison of Plasma Exosome Proteomes Between Obese and Non-Obese Patients with Type 2 Diabetes Mellitus
Wang Y, Wu Y, Yang S, Chen Y
Diabetes, Metabolic Syndrome and Obesity 2023 , 16:629-642
Published Date: 7 March 2023
Alternative Polyadenylation Results in mRNA Transcript Instability in Gestational Diabetes Mellitus
Diabetes, Metabolic Syndrome and Obesity 2023 , 16:619-628
Chinese Visceral Adipose Index Shows Superior Diagnostic Performance in Predicting the Risk of Metabolic Dysfunction Associated Fatty Liver Disease in Early Postmenopausal Chinese Women
Lu Y, Ge L, Yang H, He Y, Wang Y
Diabetes, Metabolic Syndrome and Obesity 2023 , 16:607-617
Published Date: 6 March 2023
Association of Serum Bilirubin Levels with Macro- and Microvascular Complications in Chinese People with Type 2 Diabetes Mellitus: New Insight on Gender Differences
Li S, Li N, Li L, Wang Y, Liu Y, Wang Y, Zhan J
Diabetes, Metabolic Syndrome and Obesity 2023 , 16:597-606
Published Date: 4 March 2023
The Efficacy of GLP-1 Analogues on Appetite Parameters, Gastric Emptying, Food Preference and Taste Among Adults with Obesity: Systematic Review of Randomized Controlled Trials
Aldawsari M, Almadani FA, Almuhammadi N, Algabsani S, Alamro Y, Aldhwayan M
Diabetes, Metabolic Syndrome and Obesity 2023 , 16:575-595
Published Date: 2 March 2023
Vitamin E Supplement Protects Against Gestational Diabetes Mellitus in Mice Through nuclear factor-erythroid factor 2-related factor 2/heme oxygenase-1 Signaling Pathway
Lin B, Zhang X
Diabetes, Metabolic Syndrome and Obesity 2023 , 16:565-574
Published Date: 1 March 2023
Leu72Met Polymorphism in Ghrelin Gene: A Potential Risk Factor for Hypertension in Type 2 Diabetes Patients
Buraczynska M, Golacki J, Zaluska W
Diabetes, Metabolic Syndrome and Obesity 2023 , 16:557-564
Trimethylamine N-Oxide and Related Metabolites in the Serum and Risk of Type 2 Diabetes in the Chinese Population: A Case-Control Study
Qi S, Liu L, He S, Wang L, Li J, Sun X
Diabetes, Metabolic Syndrome and Obesity 2023 , 16:547-555
Published Date: 27 February 2023
Association Between Sensitivity to Thyroid Hormone Indices and Diabetic Retinopathy in Euthyroid Patients with Type 2 Diabetes Mellitus
Yang J, Ding W, Wang H, Shi Y
Diabetes, Metabolic Syndrome and Obesity 2023 , 16:535-545
Red Blood Cell Distribution Width as a Biomarker in Type 2 Diabetes Mellitus: Technical Notes [Response to Letter]
Arkew M, Asmerom H, Tesfa T, Tsegaye S, Gemechu K, Bete T, Haile K
Diabetes, Metabolic Syndrome and Obesity 2023 , 16:533-534
Published Date: 24 February 2023
Association Between Vitamin D Levels and the Atherogenic Index of Plasma Among Chinese with Type 2 Diabetes Mellitus
Ban J, Zhao X, Jia Z, Li Z, Chen S
Diabetes, Metabolic Syndrome and Obesity 2023 , 16:523-531
Published Date: 22 February 2023
The Impact of LEP rs7799039 Polymorphism and Obesity on the Severity of Coronavirus Disease-19
Mohamed AA, Alrohaimi AH, Sayed Abdelgeliel A, Albogami S, Jafri I, Fayad E, Mohamed N, Nassar NA, Adaroas AS, Eldeeb HH, Abdel Halim A, Ramadan A, Elnagar RM, Abdelghafour RAM, Mohamed AY, Mahmoud MO, El-Kasses A, El-Sayed M, Mohammed MA, Alwaleed EA, Mousa S, Abdel Salam S, Abd el salam SM
Diabetes, Metabolic Syndrome and Obesity 2023 , 16:515-522
Published Date: 21 February 2023
Optimal Anthropometric Indicators and Cut Points for Predicting Metabolic Syndrome in Chinese Patients with Type 2 Diabetes Mellitus by Gender
Mao J, Gan S, Zhou Q, Zhou H, Deng Z
Diabetes, Metabolic Syndrome and Obesity 2023 , 16:505-514
Visceral Adiposity Index and Lipid Accumulation Product as Effective Markers of Different Obesity Phenotypes in Korean Adults: A Cross-Sectional Analysis
Yu SR, Shin KA
Diabetes, Metabolic Syndrome and Obesity 2023 , 16:495-504
Published Date: 17 February 2023
Cross-Sectional Association Between Body Fat Composition and Biomarkers of Inflammation and Endothelial Dysfunction in Children with Overweight/Obesity
Yasin J, Sharma C, Hashim MJ, Al Hamed S, AlKaabi J, Aburawi EH
Diabetes, Metabolic Syndrome and Obesity 2023 , 16:483-493
Red Blood Cell Distribution Width as a Biomarker in Type 2 Diabetes Mellitus: Technical Notes [Letter]
Diabetes, Metabolic Syndrome and Obesity 2023 , 16:479-481
Comprehensive Analysis of circRNA Expression Profiles in Human Brown Adipose Tissue
Sun X, Wan X, Khan MA, Zhang K, Yi X, Wang Z, Chen K
Diabetes, Metabolic Syndrome and Obesity 2023 , 16:469-478
Published Date: 15 February 2023
Association of Plasma Sex-Related Hormones Levels with Bone Mineral Densities and Risk of Osteoporosis and Osteopenia in Men and Menopausal Women with Type 2 Diabetes Mellitus
Lu W, Zheng S, Zhou J, Huang S, Chen N, Li Z
Diabetes, Metabolic Syndrome and Obesity 2023 , 16:457-468
Published Date: 14 February 2023
Investigation of Hyperlipidemia Associated with Increased Levels of Oxidized Low-Density Lipoproteins and 8-Hydroxy-2´-Deoxyguanosine
Babakr A, Mukhtar M, Althubiti M, Al-Amodi H, Almaimani R, Nour Eldin MM, Elzubeir Abdalla M, Nasif W
Diabetes, Metabolic Syndrome and Obesity 2023 , 16:447-455
The Mechanism of Sodium-Glucose Cotransporter-2 Inhibitors in Reducing Uric Acid in Type 2 Diabetes Mellitus
Dong M, Chen H, Wen S, Yuan Y, Yang L, Xu D, Zhou L
Diabetes, Metabolic Syndrome and Obesity 2023 , 16:437-445
The Neuronal and Non-Neuronal Pathways of Sodium-Glucose Cotransporter-2 Inhibitor on Body Weight-Loss and Insulin Resistance
Dong M, Chen H, Wen S, Yuan Y, Yang L, Li Y, Yuan X, Xu D, Zhou L
Diabetes, Metabolic Syndrome and Obesity 2023 , 16:425-435
Effects of Anterior Pituitary Adenomas’ Hormones on Glucose Metabolism and Its Clinical Implications
Li M, Zhang J, Yang G, Zhang J, Han M, Zhang Y, Liu Y
Diabetes, Metabolic Syndrome and Obesity 2023 , 16:409-424
Published Date: 13 February 2023
Correlation Between Mean Amplitude of Glycemic Excursion and Bone Turnover Markers in Patients with Type 2 Diabetes: A Cross-Sectional Study
Yang J, Zheng C, Wang Y, Yang L, Liu L
Diabetes, Metabolic Syndrome and Obesity 2023 , 16:397-407
Published Date: 10 February 2023
A Multifactorial Risk Score System for the Prediction of Diabetic Kidney Disease in Patients with Type 2 Diabetes Mellitus
Hui D, Zhang F, Lu Y, Hao H, Tian S, Fan X, Liu Y, Zhou X, Li R
Diabetes, Metabolic Syndrome and Obesity 2023 , 16:385-395
The Association Between Metabolic Dysfunction-Associated Fatty Liver Disease and Peripheral Arterial Disease in the Chinese Population
Song XH, Liu B, Lei F, Liu YM, Zhang X, Chen Z, Zhang P, Zhang XJ, She ZG, Cai J, Wang JH, Li H
Diabetes, Metabolic Syndrome and Obesity 2023 , 16:373-384
Relationship Between Maternal Serum Cortisol and Maternal Insulin Resistance and Fetal Ultrasound Characteristics in Gestational Diabetes Mellitus
Tien Nguyen S, Bui Minh T, Trung Dinh H, Dinh Le T, Phi Thi Nguyen N, Tran TTH, Hien Vu T, Luong Cong T, Ho Thi Nguyen L, Tuan Pham P, Viet Tran T, Xuan Nguyen K
Diabetes, Metabolic Syndrome and Obesity 2023 , 16:365-372
Published Date: 8 February 2023
Association Between Hypertension and New-Onset Non-Alcoholic Fatty Liver Disease in Chinese Non-Obese People: A Longitudinal Cohort Study
Yang D, Lan J, Cen J, Han Y, Hu H
Diabetes, Metabolic Syndrome and Obesity 2023 , 16:345-363
Transplantation of Human Amniotic Mesenchymal Stem Cells Up-Regulates Angiogenic Factor Expression to Attenuate Diabetic Kidney Disease in Rats
Ni Y, Chen Y, Jiang X, Pu T, Zhang L, Li S, Hu L, Bai B, Hu T, Yu L, Yang Y
Diabetes, Metabolic Syndrome and Obesity 2023 , 16:331-343
Published Date: 7 February 2023
Association Between Depression, Diabetes Self-Care Activity and Glycemic Control in an Arab Population with Type 2 Diabetes
Al-Ozairi A, Taghadom E, Irshad M, Al-Ozairi E
Diabetes, Metabolic Syndrome and Obesity 2023 , 16:321-329
Published Date: 5 February 2023
Association of HOMA-IR with Ovarian Sensitivity Index in Women Undergoing IVF/ICSI: A Retrospective Cohort Study
Li Y, Jiang Y, Zhang S, Liu H, Zhang C
Diabetes, Metabolic Syndrome and Obesity 2023 , 16:309-320
Published Date: 2 February 2023
Association Between Different Parameters of Adipose Distribution and Transient Elastography-Assessed Hepatic Steatosis in American Adults with Diabetes, Prediabetes and Normal Glucose Tolerance
Liu Y, Chai S, Zhang X
Diabetes, Metabolic Syndrome and Obesity 2023 , 16:299-308
Published Date: 1 February 2023
Identification of a tsRNA Contributor to Impaired Diabetic Wound Healing via High Glucose-Induced Endothelial Dysfunction
Zhang XT, Mao ZY, Jin XY, Wang YG, Dong YQ, Zhang C
Diabetes, Metabolic Syndrome and Obesity 2023 , 16:285-298
Published Date: 31 January 2023
A Nomogram for Predicting Vision-Threatening Diabetic Retinopathy Among Mild Diabetic Retinopathy Patients: A Case–Control and Prospective Study of Type 2 Diabetes
Ke J, Li K, Cao B
Diabetes, Metabolic Syndrome and Obesity 2023 , 16:275-283
Published Date: 27 January 2023
Managing Severe Hypoglycaemia in Patients with Diabetes: Current Challenges and Emerging Therapies
Demirbilek H, Vuralli D, Haris B, Hussain K
Diabetes, Metabolic Syndrome and Obesity 2023 , 16:259-273
Deep Sea Water Inhibited Pancreatic β-Cell Apoptosis and Regulated Glucose Homeostasis by Affecting Lipid Metabolism in Db/Db Mice
Shi M, Hu B, Hong Y, Wang M, Yao Y
Diabetes, Metabolic Syndrome and Obesity 2023 , 16:245-258
Serum Pentosidine is Associated with Cardiac Dysfunction and Atherosclerosis in T2DM
Cao Y, Ye X, Yuan X, Liu J, Zhang Q
Diabetes, Metabolic Syndrome and Obesity 2023 , 16:237-244
Published Date: 26 January 2023
A Patient-Centered Self-Management Intervention to Improve Glycemic Control, Self-Efficacy and Self-Care Behaviors in Adults with Type 2 Diabetes Mellitus: A SPIRIT Compliant Study Protocol for Randomized Controlled Trial
Asmat K, Dhamani K, Froelicher ES, Gul R
Diabetes, Metabolic Syndrome and Obesity 2023 , 16:225-236
Investigation of the Correlation Between the Polymorphism/Expression Level of RANTES and Its Receptor CCR5 Gene Promoter and Type 2 Diabetes Mellitus
Chen J, Guo W, Yin H, Ma L, Li S, Li H
Diabetes, Metabolic Syndrome and Obesity 2023 , 16:213-223
Published Date: 24 January 2023
Adiponectin Paradox More Evident in Non-Obese Than in Obese Patients with Diabetic Microvascular Complications
Satoh K, Nagasawa K, Takebe N, Kinno H, Shozushima M, Onodera K, Oda T, Hasegawa Y, Satoh J, Ishigaki Y
Diabetes, Metabolic Syndrome and Obesity 2023 , 16:201-212
Published Date: 23 January 2023
Increased Serum Adipsin Correlates with MAFLD and Metabolic Risk Abnormalities
Pan J, Li Q, Sun Y, Gu Y, Ding Y, Pang N, Zhou Y, Pei L, Gao M, Ma S, Xiao Y, Wu F, Hu D, Chen YM, Yang L
Diabetes, Metabolic Syndrome and Obesity 2023 , 16:187-200
Baseline Serum BCAAs are Related to the Improvement in Insulin Resistance in Obese People After a Weight Loss Intervention
Zhang C, Wang S, Wu Y, Guo Y, Wang X
Diabetes, Metabolic Syndrome and Obesity 2023 , 16:179-186
Published Date: 19 January 2023
Association Between Uric Acid to HDL Cholesterol Ratio and Diabetic Complications in Men and Postmenopausal Women
Xuan Y, Zhang W, Wang Y, Wang B, Xia F, Zhang K, Li Q, Wang N, Lu Y
Diabetes, Metabolic Syndrome and Obesity 2023 , 16:167-177
Perspective: Acknowledging a Hierarchy of Social Needs in Diabetes Clinical Care and Prevention
Howell CR, Harada CN, Fontaine KR, Mugavero MJ, Cherrington AL
Diabetes, Metabolic Syndrome and Obesity 2023 , 16:161-166
