Association between metabolic body composition status and risk for impaired renal function: A cross-sectional study

English version

Autoři: Wen-Cheng Li ^aff001; Jau-Yuan Chen ^aff001; Yu-Ping Liu ^aff004; Yi-Yen Lee ^aff005; Wei-Chung Yeh ^aff001; Wei Yu ^aff003; Yu-Chung Tsao ^aff002
Působiště autorů: Department of Family Medicine, Chang Gung Memorial Hospital, Linkou, Taoyuan, Taiwan ^aff001; College of Medicine, Chang Gung University, Taoyuan, Taiwan ^aff002; Department of Health Management, Xiamen Chang-Gung Hospital, Xiamen, China ^aff003; Department of Endocrinology and Metabolism, Xiamen Chang-Gung Hospital, Xiamen, China ^aff004; Division of Pediatric Neurosurgery, Neurological Institute, Taipei Veterans General Hospital, Taipei, Taiwan ^aff005; Faculty of Medicine, National Yang-Ming University, Taipei, Taiwan ^aff006; Department of Occupational Medicine, Chang Gung Memorial Hospital, Linkou, Taoyuan, Taiwan ^aff007
Vyšlo v časopise: PLoS ONE 14(11)
Kategorie: Research Article
prolekare.web.journal.doi_sk: https://doi.org/10.1371/journal.pone.0223664

Souhrn

Background

The risk for obesity-related disorders is proportional to the visceral region and had been observed to be highly related with impaired renal function. In the current study, we aimed to evaluate renal function impairment, according to sex, age, and different status of metabolic body composition.

Methods

We retrospectively collected from the medical records the basic information and metabolic titers of Chinese adults (13,373 men and 10,175 women) who underwent health checkup from 2013 to 2016. The population was divided into four groups, according to metabolic body composition, including metabolic healthy norms-weight (MHNW), metabolic healthy obesity (MHO), metabolic unhealthy norms-weight (MUNW), and metabolic unhealthy obesity (MUO). The categorical data were compared among the groups and logistic regression analyses were conducted to investigate the association between metabolic body composition status and risk for renal function impairment.

Results

Across all ages in both sexes, the odds ratios (OR) for renal function impairment were higher in the MHO, MUNW, and MUO groups than in the MHNW group, except for women <45 years old in the MUNW group. However, after adjustment, the trend was no longer significant in all groups under 45 years old. For individuals >45 years old, the relatively high risk for renal function impairment remained significantly associated with the MUNW group (OR 2.95, 95% CI 2.02–4.30 in men and OR 1.95, 95% CI 1.35–2.82 in women) and MUO group (OR 2.33, 95% CI 1.82–3.00 in men and OR 2.67, 95% CI 2.04–3.48 in women).

Conclusion

Impaired renal function was independently associated with the status of metabolic obesity. However, the trend was only observed in individuals >45 years old, with significant sex difference.

Klíčová slova:

Body Mass Index – Cholesterol – Obesity – Biomarkers – Renal system – Blood pressure – Chronic kidney disease – Protein metabolism

Zdroje

1. Gomez-Ambrosi J, Silva C, Galofre JC, Escalada J, Santos S, Millan D, et al. Body mass index classification misses subjects with increased cardiometabolic risk factors related to elevated adiposity. Int J Obes (Lond). 2012;36(2):286–94. Epub 2011/05/19. doi: 10.1038/ijo.2011.100 21587201.

2. Stefan N, Haring HU, Hu FB, Schulze MB. Metabolically healthy obesity: epidemiology, mechanisms, and clinical implications. Lancet Diabetes Endocrinol. 2013;1(2):152–62. Epub 2014/03/14. doi: 10.1016/S2213-8587(13)70062-7 24622321.

3. Tchernof A, Despres JP. Pathophysiology of human visceral obesity: an update. Physiol Rev. 2013;93(1):359–404. Epub 2013/01/11. doi: 10.1152/physrev.00033.2011 23303913.

