20-year depressive symptoms, dementia, and structural neuropathology in older women.

INTRODUCTION: The course of depressive symptoms and dementia risk is unclear, as are potential structural neuropathological common causes. METHODS: Utilizing joint latent class mixture models, we identified longitudinal trajectories of annually assessed depressive symptoms and dementia risk over 21 years in 957 older women (baseline age 72.7 years old) from the Women's Health Initiative Memory Study. In a subsample of 569 women who underwent structural magnetic resonance imaging, we examined whether estimates of cerebrovascular disease and Alzheimer's disease (AD)-related neurodegeneration were associated with identified trajectories. RESULTS: Five trajectories of depressive symptoms and dementia risk were identified. Compared to women with minimal symptoms, women who reported mild and stable and emerging depressive symptoms were at the highest risk of developing dementia and had more cerebrovascular disease and AD-related neurodegeneration. DISCUSSION: There are heterogeneous profiles of depressive symptoms and dementia risk. Common neuropathological factors may contribute to both depression and dementia. Highlights The progression of depressive symptoms and concurrent dementia risk is heterogeneous. Emerging depressive symptoms may be a prodromal symptom of dementia. Cerebrovascular disease and AD are potentially shared neuropathological factors.

A randomized controlled trial of intermittent theta burst stimulation to the medial prefrontal cortex for tobacco use disorder: Clinical efficacy and safety.

OBJECTIVE: This study aimed to evaluate the clinical efficacy and safety of administering intermittent theta burst stimulation (iTBS) to the medial prefrontal cortex for tobacco use disorder. METHODS: A randomized sham-controlled trial was conducted, with 38 participants receiving 28 sessions of active (n=25) or sham (n=13) iTBS (2 sessions/day, 600 pulses/session, 110% resting motor threshold, AFz target) along with smoking cessation education (Forever Free © booklets) over 14 visits. Primary outcomes included self-reported cigarette consumption and abstinence, verified by urinary cotinine tests. Secondary outcomes included symptoms of tobacco use disorder, negative mood, and safety/tolerability. RESULTS: Both active and sham groups reported reduced cigarette consumption (ß = -0.12, p = 0.015), cigarette craving (ß = -0.16, p = 0.002), and tobacco withdrawal symptoms (ß = -0.05, p < 0.001). However, there were no significant time x group interaction effects for any measure. Similarly, the two groups had no significant differences in urinary cotinine-verified abstinence. Adverse events occurred with similar frequency in both groups. CONCLUSION: There were no differences in cigarette consumption between the active and sham iTBS groups, both groups decreased cigarette consumption similarly. Further research is needed to compare iTBS to standard high-frequency rTMS and explore the potential differences in efficacy. Despite limitations, this study contributes to experimental design considerations for TMS as a novel intervention for tobacco and other substance use disorders, emphasizing the need for a more comprehensive understanding of the stimulation parameters and target sites.

The osteoarthritis prevention study (TOPS) - A randomized controlled trial of diet and exercise to prevent Knee Osteoarthritis: Design and rationale.

Background: Osteoarthritis (OA), the leading cause of disability among adults, has no cure and is associated with significant comorbidities. The premise of this randomized clinical trial is that, in a population at risk, a 48-month program of dietary weight loss and exercise will result in less incident structural knee OA compared to control. Methods/design: The Osteoarthritis Prevention Study (TOPS) is a Phase III, assessor-blinded, 48-month, parallel 2 arm, multicenter randomized clinical trial designed to reduce the incidence of structural knee OA. The study objective is to assess the effects of a dietary weight loss, exercise, and weight-loss maintenance program in preventing the development of structural knee OA in females at risk for the disease. TOPS will recruit 1230 ambulatory, community dwelling females with obesity (Body Mass Index (BMI) â€‹≥ â€‹30 â€‹kg/m2) and aged ≥50 years with no radiographic (Kellgren-Lawrence grade ≤1) and no magnetic resonance imaging (MRI) evidence of OA in the eligible knee, with no or infrequent knee pain. Incident structural knee OA (defined as tibiofemoral and/or patellofemoral OA on MRI) assessed at 48-months from intervention initiation using the MRI Osteoarthritis Knee Score (MOAKS) is the primary outcome. Secondary outcomes include knee pain, 6-min walk distance, health-related quality of life, knee joint loading during gait, inflammatory biomarkers, and self-efficacy. Cost effectiveness and budgetary impact analyses will determine the value and affordability of this intervention. Discussion: This study will assess the efficacy and cost effectiveness of a dietary weight loss, exercise, and weight-loss maintenance program designed to reduce incident knee OA. Trial registration: ClinicalTrials.gov Identifier: NCT05946044.