Association of Metabolically Healthy Obesity and Risk of Cardiovascular Disease Among Adults in China: A Retrospective Cohort Study
Ding J, Chen X, Shi Z, Bai K, Shi S
Diabetes, Metabolic Syndrome and Obesity 2023 , 16:151-159
Potential Roles of mtDNA Mutations in PCOS-IR: A Review
Dong XC, Liu C, Zhuo GC, Ding Y
Diabetes, Metabolic Syndrome and Obesity 2023 , 16:139-149
Published Date: 25 January 2023
Reduced Serum Levels of Klotho are Associated with Mild Cognitive Impairment in Patients with Type 2 Diabetes Mellitus
Zhang H, Yu L, Yun G
Diabetes, Metabolic Syndrome and Obesity 2023 , 16:129-137
Published Date: 15 January 2023
Molecular Pathways of Diabetic Kidney Disease Inferred from Proteomics
Wei L, Han Y, Tu C
Diabetes, Metabolic Syndrome and Obesity 2023 , 16:117-128
Published Date: 12 January 2023
Addressing the Continuum of Dysglycaemia and Vascular Complications in Prediabetes and Type 2 Diabetes: Need for Early and Intensive Treatment
Ghannam N, Alahmed S, Aldahash R, Aljohani N, Alshammary A, Amir A, Kamal A, Khader S, Salah M, Shalabi H, Abdallah A, Elboghdady A
Diabetes, Metabolic Syndrome and Obesity 2023 , 16:105-115
Published Date: 11 January 2023
Serum Magnesium is Inversely Associated with Body Composition and Metabolic Syndrome
Al Shammaa A, Al-Thani A, Al-Kaabi M, Al-Saeed K, Alanazi M, Shi Z
Diabetes, Metabolic Syndrome and Obesity 2023 , 16:95-104
The Interaction Between Age and Risk Factors for Diabetes and Prediabetes: A Community-Based Cross-Sectional Study
Yan Z, Cai M, Han X, Chen Q, Lu H
Diabetes, Metabolic Syndrome and Obesity 2023 , 16:85-93
The Impact of Insulin Staging in the Context of Pharmaceutical Care on Patients with Type 2 Diabetes Mellitus
Obeid KA, Hamasaeed AG
Diabetes, Metabolic Syndrome and Obesity 2023 , 16:71-83
Relation of Red Cell Distribution Width to Glucose Metabolism and Adverse Long-Term Prognosis in Patients with Acute Coronary Syndrome
Xiong K, Xu C, Shou X, Dong M
Diabetes, Metabolic Syndrome and Obesity 2023 , 16:61-70
An Association Between FNDC5, PGC-1α Genetic Variants and Obesity in Chinese Children: A Case-Control Study
Wang Y, Zhang L, Wu L, Cao R, Peng X, Fu L
Diabetes, Metabolic Syndrome and Obesity 2023 , 16:47-59
Clinical Characteristics and Surgical Outcomes of Complications of Proliferative Diabetic Retinopathy in Young versus Older Patients with Type 2 Diabetes
Zhang M, Xu G, Ruan L, Huang X, Zhang T
Diabetes, Metabolic Syndrome and Obesity 2023 , 16:37-45
The Effects of Switching from Dipeptidyl Peptidase-4 Inhibitors to Glucagon-Like Peptide-1 Receptor Agonists on Bone Mineral Density in Diabetic Patients
Huang CF, Mao TY, Hwang SJ
Diabetes, Metabolic Syndrome and Obesity 2023 , 16:31-36
Metabolic Characteristics of Gestational Diabetes Mellitus and the Effects on Pregnancy Outcomes
Luo P, Fan Y, Xiong Y, Zhang C, Yang Z, Sun F, Mei B
Diabetes, Metabolic Syndrome and Obesity 2023 , 16:15-29
Impact of Visceral Obesity on Structural and Functional Alterations of Gut Microbiota in Polycystic Ovary Syndrome (PCOS): A Pilot Study Using Metagenomic Analysis
Bai X, Ma J, Wu X, Qiu L, Huang R, Zhang H, Huang H, Chen X
Diabetes, Metabolic Syndrome and Obesity 2023 , 16:1-14
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- Impact Factor : COSMOS: 3.165, ISI: 1.732 (2023-24)
- ISSN : 2578-0263
- DOI : https://doi.org/10.31031/iod
- Language of Publication : English
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An Intervention in Obesity & Diabetes is an online, open access journal, committed to publishing research from all disciplines and therapeutic areas. IOD delivers unvarnished, expert assessments of developments from the previous years. IOD aspires to provide a platform for the dissemination of information and discussion of topics related to diabetes and obesity clinical research and the patient care. Obesity is most usually caused by a combination of lack of physical activity,excessive food intake, and genetic susceptibility. IOD is a platform for bariatric/metabolic surgeries, which provides an interdisciplinary forum to communicate the latest research, laparoscopic techniques, case studies and surgical procedure for the treatment of massive obesity and metabolic diseases. Diabetes is caused due to the lack of production of Insulin by the pancreas or due to the cells of the body not responding properly towards the insulin produced. IOD journal publishes a variety of contributions, including original articles, focused reviews and rapid communications that include brief articles of particular interest and apparent novelty.
Latest Articles
Research Article
Reducing Body Weight and Improving Lipid Profile with SGLT-2 Inhibitors in Type 2 Diabetes: Current Evidence and Clinical Implications
Published : July, 2024
Review Article
Chronic Diseases: A Comprehensive Review of Applications for Prediction of Obesity
Published : May, 2024
Case Report
Atypical Endometrial Cancer, Obesity, Diabetes and Hyperandrogenism
Evaluation of state’s school policy on childhood obesity: quasi-experimental study.
Published : March, 2024
Mini Review
The Incretin Response to Exercise and Dietary Interventions in Individuals with Obesity
Published : February, 2024
Insulin Autoimmune Syndrome in Graves Disease
Published : January, 2024
Confronting Inequality in Health: Tackling Diabetes and Hypertension in Mexico’s Landscape of Socioeconomic Disparities
Published : December, 2023
Potential Antihyperglycemic, Antihypolipidemic and Antioxidant Effects of Aqueous Extract of Boscia Senegalensis (Capparidaceae) on Diet- Induced Hyperlipidemia in Rat
Bariatric surgery remodels gut microbiota.
Published : November, 2023
Obesity and the Relationship between Personal Social Networks
Implantable cgm use improves glycemic control in cgm naive patients.
Published : October, 2023
Mini Review on Energy Transformation in Cells
Published : September, 2023
Pivotal Roles of Diacylglycerol O-Acyltransferase 1 (DGAT1) and Carbonic Anhydrase Enzymes in Obesity and Diabetes
Published : May, 2023
Mini Review on How to Approach the Eradication of Childhood Obesity from a Long-Term Socio-Ecological Perspective
11-keto-β-boswellic acid and 5-chloro-8-hydroxyquinoline attenuate renal damage in streptozotocin-induced diabetic mice.
Published : February, 2023
PubMed Indexed Articles
- Dendrimer-Based Nanomedicine (Paramagnetic Nanoparticle, Nanocombretastatin, Nanocurcumin) for Glioblastoma Multiforme Imaging and Therapy PMID: 35237758
- Glioblastoma: Targeting Angiogenesis and Tyrosine Kinase Pathways PMID: 32924014
- The Conflict in East Ukraine: A Growing Need for Addiction Research and Substance Use Intervention for Vulnerable Populations PMID: 32363331
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Approaches in Poultry, Dairy & Veterinary Sciences
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Advances in Complementary & Alternative Medicine
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Jiexiong Feng
Professor, Chief Doctor, Director of Department of Pediatric Surgery, Associate Director of Department of Surgery, Doctoral Supervisor Tongji hospital, Tongji medical college, Huazhong University of Science and Technology
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National Defence University of Malaysia, Malaysia
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Dogus University, Turkey
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Hope College, USA
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Russian Academy of Sciences, Russia
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Umm Al-Qura University, Saudi Arabia
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Khalifa University of Science & Technology, United Arab Emirates
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Alexandria University, Egypt
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Indian Institute of Technology Kharagpur, India
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Council for Agriculture Research and Analysis of Agri Economy (CREA), Italy
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King Abdulaziz University, Saudi Arabia
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University of Oregon, USA
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Tourin University, Italy
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Paeditric Hospital "Giovanni XXIII", Italy
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University of Nicosia, Cyprus
Jose Luis Clua Espuny
Universidad Miguel Hernández de Elche, Spain
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Oral Roberts University, USA
Jinliang Zhang
Beijing Normal University, China
Irina Koretsky
Howard University, USA
Edith Cowan University, Australia
Hamid Yahiya Hussain
Dubai Health Authority, UAE
Gundu HR Rao
University of Minnesota, USA
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Ghassan George Haddad
Serhal Hospital, Lebanon
George Gregory Buttigieg
University of Malta, Malta
Fumihiko Hinoshita
National Center for Global Health and Medicine, Japan
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Molloy College, USA
Francisco Welington de Sousa Lima
Federal University of Piauí, Brazil
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Krankenhaus Nordwest Hospital, Germany
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Laval University, Canada
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UPMC Hamot Neuroscience Institute, USA
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White Bear Associates, LLC, USA
Arkady S Voloshin
Lehigh University, USA
Alireza Heidari
California Southern University, USA
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Alex Guskov
Institute of Solid State Physics of RAS, Russia
Alan Diego Briem Stamm
University of Buenos Aires, Argentina
Ahmed Nasr Ghanem
Mansoura University, Egypt
Afaf K El Ansary
King Saud University, Saudi Arabia
A Bernardes
University of Coimbra, Portugal
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- Introduction
- Conclusions
- Article Information
T2D indicates type 2 diabetes.
a The combined total of patients is not necessarily a sum of the individuals from each of the groups because individuals could be prescribed both glucagon-like peptide 1 receptor agonists (GLP-1RAs) and insulins or metformin during the study period.
b The combined total of patients is not necessarily a sum of the individuals from each of the groups because individuals could be prescribed both insulins and metformin during the study period.
Patients were followed up for as along as 15 years after the index event for both groups. Hazard ratios (HRs) rates were calculated using a Cox proportional hazards model with censoring applied. Overall risk equals the number of patients with outcomes during the follow-up time window divided by number of patients in the group at the beginning of the time window. For each outcome, the groups were separately propensity-score matched for covariates related to the outcome, and the outcome was compared between the matched groups. Each eligible individual was followed up from the index event until the occurrence of the outcomes, death, loss to follow-up, or 15 years after the index event, whichever occurred first.
Kaplan-Meier survival analysis was used. Each eligible individual was followed up from the index event until the occurrence of the outcomes, death, loss to follow-up, or 15 years after the index event, whichever occurred first.