4. Primeau V, Coderre L, Karelis AD, Brochu M, Lavoie ME, Messier V, et al. Characterizing the profile of obese patients who are metabolically healthy. Int J Obes (Lond). 2011;35(7):971–81. Epub 2010/10/27. doi: 10.1038/ijo.2010.216 20975726

5. Janssen I, Katzmarzyk PT, Ross R. Waist circumference and not body mass index explains obesity-related health risk. Am J Clin Nutr. 2004;79(3):379–84. Epub 2004/02/27. doi: 10.1093/ajcn/79.3.379 14985210.

6. Naukkarinen J, Heinonen S, Hakkarainen A, Lundbom J, Vuolteenaho K, Saarinen L, et al. Characterising metabolically healthy obesity in weight-discordant monozygotic twins. Diabetologia. 2014;57(1):167–76. Epub 2013/10/09. doi: 10.1007/s00125-013-3066-y 24100782.

7. Fruhbeck G, Gomez-Ambrosi J, Salvador J. Leptin-induced lipolysis opposes the tonic inhibition of endogenous adenosine in white adipocytes. FASEB J. 2001;15(2):333–40. Epub 2001/02/07. doi: 10.1096/fj.00-0249com 11156949.

8. Mangge H, Zelzer S, Puerstner P, Schnedl WJ, Reeves G, Postolache TT, et al. Uric acid best predicts metabolically unhealthy obesity with increased cardiovascular risk in youth and adults. Obesity (Silver Spring). 2013;21(1):E71–7. Epub 2013/02/13. doi: 10.1002/oby.20061 23401248.

9. Messier V, Karelis AD, Robillard ME, Bellefeuille P, Brochu M, Lavoie JM, et al. Metabolically healthy but obese individuals: relationship with hepatic enzymes. Metabolism. 2010;59(1):20–4. Epub 2009/08/28. doi: 10.1016/j.metabol.2009.06.020 19709695.

10. Karelis AD, Faraj M, Bastard JP, St-Pierre DH, Brochu M, Prud'homme D, et al. The metabolically healthy but obese individual presents a favorable inflammation profile. J Clin Endocrinol Metab. 2005;90(7):4145–50. Epub 2005/04/28. doi: 10.1210/jc.2005-0482 15855252.

11. Marques-Vidal P, Velho S, Waterworth D, Waeber G, von Kanel R, Vollenweider P. The association between inflammatory biomarkers and metabolically healthy obesity depends of the definition used. Eur J Clin Nutr. 2012;66(4):426–35. Epub 2011/09/29. doi: 10.1038/ejcn.2011.170 21952696.

12. Meigs JB, Wilson PW, Fox CS, Vasan RS, Nathan DM, Sullivan LM, et al. Body mass index, metabolic syndrome, and risk of type 2 diabetes or cardiovascular disease. J Clin Endocrinol Metab. 2006;91(8):2906–12. Epub 2006/06/01. doi: 10.1210/jc.2006-0594 16735483.

13. Conus F, Allison DB, Rabasa-Lhoret R, St-Onge M, St-Pierre DH, Tremblay-Lebeau A, et al. Metabolic and behavioral characteristics of metabolically obese but normal-weight women. J Clin Endocrinol Metab. 2004;89(10):5013–20. Epub 2004/10/09. doi: 10.1210/jc.2004-0265 15472199.

14. Dvorak RV, DeNino WF, Ades PA, Poehlman ET. Phenotypic characteristics associated with insulin resistance in metabolically obese but normal-weight young women. Diabetes. 1999;48(11):2210–4. Epub 1999/10/27. doi: 10.2337/diabetes.48.11.2210 10535456.

15. Katsuki A, Sumida Y, Urakawa H, Gabazza EC, Murashima S, Maruyama N, et al. Increased visceral fat and serum levels of triglyceride are associated with insulin resistance in Japanese metabolically obese, normal weight subjects with normal glucose tolerance. Diabetes Care. 2003;26(8):2341–4. Epub 2003/07/29. doi: 10.2337/diacare.26.8.2341 12882859.

