The results of this study indicate the following: (1) among recreational runners, women sustain injuries at a higher rate than men; (2) greater knee stiffness, more common in runners with higher body weights (≥80 kg), significantly increases the odds of sustaining an overuse running injury; and (3) contrary to several long-held beliefs, flexibility, arch height, quadriceps angle, rearfoot motion, lower extremity strength, weekly mileage, footwear, and previous injury are not significant etiologic factors across all overuse running injuries.

Recently, quantitative metabolomics identified a panel of 10-plasma lipids that were highly predictive of conversion to Alzheimer’s disease (AD) in cognitively normal older individuals (N=28, area-under-the-curve; AUC=0.92, sensitivity/specificity of 90%/90%). We failed to replicate these findings in a substantially larger study from two independent cohorts - the Baltimore Longitudinal Study of Aging (BLSA, N=93, AUC=0.642, sensitivity/specificity of 51.6%/65.7%) and the Age, Gene/Environment Susceptibility-Reykjavik Study (AGES-RS, N=100, AUC=0.395, sensitivity/specificity of 47.0%/36.0%). In analyses applying machine learning methods to all 187 metabolite concentrations assayed, we find a modest signal in the BLSA with distinct metabolites associated with the preclinical and symptomatic stages of AD, whereas the same methods gave poor classification accuracies in the AGES-RS samples. We believe that ours is the largest blood biomarker study of preclinical AD to date. These findings underscore the importance of large-scale independent validation of index findings from biomarker studies with relatively small sample sizes.

Evidence suggests exposure to particulate matter with aerodynamic diameter <2.5 μm (PM2.5) may increase the risk for Alzheimer’s disease and related dementias. Whether PM2.5 alters brain structure and accelerates the preclinical neuropsychological processes remains unknown. Early decline of episodic memory is detectable in preclinical Alzheimer’s disease. Therefore, we conducted a longitudinal study to examine whether PM2.5 affects the episodic memory decline, and also explored the potential mediating role of increased neuroanatomic risk of Alzheimer’s disease associated with exposure. Participants included older females (n = 998; aged 73–87) enrolled in both the Women’s Health Initiative Study of Cognitive Aging and the Women’s Health Initiative Memory Study of Magnetic Resonance Imaging, with annual (1999–2010) episodic memory assessment by the California Verbal Learning Test, including measures of immediate free recall/new learning (List A Trials 1–3; List B) and delayed free recall (short- and long-delay), and up to two brain scans (MRI-1: 2005–06; MRI-2: 2009–10). Subjects were assigned Alzheimer’s disease pattern similarity scores (a brain-MRI measured neuroanatomical risk for Alzheimer’s disease), developed by supervised machine learning and validated with data from the Alzheimer’s Disease Neuroimaging Initiative. Based on residential histories and environmental data on air monitoring and simulated atmospheric chemistry, we used a spatiotemporal model to estimate 3-year average PM2.5 exposure preceding MRI-1. In multilevel structural equation models, PM2.5 was associated with greater declines in immediate recall and new learning, but no association was found with decline in delayed-recall or composite scores. For each interquartile increment (2.81 μg/m3) of PM2.5, the annual decline rate was significantly accelerated by 19.3% [95% confidence interval (CI) = 1.9% to 36.2%] for Trials 1–3 and 14.8% (4.4% to 24.9%) for List B performance, adjusting for multiple potential confounders. Long-term PM2.5 exposure was associated with increased Alzheimer’s disease pattern similarity scores, which accounted for 22.6% (95% CI: 1% to 68.9%) and 10.7% (95% CI: 1.0% to 30.3%) of the total adverse PM2.5 effects on Trials 1–3 and List B, respectively. The observed associations remained after excluding incident cases of dementia and stroke during the follow-up, or further adjusting for small-vessel ischaemic disease volumes. Our findings illustrate the continuum of PM2.5 neurotoxicity that contributes to early decline of immediate free recall/new learning at the preclinical stage, which is mediated by progressive atrophy of grey matter indicative of increased Alzheimer’s disease risk, independent of cerebrovascular damage.

A large segment of older adults is unable to complete short-form assessments of health literacy. Among those who were able to complete assessments, the REALM-SF and NVS performed comparably, but their relatively low convergence with the S-TOFHLA raises questions about instrument selection when studying health literacy of older adults.