Patients were followed up for as long as 15 years after the index event for both groups. Hazard ratios (HRs) were calculated using a Cox proportional hazards model with censoring applied. Overall risk equals the number of patients with outcomes during the follow-up time window divided by the number of patients in the group at the beginning of the time window. For each outcome, the groups were separately propensity-score matched for covariates related to the outcome, and the outcome was compared between the matched groups. Each eligible individual was followed up from the index event until the occurrence of the outcomes, death, loss to follow-up, or 15 years after the index event, whichever occurred first.
eAppendix. Database
eTable 1. Clinical diagnosis, and other codes used in the platform that are used to determine the status of variables for study population definitions, exposures, outcomes, and those for propensity-score matching for groups
eTable 2. Characteristics of the GLP-1RA/no insulin group and insulin/no GLP-1RA group before and after matched for baseline covariates related to esophageal cancer for the study populations of patients with T2D and no history of any OAC
eTable 3. Characteristics of the GLP-1RA/no insulin group and insulin/no GLP-1RA group before and after matched for covariates related to breast cancer for the study populations of women (age 55 and older) with T2D and no history of any OAC
eTable 4. Characteristics of the GLP-1RA/no insulin group and insulin/no GLP-1RA group before and after matched for covariates related to endometrial cancer for the study populations of women with T2D and no history of any OAC
eTable 5. Characteristics of the GLP-1RA/no insulin group and insulin/no GLP-1RA group before and after matched for covariates related to gallbladder cancer for the study populations of patients with T2D and no history of any OAC
eTable 6. Characteristics of the GLP-1RA/no insulin group and insulin/no GLP-1RA group before and after matched for covariates related to stomach cancer for the study populations of patients with T2D and no history of any OAC
eTable 7. Characteristics of the GLP-1RA/no insulin group and insulin/no GLP-1RA group before and after matched for covariates related to kidney cancer for the study populations of patients with T2D and no history of any OAC
eTable 8. Characteristics of the GLP-1RA/no insulin group and insulin/no GLP-1RA group before and after matched for covariates related to liver cancer for the study populations of patients with T2D and no history of any OAC
eTable 9. Characteristics of the GLP-1RA/no insulin group and insulin/no GLP-1RA group before and after matched for covariates related to ovarian cancer for the study populations of women with T2D and no history of any OAC
eTable 10. Characteristics of the GLP-1RA/no insulin group and insulin/no GLP-1RA group before and after matched for covariates related to pancreatic cancer for the study populations of patients with T2D and no history of any OAC
eTable 11. Characteristics of the GLP-1RA/no insulin group and insulin/no GLP-1RA group before and after matched for covariates related to thyroid cancer for the study populations of patients with T2D and no history of any OAC
eTable 12. Characteristics of the GLP-1RA/no insulin group and insulin/no GLP-1RA group before and after matched for covariates related to meningioma for the study populations of patients with T2D and no history of any OAC
eTable 13. Characteristics of the GLP-1RA/no insulin group and insulin/no GLP-1RA group before and after matched for covariates related to multiple myeloma for the study populations of patients with T2D and no history of any OAC
eTable 14. Characteristics of the GLP-1RA/no metformin group and metformin/no GLP-1RA group before and after matched for covariates related to esophageal cancer for the study populations of patients with T2D and no history of any OAC
eTable 15. Characteristics of the GLP-1RA/no metformin group and metformin/no GLP-1RA group before and after matched for covariates related to breast cancer for the study populations of women (age 55 and older) with T2D and no history of any OAC
eTable 16. Characteristics of the GLP-1RA/no metformin group and metformin/no GLP-1RA group before and after matched for covariates related to colorectal cancer for the study populations of patients with T2D and no history of any OAC
eTable 17. Characteristics of the GLP-1RA/no metformin group and metformin/no GLP-1RA group before and after matched for covariates related to endometrial cancer for the study populations of women with T2D and no history of any OAC
eTable 18. Characteristics of the GLP-1RA/no metformin group and metformin/no GLP-1RA group before and after matched for covariates related to gallbladder cancer for the study populations of patients with T2D and no history of any OAC
eTable 19. Characteristics of the GLP-1RA/no metformin group and metformin/no GLP-1RA group before and after matched for covariates related to stomach cancer for the study populations of patients with T2D and no history of any OAC
eTable 20. Characteristics of the GLP-1RA/no metformin group and metformin/no GLP-1RA group before and after matched for covariates related to kidney cancer for the study populations of patients with T2D and no history of any OAC
eTable 21. Characteristics of the GLP-1RA/no metformin group and metformin/no GLP-1RA group before and after matched for covariates related to liver cancer for the study populations of patients with T2D and no history of any OAC
eTable 22. Characteristics of the GLP-1RA/no metformin group and metformin/no GLP-1RA group before and after matched for covariates related to ovarian cancer for the study populations of women with T2D and no history of any OAC
eTable 23. Characteristics of the GLP-1RA/no metformin group and metformin/no GLP-1RA group before and after matched for covariates related to pancreatic cancer for the study populations of patients with T2D and no history of any OAC
eTable 24. Characteristics of the GLP-1RA/no metformin group and metformin/no GLP-1RA group before and after matched for covariates related to thyroid cancer for the study populations of patients with T2D and no history of any OAC
eTable 25. Characteristics of the GLP-1RA/no metformin group and metformin/no GLP-1RA group before and after matched for covariates related to meningioma for the study populations of patients with T2D and no history of any OAC
eTable 26. Characteristics of the GLP-1RA/no metformin group and metformin/no GLP-1RA group before and after matched for covariates related to multiple myeloma for the study populations of patients with T2D and no history of any OAC
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Wang L , Xu R , Kaelber DC , Berger NA. Glucagon-Like Peptide 1 Receptor Agonists and 13 Obesity-Associated Cancers in Patients With Type 2 Diabetes. JAMA Netw Open. 2024;7(7):e2421305. doi:10.1001/jamanetworkopen.2024.21305
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Glucagon-Like Peptide 1 Receptor Agonists and 13 Obesity-Associated Cancers in Patients With Type 2 Diabetes
- 1 Center for Science, Health, and Society, Case Western Reserve University School of Medicine, Cleveland, Ohio
- 2 Center for Artificial Intelligence in Drug Discovery, Case Western Reserve University School of Medicine, Cleveland, Ohio
- 3 Case Comprehensive Cancer Center, Case Western Reserve University School of Medicine, Cleveland, Ohio
- 4 Departments of Internal Medicine, Pediatrics, and Population and Quantitative Health Sciences and the Center for Clinical Informatics Research and Education, The MetroHealth System, Cleveland, Ohio
Question Is there clinical evidence supporting the potential benefits of glucagon-like peptide receptor agonists (GLP-1RAs) for the prevention of 13 obesity-associated cancers (OACs)?
Findings This cohort study of more than 1.6 million patients with type 2 diabetes (T2D) who had no prior diagnosis of 13 OACs found that patients with T2D treated with GLP-1RAs vs insulin had a significant risk reduction in 10 of 13 OACs, including esophageal, colorectal, endometrial, gallbladder, kidney, liver, ovarian, and pancreatic cancer as well as meningioma and multiple myeloma. No decrease in cancer risk was associated with GLP-1RAs compared with metformin.
Meaning This study provides clinical data suggesting that GLP-1RAs may reduce the risk of specific OACs compared with insulins.
Importance Thirteen human malignant neoplasms have been identified as obesity-associated cancers (OACs), ie, the presence of excess body fat is associated with increased risk of developing cancer and worse prognosis in patients with these specific tumors. The glucagon-like peptide receptor agonist (GLP-1RA) class of pharmaceuticals are effective agents for the treatment of type 2 diabetes (T2D) and for achieving weight loss, but the association of GLP-1RAs with the incident risk of 13 OACs is unclear.
Objective To compare the incident risk of each of the 13 OACs in patients with T2D who were prescribed GLP-1RAs vs insulins or metformin.
Design, Setting, and Participants This retrospective cohort study was based on a nationwide multicenter database of electronic health records (EHRs) of 113 million US patients. The study population included 1 651 452 patients with T2D who had no prior diagnosis of OACs and were prescribed GLP-1RAs, insulins, or metformin during March 2005 to November 2018. Data analysis was conducted on April 26, 2024.
Exposures Prescription of GLP-1RAs, insulins, or metformin.
Main Outcomes and Measures Incident (first-time) diagnosis of each of the 13 OACs occurring during a 15-year follow-up after the exposure was examined using Cox proportional hazard and Kaplan-Meier survival analyses with censoring applied. Hazard ratios (HRs), cumulative incidences, and 95% CIs were calculated. All models were adjusted for confounders at baseline by propensity-score matching baseline covariates.
Results In the study population of 1 651 452 patients with T2D (mean [SD] age, 59.8 [15.1] years; 827 873 [50.1%] male and 775 687 [47.0%] female participants; 5780 [0.4%] American Indian or Alaska Native, 65 893 [4.0%] Asian, 281 242 [17.0%] Black, 13 707 [0.8%] Native Hawaiian or Other Pacific Islander, and 1 000 780 [60.6%] White participants), GLP-1RAs compared with insulin were associated with a significant risk reduction in 10 of 13 OACs, including in gallbladder cancer (HR, 0.35; 95% CI, 0.15-0.83), meningioma (HR, 0.37; 95% CI, 0.18-0.74), pancreatic cancer (HR, 0.41; 95% CI, 0.33-0.50), hepatocellular carcinoma (HR, 0.47; 95% CI, 0.36-0.61), ovarian cancer (HR, 0.52; 95% CI, 0.03-0.74), colorectal cancer (HR, 0.54; 95% CI, 0.46-0.64), multiple myeloma (HR, 0.59; 95% CI, 0.44-0.77), esophageal cancer (HR, 0.60; 95% CI, 0.42-0.86), endometrial cancer (HR, 0.74; 95% CI, 0.60-0.91), and kidney cancer (HR, 0.76; 95% CI, 0.64-0.91). Although not statistically significant, the HR for stomach cancer was less than 1 among patients who took GLP-1RAs compared with those who took insulin (HR, 0.73; 95% CI, 0.51-1.03). GLP-1RAs were not associated with a reduced risk of postmenopausal breast cancer or thyroid cancer. Of those cancers that showed a decreased risk among patients taking GLP-1RAs compared with those taking insulin, HRs for patients taking GLP-1RAs vs those taking metformin for colorectal and gallbladder cancer were less than 1, but the risk reduction was not statistically significant. Compared with metformin, GLP-1RAs were not associated with a decreased risk of any cancers, but were associated with an increased risk of kidney cancer (HR, 1.54; 95% CI, 1.27-1.87).
Conclusions and Relevance In this study, GLP-1RAs were associated with lower risks of specific types of OACs compared with insulins or metformin in patients with T2D. These findings provide preliminary evidence of the potential benefit of GLP-1RAs for cancer prevention in high-risk populations and support further preclinical and clinical studies for the prevention of certain OACs.
Thirteen human malignant neoplasms have been identified as obesity-associated cancers (OAC), ie, the presence of excess body fat is associated with increased risk of developing cancer and worse prognosis in patients with these specific tumors. 1 Obesity also contributes to insulin resistance and type 2 diabetes (T2D), which may further increase the risk and worsen the prognosis of the OACs. 2 , 3
The glucagon-like peptide 1 receptor agonist (GLP-1RA) class of pharmaceuticals are highly effective agents for the treatment of T2D and for achieving weight loss. 4 - 9 GLP-1RAs have further been shown to reduce the risk of adverse cardiovascular outcomes in patients with obesity 10 and to contribute to the resolution of nonalcoholic steatohepatitis. 11 Because of their efficacy in controlling T2D, obesity, and related comorbidities, we hypothesized that these agents might reduce the risk of the OACs. We recently reported that GLP-1RAs were associated with lower risks for colorectal cancer, 12 an OAC. Otherwise, clinical evidence of the potential clinical benefits of GLP-1RA in preventing OAC has not been systematically assessed. Here we conducted a nationwide multicenter retrospective cohort study in patients with T2D who were prescribed GLP-1RAs vs insulins or metformin to determine whether GLP-1RAs were associated with changes in the risk of each of 13 OACs, including esophageal, breast, colorectal, endometrial, gallbladder, stomach, kidney, ovarian, pancreatic, and thyroid cancer as well as hepatocellular carcinoma, meningioma, and multiple myeloma. 1
We used the TriNetX platform to access deidentified electronic health records (EHRs) of 113 million patients from 64 health care organizations across 50 states, covering diverse age, racial and ethnic, income, and insurance groups and clinical settings. 13 , 14 The platform’s built-in analytic functions allow patient-level analyses, while only reporting population-level data. The platform has been used for retrospective cohort studies. 15 - 26 Similar to this study, we have examined the association of GLP-1RAs with colorectal cancer incidence in patients with T2D 12 and the associations of GLP-1RA (semaglutide) with suicidal ideations 27 and with cannabis use in patients with obesity and those with T2D. 28 The MetroHealth System institutional review board determined that the research as described in this study was not human participant research and institutional review board approval and informed consent were not required. This cohort study followed the Strengthening the Reporting of Observational Studies in Epidemiology ( STROBE ) reporting guideline.
