Predictor Of Health Outcomes Research Articles

PIN13 ESTIMATING THE IMPACT OF A GENDER-NEUTRAL QUADRIVALENT HUMAN PAPILLOMAVIRUS VACCINATION PROGRAM IN ALL HPV 6/11/16/18 -RELATED DISEASES IN COLOMBIA

We assessed the public health and economic impact of adding males to the existing female-only quadrivalent HPV vaccine (4vHPV) program in Colombia, analyzing different gender-neutral vaccination (GNV) vaccine coverage rates (VCRs). A published HPV-type dynamic transmission model was used to compare female-only vaccination (FOV) versus GNV with two-dose 4vHPV in the 9-10-year-old cohort over a 100-year timeframe in Colombia. The model compared 35% VCR for FOV with GNV at VCRs of 35% (scenario A), 50% (scenario B) and different VCRs between females/males (50%/35%, scenario C). The predicted health outcomes included HPV 6/11/16/18-related disease and deaths averted [cervical intraepithelial neoplasia, cervical, vaginal, vulvar, penile, anal and head and neck cancers, genital warts (GW), and recurrent respiratory papillomatosis], direct healthcare cost prevented by vaccination, and incremental cost-effectiveness ratios (ICERs). All GNV scenarios are estimated to provide faster and greater reductions in HPV 6/11/16/18-related diseases relative to FOV at 35% VCR, mainly scenarios B and C. The highest cumulative reductions in the incidence of HPV 6/11/16/18-related disease and deaths were seen in scenario B relative to FOV at 35% VCR at year 100, averting 28,001 cervical cancer (CC) cases, 11,968 non-CC cases (4,753 in females and 7,215 in males) and 15,141 deaths. The greatest projected reductions in health care costs are due to diseases caused by HPV-6/11 infection, driven by GW. The cost savings varied from 88 (scenario A) to 184 million (scenario B) relative to FOV at 35%. The ICER for all scenarios was <0, indicating that under the model assumptions it is cost-saving to implement a GNV-4vHPV in Colombia. In Colombia, a GNV-4vHPV program is a cost-saving strategy in the three scenarios analyzed relative to the current FOV program and result in greater improvement of the public health and economic impact in both women and men.

Using a genetic algorithm to derive a highly predictive and context-specific frailty index.

The frailty index (FI) is one of the most widespread tools used to predict poor, health-related outcomes in older persons. The selection of clinical and functional deficits to include in a FI is mostly based on the users’ clinical experience. However, this approach may not be sufficiently accurate to predict health outcomes in particular subgroups of individuals. In this study, we implemented an optimization algorithm, the genetic algorithm, to create a highly performant (FI) based on our prediction goals, rather than on a predetermined clinical selection of deficits, using data from the Swedish National Study on Aging and Care in Kungsholmen (SNAC-K) and 109 potential deficits identified in the dataset. The algorithm was personalized to obtain a FI with high discrimination ability in the prediction of mortality. The resulting FI included 40 deficits and showed areas under the curve consistently higher than 0.80 (range 0.81-0.90) in the prediction of 3-year and 6-year mortality in the whole sample and in sex and age subgroups. This methodology represents a promising opportunity to optimize the exploitation of medical and administrative databases in the construction of clinically relevant frailty indices.

App-based Self-administrable Clinical Tests of Physical Function: Development and Usability Study.

