Diabetes Metab J.  2018 Aug;42(4):296-307. 10.4093/dmj.2017.0106.

Insulin Resistance and the Risk of Diabetes and Dysglycemia in Korean General Adult Population

• Baek JH
• Kim H
• Kim KY
• Jung J

• KMID: 2418718
• DOI: http://doi.org/10.4093/dmj.2017.0106

Abstract

BACKGROUND
Insulin resistance is a major pathogenic hallmark of impaired glucose metabolism. We assessed the accuracy of insulin resistance and cut-off values using homeostasis model assessment of insulin resistance (HOMA-IR) to classify type 2 diabetes mellitus (T2DM) and dysglycemia according to age and sex.
METHODS
In this cross-sectional study, we analyzed 4,291 anti-diabetic drug-naïve adults (≥20 years) from the 6th Korea National Health and Nutrition Examination Survey in 2015. Metabolic syndrome (MetS) was defined by the modified National Cholesterol Education Program III guideline. Diagnosis of dysglycemia and T2DM were based on fasting glucose and glycosylated hemoglobin levels. The receiver operating characteristic curve and optimal cut-off values of HOMA-IR were assessed to identify T2DM/dysglycemia according to sex and were further analyzed by age.
RESULTS
Sex differences were found in the association of MetS and the different MetS components with T2DM/dysglycemia. The overall optimal cut-off value of HOMA-IR for identifying dysglycemia was 1.6 in both sex. The cut-off values for T2DM were 2.87 in men and 2.36 in women. However, there are differences in diagnostic range of HOMA-IR to distinguish T2DM according to sex and age, and the accuracy of HOMA-IR in identifying T2DM gradually decreased with age especially in women.
CONCLUSION
Insulin resistance is closely associated with the risk for T2DM/dysglycemia. The accuracy of HOMA-IR levels is characterized by sex- and age-specific differences in identifying T2DM. In addition to insulin resistance index, insulin secretory function, and different MetS components should be considered in the detection of early T2DM, especially in elderly.

Keyword

Diabetes mellitus; Hyperglycemia; Insulin resistance; Risk

MeSH Terms

Adult*
Aged
Cholesterol
Cross-Sectional Studies
Diabetes Mellitus
Diabetes Mellitus, Type 2
Diagnosis
Education
Fasting
Female
Glucose
Hemoglobin A, Glycosylated
Homeostasis
Humans
Hyperglycemia
Insulin Resistance*
Insulin*
Korea
Male
Metabolism
Nutrition Surveys
ROC Curve
Sex Characteristics
Cholesterol
Glucose
Insulin

Figure

• 

  Fig. 1 Prevalence of type 2 diabetes mellitus (T2DM) and dysglycemia in different age groups and sex. aP<0.05, bP<0.01.

• 

  Fig. 2 Area under the curve (AUC) (95% confidence interval [CI]) for (A) type 2 diabetes mellitus (T2DM) in men, (B) T2DM in women, (C) dysglycemia in men, and (D) dysglycemia in women in different age groups.

• 

  Fig. 3 Comparison of homeostasis model assessment of β-cell function (HOMA-β) according to sex and age group (distinguished by 50 years) in (A) low tertile of homeostasis model assessment of insulin resistance (HOMA-IR), (B) mid tertile of HOMA-IR, and (C) high tertile of HOMA-IR among those with dysglycemia. White bar, 50 years old or less; gray bar, more than 50 years old. aP<0.05.

Reference

1. Abdul-Ghani MA, Tripathy D, DeFronzo RA. Contributions of beta-cell dysfunction and insulin resistance to the pathogenesis of impaired glucose tolerance and impaired fasting glucose. Diabetes Care. 2006; 29:1130–1139. PMID: 16644654.

2. Noh J. The diabetes epidemic in Korea. Endocrinol Metab (Seoul). 2016; 31:349–353. PMID: 27586447.

3. Danaei G, Finucane MM, Lu Y, Singh GM, Cowan MJ, Paciorek CJ, Lin JK, Farzadfar F, Khang YH, Stevens GA, Rao M, Ali MK, Riley LM, Robinson CA, Ezzati M. Global Burden of Metabolic Risk Factors of Chronic Diseases Collaborating Group (Blood Glucose). National, regional, and global trends in fasting plasma glucose and diabetes prevalence since 1980: systematic analysis of health examination surveys and epidemiological studies with 370 country-years and 2·7 million participants. Lancet. 2011; 378:31–40. PMID: 21705069.

4. Kodama K, Tojjar D, Yamada S, Toda K, Patel CJ, Butte AJ. Ethnic differences in the relationship between insulin sensitivity and insulin response: a systematic review and meta-analysis. Diabetes Care. 2013; 36:1789–1796. PMID: 23704681.

