Depression, loneliness, and lower social activity as partial mediators of the association between visual impairment and cognitive decline.

OBJECTIVES: Sensory impairment is a hypothesized risk factor for cognitive decline; however, the psychosocial pathways are not well understood. We evaluated whether the association between visual impairment (VI) and cognitive decline was partially mediated via depressive symptoms, loneliness, or social activity. METHODS: We used data from 2601 older adults enrolled in the Memory and Aging Project in 1997 and the Minority Aging Research Study in 2004 with neuropsychological tests across five domains measured annually for up to 16 years. VI was assessed with the Rosenbaum Pocket Vision Screener. Depressive symptoms, loneliness, and social activity were self-reported using validated scales. We used structural equation models to estimate the associations of VI with baseline and change in cognitive function, directly and indirectly through each mediator (depressive symptoms, loneliness, and social activity). We evaluated mediation via "psychological distress" using a latent variable combining depressive symptoms and loneliness. RESULTS: The association between VI and global cognitive decline was mediated via lower social activity (indirect effect) [95% confidence interval (CI)] of linear slope: -0.025 (-0.048, -0.011), via loneliness (-0.011 [95% CI: -0.028, -0.002]), and via psychological distress (-0.017 [95% CI: -0.042, -0.003]). We did not find sufficient evidence for mediation via depressive symptoms alone. CONCLUSIONS: The harmful effect of VI on cognitive decline may be partially mediated through loneliness and lower social activity.

Extant and extinct bilby genomes combined with Indigenous knowledge improve conservation of a unique Australian marsupial.

Ninu (greater bilby, Macrotis lagotis) are desert-dwelling, culturally and ecologically important marsupials. In collaboration with Indigenous rangers and conservation managers, we generated the Ninu chromosome-level genome assembly (3.66 Gbp) and genome sequences for the extinct Yallara (lesser bilby, Macrotis leucura). We developed and tested a scat single-nucleotide polymorphism panel to inform current and future conservation actions, undertake ecological assessments and improve our understanding of Ninu genetic diversity in managed and wild populations. We also assessed the beneficial impact of translocations in the metapopulation (N = 363 Ninu). Resequenced genomes (temperate Ninu, 6; semi-arid Ninu, 6; and Yallara, 4) revealed two major population crashes during global cooling events for both species and differences in Ninu genes involved in anatomical and metabolic pathways. Despite their 45-year captive history, Ninu have fewer long runs of homozygosity than other larger mammals, which may be attributable to their boom-bust life history. Here we investigated the unique Ninu biology using 12 tissue transcriptomes revealing expression of all 115 conserved eutherian chorioallantoic placentation genes in the uterus, an XY1Y2 sex chromosome system and olfactory receptor gene expansions. Together, we demonstrate the holistic value of genomics in improving key conservation actions, understanding unique biological traits and developing tools for Indigenous rangers to monitor remote wild populations.

Five questions on how biochemistry can combat climate change.

Global warming is caused by human activity, such as the burning of fossil fuels, which produces high levels of greenhouse gasses. As a consequence, climate change impacts all organisms and the greater ecosystem through changing conditions from weather patterns to the temperature, pH and salt concentrations found in waterways and soil. These environmental changes fundamentally alter many parameters of the living world, from the kinetics of chemical reactions and cellular signaling pathways to the accumulation of unforeseen chemicals in the environment, the appearance and dispersal of new diseases, and the availability of traditional foods. Some organisms adapt to extremes, while others cannot. This article asks five questions that prompt us to consider the foundational knowledge that biochemistry can bring to the table as we meet the challenge of climate change. We approach climate change from the molecular point of view, identifying how cells and organisms - from microbes to plants and animals - respond to changing environmental conditions. To embrace the concept of "one health" for all life on the planet, we argue that we must leverage biochemistry, cell biology, molecular biophysics and genetics to fully understand the impact of climate change on the living world and to bring positive change.

​Comparative genomics of Balto, a famous historic dog, captures lost diversity of 1920s sled dogs.