BackgroundObjective measures of physical function in older adults are widely used to predict health outcomes such as disability, institutionalization, and mortality. App-based clinical tests allow users to assess their own physical function and have objective tracking of changes over time by use of their smartphones. Such tests can potentially guide interventions remotely and provide more detailed prognostic information about the participant’s physical performance for the users, therapists, and other health care personnel. We developed 3 smartphone apps with instrumented versions of the Timed Up and Go (Self-TUG), tandem stance (Self-Tandem), and Five Times Sit-to-Stand (Self-STS) tests.ObjectiveThis study aimed to test the usability of 3 smartphone app–based self-tests of physical function using an iterative design.MethodsThe apps were tested in 3 iterations: the first (n=189) and second (n=134) in a lab setting and the third (n=20) in a separate home-based study. Participants were healthy adults between 60 and 80 years of age. Assessors observed while participants self-administered the tests without any guidance. Errors were recorded, and usability problems were defined. Problems were addressed in each subsequent iteration. Perceived usability in the home-based setting was assessed by use of the System Usability Scale, the User Experience Questionnaire, and semi-structured interviews.ResultsIn the first iteration, 7 usability problems were identified; 42 (42/189, 22.0%) and 127 (127/189, 67.2%) participants were able to correctly perform the Self-TUG and Self-Tandem, respectively. In the second iteration, errors caused by the problems identified in the first iteration were drastically reduced, and 108 (108/134, 83.1%) and 106 (106/134, 79.1%) of the participants correctly performed the Self-TUG and Self-Tandem, respectively. The first version of the Self-STS was also tested in this iteration, and 40 (40/134, 30.1%) of the participants performed it correctly. For the third usability test, the 7 usability problems initially identified were further improved. Testing the apps in a home setting gave rise to some new usability problems, and for Self-TUG and Self-STS, the rates of correctly performed trials were slightly reduced from the second version, while for Self-Tandem, the rate increased. The mean System Usability Scale score was 77.63 points (SD 16.1 points), and 80-95% of the participants reported the highest or second highest positive rating on all items in the User Experience Questionnaire.ConclusionsThe study results suggest that the apps have the potential to be used to self-test physical function in seniors in a nonsupervised home-based setting. The participants reported a high degree of ease of use. Evaluating the usability in a home setting allowed us to identify new usability problems that could affect the validity of the tests. These usability problems are not easily found in the lab setting, indicating that, if possible, app usability should be evaluated in both settings. Before being made available to end users, the apps require further improvements and validation.

Relative Validity of Dietary Total Antioxidant Capacity for Predicting All-Cause Mortality in Comparison to Diet Quality Indexes in US Adults.

While traditionally diet quality index scores (DQIS) as noted later in this abstract have been used to predict health outcomes, dietary total antioxidant capacity (TAC), a useful tool for assessing total antioxidant power in the diet, may also be a novel predictor. This study evaluated the associations between dietary TAC and DQIS and all-cause mortality. Based on the National Health and Nutrition Examination Survey (NHANES) 1988–1994 and 1999–2006, 23,797 US adults were followed-up until 2015. Dietary TAC and DQIS including the Healthy Eating Index-2015 (HEI-2015), Alternative Healthy Eating Index-2010 (AHEI-2010), alternate Mediterranean Diet (aMED), and Dietary Approaches to Stop Hypertension (DASH) were calculated using a 1-day 24 h dietary recall. US adults in the highest quintiles of DQIS had lower rates of all-cause mortality compared to those in the lowest quintiles (HEI-2015 hazard ratio (HR): 0.87, 95% confidence interval (CI): 0.77–0.98; AHEI-2010 HR: 0.84, 95% CI: 0.74–0.94; aMED HR: 0.79, 95% CI: 0.69–0.90; DASH HR: 0.80, 95% CI: 0.70–0.92). Similarly, those in the highest quintile of dietary TAC also had a lower all-cause mortality than those in the lowest quintile (HR: 0.88, 95% CI: 0.79–0.98). These findings suggest that dietary TAC might be a relatively valid predictor of all-cause mortality in the US population compared to the DQIS.

Standard machine learning approaches outperform deep representation learning on phenotype prediction from transcriptomics data

