Wolverines (Gulo gulo) in the Arctic: Revisiting distribution and identifying research and conservation priorities amid rapid environmental change.

Wolverines (Gulo gulo) occupy most of the globe's Arctic tundra. Given the rapidly warming climate and expanding human activity in this biome, understanding wolverine ecology, and therefore the species' vulnerability to such changes, is increasingly important for developing research priorities and effective management strategies. Here, we review and synthesize knowledge of wolverines in the Arctic using both Western science sources and available Indigenous Knowledge (IK) to improve our understanding of wolverine ecology in the Arctic and better predict the species' susceptibility to change. To accomplish this, we update the pan-Arctic distribution map of wolverines to account for recent observations and then discuss resulting inference and uncertainties. We use these patterns to contextualize and discuss potential underlying drivers of distribution and population dynamics, drawing upon knowledge of food habits, habitat associations, and harvest, as well as studies of wolverine ecology elsewhere. We then identify four broad areas to prioritize conservation and research efforts: (1) Monitoring trends in population abundance, demographics, and distribution and the drivers thereof, (2) Evaluating and predicting wolverines' responses to ongoing climate change, particularly the consequences of reduced snow and sea ice, and shifts in prey availability, (3) Understanding wolverines' response to human development, including the possible impact of wintertime over-snow travel and seismic testing to reproductive denning, as well as vulnerability to hunting and trapping associated with increased human access, and (4) Ensuring that current and future harvest are sustainable. Supplementary Information: The online version contains supplementary material available at 10.1007/s00300-022-03079-4.

Spatiotemporally variable snow properties drive habitat use of an Arctic mesopredator.

Climate change is rapidly altering the composition and availability of snow, with implications for snow-affected ecological processes, including reproduction, predation, habitat selection, and migration. How snowpack changes influence these ecological processes is mediated by physical snowpack properties, such as depth, density, hardness, and strength, each of which is in turn affected by climate change. Despite this, it remains difficult to obtain meaningful snow information relevant to the ecological processes of interest, precluding a mechanistic understanding of these effects. This problem is acute for species that rely on particular attributes of the subnivean space, for example depth, thermal resistance, and structural stability, for key life-history processes like reproduction, thermoregulation, and predation avoidance. We used a spatially explicit snow evolution model to investigate how habitat selection of a species that uses the subnivean space, the wolverine, is related to snow depth, snow density, and snow melt on Arctic tundra. We modeled these snow properties at a 10 m spatial and a daily temporal resolution for 3 years, and used integrated step selection analyses of GPS collar data from 21 wolverines to determine how these snow properties influenced habitat selection and movement. We found that wolverines selected deeper, denser snow, but only when it was not undergoing melt, bolstering the evidence that these snow properties are important to species that use the Arctic snowpack for subnivean resting sites and dens. We discuss the implications of these findings in the context of climate change impacts on subnivean species.

Predicting nationwide obesity from food sales using machine learning.

The obesity epidemic progresses everywhere across the globe, and implementing frequent nationwide surveys to measure the percentage of obese population is costly. Conversely, country-level food sales information can be accessed inexpensively through different suppliers on a regular basis. This study applies a methodology to predict obesity prevalence at the country-level based on national sales of a small subset of food and beverage categories. Three machine learning algorithms for nonlinear regression were implemented using purchase and obesity prevalence data from 79 countries: support vector machines, random forests and extreme gradient boosting. The proposed method was validated in terms of both the absolute prediction error and the proportion of countries for which the obesity prevalence was predicted satisfactorily. We found that the most-relevant food category to predict obesity is baked goods and flours, followed by cheese and carbonated drinks.

Simplified Mercury Extraction from Coal Fly Ash for Quantification of Total Mercury by ELISA-based Immunoassay.

A simplified two-step mercury extraction procedure enabled the selective and reproducible mercury recovery from actual coal fly ash (CFA). The optimized extraction procedure involving conventional enzyme-linked immunosorbent assay (ELISA)-based immunoassay allowed the ultra-sensitive quantification of total mercury content in CFA. The total mercury content of 41 CFA samples were successfully determined using the above-mentioned method, and the results were in agreement with those obtained by standard instrumental analysis (thermal decomposition atomic absorption spectrometry) within a 15% coefficient of variation. Our method for total mercury quantification is not only simple but suitable for management of the mercury content at coal-fired electric power plants and landfill sites, which deal with large amounts of waste CFA.

