We sought to identify the distribution and cut-off value of the ‘homeostasis model assessment of insulin resistance’ (HOMA-IR) according to gender and menopausal status for metabolic syndrome in Koreans.
Data were from the Korean National Health and Nutrition Examination Survey in 2008–2010. The subjects included adults aged 20 years or older. We excluded participants who had diabetes or fasting serum glucose ≥ 7 mmol/L. Finally, 11,121 subjects (4,911 men, 3,597 premenopausal women, 2,613 postmenopausal women) were enrolled. The modified Adult Treatment Panel III criteria were used to define metabolic syndrome.
The mean HOMA-IR was 2.11 (2.07–2.15) for men, 2.0 (1.97–2.04) for premenopausal women, and 2.14 (2.2–2.19) for postmenopausal women. The first cut-off values in men, premenopausal women, and postmenopausal women were 2.23 (sensitivity 70.6%, specificity 66.9%), 2.39 (sensitivity 72.3%, specificity 76.4%), and 2.48 (sensitivity 51.9%, specificity 80.2%), respectively. Based on the first HOMA-IR cut-off value, the prevalence of metabolic syndrome was 22.9% in men, 13.7% in premenopausal women, and 51.6% in postmenopausal women. The second cut-off value was around 3.2 in all three groups. Based on the second HOMA-IR cut-off value, the prevalence of metabolic syndrome was 50.8% in men, 42.5% in premenopausal women, and 71.6% in postmenopausal women.
In conclusion, the first cut-off values for HOMA-IR were 2.2–2.5 and the second cut-off value was 3.2 in Korea. The distribution of HOMA-IR showed differences according to gender and menopausal status. When we apply HOMA-IR, we should consider gender, menopausal status, and the prevalence of metabolic syndrome.
Citation: Yun K-J, Han K, Kim MK, Park Y-M, Baek K-H, Song K-H, et al. (2016) Insulin Resistance Distribution and Cut-Off Value in Koreans from the 2008-2010 Korean National Health and Nutrition Examination Survey. PLoS ONE 11(4): e0154593. https://doi.org/10.1371/journal.pone.0154593
Editor: Angelo Scuteri, INRCA, ITALY
Received: October 29, 2015; Accepted: April 16, 2016; Published: April 29, 2016
Copyright: © 2016 Yun et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Data Availability: All data files are available from the Korea Centers for Disease Control and Prevention database through the following URLs: http://cdc.go.kr/CDC/contents/CdcKrContentView.jsp?cid=60599&menuIds=HOME001-MNU1130-MNU1639-MNU1640-MNU1642, http://cdc.go.kr/CDC/contents/CdcKrContentView.jsp?cid=60940&menuIds=HOME001-MNU1130-MNU1639-MNU1748-MNU1752, and https://knhanes.cdc.go.kr/knhanes/eng/. Anybody who signs up for membership can get raw data from the webpage. Unfortunately, the data downloads are only available on the Korean site. Kyungdo Han (firstname.lastname@example.org), the second author, can help provide the data.
Funding: The authors received no specific funding for this work.
Competing interests: The authors have declared that no competing interests exist.
Insulin resistance (IR) is closely associated with cardiovascular risk factors, including diabetes mellitus, dyslipidemia, and hypertension . Therefore, identifying and quantifying insulin resistance is important, and it can instill awareness in patients. The gold standard method for insulin sensitivity and resistance is the hyperinsulinemic-euglycemic glucose clamp technique . However, this is not readily applied in large-scale investigations because of its complex process. Alternative and simpler methods of measuring insulin resistance include the fasting insulin level, the ‘homeostasis model assessment of insulin resistance’ (HOMA-IR), and the quantitative insulin sensitivity check index (QUICKI) [3, 4].
