INTRODUCTION:Body mass index (BMI) fluctuation has adverse health consequences, yet its determinants remain poorly understood. We examined genetic and environmental influences on BMI fluctuation and its associations with BMI trajectories. DATA AND METHODS:Data from 14 longitudinal twin cohorts, including 58 311 individuals (54% women) and 22 714 complete twin pairs (9761 monozygotic), were pooled. BMI trajectories from ages 18 to 99 years were estimated using linear mixed-effects models. BMI fluctuation was defined as the average squared residual between observed and expected BMI around individual trajectories. Genetic and environmental contributions to BMI fluctuation and its associations with baseline BMI and BMI change were assessed using structural equation modelling. RESULTS:Additive genetic effects explained a moderate proportion of variance in BMI fluctuation (a2 = 0.20 in men, 0.29 in women), with the remainder attributable to unique environmental effects (e2 = 0.80 and 0.71). No evidence of sex-specific genetic effects was found. Genetic contributions varied across life stages, peaking in men at ages 31-50 (a2 = 0.36) and in women at 51-65 (a2 = 0.36). Higher baseline BMI was associated with greater fluctuation, whereas greater directional BMI change was associated with less fluctuation. Higher BMI at ages 18-30 was associated with greater subsequent BMI fluctuation across later adulthood stages. Previous associations were driven by both genetic and unique environmental factors. CONCLUSIONS:BMI fluctuation was predominantly explained by unique environmental effects, with moderate heritability that varied across life stages with no evidence of sex-specific genetic effects. Genetic and environmental factors contribute to how BMI fluctuation is associated with BMI change.

INTRODUCTION:Educational attainment (EA) is negatively associated with body mass index (BMI), but less is known about the association between EA and adult BMI change. We analysed the role of genetic and environmental factors in the associations between EA and BMI trajectory components over adulthood. DATA AND METHODS:Pooled data from 59,490 twins aged 31-99 years (49% women) across 11 cohorts with EA and repeated measures of BMI were used. BMI trajectory components (baseline BMI and BMI change per decade) were estimated using linear mixed-effects (LME) and delta slope methods. EA was derived by regressing years of education on birth year and cohort. Associations between EA and BMI trajectories were evaluated with LME models in both cohort-specific and pooled data. Genetic and environmental contributions were evaluated using structural equation modelling. RESULTS:EA was more strongly negatively associated with baseline BMI and BMI change (mean of 1.31 and 1.32 kg/m2 per decade in men and women, respectively) in women (β = -0.14 kg/m², 95% CI: -0.15 to -0.12; β = -0.02 kg/m²/decade, 95% CI: -0.03 to -0.01, respectively) than in men (β = -0.07, 95% CI: -0.08 to -0.06; β = -0.01, 95% CI: -0.02 to -0.001, respectively). The associations between baseline BMI and EA were explained by genetic factors in men (rA = -0.10) and by both genetic (rA = -0.17) and unique environmental factors (rE = -0.07) in women. For BMI change, the associations with EA were explained by genetic factors (rA = -0.04 in men; -0.06 in women). CONCLUSION:Individuals with higher EA tend to have lower baseline BMI and slower BMI increases across adulthood. These associations are primarily genetically mediated.

Obesity is a chronic disease that develops via complex interactions between human biology and the obesogenic environment. This study aimed to develop a high-resolution index of combined obesogenic neighborhood exposures in the U.S. and examine its association with body mass index (BMI), independent of familial factors shared by twins (e.g., genetics and early life environment). This cross-sectional study included 11,152 adult twins (66% female, 69% monozygotic [MZ], mean age 42 [SD 18]) from the Washington State Twin Registry. The Obesogenic Built Environment CharacterisTics United States (OBCT-US) index captured neighborhood exposure to the modified retail food environment, walkability, area deprivation, and the normalized difference vegetation index around residential addresses. Univariate twin models estimated the heritability of the OBCT-US-index and BMI. Full-informed maximum likelihood linear regressions were applied to assess associations between the OBCT-US-index and BMI at the individual level among all twins and then pairwise differences within MZ and same-sex dizygotic (ssDZ) twin pairs. BMI showed high heritability (74%), whereas the OBCT-US-index was primarily influenced by unique environmental factors (63%). A 10% higher OBCT-US-index was associated with a 0.16 [95% CI: 0.11, 0.22] kg/m2 higher BMI in individuals, but no association remained within MZ twin pairs 0.03 [-0.03, 0.09] or in ssDZ twin pairs-0.02 [-0.15, 0.11]. We conclude that while a more obesogenic environment was associated with higher BMI, this relationship was confounded by familial factors.