Available data elements of EHRs include extensive information on demographics, diagnoses ( International Statistical Classification of Diseases and Related Health Problems, Tenth Revision ), medications (Anatomical Therapeutic Chemical and medical prescription normalized medical prescription or RxNorm), procedures ( Current Procedural Terminology ), laboratory tests (Logical Observation Identifiers Names and Codes), genomics, visits, and socioeconomic and lifestyle information. The data on the analytic platform have been expanded to include oncology-specific data from cancer registry data from North American Association of Central Cancer Registries (NAACCR) records and other data resources. 14
Self-reported sex, race, and ethnicity data from contributing health care systems are mapped by according to Office of Management and Budget standards into (1) race, American Indian or Alaska Native, Asian, Black or African American, Native Hawaiian or Other Pacific Islander, White, and unknown race; and (2) ethnicity, Hispanic or Latinx, not Hispanic or Latinx, or unknown ethnicity. All covariates are either binary, categorical, or continuous but essentially guaranteed to exist. Age is guaranteed to exist. Missing sex values are represented using “unknown sex.” The missing data for race and ethnicity are presented as “unknown race” or “unknown ethnicity.” For other variables, including medical conditions, procedures, laboratory tests, and socioeconomic determinants of health, the value is either present or absent so missing is not pertinent.
The study population comprised 1 651 452 patients with a diagnosis of T2D who had medical encounters with health care organizations and were prescribed GLP-1RAs vs insulin or metformin between March 2005 and November 2018 and had no history of any of the 13 OACs. The study population was divided into exposure and comparison groups. For comparing GLP-1RAs with insulins, the study population was divided into a GLP-1RA/no insulin group (48 983 patients prescribed a GLP-1RA but not insulins) and a insulin/no GLP-1RA group (1 044 745 patients prescribed insulins but not GLP-1RAs). For comparing GLP-1RAs with metformin, the study population was divided into a GLP-1RA/no metformin group (32 365 patients prescribed a GLP-1RA but not metformin) and a metformin/no GLP-1RA group (856 160 patients prescribed metformin but not GLP-1RAs) ( Figure 1 ).
The 13 OACs are esophageal, breast, colorectal, endometrial, gallbladder, stomach, kidney, ovarian, pancreatic, and thyroid cancer as well as hepatocellular carcinoma, meningioma, and multiple myeloma. 1 Each of the 13 OACs was examined as a separate outcome in groups that were propensity-score matched for covariates related to the specific OAC. For each OAC outcome, the exposure and comparison groups (ie, GLP-1RA/no insulin vs insulin/no GLP-1RA groups and GLP-1RA/no metformin vs metformin/no GLP-1RA groups) were propensity-score matched (1:1 using nearest neighbor greedy matching) for baseline covariates related to the specific OAC, including demographic characteristics (age, sex, race, and ethnicity); adverse socioeconomic determinants of health; family and personal history of cancer; genetic susceptibility to cancer; preexisting medical conditions, including obesity and overweight; and medical procedures, including cancer screening, bariatric surgery, and prior prescription of antidiabetes medications. Each eligible individual was followed up from the index event (the first prescription of GLP-1RAs, insulins, or metformin during March 2005 to November 2018) until the occurrence of the outcomes, death, loss to follow-up, or 15 years after the index event, whichever occurred first. Cox proportional hazard analyses were used to compare rates of time to events on a daily basis during the follow-up time after the index event. Hazard ratios (HRs) and 95% CIs were calculated. Cumulative incidences were estimated using the Kaplan-Meier survival analysis. All models are adjusted for confounders at baseline by propensity-score matching baseline covariates.
The data were collected and analyzed on April 26, 2024, within the analytics platform. All statistical analyses in this study, including propensity-score matching, Kaplan-Meier survival analysis, and Cox proportional hazard analysis were done using built-in functions within the platform that are implemented using Survival version 3.2-3 in R version 4.0.2 (R Project for Statistical Computing) and libraries and utilities for data science and statistics in Python version 3.7 (Python Software Foundation) and Java version 11.0.16 (Oracle). Details of clinical codes for eligibility criteria, treatment strategies, outcomes, and baseline covariates are in eTable 1 in Supplement 1 .
The study population included 1 651 452 patients with T2D (mean [SD] age, 59.8 [15.1] years; 827 873 [50.1%] male and 775 687 [47.0%] female participants; 5780 [0.4%] American Indian or Alaska Native, 65 893 [4.0%] Asian, 281 242 [17.0%] Black, 13 707 [0.8%] Native Hawaiian or Other Pacific Islander, and 1 000 780 [60.6%] White participants). For comparing GLP-1RAs with insulins in patients with T2D, the study population included 1 093 728 patients with T2D who had no prior diagnosis of any OAC and were prescribed GLP-1RAs or insulins but not both between March 2005 and November 2018. The GLP-1RA/no insulin group (n = 48 983) compared with the insulin/no GLP-1RA group (n = 1 044 475) was younger; included more women and White participants; had a higher prevalence of family history of cancer, obesity or overweight, medical encounters for cancer screening, and prior prescriptions of other antidiabetic agents, including insulins, metformin, dipeptidyl peptidase 4 (DPP-4) inhibitors, sodium-glucose cotransporter 2 (SGLT2) inhibitors, sulfonylureas, thiazolidinediones, and α-glucosidase inhibitors. For each OAC outcome, the GLP-1RA/no insulin and the insulin/no GLP-1RA groups were separately matched for covariates associated with the OAC. The Table shows the characteristics of the GLP-1RA/no insulin and insulin/no GLP-1RA groups before and after propensity-score matching for covariates related to colorectal cancer. The characteristics of the exposure and comparison groups before and after matching for each of the other 12 OACs are in eTables 2 to 13 in Supplement 1 .
Compared with insulins, GLP-1RAs were associated with a significantly lower risk of 10 of the 13 OACs, including gallbladder cancer (HR, 0.35; 95% CI, 0.15-0.83), meningioma (HR, 0.37; 95% CI, 0.18-0.74), pancreatic cancer (HR, 0.41; 95% CI, 0.33-0.50), hepatocellular carcinoma (HR, 0.47; 95% CI, 0.36-0.61), ovarian cancer (HR, 0.52; 95% CI, 0.03-0.74), colorectal cancer (HR, 0.54; 95% CI, 0.46-0.64), multiple myeloma (HR, 0.59; 95% CI, 0.44-0.77), esophageal cancer (HR, 0.60; 95% CI, 0.42-0.86), endometrial cancer (HR, 0.74; 95% CI, 0.60-0.91), and kidney cancer (HR, 0.76; 95% CI, 0.64-0.91). The HR for stomach cancer among patients taking GLP-1RAs vs those taking insulin was less than 1, but it was not statistically significant (HR, 0.73; 95% CI, 0.51-1.03). GLP-1RAs were not associated with risk of postmenopausal breast cancer or thyroid cancer ( Figure 2 ). Figure 3 shows the cumulative incidences of colorectal cancer and liver cancer comparing GLP-1RAs with insulins. The mean (SD) follow-up time for the outcome of colorectal cancer was 2074.7 (435.3) days for the GLP-1RA/no insulin group and 1981.8 (471.1) days for the insulin/no GLP-1RA group. The mean (SD) follow-up time for the outcome of liver cancer was 2023.1 (1112.6) days for the GLP-1RA/no insulin group and 2037.9 (766.4) days for the insulin/no GLP-1RA group.
For comparing GLP-1RAs with metformin in patients with T2D, the study population included 888 525 patients with T2D who had no prior diagnosis of any OAC and were prescribed GLP-1RAs or metformin but not both between March 2005 and November 2018. For each OAC outcome, the GLP-1RA/no metformin group (n = 32 365) and the metformin/no GLP-1RA group (n = 856 160) were separately matched for covariates related to the OAC (eTables 14-26 in Supplement 1 ). Compared with metformin, GLP-1RAs were not associated with a lower risk of colorectal cancer, gallbladder cancer, and meningioma but were associated with an increased risk of kidney cancer ( Figure 4 ). Figure 3 shows the cumulative incidences of colorectal cancer and liver cancer by comparing GLP-1RAs with metformin. The mean (SD) follow-up time for the outcome of colorectal cancer was 1967.2 (592.2) days for the GLP-1RA/no metformin group and 2101.6 (576.0) days for metformin/no GLP-1RA group. The mean (SD) follow-up time for the outcome of liver cancer was 1970.9 (426.0) days for the GLP-1RA/no metformin group and 2129.8 (514.7) days for metformin/no GLP-1RA group.
Using a data platform 29 to analyze more than 15 years of longitudinal EHRs of a US population-based cohort of more than 100 million individuals, we found that in patients with T2D who had no history of any OAC, GLP-1RAs compared with insulins were associated with a significant risk reduction in 10 of 13 OACs, including esophageal, colorectal, kidney, pancreatic, gallbladder, ovarian, endometrial, and liver cancers as well as meningioma and multiple myeloma. Decreased risk reduction that did not reach statistical significance was also noted for stomach cancer. Of those cancers that showed decreased risk of GLP-1RAs compared with insulin, risk reduction was also noted for GLP-1RAs relative to metformin for colorectal cancer, gallbladder, and meningiomas, although these findings were not statistically significant.
Our observations on the reduction in the incidence of OACs in patients with T2D treated with GLP-1RAs compare favorably with the OAC-reducing effects of intensive lifestyle intervention (ILI) observed in the Look AHEAD trial (Action for Health in Diabetes) 30 and with the results of metabolic-bariatric surgery as recently reported in the SPLENDID (Surgical Procedure and Long-term Effectiveness In Neoplastic Disease Incidence and Death) trial. 31 The Look AHEAD study, a randomized clinical trial in which 4859 patients with T2D and overweight or obesity (age, 45-76 years; median follow-up, 11 years) were randomized to an ILI or diabetes support and education group, found a 16% reduction in risk for OAC (HR, 0.84; 95% CI, 0.68-1.04). 30 The SPLENDID trial, a matched cohort study, compared 5053 patients with obesity with 25 265 nonsurgical matched controls, with a median age of 46 years and median follow-up of 6.1 years, showed an OAC risk reduction of 32%, (HR, 0.68; 95% CI, 0.53-0.87). 31
A recent 9-year follow-up population-based historical cohort study 32 conducted in Israel reported a decrease (although not statistically significant) in incidence of pancreatic cancer (HR, 0.50; 95% CI, 0.15-1.71) in patients with T2D treated with GLP-1RAs compared with insulin. 32 Our US population-based study, with 15 years of follow-up and a larger sample size, now extends these observations, suggesting that treatment of patients with T2D with GLP-1RAs vs insulin is associated with a significantly decreased incidence of pancreatic cancer (HR, 0.41; 95% CI, 0.33-0.50).
In contrast to the risk reduction shown for most of the OACs, thyroid cancer showed no statistically different risk in patients treated with GLP-1RAs compared with insulins. Studies in rodents indicate that GLP-1RAs promote thyroid C-cell hyperplasia and medullary thyroid carcinoma (MTC) by a GLP-1R mediated increase in calcitonin synthesis. 33 High levels of fasting serum insulin and insulin resistance are associated with an increased risk of thyroid cancer. 34 Although clinical evidence for an association of thyroid cancer with the use of GLP-1RAs has been reported as inconclusive, 35 the findings from our study together with previous reports of insulins promoting cancer growth suggest that GLP-1RAs might be associated with increased risk of thyroid cancer. Our results are further supported by a recent report 36 by the French National Health Cancer Data System showing that the use of GLP-1RAs for 1 to 3 years was associated with increased risk of all thyroid cancers (adjusted HR, 1.78; 95% CI 1.04-3.05). 36 These studies support the package warnings included with GLP-1RAs that these agents are contraindicated in patients with multiple endocrine neoplasia syndrome type 2 and that patients should be counseled regarding the potential risk of MTC and symptoms of thyroid tumors.
Kidney cancers showed an increased risk with GLP-1RA treatment relative to that with metformin (HR, 1.54; 95% CI 1.27-1.87) but a decrease relative to insulin (HR, 0.76; 95% CI 0.64-0.91). GLP-1RAs have direct effects on kidney function mediated by GLP-1Rs in renal vasculature; however, these are not associated with increased mitogenesis, 37 and to our knowledge, there have been no previous reports of kidney cancers with the use of GLP1-RAs. These divergent risks require further clinical and mechanistic studies for full evaluation. Nonetheless, they suggest the need for continued monitoring in patients being treated with GLP-1RAs.