HOMA-IR is a widely used index and a useful assessment of IR . However, it is still unclear what cut-off value of HOMA-IR should define IR. Several studies for defining cut-off values of HOMA-IR have been published [6–10]. The values were not consistent, and showed gender and racial differences. In 7,057 healthy Korean people (4,472 men, 2,585 women), a high HOMA-IR (≥ 2.56) and fasting insulin (≥ 9.98 μIU/mL) were significantly associated with metabolic syndrome after adjusting for age, gender, and body mass index (P < 0.001) . In another study, the cut-off values of HOMA-IR for metabolic syndrome in Korean non-diabetic adults were 2.34 (sensitivity 62.8%, specificity 65.7%) . However, this study was based on single center data and did not distinguish patients by gender.
The purpose of the present study using data representing the Korean population was to identify the distribution of HOMA-IR and to define the cut-off values according to gender and menopausal status for metabolic syndrome.
Material and Methods
The data for our study were from the Korean National Health and Nutrition Examination Survey (KNHANES) in 2008–2010. The KNHANES is conducted annually by the Korean Ministry of Health and Welfare to monitor the general health and nutritional status of the South Korean population. This survey is composed of a health interview survey, a health examination survey, and a nutrition survey by trained investigators. Data are collected using a rolling sampling design that involves complex, stratified, and multistage probability samples. All participants signed an informed consent form and this survey was approved by the institutional review board of the Korea Centers for Disease Control. Additional details about the survey have been provided elsewhere . The subjects for the present study included adults aged 20 years or older. We excluded participants who had diabetes or fasting serum glucose ≥ 7 mmol/L. Participants with missing data, such as fasting glucose, fasting insulin, and lipid profile were also excluded. Finally, 11,121 subjects (4,911 men, 6,210 women) were included in our analysis.
Measurement and classification of variables
Body weight and height were measured with the subject wearing light clothing and body mass index (BMI) was calculated using the formula: BMI = weight (kg) / height (m2). Waist circumference (WC) was measured at the level midway between the costal margin and the iliac crest at the end of a normal expiration. The subjects were required to rest for at least 5 min before measuring blood pressure using a mercury sphygmomanometer (Baumanometer; Baum, Copiague, NY, USA) in the sitting position. Blood pressure (BP) was measured three times and the mean value of the second and third measurements was used for the analysis.
Blood sampling was performed after at least an 8-h fast. Fasting blood glucose and cholesterol were measured using a Hitachi automatic analyzer 7600 (Hitachi, Tokyo, Japan). Serum insulin was estimated using a radioimmunoassay method with 1470 WIZARD gamma counter (Perkin-Elmer, Turku, Finland). Insulin resistance was measured using HOMA-IR, calculated as follows: HOMA-IR = fasting insulin (μU/mL) × fasting glucose (mg/dL) / 22.5. HOMA-IR was divided into 10 deciles.
Self-reported questionnaires were used to determine menopausal status, smoking status, alcohol consumption, and exercise habit. Premenopausal women were defined as women without a history of reproductive surgery and having > 1 menstruation during the past 12 months. Postmenopausal women were defined as women having no menstruation during the past 12 months. Current smoking was defined as subjects who were currently smoking and had smoked more than 100 cigarettes in their lifetime. The amount of alcohol consumed (g/day) was determined by the amount and type of alcohol for a month. Heavy drinking was defined as the subject drinking more than 30 g/day. Regular exercise was defined as moderate exercise for longer than 30 min at a time at least five times per week, or intense exercise for longer than 20 min at a time at least three times per week.
Definitions of metabolic syndrome
Metabolic syndrome, reflecting cardiovascular risk factors, has been defined using several criteria. We used the modified Adult Treatment Panel III (ATP III) criteria to define metabolic syndrome. Specifically, metabolic syndrome was defined as the presence of three or more of the following;1) abdominal obesity (WC ≥ 90 cm in men and ≥ 80 cm in women, by World Health Organization-Asian Pacific region criteria ), 2) triglycerides ≥ 1.69 mmol/L or on drug treatment for elevated triglycerides, 3) HDL-cholesterol < 1.03 mmol/L (men) or < 1.29 mmol/L (women) or on drug treatment for reduced HDL-cholesterol, 4) BP ≥ 130/85 mmHg or on antihypertensive drug treatment, and 5) fasting plasma glucose ≥ 5.6 mmol/L or on drug treatment for elevated glucose .
