Baseline levels of adiponectin were widely distributed among DPP participants, ranging from 1.8 to 35.0 μg/ml in a skewed distribution. The median value was 7.30 and the mean was 7.93. Women comprised 67.7% of the DPP population. The previously described sex difference in adiponectin levels was seen, with 26% higher levels in women than men (P < 0.00001) () (). Distributions of standard anthropomorphic, hemodynamic, and metabolic variables across the observed range of adiponectin levels, divided into quartiles, are presented in . We observed significant univariate relationships of baseline adiponectin with many of these variables in both men and women. The strongest associations with baseline adiponectin (correlation coefficients >0.25; P < 0.001) were with age and HDL cholesterol (directly correlated) and fasting insulin and homeostasis model assessment of insulin resistance (inversely correlated). Triglyceride levels and β-cell function (insulinogenic index) were the next most strongly related to baseline adiponectin levels, with correlation coefficients on the order of 0.2. In this obese cohort, obesity measures were less tightly correlated; although weight and BMI were both significantly related to adiponectin levels, waist circumference was not.
Baseline variables for men and women by sex-specific quartiles of baseline adiponectin
FIG. 1 Box-and-whisker plot showing the distribution of adiponectin at baseline by sex and self-described race/ethnicity. The box indicates the 25th through 75th percentile of the distribution, the whiskers show the 10th to 90th percentiles, the large dot shows (more ...)
Baseline adiponectin levels across the five ethnic groups are presented in . Adiponectin levels differed by ethnicity (P < 0.00001). The highest levels were seen in non-Hispanic white participants, with these trends unaffected by adjustment for group differences in age and adiposity. The smallest subgroups, Asian/Pacific Islanders and American Indians, are disproportionately represented by men and women, respectively. Differences in baseline adiponectin levels across groups remain significant after adjusting for sex distributions.
Baseline adiponectin and progression to diabetes
Cox proportional hazards models were used to predict progression to diabetes in relation to baseline levels of adiponectin adjusted for baseline demographics (age, sex, and race/ethnicity) () (, model 1). Adiponectin levels were inversely associated with progression to diabetes in all three intervention groups. Among subjects randomized to receive lifestyle and placebo interventions, adiponectin-associated diabetes risk remained significant after adjustment for baseline adiposity (, model 2). This was seen whether weight or waist circumference was included as the measure of adiposity.
FIG. 2 Diabetes hazard rates and baseline adiponectin adjusted for sex, age, race/ethnicity, and baseline weight (, model 2). Cox proportional hazards models were used to estimate the risk of developing diabetes. Estimates of the absolute risk gradient (more ...)
Cox proportional hazards modeling predicting progression to diabetes
A ~3 μg/ml higher baseline adiponectin level corresponded to a 20–40% lower rate of progression to diabetes. The relationship between adiponectin and diabetes conversion was not different across the three treatment groups (P = 0.13). The graphical expression of this Cox model () demonstrates that this change in risk was not linear across the range of adiponectin levels, however. At lower adiponectin levels, the diabetes risk was relatively greater. Accordingly, the change in risk was disproportionately greater at lower levels of adiponectin. also demonstrates that although the point estimates for average diabetes risk in each group are markedly different by group (reflecting the diabetes prevention effects of the two interventions compared with placebo), the slopes of the exponential relationship between adiponectin and diabetes risk were not statistically different across the three groups. In other words, baseline adiponectin was strongly associated with diabetes risk, with similar effects of adiponectin across the three treatment groups.
We then examined whether naturally occurring differences in baseline adiponectin levels across demographic groups were reflected in differing rates of diabetes conversion. Although we found baseline differences in adiponectin across sex and race/ethnicity group, the relationship of adiponectin with progression to diabetes was not altered by including sex or race/ethnicity in the model. There was also no significant interaction of adiponectin with either sex or race/ethnicity on progression to diabetes (hazard ratio 1.14 [95% CI 0.53–2.47], P = 0.73, and 1.69 [0.99–2.87], P = 0.06, respectively, for the interaction terms added to model 1 in the lifestyle group; 1.45 [0.78–2.68], P = 0.24, and 0.83 [0.54–1.26], P = 0.37, respectively, in the metformin group; and 0.93 [0.60–1.45] and 1.04 [0.74–1.46], P = 0.83 in the placebo group).