Cognitive-enhancing effects of acetylcholine receptor agonists in group-housed cynomolgus monkeys who drink ethanol.

The cognitive impairments that are often observed in patients with alcohol use disorder (AUD) partially contribute to the extremely low rates of treatment initiation and adherence. Brain acetylcholine receptors (AChR) mediate and modulate cognitive and reward-related behavior and their distribution can be altered by long-term heavy drinking. Therefore, AChRs are promising pharmacotherapeutic targets for treating the cognitive symptoms of AUD. In the present study, the pro-cognitive efficacy of two AChR agonists, xanomeline and varenicline, were evaluated in group-housed monkeys who self-administered ethanol for more than one year. The muscarinic AChR antagonist scopolamine was used to disrupt performance of a serial stimulus discrimination and reversal (SDR) task designed to probe cognitive flexibility, defined as the ability to modify a previously learned behavior in response to a change in reinforcement contingencies. The ability of xanomeline and varenicline to remediate the disruptive effects of scopolamine was compared between dominant and subordinate monkeys, with lighter and heavier drinking histories, respectively. We hypothesized that subordinate monkeys would be more sensitive to all three drugs. Scopolamine dose-dependently impaired performance on the serial SDR task in all monkeys at doses lower than those that produced non-specific impairments (e.g, sedation); its potency did not differ between dominant and subordinate monkeys. However, both AChR agonists were effective in remediating the scopolamine-induced deficit in subordinate monkeys, but not in dominant monkeys. These findings suggest xanomeline and varenicline may be effective for enhancing cognitive flexibility in individuals with a history of heavy drinking. Significance Statement Pro-cognitive effects of two acetylcholine (Ach) receptor agonists were assessed in group-housed monkeys who had several years' experience drinking ethanol. The muscarinic ACh receptor agonist xanomeline and the nicotinic ACh receptor agonist varenicline reversed a cognitive deficit induced by the muscarinic ACh receptor antagonist scopolamine. However, this effect was observed only in lower-ranking (subordinate) monkeys and not higher-ranking (dominant monkeys). Results suggest that ACh agonists may effectively remediate alcohol-induced cognitive deficits in a subpopulation of those with alcohol use disorder.

D.A.R.E./keepin' it REAL elementary curriculum: Substance use outcomes.

In response to a need to implement an evidence-based prevention program, D.A.R.E. America adopted keepin' it REAL. The program was previously developed and tested in middle school settings. As part of its adoption, an elementary version of the program was developed. This study tests the effectiveness of keepin' it REAL when delivered to fifth graders. The intervention was delivered to two cohorts of students, the first in the 2019-2020 school year, the second in the 2020-2021 school year. Pretest surveys were completed by 6,122 students. The COVID-19 pandemic interfered with posttest and follow-up data collection. At immediate posttest, 2,049 students (33.5%) completed analyzable posttest surveys. One year after the pretest, 1,486 (24.3%) students completed usable follow-up surveys. We used algorithmically generated cases (virtual controls) that use treatment cases' pretest psychosocial scores to assess program effectiveness. When compared to virtual control cases, the program had identifiable improvements in both a key psychosocial measure and in terms of deterring the onset of 30-day alcohol use, drunkenness, and vaping. Outcomes suggest that the delivery of elementary school keepin' it REAL by D.A.R.E. officers is having a positive effect in terms of deterring the onset of alcohol use and vaping.

Determinants of Metabolic Syndrome and Type 2 Diabetes in the Absence of Obesity: The Jackson Heart Study.