We reconstruct the phenotype of Balto, the heroic sled dog renowned for transporting diphtheria antitoxin to Nome, Alaska, in 1925, using evolutionary constraint estimates from the Zoonomia alignment of 240 mammals and 682 genomes from dogs and wolves of the 21st century. Balto shares just part of his diverse ancestry with the eponymous Siberian husky breed. Balto's genotype predicts a combination of coat features atypical for modern sled dog breeds, and a slightly smaller stature. He had enhanced starch digestion compared with Greenland sled dogs and a compendium of derived homozygous coding variants at constrained positions in genes connected to bone and skin development. We propose that Balto's population of origin, which was less inbred and genetically healthier than that of modern breeds, was adapted to the extreme environment of 1920s Alaska.

Mediation of an association between neighborhood socioeconomic environment and type 2 diabetes through the leisure-time physical activity environment in an analysis of three independent samples.

INTRODUCTION: Inequitable access to leisure-time physical activity (LTPA) resources may explain geographic disparities in type 2 diabetes (T2D). We evaluated whether the neighborhood socioeconomic environment (NSEE) affects T2D through the LTPA environment. RESEARCH DESIGN AND METHODS: We conducted analyses in three study samples: the national Veterans Administration Diabetes Risk (VADR) cohort comprising electronic health records (EHR) of 4.1 million T2D-free veterans, the national prospective cohort REasons for Geographic and Racial Differences in Stroke (REGARDS) (11 208 T2D free), and a case-control study of Geisinger EHR in Pennsylvania (15 888 T2D cases). New-onset T2D was defined using diagnoses, laboratory and medication data. We harmonized neighborhood-level variables, including exposure, confounders, and effect modifiers. We measured NSEE with a summary index of six census tract indicators. The LTPA environment was measured by physical activity (PA) facility (gyms and other commercial facilities) density within street network buffers and population-weighted distance to parks. We estimated natural direct and indirect effects for each mediator stratified by community type. RESULTS: The magnitudes of the indirect effects were generally small, and the direction of the indirect effects differed by community type and study sample. The most consistent findings were for mediation via PA facility density in rural communities, where we observed positive indirect effects (differences in T2D incidence rates (95% CI) comparing the highest versus lowest quartiles of NSEE, multiplied by 100) of 1.53 (0.25, 3.05) in REGARDS and 0.0066 (0.0038, 0.0099) in VADR. No mediation was evident in Geisinger. CONCLUSIONS: PA facility density and distance to parks did not substantially mediate the relation between NSEE and T2D. Our heterogeneous results suggest that approaches to reduce T2D through changes to the LTPA environment require local tailoring.

Machine learning identifies T cell receptor repertoire signatures associated with COVID-19 severity.

T cell receptor (TCR) repertoires are critical for antiviral immunity. Determining the TCR repertoire composition, diversity, and dynamics and how they change during viral infection can inform the molecular specificity of host responses to viruses such as SARS-CoV-2. To determine signatures associated with COVID-19 disease severity, here we perform a large-scale analysis of over 4.7 billion sequences across 2130 TCR repertoires from COVID-19 patients and healthy donors. TCR repertoire analyses from these data identify and characterize convergent COVID-19-associated CDR3 gene usages, specificity groups, and sequence patterns. Here we show that T cell clonal expansion is associated with the upregulation of T cell effector function, TCR signaling, NF-kB signaling, and interferon-gamma signaling pathways. We also demonstrate that machine learning approaches accurately predict COVID-19 infection based on TCR sequence features, with certain high-power models reaching near-perfect AUROC scores. These analyses provide a systems immunology view of T cell adaptive immune responses to COVID-19.

Associations of four indexes of social determinants of health and two community typologies with new onset type 2 diabetes across a diverse geography in Pennsylvania.

