Endocrinol Metab.  2021 Aug;36(4):855-864. 10.3803/EnM.2021.1086.

Differences in Abdominal Body Composition According to Glycemic Status: An Inverse Probability Treatment Weighting Analysis

Affiliations
  • 1Department of Biostatistics, Korea University College of Medicine, Seoul, Korea
  • 2Department of Family Medicine, Ulsan University Hospital, University of Ulsan College of Medicine, Ulsan, Korea
  • 3Department of Internal Medicine, Ulsan University Hospital, University of Ulsan College of Medicine, Ulsan, Korea
  • 4Department of Radiology, Ulsan University Hospital, University of Ulsan College of Medicine, Ulsan, Korea
  • 5Department of Anesthesiology and Pain Medicine, Ulsan University Hospital, University of Ulsan College of Medicine, Ulsan, Korea

Abstract

Background
Several studies have reported that abdominal fat and muscle changes occur in diabetic patients. However, there are few studies about such changes among prediabetic patients. In this study, we evaluated the differences in abdominal fat and muscles based on abdominopelvic computed tomography in prediabetic and diabetic subjects compared to normal subjects.
Methods
We performed a cross-sectional study using health examination data from March 2014 to June 2019 at Ulsan University Hospital and classified subjects into normal, prediabetic, and diabetic groups. We analyzed the body mass index corrected area of intra-abdominal components among the three groups using inverse probability treatment weighting (IPTW) analysis.
Results
Overall, 8,030 subjects were enrolled; 5,137 (64.0%), 2,364 (29.4%), and 529 (6.6%) subjects were included in the normal, prediabetic, and diabetic groups, respectively. After IPTW adjustment of baseline characteristics, there were significant differences in log visceral adipose tissue index (VATI; 1.22±0.64 cm2/[kg/m2] vs. 1.30±0.63 cm2/[kg/m2] vs. 1.47±0.64 cm2/[kg/m2], P<0.001) and low-attenuation muscle index (LAMI; 1.02±0.36 cm2/[kg/m2] vs. 1.03±0.36 cm2/[kg/m2] vs. 1.09±0.36 cm2/[kg/m2], P<0.001) among the normal, prediabetic, and diabetic groups. Prediabetic subjects had higher log VATI (estimated coefficient= 0.082, P<0.001), and diabetic subjects had higher log VATI (estimated coefficient=0.248, P<0.001) and LAMI (estimated coefficient=0.078, P<0.001) compared to normal subjects.
Conclusion
Considering that VATI and LAMI represented visceral fat and lipid-rich skeletal muscle volumes, respectively, visceral obesity was identified in both prediabetic and diabetic subjects compared to normal subjects in this study. However, intra-muscular fat infiltration was observed in diabetic subjects only.

Keyword

Abdominal fat; Abdominal muscles; Prediabetic state; Diabetes mellitus

Figure

  • Fig. 1 Classification of subjects based on self-report questionnaires and laboratory results with respect to glycosylated hemoglobin and fasting plasma glucose.


