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 procognitive efficacy of two AChR agonists, xanomeline and varenicline, were evaluated in group-housed monkeys who self-administered ethanol for more than 1 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 socially 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 nonspecific 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: Procognitive 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.

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.

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.

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.

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.

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.

Using high-dimensional machine learning methods to estimate an anatomical risk factor for Alzheimer's disease across imaging databases.

INTRODUCTION: The main goal of this work is to investigate the feasibility of estimating an anatomical index that can be used as an Alzheimer's disease (AD) risk factor in the Women's Health Initiative Magnetic Resonance Imaging Study (WHIMS-MRI) using MRI data from the Alzheimer's Disease Neuroimaging Initiative (ADNI), a well-characterized imaging database of AD patients and cognitively normal subjects. We called this index AD Pattern Similarity (AD-PS) scores. To demonstrate the construct validity of the scores, we investigated their associations with several AD risk factors. The ADNI and WHIMS imaging databases were collected with different goals, populations and data acquisition protocols: it is important to demonstrate that the approach to estimating AD-PS scores can bridge these differences. METHODS: MRI data from both studies were processed using high-dimensional warping methods. High-dimensional classifiers were then estimated using the ADNI MRI data. Next, the classifiers were applied to baseline and follow-up WHIMS-MRI GM data to generate the GM AD-PS scores. To study the validity of the scores we investigated associations between GM AD-PS scores at baseline (Scan 1) and their longitudinal changes (Scan 2 -Scan 1) with: 1) age, cognitive scores, white matter small vessel ischemic disease (WM SVID) volume at baseline and 2) age, cognitive scores, WM SVID volume longitudinal changes respectively. In addition, we investigated their associations with time until classification of independently adjudicated status in WHIMS-MRI. RESULTS: Higher GM AD-PS scores from WHIMS-MRI baseline data were associated with older age, lower cognitive scores, and higher WM SVID volume. Longitudinal changes in GM AD-PS scores (Scan 2 - Scan 1) were also associated with age and changes in WM SVID volumes and cognitive test scores. Increases in the GM AD-PS scores predicted decreases in cognitive scores and increases in WM SVID volume. GM AD-PS scores and their longitudinal changes also were associated with time until classification of cognitive impairment. Finally, receiver operating characteristic curves showed that baseline GM AD-PS scores of cognitively normal participants carried information about future cognitive status determined during follow-up. DISCUSSION: 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.

Neighborhoods, sleep quality, and cognitive decline: Does where you live and how well you sleep matter?

INTRODUCTION: We evaluated the association between neighborhood socioeconomic status (NSES) and sleep quality on cognitive decline in the Health and Retirement Study. METHODS: Health and Retirement Study participants (n = 8090), aged 65+ with DNA and multiple biennial cognitive observations (abbreviated Telephone Interview for Cognitive Status), were included. Participants were grouped into quartiles of NSES and sleep quality scores. We adjusted for apolipoprotein E ε4, demographic, and cardiovascular risk factors. Random effects modeling evaluated cognitive change over time. RESULTS: NSES and sleep were significantly associated with cognitive decline, and there was a significant interaction between them (P = .02). Significant differences between high/low NSES and high/low sleep quality (P < .0001) were found. DISCUSSION: Sleep and NSES were associated with cognitive decline; the association between sleep and cognition appeared stronger among those with low NSES. The association between low NSES, poor sleep quality, and cognitive decline was roughly equivalent to the association between apolipoprotein E ε4 and cognitive decline.

Relationship between maternal depression symptoms and child weight outcomes in Latino farmworker families.