Evaluation of geographic disparities in type 2 diabetes (T2D) onset requires multidimensional approaches at a relevant spatial scale to characterize community types and features that could influence this health outcome. Using Geisinger electronic health records (2008-2016), we conducted a nested case-control study of new onset T2D in a 37-county area of Pennsylvania. The study included 15,888 incident T2D cases and 79,435 controls without diabetes, frequency-matched 1:5 on age, sex, and year of diagnosis or encounter. We characterized patients' residential census tracts by four dimensions of social determinants of health (SDOH) and into a 7-category SDOH census tract typology previously generated for the entire United States by dimension reduction techniques. Finally, because the SDOH census tract typology classified 83% of the study region's census tracts into two heterogeneous categories, termed rural affordable-like and suburban affluent-like, to further delineate geographies relevant to T2D, we subdivided these two typology categories by administrative community types (U.S. Census Bureau minor civil divisions of township, borough, city). We used generalized estimating equations to examine associations of 1) four SDOH indexes, 2) SDOH census tract typology, and 3) modified typology, with odds of new onset T2D, controlling for individual-level confounding variables. Two SDOH dimensions, higher socioeconomic advantage and higher mobility (tracts with fewer seniors and disabled adults) were independently associated with lower odds of T2D. Compared to rural affordable-like as the reference group, residence in tracts categorized as extreme poverty (odds ratio [95% confidence interval] = 1.11 [1.02, 1.21]) or multilingual working (1.07 [1.03, 1.23]) were associated with higher odds of new onset T2D. Suburban affluent-like was associated with lower odds of T2D (0.92 [0.87, 0.97]). With the modified typology, the strongest association (1.37 [1.15, 1.63]) was observed in cities in the suburban affluent-like category (vs. rural affordable-like-township), followed by cities in the rural affordable-like category (1.20 [1.05, 1.36]). We conclude that in evaluating geographic disparities in T2D onset, it is beneficial to conduct simultaneous evaluation of SDOH in multiple dimensions. Associations with the modified typology showed the importance of incorporating governmentally, behaviorally, and experientially relevant community definitions when evaluating geographic health disparities.

Urban-Rural Differences in Health Care Utilization and COVID-19 Outcomes in Patients With Type 2 Diabetes.

INTRODUCTION: Two studies in Pennsylvania aimed to determine whether community type and community socioeconomic deprivation (CSD) 1) modified associations between type 2 diabetes (hereinafter, diabetes) and COVID-19 hospitalization outcomes, and 2) influenced health care utilization among individuals with diabetes during the COVID-19 pandemic. METHODS: The hospitalization study evaluated a retrospective cohort of patients hospitalized with COVID-19 through 2020 for COVID-19 outcomes: death, intensive care unit (ICU) admission, mechanical ventilation, elevated D-dimer, and elevated troponin level. We used adjusted logistic regression models, adding interaction terms to evaluate effect modification by community type (township, borough, or city census tract) and CSD. The utilization study included patients with diabetes and a clinical encounter between 2017 and 2020. Autoregressive integrated moving average time-series models evaluated changes in weekly rates of emergency department and outpatient visits, hemoglobin A1c (HbA1c) laboratory tests, and antihyperglycemic medication orders from 2018 to 2020. RESULTS: In the hospitalization study, of 2,751 patients hospitalized for COVID-19, 1,020 had diabetes, which was associated with ICU admission and elevated troponin. Associations did not differ by community type or CSD. In the utilization study, among 93,401 patients with diabetes, utilization measures decreased in March 2020. Utilization increased in July, and then began to stabilize or decline through the end of 2020. Changes in HbA1c tests and medication order trends during the pandemic differed by community type and CSD. CONCLUSION: Diabetes was associated with selected outcomes among individuals hospitalized for COVID-19, but these did not differ by community features. Utilization trajectories among individuals with diabetes during the pandemic were influenced by community type and CSD and could be used to identify individuals at risk of gaps in diabetes care.

Arsenic Exposure, Blood DNA Methylation, and Cardiovascular Disease.

