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


Although insulin resistance is an important disorder, there are no data concerning metabolic longitudinal predictors of insulin sensitivity in which various predictors have been ranked. Furthermore, the independent and additional role of different lifestyle factors has been unclear. This is the first analysis including both metabolic and lifestyle predictors in long-term prediction of insulin sensitivity assessed by a gold standard technique. Several conclusions can be drawn from this study: 1) multiple factors including metabolic, lifestyle, and socioeconomic factors independently contribute to predict insulin sensitivity, supporting a complex background of insulin resistance; 2) saturated fat intake and socioeconomic status are independent predictors that seem to be as important as physical activity; 3) BMI is still the strongest predictor of insulin sensitivity after adjustment for lifestyle factors and HOMA-IR, even in men with normal BMI; and 4) in fact, BMI was a stronger predictor of clamp-derived insulin sensitivity than either fasting insulin or HOMA-IR in univariate analyses. The present results suggest that predictors of insulin resistance are similar, but not identical, to those reported to predict type 2 diabetes,[7] the metabolic syndrome,[11] and hyperinsulinemia.[12]

There are limited data on the association between lifestyle factors including dietary fat, smoking, and socioeconomic status and insulin sensitivity per se. Interestingly, saturated fat intake, as assessed by the serum 16:1n-7-to-16:0 ratio, was an independent predictor in all models. A high 16:1n-7-to-16:0 ratio in a western diet mirrors a relatively high intake of saturated fat.[9] It should, however, be noted that the 16:1n-7-to-16:0 ratio may also be influenced by factors other than diet, i.e., genetic factors and drugs. In line with our finding, a high intake of saturated fat impairs insulin action in intervention studies,[13] whereas reduced intake is related to decreased diabetes risk,[14] probably by affecting cell membrane function. Our results also fit with a previous cross-sectional substudy of the present 70-year-old men, in whom palmitic acid in skeletal muscle (n = 39) and serum (n = 215), in particular, was closely associated with insulin sensitivity.[8] An independent link between saturated fat (assessed by a 24-h dietary recall) and fasting insulin levels was previously reported by Marshall et al..[15] Importantly, our results accord with those of primary prevention trials, which indicated that improving multiple lifestyle factors, factors that were independently related to decreased risk of type 2 diabetes,[14] including reducing saturated fat and enhancing physical activity improves insulin sensitivity.[16,17] These effects were likely to be mediated in part by improving insulin sensitivity,[17] and lifestyle changes had beneficial long-term effects,[18] in line with the present observational data. In the present study, it would have been valuable to have data on other dietary factors such as alcohol, coffee, or fiber intake. Another novel finding was the fact that socioeconomic status predicted insulin sensitivity independently of lifestyle factors that have been closely related to socioeconomic status.

BMI was the strongest predictor, explaining the majority of the variation in insulin sensitivity 20 years later. This finding fits well with previous weight loss studies,[19] and in the Finnish Diabetes prevention study, there was a strong correlation between the 4-year changes in insulin sensitivity and weight.[17] The strong predictive effect of BMI also accords with previous cross-sectional observational studies reporting a close link between insulin resistance and overweight,[2,3,20,21] dyslipidemia,[2,4,5] and hypertension.[6] However, in general, these studies have not compared different predictors using multivariate analyses.

Interestingly, BMI was a better marker for insulin sensitivity than either insulin or HOMA-IR ( Table 2 ). To our knowledge this is the first time this finding has been described. Thus, BMI is an excellent noninvasive marker that may even be preferable to HOMA-IR and insulin, which are commonly used as surrogate markers of insulin resistance. BMI alone explained 18% of the variation in insulin sensitivity 20 years later, a result not far from the 22-25% found cross-sectionally.[3,21] Other metabolic factors explained another 2%, and adding lifestyle factors explained an additional 4%. A fully adjusted model including insulin explained 31% of the variation in insulin sensitivity, suggesting that other genetic and/or nongenetic factors are also involved. If Δ BMI (the change of BMI during follow-up) was substituted for baseline BMI, the R 2 value increased to 42%.

When the data are interpreted, it should be remembered that BMI may have a higher precision than other metabolic and lifestyle factors, thus explaining the stronger association with BMI. However, reproducibility data from this cohort indicated that although BMI had a higher intraclass correlation coefficient than, for example, diastolic blood pressure and HDL cholesterol, it was virtually the same as that for triglycerides and glucose.

Despite the fact that physical activity is a powerful predictor of insulin sensitivity, the predictive capacity of BMI persisted after adjustment for lifestyle factors including physical activity, possibly supporting a role of adiposity per se as shown previously.[19] Notably, even in normal-weight men, BMI was the strongest predictor, implying that even modest excess body fat within the normal-weight range might deteriorate insulin sensitivity.

Both triglycerides and HDL cholesterol closely correlate with insulin resistance.[22] In our prospective analyses, they were strong predictors independent of each other. The strong correlation with triglycerides, also described cross-sectionally,[2] however, disappeared after adjustment for saturated fat in particular. In contrast, HDL cholesterol remained a predictor in all models.

There are several limitations of this study. First, the lack of clamp measurements at baseline limits the interpretation regarding the direction of the effects. To address this limitation, we adjusted for insulin concentrations (reflecting subclinical insulin resistance at baseline), which did not alter the results. We also excluded subjects in the highest quartile of HOMA-IR to obtain an "insulin-sensitive" sample at baseline. In this analysis, the results remained the same. Nevertheless, HOMA-IR is only a surrogate marker of insulin sensitivity, and it would have been optimal to have clamp data at baseline and follow-up, which are not available in any cohort we are aware of. It is thus possible that associations between predictors at baseline and insulin sensitivity at follow-up might be a result of cross-sectional associations at baseline that are due to tracking of insulin resistance over time. There is a risk of survival bias, as the men with the most insulin resistance might have died during the follow-up. Such bias would, however, decrease the chance of finding associations. We did not have data on lean body mass, and, therefore, we adjusted the clamp glucose infusion rate for body weight, which might have overestimated insulin resistance,[3] nor did we have measurements of abdominal obesity, which would have relevant. The ranking among predictors may not have been affected, however. Unfortunately, we did not have waist measurements for all men. Cross-sectional data suggest, however, that waist and BMI are equally good predictors of insulin sensitivity.[3,20] It should be noted that in this 50-year-old Swedish population, the mean BMI at baseline was 24.7 kg/m2, which may be lower than that in many other countries. On the other hand, one should remember that BMI was assessed > 35 years ago, before the pronounced increase in the prevalence of obesity seen worldwide during the last few decades. It would also have been interesting to study other age-groups, especially younger subjects, to investigate further early predictors of insulin resistance. Finally, we only studied Caucasian middle-aged men, lacking data for women and other ethnic groups. The strengths of the study include the large sample of clamp measurements, the longitudinal design, assessments of insulin sensitivity, and availability of data for several lifestyle factors.

In summary, this large study investigating long-term predictors of insulin sensitivity per se demonstrated that multiple factors contribute independently from each other. Our findings accord with and partly confirm previous data on predictors of type 2 diabetes and the metabolic syndrome. Lifestyle factors including a high proportion of saturated fat in the diet, low physical activity, and socioeconomic status all contribute to the variation in insulin sensitivity, but adiposity is the most important single factor, even in individuals with "normal" BMI.


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