Change in adiponectin and progression to diabetes
Significant increases in adiponectin were observed from baseline to year 1 in all three treatment groups, with mean increases in the lifestyle, metformin, and placebo groups of 0.83 ± 0.05, 0.23 ± 0.05, and 0.10 ± 0.05 μg/ml, respectively (P < 0.001 comparing the increases across groups) (). The magnitude of these changes, and the differences across treatment groups, were essentially unchanged following adjustment for baseline adiponectin, weight, age, sex, and ethnicity (mean increases 0.83 ± 0.05, 0.22 ± 0.05, and 0.10 ± 0.05, respectively, after adjustment).
Change in adiponectin by treatment group. Unadjusted values are presented. Bars represent means ± SE for change in adiponectin over 1 year of treatment. ■, men; □, women.
Cox proportional hazards models were used to predict progression to diabetes in relation to change in levels of adiponectin adjusted for baseline adiponectin and demographics (age, sex, and race/ethnicity adjusted) (). Added to the effects of baseline weight, baseline adiponectin, and change in weight, change in adiponectin was a significant predictor of future diabetes conversion only in subjects randomized to receive the lifestyle intervention, where a ~1 μg/ml increase in adiponectin was associated with a 16% (95% CI 1.0–28) reduction in the rate of progression to diabetes (, model 3). Among subjects randomized to placebo, the overall relationships were similar, but neither change in weight nor change in adiponectin was statistically significant. These relationships differed among metformin-treated subjects, where weight and adiponectin effects were dissociated: baseline adiponectin but not baseline weight, and change in weight but not change in adiponectin, were significant determinants of progression to diabetes (, model 3).
In the DPP, the most powerful determinants of progression to diabetes evaluated to date were change in weight, change in β-cell function, and change in insulin sensitivity (28
). Adding measures of insulin sensitivity or β-cell function individually to the models predicting diabetes removed the significance of the contribution of change in adiponectin, except among placebo-treated subjects (P
= 0.02 for change in adiponectin when modeled with change in insulinogenic index and P
= 0.15 when modeled with change in inverse fasting insulin). In models including both of these variables, the change in adiponectin again retained borderline significance in placebo-treated subjects but was no longer significant in metformin- and lifestyle-treated subjects (, model 4
). demonstrates how nearly flat the slopes are for the relationship of change in adiponectin and diabetes risk after these adjustments. The contribution of baseline adiponectin was robust to the inclusion of these variables.
FIG. 4 Diabetes hazard rates and change in adiponectin (, model 4). Cox proportional hazards models were used to estimate the risk of developing diabetes. Estimates of the absolute risk gradient associated with a given value of baseline adiponectin and (more ...)
The hazard ratios in model 4 provide information about the relative strengths of each of these variables as determinants of overall diabetes risk. From this we can see that β-cell function and insulin sensitivity variables dominate the model, plus persisting strong effects of change in weight and baseline adiponectin but not change in adiponectin. Including adiponectin did not appreciably change the recognized relationships of these other variables with the diabetes prevention effect.
Changes associated with change in adiponectin
Variables associated with the change in adiponectin were evaluated first individually then in combination in multiple regression (). In univariate analysis, the change in adiponectin was strongly inversely correlated with change in waist circumference (coefficients 0.17–0.35, P < 0.0001, in all treatment groups) and change in weight (coefficients 0.24–0.42, P < 0.0001, in all treatment groups) and also directly correlated with change in insulin sensitivity (inverse fasting insulin; coefficients 0.08–0.15, P < 0.0001, in all treatment groups). Change in adiponectin was not significantly related to the change in β-cell function (insulinogenic index). By multivariable regression, the main independent determinants of change in adiponectin were change in weight and baseline adiponectin concentration (), such that greater reductions in weight, and lower baseline levels, were associated with greater increases in adiponectin. In placebo- and lifestyle-treated subjects, baseline age was also significantly and independently related to change in adiponectin. The strongest of these variables in multivariable modeling was the change in weight, where overall a 5-kg weight loss was associated with an increase of 0.3 (placebo), 0.4 (metformin), and 0.5 μg/ml (lifestyle) in circulating adiponectin levels (all P < 0.001). There was a significant interaction of treatment and weight loss effect on adiponectin (P = 0.03), with lifestyle-associated changes greater than the other groups (). Thus, weight loss was associated with increases in adiponectin in all groups, but this effect was magnified in subjects from the lifestyle group.
Changes associated with change in adiponectin