BACKGROUND: Epigenetic dysregulation has been proposed as a key mechanism for arsenic-related cardiovascular disease (CVD). We evaluated differentially methylated positions (DMPs) as potential mediators on the association between arsenic and CVD. METHODS: Blood DNA methylation was measured in 2321 participants (mean age 56.2, 58.6% women) of the Strong Heart Study, a prospective cohort of American Indians. Urinary arsenic species were measured using high-performance liquid chromatography coupled to inductively coupled plasma mass spectrometry. We identified DMPs that are potential mediators between arsenic and CVD. In a cross-species analysis, we compared those DMPs with differential liver DNA methylation following early-life arsenic exposure in the apoE knockout (apoE-/-) mouse model of atherosclerosis. RESULTS: A total of 20 and 13 DMPs were potential mediators for CVD incidence and mortality, respectively, several of them annotated to genes related to diabetes. Eleven of these DMPs were similarly associated with incident CVD in 3 diverse prospective cohorts (Framingham Heart Study, Women's Health Initiative, and Multi-Ethnic Study of Atherosclerosis). In the mouse model, differentially methylated regions in 20 of those genes and DMPs in 10 genes were associated with arsenic. CONCLUSIONS: Differential DNA methylation might be part of the biological link between arsenic and CVD. The gene functions suggest that diabetes might represent a relevant mechanism for arsenic-related cardiovascular risk in populations with a high burden of diabetes.

High Level of Selenium Exposure in the Strong Heart Study: A Cause for Incident Cardiovascular Disease?

Increasing evidence suggests that high selenium (Se) exposure is associated with adverse health effects. However, limited evidence exists on the association of Se exposure with cardiovascular disease (CVD), especially in communities affected by high naturally occurring Se in environmental media. We evaluated the prospective association between urinary Se levels and CVD incidence and mortality for 2727 American Indian adults who participated in the Strong Heart Study, with urinary Se levels measured at baseline (1989-1991) and CVD outcomes ascertained through 2017. The median (interquartile range) of urinary Se was 49.0 (36.7-67.4) µg/g creatinine. The multivariable adjusted hazard ratios (95% confidence interval) of incident CVD, coronary heart disease, and stroke comparing the 75th versus 25th percentile of urinary Se distributions were 1.11 (1.01-1.22), 1.05 (0.94-1.17), and 1.08 (0.88-1.33), respectively. In flexible dose-response models, increased risk for CVD incidence was only observed when the urinary Se level exceeded 60 µg/g creatinine. For CVD mortality, a nonstatistically significant U-shaped relationship was found across urinary Se levels. There was no evidence of effect modification by other urinary metal/metalloid levels. Our observation leads to the hypothesis that elevated Se exposure is a risk factor for CVD, especially in Se-replete populations. Antioxid. Redox Signal. 37, 990-997.

Risk factors for Lyme disease stage and manifestation using electronic health records.

BACKGROUND: Little is known about risk factors for early (e.g., erythema migrans) and disseminated Lyme disease manifestations, such as arthritis, neurological complications, and carditis. No study has used both diagnoses and free text to classify Lyme disease by disease stage and manifestation. METHODS: We identified Lyme disease cases in 2012-2016 in the electronic health record (EHR) of a large, integrated health system in Pennsylvania. We developed a rule-based text-matching algorithm using regular expressions to extract clinical data from free text. Lyme disease cases were then classified by stage and manifestation using data from both diagnoses and free text. Among cases classified by stage, we evaluated individual, community, and health care variables as predictors of disseminated stage (vs. early) disease using Poisson regression models with robust errors. Final models adjusted for sociodemographic factors, receipt of Medical Assistance (i.e., Medicaid, a proxy for low socioeconomic status), primary care contact, setting of diagnosis, season of diagnosis, and urban/rural status. RESULTS: Among 7310 cases of Lyme disease, we classified 62% by stage. Overall, 23% were classified using both diagnoses and text, 26% were classified using diagnoses only, and 13% were classified using text only. Among the staged diagnoses (n = 4530), 30% were disseminated stage (762 arthritis, 426 neurological manifestations, 76 carditis, 95 secondary erythema migrans, and 76 other manifestations). In adjusted models, we found that persons on Medical Assistance at least 50% of time under observation, compared to never users, had a higher risk (risk ratio [95% confidence interval]) of disseminated Lyme disease (1.20 [1.05, 1.37]). Primary care contact (0.59 [0.54, 0.64]) and diagnosis in the urgent care (0.22 [0.17, 0.29]), compared to the outpatient setting, were associated with lower risk of disseminated Lyme disease. CONCLUSIONS: The associations between insurance payor, primary care status, and diagnostic setting with disseminated Lyme disease suggest that lower socioeconomic status and less health care access could be linked with disseminated stage Lyme disease. Intervening on these factors could reduce the individual and health care burden of disseminated Lyme disease. Our findings demonstrate the value of both diagnostic and narrative text data to identify Lyme disease manifestations in the EHR.