Associations of multidimensional socioeconomic and built environment factors with body mass index trajectories among youth in geographically heterogeneous communities.

Understanding contextual influences on obesity requires comparison of heterogeneous communities and concurrent assessment of multiple contextual domains. We used a theoretically-based measurement model to assess multidimensional socioeconomic and built environment factors theorized to influence childhood obesity across a diverse geography ranging from rural to urban. Confirmatory factor analysis specified four factors-community socioeconomic deprivation (CSED), food outlet abundance (FOOD), fitness and recreational assets (FIT), and utilitarian physical activity favorability (UTIL)-which were assigned to communities (townships, boroughs, city census tracts) in 37 Pennsylvania counties. Using electronic health records from 2001 to 2012 from 163,820 youth aged 3-18 years from 1288 communities, we conducted multilevel linear regression analyses with factor quartiles and their cross products with age, age2, and age3 to test whether community factors impacted body mass index (BMI) growth trajectories. Models controlled for sex, age, race/ethnicity, and Medical Assistance. Factor scores were lowest in townships, indicating less deprivation, fewer food and physical activity outlets, and lower utilitarian physical activity favorability. BMI at average age was lower in townships versus boroughs (beta [SE]) (0.217 [0.027], P < 0.001) and cities (0.378 [0.036], P < 0.001), as was BMI growth over time. Factor distributions across community types lacked overlap, requiring stratified analyses to avoid extrapolation. In townships, FOOD, UTIL, and FIT were inversely associated with BMI trajectories. Across community types, youth in the lowest (versus higher) CSED quartiles had lower BMI at average age and slower BMI growth, signifying the importance of community deprivation to the obesogenicity of environments.

Neighborhood social and economic change and diabetes incidence: The HeartHealthyHoods study.

We studied the association between neighborhood social and economic change and type 2 diabetes incidence in the city of Madrid (Spain). We followed 199,621 individuals living in 393 census tracts for diabetes incidence for 6 years using electronic health records, starting in 2009. We measured neighborhood social and economic change from 2005 to 2009 using a finite mixture model with 16 indicators that resulted in four types of neighborhood change. Adjusted results showed an association between neighborhood change and diabetes incidence: compared to those living in Aging/Stable areas, people living in Declining SES, New Housing and Improving SES areas have an 8% (HR = 0.92, 95% CI 0.87 to 0.99), 9% (HR = 0.91, 95% CI 0.81 to 1.01) and 11% (HR = 0.89, 95% CI 0.81 to 0.98) decrease in diabetes incidence. This evidence can help guide policies for diabetes prevention by focusing efforts on specific urban areas.

Socioeconomic Status, Race/Ethnicity, and Diurnal Cortisol Trajectories in Middle-Aged and Older Adults.

Objectives: Slow afternoon cortisol decline may be a marker of aging. We hypothesize that lower socioeconomic status (SES) and African American race are associated with lower waking cortisol and slower afternoon decline. Method: Six salivary cortisol samples, collected within a 24-hr period from 566 cohort participants aged 56-78 years, were examined in random-effects models. SES measures included socioeconomic vulnerability (household income and assets <500% of poverty) and education (≥college, some college, and ≤high school). African Americans were compared with all others. Results: Adjusting for age and sex, intermediate, but not low, education was associated with approximately 17% lower average waking cortisol and 1% slower decline, compared with high education. Socioeconomic vulnerability was not associated with waking cortisol or linear decline. Accounting for African American race/ethnicity, socioeconomic vulnerability was associated with a 3% faster decline, and education was not associated with cortisol. African Americans had 26% lower average waking cortisol and 1% slower decline than others. Discussion: African American race/ethnicity, but not lower SES, was associated with lower waking cortisol and slower afternoon decline in middle-aged and older adults. This pattern is likely a marker of earlier biological aging in vulnerable groups. Race/ethnicity may compete with SES as a measure of cumulative vulnerability.

Associations of LEP, CRH, ICAM-1, and LINE-1 methylation, measured in saliva, with waist circumference, body mass index, and percent body fat in mid-childhood.