BackgroundThe ability to confidently predict health outcomes from gene expression would catalyze a revolution in molecular diagnostics. Yet, the goal of developing actionable, robust, and reproducible predictive signatures of phenotypes such as clinical outcome has not been attained in almost any disease area. Here, we report a comprehensive analysis spanning prediction tasks from ulcerative colitis, atopic dermatitis, diabetes, to many cancer subtypes for a total of 24 binary and multiclass prediction problems and 26 survival analysis tasks. We systematically investigate the influence of gene subsets, normalization methods and prediction algorithms. Crucially, we also explore the novel use of deep representation learning methods on large transcriptomics compendia, such as GTEx and TCGA, to boost the performance of state-of-the-art methods. The resources and findings in this work should serve as both an up-to-date reference on attainable performance, and as a benchmarking resource for further research.ResultsApproaches that combine large numbers of genes outperformed single gene methods consistently and with a significant margin, but neither unsupervised nor semi-supervised representation learning techniques yielded consistent improvements in out-of-sample performance across datasets. Our findings suggest that using l2-regularized regression methods applied to centered log-ratio transformed transcript abundances provide the best predictive analyses overall.ConclusionsTranscriptomics-based phenotype prediction benefits from proper normalization techniques and state-of-the-art regularized regression approaches. In our view, breakthrough performance is likely contingent on factors which are independent of normalization and general modeling techniques; these factors might include reduction of systematic errors in sequencing data, incorporation of other data types such as single-cell sequencing and proteomics, and improved use of prior knowledge.

Defining sarcopenia: some caveats and challenges.

To summarize the latest advances and caveats in defining sarcopenia and discuss the implications of the most recent worldwide initiatives which are trying to harmonize the definition. The evolution over time of the definitions of sarcopenia is discussed, with a focus on the European Working Group on Sarcopenia in Older People 2 (EWGSOP2) definition and the Sarcopenia Definitions and Outcomes Consortium (SDOC) conference. The EWGSOP2 and the SDOC agree on the overall concept of sarcopenia, which involves both impaired function (low muscle strength) and structural damage (low muscle mass/quality). However, physical performance is considered as a diagnostic criterion (EWGSOP), a severity grading assessment (EWGSOP2) or an outcome (SDOC) pending on the definition used. Muscle strength has been recognized as the best predictor of health outcomes. Muscle mass alone, as part of the definition of cachexia, sarcopenia and malnutrition, is a nondefining parameter. Furthermore, there is a lack of precision in measurement techniques and variability of the cut-off points in defining it. We discuss the relationship of sarcopenia with cachexia, malnutrition and frailty, and the areas that are hampering agreement. We summarize key scientific evidence, consider future study of this nutrition-related disease and raise concern about the need for a universal definition of sarcopenia.

Do healthcare needs-based population segments predict outcomes among the elderly? Findings from a prospective cohort study in an urbanized low-income community

BackgroundA rapidly ageing population with increasing prevalence of chronic disease presents policymakers the urgent task of tailoring healthcare services to optimally meet changing needs. While healthcare needs-based segmentation is a promising approach to efficiently assessing and responding to healthcare needs at the population level, it is not clear how available schemes perform in the context of community-based surveys administered by non-medically trained personnel. The aim of this prospective cohort, community setting study is to evaluate 4 segmentation schemes in terms of practicality and predictive validity for future health outcomes and service utilization.MethodsA cohort was identified from a cross-sectional health and social characteristics survey of Singapore public rental housing residents aged 60 years and above. Baseline survey data was used to assign individuals into segments as defined by 4 predefined population segmentation schemes developed in Singapore, Delaware, Lombardy and North-West London. From electronic data records, mortality, hospital admissions, emergency department visits, and specialist outpatient clinic visits were assessed for 180 days after baseline segment assignment and compared to segment membership for each segmentation scheme.ResultsOf 1324 residents contacted, 928 agreed to participate in the survey (70% response). All subjects could be assigned an exclusive segment for each segmentation scheme. Individuals in more severe segments tended to have lower quality of life as assessed by the EQ-5D Index for health utility. All population segmentation schemes were observed to exhibit an ability to differentiate different levels of mortality and healthcare utilization.ConclusionsIt is practical to assign individuals to healthcare needs-based population segments through community surveys by non-medically trained personnel. The resulting segments for all 4 schemes evaluated in this way have an ability to predict health outcomes and utilization over the medium term (180 days), with significant overlap for some segments. Healthcare needs-based segmentation schemes which are designed to guide action hold particular promise for promoting efficient allocation of services to meet the needs of salient population groups. Further evaluation is needed to determine if these schemes also predict responsiveness to interventions to meet needs implied by segment membership.

Automated body composition analysis of clinically acquired computed tomography scans using neural networks.