The COVID-19 pandemic led to stay-at-home orders, resulting in sudden changes in movement patterns and social restrictions that impacted mental health. This study examined changes in individual behaviors during the pandemic using detailed smartphone-based activity data and determined whether behaviors and green space exposure buffered against mental health concerns. A total of 224 twins from the Washington State Twin Registry provided smartphone location data and completed a baseline survey and up to seven waves of follow-up surveys on mental health outcomes (ie, anxiety, depression, and stress). Objective measures of activities, locations, and time spent in green space were collected using Google Location History. Green space exposures were assessed using all location data. Associations between activities, locations, green space, and each mental health outcome were assessed using linear mixed models. There were changes in activities and locations from baseline to wave 1; by wave 7, the direction and magnitude of changes varied across measures, with some remaining above and others below their baseline levels. Mental health outcomes were associated with a subset of locations, activities, and green space measures although associations were not observed consistently across all exposure-outcome combinations examined.

Genetic and environmental factors contribute to weight gain, but how these effects change over adulthood is largely unknown. We examined how genetic factors influence BMI changes from young adulthood to old age and how this change relates to BMI in early adulthood. Data from 16 longitudinal twin cohorts, including 111,370 adults (56

We investigated associations between neighborhood walkability and physical activity using twins (5477 monozygotic and same-sex dizygotic pairs) as "quasi-experimental" controls of genetic and shared environment (familial) factors that would otherwise confound exposure-outcome associations. Walkability comprised intersection density, population density, and destination accessibility. Outcomes included self-reported weekly minutes of neighborhood walking and moderate-to-vigorous physical activity (MVPA) and days per week using transit services (eg, bus, commuter rail). There was a positive association between walkability and walking, which remained significant after controlling for familial and demographic factors: a 1% increase in walkability was associated with a 0.42% increase in neighborhood walking. There was a positive association between walkability and MVPA, which was not significant after considering familial and demographic factors. In twins with at least 1 day of transit use, a 1-unit increase in log (walkability) was associated with a 6.7% increase in transit use days; this was not significant after considering familial and demographic factors. However, higher walkability reduced the probability of no transit use by 32%, considering familial and demographic factors. Using a twin design to improve causal inference, walkability was associated with walking, whereas walkability and both MVPA and absolute transit use were confounded by familial and demographic factors.This article is part of a Special Collection on Environmental Epidemiology.

The evidence linking urban greenspace to individual’s physical activity (PA) levels is mixed. This study examines relationships between street-level and satellite-derived greenspace measures with PA outcomes. Our sample included 7855 adult twins enrolled in the Washington State Twin Registry from 2009 to 2020 living in urban areas; 14,095 total survey observations were analyzed. We applied a deep learning segmentation algorithm to Google Street View images sampled from 100 m around residential addresses to quantify street-level greenspace. Bouts and duration of PA, including moderate to vigorous PA and neighborhood walking were self-reported. We applied mixed-effects linear regression models to determine relationships between greenspace measures and PA outcomes, overall and stratified by residential population density. Adjusted models included age, body mass index, sex, race, education, income, neighborhood deprivation, urban sprawl, and seasonality. A series of sequential models was constructed to test associations between various greenspace exposures and PA outcomes. Overall, we found no consistent associations between greenspace exposures and PA outcomes. We found that the summer normalized difference vegetation index was associated with an increase in moderate to vigorous PA in low population density areas, but this was not significant when controlling for seasonality. Both Google Street View and normalized difference vegetation index were associated with lower total walking for those residing in areas with high population density only. Findings highlight the importance of seasonality and the need to address where PA is actually done.