The data are presented as means ± standard errors (SE) for continuous variables and as proportions (SE) for categorical variables. Analysis of variance (ANOVA) or the chi-square test was used to compare the clinical characteristics between the men, premenopausal women, and postmenopausal women in Table 1. If necessary, logarithmic transformations were performed for variables with skewed distributions. A receiver operating characteristic (ROC) curve was calculated to evaluate HOMA-IR cut-off value for metabolic syndrome. The Youden index, calculated as (sensitivity + specificity—1) was estimated to determine optimal cut-off values. After those subjects with a HOMA-IR lower than the first HOMA-IR cut-off value had been excluded, the data were recalculated to obtain the second cut-off value. The odds ratios (OR) and 95% confidence intervals (CI) were calculated to identify the risk of metabolic syndrome components at the HOMA-IR cut-off level. All values were adjusted by age, BMI and lifestyle factors (current smoking, heavy drinker, regular exercise). All statistical analyses were performed with the SAS software (ver. 9.3; SAS Institute; Cary, NC, USA). A P-value < 0.05 was considered to indicate statistical significance.
The baseline clinical characteristics of the study population are shown in Table 1. Subjects were subdivided into men, premenopausal women, and postmenopausal women. Insulin resistance probably increases in women after menopause because of estrogen deficiency and increased visceral adipose tissue [6, 16]. Thus, subjects were divided into three groups for determining more accurate HOMA-IR cut-off values. The mean ages of men, premenopausal women, and postmenopausal women were 42.5±0.3, 35.2±0.2, and 61.7±0.3 years, respectively. WC, diastolic BP, and triglycerides were higher in men than in women. Systolic BP and fasting plasma glucose showed the highest levels in postmenopausal women. HDL-cholesterol was lowest in men. The mean HOMA-IR was 2.11 (2.07–2.15) for men, 2.0 (1.97–2.04) for premenopausal women, and 2.14 (2.2–2.19) for postmenopausal women.
The prevalence of metabolic syndrome was 20.6% in men, 8.9% in premenopausal women, and 40.4% in postmenopausal women (Table 1). We identified the proportion of the five components of metabolic syndrome according to each group. In men, high TG (36.2%) and BP (35.2%) accounted for a large percentage. Low HDL-cholesterol (27.7%) and abdominal obesity (25.0%) accounted for a large proportion in premenopausal women. Abdominal obesity (56.3%) and high BP (53.4%) accounted for a large proportion in postmenopausal women.
HOMA-IR values were divided into 10 deciles to identify the prevalence of metabolic syndrome according to HOMA-IR level (Table 2). Fig 1 shows the proportion of metabolic syndrome according to HOMA-IR. The prevalence of metabolic syndrome increased as HOMA-IR increased in all three groups. In men and premenopausal women, metabolic syndrome increased sharply at the ninth to tenth deciles of HOMA-IR (HOMA-IR 3.53 in men, 3.24 in premenopausal women). In postmenopausal women, metabolic syndrome increased continuously from the seventh decile of HOMA-IR (HOMA-IR 2.32–2.56).
HOMA-IR: homeostasis model assessment of insulin resistance; Pre-women: premenopausal women; Post-women: postmenopausal women.
ROC analysis was used to define HOMA-IR cut-off value for metabolic syndrome. The first cut-off values in men, premenopausal women, and postmenopausal women were 2.23 (sensitivity 70.6%, specificity 66.9%), 2.39 (sensitivity 72.3%, specificity 76.4%), and 2.48 (sensitivity 51.9%, specificity 80.2%), respectively (Fig 2). Based on the first HOMA-IR cut-off value, the prevalence of metabolic syndrome was 22.9% in men, 13.7% in premenopausal women, and 51.6% in postmenopausal women. The second cut-off values in men, premenopausal women, and postmenopausal women were 3.23 (sensitivity 48.2%, specificity 76.5%), 3.20 (sensitivity 64.8%, specificity 70.8%), and 3.28 (sensitivity 45.8%, specificity 71.6%). Based on the second HOMA-IR cut-off value, the prevalence of metabolic syndrome was 50.8% in men, 42.5% in premenopausal women, and 71.6% in postmenopausal women.
