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Background: Personalized interventions aiming to increase physical activity in individuals are effective. However, from a public health perspective, it would be important to stimulate physical activity in larger groups of people who share the vulnerability to be physically inactive throughout adulthood. To find these high-risk groups, we identified 36-year leisure-time physical activity profiles from young adulthood to late midlife in females and males. Moreover, we uncovered which anthropometric-, demographic-, lifestyle-, and health-related characteristics were associated with these physical activity profiles. Methods: We included 2,778 females and 1,938 males from the population-based older Finnish Twin Cohort Study, who responded to health and behavior surveys at the mean ages of 24, 30, 40 and 60. Latent profile analysis was used to identify longitudinal leisure-time physical activity profiles. Results: We found five longitudinal leisure-time physical activity profiles for both females and males. Females' profiles were: 1) Low increasing moderate (29%), 2) Moderate stable (23%), 3) Very low increasing low (20%), 4) Low stable (20%) and 5) High increasing high (9%). Males' profiles were: 1) Low increasing moderate (29%), 2) Low stable very low (26%), 3) Moderate decreasing low (21%), 4) High fluctuating high (17%) and 5) Very low stable (8%). In both females and males, lower leisure-time physical activity profiles were associated with lower education, higher body mass index, smoking, poorer perceived health, higher sedentary time, high blood pressure, and a higher risk for type 2 diabetes. Furthermore, lower leisure-time physical activity was linked to a higher risk of depression in females. Conclusions: We found several longitudinal leisure-time physical activity profiles with unique changes in both sexes. Fewer profiles in females than in males remained or became low physically active during the 36-year follow-up. We observed that lower education, higher body mass index, and more smoking already in young adulthood were associated with low leisure-time physical activity profiles. However, the fact that several longitudinal profiles demonstrated a change in their physical activity behavior over time implies the potential for public health interventions to improve leisure-time physical activity levels. [ABSTRACT FROM AUTHOR]

Background: The association of sleep bruxism with mortality has not been studied. Objectives: Altogether 12 040 subjects from the nationwide Finnish twin cohort were included in the analyses. We examined whether self‐reported sleep bruxism is associated with increased risk of mortality, and if so, whether the effect is independent of known common risk factors. The time span of the follow‐up was 30 years. Methods: Cox proportional hazards regression models (Hazard Ratios and their 95% Confidence Intervals) adjusted by age, sex and covariates were used to assess the effect of baseline bruxism status in 1990 on future mortality in 1990–2020. Results: The risk of mortality among all participants (n = 12 040), independent of missing covariates and adjusted by age and sex, was 40% higher in weekly bruxers than in never bruxers (HR 1.40, 95% CI 1.16–1.68, p <.001). However, when adjusted by all studied covariates, (n = 11 427) the risk was no longer observed (HR 1.04, 95% CI 0.86–1.25, p =.717). Despite the overall lack of between bruxism and mortality after adjustment for covariates, we examined the cause‐specific risks for major cause‐of‐death groups. There were no substantial associations of weekly bruxism with major disease outcomes by the fully adjusted hazard ratios for them. Conclusion: Bruxism does not kill—in line with its definition of being rather a behaviour (with all its phenotypes) than a disease. [ABSTRACT FROM AUTHOR]

Background: The influence of genetics and environment on the association of the plasma proteome with body mass index (BMI) and changes in BMI remains underexplored, and the links to other omics in these associations remain to be investigated. We characterized protein–BMI trajectory associations in adolescents and adults and how these connect to other omics layers. Methods: Our study included two cohorts of longitudinally followed twins: FinnTwin12 (N = 651) and the Netherlands Twin Register (NTR) (N = 665). Follow-up comprised 4 BMI measurements over approximately 6 (NTR: 23–27 years old) to 10 years (FinnTwin12: 12–22 years old), with omics data collected at the last BMI measurement. BMI changes were calculated in latent growth curve models. Mixed-effects models were used to quantify the associations between the abundance of 439 plasma proteins with BMI at blood sampling and changes in BMI. In FinnTwin12, the sources of genetic and environmental variation underlying the protein abundances were quantified by twin models, as were the associations of proteins with BMI and BMI changes. In NTR, we investigated the association of gene expression of genes encoding proteins identified in FinnTwin12 with BMI and changes in BMI. We linked identified proteins and their coding genes to plasma metabolites and polygenic risk scores (PRS) applying mixed-effects models and correlation networks. Results: We identified 66 and 14 proteins associated with BMI at blood sampling and changes in BMI, respectively. The average heritability of these proteins was 35%. Of the 66 BMI-protein associations, 43 and 12 showed genetic and environmental correlations, respectively, including 8 proteins showing both. Similarly, we observed 7 and 3 genetic and environmental correlations between changes in BMI and protein abundance, respectively. S100A8 gene expression was associated with BMI at blood sampling, and the PRG4 and CFI genes were associated with BMI changes. Proteins showed strong connections with metabolites and PRSs, but we observed no multi-omics connections among gene expression and other omics layers. Conclusions: Associations between the proteome and BMI trajectories are characterized by shared genetic, environmental, and metabolic etiologies. We observed few gene-protein pairs associated with BMI or changes in BMI at the proteome and transcriptome levels. [ABSTRACT FROM AUTHOR]

