Karri Silventoinen

Understanding obesity involves evaluating numerous contributing factors. Collecting the necessary data for this can impose a significant burden on both research participants during clinical studies and real‐life deployments of digital tools aiding obesity management. The BETTER4U project (European Union's Horizon Europe, Grant Agreement ID 101080117) introduces 36 behavioral and 9 living environment indicators designed to unobtrusively and efficiently capture daily life through passive monitoring using market‐available smartphones and smartwatches while accounting for environmental confounders. This concise set, intended for dietitians, physical activity experts, and healthcare professionals as a standard for assessing obesity‐related behavior, summarizes the behavioral and environmental profile of a person with obesity/overweight. We present the selected indicators, their selection and implementation methodology and the high‐level plan for their verification, describing their conceptual foundations and technical realization. These indicators can be deployed in a modular framework: by transforming raw sensor data into structured, interpretable measures they provide a scalable foundation for long‐term monitoring of obesogenic behaviors, individual assessment, and evaluation of interventions targeting physical activity, diet, stress, and sleep. The indicators are intended for an expert‐facing dashboard within the BETTER4U Intervention Platform, a participant‐side interface within the BETTER4U application, and as input to the BETTER4U AI‐based causal model, supporting relationship modeling between behavior, environment, and obesity outcomes to inform personalized interventions. This work lays the basis for data‐driven validation of these indicators following the “BETTER4ALL Pilot Study” ( ClinicalTrials.gov ID: NCT07120802), ahead of their use in the BETTER4U RCT among individuals with obesity/overweight.

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/m ² 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 ( r _A = –0.10) and by both genetic ( r _A = –0.17) and unique environmental factors ( r _E = –0.07) in women. For BMI change, the associations with EA were explained by genetic factors ( r _A = –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.

Objective: BMI is influenced by both genetic and environmental factors, including features of the obesogenic environment such as walkability. This study investigated the interplay between residential walkability, genetics, and BMI among 4312 individuals from two Finnish twin cohorts (mean age: 43). Methods: Residential walkability was quantified using a composite index including built and natural environmental features. Results: Multiple linear regression, adjusted for sociodemographic factors, showed that higher walkability was associated with lower BMI (coefficient: −0.02, 95% CI: −0.04, −0.01), but pairwise twin analyses indicated that this association was not independent of genetic and early shared environmental influences. Univariate twin modeling revealed that additive genetic effects accounted for 22% of the variation in walkability when participants were middle‐aged. Bivariate moderation twin modeling showed that the additive genetic influence on BMI was significantly stronger among individuals living in areas with average or moderately low residential walkability. The unique environmental component played a larger role among those living in areas with either very low or very high residential walkability. Conclusions: These findings underscored the complex gene–environment interplay between walkability and BMI. While genetic susceptibility cannot be modified, public health strategies that focus on the obesogenic environment, such as improving walkability, may still help mitigate obesity risk.

Background Polygenic indices (PGIs) of various traits abound, but knowledge remains limited on how they predict wide-ranging health indicators, including the risk of death. We investigated the associations between mortality and 35 different PGIs related to social, psychological, and behavioural traits, and typically non-fatal health conditions. Methods Data consist of Finnish adults from population-representative genetically informed epidemiological surveys (FINRISK 1992–2012, Health 2000/2011, FinHealth 2017), linked to administrative registers (N: 40,097 individuals, 5948 deaths). Within-sibship analysis was complemented with dizygotic twins from Finnish twin study cohorts (N: 10,174 individuals, 2116 deaths). We estimated Cox proportional hazards models with mortality follow-up 1995–2019. Results PGIs most strongly predictive of all-cause mortality were ever smoking (hazard ratio [HR]=1.12, 95% confidence interval [95% CI] 1.09; 1.14 per one standard deviation larger PGI), self-rated health (HR=0.90, 95% CI 0.88; 0.93), body mass index (HR=1.10, 95% CI 1.07; 1.12), educational attainment (HR=0.91, 95% CI 0.89; 0.94), depressive symptoms (HR=1.07, 95% CI 1.04; 1.10), and alcohol drinks per week (HR=1.06, 95% CI 1.04; 1.09). Within-sibship estimates were approximately consistent with the population analysis, suggesting no evidence for inflation of PGI–mortality associations by population phenomena. The investigated PGIs were typically more predictive for external than for natural causes of death. PGIs were more strongly associated with death occurring at younger ages, while among those who survived to age 80, the PGI–mortality associations were negligible. Conclusions PGIs related to the best-established mortality risk phenotypes had the strongest associations with mortality. They offer moderate additional prediction even when mutually adjusting with their phenotype.

Individuals with high self-esteem experience less social anxiety, fewer depressive symptoms, and exhibit lower neuroticism and higher extraversion. We aimed to explore the genetic and environmental influences behind these associations. Self-esteem, four mental health indicators, and five personality factors were assessed in 1,288 Finnish young adult twins, including 583 complete pairs. The mean age of the participants was 21.9 years (SD = 0.8). Classical twin modelling was used to estimate genetic and environmental correlations. Additionally, regression models were used to examine the association between self-esteem and polygenic scores (PGS) of several mental health traits. Among all participants, self-esteem associated positively with extraversion, agreeableness, and conscientiousness and negatively with depressive symptoms, alexithymia, schizotypal personality, overall mental health problems and neuroticism. These associations were explained by additive genetic factors (19–66% of covariation) and unique environmental factors (36–81% of covariation) when using twin modelling. Self-esteem correlated only with the PGS of subjective well-being in men and women. The proportion of variance of self-esteem explained by the PGSs was minor (1.5% or less). These findings suggest that while self-esteem shares a genetic background with mental health and personality traits, unique environmental factors can also influence these connections. Our findings are consistent with a hypothesis that enhancing self-esteem can have a positive impact on mental health.