Environmental Heat Exposure Among Pet Dogs in Rural and Urban Settings in the Southern United States.

With advancing global climate change, heat-related illnesses and injuries are anticipated to become more prevalent for humans and other species. Canine hyperthermia is already considered an important seasonal emergency. Studies have been performed on the risk factors for heat stroke in canine athletes and military working dogs; however there is limited knowledge on environmental risk factors for the average pet dog. This observational study explores variation in individually experienced environmental temperatures of pet dogs (N = 30) in rural and urban environments in central Alabama. Temperature data from dogs and their owners was collected using wearable personal thermometers. Demographic data on the dogs was collected using a brief survey instrument completed by their owners. Dogs included in the study varied in signalment, activity level, and home environment. Linear mixed effects regression models were used to analyze repeated measure temperature and heat index values from canine thermometers to explore the effect of environmental factors on the overall heat exposure risk of canine pets. Specifically, the heat exposures of dogs were modeled considering their owner's experienced temperatures, as well as neighborhood and local weather station measurements, to identify factors that contribute to the heat exposure of individual dogs, and therefore potentially contribute to heat stress in the average pet dog. Results show hourly averaged temperatures for dogs followed a diurnal pattern consistent with both owner and ambient temperature measurements, except for indoor dogs whose recordings remained stable throughout the day. Heat index calculations showed that owners, in general, had more hours categorized into the National Weather Station safe category compared to their dogs, and that indoor dogs had a greater proportion of hours categorized as safe compared to outdoor dogs. Our results suggest that the risk of the average pet dog to high environmental heat exposure may be greater than traditional measures indicate, emphasizing that more localized considerations of temperature are important when assessing a dog's environmental risk for heat-related injury or illness.

Association of community socioeconomic deprivation with evidence of reduced kidney function at time of type 2 diabetes diagnosis.

BACKGROUND: While there are known individual-level risk factors for kidney disease at time of type 2 diabetes diagnosis, little is known regarding the role of community context. We evaluated the association of community socioeconomic deprivation (CSD) and community type with estimated glomerular filtration rate (eGFR) when type 2 diabetes is diagnosed. METHODS: This was a retrospective cohort study of 13,144 adults with newly diagnosed type 2 diabetes in Pennsylvania. The outcome was the closest eGFR measurement within one year prior to and two weeks after type 2 diabetes diagnosis, calculated using the Chronic Kidney Disease Epidemiology Collaboration (CKD-Epi) equation. We used adjusted multinomial regression models to estimate associations of CSD (quartile 1, least deprivation) and community type (township, borough, city) with eGFR and used adjusted generalized estimating equation models to evaluate whether community features were associated with the absence of diabetes screening in the years prior to type 2 diabetes diagnosis. RESULTS: Of the participants, 1279 (9.7%) had hyperfiltration and 1377 (10.5%) had reduced eGFR. Women were less likely to have hyperfiltration and more likely to have reduced eGFR. Black (versus White) race was positively associated with hyperfiltration when the eGFR calculation was corrected for race but inversely associated without the correction. Medical Assistance (ever versus never) was positively associated with reduced eGFR. Higher CSD and living in a city were each positively associated (odds ratio [95% confidence interval]) with reduced eGFR (CSD quartiles 3 and 4 versus quartile 1, 1.23 [1.04, 1.46], 1.32 [1.11, 1.58], respectively; city versus township, 1.38 [1.15, 1.65]). These features were also positively associated with the absence of a type 2 diabetes screening measure. CONCLUSIONS: In a population-based sample, more than twenty percent had hyperfiltration or reduced eGFR at time of type 2 diabetes diagnosis. Individual- and community-level factors were associated with these outcomes.

