Longitudinal Patterns of Potentially Inappropriate Medication Use Following Incident Dementia Diagnosis

Introduction: Potentially inappropriate medication (PIM) use in older adults with dementia is an understudied area. We assessed longitudinal changes in PIM exposure by dementia type following dementia diagnosis. Methods: We followed 2448 participants aged ≥ 65 years (52% women, 85.5% Caucasian, mean age 80.9 ± 7.5 years) diagnosed with dementia after enrollment in the National Alzheimer\u27s Coordinating Center (2005-2014). We estimated the association between dementia type and PIM annually for 2 years after diagnosis, using Generalized Estimating Equations. Results:Participants with Lewy body dementia had more PIM use, and participants with frontotemporal dementia had less PIM use than participants with Alzheimer\u27s disease. In the first year following diagnosis, total number of medications increased, on average, by 10% for Alzheimer\u27s disease and 15% for Lewy body dementia (P \u3c .05 for both). Discussion: A tailored approach aimed at optimizing drug therapy is needed to mitigate PIM exposure to improve medical care for individuals with dementia

Southeast of What? Reflections on SEALS\u27 Success

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

AI is a viable alternative to high throughput screening: a 318-target study

: High throughput screening (HTS) is routinely used to identify bioactive small molecules. This requires physical compounds, which limits coverage of accessible chemical space. Computational approaches combined with vast on-demand chemical libraries can access far greater chemical space, provided that the predictive accuracy is sufficient to identify useful molecules. Through the largest and most diverse virtual HTS campaign reported to date, comprising 318 individual projects, we demonstrate that our AtomNet® convolutional neural network successfully finds novel hits across every major therapeutic area and protein class. We address historical limitations of computational screening by demonstrating success for target proteins without known binders, high-quality X-ray crystal structures, or manual cherry-picking of compounds. We show that the molecules selected by the AtomNet® model are novel drug-like scaffolds rather than minor modifications to known bioactive compounds. Our empirical results suggest that computational methods can substantially replace HTS as the first step of small-molecule drug discovery

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 twopart 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

Associations of food groups and cardiometabolic and inflammatory biomarkers - Does the meal matter?

Increased attention has been paid to circadian patterns and how predisposition to metabolic disorders can be affected by meal timing. Currently, it is not clear which role can be attributed to the foods selected at meals. On a cross-sectional sub-cohort study (815 adults) within the EPIC-Potsdam study we investigated whether the same foods (vegetables, fruits, refined grains, whole grains, red and processed meats) eaten at different meals (breakfast, lunch, dinner) show different associations with biomarkers of cardiometabolic risk. Meal-specific usual intakes were calculated from multiple 24h dietary recalls. Multivariable-adjusted linear regression models showed that intake of vegetables at breakfast was associated with lower LDL cholesterol (LDL-C) (-0.37 mmol/l per 50g; 95%CI: -0.61 to -0.12) and vegetables at dinner was associated with higher HDL cholesterol (HDL-C) (0.05 mmol/l per 50g; 95%CI: 0 to 0.10). Fruit intake at breakfast was associated with lower glycated hemoglobin (HbA1c) (-0.06% per 50g; 95%CI: -0.10 to -0.01) and fruits at dinner with lower CRP (-0.21 mg/l per 50g; 95%CI: -0.42 to -0.01). Red and processed meat intake at breakfast was associated with higher HbA1c (0.25% per 50g; 95%CI: 0.05 to 0.46) and CRP (0.76 mg/l per 50g; 95%CI: 0.15 to 1.36). Our results suggest that by preferring fruits and vegetables and avoiding red and processed meats at specific meals (i.e., breakfast and dinner), cardiometabolic profiles and ultimately chronic disease risk could be improved. Lunch seemed to be a less important meal in terms of food-biomarker associations

Frailty modifies the intervention effect of chair yoga on pain among older adults with lower extremity osteoarthritis: Secondary analysis of a nonpharmacological intervention trial

