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Circulating cell-free DNA (cf-DNA) has emerged as a promising biomarker of ageing, tissue damage and cellular stress. However, less is known about health behaviours, ageing phenotypes and metabolic processes that lead to elevated cf-DNA levels. We sought to analyse the relationship of circulating cf-DNA level to age, sex, smoking, physical activity, vegetable consumption, ageing phenotypes (physical functioning, the number of diseases, frailty) and an extensive panel of biomarkers including blood and urine metabolites and inflammatory markers in three human cohorts (N = 5385; 17‐82 years). The relationships were assessed using correlation statistics, and linear and penalised regressions (the Lasso), also stratified by sex. cf-DNA levels were significantly higher in men than in women, and especially in middle-aged men and women who smoke, and in older more frail individuals. Correlation statistics of biomarker data showed that cf-DNA level was higher with elevated inflammation (C-reactive protein, interleukin-6), and higher levels of homocysteine, and proportion of red blood cells and lower levels of ascorbic acid. Inflammation (C-reactive protein, glycoprotein acetylation), amino acids (isoleucine, leucine, tyrosine), and ketogenesis (3-hydroxybutyrate) were included in the cf-DNA level-related biomarker profiles in at least two of the cohorts. In conclusion, circulating cf-DNA level is different by sex, and related to health behaviour, health decline and metabolic processes common in health and disease. These results can inform future studies where epidemiological and biological pathways of cf-DNA are to be analysed in details, and for studies evaluating cf-DNA as a potential clinical marker.

This study examined the association between daily working hours and accelerometer-based sleep duration, sleep efficiency, and number of awakenings per hour of sleep on the following night among 800 older public sector employees in Finland (mean age 63 years in the first measurement they participated in, 87% women) with 4,818 measurement nights in total. Information on working hours was derived from daily logs and categorized into: 1) 6 h, 2) 7, hours 3) 8 h, 4) 9 h, and 5) 10 or more hours of work. The most common category (i.e. workdays with 8 h of work) was used as the reference category in the analyses. Nights followed by a workday and a free day were analyzed separately. No differences were observed in sleep duration between the reference group and the other working hour categories when the next day was a workday nor when the next day was a free day. After a 6-hour workday, sleep efficiency was on average 1.0 percentage points higher and there were on average 0.13 less awakenings per hour of sleep when compared with the reference category. When the next day was a free day, no differences in sleep quality were observed. Thus, no clear indication of a dose-response relationship between working hours and either duration or quality of sleep was found. Furthermore, future research should further examine the possibility that the association between working hours and sleep is somewhat different depending on whether the workday is followed by another workday or a free day.

Background: Transition to retirement is shown to affect sleep, sedentary time and physical activity, but no previous studies have examined how retirement changes the distribution of time spent daily in these movement behaviors. The aim of this study was to examine longitudinally how the composition of 24-h movement behaviors changes during the transition to retirement using compositional data analysis (CoDA). Methods: We included 551 retiring public sector workers (mean age 63.2 years, standard deviation 1.1) from the Finnish Retirement and Aging study. The study participants wore a wrist-worn ActiGraph accelerometer for one week 24 h per day before and after retirement, with one year between the measurements. The daily proportions to time spent sleeping, in sedentary behavior (SED), light physical activity (LPA) and moderate-to-vigorous physical activity (MVPA) were estimated using the GGIR package. Changes in the daily proportions of movement behaviors were examined using Compositional Data Analysis version of linear mixed models. Results: In general, the proportion of time spent in active behaviors decreased relative to time spent in passive behaviors after retirement (p < .001). This change depended on occupation (occupation*time interaction p < .001). After retirement manual workers increased the proportions of both sleep and SED in relation to active behaviors, whereas non-manual workers increased the proportion of sleep in relation to active behaviors and SED. The proportion of MVPA decreased relatively more than the proportion of LPA (p = 0.01), independently of gender and occupation. Conclusions: Retirement induced a decrease in the proportion of time spent in active behaviors, especially time spent in MVPA. Future studies are needed to find ways to maintain or increase daily physical activity levels at the cost of sedentary behaviors among retirees.