Lifetime mobility of an Arctic woolly mammoth.

Little is known about woolly mammoth (Mammuthus primigenius) mobility and range. Here we use high temporal resolution sequential analyses of strontium isotope ratios along an entire 1.7-meter-long tusk to reconstruct the movements of an Arctic woolly mammoth that lived 17,100 years ago, during the last ice age. We use an isotope-guided random walk approach to compare the tusk's strontium and oxygen isotope profiles to isotopic maps. Our modeling reveals patterns of movement across a geographically extensive range during the animal's ~28-year life span that varied with life stages. Maintenance of this level of mobility by megafaunal species such as mammoth would have been increasingly difficult as the ice age ended and the environment changed at high latitudes.

Relations of peri-residential temperature and humidity in tick-life-cycle-relevant time periods with human Lyme disease risk in Pennsylvania, USA.

How weather affects tick development and behavior and human Lyme disease remains poorly understood. We evaluated relations of temperature and humidity during critical periods for the tick lifecycle with human Lyme disease. We used electronic health records from 479,344 primary care patients in 38 Pennsylvania counties in 2006-2014. Lyme disease cases (n = 9657) were frequency-matched (5:1) by year, age, and sex. Using daily weather data at ~4 km2 resolution, we created cumulative metrics hypothesized to promote (warm and humid) or inhibit (hot and dry) tick development or host-seeking during nymph development (March 1-May 31), nymph activity (May 1-July 30), and prior year larva activity (Aug 1-Sept 30). We estimated odds ratios (ORs) of Lyme disease by quartiles of each weather variable, adjusting for demographic, clinical, and other weather variables. Exposure-response patterns were observed for higher cumulative same-year temperature, humidity, and hot and dry days (nymph-relevant), and prior year hot and dry days (larva-relevant), with same-year hot and dry days showing the strongest association (4th vs. 1st quartile OR = 0.40; 95% confidence interval [CI] = 0.36, 0.43). Changing temperature and humidity could increase or decrease human Lyme disease risk.

Urinary arsenic and heart disease mortality in NHANES 2003-2014.

BACKGROUND: Evidence evaluating the prospective association between low-to moderate-inorganic arsenic (iAs) exposure and cardiovascular disease in the general US population is limited. We evaluated the association between urinary arsenic concentrations in National Health and Nutrition Examination Survey (NHANES) 2003-2014 and heart disease mortality linked from the National Death Index through 2015. METHODS: We modeled iAs exposure as urinary total arsenic and dimethylarsinate among participants with low seafood intake, based on low arsenobetaine levels (N = 4990). We estimated multivariable adjusted hazard ratios (HRs) for heart disease mortality per interquartile range (IQR) increase in urinary arsenic levels using survey-weighted, Cox proportional hazards models, and evaluated flexible dose-response analyses using restricted quadratic spline models. We updated a previously published relative risk of coronary heart disease mortality from a dose-response meta-analysis per a doubling of water iAs (e.g., from 10 to 20 µg/L) with our results from NHANES 2003-2014, assuming all iAs exposure came from drinking water. RESULTS: A total of 77 fatal heart disease events occurred (median follow-up time 75 months). The adjusted HRs (95% CI) of heart disease mortality for an increase in urinary total arsenic and DMA corresponding to the interquartile range were 1.20 (0.83, 1.74) and 1.18 (0.68, 2.05), respectively. Restricted quadratic splines indicate a significant association between increasing urinary total arsenic and the HR of fatal heart disease for all participants at the lowest exposure levels <4.5 µg/L. The updated pooled relative risk of coronary heart disease mortality per doubling of water iAs (µg/L) was 1.16 (95% CI 1.07, 1.25). CONCLUSIONS: Despite a small number of events, relatively short follow-up time, and high analytical limits of detection for urinary arsenic species, iAs exposure at low-to moderate-levels is consistent with increased heart disease mortality in NHANES 2003-2014 although the associations were only significant in flexible dose-response models.