Context: Multiple studies suggest that adults who were normal weight at diabetes diagnosis are at higher risk for all-cause mortality than those who had overweight or obesity at diagnosis. Objective: While obesity is a known risk factor for cardiometabolic disease, differences in body fat distribution in those without obesity are understudied, especially in African Americans. Methods: In 1005 participants of the Jackson Heart Study, without cardiovascular disease at baseline, we used logistic regression to investigate the longitudinal association of body fat distribution by CT scan with metabolic syndrome (MetS) or type 2 diabetes (T2D). We used the harmonized International Diabetes Federation criteria to define MetS. We included only normal weight or overweight participants (BMI: 18.5 to < 30.0 kg/m2). We created separate models for MetS and T2D adjusted for a standard set of covariates. We excluded participants with prevalent MetS or T2D, respectively in sensitivity. Results: Higher visceral fat, subcutaneous fat, BMI, and insulin resistance (HOMA-IR) were significantly associated with MetS and T2D after adjustment. Visceral fat was strongly associated with both outcomes (MetS OR = 2.07 [1.66-2.68]; T2D OR = 1.51 [1.21-1.88]), and the association for MetS persisted in the normal weight only group. Estimates were robust to sensitivity analysis and were only modestly mediated by insulin resistance. Physical activity was not associated with MetS or T2D. Conclusion: Visceral fat is strongly associated with developing MetS, even in normal weight individuals, suggesting that excess visceral fat plays a role in cardiometabolic risk beyond that of overall adiposity and obesity in African Americans.

Psychosocial Indicators of Adolescent Alcohol, Cigarette, and Marijuana Use: An Analysis of Normalized, Harmonized, and Pooled Data.

We normalized, harmonized, and pooled 344,429 surveys collected from 106,470 research participants from 25 research studies that assessed past 30-day alcohol use, drunkenness, smoking cigarettes, using marijuana, and a host of psychosocial variables. After normalizing and harmonizing psychosocial measures, we completed analyses to examine the ability of psychosocial variables to serve as proxy indicators of use. Intentionality, peer descriptive normative beliefs, and age emerged as being of primary importance in indicating use. Additional variables - peer injunctive norms, beliefs about the positive and negative consequences of use, and attitudes - were also demonstrated to have the potential to serve as proxies in the assessment of substance use risk. There were developmental patterns in how intentionality and descriptive normative beliefs changed with age. Young adolescents had scores that are protective; they have positive intentionality and do not see the prevalence of alcohol and other drug use as widespread. These and other psychosocial variable's mean scores generally erode with age while the distribution of scores widens as youth grow older. The goal of analyses was to define age-related psychosocial profiles that can be used prospectively to estimate substance use risk. These profiles are useful in creating virtual control cases for evaluating disseminated prevention programs.

Are Machine Learning Models on Wrist Accelerometry Robust against Differences in Physical Performance among Older Adults?

Sufficient physical activity (PA) reduces the risk of a myriad of diseases and preserves physical capabilities in later life. While there have been significant achievements in mapping accelerations to real-life movements using machine learning (ML), errors continue to be common, particularly for wrist-worn devices. It remains unknown whether ML models are robust for estimating age-related loss of physical function. In this study, we evaluated the performance of ML models (XGBoost and LASSO) to estimate the hallmark measures of PA in low physical performance (LPP) and high physical performance (HPP) groups. Our models were built to recognize PA types and intensities, identify each individual activity, and estimate energy expenditure (EE) using wrist-worn accelerometer data (33 activities per participant) from a large sample of participants (n = 247, 57% females, aged 60+ years). Results indicated that the ML models were accurate in recognizing PA by type and intensity while also estimating EE accurately. However, the models built to recognize individual activities were less robust. Across all tasks, XGBoost outperformed LASSO. XGBoost obtained F1-Scores for sedentary (0.932 ± 0.005), locomotion (0.946 ± 0.003), lifestyle (0.927 ± 0.006), and strength flexibility exercise (0.915 ± 0.017) activity type recognition tasks. The F1-Scores for recognizing low, light, and moderate activity intensity were (0.932 ± 0.005), (0.840 ± 0.004), and (0.869 ± 0.005), respectively. The root mean square error for EE estimation was 0.836 ± 0.059 METs. There was no evidence showing that splitting the participants into the LPP and HPP groups improved the models' performance on estimating the hallmark measures of physical activities. In conclusion, using features derived from wrist-worn accelerometer data, machine learning models can accurately recognize PA types and intensities and estimate EE for older adults with high and low physical function.

