Artificial Intelligence: An Interprofessional Perspective on Implications for Geriatric Mental Health Research and Care

Artificial intelligence (AI) in healthcare aims to learn patterns in large multimodal datasets within and across individuals. These patterns may either improve understanding of current clinical status or predict a future outcome. AI holds the potential to revolutionize geriatric mental health care and research by supporting diagnosis, treatment, and clinical decision-making. However, much of this momentum is driven by data and computer scientists and engineers and runs the risk of being disconnected from pragmatic issues in clinical practice. This interprofessional perspective bridges the experiences of clinical scientists and data science. We provide a brief overview of AI with the main focus on possible applications and challenges of using AI-based approaches for research and clinical care in geriatric mental health. We suggest future AI applications in geriatric mental health consider pragmatic considerations of clinical practice, methodological differences between data and clinical science, and address issues of ethics, privacy, and trust

Short Sleep Is Associated With Low Bone Mineral Density and Osteoporosis in the Women’s Health Initiative

Short sleep duration, recognized as a public health epidemic, is associated with adverse health conditions, yet little is known about the association between sleep and bone health. We tested the associations of usual sleep behavior and bone mineral density (BMD) and osteoporosis. In a sample of 11,084 postmenopausal women from the Women’s Health Initiative (WHI; mean age 63.3â years, SD = 7.4), we performed a crossâ sectional study of the association of selfâ reported usual hours of sleep and sleep quality (WHI Insomnia Rating Score) with whole body, total hip, femoral neck, and spine BMD using linear regression models. We also studied the association of sleep duration and quality with dualâ energy Xâ ray absorptiometry (DXA)â defined low bone mass (Tâ scoreâ <â â 2.5 to <â 1) and osteoporosis (Tâ scoreâ â ¤â â 2.5) using multinomial regression models. We adjusted for age, DXA machine, race, menopausal symptoms, education, smoking, physical activity, body mass index, alcohol use, physical function, and sleep medication use. In adjusted linear regression models, women who reported sleeping 5â hours or less per night had on average 0.012 to 0.018â g/cm2 significantly lower BMD at all four sites compared with women who reported sleeping 7â hours per night (reference). In adjusted multinomial models, women reporting 5â hours or less per night had higher odds of low bone mass and osteoporosis of the hip (odds ratio [OR] =â 1.22; 95% confidence interval [CI] 1.03â 1.45, and 1.63; 1.15â 2.31, respectively). We observed a similar pattern for spine BMD, where women with 5â hours or less per night had higher odds of osteoporosis (adjusted OR = 1.28; 95% CI 1.02â 1.60). Associations of sleep quality and DXA BMD failed to reach statistical significance. Short sleep duration was associated with lower BMD and higher risk of osteoporosis. Longitudinal studies are needed to confirm the crossâ sectional effects of sleep duration on bone health and explore associated mechanisms. © 2019 American Society for Bone and Mineral Research.Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/154418/1/jbmr3879_am.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/154418/2/jbmr3879.pd

Associations between ACE-Inhibitors, Angiotensin Receptor Blockers, and Lean Body Mass in Community Dwelling Older Women

Studies suggest that ACE-inhibitors (ACE-I) and angiotensin receptor blockers (ARBs) may preserve skeletal muscle with aging. We evaluated longitudinal differences in lean body mass (LBM) among women diagnosed with hypertension and classified as ACE-I/ARB users and nonusers among Women’s Health Initiative participants that received dual energy X-ray absorptiometry scans to estimate body composition (n=10,635) at baseline and at years 3 and 6 of follow-up. Of those, 2642 were treated for hypertension at baseline. Multivariate linear regression models, adjusted for relevant demographics, behaviors, and medications, assessed ACE-I/ARB use/nonuse and LBM associations at baseline, as well as change in LBM over 3 and 6 years. Although BMI did not differ by ACE-I/ARB use, LBM (%) was significantly higher in ACE-I/ARB users versus nonusers at baseline (52.2% versus 51.3%, resp., p=0.001). There was no association between ACE-I/ARB usage and change in LBM over time. Reasons for higher LBM with ACE-I/ARB use cross sectionally, but not longitundinally, are unclear and may reflect a threshold effect of these medications on LBM that is attenuated over time. Nevertheless, ACE-I/ARB use does not appear to negatively impact LBM in the long term

