Adherence to the MIND diet and the odds of mild cognitive impairment in generally healthy older adults: The 3-year DO-HEALTH study.

BACKGROUND: The Mediterranean-DASH Intervention for Neurodegenerative Delay (MIND) diet may slow cognitive decline in older adults. A potential mechanism could be possible anti-inflammatory properties of the MIND-diet. OBJECTIVE: To examine whether adherence to the MIND diet at baseline is associated with the odds of mild cognitive impairment (MCI) and changes in biomarkers of inflammation (High-sensitivity C-reactive Protein(hsCRP), interleukin-6(IL-6)) over three years in adults ≥70 years. METHODS: Adherence to the MIND diet was assessed by food frequency questionnaire (FFQ) at baseline and after three years. Presence of MCI based on the Montreal Cognitive Assessment (MoCA) was defined as <26 (MCI26), or <24 (MCI24). We performed a minimally adjusted model controlling for sex, prior fall, linear spline at age 85, time, treatment and study site. The fully adjusted model also adjusted for education, BMI, physical activity, depression score, daily energy intake, and comorbidity score. To assess the change in inflammatory markers from baseline, we used linear-mixed-effect models adjusted for the same variables plus the respective baseline concentrations. Sensitivity analyses accounting for practice effects of repeated cognitive tests using the reliable change index for both MoCA cut-offs were done. RESULTS: We included 2028 of 2157 DO-HEALTH participants (60.5% women; mean age 74.88 years) with complete data. Adherence to the MIND diet at baseline was not associated with cognitive decline over three years, neither at MoCA < 26 (OR (95%CI) = 0.99 (0.94-1.04)) nor at MoCA < 24 (OR (95%CI) = 1.03 (0.96-1.1)). Applying the reliable change index to the two cut-offs confirmed the findings. Further, the MIND diet adherence was not associated with the change in MoCA score from baseline in DO-HEALTH. For inflammatory biomarkers MIND-diet baseline adherence was not associated with changes in hsCRP or IL-6. CONCLUSION: Adherence to the MIND-diet was neither associated with the odds of MCI, nor with hsCRP or IL-6 at baseline. Moreover, change in MIND-diet over three years was not associated with changes in hsCRP or IL-6.

Multiple measures derived from 3D photonic body scans improve predictions of fat and muscle mass in young Swiss men.

INTRODUCTION: Digital tools like 3D laser-based photonic scanners, which can assess external anthropometric measurements for population based studies, and predict body composition, are gaining in importance. Here we focus on a) systematic deviation between manually determined and scanned standard measurements, b) differences regarding the strength of association between these standard measurements and body composition, and c) improving these predictions of body composition by considering additional scan measurements. METHODS: We analysed 104 men aged 19-23. Bioelectrical Impedance Analysis was used to estimate whole body fat mass, visceral fat mass and skeletal muscle mass (SMM). For the 3D body scans, an Anthroscan VITUSbodyscan was used to automatically obtain 90 body shape measurements. Manual anthropometric measurements (height, weight, waist circumference) were also taken. RESULTS: Scanned and manually measured height, waist circumference, waist-to-height-ratio, and BMI were strongly correlated (Spearman Rho>0.96), however we also found systematic differences. When these variables were used to predict body fat or muscle mass, explained variation and prediction standard errors were similar between scanned and manual measurements. The univariable predictions performed well for both visceral fat (r2 up to 0.92) and absolute fat mass (AFM, r2 up to 0.87) but not for SMM (r2 up to 0.54). Of the 90 body scanner measures used in the multivariable prediction models, belly circumference and middle hip circumference were the most important predictors of body fat content. Stepwise forward model selection using the AIC criterion showed that the best predictive power (r2 up to 0.99) was achieved with models including 49 scanner measurements. CONCLUSION: The use of a 3D full body scanner produced results that strongly correlate to manually measured anthropometric measures. Predictions were improved substantially by including multiple measurements, which can only be obtained with a 3D body scanner, in the models.