BackgroundDiabetic retinopathy (DR) is one of the leading causes of blindness in the United States and world-wide. DR is a silent disease that may go unnoticed until it is too late for effective treatment. Therefore, early detection could improve the chances of therapeutic interventions that would alleviate its effects.MethodologyGraded fundus photography and systemic data from 3443 ACCORD-Eye Study participants were used to estimate Random Forest (RF) and logistic regression classifiers. We studied the impact of sample size on classifier performance and the possibility of using RF generated class conditional probabilities as metrics describing DR risk. RF measures of variable importance are used to detect factors that affect classification performance.Principal FindingsBoth types of data were informative when discriminating participants with or without DR. RF based models produced much higher classification accuracy than those based on logistic regression. Combining both types of data did not increase accuracy but did increase statistical discrimination of healthy participants who subsequently did or did not have DR events during four years of follow-up. RF variable importance criteria revealed that microaneurysms counts in both eyes seemed to play the most important role in discrimination among the graded fundus variables, while the number of medicines and diabetes duration were the most relevant among the systemic variables.Conclusions and SignificanceWe have introduced RF methods to DR classification analyses based on fundus photography data. In addition, we propose an approach to DR risk assessment based on metrics derived from graded fundus photography and systemic data. Our results suggest that RF methods could be a valuable tool to diagnose DR diagnosis and evaluate its progression.

Objective Adiposity rebound (AR) or BMI (body mass index) rebound refers to the increase in BMI following the minimum BMI in early childhood. Early AR (before age 5) is predictive of adult obesity. To determine how 4 domains–demographics, maternal BMI, food security, and behavioral characteristics–may affect timing of AR. Study design 248 children, ages 2.5 to 3.5, in Latino farmworker families in North Carolina were examined at baseline and every 3 months for 2 years. BMI was plotted serially for each child and the onset of BMI rebound was determined by visual inspection of the graphs. Given the ages of the children, all rebounds were detected prior to age 5 and were deemed “early,” while other children were classified as “non-rebounders.” Classes were then compared in terms of the 4 domains using bivariate analyses and linear mixed models. Results 131 children demonstrated early rebound, 59 children were non-rebounders, and a further 35 had inconclusive data. Parents of early rebounders were less likely to have documentation permitting legal residence in the United States. Mothers of early rebounders were on average 3 BMI units heavier. Sex, household food security, diet quality, caloric intake, and daily activity did not differ between classes. In multivariable analysis, female sex, limited maternal education, increased maternal BMI, and increased caloric intake were significant predictors of early rebound. Conclusion High maternal BMI was the strongest predictor of early BMI rebound, but increased caloric intake was also significant. Limiting excess calories could delay premature AR and lower the risk of future obesity.

Abstract Objectives Genetic risks for cognitive decline are not modifiable; however their relative importance compared to modifiable factors is unclear. We used machine learning to evaluate modifiable and genetic risk factors for Alzheimer’s disease (AD), to predict cognitive decline. Methods Health and Retirement Study participants, aged 65–90 years, with DNA and >2 cognitive evaluations, were included (n = 7,142). Predictors included age, body mass index, gender, education, APOE ε4, cardiovascular, hypertension, diabetes, stroke, neighborhood socioeconomic status (NSES), and AD risk genes. Latent class trajectory analyses of cognitive scores determined the form and number of classes. Random Forests (RF) classification investigated predictors of cognitive trajectories. Performance metrics (accuracy, sensitivity, and specificity) were reported. Results Three classes were identified. Discriminating highest from lowest classes produced the best RF performance: accuracy = 78% (1.0%), sensitivity = 75% (1.0%), and specificity = 81% (1.0%). Top ranked predictors were education, age, gender, stroke, NSES, and diabetes, APOE ε4 carrier status, and body mass index (BMI). When discriminating high from medium classes, top predictors were education, age, gender, stroke, diabetes, NSES, and BMI. When discriminating medium from the low classes, education, NSES, age, diabetes, and stroke were top predictors. Discussion The combination of latent trajectories and RF classification techniques suggested that nongenetic factors contribute more to cognitive decline than genetic factors. Education was the most relevant predictor for discrimination.

We applied a high-dimensional machine learning approach to estimate a novel AD risk factor for WHIMS-MRI study participants using ADNI data. The GM AD-PS scores showed strong associations with incident cognitive impairment and cross-sectional and longitudinal associations with age, cognitive function, cognitive status and WM SVID volume lending support to the ongoing validation of the GM AD-PS score.