Our study, with follow-up over 15 years, found no signs of increase or decrease in risk for breast cancer in postmenopausal women with T2D being treated with GLP-1RAs compared with those being treated with insulin or metformin. GLP-1RAs have been shown to reduce the growth of murine and human breast cancer cell lines in vitro and in vivo murine models. 38 However, a meta-analysis of more than 50 randomized clinical trials, evaluating GLP-1RAs in women aged between 45 to 70 years and followed up from 24 weeks to 7.5 years, showed no differences in benign, premalignant, or malignant breast neoplasms in patients treated with GLP-1RAs compared with other antidiabetic agents or placebos. 39 A more recent population-based cohort study of 44 984 women 40 years and older treated with GLP-1RAs or other antidiabetic agents for a mean of 3.5 years showed no overall significant difference in the risk for breast cancer occurrence. However, an increased risk (HR, 2.66; 95% CI, 1.32-5.38) was noted for those treated between 2 to 3 years with a return to null after more than 3 years’ treatment. 40 Interestingly, the SPLENDID trial of bariatric surgery for weight reduction, which found an overall 32% risk reduction for OACs, showed no significant difference among women for incidence of overall or postmenopausal breast cancer. 31 This lack of effect on breast cancer risk needs to be further investigated to determine the impact of longer duration of therapy as well as to more fully understand the relation between GLP-1RAs and estrogen metabolism. The lack of breast cancer risk reduction by GLP-1RAs and the similar lack of protection by bariatric surgery may also suggest the possibility that factors determining the incidence of breast cancer in patients with overweight or obesity may have been initiated long before intervention with GLP-1RAs and/or bariatric surgery and therefore require earlier intervention to affect risk reduction. The concept that early intervention might reduce breast cancer incidence is supported by the observation that both pregnancy and breastfeeding reduce the incidence of breast cancer. 41 , 42
Our study has several limitations. First, this is a retrospective observational study of patient EHRs, which has inherent limitations including overdiagnosis, underdiagnosis, and misdiagnosis; unmeasured or uncontrolled confounders; and biases. Although we controlled for an extensive list of variables, these limitations and biases could not be fully eliminated; therefore, no causal inferences can be drawn. Second, patients in our study represented those who had medical encounters with health care systems contributing to the data platform. Although both the exposure and comparison groups were drawn from the same EHR database and from the same time period, which should not significantly affect the HR calculations, results from the platform need to be validated in other EHR databases and analytics platforms. Third, the status of incident cancer was based on the presence of first-ever diagnosis codes of OACs documented in patient EHRs, which also included oncology-specific data from cancer registry data, such as NAACCR records. However, it is unknown how well cancer diagnoses are captured in patient EHRs. For this study, the main interest was the relative risk (or HR) of cancer diagnosis. Since all patients in the study population were drawn from the same health care organizations in the data platform, cancer underdiagnosis, misdiagnosis, or overdiagnosis should not have a substantial impact on the relative risk analysis. Fourth, the built-in functions did not allow us to control for variables (eg, weight loss) that occurred after the index event and to identify individual patient data, which precludes our ability to correlate risk reduction with a degree of weight loss, which was demonstrated to be particularly important in the SPLENDID bariatric study. 25 In addition, we could not explicitly control for health care utilization and insurance type although the study population included patients who had medical encounters with health care organizations and were withdrawn from the same 64 health care organizations in the network. Finally, due to the lack of patients’ medication adherence information in EHRs, we used intention-to-treat (medication prescriptions) as a causal contrast of interest regardless of whether the individuals adhered to their medications and the duration of the medication use.
In this study of patients with T2D who were cancer free at baseline, taking GLP-1RAs compared with insulin was associated with a lower risk of 10 of 13 OACs. The potential cancer-preventative effects of OACs by GLP-1RAs warrant further long-term studies as well as studies of individual newer and possibly more effective antidiabetic and weight loss agents as well as those with multihormone agonist activities. Studies are also warranted to evaluate the preventive effects of these agents on non-OACs. In addition, the associations of the GLP-1RA targeted pharmacologic agents with cancer risk should be compared with the use of ILI and metabolic-bariatric surgery for the control of obesity and diabetes. As noted previously, it will be important to correlate these associations with the control of T2D and obesity. Moreover, given that T2D and overweight or obesity have negative impacts on patients during cancer therapy, GLP-1RAs should be evaluated for control of these comorbid conditions during cancer therapy as well as for secondary prevention to delay cancer recurrence.
Accepted for Publication: May 9, 2024.
Published: July 5, 2024. doi:10.1001/jamanetworkopen.2024.21305
Open Access: This is an open access article distributed under the terms of the CC-BY License . © 2024 Wang L et al. JAMA Network Open .
Corresponding Authors: Nathan A. Berger, MD, Center for Science, Health, and Society, Case Comprehensive Cancer Center ( [email protected] ), and Rong Xu, PhD, Center for Artificial Intelligence in Drug Discovery, Case Western Reserve University School of Medicine, 10900 Euclid Ave, Cleveland, OH 44106 ( [email protected] ).
Author Contributions: Dr Xu and Ms Wang had full access to all of the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis.
Concept and design: Xu, Berger.
Acquisition, analysis, or interpretation of data: All authors.
Drafting of the manuscript: Xu, Berger.
Critical review of the manuscript for important intellectual content: All authors.
Statistical analysis: Wang, Xu.
Obtained funding: Xu, Berger.
Administrative, technical, or material support: Xu, Kaelber, Berger.
Supervision: Xu, Kaelber, Berger.
Conflict of Interest Disclosures: Drs Kaelber and Berger reported receiving grants from the National Institutes of Health (NIH) during the conduct of the study. No other disclosures were reported.
Funding/Support: We acknowledge support from National Cancer Institute Case Comprehensive Cancer Center (grant Nos. CA221718 and CA043703), American Cancer Society (grant No. RSG-16-049-01–MPC), The Landon Foundation–American Association for Cancer Research (award No. 15-20-27-XU), NIH Director’s New Innovator Award Program (award No. DP2HD084068), National Institute on Aging (grant Nos. AG057557, AG061388, AG062272, and AG07664), and the National Institute on Alcohol Abuse and Alcoholism (grant No. AA029831).
Role of the Funder/Sponsor: The funders had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.
Data Sharing Statement: See Supplement 2 .
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Obesity: causes, consequences, treatments, and challenges
Obesity has become a global epidemic and is one of today’s most public health problems worldwide. Obesity poses a major risk for a variety of serious diseases including diabetes mellitus, non-alcoholic liver disease (NAFLD), cardiovascular disease, hypertension and stroke, and certain forms of cancer ( Bluher, 2019 ).
Obesity is mainly caused by imbalanced energy intake and expenditure due to a sedentary lifestyle coupled with overnutrition. Excess nutrients are stored in adipose tissue (AT) in the form of triglycerides, which will be utilized as nutrients by other tissues through lipolysis under nutrient deficit conditions. There are two major types of AT, white AT (WAT) and brown AT, the latter is a specialized form of fat depot that participates in non-shivering thermogenesis through lipid oxidation-mediated heat generation. While WAT has been historically considered merely an energy reservoir, this fat depot is now well known to function as an endocrine organ that produces and secretes various hormones, cytokines, and metabolites (termed as adipokines) to control systemic energy balance. Studies over the past decade also show that WAT, especially subcutaneous WAT, could undergo ‘beiging’ remodeling in response to environmental or hormonal perturbation. In the first paper of this special issue, Cheong and Xu (2021) systematically review the recent progress on the factors, pathways, and mechanisms that regulate the intercellular and inter-organ crosstalks in the beiging of WAT. A critical but still not fully addressed issue in the adipose research field is the origin of the beige cells. Although beige adipocytes are known to have distinct cellular origins from brown and while adipocytes, it remains unclear on whether the cells are from pre-existing mature white adipocytes through a transdifferentiation process or from de novo differentiation of precursor cells. AT is a heterogeneous tissue composed of not only adipocytes but also nonadipocyte cell populations, including fibroblasts, as well as endothelial, blood, stromal, and adipocyte precursor cells ( Ruan, 2020 ). The authors examined evidence to show that heterogeneity contributes to different browning capacities among fat depots and even within the same depot. The local microenvironment in WAT, which is dynamically and coordinately controlled by inputs from the heterogeneous cell types, plays a critical role in the beige adipogenesis process. The authors also examined key regulators of the AT microenvironment, including vascularization, the sympathetic nerve system, immune cells, peptide hormones, exosomes, and gut microbiota-derived metabolites. Given that increasing beige fat function enhances energy expenditure and consequently reduces body weight gain, identification and characterization of novel regulators and understanding their mechanisms of action in the beiging process has a therapeutic potential to combat obesity and its associated diseases. However, as noticed by the authors, most of the current pre-clinical research on ‘beiging’ are done in rodent models, which may not represent the exact phenomenon in humans ( Cheong and Xu, 2021 ). Thus, further investigations will be needed to translate the findings from bench to clinic.
While both social–environmental factors and genetic preposition have been recognized to play important roles in obesity epidemic, Gao et al. (2021) present evidence showing that epigenetic changes may be a key factor to explain interindividual differences in obesity. The authors examined data on the function of DNA methylation in regulating the expression of key genes involved in metabolism. They also summarize the roles of histone modifications as well as various RNAs such as microRNAs, long noncoding RNAs, and circular RNAs in regulating metabolic gene expression in metabolic organs in response to environmental cues. Lastly, the authors discuss the effect of lifestyle modification and therapeutic agents on epigenetic regulation of energy homeostasis. Understanding the mechanisms by which lifestyles such as diet and exercise modulate the expression and function of epigenetic factors in metabolism should be essential for developing novel strategies for the prevention and treatment of obesity and its associated metabolic diseases.
A major consequence of obesity is type 2 diabetes, a chronic disease that occurs when body cannot use and produce insulin effectively. Diabetes profoundly and adversely affects the vasculature, leading to various cardiovascular-related diseases such as atherosclerosis, arteriosclerotic, and microvascular diseases, which have been recognized as the most common causes of death in people with diabetes ( Cho et al., 2018 ). Love et al. (2021) systematically review the roles and regulation of endothelial insulin resistance in diabetes complications, focusing mainly on vascular dysfunction. The authors review the vasoprotective functions and the mechanisms of action of endothelial insulin and insulin-like growth factor 1 signaling pathways. They also examined the contribution and impart of endothelial insulin resistance to diabetes complications from both biochemical and physiological perspectives and evaluated the beneficial roles of many of the medications currently used for T2D treatment in vascular management, including metformin, thiazolidinediones, glucagon-like receptor agonists, dipeptidyl peptidase-4 inhibitors, sodium-glucose cotransporter inhibitors, as well as exercise. The authors present evidence to suggest that sex differences and racial/ethnic disparities contribute significantly to vascular dysfunction in the setting of diabetes. Lastly, the authors raise a number of very important questions with regard to the role and connection of endothelial insulin resistance to metabolic dysfunction in other major metabolic organs/tissues and suggest several insightful directions in this area for future investigation.
Following on from the theme of obesity-induced metabolic dysfunction, Xia et al. (2021) review the latest progresses on the role of membrane-type I matrix metalloproteinase (MT1-MMP), a zinc-dependent endopeptidase that proteolytically cleaves extracellular matrix components and non-matrix proteins, in lipid metabolism. The authors examined data on the transcriptional and post-translational modification regulation of MT1-MMP gene expression and function. They also present evidence showing that the functions of MT1-MMP in lipid metabolism are cell specific as it may either promote or suppress inflammation and atherosclerosis depending on its presence in distinct cells. MT1-MMP appears to exert a complex role in obesity for that the molecule delays the progression of early obesity but exacerbates obesity at the advanced stage. Because inhibition of MT1-MMP can potentially lower the circulating low-density lipoprotein cholesterol levels and reduce the risk of cancer metastasis and atherosclerosis, the protein has been viewed as a very promising therapeutic target. However, challenges remain in developing MT1-MMP-based therapies due to the tissue-specific roles of MT1-MMP and the lack of specific inhibitors for this molecule. Further investigations are needed to address these questions and to develop MT1-MMP-based therapeutic interventions.
Lastly, Huang et al. (2021) present new findings on a critical role of puromycin-sensitive aminopeptidase (PSA), an integral non-transmembrane enzyme that catalyzes the cleavage of amino acids near the N-terminus of polypeptides, in NAFLD. NAFLD, ranging from simple nonalcoholic fatty liver to the more aggressive subtype nonalcoholic steatohepatitis, has now become the leading chronic liver disease worldwide ( Loomba et al., 2021 ). At present, no effective drugs are available for NAFLD management in the clinic mainly due to the lack of a complete understanding of the mechanisms underlying the disease progress, reinforcing the urgent need to identify and validate novel targets and to elucidate their mechanisms of action in NAFLD development and pathogenesis. Huang et al. (2021) found that PSA expression levels were greatly reduced in the livers of obese mouse models and that the decreased PSA expression correlated with the progression of NAFLD in humans. They also found that PSA levels were negatively correlated with triglyceride accumulation in cultured hepatocytes and in the liver of ob/ob mice. Moreover, PSA suppresses steatosis by promoting lipogenesis and attenuating fatty acid β-oxidation in hepatocytes and protects oxidative stress and lipid overload in the liver by activating the nuclear factor erythroid 2-related factor 2, the master regulator of antioxidant response. These studies identify PSA as a pivotal regulator of hepatic lipid metabolism and suggest that PSA may be a potential biomarker and therapeutic target for treating NAFLD.
In summary, papers in this issue review our current knowledge on the causes, consequences, and interventions of obesity and its associated diseases such as type 2 diabetes, NAFLD, and cardiovascular disease ( Cheong and Xu, 2021 ; Gao et al., 2021 ; Love et al., 2021 ). Potential targets for the treatment of dyslipidemia and NAFLD are also discussed, as exemplified by MT1-MMP and PSA ( Huang et al., 2021 ; Xia et al., 2021 ). It is noted that despite enormous effect, few pharmacological interventions are currently available in the clinic to effectively treat obesity. In addition, while enhancing energy expenditure by browning/beiging of WAT has been demonstrated as a promising alternative approach to alleviate obesity in rodent models, it remains to be determined on whether such WAT reprogramming is effective in combating obesity in humans ( Cheong and Xu, 2021 ). Better understanding the mechanisms by which obesity induces various medical consequences and identification and characterization of novel anti-obesity secreted factors/soluble molecules would be helpful for developing effective therapeutic treatments for obesity and its associated medical complications.