Reference

1. Rao Kondapally Seshasai S, Kaptoge S, Thompson A, Di Angelantonio E, Gao P, Sarwar N, et al. Diabetes mellitus, fasting glucose, and risk of cause-specific death. N Engl J Med. 2011; 364:829–41.
Article
2. Lloyd-Jones DM, Hong Y, Labarthe D, Mozaffarian D, Appel LJ, Van Horn L, et al. Defining and setting national goals for cardiovascular health promotion and disease reduction: the American Heart Association’s strategic Impact Goal through 2020 and beyond. Circulation. 2010; 121:586–613.
Article
3. Flint AJ, Hu FB, Glynn RJ, Caspard H, Manson JE, Willett WC, et al. Excess weight and the risk of incident coronary heart disease among men and women. Obesity (Silver Spring). 2010; 18:377–83.
Article
4. Abdullah A, Peeters A, de Courten M, Stoelwinder J. The magnitude of association between overweight and obesity and the risk of diabetes: a meta-analysis of prospective cohort studies. Diabetes Res Clin Pract. 2010; 89:309–19.
Article
5. Tanaka M, Okada H, Hashimoto Y, Kumagai M, Nishimura H, Fukui M. Low-attenuation muscle is a predictor of diabetes mellitus: a population-based cohort study. Nutrition. 2020; 74:110752.
Article
6. Dube MC, Joanisse DR, Prud’homme D, Lemieux S, Bouchard C, Perusse L, et al. Muscle adiposity and body fat distribution in type 1 and type 2 diabetes: varying relationships according to diabetes type. Int J Obes (Lond). 2006; 30:1721–8.
Article
7. Martin M, Almeras N, Despres JP, Coxson HO, Washko GR, Vivodtzev I, et al. Ectopic fat accumulation in patients with COPD: an ECLIPSE substudy. Int J Chron Obstruct Pulmon Dis. 2017; 12:451–60.
Article
8. Neeland IJ, Turer AT, Ayers CR, Powell-Wiley TM, Vega GL, Farzaneh-Far R, et al. Dysfunctional adiposity and the risk of prediabetes and type 2 diabetes in obese adults. JAMA. 2012; 308:1150–9.
Article
9. Zhang M, Hu T, Zhang S, Zhou L. Associations of different adipose tissue depots with insulin resistance: a systematic review and meta-analysis of observational studies. Sci Rep. 2015; 5:18495.
Article
10. Lopes HF, Correa-Giannella ML, Consolim-Colombo FM, Egan BM. Visceral adiposity syndrome. Diabetol Metab Syndr. 2016; 8:40.
Article
11. Larsen BA, Wassel CL, Kritchevsky SB, Strotmeyer ES, Criqui MH, Kanaya AM, et al. Association of muscle mass, area, and strength with incident diabetes in older adults: the Health ABC Study. J Clin Endocrinol Metab. 2016; 101:1847–55.
Article
12. Goodpaster BH, Thaete FL, Kelley DE. Thigh adipose tissue distribution is associated with insulin resistance in obesity and in type 2 diabetes mellitus. Am J Clin Nutr. 2000; 71:885–92.
Article
13. Chait A, den Hartigh LJ. Adipose tissue distribution, inflammation and its metabolic consequences, including diabetes and cardiovascular disease. Front Cardiovasc Med. 2020; 7:22.
Article
14. Haggmark T, Jansson E, Svane B. Cross-sectional area of the thigh muscle in man measured by computed tomography. Scand J Clin Lab Invest. 1978; 38:355–60.
Article
15. American Diabetes Association. 2. Classification and diagnosis of diabetes: standards of medical care in diabetes-2020. Diabetes Care. 2020; 43(Suppl 1):S14–31.
16. Lee K, Shin Y, Huh J, Sung YS, Lee IS, Yoon KH, et al. Recent issues on body composition imaging for sarcopenia evaluation. Korean J Radiol. 2019; 20:205–17.
Article
17. Park HJ, Shin Y, Park J, Kim H, Lee IS, Seo DW, et al. Development and validation of a deep learning system for segmentation of abdominal muscle and fat on computed tomography. Korean J Radiol. 2020; 21:88–100.
Article
18. McLaughlin T, Lamendola C, Liu A, Abbasi F. Preferential fat deposition in subcutaneous versus visceral depots is associated with insulin sensitivity. J Clin Endocrinol Metab. 2011; 96:E1756–60.
Article
19. Mittal B. Subcutaneous adipose tissue & visceral adipose tissue. Indian J Med Res. 2019; 149:571–3.
Article
20. Wajchenberg BL. Subcutaneous and visceral adipose tissue: their relation to the metabolic syndrome. Endocr Rev. 2000; 21:697–738.