This study sought to characterize depressive symptoms among mothers in Latino farmworker families, determine if maternal depression increases children's risk of obesity, and ascertain whether relevant risk factors such as physical activity, diet, and feeding style mediate this relationship. Mothers from 248 families completed the 10-item Center for Epidemiologic Studies Depression Scale 9 times over a 2-year period. Four distinct patterns were used to describe mothers: few symptoms, moderate episodic symptoms, severe episodic symptoms, and chronic symptoms. Approximately two-thirds of women experienced moderate symptoms of depression at least once. Children of mothers fitting each pattern were compared. At the end of the study, children of mothers with severe episodic and chronic symptoms were significantly more likely to be overweight and obese than children of mothers with few symptoms (p < .05). After controlling for covariates, differences in weight status for children of mothers with severe episodic symptoms remained significant. Children of mothers with either moderate episodic or chronic symptoms were fed in a less responsive fashion (p < .05), and children of chronically symptomatic mothers had lower diet quality (p < .01). Although nonresponsive feeding has been linked to childhood obesity, in this analysis, feeding style did not mediate the relationship between maternal depression and diet quality. Elevated levels of depressive symptoms are common in this population, and those symptoms, especially when severe or chronic in nature, may increase children's risk of obesity. Additional research is needed to characterize the pathways through which maternal depression influences children's weight.

Determinants of Adiposity Rebound Timing in Children.

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: A total of 248 children, ages 2.5-3.5 years, 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 before age 5 years and were deemed "early," whereas other children were classified as "nonrebounders." Classes were then compared in terms of the 4 domains with the use of bivariate analyses and linear mixed models. RESULTS: A total of 131 children demonstrated early rebound, 59 children were nonrebounders, and a further 35 had inconclusive data. Parents of early rebounders were less likely to have documentation permitting legal residence in the US. 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 also was significant. Limiting excess calories could delay premature AR and lower the risk of future obesity.

Cumulative Number of Treatment Interventions Predicts Health-Related Quality of Life in Patients with Critical Limb Ischemia.

BACKGROUND: Health-related quality of life (QOL) is usually assessed after a defined interval following a single intervention, but critical limb ischemia (CLI) is a chronic condition where multiple interventions are often required over a patient's lifetime. We hypothesized that the impact of CLI treatment interventions on QOL is diminished in the setting of multiple previous interventions. To test this hypothesis, we performed a cross-sectional study evaluating associations between cumulative number of previous peripheral artery disease (PAD) treatment interventions and QOL adjusting for both comorbidity and disease severity. METHODS: Participants with CLI (abnormal ankle brachial index [ABI] plus rest pain and/or tissue loss) were enrolled in a cross-sectional study and completed a disease-specific QOL assessment, (the Vascular Quality of Life Questionnaire-6 [VascuQol-6]). Minimum ABI was used to assess disease severity, and comorbidity was evaluated based on Charlson Comorbidity Index. Cumulative number of PAD treatment interventions was defined based on the lifelong total for both legs. QOL associations were evaluated using a multivariable linear regression model adjusted for age and gender. RESULTS: Thirty-two patients with CLI participated. Mean age was 63 ± 10 years, 72% were men, and 63% were white; mean ABI was 0.6 ± 0.2. Mean VQ-6 score was 11.6 ± 4.2, and QOL was lower in patients with more previous interventions. Multivariable models demonstrated that an increasing number of previous treatment interventions negatively impacted QOL (P = 0.047), whereas positive associations were identified for female gender (P = 0.006) and ABI (P = 0.006). No association between comorbidity and QOL was identified. CONCLUSIONS: Vascular-specific factors appear to be key determinants of QOL among patients with CLI, whereas comorbidity appears less important. Strategies focused on definitive and durable revascularization may reduce cumulative interventions and potentially maximize QOL for patients with CLI.

Blood metabolite markers of preclinical Alzheimer's disease in two longitudinally followed cohorts of older individuals.

INTRODUCTION: 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%). METHODS: Quantitative targeted metabolomics in serum using an identical method as in the index study. RESULTS: 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. DISCUSSION: 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.

Profiles of Food Security for US Farmworker Households and Factors Related to Dynamic of Change.

