Högskolan i Skövde

Background: Midlife obesity is a well-established risk factor for dementia, whereas late-life obesity has been associated with no increased risk, or even a reduced risk in some studies. However, the joint associations of obesity (body mass index ≥ 30 kg/m2) and metabolic health phenotypes (defined by the presence of hyperglycemia, hypertension and dyslipidemia) with dementia risk are less explored, particularly with regard to age- and sex-related differences. Therefore, we investigated how obesity and metabolic health phenotypes jointly associate with dementia risk and whether this risk differs between midlife (≤ 65 years) and late-life (> 65 years), and sex.

Methods: We analysed data from 11,482 participants, aged 51 to 100 years, from the US Health and Retirement Study (HRS), and 13,068 participants, aged 45 to 90 years, from the Swedish Twin Registry (STR). Cox regression models were used to estimate dementia risk in relation to metabolically healthy obesity (MHO), metabolically unhealthy obesity (MUO), and metabolically unhealthy no obesity (MUNO), relative to metabolically healthy no obesity (reference). Models were adjusted for age, sex, smoking status, and education level. Analyses were stratified by midlife and late-life and conducted in the entire sample and separately by sex.

Results: Metabolically unhealthy status in midlife and late-life indicated increased dementia risk regardless of obesity status, reaching statistical significance for midlife MUNO in females in HRS (Hazard ratio (HR): 1.62, 95% confidence intervals (CI): 1.05–2.49) and in late-life MUNO for the full sample in STR (HR: 1.13, CI: 1.02–1.25) and males in the STR (HR: 1.22, CI: 1.04–1.42). The associations between MUO and dementia risk were not statistically significant, but trends suggested midlife MUO was associated with higher dementia risk. The associations between mid and late-life MHO with dementia risk were also not statistically significant, although the associations showed trends towards lower dementia risk.

Conclusions: Being metabolically unhealthy, especially in midlife, may be associated with increased dementia risk, regardless of obesity status. Mid- and late-life MHO showed no increased risk and suggested potential inverse associations. These findings underscore the importance of evaluating dementia risk in the context of obesity, metabolic health, age and sex simultaneously.

Background: Loneliness is recognized as a global health threat. Older adults are vulnerable to loneliness due to life changes common in old age. While individual risk factors for loneliness in old age are well-documented, contextual factors are scarcely explored, such as digitalization. Rapid digitalization underscores the need to explore the long-term association between the use of digital devices and loneliness.

Objective: This study aimed to explore whether and how the use of digital devices is associated with changes in loneliness in a population-based sample of middle-aged and older adults in a context where digital technology and digital environments were introduced and rapidly evolving.

Methods: Data were obtained from the longitudinal cohort study, the Swedish Adoption/Twin Study of Aging (SATSA; N=771; mean age 69.64 years, SD 11.00). Daily digital use and loneliness were assessed across 5 waves between 2004 and 2014. Age, sex, education, living situation, self-rated health, and the personality traits openness and extraversion were assessed at baseline. Growth mixture modeling was used to identify latent trajectories of loneliness, and multinomial logistic regression was used to predict class membership based on baseline daily digital use and covariates.

Results: Three latent loneliness classes were identified: class 1 (10.5%; high intercept and significant increases in loneliness), class 2 (33.2%; intermediate stable loneliness), and class 3 (56.3%; low stable loneliness). Daily digital use at baseline is significantly associated with lower odds of belonging to the high-increasing loneliness group (class 1) compared to the low-stable group (class 3; odds ratio 0.27, 95% CI 0.08-0.90; P=.03). Differences between classes were not explained by the personality traits.

Conclusions: Daily use of digital devices is associated with lower and more stable levels of loneliness over time, compared to not using digital devices daily, which is associated with higher and increasing levels of loneliness. These findings, based on data from early adopters of digital technology, lend further support to the idea that digital use is associated with lower levels of loneliness and less increase in loneliness. Hence, these findings suggest that use of digital devices may represent a noninvasive approach to addressing loneliness in older adults.

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: 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 (rA = −0.27) and non-shared environmental (rE = −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 (rA=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.

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 (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.

