Altered plasma serine and 1-deoxydihydroceramide profiles are associated with diabetic neuropathy in type 2 diabetes and obesity

Recent studies suggest that the accumulation of atypical, 1-deoxysphingolipids that lack the C1 hydroxyl group may be associated with diabetic neuropathy (DN). We hypothesized that specific plasma 1-deoxysphingolipids associate with DN severity, and that alterations in plasma serine and alanine associate with 1-deoxysphingolipid elevation in patients with type 2 diabetes (T2D). We examined individual 1-deoxysphingolipid species using LC/MS/MS in plasma samples from 75 individuals including lean controls (LC, n = 19), those with obesity (n = 19), obesity with T2D without DN (ob/T2D, n = 18), and obesity with T2D with DN (Ob/T2D/DN, n = 19). We observed a step wise increase in 1-deoxydihydroceramides across these four groups (spearman correlation coefficient r = 0.41, p = 0.0002). Mean total concentrations of 1-deoxydihydroceramides, and most individual 1-deoxydihydroceramide species, were higher in ob/T2D/DN versus LC group (8.939 vs. 5.195 pmol/100 μL for total 1-deoxydihydroceramides p = 0.005). No significant differences in 1-deoxydihydroceramides were observed between the ob/T2D and ob/T2D/DN groups. l-alanine was higher and l-serine lower in ob/T2D/DN versus LC groups (326.2 vs. 248.0 μM, p = 0.0086 and 70.2 vs. 89.8 μM, p = 0.0110), consistent with a potential contribution of these changes to the observed 1-deoxysphingolipids profiles. 1-deoxydihydroceramides correlated inversely with leg intraepidermal nerve fiber density (CC -0.40, p = 0.003). These findings indicate that 1-deoxydihydroceramides may be important biomarkers and/or mediators of DN.

Regression calibration with instrumental variables for longitudinal models with interaction terms, and application to air pollution studies

In this paper, we derive forms of estimators and associated variances for regression calibration with instrumental variables in longitudinal models that include interaction terms between two unobservable predictors and interactions between these predictors and covariates not measured with error; the inclusion of the latter interactions generalize results we previously reported. The methods are applied to air pollution and health data collected on children with asthma. The new methods allow for the examination of how the relationship between health outcome leukotriene E4 (LTE₄, a biomarker of inflammation) and two unobservable pollutant exposures and their interaction are modified by the presence or absence of upper respiratory infections. The pollutant variables include secondhand smoke and ambient (outdoor) fine particulate matter. Simulations verify the accuracy of the proposed methods under various conditions.