BACKGROUND: Genetics explains a small proportion of variance in body mass index at the population level. Epigenetics, commonly measured by gene methylation, holds promise for understanding obesity risk factors and mechanisms. METHODS: Participants were 431 adolescents aged 10-15 years. BMI z-score, waist circumference z-score, and percent body fat were measured. Saliva samples were collected and methylation of promoter regions of four candidate genes or sequences (LEP, ICAM-1, CRH, and LINE-1) were measured in 3-4 CpG sites each. Linear regression was used to identify associations of methylation with obesity-related outcomes. RESULTS: After adjusting for age, in sex-stratified analysis, the three obesity-related outcomes were negatively associated with LEP methylation in obese boys only. There were no associations of methylation of the other genes or sequences and the obesity-related outcomes. CONCLUSIONS: Our results are consistent with prior studies that reported sex differences in associations of obesity-related outcomes with LEP methylation, and also as would be expected in adipose tissue, the source of circulating leptin. The findings suggest that saliva might be an acceptable tissue for epigenetics studies in adolescents.

Economic growth and mortality: do social protection policies matter?

Background: In the 20th century, periods of macroeconomic growth have been associated with increases in population mortality. Factors that cause or mitigate this association are not well understood. Evidence suggests that social policy may buffer the deleterious impact of economic growth. We sought to explore associations between changing unemployment (as a proxy for economic change) and trends in mortality over 30 years in the context of varying social protection expenditures. Methods: We model change in all-cause mortality in 21 OECD (Organization for Economic Cooperation and Development) countries from 1980 to 2010. Data from the Comparative Welfare States Data Set and the WHO Mortality Database were used. A decrease in the unemployment rate was used as a proxy for economic growth and age-adjusted mortality rates as the outcome. Social protection expenditure was measured as percentage of gross domestic product expended. Results: A 1% decrease in unemployment (i.e. the proxy for economic growth) was associated with a 0.24% increase in the overall mortality rate (95% confidence interval: 0.07;0.42) in countries with no changes in social protection. Reductions in social protection expenditure strengthened this association between unemployment and mortality. The magnitude of the association was diminished over time. Conclusions: Our results are consistent with the hypothesis that social protection policies that accompany economic growth can mitigate its potential deleterious effects on health. Further research should identify specific policies that are most effective.

Community Audit of Social, Civil, and Activity Domains in Diverse Environments (CASCADDE).

There are currently no direct observation environmental audit tools that measure diverse aspects of the obesity-related environment efficiently and reliably in a variety of geographic settings. The goal was to develop a new instrument to reliably characterize the overall properties and features of rural, suburban, and urban settings along multiple dimensions. The Community Audit of Social, Civil, and Activity Domains in Diverse Environments (CASCADDE) is an iPad-based instrument that incorporates GPS coordinates and photography and comprises 214 items yielding seven summary indices. A comprehensive spatial sampling strategy, training manual, and supporting data analysis code were also developed. Random geospatial sampling using GIS was used to capture features of the community as a whole. A single auditor collected 510 observation points in 30 communities (2013-2015). This analysis was done in 2015-2016. Correlation coefficients were used to compare items and indices to each other and to standard measures. Multilevel unconditional means models were used to calculate intraclass correlation coefficients to determine if there was significant variation between communities. Results suggest that CASCADDE measures aspects of communities not previously captured by secondary data sources. Additionally, seven summary indices capture meaningful differences between communities based on 15 observations per community. Community audit tools such as CASCADDE complement secondary data sources and have the potential to offer new insights about the mechanisms through which communities affect obesity and other health outcomes.

Associations of prenatal and childhood antibiotic use with child body mass index at age 3 years.

OBJECTIVE: Early-life antibiotic exposure, whether through prenatal or childhood antibiotic use, may contribute to increased child body mass. Associations of prenatal and childhood antibiotic use with body mass index z-score (BMIz) were evaluated at age 3 years. METHODS: Electronic health records were utilized from 8,793 mothers and singleton children delivered at Geisinger Clinic in Pennsylvania between 2006 and 2012. Antibiotic orders were ascertained for mothers during pregnancy and for children through their age-3 BMI measurement. Linear mixed-effects regression models evaluated associations of prenatal and childhood antibiotic use with child BMIz. RESULTS: Prenatal antibiotic orders were not associated with child BMIz. Children in the three largest categories of lifetime antibiotic orders had higher BMIz compared with children with no orders; associations persisted when controlling for prenatal antibiotics (ß [95% confidence interval]) (4-5 child orders: 0.090 [0.011 to 0.170]; 6 to 8: 0.113 [0.029 to 0.197]; ≥9: 0.175 [0.088 to 0.263]; trend P value <0.001). Two or more first-year orders were also associated with BMIz (1: 0.021 [-0.038 to 0.081]; 2: 0.088 [0.017 to 0.160]; ≥3: 0.104 [0.038 to 0.170]; trend P value < 0.001). CONCLUSIONS: Associations of early-life and lifetime childhood antibiotic use with increased child BMI highlight antibiotic exposure as a modifiable factor for reducing population-level excess weight.