Introduction: Loneliness, a global health problem, increases with advancing age. The digitalization of society has the potential to either increase or decrease loneliness. This study aims to investigate the cross-sectional association between digital living and loneliness in the context of other risk factors in a sample of late middle-aged and older adults, using a measure of embracement of digitalization in daily life.

Methods: In total, 441 Swedish adults (response rate 44%) aged 55 to 93 years of age who responded from December 2023 to January 2024. Embracement of digitalization was measured using the Digital Living Index, and loneliness with the UCLA Loneliness Scale.

Results: The mean score for perceived loneliness was 35.64 (SD = 10.55), positioning the participants at the threshold between low and moderate levels of loneliness. In the final multivariate linear regression model, including established risk factors for loneliness, low digital living was estimated to be 3.3 and 4.1 units higher in loneliness compared to mid and high digital living (p = 0.005), respectively. Mental health was estimated to be the strongest predictor of loneliness, with a difference of 14.1 units between bad or very bad mental health and very good (p < 0.001).

Conclusion: Higher digital living appears to be associated with lower levels of loneliness even when other established risk factors for loneliness are controlled for. Supporting late middle-aged and older adults to overcome the digital divide, from access and use to embracement, could potentially be a tool to battle loneliness, and hence to improve public health. 

Background: Obesity and aging share biological processes, but their relationship remains unclear, especially in late life. Understanding how body mass index (BMI) and biological aging influence each other can guide strategies to reduce age- and obesity-related health risks. We examined the bidirectional, longitudinal association between changes in BMI and biological aging, measured by frailty index (FI) and functional aging index (FAI), across late life.

Methods: This longitudinal cohort study used data from the Swedish Twin Registry substudies, GENDER, OCTO-Twin and SATSA, collected via in-person assessments from 1986 to 2014 at 2- to 4-year intervals. We analysed 6216–6512 evaluations from 1902 to 1976 Swedish twins. Dual change score models were applied to assess the bidirectional, longitudinal association between BMI and FI or FAI from ages 60.0–91.9. FI measured physiological aging, while FAI assessed functional aging through a composite score of functional abilities.

Results: At first measurement, mean age was 74 ± 8, and 41% were males. BMI–FI relationship was bidirectional (p value ≤ 0.001): Higher BMI predicted a greater increase in FI over time (coupling effect [γ] = 0.86, 95% confidence interval [CI] = 0.65–1.06, p value ≤ 0.001), and higher FI predicted steeper decline in BMI (γ = −0.04, 95% CI = −0.05 to −0.03, p value ≤ 0.001). When including coupling from FI, BMI showed a nonlinear trajectory with a mean intercept of 26.32 kg/m2 (95% CI = 25.76–26.88), declining more rapidly after age 75. When including BMI coupling, FI increased from a mean intercept of 7.91% (95% CI = 6.41–9.42), with steeper growth from ages 60–75. BMI–FAI relationship was unidirectional (p value ≤ 0.001): Higher FAI predicted a steeper BMI decline (γ = −0.02, 95% CI = −0.02 to −0.01, p value ≤ 0.001). By including FAI coupling, BMI had a mean intercept of 26.10 kg/m2 (95% CI = 25.47–26.74), declining rapidly after age 75. FAI increased exponentially from a mean intercept of 36.49 (95% CI = 34.54–38.43).

Conclusions: Higher BMI predicted a steeper increase in FI, substantiating the hypothesis that obesity accelerates biological aging. Higher biological aging, measured as FI and FAI, drove a steeper BMI decline in late life, signalling that late-life weight loss may result from accelerated aging. Higher BMI may accelerate aspects of the aging process, and the aging process, in turn, accelerates late-life BMI decline, necessitating an integrated approach to manage both obesity and unintentional weight loss among older adults.

Aims: About 10%–30% of individuals with obesity are metabolically healthy, but the specific characteristics of the metabolically healthy obesity (MHO) phenotype remain unclear. We aimed to examine how physical activity, education, depressive symptoms and genetic predisposition to obesity differ between individuals with MHO and those with metabolically unhealthy obesity (MUO), and whether these factors predict stability in MHO or conversion to a metabolically unhealthy state.

