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Purpose Methods Results Conclusion To examine age‐related macular degeneration (AMD) and retinal pigment epithelium (RPE)–Bruch's membrane (BrM) complex volume associations in monozygotic twin pairs.In this study, 106 elderly twins (53 twin pairs) from the Finnish Twin Cohort study were recruited. Each participant underwent dilated 35‐degree digital colour fundus photography (CFP), and spectral domain optical coherence tomography (OCT) and replied to a structured study questionnaire. The CFPs were graded according to the Age‐Related Eye Disease Study (AREDS) classification. The OCT images were segmented and volumetric data of the RPE‐BrM complex volume was calculated with the Orion™ software. The worse eye according to AREDS classification was used for the analysis.Twenty‐nine (55%) of the twin pairs were discordant with regard to AREDS classification. Fourteen (26%) pairs were discordant with one twin participant having AMD (AREDS 2–4) and the other being unaffected (AREDS 1). Four (8%) pairs had one twin participant with intermediate or late AMD (AREDS 3–4) versus the other being unaffected (AREDS 1). The within‐pair polychoric correlation for AREDS was 0.605 (95% confidence interval 0.418–0.792).In multivariate analysis intermediate and late AMD as well as age associated with RPE‐BrM complex volume. RPE‐BrM complex volume showed a within twin pair correlation, r = 0.430 (95% confidence interval 0.172–0.688, p < 0.01).A substantial proportion of monozygotic twin pairs are discordant with regard to age‐related macular degeneration phenotype. RPE‐BrM complex volume associated with age and intermediate and late AMD. [ABSTRACT FROM AUTHOR]

Background: Machine learning (ML) classifiers are increasingly used for predicting cardiovascular disease (CVD) and related risk factors using omics data, although these outcomes often exhibit categorical nature and class imbalances. However, little is known about which ML classifier, omics data, or upstream dimension reduction strategy has the strongest influence on prediction quality in such settings. Our study aimed to illustrate and compare different machine learning strategies to predict CVD risk factors under different scenarios. Methods: We compared the use of six ML classifiers in predicting CVD risk factors using blood-derived metabolomics, epigenetics and transcriptomics data. Upstream omic dimension reduction was performed using either unsupervised or semi-supervised autoencoders, whose downstream ML classifier performance we compared. CVD risk factors included systolic and diastolic blood pressure measurements and ultrasound-based biomarkers of left ventricular diastolic dysfunction (LVDD; E/e' ratio, E/A ratio, LAVI) collected from 1,249 Finnish participants, of which 80% were used for model fitting. We predicted individuals with low, high or average levels of CVD risk factors, the latter class being the most common. We constructed multi-omic predictions using a meta-learner that weighted single-omic predictions. Model performance comparisons were based on the F1 score. Finally, we investigated whether learned omic representations from pre-trained semi-supervised autoencoders could improve outcome prediction in an external cohort using transfer learning. Results: Depending on the ML classifier or omic used, the quality of single-omic predictions varied. Multi-omics predictions outperformed single-omics predictions in most cases, particularly in the prediction of individuals with high or low CVD risk factor levels. Semi-supervised autoencoders improved downstream predictions compared to the use of unsupervised autoencoders. In addition, median gains in Area Under the Curve by transfer learning compared to modelling from scratch ranged from 0.09 to 0.14 and 0.07 to 0.11 units for transcriptomic and metabolomic data, respectively. Conclusions: By illustrating the use of different machine learning strategies in different scenarios, our study provides a platform for researchers to evaluate how the choice of omics, ML classifiers, and dimension reduction can influence the quality of CVD risk factor predictions. [ABSTRACT FROM AUTHOR]

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 We used a polygenic score for hand grip strength (PGS HGS) to investigate whether genetic predisposition for higher muscle strength predicts age-related noncommunicable diseases, survival from acute adverse health events, and mortality. Methods This study consisted of 342 443 Finnish biobank participants from FinnGen Data Freeze 10 (53% women) aged 40–108 with combined genotype and health registry data. Associations between PGS HGS and a total of 27 clinical endpoints were explored with linear or Cox regression models. Results A higher PGS HGS was associated with a reduced risk of selected common noncommunicable diseases and mortality by 2%–10%. The risk for these medical conditions decreased by 5%–23% for participants in the highest PGS HGS quintile compared to those in the lowest PGS HGS quintile. A 1 standard deviation (SD) increase in the PGS HGS predicted a lower body mass index (β = −0.112 kg/m2, standard error [ SE ] = 0.017, p  = 1.69E-11) in women but not in men (β = 0.004 kg/m2, p  = .768). PGS HGS was not associated with better survival after acute adverse health events compared to the nondiseased period. Conclusions The genotype that supports higher muscle strength appears to protect against future health adversities, albeit with modest effect sizes. Further research is needed to investigate whether or how a favorable lifestyle modifies this intrinsic capacity to resist diseases, and if the impacts of lifestyle behavior on health differs due to genetic predisposition for muscle strength. [ABSTRACT FROM AUTHOR]

