Long-Term Predictors of Insulin Resistance: Role of Lifestyle and Metabolic Factors in Middle-Aged Men

Ulf Risérus, MMED, PHD; Johan Ärnlöv, MD, PHD; Lars Berglund, BSC


Diabetes Care. 2007;30(11):2928-2933. 

In This Article

Research Design and Methods

ULSAM is a longitudinal population-based cohort study (www.pubcare.uu.se/ULSAM/). In 1970-1973, all 50-year-old men living in Uppsala, Sweden, were invited to participate in a health survey, and of the men invited, 2,322 (82%) participated in the baseline investigation. At a reexamination 20 years later in 1991-1995 (at age 70 years), 1,221 men (73% of surviving men still living in the Uppsala region) participated. At baseline, we excluded 124 subjects who were taking glucose-lowering medication or who had diabetes and subjects treated with drugs for cardiovascular disease that may affect insulin sensitivity, i.e., glucocorticoids (n = 64), antihypertensive agents (n = 130), and lipid lowering drugs (n = 57). For the follow-up, 422 men died, 219 moved, and another 460 did not participate. We also excluded subjects with incomplete data for variables at baseline and follow-up (n = 76). Thus, after all exclusions the present study included all men with baseline data on all included variables who also had follow-up data on insulin sensitivity (n = 770) ( Table 1 ). In addition, of the 770 subjects, we also examined a subsample of 440 normal-weight men (excluding subjects with BMI > 25). All men gave informed consent, and the Ethics Committee of Uppsala University approved the study.

We selected specific variables that have been associated with insulin sensitivity in cross-sectional studies, i.e., metabolic factors (BMI, HDL cholesterol, triglycerides, glucose, and blood pressure) and lifestyle factors that were available at baseline (physical activity, smoking, and estimated saturated fat intake). In addition, socioeconomic status was regarded as a lifestyle factor and was included in the lifestyle model. In the baseline examination (at age 50 years), blood samples were drawn in the morning after an overnight fast. Blood glucose was measured by spectrophotometry using the glucose oxidase method. BMI was calculated as the weight in kilograms divided by the square of height in meters. Serum insulin concentrations were determined with the Phadebas Insulin Test (Pharmacia, Uppsala, Sweden), using a radioimmunosorbent technique. Serum triglyceride concentrations and HDL cholesterol were assayed by enzymatic techniques. Supine blood pressure was measured twice in the right arm after a 10-min rest, and means were calculated. Fatty acid composition was analyzed in serum cholesterol esters by gas chromatography as described previously.[8] As a biomarker of saturated fat intake ("saturated fat index"), the 16:1n-7-to-16:0 ratio was calculated; a high ratio reflects a high intake of saturated fat in a western diet as shown in controlled studies.[9] Leisure-time physical activity was assessed using a validated questionnaire providing four activity levels: sedentary, moderate, regular, and athletic. Coding of smoking was based on interview reports and defined as smoking (1) or nonsmoking (0). Socioeconomic status was based on interview and defined as three conventional social classes according to the Central Bureau of Statistics.

Insulin sensitivity was determined at age 70 years using a hyperinsulinemic-euglycemic clamp, according to the method of DeFronzo et al.,[10] slightly modified [insulin was infused at a constant rate of 56 mU/(min per m2)]. Insulin sensitivity (M) was calculated as glucose infusion rate (mg · kg body wt-1 · min-1) during the last 60 min of the 2-h clamp.

All analyses were defined a priori. All variables except smoking were treated as continuous variables. Triglycerides, saturated fat index, insulin, glucose, and diastolic blood pressure were skewed, but all variables were normally distributed after log transformation. The intraclass coefficients of variation (representing the biological variation and measurement error) were as follows: BMI, 1.0; triglycerides, 0.96; glucose, 0.95; HDL cholesterol, 0.84; diastolic blood pressure, 0.73; and insulin, 0.80. Initially, univariate linear regression was performed between metabolic factors at baseline and insulin sensitivity at follow-up. Then multivariate regression models were used to identify independent predictors (assessed at baseline) of insulin sensitivity. All metabolic independent variables were standardized to 1 SD. Three multivariate models were used. First, we included all metabolic factors (metabolic model: BMI, HDL cholesterol, triglycerides, glucose, and diastolic blood pressure). Because diastolic blood pressure was slightly better correlated to insulin sensitivity than to systolic blood pressure in univariate analyses (r = 0.23 vs. r = 0.21, both P < 0.0001), we only included diastolic blood pressure in multivariate models to avoid colinearity. Second, we added lifestyle factors (smoking status, saturated fat index, physical activity level, and socioeconomic status) to the metabolic model (lifestyle model). Finally, to adjust for "subclinical insulin resistance" we added fasting insulin levels to the lifestyle model (insulin model). We used insulin instead of homeostasis model assessment of insulin resistance (HOMA-IR) because these measures are equally good predictors of directly measured insulin sensitivity,[5] and if we had used HOMA-IR instead of insulin, we would not have been able to keep glucose in the model because of colinearity. JMP software (SAS Institute, Cary, NC) was used for statistical analyses.


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