Materials and Methods: We retrieved data on 9809 individuals with obesity from the Health and Retirement Study collected between 2006 and 2016. We compared how physical activity, education, depressive symptoms and a polygenic score for higher body mass index (BMI) (PGSBMI) differed cross-sectionally between MHO and MUO using logistic regression. We then examined if the same factors predict conversion to a metabolically unhealthy state over 4 years in individuals with MHO.

Results: Individuals with MHO had higher physical activity (odds ratio [OR] = 0.81), higher education (OR = 0.83) and lower depressive symptoms (OR = 1.14) compared to those with MUO but did not differ in the PGSBMI. The associations were slightly attenuated in mutually adjusted models. None of the factors were associated with conversion from MHO to a metabolically unhealthy state. However, a higher PGSBMI indicated 24% lower risk of conversion to a metabolically unhealthy state (p = 0.07).

Conclusions: Physical activity, education and depressive symptoms differed between MHO and MUO, even when mutually adjusted for, but did not predict conversion from a metabolically healthy to unhealthy state. Although not statistically significant, the results indicated that those with genetically predicted high BMI are more likely to maintain MHO and not convert to a metabolically unhealthy state.

Background

Older persons (those 65 years of age and older) have the lowest digital competence and are thus at an increased risk of exclusion from a digitalizing society. To bridge this digital divide, several Swedish municipalities have introduced a service called Digital Coach (DC), where older persons can request in-home support for their digital needs. Little is known about older persons’ experiences of this unique service.

Objective

To study older persons’ experiences of one-to-one in-home support, with their digital needs, through the DC service.

Methods

A qualitative study was conducted through interviews, and the data were analysed using qualitative content analysis following the approach of Graneheim and Lundman.

Results

The older persons (n = 14) expressed awareness of the importance of digital competence to keep up with the times to avoid exclusion and vulnerability. They requested support to become part of a digital society. The DC service was experienced as providing respectful support at home, which is considered a calm, safe environment. They expressed that the support made them feel valued and increased opportunities for independence and social participation.

Conclusions

Older persons found one-to-one in-home support to be the optimal form of support for their digital needs. The support gave them the best conditions to achieve digital competence, and without the support, would have felt stranded. Efforts to support older persons’ digital competence should be based on the older persons’ own digital needs and be provided in their home.

Background: The role of biological ageing in the association between body mass index (BMI) and survival remains unclear. We examined whether epigenetic age acceleration (EAA), a biomarker of biological ageing, mediates the BMI-survival association. Methods: We analysed data from 3840 participants (aged 51–100) in the 2016 US Health and Retirement Study, with survival information through 2020. Mediation analyses were performed using linear regression and Gompertz proportional hazards models with restricted cubic splines, adjusting for age, sex, ethnicity/race, smoking, education, and metabolic health. Average direct effects (ADE) of BMI and average causal mediation effects of EAA (HannumAgeAcc, PhenoAgeAcc, GrimAgeAcc, and DunedinPace) on survival time were estimated with 95% confidence intervals (CI). Findings: Associations between BMI, EAA, and survival were nonlinear: high and low BMIs were associated with higher EAA and reduced survival time. ADEs of high BMI (35 kg/m2 versus 27 kg/m2) were not statistically significant (reduced survival time: 1.21–1.58 years) but significant for low BMI (19 kg/m2 versus 27 kg/m2, reduced survival time: 5.60–6.38 years). For high BMI, mediation was significant through all EAAs, with reduced survival time ranging from 0.28 to 0.71 years, accounting for 15–37% of total effects. For low BMI, mediation was statistically significant through HannumAgeAcc (reduced survival time: 0.44, CI: 0.08–0.86) and GrimAgeAcc (reduced survival time: 0.73, CI: 0.15–1.38), accounting for 7–11% of total effects. Interpretation: EAA partially mediated the high BMI-survival association, supporting the mediating role of accelerated ageing in the obesity-survival relationship. Mediation through EAA in the low BMI-survival association was weaker, indicating that alternative mechanisms, other than accelerated ageing, may dominate. Funding: Forte, Vetenskaprådet, SFOepi, Karolinska Institutet's Research Foundation, Loo and Hans Osterman Foundation, the Foundation for Geriatric Diseases at Karolinska Institutet.