The Youden index, calculated as (sensitivity + specificity − 1) was estimated to determine optimal cut-off values. After those subjects with a HOMA-IR lower than the first HOMA-IR cut-off value had been excluded, the data were recalculated to obtain the second cut-off. The first cut-off values in men, premenopausal women, and postmenopausal women were 2.23 (AUC 0.75, sensitivity 70.6%, specificity 66.9%), 2.39 (AUC 0.82, sensitivity 72.3%, specificity 76.4%), and 2.48 (AUC 0.71, sensitivity 51.9%, specificity 80.2%). The second cut-off values in men, premenopausal women, and postmenopausal women were 3.23 (AUC 0.65, sensitivity 48.2%, specificity 76.5%), 3.20 (AUC 0.71, sensitivity 64.8%, specificity 70.8%), and 3.28 (AUC 0.61, sensitivity 45.8%, specificity 71.6%). HOMA-IR: homeostasis model assessment of insulin resistance; Pre-women: premenopausal women; Post-women: postmenopausal women.
Table 3 shows the OR (95% CI) for each metabolic syndrome component according to HOMA-IR cut-off value after adjustment for age, BMI, and lifestyle factors (current smoking, heavy drinker, exercise). Results showed that increased fasting glucose had the highest OR value. The OR of metabolic syndrome according to HOMA-IR first cut-off value was 2.44 for men (95% CI = 1.98–3.00), 2.47 for premenopausal women (95% CI = 1.62–3.77), and 2.17 for postmenopausal women (95% CI = 1.65–2.85).
HOMA-IR is a simple, less invasive, inexpensive and useful method to measure insulin resistance. Insulin resistance has a close association with cardiovascular risk factors and it is similar to the metabolic syndrome component [17, 18]. So, we calculated the HOMA-IR cut-off values for predicting metabolic syndrome in Koreans. In the present study, the first HOMA-IR cut-off values in men, premenopausal women, and postmenopausal women were 2.23, 2.39, and 2.48. The second cut-off value was around 3.2 in all three groups. Several other studies have attempted to define cut-off values of HOMA-IR using the ROC curves. The HOMA-IR cut-off values in Japanese, Iranian and Spanish subjects were 1.7–2.0 [6, 8, 19] These values were lower than first cut-off value of HOMA-IR in Korea. In Portuguese and Brazilian studies, the HOMA-IR cut-off values were 2.4–2.7, and it was similar to Korea [7, 20] However, the cut-off value in 1,854 Mexican Americans was 3.80 (specificity = 0.778, sensitivity = 0.616) . The values showed variability by race and ethnicity. Thus, the ‘best’ cut-off for insulin resistance may need to be measured by race or country.
Additionally, the HOMA-IR cut-off value has shown different results depending on gender. In the present study, first cut-off value in women was little higher than in men. Furthermore, the cut-off values in the same age group were higher in women than in men. (Data not shown in tables.) Similar results were observed in Iran (1.7 in men and 1.8 in women), Spain (1.85 in men and 2.07 in women), and China (using 75th percentile for threshold of HOMA-IR, 2.48 in men and 2.67 in women) [6, 10, 22]. However, some studies showed that there were no differences in values between genders [19, 23]. The different results of each study can occur due to definitions of the metabolic syndrome and characteristics of subjects.