We utilized compositional data analysis (CoDA) to study changes in the composition of the 24-h movement behaviors during an activity tracker based physical activity intervention. A total of 231 recently retired Finnish retirees were randomized into intervention and control groups. The intervention participants were requested to use a commercial activity tracker bracelet with daily activity goal and inactivity alerts for 12 months. The controls received no intervention. The 24-h movement behaviors, i.e., sleep, sedentary time (SED), light physical activity (LPA), and moderate-to-vigorous physical activity (MVPA) were estimated from wrist-worn ActiGraph data using the GGIR R-package. Three balance coordinates describing the composition of movement behaviors were applied: ratio of active vs. passive behaviors, LPA vs. MVPA, and sleep vs. SED. A linear mixed model was used to study changes between the baseline and 6-month time point. Overall, the changes in the 24-h movement behaviors were small and did not differ between the groups. Only the ratio of LPA to MVPA tended to change differently between the groups (group*time interaction p = 0.08) as the intervention group increased LPA similarly to controls but decreased their MVPA. In conclusion, the use of a commercial activity tracker may not be enough to induce changes in the 24-h movement behaviors among retirees.

There is limited knowledge on the characteristics of employees who engage in catch-up sleep, the extension of sleep duration on free days to compensate for sleep loss accumulating during workdays. This study examined associations of accelerometer-measured free day catch-up sleep with sleep duration, sleep timing, and socio-demographic, health and lifestyle factors among older employees. We measured sleep repeatedly with accelerometers among 824 public sector employees in Finland (mean age 63 years; 86% women). On average, the participants provided 1.7 annual accelerometer measurements and 1,437 person-observations in total. Catch-up sleep was defined as longer average sleep duration on free days compared with average sleep duration on workdays. Prevalence of catch-up sleep was 78%. On average, the catch-up sleep group extended their sleep on free days by 1 hour 22 min (95% confidence interval [CI] 1 h 19 min — 1 h 26 min), whereas the non-catch-up sleep group reduced their sleep duration by 45 min (95% CI -50 min — -40 min). Catch-up sleep was mainly associated with delayed awakening time on free days (by 1 h 57 min, 95% CI 1 h 52 min — 2 h 2 min). We also observed a greater social jetlag in the catch-up sleep group in comparison to the non-catch-up sleep group, whereas no differences were observed in chronotype, self-reported sleep, or other individual characteristic. In conclusion, accelerometer-measured catch-up sleep is common among older employees in Finland and major differences in duration and timing of sleep occur between those with and without catch-up sleep.

Background: Long-term associations between type 2 diabetes, prediabetes, and muscle strength are obscure in older adults. The aim of this study was to examine type 2 diabetes as a predictor of handgrip strength decline during an 11-year follow-up among men and women aged 55 years. Methods: We used Finnish population-based Health 2000 Survey and its follow-up measurements in 2011. The study population consisted of 1,200 individuals, aged 55–86 years at baseline. Baseline fasting glucose level or use of diabetes medication was used to categorize the participants into diabetes (≥7.0 mmol/L), prediabetes (≥6.1 mmol/L but <7.0 mmol/L), and nondiabetes (<6.1 mmol/L) groups. Handgrip strength was measured at baseline and during 11-year follow-up. Results: Mean handgrip strength at baseline did not differ between diabetes groups in men or women. Among men during the 11-year follow-up, decline in muscle strength was significantly greater among diabetes group (−131.5 Newtons [N], 95% confidence interval (CI) −182.7 to −80.4) than nondiabetes group (−98.9 N, 95% CI −139.5 to −58.3) after adjusting for age, education, lifestyle factors, chronic conditions, and diabetes status at follow-up. Among women, muscle strength decline did not differ between diabetes, prediabetes, or nondiabetes groups after adjusting for all potential covariates, the results being −45.0 N (95% CI −73.2 to −16.7), −29.4 N (95% CI −59.7 to 0.9), and −42.3 N (95% CI −70.5 to −14.1), respectively. Conclusions: The presence of type 2 diabetes was associated with greater muscle strength decline among older men but not among older women.