Anthropometric measures and serum estrogen metabolism in postmenopausal women: the Women’s Health Initiative Observational Study

Background: Several anthropometric measures have been associated with hormone-related cancers. However, it is unknown whether estrogen metabolism plays an important role in these relationships. We examined whether measured current body mass index (BMI), waist-to-hip ratio (WHR), height, and self-reported BMI at age 18 years were associated with serum estrogens/estrogen metabolites using baseline, cross-sectional data from 1835 postmenopausal women enrolled in the Women’s Health Initiative Observational Study. Methods: Fifteen estrogens/estrogen metabolites were quantified using liquid chromatography-tandem mass spectrometry. Geometric means (GMs) of estrogens/estrogen metabolites (in picomoles per liter) were estimated using inverse probability weighted linear regression, adjusting for potential confounders and stratified on menopausal hormone therapy (MHT) use. Results: Among never or former MHT users, current BMI (≥30 vs. <25 kg/m2) was positively associated with parent estrogens (multivariable adjusted GM 432 vs. 239 pmol/L for estrone, 74 vs. 46 pmol/L for estradiol; p-trend < 0.001 for both) and all of the 2-, 4-, and 16-pathway estrogen metabolites evaluated (all p-trend ≤ 0.02). After additional adjustment for estradiol, unconjugated methylated 2-catechols were inversely associated (e.g., 2-methoxyestrone multivariable GM 9.3 vs. 12.0 pmol/L; p-trend < 0.001). Among current MHT users, current BMI was not associated with parent estrogens but was inversely associated with methylated catechols (e.g., 2-methoxyestrone multivariable GM 216 vs. 280 pmol/L; p-trend = 0.008). Similar patterns of association were found with WHR; however, the associations were not independent of BMI. Height and BMI at age 18 years were not associated with postmenopausal estrogens/estrogen metabolite levels. Conclusions: Our data suggest that postmenopausal BMI is associated with increased circulating levels of parent estrogens and reduced methylation of catechol estrogen metabolites, the estrogen metabolism patterns that have previously been associated with higher breast cancer risk. Electronic supplementary material The online version of this article (doi:10.1186/s13058-017-0810-0) contains supplementary material, which is available to authorized users

Longitudinal Dynamics in Indicators of Frailty: Predictors and Long-Term Outcomes

Thesis (Ph.D.)--University of Washington, 2012Frailty is a common geriatric condition with a wide array of sequelae, including increased risks of mortality, morbidity and disability. Despite its long conceptual and operational history in research and publications, both frailty and mechanisms of frailty development are still poorly understood. A detailed description of trajectories of frailty indicators could offer vital insights on unfolding longitudinal dynamics involved in the development of frailty. Such a longitudinal modeling could also provide researchers and clinicians with a better foundational understanding of the phenomenon and facilitate targeted care approach. The purpose of this study was to: 1. Describe longitudinal (~10 years) trajectories of change in musculoskeletal and neuro-cognitive indicators of frailty in older (>/=65 years) women enrolled in the Women's Health Initiative Clinical Trial. 2. Estimate the extent to which baseline factors (e.g., demographic characteristics, health status and behaviors) conjointly were associated with a likelihood of membership in the derived longitudinal clusters. 3. To determine the extent to which membership in longitudinal trajectories predicts the incidence of clinically relevant geriatric health outcomes (i.e., mortality and hospitalization) over 5-years of follow up (2005-2010 WHI Extension Study) in a model adjusted for all other baseline predictors. The study findings demonstrated a high degree of heterogeneity in longitudinal dynamics of individual frailty criteria. We also showed that age, socio-demographic variables, health status, health behavior, environmental factors and personality traits are important determinants of individual frailty criteria. However the effect of these determinants on frailty phenotype is complex, presumably due to the multidimensional nature of frailty phenomenon. Thirdly, we found that the magnitude of risk carried by a membership in a certain longitudinal group for each of the defining elements of frailty is closely linked to the distance of that trajectory estimates from the one that represents the most optimal criterion-specific functioning over time. The further the distance between trajectory estimates of an individual who maintained the highest level of performance (specific to that indicator) and those who demonstrated less optimal functioning, the higher the risk of incidence of adverse health events. Finally, we empirically determined that distribution based cross sectional partitioning of frailty criteria seems to be a valid method for defining frailty given that elderly women maintained approximately similar levels of functioning over time without demonstrating clear increasing or decreasing longitudinal patterns