The quantity and quality of skeletal muscle and adipose tissue is an important prognostic factor for clinical outcomes across several illnesses. Clinically acquired computed tomography (CT) scans are commonly used for quantification of body composition, but manual analysis is laborious and costly. The primary aim of this study was to develop an automated body composition analysis framework using CT scans. CT scans of the 3rd lumbar vertebrae from critically ill, liver cirrhosis, pancreatic cancer, and clear cell renal cell carcinoma patients, as well as renal and liver donors, were manually analyzed for body composition. Ninety percent of scans were used for developing and validating a neural network for the automated segmentation of skeletal muscle and adipose tissues. Network accuracy was evaluated with the remaining 10 percent of scans using the Dice similarity coefficient (DSC), which quantifies the overlap (0=no overlap, 1=perfect overlap) between human and automated segmentations. Of the 893 patients, 44% were female, with a mean (±SD) age and body mass index of 52.7 (±15.8) years old and 28.0 (±6.1) kg/m2, respectively. In the testing cohort (n=89), DSC scores indicated excellent agreement between human and network-predicted segmentations for skeletal muscle (0.983±0.013), and intermuscular (0.900±0.034), visceral (0.979±0.019), and subcutaneous (0.986±0.016) adipose tissue. Network segmentation took ~350 milliseconds/scan using modern computing hardware. Our network displayed excellent ability to analyze diverse body composition phenotypes and clinical cohorts, which will create feasible opportunities to advance our capacity to predict health outcomes in clinical populations.

Evaluating foods and diets from a multi-dimensional perspective: nutrition, health and environment

AbstractIntroductionThe study of the environmental impact of dietary patterns in relation to their nutrition quality and health impact is of recent development and great interest for both nutrition and environmental scientists. Preliminary research has been conducted on the potential application of nutrition and health indexes as reference units (i.e. functional units) in the life cycle assessment of foods. Although proposed methods to include nutrition and health aspects exist, more research is needed to increase the scientific quality and societal usefulness of such assessments.Materials and MethodsSLF Healthy Diets is a 20-month project where 64 food items from the FFQ used in the population study “Västerbotten Intervention Programme” (VIP) will be characterized for their nutrition density and matched to the carbon footprint from life cycle assessment studies. Several nutrition density scores will be analyzed, among which the NRF9.3 score, a dietary-dependent NQI score, and a new nutrient index tailored for the Swedish population. Hazard ratios for total mortality will be estimated for 100.000 participants to the VIP study, and associations with reported intake of food products, nutrient density and environmental performance described.Results and DiscussionMultiple results are expected from the project, among which a synthesis of the combined nutritional and environmental performance of the analyzed foods according to different methods, and hence the identification of the best nutritional index to apply in environmental studies. Additionally, the assessment in the VIP cohort of the associations between reported intake of food products and observed health outcomes will evaluate the ability of the suggested nutrition scores to predict the total mortality in the studied population.The present project will allow for more robust quantification and communication of food products’ sustainability performance. Specifically, the project will: develop clear advice on which nutrition scores can best be used in LCA food studies; evaluate pros and cons of combined environmental, nutritional, and health metrics; validate nutrition and health metrics ability to predict health outcomes within a Swedish population-based cohort; investigate and propose how combined environmental, nutritional and health metrics can be implemented and used by food chain stakeholders.

The Application of Community-Based Information from the American Community Survey in a Large Integrated Health Care Organization.

The American Community Survey (ACS) is the largest household survey conducted by the US Census Bureau. We sought to describe the community-level characteristics derived from the ACS among enrollees of Kaiser Permanente Southern California (KPSC), evaluate the associations between ACS estimates and selective individual-level health outcomes, and explore how using different scales of the census geography and the linearity assumption affect the associations. We examined the associations between track-level and block group-level ACS 5-year estimates and 4 individual-level Healthcare Effectiveness Data and Information Set (HEDIS) outcome measures (comprehensive diabetes care, postpartum care, antidepressant medication management, and childhood immunization status) using multilevel generalized linear models. Odds ratios and their 95% confidence intervals were estimated for every 10% increase in ACS measures. 6,357,841 addresses were successfully geocoded to at least the tract level. The community-level demographic, socioeconomic, residential, and other ACS measures varied among KPSC health plan enrollees. A majority of these ACS measures were associated with the selected HEDIS health outcomes. The directions of the effects were consistent across health outcomes; however, the magnitudes of the effect sizes varied. Within each HEDIS health outcome, the relative size of the effects appeared to remain similar. Differences between the census tract- and block group-level estimates were minor, especially for measures related to race/ethnicity, education, income, and occupation. These findings support the use of many ACS measures at neighborhood levels to predict health outcomes. The geographic units might have little effect on the results. The linearity assumption should be made with caution.