Objective: This study investigated 36‐year BMI trajectories in twins whose BMI in young adulthood was below, within, or above their genetically predicted BMI, with a focus on twin pairs with large intrapair BMI differences (within‐pair ΔBMI ≥ 3 kg/m2). Methods: Together, 3227 like‐sexed twin pairs (34% monozygotic) were examined at age ~30 years in 1975 and followed up in 1981, 1990, and 2011. An individual's observed BMI in 1975 was considered within (±2.0), below (<−2.0), or above (>+2.0) genetically predicted BMI, measured by a polygenic risk score of 996,919 single nucleotide polymorphisms. Results: In monozygotic and dizygotic twin pairs with large intrapair BMI differences, the co‐twin with a higher observed BMI in 1975 deviated above predicted BMI more frequently (~2/3) than the co‐twin with a lower BMI deviated below prediction (~1/3). Individuals below, within, and above prediction in 1975 reached, respectively, normal weight, overweight, and obesity by 2011, with a mean BMI increase of 4.5 (95% CI: 4.3–4.8). Conclusions: Categorizing BMI as below, within, or above polygenic risk score‐predicted BMI helps identifying individuals who have been resistant or susceptible to weight gain. This may provide new insights into determinants and consequences of obesity. [ABSTRACT FROM AUTHOR]

Physical activity (PA), aerobic fitness, and cardiometabolic diseases (CMD) are highly heritable multifactorial phenotypes. Shared genetic factors may underlie the associations between higher levels of PA and better aerobic fitness and a lower risk for CMDs. We aimed to study how PA genotype associates with self-reported PA, aerobic fitness, cardiometabolic risk factors and diseases. PA genotype, which combined variation in over one million of gene variants, was composed using the SBayesR polygenic scoring methodology. First, we constructed a polygenic risk score for PA in the Trøndelag Health Study (N = 47,148) using UK Biobank single nucleotide polymorphism-specific weights (N = 400,124). The associations of the PA PRS and continuous variables were analysed using linear regression models and with CMD incidences using Cox proportional hazard models. The results showed that genotypes predisposing to higher amount of PA were associated with greater self-reported PA (Beta [B] = 0.282 MET-h/wk per SD of PRS for PA, 95% confidence interval [CI] = 0.211, 0.354) but not with aerobic fitness. These genotypes were also associated with healthier cardiometabolic profile (waist circumference [B = -0.003 cm, 95% CI = -0.004, -0.002], body mass index [B = -0.002 kg/m2, 95% CI = -0.004, -0.001], high-density lipoprotein cholesterol [B = 0.004 mmol/L, 95% CI = 0.002, 0.006]) and lower incidence of hypertensive diseases (Hazard Ratio [HR] = 0.97, 95% CI = 0.951, 0.990), stroke (HR = 0.94, 95% CI = 0.903, 0.978) and type 2 diabetes (HR = 0.94, 95 % CI = 0.902, 0.970). Observed associations were independent of self-reported PA. These results support earlier findings suggesting small pleiotropic effects between PA and CMDs and provide new evidence about associations of polygenic inheritance of PA and intermediate cardiometabolic risk factors. [ABSTRACT FROM AUTHOR]

Breast cancer is highly prevalent yet a more complete understanding of the interplay between genes and probable environmental risk factors, such as night work, remains lagging. Using a discordant twin pair design, we examined the association between night shift work and breast cancer risk, controlling for familial confounding. Shift work pattern was prospectively assessed by mailed questionnaires among 5,781 female twins from the Older Finnish Twin Cohort. Over the study period (1990–2018), 407 incident breast cancer cases were recorded using the Finnish Cancer Registry. Cox proportional hazards models were used to calculate hazard ratios (HRs) and 95% confidence intervals (CIs) adjusting for potential confounders. Within-pair co-twin analyses were employed in 57 pairs to account for potential familial confounding. Compared to women who worked days only, women with shift work that included night shifts had a 1.58-fold higher risk of breast cancer (HR = 1.58; 95%CI, 1.16–2.15, highest among the youngest women i.e. born 1950–1957, HR = 2.08; 95%CI, 1.32–3.28), whereas 2-shift workers not including night shifts, did not (HR = 0.84; 95%CI, 0.59–1.21). Women with longer sleep (average sleep duration > 8 h/night) appeared at greatest risk of breast cancer if they worked night shifts (HR = 2.91; 95%CI, 1.55–5.46; Pintx=0.32). Results did not vary by chronotype (Pintx=0.74). Co-twin analyses, though with limited power, suggested that night work may be associated with breast cancer risk independent of early environmental and genetic factors. These results confirm a previously described association between night shift work and breast cancer risk. Genetic influences only partially explain these associations. [ABSTRACT FROM AUTHOR]

The article discusses research which found that smoking cessation is not associated with a decline in reported sleep bruxism in middle-aged Finnish twins. The study considered the role of environmental factors in the aetiology of sleep bruxism. It used Chi-square test to analyze the bruxism status of Finnish twins in 2011 and whether smoking cessation during 1990-2011 was associated with it.