16. Katsuki A, Sumida Y, Urakawa H, Gabazza EC, Murashima S, Matsumoto K, et al. Plasma levels of adiponectin are associated with insulin resistance and serum levels of triglyceride in Japanese metabolically obese, normal-weight men with normal glucose tolerance. Diabetes Care. 2003;26(10):2964–5. Epub 2003/09/30. doi: 10.2337/diacare.26.10.2964 14514619

17. Marini MA, Succurro E, Frontoni S, Hribal ML, Andreozzi F, Lauro R, et al. Metabolically healthy but obese women have an intermediate cardiovascular risk profile between healthy nonobese women and obese insulin-resistant women. Diabetes Care. 2007;30(8):2145–7. Epub 2007/05/18. doi: 10.2337/dc07-0419 17507694.

18. Ruderman N, Chisholm D, Pi-Sunyer X, Schneider S. The metabolically obese, normal-weight individual revisited. Diabetes. 1998;47(5):699–713. Epub 1998/05/20. doi: 10.2337/diabetes.47.5.699 9588440.

19. St-Onge MP, Janssen I, Heymsfield SB. Metabolic syndrome in normal-weight Americans: new definition of the metabolically obese, normal-weight individual. Diabetes Care. 2004;27(9):2222–8. Epub 2004/08/31. doi: 10.2337/diacare.27.9.2222 15333488.

20. Bosetti F, Galis ZS, Bynoe MS, Charette M, Cipolla MJ, Zoppo GJd, et al. “Small Blood Vessels: Big Health Problems?”: Scientific Recommendations of the National Institutes of Health Workshop. Journal of the American Heart Association. 2016;5(11):e004389. doi: 10.1161/JAHA.116.004389 27815267

21. Mogensen CE, Chachati A, Christensen CK, Close CF, Deckert T, Hommel E, et al. Microalbuminuria: an early marker of renal involvement in diabetes. Uremia Invest. 1985;9(2):85–95. Epub 1985/01/01. 3915933.

22. Weir MR. Microalbuminuria and Cardiovascular Disease. Clinical Journal of the American Society of Nephrology. 2007;2(3):581–90. doi: 10.2215/CJN.03190906 17699466

23. Levey AS, Stevens LA, Schmid CH, Zhang YL, Castro AF, 3rd, Feldman HI, et al. A new equation to estimate glomerular filtration rate. Ann Intern Med. 2009;150(9):604–12. Epub 2009/05/06. doi: 10.7326/0003-4819-150-9-200905050-00006 19414839; PubMed Central PMCID: PMC2763564.

24. Keane WF, Eknoyan G. Proteinuria, albuminuria, risk, assessment, detection, elimination (PARADE): a position paper of the National Kidney Foundation. Am J Kidney Dis. 1999;33(5):1004–10. Epub 1999/04/23. doi: 10.1016/s0272-6386(99)70442-7 10213663.

25. Levey AS, Coresh J, Balk E, Kausz AT, Levin A, Steffes MW, et al. National Kidney Foundation practice guidelines for chronic kidney disease: evaluation, classification, and stratification. Ann Intern Med. 2003;139(2):137–47. Epub 2003/07/16. doi: 10.7326/0003-4819-139-2-200307150-00013 12859163.

26. Singh Y, Garg MK, Tandon N, Marwaha RK. A study of insulin resistance by HOMA-IR and its cut-off value to identify metabolic syndrome in urban Indian adolescents. J Clin Res Pediatr Endocrinol. 2013;5(4):245–51. Epub 2014/01/01. doi: 10.4274/Jcrpe.1127 24379034; PubMed Central PMCID: PMC3890224.

27. Yamada C, Mitsuhashi T, Hiratsuka N, Inabe F, Araida N, Takahashi E. Optimal reference interval for homeostasis model assessment of insulin resistance in a Japanese population. J Diabetes Investig. 2011;2(5):373–6. Epub 2011/10/07. doi: 10.1111/j.2040-1124.2011.00113.x 24843516; PubMed Central PMCID: PMC4019305.