Associations of 4AT with mobility, length of stay and mortality in hospital and discharge destination among patients admitted with hip fractures.

the 4AT (Alertness, Abbreviated Mental Test-4, Attention and Acute change or fluctuating course), a tool to screen cognitive impairment and delirium, has recently been recommended by the Scottish Intercollegiate Guidelines Network. We examined its ability to predict health outcomes among patients admitted with hip fractures to a single hospital between January 2018 and June 2019. the 4AT was performed within 1day after hip surgery. A 4AT score of 0 means unlikely delirium or severe cognitive impairment (reference group); a score of 1-3 suggests possible chronic cognitive impairment, without excluding possibility of delirium; a score ≥ 4 suggests delirium with or without chronic cognitive impairment. Logistic regression, adjusted for: age; sex; nutritional status; co-morbidities; polypharmacy; and anticholinergic burden, used the 4AT to predict mobility, length of stay (LOS), mortality and discharge destination, compared with the reference group. from 537 (392 women, 145 men: mean = 83.7 ± standard deviation [SD] = 8.8years) consecutive patients, 522 completed the 4AT; 132 (25%) had prolonged LOS (>2 weeks) and 36 (6.8%) died in hospital. Risk of failure to mobilise within 1day of surgery was increased with a 4AT score ≥ 4 (OR = 2.4, 95% confidence interval [CI] = 1.3-4.3). Prolonged LOS was increased with 4AT scores of 1-3 (OR = 2.4, 95%CI = 1.4-4.1) or ≥4 (OR = 3.1, 95%CI = 1.9-6.7). In-patient mortality was increased with a 4AT score ≥ 4 (OR = 3.1, 95%CI = 1.2-8.2) but not with a 4AT score of 1-3. Change of residence on discharge was increased with a 4AT score ≥ 4 (OR = 3.1, 95%CI = 1.4-6.8). These associations persisted after excluding patients with dementia. 4AT score = 1-3 and ≥ 4 associated with increased LOS by 3 and 6days, respectively. for older adults with hip fracture, the 4AT independently predicts immobility, prolonged LOS, death in hospital and change in residence on discharge.

Different Birth Size Measures are Not Equivalent in Predicting Health Outcomes in Young Women

Different Birth Size Measures are Not Equivalent in Predicting Health Outcomes in Young Women

CONSERVATION OF INTEGRATIVE PHYSIOLOGICAL AGING MECHANISMS ACROSS PRIMATES

Physiological dysregulation (PD) and integrated albunemia (IA) are organism-level aging mechanisms that can be measured using standard biomarkers, and in humans they have been shown to increase with age and predict health outcomes. Here, we use 10 species from the Internet Primate Aging Database (iPAD), a longitudinal database of biomarkers and mortality in captive primates, to analyze the generalizability of the role of PD and IA in aging, as well as the conservation of the underlying physiology. Human patterns are broadly but not universally replicated in primates. For example, PD increases with age in nine of eleven species, and predicts mortality in three of four. Both IA and PD can to some extent be cross-calibrated across species, indicating surprising conservation of underlying homeostatic norms; in the case of PD, the calibration weakens with phylogenetic distance.