Family history is the standard indirect measure of inherited susceptibility in clinical care, whereas polygenic risk scores (PRSs) have more recently demonstrated potential for more directly capturing genetic risk in many diseases. Few studies have systematically compared how these overlap and complement each other across common diseases. Within FinnGen (N = 306,418), we leverage family relationships, up to 50 years of nationwide registries, and genome-wide genotyping to examine the interplay of family history and genome-wide PRSs. We explore the dynamic for three types of family history across 24 common diseases: first- and second-degree family history and parental causes of death. Covering a large proportion of the burden of non-communicable diseases in adults, we show that family history and PRS are independent and not interchangeable measures, but instead provide complementary information on inherited disease susceptibility. The PRSs explained on average 10% of the effect of first-degree family history, and first-degree family history 3% of PRSs, and PRS effects were independent of both early- and late-onset family history. The PRS stratified the risk similarly in individuals with and without family history. In most diseases, including coronary artery disease, glaucoma, and type 2 diabetes, a positive family history with a high PRS was associated with a considerably elevated risk, whereas a low PRS compensated completely for the risk implied by positive family history. This study provides a catalogue of risk estimates for both family history of disease and PRSs and highlights opportunities for a more comprehensive way of assessing inherited disease risk across common diseases. [Display omitted] Leveraging family relationships, nationwide registries, and genome-wide genotyping, Mars et al. systematically compared two measures of inherited disease risk across 24 diseases: family history and polygenic risk scores. The measures provided complementary information for risk assessment, demonstrating opportunities for a more comprehensive way of assessing inherited risk in clinical care. [ABSTRACT FROM AUTHOR]

Purpose: Hand grip strength (HGS) is a widely used indicator of overall muscle strength and general health. We computed a polygenic risk score (PRS) for HGS and examined whether it predicted muscle strength, functional capacity, and disability outcomes. Methods: Genomewide association study summary statistics for HGS from the Pan-UK Biobank was used. PRS were calculated in the Finnish Twin Study on Aging (N = 429 women, 63–76 yr). Strength tests included HGS, isometric knee extension, and ankle plantarflexion strength. Functional capacity was examined with the Timed Up and Go, 6-min and 10-m walk tests, and dual-task tests. Disabilities in the basic activities of daily living (ADL) and instrumental ADL (IADL) were investigated with questionnaires. The proportion of variation in outcomes accounted for by PRS HGS was examined using linear mixed models and extended logistic regression. Results: The measured HGS increased linearly over increasing PRS (β = 4.8, SE = 0.93, P < 0.001). PRS HGS independently accounted for 6.1% of the variation in the measured HGS (β = 14.2, SE = 3.1, P < 0.001), 5.4% of the variation in knee extension strength (β = 19.6, SE = 4.7, P < 0.001), 1.2% of the variation in ankle plantarflexion strength (β = 9.4, SE = 4.2, P = 0.027), and 0.1%–1.5% of the variation in functional capacity tests (P = 0.016–0.133). Further, participants with higher PRS HGS were less likely to have ADL/IADL disabilities (odds ratio = 0.74–0.76). Conclusions: Older women with genetic risk for low muscle strength were significantly weaker than those with genetic susceptibility for high muscle strength. PRS HGS was also systematically associated with overall muscle strength and proximal and distal functional outcomes that require muscle strength. [ABSTRACT FROM AUTHOR]

Abnormal blood pressure is strongly associated with risk of high-prevalence diseases, making the study of blood pressure a major public health challenge. Although biological mechanisms underlying hypertension at the single omic level have been discovered, multi-omics integrative analyses using continuous variations in blood pressure values remain limited. We used a multi-omics regression-based method, called sparse multi-block partial least square, for integrative, explanatory, and predictive interests in study of systolic and diastolic blood pressure values. Various datasets were obtained from the Finnish Twin Cohort for up to 444 twins. Blocks of omics—including transcriptomic, methylation, metabolomic—data as well as polygenic risk scores and clinical data were integrated into the modeling and supported by cross-validation. The predictive contribution of each omics block when predicting blood pressure values was investigated using external participants from the Young Finns Study. In addition to revealing interesting inter-omics associations, we found that each block of omics heterogeneously improved the predictions of blood pressure values once the multi-omics data were integrated. The modeling revealed a plurality of clinical, transcriptomic, and metabolomic factors consistent with the literature and that play a leading role in explaining unit variations in blood pressure. These findings demonstrate (1) the robustness of our integrative method to harness results obtained by single omics discriminant analyses, and (2) the added value of predictive and exploratory gains of a multi-omics approach in studies of complex phenotypes such as blood pressure. [ABSTRACT FROM AUTHOR]