Structural and functional brain changes in middle-aged type 2 diabetic patients: a cross-sectional study.

BACKGROUND Type 2 diabetes mellitus (T2DM) is an emerging risk factor for cognitive impairment. Whether this impairment is a direct effect of this metabolic disorder on brain function, a consequence of vascular disease, or both, remains unknown. Structural and functional neuroimaging studies in patients with T2DM could help to elucidate this question. OBJECTIVE We designed a cross-sectional study comparing 25 T2DM patients with 25 age- and gender-matched healthy control participants. Clinical information, APOE genotype, lipid and glucose analysis, structural cerebral magnetic resonance imaging including voxel-based morphometry, and F-18 fluorodeoxyglucose positron emission tomography were obtained in all subjects. METHODS Gray matter densities and metabolic differences between groups were analyzed using statistical parametric mapping. In addition to comparing the neuroimaging profiles of both groups, we correlated neuroimaging findings with HbA1c levels, duration of T2DM, and insulin resistance measurement (HOMA-IR) in the diabetic patients group. Results: Patients with T2DM presented reduced gray matter densities and reduced cerebral glucose metabolism in several fronto-temporal brain regions after controlling for various vascular risk factors. Furthermore, within the T2DM group, longer disease duration, and higher HbA1c levels and HOMA-IR were associated with lower gray matter density and reduced cerebral glucose metabolism in fronto-temporal regions. CONCLUSION In agreement with previous reports, our findings indicate that T2DM leads to structural and metabolic abnormalities in fronto-temporal areas. Furthermore, they suggest that these abnormalities are not entirely explained by the role of T2DM as a cardiovascular risk factor.

Use of Two-Part Regression Calibration Model to Correct for Measurement Error in Episodically Consumed Foods in a Single-Replicate Study Design: EPIC Case Study.

In epidemiologic studies, measurement error in dietary variables often attenuates association between dietary intake and disease occurrence. To adjust for the attenuation caused by error in dietary intake, regression calibration is commonly used. To apply regression calibration, unbiased reference measurements are required. Short-term reference measurements for foods that are not consumed daily contain excess zeroes that pose challenges in the calibration model. We adapted two-part regression calibration model, initially developed for multiple replicates of reference measurements per individual to a single-replicate setting. We showed how to handle excess zero reference measurements by two-step modeling approach, how to explore heteroscedasticity in the consumed amount with variance-mean graph, how to explore nonlinearity with the generalized additive modeling (GAM) and the empirical logit approaches, and how to select covariates in the calibration model. The performance of two-part calibration model was compared with the one-part counterpart. We used vegetable intake and mortality data from European Prospective Investigation on Cancer and Nutrition (EPIC) study. In the EPIC, reference measurements were taken with 24-hour recalls. For each of the three vegetable subgroups assessed separately, correcting for error with an appropriately specified two-part calibration model resulted in about three fold increase in the strength of association with all-cause mortality, as measured by the log hazard ratio. Further found is that the standard way of including covariates in the calibration model can lead to over fitting the two-part calibration model. Moreover, the extent of adjusting for error is influenced by the number and forms of covariates in the calibration model. For episodically consumed foods, we advise researchers to pay special attention to response distribution, nonlinearity, and covariate inclusion in specifying the calibration model.