Tolerability of bevacizumab and chemotherapy in a phase 3 clinical trial with human epidermal growth factor receptor 2-negative breast cancer: A trajectory analysis of adverse events.

BACKGROUND: E5103 was a study designed to evaluate the efficacy and safety of bevacizumab. It was a negative trial for the end points of invasive disease-free survival and overall survival. The current work examines the tolerability of bevacizumab and other medication exposures with respect to clinical outcomes and patient-reported outcomes (PROs). METHODS: Adverse events (AEs) collected from the Common Terminology Criteria for Adverse Events were summarized to form an AE profile at each treatment cycle. All-grade and high-grade events were separately analyzed. The change in the AE profile over the treatment cycle was delineated as distinct AE trajectory clusters. AE-related and any-reason early treatment discontinuations were treated as clinical outcome measures. PROs were measured with the Functional Assessment of Cancer Therapy-Breast + Lymphedema. The relationships between the AE trajectory and early treatment discontinuation as well as PROs were analyzed. RESULTS: More than half of all AEs (57.5%) were low-grade. A cluster of patients with broad and mixed AE (all-grade) trajectory grades was significantly associated with any-reason early treatment discontinuation (odds ratio [OR], 2.87; P = .01) as well as AE-related discontinuation (OR, 4.14; P = .001). This cluster had the highest count of all-grade AEs per cycle in comparison with other clusters. Another cluster of patients with primary neuropathic AEs in their trajectories had poorer physical well-being in comparison with a trajectory of no or few AEs (P < .01). A high-grade AE trajectory did not predict discontinuations. CONCLUSIONS: A sustained and cumulative burden of across-the-board toxicities, which were not necessarily all recognized as high-grade AEs, contributed to early treatment discontinuation. Patients with neuropathic all-grade AEs may require additional attention for preventing deterioration in their physical well-being.

Age Differences in Estimating Physical Activity by Wrist Accelerometry Using Machine Learning.

Accelerometer-based fitness trackers and smartwatches are proliferating with incessant attention towards health tracking. Despite their growing popularity, accurately measuring hallmark measures of physical activities has yet to be accomplished in adults of all ages. In this work, we evaluated the performance of four machine learning models: decision tree, random forest, extreme gradient boosting (XGBoost) and least absolute shrinkage and selection operator (LASSO), to estimate the hallmark measures of physical activities in young (20-50 years), middle-aged (50-70 years], and older adults (70-89 years]. Our models were built to recognize physical activity types, recognize physical activity intensities, estimate energy expenditure (EE) and recognize individual physical activities using wrist-worn tri-axial accelerometer data (33 activities per participant) from a large sample of participants (n = 253, 62% women, aged 20-89 years old). Results showed that the machine learning models were quite accurate at recognizing physical activity type and intensity and estimating energy expenditure. However, models performed less optimally when recognizing individual physical activities. F1-Scores derived from XGBoost's models were high for sedentary (0.955-0.973), locomotion (0.942-0.964) and lifestyle (0.913-0.949) activity types with no apparent difference across age groups. Low (0.919-0.947), light (0.813-0.828) and moderate (0.846-0.875) physical activity intensities were also recognized accurately. The root mean square error range for EE was approximately 1 equivalent of resting EE [0.835-1.009 METs]. Generally, random forest and XGBoost models outperformed other models. In conclusion, machine learning models to label physical activity types, activity intensity and energy expenditure are accurate and there are minimal differences in their performance across young, middle-aged and older adults.

Severity of Overuse Injury Impacts Self-Efficacy and Quality of Life in Runners: A 2-Year Prospective Cohort Study.