Objective: In an 8-week nonpharmacological pain intervention trial among older adults with lower extremity osteoarthritis (OA), we aimed to examine: a) the baseline frailty level of the participants; b) whether such intervention is more beneficial for baseline frailer older adults than for their counterparts with less frailty; and c) whether the intervention could also alter frailty. Methods: Participants were randomly assigned to either chair yoga (CY) or health education program (HEP) groups and attended twice-weekly 45-minute CY or HEP sessions for 8 weeks. Following a standard procedure, 82 variables were used to construct a frailty index (FI, 0–1). Primary outcomes were: Western Ontario and McMaster Universities (WOMAC) pain and pain interference. Linear mixed-effects models were used to evaluate the modifying effect of baseline frailty on the intervention effect of CY on primary outcomes. Similar models were used to evaluate the effect of CY on frailty. Results: A total of 112 participants (n = 63 CY, n = 49 HEP; 75.3 [SD = 7.5] years) with 85 females (75.9%) were included. The mean values of baseline FI for the CY and HEP groups were similar (0.428 [0.05] and 0.433 [0.05], P = 0.355). Each 0.01 increment in baseline FI was associated with higher WOMAC pain (beta = 0.28, P < 0.001) and pain interference (beta = 0.51, P < 0.001). There was a significant interaction effect between intervention, time, and baseline FI (P = 0.020 for WOMAC pain; P = 0.010 for pain interference), indicating that participants with higher level of baseline FI had greater declines in WOMAC pain and pain interference. There was no significantly greater decline in FI for the CY group compared to the HEP group (between-group difference − 0.01; P = 0.509) and there were no significant trend changes in FI (P for interaction = 0.605). Conclusions: Frailty modifies the intervention effect of CY on pain among older adults with lower extremity OA, underscoring the importance of assessing frailty to improve the management of pain in this population

Correction to: Meal analysis for understanding eating behavior: meal- and participant-specific predictors for the variance in energy and macronutrient intake

Following publication of the original article [1], the authors reported an error in Table 3. The correct Table 3 is provided below

Meal analysis for understanding eating behavior: meal- and participant-specific predictors for the variance in energy and macronutrient intake

Abstract Background Meals differ in their nutritional content. This variation has not been fully addressed despite its potential contribution in understanding eating behavior. The aim of this study was to investigate the between-meal and between-individual variance in energy and macronutrient intake as a measure of variation in intake and the meal type-specific relative importance of predictors of these intake variations. Methods Energy and macronutrient intake were derived from three 24 h dietary recalls in an EPIC-Potsdam sub-cohort of 814 German adults. Intra-class correlation was calculated for participants and meal type. Predictors of intake were assessed using meal type-specific multilevel regression models in a structural equation modeling framework at intake and participant levels using the Pratt Index. The importance of the predictor energy misreporting was assessed in sensitivity analyses on 682 participants. 95% confidence intervals were calculated based on 1000 bootstrap samples. Results Differences between meal types explain a large proportion of the variation in intake (intra-class correlation: 39% for energy, 25% for carbohydrates, 47% for protein, and 33% for fat). Between-participant variation in intake was much lower, with a maximum of 3% for carbohydrate and fat. Place of meal was the most important intake-level predictor of energy and macronutrient intake (Pratt Index of up to 65%). Week/weekend day was important in the breakfast meal, and prior interval (hours passed since last meal) was important for the afternoon snack and dinner. On the participant level, sex was the most important predictor, with Pratt Index of up to 95 and 59% in the main and in the sensitivity analysis, respectively. Energy misreporting was especially important at the afternoon snack, accounting for up to 69% of the explained variance. Conclusions The meal type explains the highest variation in energy and macronutrient intakes. We identified key predictors of variation in the intake and in the participant levels. These findings suggest that successful dietary modification efforts should focus on improving specific meals