Background: Due to different assessment modes employed, a clear picture of the prevalence of sleep bruxism across time cannot be formed. Moreover, studies on the persistent or fluctuating nature of sleep bruxism have yielded divergent and even contradictory results. The aim of the present study was to evaluate in a nationwide twin cohort whether self‐reported sleep bruxism was correlated longitudinally, pairwise and cross‐twin over a 20‐year period. Objectives: Self‐reported bruxism was assessed in 1990 and 2011 by mailed questionnaires in the Finnish Twin Cohort study of same‐sex twins born 1945–1957. Methods: We assessed the phenotypic stability over time for all participating individuals (n = 4992). Among zygosity verified pairs (n = 516 MZ and n = 837 DZ), we estimated the cross‐sectional zygosity correlations and the zygosity‐specific cross‐twin cross‐time correlations. Results: Reported bruxism appeared rather persistent over time without significant difference regarding zygosity. The overall phenotypic longitudinal correlation was 0.540 and somewhat higher in men (0.596) than in women (0.507). Pairwise trait correlations in 1990 and 2011 were higher in MZ than in DZ pairs. The cross‐twin cross‐time correlations were higher in MZ twins than in DZ twins, but less than the cross‐sectional MZ and DZ pairwise correlations. Conclusions: The higher correlation of reported sleep bruxism in the cross‐twin cross‐time analyses in MZ than in DZ pairs implies a genetic background for bruxism persistence. Also, bruxism over time in individual twins appears to be fairly persistent and somewhat higher in men than women. [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]

Aim: High body weight is a protective factor against osteoporosis, but obesity also suppresses bone metabolism and whole‐body insulin sensitivity. However, the impact of body weight and regular training on bone marrow (BM) glucose metabolism is unclear. We studied the effects of regular exercise training on bone and BM metabolism in monozygotic twin pairs discordant for body weight. Methods: We recruited 12 monozygotic twin pairs (mean ± SD age 40.4 ± 4.5 years; body mass index 32.9 ± 7.6, mean difference between co‐twins 7.6 kg/m2; eight female pairs). Ten pairs completed the 6‐month long training intervention. We measured lumbar vertebral and femoral BM insulin‐stimulated glucose uptake (GU) using 18F‐FDG positron emission tomography, lumbar spine bone mineral density and bone turnover markers. Results: At baseline, heavier co‐twins had higher lumbar vertebral BM GU (p <.001) and lower bone turnover markers (all p <.01) compared with leaner co‐twins but there was no significant difference in femoral BM GU, or bone mineral density. Training improved whole‐body insulin sensitivity, aerobic capacity (both p <.05) and femoral BM GU (p =.008). The training response in lumbar vertebral BM GU was different between the groups (time × group, p =.02), as GU tended to decrease in heavier co‐twins (p =.06) while there was no change in leaner co‐twins. Conclusions: In this study, regular exercise training increases femoral BM GU regardless of weight and genetics. Interestingly, lumbar vertebral BM GU is higher in participants with higher body weight, and training counteracts this effect in heavier co‐twins even without reduction in weight. These data suggest that BM metabolism is altered by physical activity. [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.

The understanding of the molecular genetic contributions to smoking is largely limited to the additive effects of individual single nucleotide polymorphisms (SNPs), but the underlying genetic risk is likely to also include dominance, epistatic, and gene-environment interactions. To begin to address this complexity, we attempted to identify genetic interactions between rs16969968, the most replicated SNP associated with smoking quantity, and all SNPs and genes across the genome. Using the UK Biobank European subsample, we found one SNP, rs1892967, and two genes, PCNA and TMEM230 , that showed a significant genome-wide interaction with rs16969968 for log10 CPD and raw CPD, respectively, in a sample of 116 442 individuals who self-reported currently or previously smoking. We extended these analyses to individuals of South Asian descent and meta-analyzed the combined sample of 117 212 individuals of European and South Asian ancestry. We replicated the gene findings in a meta-analysis of five Finnish samples (N=40 140): FinHealth, FINRISK, Finnish Twin Cohort, GeneRISK, and Health-2000–2011. To our knowledge, this represents the first reliable epistatic association between single nucleotide polymorphisms for smoking behaviors and provides a novel direction for possible future functional studies related to this interaction. Furthermore, this work demonstrates the feasibility of these analyses by pooling multiple datasets across various ancestries, which may be applied to other top SNPs for smoking and/or other phenotypes. • We explored interactions between rs16969968 and all SNPs and genes genome-wide. • One SNP, rs1892967, and two genes, PCNA and TMEM230 , significantly interacted with rs16969968 for smoking quantity. • We replicated the gene findings in a meta-analysis of five Finnish samples comprising over 40, 000 individuals. • This is the first reliable epistatic association between single nucleotide polymorphisms for smoking behaviors. [ABSTRACT FROM AUTHOR]