CONTEXT: While 55 million Americans incorporate running into their exercise routines, up to 65% of runners sustain an overuse injury annually. It has been consistently shown that regular physical activity positively impacts quality of life (QOL), an essential public health indicator; however, the impact of running-related injuries on QOL is unknown. This study seeks to determine whether overuse injury severity impacts QOL in recreational runners, and if self-efficacy mediates this relationship. DESIGN: Community-based prospective cohort study of 300 runners who had been running injury free for at least 5 miles/wk in the past 6 months. METHODS: Self-efficacy for running and QOL measures (Short Form-12 Physical Component and Mental Component, Satisfaction with Life, Positive Affect and Negative Affect) were assessed at baseline, time of injury, and follow-up visits. Over 2 years of observation, overuse injuries were diagnosed by an orthopedic surgeon and injured runners were referred to a physical therapist. RESULTS: Injury severity was significantly (P < .01) related with 2 indices of QOL, such that the effect of injury severity was -2.28 units on the Short Form-12 physical component and -0.73 units on positive affect. Self-efficacy accounted for 19% and 48% of the indirect effects on Short Form-12 physical component and positive affect, respectively. CONCLUSIONS: Since self-efficacy is a modifiable factor related to decreased QOL, these findings have important clinical implications for rehabilitation interventions.

Deployment and testing of an automated medical equipment service communication and documentation system at a rural hospital in Kenya.

BACKGROUND: The Medical Equipment Network Documentation System (MENDS) provides a simple communication network for equipment servicing from failure to restoration. It is a text messaging-based platform, designed to use existing technologies in place in low- and middle-income settings. The system gathers and relays information about equipment service requests and reports and automatically saves them to an online database. METHODS: MENDS was deployed at a high volume, rural, charity medical facility in Kijabe, Kenya for a 3-mo pilot test. RESULTS: The results show MENDS more than tripled documentation and enhanced ease and speed of communication. CONCLUSIONS: Comprehensive data provided by MENDS created more accurate measures of equipment performance, which can be used to decrease the time that equipment is out of service and improve the efficiency of repairs, equipment quality and procurement.

Leveraging Emergent Social Networks to Reduce Sedentary Behavior in Low-Income Parents With Preschool-Aged Children.

This study tested the hypothesis that parents participating in a pediatric obesity intervention who formed social network ties with a parent in the intervention arm would engage in more daily physical activity and less sedentary behavior (after controlling for participant covariates). Data were collected at baseline, 12 months, and 36 months from 610 low-income parent-child pairs participating in an obesity prevention intervention for 3- to 5-year-old children. A network survey was used to identify social network ties among parents and accelerometers were used to measure parental physical activity and sedentary time. Longitudinal regression analyses tested effects of social network ties on parents' physical activity and sedentary behavior. Compared with parents without a social network tie, having a tie with an intervention group participant was associated with a clinically meaningful 11.04 min/day decrease in parental sedentary behavior that approached statistical significance (95% confidence interval [Cl] = [-22.71, 0.63], p = .06). Social network ties among parents in a pediatric obesity prevention intervention were not clearly associated with reduced sedentary behavior among those parents at the traditional level of p = .05. The large effect size (over 77min per week improvement) suggests there might be potential importance of promoting new social networks in community-based health promotion interventions to elicit and support behavior change, but further examination is needed.

PM2.5 associated with gray matter atrophy reflecting increased Alzheimers risk in older women.

OBJECTIVE: To examine whether late-life exposure to PM2.5 (particulate matter with aerodynamic diameters <2.5-µm) contributes to progressive brain atrophy predictive of Alzheimer's disease (AD) using a community-dwelling cohort of women (aged 70-89) with up to two brain MRI scans (MRI-1: 2005-6; MRI-2: 2010-11). METHODS: AD pattern similarity (AD-PS) scores, developed by supervised machine learning and validated with MRI data from the AD Neuroimaging Initiative, was used to capture high-dimensional gray matter atrophy in brain areas vulnerable to AD (e.g., amygdala, hippocampus, parahippocampal gyrus, thalamus, inferior temporal lobe areas and midbrain). Based on participants' addresses and air monitoring data, we implemented a spatiotemporal model to estimate 3-year average exposure to PM2.5 preceding MRI-1. General linear models were used to examine the association between PM2.5 and AD-PS scores (baseline and 5-year standardized change), accounting for potential confounders and white matter lesion volumes. RESULTS: For 1365 women aged 77.9±3.7 years in 2005-6, there was no association between PM2.5 and baseline AD-PS score in cross-sectional analyses (ß=-0.004; 95% CI: -0.019, 0.011). Longitudinally, each interquartile range increase of PM2.5 (2.82-µg/m3) was associated with increased AD-PS scores during the follow-up, equivalent to a 24% (hazard ratio=1.24; 95% CI: 1.14, 1.34) increase in AD risk over 5-years (n=712; aged 77.4±3.5 years). This association remained after adjustment for socio-demographics, intracranial volume, lifestyle, clinical characteristics, and white matter lesions, and was present with levels below US regulatory standards (<12-µg/m3). CONCLUSIONS: Late-life exposure to PM2.5 is associated with increased neuroanatomical risk of AD, which may not be explained by available indicators of cerebrovascular damage.