In the present study, the mean HOMA-IR values in men decreased with age, the mean HOMA-IR values in women showed a tendency to increase from 50 years of age. (Data not shown in tables.) Also, previous other studies showed that the HOMA-IR value increases significantly from 50 years of age in non-diabetic women [6, 10, 24]. Thus, in this study, we analyzed separately for premenopausal and postmenopausal women. Causes of the deterioration of insulin resistance in women more than 50 years old may involve estrogen deficiency. Physiological levels of estradiol plays a role in maintaining insulin sensitivity [25, 26]. Estrogen deficiency due to menopause affects glucose and insulin metabolism [16, 27] and body fat distribution . Thus, estrogen deficiency and central obesity in postmenopausal women may aggravate insulin resistance and metabolic syndrome.
Some studies have reported HOMA-IR cut-off values in non-diabetic healthy Korean people [11, 12]. The results showed that the cut-off value of HOMA-IR for metabolic syndrome by the ATP III criteria was 2.3–2.5. It was similar to our results. However, to our knowledge, no reported study has identified second cut-off values of HOMA-IR or performed subgroup analysis by gender and menopausal status in Koreans before. In the present study, there were great differences in the prevalence of metabolic syndrome according to HOMA-IR in the three groups. The prevalence of metabolic syndrome at the second cut-off value of HOMA-IR was 40–50% in men and premenopausal women. However, in postmenopausal women, the prevalence of metabolic syndrome at the first cut-off value was 51.6%. Considering the prevalence of metabolic syndrome, second cut-off value (about 3.2) is more clinically useful in men and premenopausal women and first cut-off value (about 2.5) is useful in postmenopausal women. Thus, we have a choice between the first and second cut-off values of HOMA-IR in consideration of the gender, menopausal status, and the prevalence of metabolic syndrome.
Previous studies about HOMA-IR in non-diabetic Koreans showed that the prevalence of metabolic syndrome was 10–15% [11, 23]. However, the prevalence of metabolic syndrome in our study was 21.5% (men 20.6%, women 22.1%). The first possible cause of this difference is the different diagnostic criteria for metabolic syndrome. Therefore, there may have been an increase in the prevalence of metabolic syndrome by the reduced fasting glucose standard (6.1 → 5.6 mmol/L). Additionally, the other studies were single center studies and subjects who visited for medical check-ups were assessed. Thus, relatively healthy people may have been included in the existing research. Also, the number of people with metabolic syndrome in Korea is increasing, due to lifestyle changes .
There are several strengths of the present study. First, this study used a large, nationally representative, population-based data set in Korea. Second, subjects were divided into three groups by gender and menopausal status. We could identify the HOMA-IR cut-off value and prevalence of metabolic syndrome for each group. Third, both the first and second HOMA-IR cut-off values were calculated. Thus, the values can be used differently, depending on the clinical situation. However, the study also has some limitations. First, this study was cross-sectional in design. Thus, insulin resistance was identified by HOMA-IR based on single tests of fasting blood glucose and insulin. Another limitation is that the subjects consisted of only non-diabetic patients. Insulin resistance in diabetic patients may vary depending on the duration of diabetes mellitus, use of oral hypoglycemic agents or insulin, and glycemic control status. However, these are limitations due to the nature of the data set.
In conclusion, the first cut-off values of HOMA-IR were 2.2–2.5 and the second cut-off value of HOMA-IR was 3.2 in Koreans. The distribution of HOMA-IR and prevalence of metabolic syndrome showed differences according to gender and menopausal status. When we apply HOMA-IR, we should consider gender, menopausal status, and the prevalence of metabolic syndrome.
The English in this document has been checked by at least two professional editors, both native speakers of English. For a certificate, please see: http://www.textcheck.com/certificate/5z7FPo
Conceived and designed the experiments: HK. Performed the experiments: KY KH. Analyzed the data: KH YP. Contributed reagents/materials/analysis tools: MK KB KS. Wrote the paper: KY. Contributed to discussion: MK KB KS HK.
- 1. DeFronzo RA, Ferrannini E. Insulin resistance. A multifaceted syndrome responsible for NIDDM, obesity, hypertension, dyslipidemia, and atherosclerotic cardiovascular disease. Diabetes care. 1991;14(3):173–94. Epub 1991/03/01. pmid:2044434.