Home Food Rules in Relation to Youth Eating Behaviors, Body Mass Index, Waist Circumference, and Percent Body Fat.

PURPOSE: To investigate agreement and associations between parent and youth acknowledgment of home food rules, youth eating behaviors, and measures of body composition and excess weight. METHODS: Parent-youth dyads (N = 413) completed the "rules for eating at home" scale (Active Where Survey) and reported dietary intake. Trained research staff obtained anthropometric data. Linear regression analyses separately evaluated relationships between youth and parent acknowledgment of rules and youth-reported eating behaviors and anthropometric outcomes. Food rules were evaluated as a 12-item scale and individually. RESULTS: Score on the food rule scale was positively associated with fruit and vegetable servings by youth acknowledgment only (ß = .09, p = .006), and not with anthropometric outcomes. The rule "no desserts except fruit" was positively associated with fruit and vegetable servings by youth (ß = .72, p = .002) and parent (ß = .53, p = .03) acknowledgment. The rules "no second helpings at meals" and "limited fast food" were positively associated with body mass index z-score by youth (ß = .38, p = .002; ß = .32, p = .02, respectively) and parent (ß = .74, p < .001; ß = .41, p = .006, respectively) acknowledgment, with similar results for waist circumference z-score and percent body fat. CONCLUSIONS: Inverse associations between specific food rules and healthful eating behaviors but positive associations with anthropometric outcomes suggest potentially bidirectional relationships between food rule implementation and youth weight. Future studies should disentangle how food rules guide youth behavior in the context of youth weight status.

Comparing methods of targeting obesity interventions in populations: An agent-based simulation.

Social networks as well as neighborhood environments have been shown to effect obesity-related behaviors including energy intake and physical activity. Accordingly, harnessing social networks to improve targeting of obesity interventions may be promising to the extent this leads to social multiplier effects and wider diffusion of intervention impact on populations. However, the literature evaluating network-based interventions has been inconsistent. Computational methods like agent-based models (ABM) provide researchers with tools to experiment in a simulated environment. We develop an ABM to compare conventional targeting methods (random selection, based on individual obesity risk, and vulnerable areas) with network-based targeting methods. We adapt a previously published and validated model of network diffusion of obesity-related behavior. We then build social networks among agents using a more realistic approach. We calibrate our model first against national-level data. Our results show that network-based targeting may lead to greater population impact. We also present a new targeting method that outperforms other methods in terms of intervention effectiveness at the population level.

Depression, its comorbidities and treatment, and childhood body mass index trajectories.

OBJECTIVE: No prior studies have evaluated depression diagnoses and cumulative antidepressant use in relation to longitudinal body mass index (BMI) trajectories in a population-representative sample. METHODS: Electronic health record data from 105,163 children ages 8 to 18 years with 314,648 BMI values were used. Depression diagnoses were evaluated as ever versus never, cumulative number of encounters with diagnoses, and total duration of diagnoses. Antidepressants were evaluated as months of use. Associations were evaluated with diagnoses alone, antidepressants alone, and then together, adjusting for covariates. RESULTS: A total of 6,172 (5.9%) and 10,628 (10.1%) children had a diagnosis of depression or received antidepressant treatment, respectively. At all ages, children receiving Medical Assistance (30.9%) were more likely to be treated with antidepressants. Depression diagnosis and antidepressant use were each independently and positively associated with BMI trajectories; associations were stronger with longer durations of diagnosis and treatment. Among children who received 12 or more months of antidepressants (vs. none), the mean (95% CI) weight gain at 18 years associated with antidepressant use (all classes) was 2.10 (1.76-2.45) kg. CONCLUSIONS: Depression and antidepressant use were both independently associated with increasing BMIs over time, suggesting an important unintended consequence of healthcare to the obesity epidemic.