28. Kramer CK, Zinman B, Retnakaran R. Are metabolically healthy overweight and obesity benign conditions?: A systematic review and meta-analysis. Ann Intern Med. 2013;159(11):758–69. Epub 2013/12/04. doi: 10.7326/0003-4819-159-11-201312030-00008 24297192.

29. Chang Y, Kim BK, Yun KE, Cho J, Zhang Y, Rampal S, et al. Metabolically-healthy obesity and coronary artery calcification. J Am Coll Cardiol. 2014;63(24):2679–86. Epub 2014/05/06. doi: 10.1016/j.jacc.2014.03.042 24794119.

30. Morkedal B, Vatten LJ, Romundstad PR, Laugsand LE, Janszky I. Risk of myocardial infarction and heart failure among metabolically healthy but obese individuals: HUNT (Nord-Trondelag Health Study), Norway. J Am Coll Cardiol. 2014;63(11):1071–8. Epub 2013/12/19. doi: 10.1016/j.jacc.2013.11.035 24345592.

31. Stefan N, Kantartzis K, Machann J, Schick F, Thamer C, Rittig K, et al. Identification and characterization of metabolically benign obesity in humans. Arch Intern Med. 2008;168(15):1609–16. Epub 2008/08/13. doi: 10.1001/archinte.168.15.1609 18695074.

32. Ruderman NB, Berchtold P, Schneider S. Obesity-associated disorders in normal-weight individuals: some speculations. Int J Obes. 1982;6 Suppl 1:151–7. Epub 1982/01/01. 6749721.

33. Fujioka S, Matsuzawa Y, Tokunaga K, Tarui S. Contribution of intra-abdominal fat accumulation to the impairment of glucose and lipid metabolism in human obesity. Metabolism. 1987;36(1):54–9. Epub 1987/01/01. doi: 10.1016/0026-0495(87)90063-1 3796297.

34. Romero-Corral A, Somers VK, Sierra-Johnson J, Korenfeld Y, Boarin S, Korinek J, et al. Normal weight obesity: a risk factor for cardiometabolic dysregulation and cardiovascular mortality. Eur Heart J. 2010;31(6):737–46. Epub 2009/11/26. doi: 10.1093/eurheartj/ehp487 PubMed Central PMCID: PMC2838679. 19933515

35. Chang Y, Ryu S, Choi Y, Zhang Y, Cho J, Kwon MJ, et al. Metabolically Healthy Obesity and Development of Chronic Kidney Disease: A Cohort Study. Ann Intern Med. 2016;164(5):305–12. Epub 2016/02/10. doi: 10.7326/M15-1323 26857595.

36. Jung CH, Lee MJ, Kang YM, Hwang JY, Kim EH, Park JY, et al. The risk of chronic kidney disease in a metabolically healthy obese population. Kidney Int. 2015;88(4):843–50. Epub 2015/06/25. doi: 10.1038/ki.2015.183 26108064.

37. de Jong PE, Verhave JC, Pinto-Sietsma SJ, Hillege HL, group Ps. Obesity and target organ damage: the kidney. Int J Obes Relat Metab Disord. 2002;26 Suppl 4:S21–4. Epub 2002/11/29. doi: 10.1038/sj.ijo.0802213 12457295.

38. de Vries AP, Ruggenenti P, Ruan XZ, Praga M, Cruzado JM, Bajema IM, et al. Fatty kidney: emerging role of ectopic lipid in obesity-related renal disease. Lancet Diabetes Endocrinol. 2014;2(5):417–26. Epub 2014/05/06. doi: 10.1016/S2213-8587(14)70065-8 24795255.

39. Wickman C, Kramer H. Obesity and kidney disease: potential mechanisms. Semin Nephrol. 2013;33(1):14–22. Epub 2013/02/05. doi: 10.1016/j.semnephrol.2012.12.006 23374890.