- 2. DeFronzo RA, Tobin JD, Andres R. Glucose clamp technique: a method for quantifying insulin secretion and resistance. The American journal of physiology. 1979;237(3):E214–23. Epub 1979/09/01. pmid:382871.
- 3. Katz A, Nambi SS, Mather K, Baron AD, Follmann DA, Sullivan G, et al. Quantitative insulin sensitivity check index: a simple, accurate method for assessing insulin sensitivity in humans. The Journal of clinical endocrinology and metabolism. 2000;85(7):2402–10. Epub 2000/07/21. pmid:10902785.
- 4. 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(7):412–9. Epub 1985/07/01. pmid:3899825.
- 5. Antuna-Puente B, Disse E, Rabasa-Lhoret R, Laville M, Capeau J, Bastard JP. How can we measure insulin sensitivity/resistance? Diabetes & metabolism. 2011;37(3):179–88. Epub 2011/03/26. pmid:21435930.
- 6. Gayoso-Diz P, Otero-González A, Rodriguez-Alvarez MX, Gude F, García F, De Francisco A, et al. 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 endocrine disorders. 2013;13(1):47. Epub 2013/10/18. pmid:24131857; PubMed Central PMCID: PMCPmc4016563.
- 7. Geloneze B, Vasques AC, Stabe CF, Pareja JC, Rosado LE, Queiroz EC, et al. HOMA1-IR and HOMA2-IR indexes in identifying insulin resistance and metabolic syndrome: Brazilian Metabolic Syndrome Study (BRAMS). Arquivos brasileiros de endocrinologia e metabologia. 2009;53(2):281–7. Epub 2009/05/26. pmid:19466221.
- 8. Esteghamati A, Ashraf H, Khalilzadeh O, Zandieh A, Nakhjavani M, Rashidi A, et al. Optimal cut-off of homeostasis model assessment of insulin resistance (HOMA-IR) for the diagnosis of metabolic syndrome: third national surveillance of risk factors of non-communicable diseases in Iran (SuRFNCD-2007). Nutrition & metabolism. 2010;7:26. Epub 2010/04/09. pmid:20374655; PubMed Central PMCID: PMCPmc2857836.
- 9. Sumner AE, Cowie CC. Ethnic differences in the ability of triglyceride levels to identify insulin resistance. Atherosclerosis. 2008;196(2):696–703. Epub 2007/01/27. pmid:17254586.
- 10. Esteghamati A, Ashraf H, Esteghamati AR, Meysamie A, Khalilzadeh O, Nakhjavani M, et al. Optimal threshold of homeostasis model assessment for insulin resistance in an Iranian population: the implication of metabolic syndrome to detect insulin resistance. Diabetes research and clinical practice. 2009;84(3):279–87. Epub 2009/04/11. pmid:19359063.
- 11. Park SH, Lee WY, Rhee EJ, Jeon WK, Kim BI, Ryu SH, et al. Relative risks of the metabolic syndrome according to the degree of insulin resistance in apparently healthy Korean adults. Clinical science (London, England: 1979). 2005;108(6):553–9. Epub 2005/01/27. pmid:15669921.
- 12. Lee S, Choi S, Kim HJ, Chung YS, Lee KW, Lee HC, et al. Cutoff values of surrogate measures of insulin resistance for metabolic syndrome in Korean non-diabetic adults. Journal of Korean medical science. 2006;21(4):695–700. Epub 2006/08/08. pmid:16891815; PubMed Central PMCID: PMCPmc2729893.
- 13. (KCDC). KCfDCaP. Korea National Health and Nutrition Examination Survey.
- 14. Region WWP. The asia pacific perspective: redefining obesity and its treatment 2000.
- 15. Grundy SM, Cleeman JI, Daniels SR, Donato KA, Eckel RH, Franklin BA, et al. Diagnosis and management of the metabolic syndrome: an American Heart Association/National Heart, Lung, and Blood Institute Scientific Statement. Circulation. 2005;112(17):2735–52. Epub 2005/09/15. pmid:16157765.
