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Cadernos de Saúde Pública

ISSN 1678-4464

36 nº.8

Rio de Janeiro, Agosto 2020


Modelo de avaliação da homeostase de resistência à insulina (HOMA-IR) e síndrome metabólica na linha de base de uma coorte brasileira multicêntrica: estudo ELSA-Brasil

Maria de Fátima Haueisen Sander Diniz, Alline Maria Rezende Beleigoli, Maria Inês Schmidt, Bruce B. Duncan, Antônio Luiz P. Ribeiro, Pedro G. Vidigal, Isabela M. Benseñor, Paulo A. Lotufo, Itamar S. Santos, Rosane H. Griep, Sandhi Maria Barreto


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O modelo de avaliação da homeostase da resistência à insulina (HOMA-IR) é um método para medir a resistência à insulina. Os pontos de corte do HOMA-IR para identificar a síndrome metabólica podem variar entre as populações e os níveis de índice de massa corporal (IMC). Nosso objetivo foi investigar os pontos de corte do HOMA-IR que melhor discriminam indivíduos com resistência à insulina e com síndrome metabólica para cada categoria de IMC em uma grande amostra de adultos sem diabetes na linha de base do Estudo Longitudinal de Saúde do Adulto (ELSA-Brasil). Entre os 12.313 participantes com média de idade de 51,2 (DP 8,9) anos, a prevalência de síndrome metabólica foi de 34,6%, e 60,1% apresentavam sobrepeso ou obesidade. As prevalências de síndrome metabólica nas categorias de peso normal, sobrepeso e obesidade foram, respectivamente, 13%, 43,2% e 60,7%. O ponto de máxima sensibilidade e especificidade combinadas do HOMA-IR para discriminar a síndrome metabólica foi de 2,35 em toda a amostra, com valores crescentes nas categorias de IMC mais elevadas. Esta investigação contribui para o melhor entendimento dos valores de HOMA-IR associados à resistência à insulina e síndrome metabólica em uma grande amostra de adultos brasileiros, e que o uso de pontos de corte de acordo com a curva ROC pode ser a melhor estratégia. Também sugere que valores diferentes podem ser apropriados nas categorias de IMC.

Síndrome Metabólica; Resistência à Insulina; Estudos de Coortes



Insulin resistance is one of the pathogenic mechanisms of the metabolic syndrome and is a common condition that allows identification of the risk of diabetes and metabolic syndrome 1. The gold standard method to assess insulin resistance is the hyperinsulinemic-euglycemic clamp, which is not useful for clinical and epidemiological investigations. The homeostasis model assessment of insulin resistance (HOMA-IR) is a method based on fasting glucose and insulin plasmatic levels, which was validated by Matthews et al. 2 and has been used for defining insulin resistance for clinical and research purposes in several populations. In Brazil, the Brazilian Metabolic Syndrome Study (BRAMS), with a population from 18 to 78 years old, used the 90th percentile to establish 2.7 as a cut-off to define insulin resistance in healthy people (n = 297) with body mass index (BMI) < 30kg/m², and 2.3 as the value that best discriminates the presence of metabolic syndrome 3. However, HOMA-IR cut-offs might differ across populations and BMI levels and establishing HOMA-IR values that correlate with insulin resistance and with metabolic syndrome is still necessary 4.

The Brazilian Longitudinal Study of Adult Health (ELSA-Brasil) is a large multicentric cohort conducted in six Brazilian capitals, which analyzed data of 15,105 civil servants from three different geographical regions 5. We aimed to investigate HOMA-IR cut-offs that best discriminate insulin resistance and metabolic syndrome for each BMI category among individuals without diabetes mellitus in this large sample.


This is a cross-sectional analysis of the ELSA-Brasil study, described previously 5. Participants were enrolled between August 2008 and December 2010. All participants were volunteers, between 35 and 74 years old, and provided an informed consent form. All Institutional Review Boards approved this study.

For this analysis, we excluded 7 participants with missing data of fasting glucose, 12 of insulin, 52 of metabolic syndrome, 141 with underweight (BMI < 18.5kg/m2), and 2,580 with diabetes mellitus, which led to a final sample of 12,313 participants.

Height (in cm) was measured using a fixed stadiometer (accuracy of 0.1cm), and weight (kg) was measured with an electronic digital scale (Toledo, Brazil, to the nearest 100g). Waist (mid-point between lowest rib and iliac crest) circumference was measured by inelastic tapes (cm). The average of two measures was used for analyses. BMI [weight (kg)/height (m)2] was calculated. According to BMI, participants were stratified into categories: normal weight ≥ 18.5 to 24.9kg/m2, overweight 25-29.9kg/m2, obesity ≥ 30kg/m2. Blood pressure was defined by the average of two measures, after five minutes of rest in the sitting position 5.