Fifty million years of beetle evolution along the Antarctic Polar Front.

Global cooling and glacial-interglacial cycles since Antarctica's isolation have been responsible for the diversification of the region's marine fauna. By contrast, these same Earth system processes are thought to have played little role terrestrially, other than driving widespread extinctions. Here, we show that on islands along the Antarctic Polar Front, paleoclimatic processes have been key to diversification of one of the world's most geographically isolated and unique groups of herbivorous beetles-Ectemnorhinini weevils. Combining phylogenomic, phylogenetic, and phylogeographic approaches, we demonstrate that these weevils colonized the sub-Antarctic islands from Africa at least 50 Ma ago and repeatedly dispersed among them. As the climate cooled from the mid-Miocene, diversification of the beetles accelerated, resulting in two species-rich clades. One of these clades specialized to feed on cryptogams, typical of the polar habitats that came to prevail under Miocene conditions yet remarkable as a food source for any beetle. This clade's most unusual representative is a marine weevil currently undergoing further speciation. The other clade retained the more common weevil habit of feeding on angiosperms, which likely survived glaciation in isolated refugia. Diversification of Ectemnorhinini weevils occurred in synchrony with many other Antarctic radiations, including penguins and notothenioid fishes, and coincided with major environmental changes. Our results thus indicate that geo-climatically driven diversification has progressed similarly for Antarctic marine and terrestrial organisms since the Miocene, potentially constituting a general biodiversity paradigm that should be sought broadly for the region's taxa.

A Novel Behavioral Intervention for Rural Appalachian Cancer Survivors (weSurvive): Participatory Development and Proof-of-Concept Testing.

BACKGROUND: Addressing the modifiable health behaviors of cancer survivors is important in rural communities that are disproportionately impacted by cancer (eg, those in Central Appalachia). However, such efforts are limited, and existing interventions may not meet the needs of rural communities. OBJECTIVE: This study describes the development and proof-of-concept testing of weSurvive, a behavioral intervention for rural Appalachian cancer survivors. METHODS: The Obesity-Related Behavioral Intervention Trials (ORBIT) model, a systematic model for designing behavioral interventions, informed the study design. An advisory team (n=10) of community stakeholders and researchers engaged in a participatory process to identify desirable features for interventions targeting rural cancer survivors. The resulting multimodal, 13-week weSurvive intervention was delivered to 12 participants across the two cohorts. Intervention components included in-person group classes and group and individualized telehealth calls. Indicators reflecting five feasibility domains (acceptability, demand, practicality, implementation, and limited efficacy) were measured using concurrent mixed methods. Pre-post changes and effect sizes were assessed for limited efficacy data. Descriptive statistics and content analysis were used to summarize data for other domains. RESULTS: Participants reported high program satisfaction (acceptability). Indicators of demand included enrollment of cancer survivors with various cancer types and attrition (1/12, 8%), recruitment (12/41, 30%), and attendance (median 62%) rates. Dietary (7/12, 59%) and physical activity (PA; 10/12, 83%) behaviors were the most frequently chosen behavioral targets. However, the findings indicate that participants did not fully engage in action planning activities, including setting specific goals. Implementation indicators showed 100% researcher fidelity to delivery and retention protocols, whereas practicality indicators highlighted participation barriers. Pre-post changes in limited efficacy outcomes regarding cancer-specific beliefs and knowledge and behavior-specific self-efficacy, intentions, and behaviors were in desired directions and demonstrated small and moderate effect sizes. Regarding dietary and PA behaviors, effect sizes for fruit and vegetable intake, snacks, dietary fat, and minutes of moderate-to-vigorous activity were small (Cohen d=0.00 to 0.32), whereas the effect sizes for change in PA were small to medium (Cohen d=0.22 to 0.45). CONCLUSIONS: weSurvive has the potential to be a feasible intervention for rural Appalachian cancer survivors. It will be refined and further tested based on the study findings, which also provide recommendations for other behavioral interventions targeting rural cancer survivors. Recommendations included adding additional recruitment and engagement strategies to increase demand and practicality as well as increasing accountability and motivation for participant involvement in self-monitoring activities through the use of technology (eg, text messaging). Furthermore, this study highlights the importance of using a systematic model (eg, the ORBIT framework) and small-scale proof-of-concept studies when adapting or developing behavioral interventions, as doing so identifies the intervention's potential for feasibility and areas that need improvement before time- and resource-intensive efficacy trials. This could support a more efficient translation into practice.