- 16. Walton C, Godsland IF, Proudler AJ, Wynn V, Stevenson JC. The effects of the menopause on insulin sensitivity, secretion and elimination in non-obese, healthy women. European journal of clinical investigation. 1993;23(8):466–73. Epub 1993/08/01. pmid:8404998.
- 17. Ginsberg HN. Insulin resistance and cardiovascular disease. Journal of Clinical Investigation. 2000;106(4):453. pmid:10953019
- 18. McFarlane SI, Banerji M, Sowers JR. Insulin resistance and cardiovascular disease. The Journal of clinical endocrinology and metabolism. 2001;86(2):713–8. Epub 2001/02/07. pmid:11158035.
- 19. 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. Journal of diabetes investigation. 2012;3(4):384–7. Epub 2012/08/20. pmid:24843594; PubMed Central PMCID: PMCPmc4019259.
- 20. Timoteo AT, Miranda F, Carmo MM, Ferreira RC. Optimal cut-off value for homeostasis model assessment (HOMA) index of insulin-resistance in a population of patients admitted electively in a Portuguese cardiology ward. Acta medica portuguesa. 2014;27(4):473–9.
- 21. Qu HQ, Li Q, Rentfro AR, Fisher-Hoch SP, McCormick JB. The definition of insulin resistance using HOMA-IR for Americans of Mexican descent using machine learning. PloS one. 2011;6(6):e21041. Epub 2011/06/23. pmid:21695082; PubMed Central PMCID: PMCPmc3114864.
- 22. Sun Y, Li W, Hou X, Wang C, Li C, Zhang X, et al. Triglycerides and ratio of triglycerides to high-density lipoprotein cholesterol are better than liver enzymes to identify insulin resistance in urban middle-aged and older non-obese Chinese without diabetes. Chinese medical journal. 2014;127(10):1858–62. Epub 2014/05/16. pmid:24824245.
- 23. Lee JG, Lee S, Kim YJ, Jin HK, Cho BM, Kim YJ, et al. Multiple biomarkers and their relative contributions to identifying metabolic syndrome. Clinica chimica acta; international journal of clinical chemistry. 2009;408(1–2):50–5. Epub 2009/07/23. pmid:19622349.
- 24. Gayoso-Diz P, Otero-Gonzalez A, Rodriguez-Alvarez MX, Gude F, Cadarso-Suarez C, Garcia F, et al. Insulin resistance index (HOMA-IR) levels in a general adult population: curves percentile by gender and age. The EPIRCE study. Diabetes research and clinical practice. 2011;94(1):146–55. Epub 2011/08/10. pmid:21824674.
- 25. Livingstone C, Collison M. Sex steroids and insulin resistance. Clinical science (London, England: 1979). 2002;102(2):151–66. Epub 2002/02/09. pmid:11834135.
- 26. Louet JF, LeMay C, Mauvais-Jarvis F. Antidiabetic actions of estrogen: insight from human and genetic mouse models. Current atherosclerosis reports. 2004;6(3):180–5. Epub 2004/04/08. pmid:15068742.
- 27. Proudler AJ, Felton CV, Stevenson JC. Ageing and the response of plasma insulin, glucose and C-peptide concentrations to intravenous glucose in postmenopausal women. Clinical science (London, England: 1979). 1992;83(4):489–94. Epub 1992/10/01. pmid:1330412.
- 28. Ley CJ, Lees B, Stevenson JC. Sex- and menopause-associated changes in body-fat distribution. The American journal of clinical nutrition. 1992;55(5):950–4. Epub 1992/05/01. pmid:1570802.
- 29. Lim S, Shin H, Song JH, Kwak SH, Kang SM, Won Yoon J, et al. Increasing prevalence of metabolic syndrome in Korea: the Korean National Health and Nutrition Examination Survey for 1998–2007. Diabetes care. 2011;34(6):1323–8. Epub 2011/04/21. pmid:21505206; PubMed Central PMCID: PMCPmc3114326.