Article
21. Maddocks M, Shrikrishna D, Vitoriano S, Natanek SA, Tanner RJ, Hart N, et al. Skeletal muscle adiposity is associated with physical activity, exercise capacity and fibre shift in COPD. Eur Respir J. 2014; 44:1188–98.
Article
22. Virkamaki A, Korsheninnikova E, Seppala-Lindroos A, Vehkavaara S, Goto T, Halavaara J, et al. Intramyocellular lipid is associated with resistance to in vivo insulin actions on glucose uptake, antilipolysis, and early insulin signaling pathways in human skeletal muscle. Diabetes. 2001; 50:2337–43.
Article
23. Kim D, Nam S, Ahn C, Kim K, Yoon S, Kim J, et al. Correlation between midthigh low-density muscle and insulin resistance in obese nondiabetic patients in Korea. Diabetes Care. 2003; 26:1825–30.
24. Mitsiopoulos N, Baumgartner RN, Heymsfield SB, Lyons W, Gallagher D, Ross R. Cadaver validation of skeletal muscle measurement by magnetic resonance imaging and computerized tomography. J Appl Physiol (1985). 1998; 85:115–22.
Article
25. Aubrey J, Esfandiari N, Baracos VE, Buteau FA, Frenette J, Putman CT, et al. Measurement of skeletal muscle radiation attenuation and basis of its biological variation. Acta Physiol (Oxf). 2014; 210:489–97.
Article
26. Studenski SA, Peters KW, Alley DE, Cawthon PM, McLean RR, Harris TB, et al. The FNIH sarcopenia project: rationale, study description, conference recommendations, and final estimates. J Gerontol A Biol Sci Med Sci. 2014; 69:547–58.
Article
27. Imai K, Ratkovic M. Covariate balancing propensity score. J R Stat Soc Series B Stat Methodol. 2014; 76:243–63.
Article
28. Fong C, Ratkovic M, Hazlett C, Imai K. CBPS: covariate balancing propensity score [R package version 0.21] [Internet]. Vienna (AT): Comprehensive R Archive Network (CRAN);2014. [cited 2021 Jul 21]. Available from: https://CRAN.R-project.org/package=CBPS .
29. Bjorntorp P. “Portal” adipose tissue as a generator of risk factors for cardiovascular disease and diabetes”. Arteriosclerosis. 1990; 10:493–6.
Article
30. Manolopoulos KN, Karpe F, Frayn KN. Gluteofemoral body fat as a determinant of metabolic health. Int J Obes (Lond). 2010; 34:949–59.
Article
31. Tchkonia T, Thomou T, Zhu Y, Karagiannides I, Pothoulakis C, Jensen MD, et al. Mechanisms and metabolic implications of regional differences among fat depots. Cell Metab. 2013; 17:644–56.
Article
32. Goodpaster BH, Thaete FL, Simoneau JA, Kelley DE. Subcutaneous abdominal fat and thigh muscle composition predict insulin sensitivity independently of visceral fat. Diabetes. 1997; 46:1579–85.
Article
33. Borkan GA, Hults DE, Gerzof SG, Robbins AH, Silbert CK. Age changes in body composition revealed by computed tomography. J Gerontol. 1983; 38:673–7.
Article
34. Simoneau JA, Colberg SR, Thaete FL, Kelley DE. Skeletal muscle glycolytic and oxidative enzyme capacities are determinants of insulin sensitivity and muscle composition in obese women. FASEB J. 1995; 9:273–8.
Article
35. Visser M, Kritchevsky SB, Goodpaster BH, Newman AB, Nevitt M, Stamm E, et al. Leg muscle mass and composition in relation to lower extremity performance in men and women aged 70 to 79: the health, aging and body composition study. J Am Geriatr Soc. 2002; 50:897–904.
Article
36. Lang T, Cauley JA, Tylavsky F, Bauer D, Cummings S, Harris TB, et al. Computed tomographic measurements of thigh muscle cross-sectional area and attenuation coefficient predict hip fracture: the health, aging, and body composition study. J Bone Miner Res. 2010; 25:513–9.
Article
37. Violan C, Foguet-Boreu Q, Hermosilla-Perez E, Valderas JM, Bolibar B, Fabregas-Escurriola M, et al. Comparison of the information provided by electronic health records data and a population health survey to estimate prevalence of selected health conditions and multimorbidity. BMC Public Health. 2013; 13:251.
Article
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