Rationale and Design of the Hamburg City Health Study

The Hamburg City Health Study (HCHS) is a large, prospective, long-term, population-based cohort study and a unique research platform and network to obtain substantial knowledge about several important risk and prognostic factors in major chronic diseases. A random sample of 45,000 participants between 45 and 74 years of age from the general population of Hamburg, Germany, are taking part in an extensive baseline assessment at one dedicated study center. Participants undergo 13 validated and 5 novel examinations primarily targeting major organ system function and structures including extensive imaging examinations. The protocol includes validate self-reports via questionnaires regarding lifestyle and environmental conditions, dietary habits, physical condition and activity, sexual dysfunction, professional life, psychosocial context and burden, quality of life, digital media use, occupational, medical and family history as well as healthcare utilization. The assessment is completed by genomic and proteomic characterization. Beyond the identification of classical risk factors for major chronic diseases and survivorship, the core intention is to gather valid prevalence and incidence, and to develop complex models predicting health outcomes based on a multitude of examination data, imaging, biomarker, psychosocial and behavioral assessments. Participants at risk for coronary artery disease, atrial fibrillation, heart failure, stroke and dementia are invited for a visit to conduct an additional MRI examination of either heart or brain. Endpoint assessment of the overall sample will be completed through repeated follow-up examinations and surveys as well as related individual routine data from involved health and pension insurances. The study is targeting the complex relationship between biologic and psychosocial risk and resilience factors, chronic disease, health care use, survivorship and health as well as favorable and bad prognosis within a unique, large-scale long-term assessment with the perspective of further examinations after 6 years in a representative European metropolitan population.

PATTERN OF HEALTH-RELATED BEHAVIORS AND RESOURCES AS PREDICTOR OF MEDICAL CARE AND MORTALITY RISK AMONG OLDER ADULTS

This study investigates the patterns and consequences of a wide range of health-related behaviors and resources that include health-compromising behaviors, health-promoting behaviors, preventive health behaviors, and health risks coping resources. We aim to identify the empirically-derived subgroups of individuals with unique profiles of health behaviors and resources to determine how subgroup membership predicts health outcomes and medical care utilization four years later. Data derived from 5,067 respondents in the 2010 and 2014 waves of the Health and Retirement Study. Latent class analysis was used to define classes based on 13 indicators in the 2010 wave, which also provided sociodemographic and health-related covariates. Outcomes were measured over 4 years. Six latent subgroups were identified: “Best Behavior/Resources”, “Low Social Support “, “Low Physical Activity”, “High Substance Abuse”, “Low Preventive Tests”, and “Low Governmental Health Insurance”. Compared with the “Best” group, older adults identified as “Low Physical Activity” and “High Substance Abuse” were found to have higher mortality risks and a lower likelihood of seeing doctors and less nursing home nights; older adults with the lowest level of receiving flu shots, cholesterol and cancer screen test (“Low Preventive Tests”) reported a less likelihood of seeing doctors; respondents in “Low Governmental Health Insurance” subgroup were associated with a lower likelihood of hospital stay and more nursing home nights. Results suggest that distinct groups of older individuals characterized by their health behaviors and resources provide a basis for identifying the high-risk segment of the older population for intervention.

Intelligent Sensing to Inform and Learn (InSTIL): A Scalable and Governance-Aware Platform for Universal, Smartphone-Based Digital Phenotyping for Research and Clinical Applications.

In this viewpoint we describe the architecture of, and design rationale for, a new software platform designed to support the conduct of digital phenotyping research studies. These studies seek to collect passive and active sensor signals from participants' smartphones for the purposes of modelling and predicting health outcomes, with a specific focus on mental health. We also highlight features of the current research landscape that recommend the coordinated development of such platforms, including the significant technical and resource costs of development, and we identify specific considerations relevant to the design of platforms for digital phenotyping. In addition, we describe trade-offs relating to data quality and completeness versus the experience for patients and public users who consent to their devices being used to collect data. We summarize distinctive features of the resulting platform, InSTIL (Intelligent Sensing to Inform and Learn), which includes universal (ie, cross-platform) support for both iOS and Android devices and privacy-preserving mechanisms which, by default, collect only anonymized participant data. We conclude with a discussion of recommendations for future work arising from learning during the development of the platform. The development of the InSTIL platform is a key step towards our research vision of a population-scale, international, digital phenotyping bank. With suitable adoption, the platform will aggregate signals from large numbers of participants and large numbers of research studies to support modelling and machine learning analyses focused on the prediction of mental illness onset and disease trajectories.