Race/skin color, physical activity, alcohol and tobacco use were self-reported 5. Blood samples were collected after an overnight fast for fasting glucose, total cholesterol, triglycerides, high-density lipoprotein cholesterol (HDL-c), insulin. fasting glucose was determined by the hexokinase method (enzymatic colorimetric); total cholesterol by cholesterol oxidase method (enzymatic colorimetric), triglycerides by glycerol-phosphate peroxidase; HDL-c by homogeneous colorimetric without precipitation, insulin by immunoenzymatic assay, all of them with an ADVIA 1200 Siemens system (Deerfield, United States) 6. HOMA-IR was calculated from fasting glucose and insulin as [fasting glucose (mg/dL) X 0.0555 X fasting serum insulin (mUI/L)/22.5]2.

The quality and control of all data collected and stored were ensured according to the study protocol 5.

Since there is no consensus about whether the 75th and the 90th percentile of HOMA-IR should be used as cut-off points for identifying individuals with insulin resistance, we calculated both for the overall population and for each BMI category 4,7. We defined metabolic syndrome by the 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 criteria for Latin American populations 1.

Data are described as means and standard deviation, median with interquartile range (IQR) and frequencies. Pairwise group comparisons were performed using the Mann-Whitney U test or the chi-square test. Receiving operator characteristic (ROC) analyses were conducted and the area under the curve (AUC) with 95% confidence intervals (95%CI) for the whole population and for each BMI category was estimated to investigate HOMA-IR accuracy at identifying metabolic syndrome. The point of the ROC with maximum sensitivity and specificity was determined by the Youden index 8.

We performed a sub-analysis with the exclusion of participants with BMI ≥ 30kg/m2 (n = 2,399). All analyses were performed using the statistical software Stata 14 (https://www.stata.com).

Results and discussion

Among the 12,313 participants studied, 34.6% (n = 4,262) had metabolic syndrome and 60.1% (n = 7,399) had overweight or obesity. The prevalence of metabolic syndrome among normal weight, overweight and obesity categories were, respectively, 13%, 43.2% and 60.7%. Table 1 presents the population characteristics. After exclusion of participants with obesity, 2,805 (28.3%) had metabolic syndrome.


Tab.: 1
Table 1 Characteristics of the population studied according to the metabolic syndrome *, at baseline of the Brazilian Longitudinal Study of Adult Health (ELSA-Brasil), 2008-2010.


For the population without metabolic syndrome, HOMA-IR 75th and 90th percentiles were 2.75 and 3.73, respectively. Regarding the participants without obesity and metabolic syndrome (n = 7,109), HOMA-IR 75th and 90th percentiles were, respectively, 2.55 and 3.43. The 90th percentile of the population without obesity and metabolic syndrome at ELSA-Brasil baseline was higher than that of the healthy population in the BRAMS study (2.7). This difference might be related to the inclusion of younger participants compared to ELSA-Brasil (≥ 18 years vs. ≥ 35 years, respectively) 9, and perhaps because ELSA-Brasil included participants from six capitals of different parts of the country: South, Southeast and Northeast.

The value with the maximum combined sensitivity and specificity to discriminate metabolic syndrome was 2.35 both for the whole population and among participants without obesity. Area under the curve ROC (95%CI) for total sample was 0.78 (0.77-0.79). There was a clear gradient of HOMA-IR values that best discriminate the metabolic syndrome across BMI categories with the highest values within the obese subgroup Table 2. This suggests that it may be appropriate to apply different HOMA-IR values to define insulin resistance, according to the BMI category.


Tab.: 2
Table 2 Homeostasis model assessment of insulin resistance (HOMA-IR) values for the overall population and according to the body mass index (BMI) at baseline of the Brazilian Longitudinal Study of Adult Health (ELSA-Brasil), 2008-2010.


The large sample that included participants from diverse Brazilian states, the methodological rigor in data collection, centralized analysis of the laboratory tests, and the rigorous quality control procedures are strengths of this study. However, we acknowledge that the inclusion of participants with a minimum age of 35 years limits generalizing these results to younger Brazilian populations. Also, ELSA-Brasil is not a population-based study, and generalization to the entire Brazilian population should be done with caution.

The point of maximum combined sensitivity and specificity of HOMA-IR to discriminate the metabolic syndrome was 2.35 in the whole sample, with increasing values at higher BMI categories. In a Spanish population the threshold value of HOMA-IR, considering, metabolic syndrome components was 2.05 9. Different values were found in the literature according to the HOMA-IR percentile used as criteria to define insulin resistance, mean of age and BMI of studied population 4,9. Our investigation contributes to better understanding HOMA-IR values associated with insulin resistance and metabolic syndrome in a large Brazilian adult sample, and that use of cut-off points according to ROC curve may be the better strategy. Our findings also suggest that different values might be appropriate and should be adopted across the different BMI categories.


The authors thank all participants and the staff of the ELSA-Brasil for their important contributions.


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