Diabetes Metab J.  2022 Sep;46(5):767-780. 10.4093/dmj.2021.0258.

Waist Circumference and Body Mass Index Variability and Incident Diabetic Microvascular Complications: A Post Hoc Analysis of ACCORD Trial

• Hukportie DN
• Li FR
• Zhou R
• Zheng JZ
• Wu XX
• Wu XB

• KMID: 2533645
• DOI: http://doi.org/10.4093/dmj.2021.0258

Abstract

Background
Obesity is associated with adverse health events among diabetic patients, however, the relationship between obesity fluctuation and risk of microvascular complications among this specific population is unclear. We aimed to examine the effect of waist circumference (WC) and body mass index (BMI) variability on the risk of diabetic microvascular outcome
Methods
Annually recorded anthropometric data in the Action to Control Cardiovascular Risk in Diabetes (ACCORD) study was used to examine the association of WC and BMI variability defined as variability independent of mean, with the risk of microvascular outcomes, including neuropathy, nephropathy, and retinopathy. Cox proportional hazards models were used to estimate the hazard ratios (HRs) and 95% confidence intervals (CIs) (Trial registration: ClinicalTrials.gov., no. NCT00000620).
Results
There were 4,031, 5,369, and 2,601 cases of neuropathy, nephropathy, and retinopathy during a follow-up period of 22,524, 23,941, and 23,850 person-years, respectively. Higher levels of WC and BMI variability were associated with an increased risk of neuropathy. Compared with the lowest quartile, the fully-adjusted HR (95% CI) for the highest quartile of WC and BMI variability for neuropathy risk were 1.21 (1.05 to 1.40) and 1.16 (1.00 to 1.33), respectively. Also, higher quartiles of BMI variability but not WC variability were associated with increased risk of nephropathic events. The fully-adjusted HR (95% CI) for the highest quartile compared with the lowest quartile of BMI variability was 1.31 (1.18 to 1.46). However, the results for retinopathic events were all insignificant.
Conclusion
Among participants with type 2 diabetes mellitus, WC and BMI variability were associated with a higher risk of neuropathic events, whereas BMI variability was associated with an increased risk of nephropathic events.

Keyword

Body mass index; Diabetes complications; Diabetes mellitus, type 2; Obesity; Waist circumference

Figure

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  Fig. 1. Rates of any microvascular event in quartiles of (A, B, C) waist circumference (WC) and (D, E, F) body mass index (BMI) variability as measured by corrected variability independent of mean (cVIM).

Reference

1. Bommer C, Sagalova V, Heesemann E, Manne-Goehler J, Atun R, Barnighausen T, et al. Global economic burden of diabetes in adults: projections from 2015 to 2030. Diabetes Care. 2018; 41:963–70.

2. World Health Organization: Diabetes. Available from: https://www.who.int/news-room/fact-sheets/detail/diabetes (updated 2021 Nov 10).

3. Hwang IC, Bae JH, Kim JM. Relationship between body fat and diabetic retinopathy in patients with type 2 diabetes: a nationwide survey in Korea. Eye (Lond). 2019; 33:980–7.

4. American Diabetes Association. 11. Microvascular complications and foot care: standards of medical care in diabetes-2020. Diabetes Care. 2020; 43(Suppl 1):S135–51.

5. Wong TY, Cheung N, Tay WT, Wang JJ, Aung T, Saw SM, et al. Prevalence and risk factors for diabetic retinopathy: the Singapore Malay Eye Study. Ophthalmology. 2008; 115:1869–75.

6. Zhou R, Li F, Chen G, Fu Q, Gu S, Wu X. Associations between general and abdominal obesity and incident diabetic neuropathy in participants with type 2 diabetes mellitus. J Diabetes. 2021; 13:33–42.

7. Dirani M, Xie J, Fenwick E, Benarous R, Rees G, Wong TY, et al. Are obesity and anthropometry risk factors for diabetic retinopathy?: the diabetes management project. Invest Ophthalmol Vis Sci. 2011; 52:4416–21.

8. Bavbek N, Isik B, Kargili A, Uz E, Uz B, Kanbay M, et al. Association of obesity with inflammation in occult chronic kidney disease. J Nephrol. 2008; 21:761–7.

9. Neter JE, Stam BE, Kok FJ, Grobbee DE, Geleijnse JM. Influence of weight reduction on blood pressure: a meta-analysis of randomized controlled trials. Hypertension. 2003; 42:878–84.

10. Magkos F, Fraterrigo G, Yoshino J, Luecking C, Kirbach K, Kelly SC, et al. Effects of moderate and subsequent progressive weight loss on metabolic function and adipose tissue biology in humans with obesity. Cell Metab. 2016; 23:591–601.

11. Kittiskulnam P, Kanjanabuch T, Tangmanjitjaroen K, Chancharoenthana W, Praditpornsilpa K, Eiam-Ong S. The beneficial effects of weight reduction in overweight patients with chronic proteinuric immunoglobulin a nephropathy: a randomized controlled trial. J Ren Nutr. 2014; 24:200–7.

12. Ryan DH, Yockey SR. Weight loss and improvement in comorbidity: differences at 5%, 10%, 15%, and over. Curr Obes Rep. 2017; 6:187–94.

13. Gong W, Hu Y, Niu Y, Wang D, Wang Y, Li Y, et al. Effects of longitudinal body mass index variability on microvasculature over 5 years in adult Chinese. Obesity (Silver Spring). 2016; 24:743–9.