- Bluher M. (2019). Obesity: global epidemiology and pathogenesis . Nat. Rev. Endocrinol . 15 , 288–298. [ PubMed ] [ Google Scholar ]
- Cheong L.Y., Xu A. (2021). Intercellular and inter-organ crosstalk in browning of white adipose tissue: molecular mechanism and therapeutic complications . J. Mol. Cell Biol . 13 , 466–479. [ PMC free article ] [ PubMed ] [ Google Scholar ]
- Cho N.H., Shaw J.E., Karuranga S., et al. (2018). IDF Diabetes Atlas: global estimates of diabetes prevalence for 2017 and projections for 2045 . Diabetes Res. Clin. Pract . 138 , 271–281. [ PubMed ] [ Google Scholar ]
- Gao W., Liu J.-L., Lu X., et al. (2021). Epigenetic regulation of energy metabolism in obesity . J. Mol. Cell Biol . 13 , 480–499. [ PMC free article ] [ PubMed ] [ Google Scholar ]
- Huang B., Xiong X., Zhang L., et al. (2021). PSA controls hepatic lipid metabolism by regulating the NRF2 signaling pathway . J. Mol. Cell Biol . 13 , 527–539. [ PMC free article ] [ PubMed ] [ Google Scholar ]
- Loomba R., Friedman S.L., Shulman G.I. (2021). Mechanisms and disease consequences of nonalcoholic fatty liver disease . Cell 184 , 2537–2564. [ PubMed ] [ Google Scholar ]
- Love K.M., Barrett E.J., Malin S.K., et al. (2021). Diabetes pathogenesis and management: the endothelium comes of age . J. Mol. Cell Biol . 13 , 500–512. [ PMC free article ] [ PubMed ] [ Google Scholar ]
- Ruan H.-B. (2020). Developmental and functional heterogeneity of thermogenic adipose tissue . J. Mol. Cell Biol . 12 , 775–784. [ PMC free article ] [ PubMed ] [ Google Scholar ]
- Xia X.-D., Alabi A., Wang M., et al. (2021). Membrane-type I matrix metalloproteinase (MT1-MMP), lipid metabolism, and therapeutic implications . J. Mol. Cell Biol . 13 , 513–526. [ PMC free article ] [ PubMed ] [ Google Scholar ]
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Article Contents
Introduction, conclusions, acknowledgements, supplementary data, declarations, adulthood adiposity affects cardiac structure and function in later life.
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Lamia Al Saikhan, Nish Chaturvedi, Arjun K Ghosh, Rebecca Hardy, Alun Hughes, on behalf of the Medical Research Council National Survey of Health and Development (NHSD) Scientific and Data Collection Team, Adulthood adiposity affects cardiac structure and function in later life, European Heart Journal , 2024;, ehae403, https://doi.org/10.1093/eurheartj/ehae403
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Excess adiposity is associated with poorer cardiac function and adverse left ventricular (LV) remodelling. However, its importance over the adult life course on future cardiac structure and systolic and diastolic function is unknown.
A total of 1690 participants in the National Survey of Health and Development birth cohort underwent repeated adiposity [body mass index (BMI)/waist-to-hip ratio (WHR)] measurements over adulthood and investigation, including echocardiography at age 60–64 years. The relationship between LV structure [LV mass (LVM), relative wall thickness, and LV internal diameter in diastole (LVIDd)] and function (diastolic: E/e ʹ , e ʹ , and left atrial volume indexed to body surface area; systolic: ejection fraction, S ʹ , and myocardial contraction fraction) was investigated using multivariable linear regression models.
Increased BMI from age 20 years onwards was associated with greater LVM and LVIDd independent of confounders. Associations remained independent of current BMI for LVIDd and at age 26, 43, and 53 years for LVM. Increased BMI from 43 years onwards was associated with greater relative wall thickness, but not when BMI at age 60–64 years was accounted for. Increased BMI at age 26, 36, and 53 years and at 20 years onwards was associated with lower ejection fraction and myocardial contraction fraction, respectively, but not independently of BMI at 60–64 years. Higher BMI from 20 years onwards was associated with poorer diastolic function independent of confounders. Associations between BMI and left atrial volume indexed to body surface area persisted from 26 years onwards after adjustment for BMI at 60–64 years. Similar relationships were observed for WHR from age 43 years onwards.
Higher adiposity (BMI/WHR) over adulthood is associated with evidence of adverse cardiac structure and function. Some of these associations are independent of adiposity in later life.
Excess adiposity over the adult life course and its associations with cardiac structure and function in later life. BMI, body mass index; CI, confidence interval.
Worldwide obesity has nearly tripled since 1975 with 39% of adults aged ≥18 years being classified as overweight in 2016 and 13% obese according to the World Health Organization. 1 Obesity and overweight are associated with an increased risk of cardiovascular disease (CVD), including heart failure, and mortality. 2 , 3
In cross-sectional studies, elevated body mass index (BMI) is associated with poorer cardiac function (i.e. poorer diastolic and systolic function) and adverse left ventricular (LV) remodelling [i.e. elevated LV mass (LVM) and increased LV cavity size] albeit with inconsistent relationships across studies. 4–6 However, evidence on the relationship between excess adiposity over the adult life course and future cardiac structure and function, and its reversibility is limited. This is important because understanding the temporal relationship between adiposity gain and cardiac measures provides insight into effective preventative strategies.
This study, therefore, aimed to investigate the relationship of adiposity, measured by BMI/waist-to-hip ratio (WHR), over the adult life course with cardiac structure and function, measured by echocardiography, at age 60–64 years based on longitudinal data derived from the Medical Research Council National Survey of Health and Development (MRC NSHD) birth cohort. We hypothesized that antecedent adiposity prior to age 60–64 would be adversely associated with subsequent cardiac structure and function in later life, and that this would not be completely explained by current adiposity.
Study population
The MRC NSHD is a birth cohort study consisting of 5362 individuals born in March 1946 in England, Scotland, and Wales. 7 The design of the study has been described previously. 7 , 8 In brief, the whole sample has been followed up over 20 times since birth with adult BMI ascertained at 20, 26, 36, 43, and 53 years. At age 60–64 years (2006–10), 2856 eligible study members were invited to participate, 2229 (78%) responded, and of these, 1690 (59.1%) attended clinic, and 539 (18.9%) had a home visit (see Supplementary data online , Figure S1 ). Clinic attenders underwent a comprehensive examination that included health, lifestyle, and sociodemographic questionnaires, height and weight measurements, and detailed cardiovascular assessments. 7 , 8 Data from individuals with echocardiographic measurements were used in this analysis. The study received regional Ethics Committee approval (MREC98/2/121, 08/MRE00/12, and 07/H1008/245) and was performed in accordance with the principles of the Helsinki Declaration. All participants provided written informed consent for each component of data collection.
Clinical examination
At the clinic visit, anthropometrics including height and weight were measured in light indoor clothing without shoes and BMI was calculated. Waist and hip circumferences were measured to the nearest millimetres using identical protocol from age 43 years onwards, and WHR was derived. Waist measurements were taken at the midpoint between the costal margin and the iliac crest, and hip at the level of the greater trochanter on skin or under light clothing. Two sitting blood pressure measurements from the upper right arm were recorded in clinic using an appropriately sized cuff after resting for 5 min, and the average of the two readings was used. Socioeconomic position was defined as the highest household occupational social class at 53 years [using Registrar General’s Social Class (RGSC)]. 9 Education was defined as the highest educational level achieved by 26 years classified using the Burnham scale as follows: (i) no qualifications, (ii) sub-GCE or sub-Burnham C, (iii) GCE O level or Burnham C, (iv) GCE A level or Burnham B, and (v) degree or higher. The presence of Type 2 diabetes mellitus at 60–64 years was defined based on self-report or clinic blood tests.
Echocardiographic imaging and analysis
Of the 1690 participants who attended clinic, 1653 participants underwent echocardiography using GE Vivid I machines (General Electric, Fairfield, Connecticut, USA) performed by trained experienced sonographers according to a strict protocol. The imaging protocol included the following echocardiographic views: parasternal long axis and short axis, apical five-, four-, three-, two-chamber, and aortic views in addition to conventional and tissue Doppler in the four-chamber view. Images were optimized if required using second harmonic imaging. Image analysis was performed by three experienced echocardiographers blinded to patient identity using GE EchoPAC Software (GE Connecticut, USA) in a single core laboratory.
Left ventricular wall thickness and cavity dimensions from 2D-guided M-mode were measured from parasternal long-axis view from which LV relative wall thickness (RWT) was calculated. 10 Left ventricular mass was also calculated following the American Society of Echocardiography (ASE) recommendations. 10 Indexation to body size was achieved by adjusting LVM for height 1.7 , 11 since indexing to body surface area is known to bias results when there is obesity. 10 Left ventricular volumes and LV ejection fraction (EF) was calculated using the modified biplane Simpson’s rule. 10 Diastolic function was evaluated in accordance with the ASE recommendations and included the following markers: mitral inflow diastolic filling velocities [early (E-wave), late (A-wave), and E / A ratio] and tissue Doppler analysis averaged of the lateral and septal mitral annular velocities (eʹ, aʹ, and sʹ) from which E / e ʹ ratio was calculated. 10 Maximum left atrial volume [indexed (LAVi)] to BMI was calculated by the biplane method of discs at end-systole as a marker of chronically elevated LV filling pressures, and indexation to height and height 2 was also performed in sensitivity analyses. 10 , 12 , 13 Myocardial contraction fraction (MCF), a measure of LV myocardial shortening, was calculated as the ratio of LV stroke volume to LV myocardial volume.
Quality checks were performed throughout the study including blind duplicate reading reproducibility studies ( n = 40; in similar numbers of men and women and with varying image qualities) to establish inter- and intrareader variability. The reproducibility was excellent with intraclass correlation coefficients >0.8 for all measurements.
Statistical analysis
Statistical analyses were performed using STATA 15.1 (StataCorp LLC, College Station, TX, USA). Continuous variables are presented as mean ± standard deviation or median (interquartile range) as appropriate, and categorical variables are presented as counts (percentages). The relationship between BMI at each of the six ages when it was measured and cardiac structural and functional measures at 60–64 years was investigated using multivariable linear regression models, and regression coefficients with 95% confidence intervals (CIs) are reported. Models were adjusted for prespecified covariables as follows: Model 1: adjusted for age and sex [plus height for LV internal diameter in diastole (LVIDd); and height 1.7 for LVM]; Model 2: Model 1 plus occupational social class and education; and Model 3: Model 2 plus current BMI. The analysis was repeated replacing BMI, as a measure of adiposity, with WHR, which was available at NSHD only from age 43 onwards. Model 3 aimed to establish whether antecedent adiposity (BMI/WHR) was predictive of cardiac structure and function independent of current adiposity. A sensitivity analysis was performed omitting participants with Type 2 diabetes mellitus (T2DM). Sex-stratified analyses were performed as a sensitivity analysis, and interaction P < .05 was taken as indicative of sex modification of associations. Sex-stratified models are reported in the supplement for completeness, although results did not differ markedly from pooled models. Missing values were rare and were dealt with by listwise deletion, which is valid when missingness is rare and data are missing completely at random. Inference was made on the basis of two-tailed P -values, CIs, and effect size.
Population characteristics
Sample characteristics are summarized in Table 1 . Mean age at outcome measurement was 63.2 ± 1.1 years, and 48.3% were male. Figure 1 illustrates the increase in mean BMI with age over the adult life course.