40. Garland JS. Elevated body mass index as a risk factor for chronic kidney disease: current perspectives. Diabetes Metab Syndr Obes. 2014;7:347–55. Epub 2014/08/13. doi: 10.2147/DMSO.S46674 25114577; PubMed Central PMCID: PMC4122576.

41. Hashimoto Y, Tanaka M, Okada H, Senmaru T, Hamaguchi M, Asano M, et al. Metabolically healthy obesity and risk of incident CKD. Clin J Am Soc Nephrol. 2015;10(4):578–83. Epub 2015/01/31. doi: 10.2215/CJN.08980914 25635035; PubMed Central PMCID: PMC4386260.

42. Ishizaka N, Ishizaka Y, Toda E, Koike K, Seki G, Nagai R, et al. Association between obesity and chronic kidney disease in Japanese: differences in gender and hypertensive status? Hypertens Res. 2007;30(11):1059–64. Epub 2008/02/06. doi: 10.1291/hypres.30.1059 18250555.

43. Iseki K, Ikemiya Y, Kinjo K, Inoue T, Iseki C, Takishita S. Body mass index and the risk of development of end-stage renal disease in a screened cohort. Kidney Int. 2004;65(5):1870–6. Epub 2004/04/17. doi: 10.1111/j.1523-1755.2004.00582.x 15086929.

44. Gretz N, Zeier M, Geberth S, Strauch M, Ritz E. Is gender a determinant for evolution of renal failure? A study in autosomal dominant polycystic kidney disease. Am J Kidney Dis. 1989;14(3):178–83. Epub 1989/09/01. doi: 10.1016/s0272-6386(89)80068-x 2672797.

45. Sakemi T, Toyoshima H, Morito F. Testosterone eliminates the attenuating effect of castration on the progressive glomerular injury in hypercholesterolemic male Imai rats. Nephron. 1994;67(4):469–76. Epub 1994/01/01. doi: 10.1159/000188022 7969682.

46. Lee JM, Okumura MJ, Davis MM, Herman WH, Gurney JG. Prevalence and determinants of insulin resistance among U.S. adolescents: a population-based study. Diabetes Care. 2006;29(11):2427–32. Epub 2006/10/27. doi: 10.2337/dc06-0709 17065679.

47. Kubo S, Kitamura A, Imano H, Cui R, Yamagishi K, Umesawa M, et al. Serum Albumin and High-Sensitivity C-reactive Protein are Independent Risk Factors of Chronic Kidney Disease in Middle-Aged Japanese Individuals: the Circulatory Risk in Communities Study. J Atheroscler Thromb. 2016;23(9):1089–98. Epub 2016/02/26. doi: 10.5551/jat.33530 26911856; PubMed Central PMCID: PMC5090815.

48. Shankar A, Sun L, Klein BE, Lee KE, Muntner P, Nieto FJ, et al. Markers of inflammation predict the long-term risk of developing chronic kidney disease: a population-based cohort study. Kidney Int. 2011;80(11):1231–8. Epub 2011/08/26. doi: 10.1038/ki.2011.283 21866089; PubMed Central PMCID: PMC3260339.

49. Tsao YC, Lee YY, Chen JY, Yeh WC, Chuang CH, Yu W, et al. Gender- and Age-Specific Associations Between Body Fat Composition and C-Reactive Protein with Lung Function: A Cross-Sectional Study. Sci Rep. 2019;9(1):384. Epub 2019/01/25. doi: 10.1038/s41598-018-36860-9 30674938; PubMed Central PMCID: PMC6344558.

50. Tsao YC, Chen JY, Yeh WC, Li WC. Gender- and Age-Specific Associations between Visceral Obesity and Renal Function Impairment. Obes Facts. 2019;12(1):67–77. Epub 2019/02/07. doi: 10.1159/000496626 30726849; PubMed Central PMCID: PMC6465737.