Epidemiological studies typically rely on exposure assessments based on ambient PM2.5 concentrations at participants' home addresses. However, these approaches neglect personal exposures indoors and across different non-residential microenvironments. To address this problem, our study combined low-cost sensors and GPS to conduct two-week personal PM2.5 monitoring in 168 adults recruited from the Washington State Twin Registry between 2018 and 2021. PM2.5 mass concentration, size-resolved particle number concentration, temperature, humidity, and GPS coordinates were recorded at 1-min intervals, providing 5,161,737 data points. We used GPS coordinates and a processing algorithm for automatic classification of microenvironments, including seven land use types and vehicles, and time spent indoors/outdoors. The low-cost sensors were calibrated in-situ, using regulatory monitoring data within 600 m of participants' outdoor measurements (R2 = 0.93). A linear mixed model was used to estimate the associations of multiple spatiotemporal factors with personal exposure concentrations. The average PM2.5 exposure concentration was 8.1 ± 15.8 μg/m3 for all participants. Indoor exposure concentration was higher than outdoor exposure level, and indoor exposure dose contributed 77 % to the total exposure. Exposures in residential and industrial land use had a higher concentration than in other areas, and accounted for 69 % of the total exposure dose. Furthermore, personal exposure concentration was the highest during winter and evening hours, possibly due to cooking and heating-related behaviors. This study demonstrates that personal monitoring can capture spatiotemporal variations in PM2.5 exposure more accurately than home-based approaches based on ambient air quality, and suggests opportunities for controlling exposures in certain microenvironments.

INTRODUCTION:Research has focused on the built environment (e.g., neighborhood walkability) that supports or hinders physical activity because it is potentially modifiable. This study investigated the associations between changes in neighborhood walkability and changes in physical activity in an adult twin cohort. METHODS:Longitudinal data (2009-2020) from 7,439 identical and fraternal twins comprising 2,800 complete pairs from a community-based registry were analyzed. Participants were free of mobility limitations and resided at their current residential location for at least 1 year. A series of phenotypic (nongenetically informed) models were used to test the effect of walkability change on change in physical activity. These were re-estimated in a series of quasi-causal models by leveraging the genetically informed nature of the twin design to test the effect of walkability change on change in physical activity while controlling for genetic and shared environmental confounds. RESULTS:Change in neighborhood walkability was associated with change in neighborhood walking but not in moderate-to-vigorous physical activity, which held after controlling for genetic and shared environmental confounding, plus standard demographic covariates, length of follow-up, and moving status. A 1-unit increased change in neighborhood walkability was associated with a 2.7-minute increased change in neighborhood walking per week, independent of familial confounds and covariates. Moving to a neighborhood that is 5.5 units greater in walkability could increase neighborhood walking by about 15 minutes per week. CONCLUSIONS:This study supports a quasi-causal relationship between changes in neighborhood walkability and changes in neighborhood walking, extending previous cross-sectional findings in the same twin cohort by establishing temporality.

OBJECTIVE:Neighbourhood deprivation has been found to be associated with many health conditions, but its association with low back pain (LBP) and arthritis is unclear. This study aimed to examine the association between neighbourhood deprivation with LBP and arthritis, and its potential interaction with individual socioeconomic status (SES) on these outcomes.METHODS:Monozygotic (MZ) twins from the Washington State Twin Registry were used to control for genetic and common environmental factors that could otherwise confound the purported relationship. Multilevel models were employed to examine the association between neighbourhood deprivation as well as individual-level SES with LBP/arthritis, adjusting for age, sex, body mass index (BMI) and residence rurality.RESULTS:There were 6,380 individuals in the LBP sample and 2,030 individuals in the arthritis sample. Neighbourhood deprivation was not associated with LBP (P = 0.26) or arthritis (P = 0.61), and neither was its interaction with individual-level SES. People without a bachelor's degree were more likely to report LBP (OR 1.44, 95% CI 1.26-1.65) or both LBP and arthritis (OR 1.67, 95% CI 1.14-2.45) than those with a bachelor's degree, but not for arthritis alone (P = 0.17). Household income was not significantly associated with LBP (P = 0.16) or arthritis (p = 0.23) independent of age, sex, and BMI.CONCLUSION:Our study did not find significant associations between neighbourhood deprivation and the presence of LBP or arthritis. More research using multilevel modelling to investigate neighbourhood effects on LBP and arthritis is recommended.