5. Ohn JH, Kwak SH, Cho YM, Lim S, Jang HC, Park KS, Cho NH. 10-Year trajectory of β-cell function and insulin sensitivity in the development of type 2 diabetes: a community-based prospective cohort study. Lancet Diabetes Endocrinol. 2016; 4:27–34. PMID: 26577716.

6. Matthews DR, Hosker JP, Rudenski AS, Naylor BA, Treacher DF, Turner RC. Homeostasis model assessment: insulin resistance and beta-cell function from fasting plasma glucose and insulin concentrations in man. Diabetologia. 1985; 28:412–419. PMID: 3899825.

7. Bonora E, Targher G, Alberiche M, Bonadonna RC, Saggiani F, Zenere MB, Monauni T, Muggeo M. Homeostasis model assessment closely mirrors the glucose clamp technique in the assessment of insulin sensitivity: studies in subjects with various degrees of glucose tolerance and insulin sensitivity. Diabetes Care. 2000; 23:57–63. PMID: 10857969.

8. Gayoso-Diz P, Otero-Gonzalez A, Rodriguez-Alvarez MX, Gude F, Cadarso-Suarez C, Garcia F, De Francisco A. Insulin resistance index (HOMA-IR) levels in a general adult population: curves percentile by gender and age. The EPIRCE study Diabetes Res Clin Pract. 2011; 94:146–155. PMID: 21824674.

9. Esteghamati A, Ashraf H, Esteghamati AR, Meysamie A, Khalilzadeh O, Nakhjavani M, Abbasi M. Optimal threshold of homeostasis model assessment for insulin resistance in an Iranian population: the implication of metabolic syndrome to detect insulin resistance. Diabetes Res Clin Pract. 2009; 84:279–287. PMID: 19359063.

10. Gayoso-Diz P, Otero-Gonzalez A, Rodriguez-Alvarez MX, Gude F, Garcia F, De Francisco A, Quintela AG. Insulin resistance (HOMA-IR) cut-off values and the metabolic syndrome in a general adult population: effect of gender and age. EPIRCE cross-sectional study. BMC Endocr Disord. 2013; 13:47. PMID: 24131857.

11. Morimoto A, Tatsumi Y, Soyano F, Miyamatsu N, Sonoda N, Godai K, Ohno Y, Noda M, Deura K. Increase in homeostasis model assessment of insulin resistance (HOMA-IR) had a strong impact on the development of type 2 diabetes in Japanese individuals with impaired insulin secretion: the Saku study. PLoS One. 2014; 9:e105827. PMID: 25166121.

12. Lee CH, Shih AZ, Woo YC, Fong CH, Leung OY, Janus E, Cheung BM, Lam KS. Optimal cut-offs of homeostasis model assessment of insulin resistance (HOMA-IR) to identify dysglycemia and type 2 diabetes mellitus: a 15-year prospective study in Chinese. PLoS One. 2016; 11:e0163424. PMID: 27658115.

13. Ghasemi A, Tohidi M, Derakhshan A, Hasheminia M, Azizi F, Hadaegh F. Cut-off points of homeostasis model assessment of insulin resistance, beta-cell function, and fasting serum insulin to identify future type 2 diabetes: Tehran Lipid and Glucose Study. Acta Diabetol. 2015; 52:905–915. PMID: 25794879.

14. Yun KJ, Han K, Kim MK, Park YM, Baek KH, Song KH, Kwon HS. Insulin resistance distribution and cut-off value in Koreans from the 2008-2010 Korean National Health and Nutrition Examination Survey. PLoS One. 2016; 11:e0154593. PMID: 27128847.

15. Korea Centers for Disease Control and Prevention. The Sixth Korea National Health and Nutrition Examination Survey (KNHANES VI). 2018 Mar 23. Available from: https://knhanes.cdc.go.kr/knhanes/intro.html.

16. Kweon S, Kim Y, Jang MJ, Kim Y, Kim K, Choi S, Chun C, Khang YH, Oh K. Data resource profile: the Korea National Health and Nutrition Examination Survey (KNHANES). Int J Epidemiol. 2014; 43:69–77. PMID: 24585853.