14. Saito I, Iso H, Kokubo Y, Inoue M, Tsugane S. Body mass index, weight change and risk of stroke and stroke subtypes: the Japan Public Health Center-based prospective (JPHC) study. Int J Obes (Lond). 2011; 35:283–91.

15. Bangalore S, Fayyad R, DeMicco DA, Colhoun HM, Waters DD. Body weight variability and cardiovascular outcomes in patients with type 2 diabetes mellitus. Circ Cardiovasc Qual Outcomes. 2018; 11:e004724.

16. Nam GE, Kim W, Han K, Lee CW, Kwon Y, Han B, et al. Body weight variability and the risk of cardiovascular outcomes and mortality in patients with type 2 diabetes: a nationwide cohort study. Diabetes Care. 2020; 43:2234–41.

17. Yeboah P, Hsu FC, Bertoni AG, Yeboah J. Body mass index, change in weight, body weight variability and outcomes in type 2 diabetes mellitus (from the ACCORD Trial). Am J Cardiol. 2019; 123:576–81.

18. Booth ML, Hunter C, Gore CJ, Bauman A, Owen N. The relationship between body mass index and waist circumference: implications for estimates of the population prevalence of overweight. Int J Obes Relat Metab Disord. 2000; 24:1058–61.

19. Chan DC, Watts GF, Barrett PH, Burke V. Waist circumference, waist-to-hip ratio and body mass index as predictors of adipose tissue compartments in men. QJM. 2003; 96:441–7.

20. Nazare JA, Smith J, Borel AL, Aschner P, Barter P, Van Gaal L, et al. Usefulness of measuring both body mass index and waist circumference for the estimation of visceral adiposity and related cardiometabolic risk profile (from the INSPIRE ME IAA study). Am J Cardiol. 2015; 115:307–15.

21. ACCORD: ACCORDION: the ACCORD follow-on study protocol (March 18, 2011–Version G). 2011; Available from: https://biolincc.nhlbi.nih.gov/media/studies/accord/ACCORDION_Protocol.pdf (cited 2022 Mar 16).

22. ACCORD Study Group, Buse JB, Bigger JT, Byington RP, Cooper LS, Cushman WC, et al. Action to Control Cardiovascular Risk in Diabetes (ACCORD) trial: design and methods. Am J Cardiol. 2007; 99(12A):21i–33i.

23. Papademetriou V, Zaheer M, Doumas M, Lovato L, Applegate WB, Tsioufis C, et al. Cardiovascular outcomes in action to control cardiovascular risk in diabetes: impact of blood pressure level and presence of kidney disease. Am J Nephrol. 2016; 43:271–80.

24. Elam MB, Ginsberg HN, Lovato LC, Corson M, Largay J, Leiter LA, et al. Association of fenofibrate therapy with long-term cardiovascular risk in statin-treated patients with type 2 diabetes. JAMA Cardiol. 2017; 2:370–80.

25. ClinicalTrials.gov: Action to Control Cardiovascular Risk in Diabetes (ACCORD). Available from: https://www.clinicaltrials.gov/ct2/show/NCT00000620 (updated 2016 Nov 22).

26. Bangalore S, Messerli FH, Waters DD. Body-weight fluctuations and outcomes in coronary disease. N Engl J Med. 2017; 377:95–6.

27. Lee EY, Yang Y, Kim HS, Cho JH, Yoon KH, Chung WS, et al. Effect of visit-to-visit LDL-, HDL-, and non-HDL-cholesterol variability on mortality and cardiovascular outcomes after percutaneous coronary intervention. Atherosclerosis. 2018; 279:1–9.

28. Kim DH, Nam GE, Han K, Kim YH, Park KY, Hwang HS, et al. Variabilities in weight and waist circumference and risk of myocardial infarction, stroke, and mortality: a nationwide cohort study. Endocrinol Metab (Seoul). 2020; 35:933–42.

29. Okada H, Hamaguchi M, Habu M, Kurogi K, Murata H, Ito M, et al. Association between variability in body mass index and development of type 2 diabetes: Panasonic cohort study. BMJ Open Diabetes Res Care. 2021; 9:e002123.

30. Lim YM, Yang PS, Jang E, Yu HT, Kim TH, Uhm JS, et al. Body mass index variability and long-term risk of new-onset atrial fibrillation in the general population: a Korean Nationwide Cohort Study. Mayo Clin Proc. 2019; 94:225–35.

31. Stienen S, Ferreira JP, Girerd N, Duarte K, Lamiral Z, McMurray JJ, et al. Mean BMI, visit-to-visit BMI variability and BMI changes during follow-up in patients with acute myocardial infarction with systolic dysfunction and/or heart failure: insights from the High-Risk Myocardial Infarction Initiative. Clin Res Cardiol. 2019; 108:1215–25.

32. Ashraf M, Shokrollahi S, Pisig AU, Sampani K, Abdelal O, Cavallerano JD, et al. Retinal vascular caliber association with nonperfusion and diabetic retinopathy severity depends on vascular caliber measurement location. Ophthalmol Retina. 2021; 5:571–9.

33. Ceriello A, Prattichizzo F. Variability of risk factors and diabetes complications. Cardiovasc Diabetol. 2021; 20:101.

34. Ahmadi N, Hajsadeghi F, Conneely M, Mingos M, Arora R, Budoff M, et al. Accurate detection of metabolically active “brown” and “white” adipose tissues with computed tomography. Acad Radiol. 2013; 20:1443–7.

35. Park K, Lee DH, Erickson DJ, Himes JH, Shikany JM, Jacobs DR Jr. Association of long-term change in waist circumference with insulin resistance. Obesity (Silver Spring). 2010; 18:370–6.