Although research shows a strong positive association between perceived stress and loneliness, the genetic and environmental etiology underlying their association remains unknown. People with a genetic predisposition to perceived stress, for example, may be more prone to feeling lonely and vice versa. Conversely, unique factors in people’s lives may explain differences in perceived stress levels that, in turn, affect feelings of loneliness. We tested whether genetic factors, environmental factors, or both account for the association between perceived stress and loneliness. Participants were 3,066 individual twins ( n Female = 2,154, 70.3%) from the Washington State Twin Registry who completed a survey during April–May, 2020. Structural equation modeling was used to analyze the item-level perceived stress and loneliness measures. The correlation between latent perceived stress and latent loneliness was .68. Genetic and nonshared environmental variance components underlying perceived stress accounted for 3.71% and 23.26% of the total variance in loneliness, respectively. The genetic correlation between loneliness and perceived stress was .45 and did not differ significantly between men and women. The nonshared environmental correlation was .54 and also did not differ between men and women. Findings suggest that holding constant the strong genetic association between perceived stress and loneliness, unique life experiences underlying people’s perceived stress account for individual differences in loneliness.

We examined relationships between walkability and health behaviors between and within identical twin pairs, considering both home (neighborhood) walkability and each twin's measured activity space. Continuous activity and location data (via accelerometry and GPS) were obtained in 79 pairs over 2 weeks. Walkability was estimated using Walk Score® (WS); home WS refers to neighborhood walkability, and GPS WS refers to the mean of individual WSs matched to every GPS point collected by each participant. GPS WS was assessed within (WHN) and out of the neighborhood (OHN), using 1-mile Euclidean (air1mi) and network (net1mi) buffers. Outcomes included walking and moderate-to-vigorous physical activity (MVPA) bouts, dietary energy density (DED), and BMI. Home WS was associated with WHN GPS WS (b = 0.71, SE = 0.03, p < 0.001 for air1mi; b = 0.79, SE = 0.03, p < 0.001 for net1mi), and OHN GPS WS (b = 0.18, SE = 0.04, p < 0.001 for air1mi; b = 0.22, SE = 0.04, p < 0.001 for net1mi). Quasi-causal relationships (within-twin) were observed for home and GPS WS with walking (ps < 0.01), but not MVPA, DED, or BMI. Results support previous literature that neighborhood walkability has a positive influence on walking.

Objective: The evening ("night owl") chronotype is associated with greater severity and lifetime prevalence of post-traumatic stress disorder (PTSD) symptoms compared to morning or intermediate chronotypes. This twin study investigated the gene-environment relationships between chronotype, recent PTSD symptoms, and lifetime intrusive symptoms. Methods: We used the reduced Horne-Ostberg Morningness-Eveningness Questionnaire (rMEQ) to assess chronotype in a sample of 3777 same-sex adult twin pairs raised together (70.4% monozygotic, 29.6% dizygotic) in the community-based Washington State Twin Registry. PTSD symptoms were reported on the Impact of Events Scale (IES) and a single item for lifetime experience of intrusive symptoms after a stressful or traumatic event. Results: Genetic influences accounted for 50% of chronotype variance, 30% of IES score variance, and 14% of lifetime intrusive symptom variance. Bivariate twin models showed a phenotypic association (b(p)) between evening chronotype and more severe PTSD symptoms (b(p) = -0.16, SE = 0.02, p < .001) that remained significant even after adjusting for shared genetic and environmental influences (b(p) = -0.10, SE = 0.04, p = .009), as well as age, sex, and self-reported sleep duration (b(p) = -0.11, SE = 0.04, p = .004). An association was found between evening chronotype and lifetime intrusive symptoms (b(p) = -0.11, SE = 0.03, p < .001) that was no longer significant after adjusting for shared genetic and environmental influences (b(p) = 0.04, SE = 0.06, p = .558). Conclusions: Our results suggest a "quasi-causal" relationship between evening chronotype and PTSD symptoms that is not purely attributable to genetic or shared environmental factors. Evening chronotype may increase vulnerability to pathologic stress responses in the setting of circadian misalignment, providing potential avenues of prevention and treatment using chronobiological strategies.