Environmental genomics of Late Pleistocene black bears and giant short-faced bears.

Analysis of ancient environmental DNA (eDNA) has revolutionized our ability to describe biological communities in space and time,1-3 by allowing for parallel sequencing of DNA from all trophic levels.4-8 However, because environmental samples contain sparse and fragmented data from multiple individuals, and often contain closely related species,9 the field of ancient eDNA has so far been limited to organellar genomes in its contribution to population and phylogenetic studies.5,6,10,11 This is in contrast to data from fossils12,13 where full-genome studies are routine, despite these being rare and their destruction for sequencing undesirable.14-16 Here, we report the retrieval of three low-coverage (0.03×) environmental genomes from American black bear (Ursus americanus) and a 0.04× environmental genome of the extinct giant short-faced bear (Arctodus simus) from cave sediment samples from northern Mexico dated to 16-14 thousand calibrated years before present (cal kyr BP), which we contextualize with a new high-coverage (26×) and two lower-coverage giant short-faced bear genomes obtained from fossils recovered from Yukon Territory, Canada, which date to ∼22-50 cal kyr BP. We show that the Late Pleistocene black bear population in Mexico is ancestrally related to the present-day Eastern American black bear population, and that the extinct giant short-faced bears present in Mexico were deeply divergent from the earlier Beringian population. Our findings demonstrate the ability to separately analyze genomic-scale DNA sequences of closely related species co-preserved in environmental samples, which brings the use of ancient eDNA into the era of population genomics and phylogenetics.

Association of community types and features in a case-control analysis of new onset type 2 diabetes across a diverse geography in Pennsylvania.

OBJECTIVES: To evaluate associations of community types and features with new onset type 2 diabetes in diverse communities. Understanding the location and scale of geographic disparities can lead to community-level interventions. DESIGN: Nested case-control study within the open dynamic cohort of health system patients. SETTING: Large, integrated health system in 37 counties in central and northeastern Pennsylvania, USA. PARTICIPANTS AND ANALYSIS: We used electronic health records to identify persons with new-onset type 2 diabetes from 2008 to 2016 (n=15 888). Persons with diabetes were age, sex and year matched (1:5) to persons without diabetes (n=79 435). We used generalised estimating equations to control for individual-level confounding variables, accounting for clustering of persons within communities. Communities were defined as (1) townships, boroughs and city census tracts; (2) urbanised area (large metro), urban cluster (small cities and towns) and rural; (3) combination of the first two; and (4) county. Community socioeconomic deprivation and greenness were evaluated alone and in models stratified by community types. RESULTS: Borough and city census tract residence (vs townships) were associated (OR (95% CI)) with higher odds of type 2 diabetes (1.10 (1.04 to 1.16) and 1.34 (1.25 to 1.44), respectively). Urbanised areas (vs rural) also had increased odds of type 2 diabetes (1.14 (1.08 to 1.21)). In the combined definition, the strongest associations (vs townships in rural areas) were city census tracts in urban clusters (1.41 (1.22 to 1.62)) and city census tracts in urbanised areas (1.33 (1.22 to 1.45)). Higher community socioeconomic deprivation and lower greenness were each associated with increased odds. CONCLUSIONS: Urban residence was associated with higher odds of type 2 diabetes than for other areas. Higher community socioeconomic deprivation in city census tracts and lower greenness in all community types were also associated with type 2 diabetes.