The mean body mass index at each of the six age periods at which it was measured, demonstrating a steady increase in median body mass index with increasing age
Echocardiographic and cardiac risk factor characteristics of the study sample
| Variable (at age 60–64 years unless stated otherwise) . | (%) . | Mean (SD) . |
|---|---|---|
| Age at echo, years | 1638 | 63.2 (1.1) |
| Male | 791 (48.3) | |
| Socioeconomic status | 1639 | |
| Professional | 137 (8.4) | |
| Intermediate | 726 (44.3) | |
| Skilled (non-manual) | 385 (23.5) | |
| Skilled (manual) | 215 (13.1) | |
| Partly skilled | 130 (7.9) | |
| Unskilled | 37 (2.3) | |
| Education | 1639 | |
| None attempted | 391 (23.9) | |
| Vocational course, proficiency only | 44 (2.7) | |
| Sub-GCE or sub-Burnham C | 68 (4.2) | |
| GCE ‘O’ level or Burnham C | 340 (20.7) | |
| GCE ‘A’ level or Burnham B | 269 (16.4) | |
| Burnham A2 | 228 (13.9) | |
| 1st degree or graduate equivalent | 198 (12.1) | |
| Higher degree, master’s or doctorate | 18 (1.1) | |
| Unknown | 52 (3.2) | |
| No data | 31 (1.9) | |
| Smoking status | 1639 | |
| Current smoker | 144 (8.8) | |
| Ex-smoker | 620 (37.8) | |
| Never smoked | 739 (45.1) | |
| Unknown | 136 (8.3) | |
| Interventricular septum, cm | 1479 | 1.1 (0.25) |
| Posterior wall thickness, cm | 1473 | 1.0 (0.18) |
| Relative wall thickness | 1473 | 0.42 (0.1) |
| LV mass, g | 1473 | 182.1 (62.1) |
| LV mass/height , g/m | 1473 | 74.7 (23.6) |
| LV internal diameter at diastole, cm | 1494 | 4.8 (0.6) |
| LV internal diameter at systole, cm | 1498 | 2.9 (0.5) |
| E-wave, cm/s | 1576 | 0.67 (0.15) |
| A-wave, cm/s | 1578 | 0.70 (0.17) |
| E/A ratio | 1576 | 1.0 (0.27) |
| e , cm/s | 1600 | 8.9 (1.9) |
| E/e ratio | 1558 | 7.9 (2.2) |
| LA volume index, mL/m | 1404 | 23.6 (8.8) |
| LV ejection fraction, % | 1447 | 64.3 (7.8) |
| S , cm/s | 1505 | 7.8 (1.6) |
| Myocardial contraction fraction, % | 1470 | 45.7 (12.8) |
| BMI, kg/m | 1639 | 27.6 (4.6) |
| BMI at age 53, kg/m | 1555 | 26.9 (4.4) |
| BMI at age 43, kg/m | 1544 | 24.9 (3.7) |
| BMI at age 36, kg/m | 1486 | 23.7 (3.2) |
| BMI at age 26, kg/m | 1445 | 22.5 (2.8) |
| BMI at age 20, kg/m | 1352 | 22.0 (2.6) |
| Waist-to-hip ratio | 1634 | 0.91 (0.1) |
| Waist-to-hip ratio at age 53 | 1558 | 0.86 (0.1) |
| Waist-to-hip ratio at age 43 | 1425 | 0.83 (0.1) |
| Systolic blood pressure, mmHg | 1634 | 136.6 (17.8) |
| Diastolic blood pressure, mmHg | 1635 | 77.9 (9.6) |
| Heart rate, b.p.m. | 1637 | 68.5 (11.2) |
| T2DM | 105 (6.4) | |
| HbA1c, % | 1534 | 5.8 (0.6) |
| Glucose, mmol/L | 1525 | 3.9 (1.2) |
| Creatinine, mmol/L | 1525 | 0.06 (0.02) |
| Cystatin C, mg/L | 1570 | 0.81 (0.13) |
| Variable (at age 60–64 years unless stated otherwise) . | (%) . | Mean (SD) . |
|---|---|---|
| Age at echo, years | 1638 | 63.2 (1.1) |
| Male | 791 (48.3) | |
| Socioeconomic status | 1639 | |
| Professional | 137 (8.4) | |
| Intermediate | 726 (44.3) | |
| Skilled (non-manual) | 385 (23.5) | |
| Skilled (manual) | 215 (13.1) | |
| Partly skilled | 130 (7.9) | |
| Unskilled | 37 (2.3) | |
| Education | 1639 | |
| None attempted | 391 (23.9) | |
| Vocational course, proficiency only | 44 (2.7) | |
| Sub-GCE or sub-Burnham C | 68 (4.2) | |
| GCE ‘O’ level or Burnham C | 340 (20.7) | |
| GCE ‘A’ level or Burnham B | 269 (16.4) | |
| Burnham A2 | 228 (13.9) | |
| 1st degree or graduate equivalent | 198 (12.1) | |
| Higher degree, master’s or doctorate | 18 (1.1) | |
| Unknown | 52 (3.2) | |
| No data | 31 (1.9) | |
| Smoking status | 1639 | |
| Current smoker | 144 (8.8) | |
| Ex-smoker | 620 (37.8) | |
| Never smoked | 739 (45.1) | |
| Unknown | 136 (8.3) | |
| Interventricular septum, cm | 1479 | 1.1 (0.25) |
| Posterior wall thickness, cm | 1473 | 1.0 (0.18) |
| Relative wall thickness | 1473 | 0.42 (0.1) |
| LV mass, g | 1473 | 182.1 (62.1) |
| LV mass/height , g/m | 1473 | 74.7 (23.6) |
| LV internal diameter at diastole, cm | 1494 | 4.8 (0.6) |
| LV internal diameter at systole, cm | 1498 | 2.9 (0.5) |
| E-wave, cm/s | 1576 | 0.67 (0.15) |
| A-wave, cm/s | 1578 | 0.70 (0.17) |
| E/A ratio | 1576 | 1.0 (0.27) |
| e , cm/s | 1600 | 8.9 (1.9) |
| E/e ratio | 1558 | 7.9 (2.2) |
| LA volume index, mL/m | 1404 | 23.6 (8.8) |
| LV ejection fraction, % | 1447 | 64.3 (7.8) |
| S , cm/s | 1505 | 7.8 (1.6) |
| Myocardial contraction fraction, % | 1470 | 45.7 (12.8) |
| BMI, kg/m | 1639 | 27.6 (4.6) |
| BMI at age 53, kg/m | 1555 | 26.9 (4.4) |
| BMI at age 43, kg/m | 1544 | 24.9 (3.7) |
| BMI at age 36, kg/m | 1486 | 23.7 (3.2) |
| BMI at age 26, kg/m | 1445 | 22.5 (2.8) |
| BMI at age 20, kg/m | 1352 | 22.0 (2.6) |
| Waist-to-hip ratio | 1634 | 0.91 (0.1) |
| Waist-to-hip ratio at age 53 | 1558 | 0.86 (0.1) |
| Waist-to-hip ratio at age 43 | 1425 | 0.83 (0.1) |
| Systolic blood pressure, mmHg | 1634 | 136.6 (17.8) |
| Diastolic blood pressure, mmHg | 1635 | 77.9 (9.6) |
| Heart rate, b.p.m. | 1637 | 68.5 (11.2) |
| T2DM | 105 (6.4) | |
| HbA1c, % | 1534 | 5.8 (0.6) |
| Glucose, mmol/L | 1525 | 3.9 (1.2) |
| Creatinine, mmol/L | 1525 | 0.06 (0.02) |
| Cystatin C, mg/L | 1570 | 0.81 (0.13) |
A, late mitral inflow diastolic filling velocity; BMI, body mass index; e ʹ , early diastolic mitral annular velocity; E, early mitral inflow diastolic filling velocity; HbA1c, glycosylated haemoglobin; LA, left atrium; LV, left ventricle; S ʹ , systolic mitral annular velocity; T2DM, Type 2 diabetes mellitus.
Associations between left ventricular structural measures and adiposity at various ages
The associations of BMI at different ages with LVM, LVIDd, and RWT are presented in Figure 2 (and Supplementary data online , Figure S2 and Table S1 ). Increased BMI from the age of 20 years onwards was associated with greater LVM in confounder-adjusted models (Model 1, Model 2). When BMI at age 60–64 years was included in models, the strength of association of BMI at prior ages was substantially attenuated for LVM, although there was still evidence of associations at age 26, 43, and 53 years independent of current BMI.
Association between body mass index at different ages and left ventricular structure measured at age 60–64 years. A) Left ventricular (LV) mass, B) left ventricular internal diameter in diastole (LVIDd) and C) relative wall thickness (RWT). Left column: Model 2 adjusted for age and sex (plus height for left ventricular internal diameter in diastole and height 1.7 for left ventricular mass), socioeconomic status/social class, and education. Right column: Model 3 adjusted for Model 2 plus current body mass index
Increased BMI from the age of 20 years onwards was associated with greater LVIDd in confounder-adjusted models (Model 1, Model 2). When BMI at age 60–64 years was included in models, the effect of BMI at prior ages was attenuated slightly, but the associations remained independent of current BMI.
Higher BMI from age 43 years onwards was associated with greater RWT in confounder-adjusted models (Model 1, Model 2). At younger ages, associations were in the same direction but did not convincingly differ from zero. When BMI at age 60–64 years was included in models, the associations were attenuated to the null and in one case (age 36 years) reversed.
Similar relationships were observed for WHR from age 43 onwards (see Supplementary data online , Table S2 ), with the exception of LVIDd at age 43, and all associations (i.e. LVM, LVIDd, and RWT) were independent of current WHR.
Associations between left ventricular functional measures and adiposity at various ages
Systolic function.
The associations of BMI at different ages with EF, S ʹ , and MCF are presented in Figure 3 (and Supplementary data online , Figure S3 and Table S3 ). Higher BMI at age 26, 36, and 53 years was associated with lower EF, i.e. worse systolic function in confounder-adjusted models (Model 1, Model 2). When BMI at age 60–64 years was included in models, the associations were attenuated to null.
Association between body mass index at different ages and left ventricular systolic function measured at age 60–64 years. A) Ejection fraction (EF), B) systolic mitral annular velocity (S') and C) myocardial contraction fraction (MCF). Left column: Model 2 adjusted for age and sex, socioeconomic status/social class, and education. Right column: Model 3 adjusted for Model 2 plus current body mass index
Most associations between higher BMI and S ʹ were unconvincing, although there was some evidence that higher BMI at age 43 years was associated with higher S ʹ . After adjusting for current BMI, there was no evidence of associations with S ʹ .
Higher BMI from age 20 years onwards was associated with a lower MCF, i.e. worse systolic function in confounder-adjusted models (Model 1, Model 2). When BMI at age 60–64 years was included in models, all associations were abolished.
Similar associations were observed for WHR from age 43 onwards (see Supplementary data online , Table S4 ), although associations between higher WHR from age 43 onwards and lower MCF (i.e. worse systolic function) were independent of WHR at age 60–64 years.
Diastolic function
The associations of BMI at different ages with E/e ʹ , e ʹ , and LAVi are presented in Figure 4 (and Supplementary data online , Figure S4 and Table S5 ). Higher BMI from the age of 26 years onwards was associated with higher E/e ʹ , i.e. worse diastolic function in confounder-adjusted models (Model 1, Model 2). When BMI at 60–64 years was included in the models, no convincing associations were evident at ages 26–53 years and the association became negative at age 20 years, i.e. higher BMI at age 20 years was associated with lower E/e ʹ , i.e. better diastolic function after accounting for current BMI.
Association between body mass index at different ages and left ventricular diastolic function measured at age 60–64 years. A) Ratio of early mitral inflow diastolic filling velocity to early diastolic mitral annular velocity (E/e'), B) early diastolic mitral annular velocity (e') and C) left atrial volume index (LAVi). Left column: Model 2 adjusted for age and sex, socioeconomic status/social class, and education. Right column: Model 3 adjusted for model 2 plus current body mass index
Increased BMI from the age of 36 years onwards was associated with lower e ʹ , i.e. worse diastolic function in confounder-adjusted models. On including BMI at age 60–64 (Model 3), the associations at age 36 years and above were reversed at age 20 and 26 years.
Increased BMI from the age of 20 years onwards was associated with greater LAVi in confounder-adjusted models (Model 1, Model 2). On including BMI at age 60–64, associations were moderately attenuated, although there was evidence of independent associations from age 26 years onwards.
Similar relationships were observed for WHR from age 43 onwards (see Supplementary data online , Table S6 ) with associations between WHR and LAVi being independent of current WHR. Results stratified by sex are shown in Supplementary data online, (Figures S5–S7). Exclusion of people with T2DM had little impact on associations (Supplementary data online, Tables S7–S9).
In a birth cohort of men and women aged 60–64 years, excess adiposity measured as higher BMI or WHR from early or mid-adulthood was associated with adverse cardiac structure and function. For LVM, RWT, and LVIDd, associations with adiposity after age 43 years were independent of adiposity at age 60–64 years, consistent with an enduring influence of adiposity earlier in life. Higher adiposity (BMI/WHR) from early adulthood onwards was also associated with evidence of poorer systolic and diastolic function in older age. These associations were mostly explained by adiposity at age 60–64 years, but in the case of LAVi and MCF, there was evidence of adverse associations with antecedent adiposity independent of current adiposity ( Structured Graphical Abstract ).
Our study adds to existing knowledge. Previous cross-sectional studies have shown that elevated BMI is associated with adverse LV remodelling (i.e. elevated LVM and increased LV cavity size), poorer systolic and diastolic function, and increased risk of heart failure. 4–6 , 14–16 Based on Mendelian randomization studies, it is likely that at least some of these relationships are causal. 17 , 18 A few previous studies have reported a relationship between high BMI in childhood and adverse cardiac structure in later life, 19–21 and in adulthood, increasing BMI over a decade of adult life has been reported to be associated with higher LVM. 22 However, we are unaware of previous studies that have examined the association between BMI (and/or WHR) measured repeatedly across adulthood with cardiac measures in older life and that also account for the influence of adiposity in older life.