Identifying Social Network Conditions that Facilitate Sedentary Behavior Change: The Benefit of Being a "Bridge" in a Group-based Intervention.

Using data from one of the first trials to try to leverage social networks as a mechanism for obesity intervention, we examined which social network conditions amplified behavior change. Data were collected as part of a community-based healthy lifestyle intervention in Nashville, USA, between June 2014 and July 2017. Adults randomized to the intervention arm were assigned to a small group of 10 participants that met in person for 12 weekly sessions. Intervention small group social networks were measured three times; sedentary behavior was measured by accelerometry at baseline and 12 months. Multivariate hidden Markov models classified people into distinct social network trajectories over time, based on the structure of the emergent network and where the individual was embedded. A multilevel regression analysis assessed the relationship between network trajectory and sedentary behavior (N = 261). Being a person that connected clusters of intervention participants at any point during the intervention predicted an average reduction of 31.3 min/day of sedentary behavior at 12 months, versus being isolated [95% CI: (-61.4, -1.07), p = 0.04]. Certain social network conditions may make it easier to reduce adult sedentary behavior in group-based interventions. While further research will be necessary to establish causality, the implications for intervention design are discussed.

Investigating Predictors of Cognitive Decline Using Machine Learning.

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.

Particulate matter and episodic memory decline mediated by early neuroanatomic biomarkers of Alzheimer's disease.

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.

The association between neighborhood socioeconomic status, cardiovascular and cerebrovascular risk factors, and cognitive decline in the Health and Retirement Study (HRS).

Background: A small but growing body of evidence supports a relationship between neighborhood socioeconomic status (NSES) and cognitive decline. Additional work is needed to characterize this relationship controlling for risk factors such as cardiovascular, cerebrovascular, and genetic risk factors.Methods: Cognitive decline was assessed in association with NSES, and cardiovascular and cerebrovascular risk factors (heart disease, diabetes, hypertension, and stroke) in 8,198 individuals from the 1992-2010 waves of the Health and Retirement Study (HRS). Latent class trajectory analysis determined the number of cognitive trajectory classes that best fit the data, and a multinomial logistic regression model in the latent class framework assessed the risk for cognitive classes conferred by NSES index score and heart disease, diabetes, hypertension, and stroke across three trajectory classes of cognitive function. The analyses controlled for genetic risk for cognitive decline (including APOE genotype) and demographic variables, including education.Results: The HRS sample was 57.6% female and 85.5% White, with a mean age of 67.5(3.5) years at baseline. The three-quadratic-class model best fit the data, where higher classes represented better cognitive function. Those with better cognitive function were mainly younger white females. Those in the highest quartile of NSES had 57% higher odds of being in the high cognitive function class. Heart disease, diabetes, hypertension, and stroke each increased the odds having of lower cognitive function.Conclusions: In examining the relationship of cognitive status with various variables, neighborhood socioeconomic status, cardiovascular risk, and cerebrovascular risk persisted across the cognitive trajectory classes.

Deep CHORES: Estimating Hallmark Measures of Physical Activity Using Deep Learning.