The current study was designed to use an epigenome-wide association approach (EWAS) to identify potential systemic DNA methylation alterations that are associated with obesity using 22 discordant twin pairs. Buccal cells (from a cheek swab) were used as a non-obesity relevant purified marker cell for the epigenetic analysis. Analysis of differential DNA methylation regions (DMRs) was used to identify epigenetic associations with metabolic and dietary measures related to obesity with discordant twins. An edgeR analysis provided a DMR signature with p < 1e-04, but statistical significance was reduced due to low sample size and known multiple origins of obesity. A weighted gene coexpression network analysis (WGCNA) was performed and identified modules (p < 0.005) of epigenetic sites that correlated with different metabolic and dietary measures. The DMR and WGCNA epigenetic sites were near genes (e.g., CIDEC, SPP1, ZFPG9, and POMC) with previously identified obesity associated pathways (e.g., metabolism, cholesterol, and fat digestion). Observations demonstrate the feasibility of identifying systemic epigenetic biomarkers for obesity, which can be further investigated for clinical relevance in future research with larger sample sizes. The availability of a systemic epigenetic biomarker for obesity susceptibility may facilitate preventative medicine and clinical management of the disease early in life.

Due to social distancing measures implemented to mitigate the COVID-19 pandemic, individuals are spending more time isolated at home with limited physical social interactions. The current study investigated whether marriage and/or cohabitation is associated with satisfaction with life and depression among 732 adult same-sex twin pairs (monozygotic and dizygotic) in the US using online survey data. Twin analysis showed that married and/or cohabiting individuals were more satisfied with life and less depressed than those not married and/or cohabiting. The association between marriage and/or cohabiting and satisfaction with life was not confounded by between-family factors, whereas that between depression was mediated by familial factors. These findings suggest that being in a close relationship may mitigate some of the adverse consequences of the COVID-19 pandemic. Close relationships may be an essential source of support as individuals rely on their intimate partners when faced with the uncertainty and stress of the pandemic.

BACKGROUND:Low back pain (LBP) is more likely to occur in people with a family history of this condition, highlighting the importance of accounting for familial factors when studying the individual risk of LBP. We conducted a study of opposite-sex twin pairs investigating sex differences in LBP while accounting for (genetic and shared environmental) familial factors. METHODS:We applied a matched co-twin control design to study 795 adult opposite-sex pairs from Australia, Spain, and the United States (US). We used mixed-effects logistic regression to assess the within-pair association between female sex and lifetime prevalence of LBP in unadjusted and adjusted models for body-mass-index, and depression, as well as interactions between female sex and age (<median age vs. ≥median age) in this association. RESULTS:The mean age of the sample was 47.4 years (Standard Deviation = 16.5). The adjusted odds ratio (aOR) of the association between sex and LBP in the merged sample was 1.11 (95% Confidence Interval = 0.88-1.40), with 87.4% of the variance in the studied association explained by between-site heterogeneity (Q test; p = 0.001). Females had 2.37 (95% CI: 1.48-3.78) higher odds of LBP compared to their male co-twins in the Spanish sample (adjusted), but a sex association was not found in the Australian nor US samples. CONCLUSIONS:We found no evidence of the association between sex and LBP in our merged sample. Between-population differences (i.e. cultural background or health system characteristics) are likely to be major factors leading to variation in the sex association with LBP when familial factors are accounted for. SIGNIFICANCE:Our study of adult opposite-sex twin pairs found no evidence of an association between female sex and lifetime prevalence of low back pain after controlling for familial factors in the merged sample from Australia, Spain and USA, contrary to findings from previous studies of unrelated individuals. Our findings indicate potentially relevant between-country genetic, cultural and environmental differences which may need to be considered for optimal and individualized strategies for the prevention and management of low back pain across the lifespan.

