Lack of significant association between type 2 diabetes mellitus with longitudinal change in diurnal salivary cortisol: the multiethnic study of atherosclerosis

Abstract

Cross-sectional association has been shown between type 2 diabetes and hypothalamic–pituitary–adrenal (HPA) axis dysregulation; however, the temporality of this association is unknown. Our aim was to determine if type 2 diabetes is associated with longitudinal change in daily cortisol curve features. We hypothesized that the presence of type 2 diabetes may lead to a more blunted and abnormal HPA axis profile over time, suggestive of increased HPA axis dysregulation. This was a longitudinal cohort study, including 580 community-dwelling individuals (mean age 63.7 ± 9.1 years; 52.8 % women) with (n = 90) and without (n = 490) type 2 diabetes who attended two MultiEthnic Study of Atherosclerosis Stress ancillary study exams separated by 6 years. Outcome measures that were collected were wake-up and bedtime cortisol, cortisol awakening response (CAR), total area under the curve (AUC), and early, late, and overall decline slopes. In univariate analyses, wake-up and AUC increased over 6 years more in persons with as compared to those without type 2 diabetes (11 vs. 7 % increase for wake-up and 17 vs. 11 % for AUC). The early decline slope became flatter over time with a greater flattening observed in diabetic compared to non-diabetic individuals (23 vs. 9 % flatter); however, the change was only statistically significant for wake-up cortisol (p-value: 0.03). Over time, while CAR was reduced more, late decline and overall decline became flatter, and bedtime cortisol increased less in those with as compared to those without type 2 diabetes, none of these changes were statistically significant in adjusted models. We did not identify any statistically significant change in cortisol curve features over 6 years by type 2 diabetes status.

Appendix 1

Let $y_{ijdk}$ be the kth measure of cortisol of subject $i$ at MESA Stress study $j$ ($j$ = 1, 2) on day $d$. The piecewise linear mixed effect model was specified as follows:

$$ \begin{aligned} y_{ijdk} = & \beta_{0i} + \beta_{1i} \cdot t_{ijdk} + \beta_{2} \cdot \left( {t_{ijdk} - 0.5} \right)_{ + } + \beta_{3i} \cdot \left( {t_{ijdk} - 2} \right)_{ + } \\ + \, \beta_{4i} \cdot {\text{Time}}_{ij} + \beta_{5i} \cdot {\text{Time}}_{ij} \cdot t_{ijdk} + \beta_{6} \cdot {\text{Time}}_{ij} \cdot \left( {t_{ijdk} - 0.5} \right)_{ + } + \beta_{7i} \cdot {\text{Time}}_{ij} \cdot \left( {t_{ijdk} - 2} \right)_{ + } \\ + \,\beta_{8} \cdot {\text{Diab}}_{i} + \beta_{9} \cdot {\text{Diab}}_{i} \cdot t_{ijdk} + \beta_{10} \cdot {\text{Diab}}_{i} \cdot \left( {t_{ijdk} - 0.5} \right)_{ + } + \beta_{11} \cdot {\text{Diab}}_{i} \cdot \left( {t_{ijdk} - 2} \right)_{ + } \\ + \,\beta_{12} \cdot {\text{Cov}}_{i} + \beta_{13} \cdot {\text{Cov}}_{i} \cdot t_{ijdk} + \beta_{14} \cdot {\text{Cov}}_{i} \cdot \left( {t_{ijdk} - 0.5} \right)_{ + } + \beta_{15} \cdot {\text{Cov}}_{i} \cdot \left( {t_{ijdk} - 2} \right)_{ + } \\ + \,e_{ijdk} , \\ \end{aligned}, $$

where $\beta_{li} = \beta_{l} + b_{li} , l = 0, 1, 3, 4,5,7$ and $b_{0i} , b_{1i} ,b_{3i} ,b_{4i} , b_{5i} ,b_{7i}$ are individual-level random intercept and slope for individual $i$;

${\text{Time}}_{ij}$ is the time (years) since the baseline study (MESA Stress I) for individual $i$ at study $j$. Note that ${\text{Time}}_{{{\text{i}}1}} = 0$; $t_{ijdk}$ is the time (h) since wake-up when the cortisol sample $y_{ijdk}$ was collected;

${\text{Cov}}_{i}$ represents a set of sociodemographic factors and health-related factors for individual $i$ at baseline study; ${\text{Cov}}_{i}$ is excluded in Model 0 as the model is for an unadjusted analysis; ${\text{Cov}}_{i}$ includes sociodemographic factors including age, sex, race/ethnicity, and socioeconomic status in Model 1; and additionally includes waist circumference, depressive symptoms, smoking status, and medication usage (beta-blocker, aspirins, inhaled or oral steroids, and hormone replacement therapy) in Model 2. Also, all covariates included in ${\text{Cov}}_{i}$ are centered at their population average in the analysis; therefore, the estimates on the difference in cortisol feature change over time for each diabetes groups and the difference between groups (as shown in Supplementary Table 1) are interpreted at the population average, i.e., average level of sociodemographic characteristics and health-related factors.

${\text{Diab}}_{i}$ is a binary variable indicating individual’s diabetes status (1: diabetes; 0: non-diabetes); $e_{ijdk}$ is the unexplained deviation from the mean for the kth cortisol measure on day d at MESA Stress study j for individual i.

The estimates of the coefficients for the terms that involve diabetes status were used to derive estimates of the cortisol features by diabetes groups, and the difference in the change of daily cortisol features over time between diabetes groups, as shown in Supplementary Table 1.