OBJECTIVES: We recruited 248 farmworker families with preschool-aged children in North Carolina and examined food security indicators over 24 months to identify food security patterns and examine the dynamic of change over time. METHODS: Participants in the Niños Sanos study, conducted 2011 to 2014, completed quarterly food security assessments. Based on responses to items in the US Household Food Security Survey Module, we identified different states of food security by using hidden Markov model analysis, and examined factors associated with different states. We delineated factors associated with changes in state by using mixed-effect ordinal logistic regression. RESULTS: About half of the households (51%) consistently stayed in the most food-secure state. The least food-secure state was transient, with only 29% probability of this state for 2 consecutive quarters. Seasonal (vs migrant) work status, having immigration documents (vs not documented), and season predicted higher levels of food security. CONCLUSIONS: Heterogeneity in food security among farmworker households calls for tailoring intervention strategies. The transiency and unpredictability of low food security suggest that access to safety-net programs could reduce low food security risk in this population.

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.

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.

Performance of health literacy tests among older adults with diabetes.

BACKGROUND: Knowing a patient's health literacy can help clinicians and researchers anticipate a patient's ability to understand complex health regimens and deliver better patient-centered instructions and information. Poor health literacy has been linked with lower ability to function adequately in health care systems. OBJECTIVE: We evaluated and compared three measures of health literacy and performance among older patients with diabetes. DESIGN: Cross-sectional study utilizing in-person interviews conducted in participants' homes. PARTICIPANTS: A tri-ethnic sample (n = 563) of African American, American Indian, and white older adults with diabetes from eight counties in south-central North Carolina. MAIN MEASURE: Participants completed interviews and health literacy assessments using the Short-Form Test of Functional Health Literacy in Adults (S-TOFHLA), the Rapid Estimates of Adult Literacy in Medicine Short-Form (REALM-SF), or the Newest Vital Signs (NVS). Scores for reading comprehension and numeracy were calculated. RESULTS: Over 90% completed the S-TOFHLA numeracy and approximately 85% completed the S-TOFHLA reading and REALM-SF. Only 73% completed the NVS. The correlation of S-TOFHLA total scores with REALM-SF and NVS were 0.48 and 0.54, respectively. Age, gender, ethnic, educational and income differences in health literacy emerged for several instruments, but the pattern of results across the instruments was highly variable. CONCLUSIONS: 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.

Cultural basis for diabetes-related beliefs among low- and high-education African American, American Indian, and white older adults.

OBJECTIVES: Racial and ethnic disparities in diabetes and subsequent complications are often attributed to culture; however, previous diabetes disparities research is restricted to in-depth ethnic-specific samples or to comparative study designs with limited belief assessment. The goal of our study was to improve understanding of the cultural basis for variation in diabetes beliefs. DESIGN: Cross-sectional. SETTING: Rural North Carolina. PARTICIPANTS: Older adults (aged 60+) with diabetes, equally divided by ethnicity (White, African American, American Indian) and sex (N=593). INTERVENTIONS: Guided by Explanatory Models of Illness and Cultural Consensus research traditions, trained interviewers collected data using 38 items in four diabetes belief domains: causes, symptoms, consequences, and medical management. Items were obtained from the Common Sense Model of Diabetes Inventory (CSMDI). MAIN OUTCOME: Beliefs about diabetes. Response options for each diabetes belief item were "agree," "disagree" and "don't know." Collected data were analyzed using Anthropac (version 4.98) and Latent Gold (version 4.5) programs. RESULTS: There is substantial similarity in diabetes beliefs among African Americans, American Indians and Whites. Diabetes beliefs were most similar in the symptoms and consequences domains compared to beliefs pertaining to causes and medical management. Although some discrete beliefs differed by ethnicity, systematic differences by ethnicity were observed for specific educational groups. CONCLUSIONS: Socioeconomic conditions influence diabetes beliefs rather than ethnicity per se.