17. American Diabetes Association. 2. Classification and diagnosis of diabetes. Diaetes Care. 2016; 39(Suppl 1):S13–S22.

18. Alberti KG, Eckel RH, Grundy SM, Zimmet PZ, Cleeman JI, Donato KA, Fruchart JC, James WP, Loria CM, Smith SC Jr. International Diabetes Federation Task Force on Epidemiology and Prevention. Hational Heart, Lung, and Blood Institute. American Heart Association; World Heart Federation. Inter national Atherosclerosis Society. International Association for the Study of Obesity. Harmonizing the metabolic syndrome: a joint interim statement of the International Diabetes Federation Task Force on Epidemiology and Prevention; National Heart, Lung, and Blood Institute; American Heart Association; World Heart Federation; International Atherosclerosis Society; and International Association for the Study of Obesity. Circulation. 2009; 120:1640–1645. PMID: 19805654.

19. Lee SY, Park HS, Kim DJ, Han JH, Kim SM, Cho GJ, Kim DY, Kwon HS, Kim SR, Lee CB, Oh SJ, Park CY, Yoo HJ. Appropriate waist circumference cutoff points for central obesity in Korean adults. Diabetes Res Clin Pract. 2007; 75:72–80. PMID: 16735075.

20. Kuk JL, Ardern CI. Age and sex differences in the clustering of metabolic syndrome factors: association with mortality risk. Diabetes Care. 2010; 33:2457–2461. PMID: 20699434.

21. Schmidt MI, Duncan BB, Bang H, Pankow JS, Ballantyne CM, Golden SH, Folsom AR, Chambless LE. Atherosclerosis Risk in Communities Investigators. Identifying individuals at high risk for diabetes: the Atherosclerosis Risk in Communities study. Diabetes Care. 2005; 28:2013–2018. PMID: 16043747.

22. Wilson PW, Meigs JB, Sullivan L, Fox CS, Nathan DM, D'Agostino RB Sr. Prediction of incident diabetes mellitus in middle-aged adults: the Framingham Offspring Study. Arch Intern Med. 2007; 167:1068–1074. PMID: 17533210.

23. Freiberg MS, Cabral HJ, Heeren TC, Vasan RS, Curtis Ellison R. Third National Health and Nutrition Examination Survey. Alcohol consumption and the prevalence of the metabolic syndrome in the US: a cross-sectional analysis of data from the Third National Health and Nutrition Examination Survey. Diabetes Care. 2004; 27:2954–2959. PMID: 15562213.

24. Golbidi S, Mesdaghinia A, Laher I. Exercise in the metabolic syndrome. Oxid Med Cell Longev. 2012; 2012:349710. PMID: 22829955.

25. Oh SW, Yoon YS, Lee ES, Kim WK, Park C, Lee S, Jeong EK, Yoo T. Korea National Health and Nutrition Examination Survey. Association between cigarette smoking and metabolic syndrome: the Korea National Health and Nutrition Examination Survey. Diabetes Care. 2005; 28:2064–2066. PMID: 16043763.

26. Tang Q, Li X, Song P, Xu L. Optimal cut-off values for the homeostasis model assessment of insulin resistance (HOMA-IR) and pre-diabetes screening: developments in research and prospects for the future. Drug Discov Ther. 2015; 9:380–385. PMID: 26781921.

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; 2:373–376.

28. Yamada C, Moriyama K, Takahashi E. Optimal cut-off point for homeostasis model assessment of insulin resistance to discriminate metabolic syndrome in non-diabetic Japanese subjects. J Diabetes Investig. 2012; 3:384–387.

29. Bryhni B, Arnesen E, Jenssen TG. Associations of age with serum insulin, proinsulin and the proinsulin-to-insulin ratio: a cross-sectional study. BMC Endocr Disord. 2010; 10:21. PMID: 21162746.

30. Tohidi M, Ghasemi A, Hadaegh F, Derakhshan A, Chary A, Azizi F. Age- and sex-specific reference values for fasting serum insulin levels and insulin resistance/sensitivity indices in healthy Iranian adults: Tehran Lipid and Glucose Study. Clin Biochem. 2014; 47:432–438. PMID: 24530467.

31. Gupte AA, Pownall HJ, Hamilton DJ. Estrogen: an emerging regulator of insulin action and mitochondrial function. J Diabetes Res. 2015; 2015:916585. PMID: 25883987.

32. Borai A, Livingstone C, Kaddam I, Ferns G. Selection of the appropriate method for the assessment of insulin resistance. BMC Med Res Methodol. 2011; 11:158. PMID: 22112229.

33. Monnier L, Lapinski H, Colette C. Contributions of fasting and postprandial plasma glucose increments to the overall diurnal hyperglycemia of type 2 diabetic patients: variations with increasing levels of HbA(1c). Diabetes Care. 2003; 26:881–885. PMID: 12610053.

34. Ketema EB, Kibret KT. Correlation of fasting and postprandial plasma glucose with HbA1c in assessing glycemic control: systematic review and meta-analysis. Arch Public Health. 2015; 73:43. PMID: 26413295.