Contributions of the Women's Health Initiative to understanding associations between sleep duration, insomnia symptoms, and sleep-disordered breathing across a range of health outcomes in postmenopausal women

The Women's Health Initiative (WHI), a longitudinal study of more than 161,000 postmenopausal women across the United States, provides an opportunity to investigate the link between sleep health and healthy aging. The purpose of this paper was to systematically review all published WHI articles examining sleep as a predictor of health outcomes and health behaviors/quality of life outcomes. A strength of the WHI is that for most participants, sleep measures were completed before a major health diagnosis, with a significant portion of participants also providing sleep measures after diagnosis. Twenty-three WHI articles were identified and examined for this review. The combination of sleep duration and insomnia symptoms was the most commonly investigated sleep measure. The results indicated that both short (≤6 hours) and long (≥9 hours) sleep duration were associated with a higher risk of cardiovascular disease, colorectal cancer, mortality, cognitive decline, and poor diet. Insomnia symptoms, frequent snoring, and risk of sleep-disordered breathing (SDB) were also associated with increased risk for ischemic stroke and cardiovascular disease. However, many significant results were attenuated after multivariable adjustment. Limitations of these WHI examinations include the use of different categories for sleep measures across studies and a lack of examination by race/ethnicity. Owing to the longitudinal study design, large sample size, and long-term follow-up for health outcomes, the WHI serves as a rich resource for examining associations between sleep characteristics, demographics, and health in postmenopausal women.

Do we have simple and accurate predictors of health outcomes in apparently healthy populations other than estimated peak oxygen uptake?

Do we have simple and accurate predictors of health outcomes in apparently healthy populations other than estimated peak oxygen uptake?

Divergent immune responses in behaviorally-inhibited vs. non-inhibited male rats

Stable behavioral traits (temperament, personality) often predict health outcomes. Temperament-specific differences in immune function could explain temperament-specific health outcomes, however, we have limited information on whether immune function varies by personality. In the present study, we examined the relationship between a basic behavioral trait (behavioral-inhibition vs. non-inhibition) and two immune responses (innate inflammation and delayed-type hypersensitivity, DTH) in a rodent model. In humans, behavioral inhibition (fearful temperament) is associated with altered stress physiology and allergies. In laboratory rats, the trait is associated with elevated glucocorticoid production. We hypothesized that behavioral inhibition is associated with glucocorticoid resistance and dampened T-helper 1 cell responses often associated with chronic stress and allergies. Further, this immune profile would predict poorly-regulated innate inflammation and dampened DTH. In male Sprague-Dawley rats, we quantified consistent behavioral phenotypes by measuring latency to contact two kinds of novelty (object vs. social), then measured lipopolysaccharide(LPS)-induced innate inflammation or keyhole limpet hemocyanin(KLH)-induced DTH. Behaviorally-inhibited rats had heightened glucocorticoid and interleukin-6 responses to a low/moderate dose of LPS and reduced DTH swelling to KLH re-exposure compared to non-inhibited rats. These results suggest that behavioral inhibition is associated with a glucocorticoid resistant state with poorly regulated innate inflammation and dampened cell-mediated immune responses. This immune profile may be associated with exaggerated T-helper 2 responses, which could set the stage for an allergic/asthmatic/atopic predisposition in inhibited individuals. Human and animal models of temperament-specific immune responses represent an area for further exploration of mechanisms involved in individual differences in health.

Atheism, Social Networks and Health: A Review and Theoretical Model

Despite accumulating evidence of the importance of social capital in predicting health outcomes, no work has yet systematically investigated the structural differences between the social networks of god-believers and atheists. This is an especially important gap in the religion/secularism research because religiosity appears to be declining throughout the Western world (Zuckerman, Galen & Pasquale, 2016). Despite stereotypes of atheists as atomized, psychologically unhealthy and anti-social (e.g., Bainbridge, 2005), a growing body of evidence suggests that strongly-identified atheists are more likely to join secular social clubs as well as benefit from better mental and physical health compared to less affirmatively-identified secular individuals. As a step toward developing this line of research, the present article operationalizes social network structure within the study of secularism, discusses the available research with a focus on atheism in particular, and integrates this research into a schematic theoretical model of atheist self-identity, network structure and health.