Wrist accelerometers for assessing hallmark measures of physical activity (PA) are rapidly growing with the advent of smartwatch technology. Given the growing popularity of wrist-worn accelerometers, there needs to be a rigorous evaluation for recognizing (PA) type and estimating energy expenditure (EE) across the lifespan. Participants (66% women, aged 20-89 yrs) performed a battery of 33 daily activities in a standardized laboratory setting while a tri-axial accelerometer collected data from the right wrist. A portable metabolic unit was worn to measure metabolic intensity. We built deep learning networks to extract spatial and temporal representations from the time-series data, and used them to recognize PA type and estimate EE. The deep learning models resulted in high performance; the F1 score was: 0.82, 0.81, and 95 for recognizing sedentary, locomotor, and lifestyle activities, respectively. The root mean square error was 1.1 (+/-0.13) for the estimation of EE.

Respuesta farmacogenética a escitalopram en pacientes geriátricos en Guatemala / Pharmacogenetic response to escitalopram in geriatric patients in Guatemala

OBJETIVOS: El escitalopram es un fármaco antidepresivo del grupo de los inhibidores selectivos de la recaptación de serotonina utilizado ampliamente en el tratamiento del síndrome depresivo mayor. Este medicamento es metabolizado por la enzima CYP2C19 del complejo enzimático citocromo P450, y se conoce que variaciones genéticas en esta enzima están directamente relacionadas a variaciones en la respuesta farmacológica al escitalopram, en eficacia y toxicidad. El objetivo de este estudio fue determinar la presencia de variantes alélicas para CYP2C19 en pacientes geriátricos tratados con escitalopram y evaluar el ajuste al tratamiento farmacológico con escitalopram de acuerdo con su perfil farmacogenético. MATERIALES Y MÉTODOS: Se realizó un estudio cuantitativo, descriptivo y transversal. La muestra se conformó por 36 pacientes geriátricos de ambos sexos, diagnosticados con síndrome depresivo mayor y tratados con escitalopram en la Clínica del Adulto Mayor del Hospital Roosevelt durante marzo a agosto de 2017. Para la determinación de la presencia de variantes alélicas se utilizó la técnica de análisis de reacción en cadena de la polimerasa-polimorfismos de longitud de fragmentos de restricción (PCR-RFLP). RESULTADOS: Se encontró presencia de variantes alélicas en 4 pacientes (11,1%). Según los diplotipos encontrados, 32 pacientes (88,9%) correspondieron al fenotipo metabolizador extensivo y 4 (11,1%) al intermedio, no se encontraron casos de fenotipos metabolizadores pobres. CONCLUSIONES: En todos los casos se realizó intervención farmacéutica con recomendación terapéutica al médico. En ningún caso fue necesario reajuste de dosis. La frecuencia de la presencia de variantes alélicas encontrada para Guatemala fue similar al promedio de frecuencia multiétnica conocida

OBJECTIVES: Escitalopram is an antidepressant drug from the group of selective serotonin reuptake inhibitors widely used in the treatment of major depressive syndrome. This drug is metabolized by the enzyme CYP2C19 of the cytochrome P450 enzyme complex and it is known that genetic variations in this enzyme are directly related to variations in the pharmacological response to escitalopram, in efficacy and toxicity. The aim of this study was to determine the presence of allelic variants for CYP2C19 in geriatric patients treated with escitalopram and to evaluate the adjustment to pharmacological treatment with escitalopram according to their pharmacogenetic profile. MATERIALS AND METHODS: A quantitative, descriptive and cross-sectional study was conducted. The sample consisted of 36 geriatric patients of both sexes, diagnosed with major depressive syndrome and treated with escitalopram in the Senior Adult Clinic of the Roosevelt Hospital of Guatemala during March to August 2017. For the determination of the presence of allelic variants the analysis technique of polymerase chain reaction-restriction fragment length polymorphism (PCR-RFLP) was used. RESULTS: Allelic variants were found in 4 patients (11.1%). 32 patients (88.9%) corresponded to the extensive metabolizer phenotype and 4 (11.1%) to the intermediate, no cases of poor metabolizing phenotypes were found. CONCLUSIONS: In all cases, a pharmaceutical intervention was carried out with therapeutic recommendation to the doctor. In no case was dose readjustment necessary. The frequency of the presence of allelic variants found for Guatemala was similar to the known multiethnic frequency average