Our findings show that excess adiposity over the life course is associated with adverse cardiac structure and function that is only partly attributable to tracking of BMI (and/or WHR where applicable). This suggests a lasting impact of being overweight/obese early in adulthood on the heart. There was no compelling evidence of a sensitive period during adulthood when associations between BMI (and/or WHR) and later life cardiac outcomes were markedly stronger than at other ages, although most associations with BMI in earlier adulthood (below 36 years) were weaker than those subsequently. It is possible that interpreting BMI as a measure of adiposity is less secure at these younger ages since muscle mass will also contribute to BMI, especially in a cohort where BMI was largely in the normal range at younger ages.
Limitations
The MRC NSHD was a representative sample of native-born adults living in England, Scotland, and Wales at the time of data collection. 7 , 23 However, our findings cannot be generalized beyond the White British population of similar age. The repeated measurements of adiposity in our study, the longest running birth cohort in Britain, allowed us a rare opportunity to perform a longitudinal analysis across adult life investigating the associations of adiposity and changes in adiposity with cardiac structure and function using echocardiography in a large sample of people. Heights and weights recorded at ages 20 and 26 years were based on self-report and thus could be subject to reporting bias. Unmeasured or residual confounders that influence BMI or WHR and cardiac structure and function may have biased the associations observed. Probably the most important of these is likely to be dietary calorie intake; this was not accounted for in our models. Our analyses examined multiple associations, which increase the possibility of ‘false discovery’.
Future directions
Our data suggest lasting effects of adiposity on cardiac structure and function and reinforce calls for system approaches to the obesity syndemic. 24 Early prevention of excessive adiposity gain may be important to prevent future cardiac target organ damage in later life. This possibility should be explored in future studies, including in more diverse populations.
Higher adiposity (measured by BMI/WHR) over adult life is associated with evidence of adverse cardiac structure and function. Some of these associations are independent of adiposity in later life.
We thank the National Survey of Health and Development study members for their lifelong participation and past and present members of the National Survey of Health and Development study team who helped to collect the data.
Supplementary data are available at European Heart Journal online.
Disclosure of Interest
All authors declare no disclosure of interest for this contribution.
Data Availability
Data used in this publication are available to bona fide researchers on request to the National Survey of Health and Development Data Sharing Committee via a standard application procedure. Further details can be found at http://www.nshd.mrc.ac.uk/data , https://doi.org/10.5522/NSHD/Q101 , https://doi.org/10.5522/NSHD/Q102 , and https://doi.org/10.5522/NSHD/Q103 .
This work was supported by the Medical Research Council (MC_UU_12019/1), which provides core funding for the Medical Research Council National Survey of Health and Development and the Medical Research Council Unit for Lifelong Health and Ageing at UCL. The funders had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.
Ethical Approval
The study was approved by various regional Ethics Committees and was performed in accordance with the principles of the Helsinki Declaration. All participants provided written informed consent for each component of data collection.
Pre-registered Clinical Trial Number
None supplied.
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Associations of obstructive sleep apnea risk with obesity, body composition and metabolic abnormalities in school-aged children and adolescents.
1. Introduction
2. materials and methods, 2.1. study design and participants, 2.2. data collection and measurements, 2.2.1. sleep assessment, 2.2.2. anthropometric measurements, 2.2.3. metabolic indicators, 2.2.4. covariates, 2.3. statistical analyses, 4. discussion, 5. conclusions, supplementary materials, author contributions, institutional review board statement, informed consent statement, data availability statement, conflicts of interest.
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Click here to enlarge figure
| Characteristics | Low-Risk for OSA (n = 4428) | High-Risk for OSA (n = 572) | p |
|---|---|---|---|
| Maternal age at delivery (year), Mean ± SD | 27.8 ± 4.3 | 27.6 ± 4.5 | 0.267 |
| Maternal age at delivery (year), n (%) | |||
| <35 | 3917 (93.0) | 508 (94.1) | 0.368 |
| ≥35 | 296 (7.0) | 32 (5.9) | |
| Maternal history of gestational diabetes mellitus/hypertension, n (%) | |||
| Without | 4082 (92.2) | 525 (91.8) | 0.741 |
| With | 346 (7.8) | 47 (8.2) | |
| Delivery mode, n (%) | |||
| Vaginal delivery | 1985 (45.9) | 268 (48.3) | 0.298 |
| Cesarean delivery | 2340 (54.1) | 287 (51.7) | |
| Child sex, n (%) | |||
| Male | 2194 (49.5) | 343 (60.0) | <0.001 |
| Female | 2234 (50.5) | 229 (40.0) | |
| Birthweight (g), Mean ± SD | 3393.7 ± 659.3 | 3369.1 ± 592.6 | 0.426 |
| Birthweight (g), n (%) | |||
| <2500 | 136 (3.9) | 16 (3.8) | 0.893 |
| 2500–3999 | 2927 (84.2) | 355 (83.5) | |
| ≥4000 | 415 (11.9) | 54 (12.7) | |
| Exclusive breastfeeding duration in the first six months, n (%) | |||
| yes | 2528 (59.7) | 302 (55.2) | 0.047 |
| no | 1703 (40.3) | 245 (44.8) | |
| Family history of metabolic abnormalities, n (%) | |||
| Without | 1490 (33.6) | 163 (28.5) | 0.014 |
| With | 2938 (66.4) | 409 (71.5) | |
| Annual family income (CNY), n (%) | |||
| <20,000 | 760 (18.4) | 105 (19.7) | 0.708 |
| 20,000–120,000 | 1403 (33.9) | 175 (32.9) | |
| 120,000–250,000 | 1284 (31) | 171 (32.1) | |
| ≥250,000 | 689 (16.7) | 81 (15.2) | |
| Maternal education, n (%) | |||
| High School or less | 2371 (55.5) | 308 (55.8) | 0.749 |
| College | 1838 (43) | 238 (43.1) | |
| Above college | 64 (1.5) | 6 (1.1) | |
| Paternal education, n (%) | |||
| High School or less | 2518 (59.2) | 335 (60.4) | 0.548 |
| College | 1645 (38.7) | 205 (36.9) | |
| Above college | 91 (2.1) | 15 (2.7) | |
| Maternal current BMI (kg/m ), Mean ± SD | 24.1 (5.4) | 24.3 (5.5) | 0.376 |
| Maternal current BMI (kg/m ), n (%) | |||
| <18.5 | 206 (4.9) | 19 (3.5) | 0.443 |
| 18–23 | 1843 (44) | 231 (43) | |
| 23–27.5 | 1527 (36.5) | 208 (38.7) | |
| ≥27.5 | 612 (14.6) | 79 (14.7) | |
| Paternal current BMI (kg/m ), Mean ± SD | 26.3 (5.6) | 26.6 (5.2) | 0.298 |
| Paternal current BMI (kg/m ), n (%) | |||
| <23 | 916 (22.1) | 106 (19.8) | 0.006 |
| 23–27.5 | 2011 (48.4) | 235 (43.9) | |
| ≥27.5 | 1227 (29.5) | 194 (36.3) | |
| Child age (year), Mean ± SD | 11.0 ± 3.0 | 11.1 ± 2.9 | 0.708 |
| Sugary beverage consumption pattern, n (%) | |||
| ≥1/day | 296 (6.8) | 41 (7.3) | 0.001 |
| 4–6/week | 372 (8.6) | 58 (10.3) | |
| 1–3/week | 1371 (31.7) | 217 (38.4) | |
| <1/week | 2291 (52.9) | 249 (44.1) | |
| Passive smoking in the past week, n (%) | |||
| no | 3141 (73.4) | 349 (62.9) | <0.001 |
| yes | 1137 (26.6) | 206 (37.1) | |
| Child weight status, n (%) | |||
| Normal | 2592 (58.7) | 249 (43.6) | <0.001 |
| Overweight | 869 (19.7) | 131 (22.9) | |
| Obesity | 952 (21.6) | 191 (33.5) | |
| Child body composition phenotype, n (%) | |||
| Normal fat-normal muscle mass | 3459 (78.4) | 398 (69.7) | <0.001 |
| High fat-normal muscle mass | 351 (7.9) | 59 (10.3) | |
| Normal fat-high muscle mass | 265 (6.0) | 37 (6.5) | |
| High fat-high muscle mass | 338 (7.7) | 77 (13.5) |
| Crude OR (95% CI) | p | Adjusted OR (95% CI) * | p | |
|---|---|---|---|---|
| Child weight status | ||||
| Normal | 1 | 1 | ||
| Overweight | 1.57 (1.25, 1.97) | <0.001 | 1.53 (1.22, 1.92) | <0.001 |
| Obesity | 2.09 (1.71, 2.56) | <0.001 | 1.94 (1.57, 2.40) | <0.001 |
| Abdominal obesity | ||||
| WHtR < 0.5 | 1 | 1 | ||
| WHtR ≥ 0.5 | 1.74 (1.45, 2.09) | <0.001 | 1.59 (1.31, 1.93) | <0.001 |
| Body composition phenotype | ||||
| Normal fat -normal muscle mass | 1 | 1 | ||
| High fat-normal muscle mass | 1.46 (1.09, 1.96) | 0.012 | 1.39 (1.03, 1.88) | 0.032 |
| Normal fat-high muscle mass | 1.21 (0.85, 1.74) | 0.291 | 1.21 (0.84, 1.73) | 0.314 |
| High fat-high muscle mass | 1.98 (1.51, 2.59) | <0.001 | 2.05 (1.56, 2.69) | <0.001 |
| Metabolic Abnormalities | Crude OR (95% CI) | p | Model 1 | Model 2 | ||
|---|---|---|---|---|---|---|
| Adjusted OR (95% CI) | p | Adjusted OR (95% CI) | p | |||
| High TC levels | 1.22 (0.89, 1.67) | 0.222 | 1.22 (0.88, 1.68) | 0.228 | 1.17 (0.85, 1.61) | 0.339 |
| High LDL-C levels | 1.31 (1.02, 1.68) | 0.036 | 1.27 (0.99, 1.64) | 0.063 | 1.11 (0.86, 1.44) | 0.425 |
| Low HDL-C levels | 1.41 (1.04, 1.91) | 0.028 | 1.37 (1.01, 1.88) | 0.045 | 1.11 (0.80, 1.54) | 0.546 |
| High TG levels | 1.38 (1.09, 1.75) | 0.007 | 1.34 (1.06, 1.71) | 0.015 | 1.08 (0.84, 1.40) | 0.531 |
| IFG | 0.89 (0.56, 1.43) | 0.642 | 0.81 (0.50, 1.31) | 0.396 | 0.77 (0.48, 1.25) | 0.287 |
| High UA levels | 1.13 (0.93, 1.36) | 0.216 | 1.13 (0.92, 1.37) | 0.245 | 0.88 (0.70, 1.09) | 0.240 |
| Hypertension | 1.08 (0.84, 1.39) | 0.568 | 1.05 (0.81, 1.36) | 0.704 | 0.79 (0.59, 1.04) | 0.091 |
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Liao, Z.; Chen, Y.; Wu, L.; Huang, Y.; Li, S.; Liu, J.; Zong, X.; Tai, J.; Chen, F. Associations of Obstructive Sleep Apnea Risk with Obesity, Body Composition and Metabolic Abnormalities in School-Aged Children and Adolescents. Nutrients 2024 , 16 , 2419. https://doi.org/10.3390/nu16152419
Liao Z, Chen Y, Wu L, Huang Y, Li S, Liu J, Zong X, Tai J, Chen F. Associations of Obstructive Sleep Apnea Risk with Obesity, Body Composition and Metabolic Abnormalities in School-Aged Children and Adolescents. Nutrients . 2024; 16(15):2419. https://doi.org/10.3390/nu16152419
Liao, Zijun, Yiren Chen, Lijun Wu, Yiying Huang, Shaoli Li, Junting Liu, Xinnan Zong, Jun Tai, and Fangfang Chen. 2024. "Associations of Obstructive Sleep Apnea Risk with Obesity, Body Composition and Metabolic Abnormalities in School-Aged Children and Adolescents" Nutrients 16, no. 15: 2419. https://doi.org/10.3390/nu16152419
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Obesity has become a global epidemic and is one of today's most public health problems worldwide. Obesity poses a major risk for a variety of serious diseases including diabetes mellitus, non-alcoholic liver disease (NAFLD), cardiovascular disease, hypertension and stroke, and certain forms of cancer (Bluher, 2019).Obesity is mainly caused by imbalanced energy intake and expenditure due to a ...
Introduction. Worldwide obesity has nearly tripled since 1975 with 39% of adults aged ≥18 years being classified as overweight in 2016 and 13% obese according to the World Health Organization. 1 Obesity and overweight are associated with an increased risk of cardiovascular disease (CVD), including heart failure, and mortality. 2, 3 In cross-sectional studies, elevated body mass index (BMI ...
The objective of this study is to explore the associations between obesity, body composition, and the self-reported risk of obstructive sleep apnea (OSA) and to examine whether the risk of OSA is related to metabolic abnormalities in children and adolescents aged 6-17 years. Utilizing data from the 2022 to 2023 Beijing Children and Adolescents Health Cohort baseline survey, 5000 school-aged ...