Introduction 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 and methods Data from 16 longitudinal twin cohorts, including 111,370 adults (56% women) and 55,657 complete twin pairs (42% monozygotic), were pooled. The data were divided into three stages (young adulthood-early middle age, late middle age, and old age). BMI changes were calculated via linear mixed effects and delta slope methods. Genetic and environmental contributions to these changes and their correlations with BMI in early young adulthood were estimated through structural equation modeling. Results The average BMI increase per year was 0.18 kg/m² in men and 0.15 kg/m² in women during young adulthood-early middle age (18–50 years), decreasing to ≤0.07 kg/m² at older ages. Genetic effects contributed to variance of BMI changes during young adulthood-early middle age (men a² = 0.29; women a² = 0.26) and less so in late middle age (51–64 years) (men a² = 0.05; women a² = 0.16) and old age ( > 65 years) (men a² = 0.13; women a² = 0.18). Most variation was explained by non-shared environmental effects (e² = 0.71–0.95 in men and e²= 0.74–0.84 in women). In men, greater BMI during early young adulthood (18–30 years) was associated with lower BMI changes later in life (r = −0.22 to −0.13), and the association was driven by genetic (r_A = −0.27) and non-shared environmental (r_E = −0.22 to −0.14) factors. In contrast, the association was positive in women (r = 0.05–0.28) and was explained by genetic factors (r_A=0.27–0.51). Conclusion Genotype influences BMI changes across adulthood, with its effect varying by age and sex. Environmental effects are the main drivers of adult BMI changes, highlighting the role of modifiable factors in long-term weight regulation.

Aims: There is limited research on how family-related events are associated with sustainable working life, which is defined as the absence of long-term periods of unemployment, sickness absence, or disability pension. We aimed to investigate whether family-related events, such as living with children, marriage, or divorce, are associated with sustainable working life. We also examined whether these associations could be explained by health behaviours including physical activity, smoking, and alcohol consumption, as well as by genetic factors and early life environment. Methods: Registry data and surveys on family-related events, sustainable working life, and health behaviours of 49,611 Swedish twins in 1993–2016 were used. Cox proportional hazards regressions were applied to estimate the associations between family-related events and sustainable working life, while conditional Cox models for twin pairs accounted for confounding genetic factors and early life environment. Further, the analyses were stratified by health behaviours. Results: The results showed that a change from being single living without children to being married and living with children was associated with a sustainable working life. Moreover, individuals who experienced divorce and single individuals who changed their status of living with children were less likely to experience a sustainable working life. Genetic factors and early life environment played a role in some associations. Those with healthy behaviours were more likely to have sustainable working lives despite changes in family life, compared with those with unhealthy behaviours. Conclusions: Family formation (i.e. change from being single to getting married and having children) is associated with sustainable working life, whereas divorce seems to decrease the possibility of sustainable working life. Health behaviours, as well as genetic factors and early life environment, also need to be considered with changes in family life to support sustainable working life.

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 analyzed the role of genetic and environmental factors in the association 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 modeling. Results: EA was more strongly negatively associated with baseline BMI and BMI change (mean of 1.31 and 1.32 kg/m ² 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 (r _A = –0.10) and by both genetic (r _A = –0.17) and unique environmental factors (r _E = –0.07) in women. For BMI change, the associations with EA were explained solely by genetic factors (r _A = –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. The majority of the associations are primarily genetically mediated.

We examined how BMI, BMI trajectories, and BMI fluctuation around these trajectories in adolescence were correlated with BMI trajectories and BMI fluctuation in early adulthood, as well as the genetic basis of these associations. BMI data from Finnish twins ( N = 1379, 48% males) were collected at ages 11.5, 14, 17.5, 24, and 37 years. BMI trajectories in adolescence (11.5–17.5 years) and early adulthood (17.5–37 years) were estimated using linear mixed-effect models. BMI fluctuation was calculated as the average squared differences between observed and expected BMI around these trajectories. Genetic twin models and a polygenic risk score for BMI (PRS _BMI ) were used to assess genetic contributions to BMI fluctuation and its associations with BMI and BMI trajectories. Adolescent BMI fluctuation was positively correlated with early adulthood BMI trajectories in females, while in males, adolescent BMI trajectories were positively associated with BMI fluctuation in early adulthood. Genetic factors affected BMI fluctuation in both adolescence and early adulthood when estimated using twin modelling and PRS _BMI . Adolescent BMI was positively associated with early adulthood fluctuation in both sexes, with genetic factors playing a role (genetic correlations .08–.29). It was concluded that genetic factors play a significant role in BMI fluctuations in adolescence and early adulthood, with some overlap with the genetics of BMI.