Background Body mass index (BMI) shows strong continuity over childhood and adolescence and high childhood BMI is the strongest predictor of adult obesity. Genetic factors strongly contribute to this continuity, but it is still poorly known how their contribution changes over childhood and adolescence. Thus, we used the genetic twin design to estimate the genetic correlations of BMI from infancy to adulthood and compared them to the genetic correlations of height. Methods We pooled individual level data from 25 longitudinal twin cohorts including 38,530 complete twin pairs and having 283,766 longitudinal height and weight measures. The data were analyzed using Cholesky decomposition offering genetic and environmental correlations of BMI and height between all age combinations from 1 to 19 years of age. Results The genetic correlations of BMI and height were stronger than the trait correlations. For BMI, we found that genetic correlations decreased as the age between the assessments increased, a trend that was especially visible from early to middle childhood. In contrast, for height, the genetic correlations were strong between all ages. Age-to-age correlations between environmental factors shared by co-twins were found for BMI in early childhood but disappeared altogether by middle childhood. For height, shared environmental correlations persisted from infancy to adulthood. Conclusions Our results suggest that the genes affecting BMI change over childhood and adolescence leading to decreasing age-to-age genetic correlations. This change is especially visible from early to middle childhood indicating that new genetic factors start to affect BMI in middle childhood. Identifying mediating pathways of these genetic factors can open possibilities for interventions, especially for those children with high genetic predisposition to adult obesity.

BACKGROUND:Perceptions of the built environment, such as nature quality, beauty, relaxation, and safety, may be key factors linking the built environment to human health. However, few studies have examined these types of perceptions due to the difficulty in quantifying them objectively in large populations. OBJECTIVE:To measure and predict perceptions of the built environment from street-view images using crowd-sourced methods and deep learning models for application in epidemiologic studies. METHODS:We used the Amazon Mechanical-Turk crowdsourcing platform where participants compared two street-view images and quantified perceptions of nature quality, beauty, relaxation, and safety. We optimized street-view image sampling methods to improve the quality and resulting perception data specific to participants enrolled in the Washington State Twin Registry (WSTR) health study. We used a transfer learning approach to train deep learning models by leveraging existing image perception data from the PlacePulse 2.0 dataset, which includes 1.1 million image comparisons, and refining based on new WSTR perception data. Resulting models were applied to WSTR addresses to estimate exposures and evaluate associations with traditional built environment measures. RESULTS:We collected over 36,000 image comparisons and calculated perception measures for each image. Our final deep learning models explained 77.6% of nature quality, 68.1% of beauty, 72.0% of relaxation, and 64.7% of safety in pairwise image comparisons. Applying transfer learning with the new perception labels specific to the WSTR yielded an average improvement of 3.8% for model performance. Perception measures were weakly to moderately correlated with traditional built environment exposures for WSTR participant addresses; for example, nature quality and NDVI (r = 0.55), neighborhood area deprivation (r = -0.16), and walkability (r = -0.20), respectively. SIGNIFICANCE:We were able to measure and model perceptions of the built environment optimized for a specific health study. Future applications will examine associations between these exposure measures and mental health in the WSTR. IMPACT STATEMENT:Built environments influence health through complex pathways. Perceptions of nature quality, beauty, relaxation and safety may be particularly import for understanding these linkages, but few studies to-date have examined these perceptions objectively for large populations. For quantitative research, an exposure measure must be reproducible, accurate, and precise--here we work to develop such measures for perceptions of the urban environment. We created crowd-sourced and image-based deep learning methods that were able to measure and model these perceptions. Future applications will apply these models to examine associations with mental health in the Washington State Twin Registry.

Abstracts from the 19th International Congress on Twin Studies, 11–14 November 2021s from the 19th International Congress on Twin Studies, 11–14 November 2021 Exceptional Sibships and Curious Couples: General Intelligence Findings From Chinese Twins Reared Apart and Together and Virtual Twins