A novel approach for measuring sphingosine-1-phosphate and lysophosphatidic acid binding to carrier proteins using monoclonal antibodies and the Kinetic Exclusion Assay.

Sphingosine-1-phosphate (S1P) and lysophosphatidic acid (LPA) are bioactive signaling lysophospholipids that activate specific G protein-coupled receptors on the cell surface triggering numerous biological events. In circulation, S1P and LPA associate with specific carrier proteins or chaperones; serum albumin binds both S1P and LPA while HDL shuttles S1P via interactions with apoM. We used a series of kinetic exclusion assays in which monoclonal anti-S1P and anti-LPA antibodies competed with carrier protein for the lysophospholipid to measure the equilibrium dissociation constants (Kd) for these carrier proteins binding S1P and the major LPA species. Fatty acid-free (FAF)-BSA binds these lysophospholipids with the following Kd values: LPA(16:0), 68 nM; LPA(18:1), 130 nM; LPA(18:2), 350 nM; LPA(20:4), 2.2 µM; and S1P, 41 µM. FAF human serum albumin binds each lysophospholipid with comparable affinities. By measuring the apoM concentration and expanding the model to include endogenous ligand, we were able to resolve the Kd values for S1P binding apoM in the context of human HDL and LDL particles (21 nM and 2.4 nM, respectively). The novel competitive assay and analysis described herein enables measurement of Kd values of completely unmodified lysophospholipids binding unmodified carrier proteins in solution, and thus provide insights into S1P and LPA storage in the circulation system and may be useful in understanding chaperone-dependent receptor activation and signaling.

Understanding differences in the local food environment across countries: A case study in Madrid (Spain) and Baltimore (USA).

Places where we buy food influence dietary patterns, making local food environments a good example of a mass influence on population diets. Cross-cultural studies, using reliable methods, may help understanding the relationship between food environments and diet-related health outcomes. We aimed to understand cross-national differences in the local food environment between Madrid and Baltimore by comparing an average neighborhood in each city in terms of food store types, healthy food availability, and residents' pedestrian access. During 2012-2013, we assessed one neighborhood (~15,000 residents) in each city selecting median areas in terms of socio-demographic characteristics (segregation, education, aging, and population density). We collected on-field data on (a) number and types of all food stores, (b) overall healthy food availability and (c) specific availability of fruits & vegetables. Throughout a street network analysis (200m, 400m and 800m) of food stores with high healthy food availability, we estimated residents' pedestrian accessibility. We found 40 stores in Madrid and 14 in Baltimore. Small food stores carrying fresh foods in Madrid contrasted with the high presence of corner and chain convenience stores in Baltimore. In Madrid, 77% of the residents lived within less than 200m from a food store with high healthy food availability. In contrast, 95% of Baltimore's residents lived further than 400m from these stores. Our results may help promoting interventions from local city agencies to allocate resources to existing small-sized food stores, and to improve walkable urban environments. These actions may influence food choices, especially for those residents lacking access to private vehicles.

Are neighborhoods causal? Complications arising from the 'stickiness' of ZNA.

Are neighborhoods causal? The answer remains elusive. Armed with new multilevel methods, enthusiasm for neighborhoods research surged at the turn of the century. However, a wave of skepticism has arisen based on the difficulty of drawing causal inferences from observational studies in which selection to neighborhoods is non-random. Researchers have sought answers from experimental and quasi-experimental studies of movers vs. stayers. We develop two related concepts in this essay in the hopes of shedding light on this problem. First, the inceptive environment into which persons are born (which we term ZNA for Zip code Nativity Area) exerts a potentially powerful causal impact on health. Detecting that causal effect is challenging for reasons similar that obtain in other fields (including genetics). Second, we explicate the problem of neighborhood 'stickiness' in terms of the persistence of neighborhood treatment assignment, and argue that under-appreciation of stickiness has led to systematic bias in causal estimates of neighborhoods proportional to the degree of stickiness. In sticky contexts, failure to account for the lasting influences of ZNA by adjusting for intermediate individual socioeconomic and health variables on the causal pathway can result in neighborhood effects estimates that are biased toward the null. We follow with an example drawn from evidence of neighborhood 'stickiness' and obesity. The stickiness of ZNA cautions us that experimental evidence may be insufficient or misleading as a solution to causal inference problems in neighborhood research.