Background: Forty percent of patients with aneurysmatic subarachnoid hemorrhage (aSAH) develop acute hydrocephalus requiring treatment with cerebrospinal fluid (CSF) drainage. CSF cell parameters are used in the diagnosis of nosocomial infections but also reflect sterile inflammation after aSAH. We aimed to study the temporal changes in CSF parameters and compare external ventricular drain (EVD)–derived and lumbar spinal drain–derived samples.We retrospectively identified consecutive patients with aSAH treated at our neurointensive care unit between January 2014 and May 2019. We mapped the temporal changes in CSF leucocyte count, erythrocyte count, cell ratio, and cell index during the first 19 days after aSAH separately for EVD-derived and spinal drain–derived samples. We compared the sample sources using a linear mixed model, controlling for repeated sampling.We included 1360 CSF samples from 197 patients in the analyses. In EVD-derived samples, the CSF leucocyte count peaked at days 4–5 after aSAH, reaching a median of 225 × 106 (interquartile range [IQR] 64–618 × 106). The cell ratio and index peaked at 8–9 days (0.90% [IQR 0.35–1.98%] and 2.71 [IQR 1.25–6.73], respectively). In spinal drain–derived samples, the leucocyte count peaked at days 6–7, reaching a median of 238 × 106 (IQR 60–396 × 106). The cell ratio and index peaked at 14–15 days (4.12% [IQR 0.63–10.61%]) and 12–13 days after aSAH (8.84 [IQR 3.73–18.84]), respectively. Compared to EVD-derived samples, the leucocyte count was significantly higher in spinal drain–derived samples at days 6–17, and the cell ratio as well as the cell index was significantly higher in spinal drain–derived samples compared to EVD samples at days 10–15.CSF cell parameters undergo dynamic temporal changes after aSAH. CSF samples from different CSF compartments are not comparable.Methods: Forty percent of patients with aneurysmatic subarachnoid hemorrhage (aSAH) develop acute hydrocephalus requiring treatment with cerebrospinal fluid (CSF) drainage. CSF cell parameters are used in the diagnosis of nosocomial infections but also reflect sterile inflammation after aSAH. We aimed to study the temporal changes in CSF parameters and compare external ventricular drain (EVD)–derived and lumbar spinal drain–derived samples.We retrospectively identified consecutive patients with aSAH treated at our neurointensive care unit between January 2014 and May 2019. We mapped the temporal changes in CSF leucocyte count, erythrocyte count, cell ratio, and cell index during the first 19 days after aSAH separately for EVD-derived and spinal drain–derived samples. We compared the sample sources using a linear mixed model, controlling for repeated sampling.We included 1360 CSF samples from 197 patients in the analyses. In EVD-derived samples, the CSF leucocyte count peaked at days 4–5 after aSAH, reaching a median of 225 × 106 (interquartile range [IQR] 64–618 × 106). The cell ratio and index peaked at 8–9 days (0.90% [IQR 0.35–1.98%] and 2.71 [IQR 1.25–6.73], respectively). In spinal drain–derived samples, the leucocyte count peaked at days 6–7, reaching a median of 238 × 106 (IQR 60–396 × 106). The cell ratio and index peaked at 14–15 days (4.12% [IQR 0.63–10.61%]) and 12–13 days after aSAH (8.84 [IQR 3.73–18.84]), respectively. Compared to EVD-derived samples, the leucocyte count was significantly higher in spinal drain–derived samples at days 6–17, and the cell ratio as well as the cell index was significantly higher in spinal drain–derived samples compared to EVD samples at days 10–15.CSF cell parameters undergo dynamic temporal changes after aSAH. CSF samples from different CSF compartments are not comparable.Results: Forty percent of patients with aneurysmatic subarachnoid hemorrhage (aSAH) develop acute hydrocephalus requiring treatment with cerebrospinal fluid (CSF) drainage. CSF cell parameters are used in the diagnosis of nosocomial infections but also reflect sterile inflammation after aSAH. We aimed to study the temporal changes in CSF parameters and compare external ventricular drain (EVD)–derived and lumbar spinal drain–derived samples.We retrospectively identified consecutive patients with aSAH treated at our neurointensive care unit between January 2014 and May 2019. We mapped the temporal changes in CSF leucocyte count, erythrocyte count, cell ratio, and cell index during the first 19 days after aSAH separately for EVD-derived and spinal drain–derived samples. We compared the sample sources using a linear mixed model, controlling for repeated sampling.We included 1360 CSF samples from 197 patients in the analyses. In EVD-derived samples, the CSF leucocyte count peaked at days 4–5 after aSAH, reaching a median of 225 × 106 (interquartile range [IQR] 64–618 × 106). The cell ratio and index peaked at 8–9 days (0.90% [IQR 0.35–1.98%] and 2.71 [IQR 1.25–6.73], respectively). In spinal drain–derived samples, the leucocyte count peaked at days 6–7, reaching a median of 238 × 106 (IQR 60–396 × 106). The cell ratio and index peaked at 14–15 days (4.12% [IQR 0.63–10.61%]) and 12–13 days after aSAH (8.84 [IQR 3.73–18.84]), respectively. Compared to EVD-derived samples, the leucocyte count was significantly higher in spinal drain–derived samples at days 6–17, and the cell ratio as well as the cell index was significantly higher in spinal drain–derived samples compared to EVD samples at days 10–15.CSF cell parameters undergo dynamic temporal changes after aSAH. CSF samples from different CSF compartments are not comparable.Conclusions: Forty percent of patients with aneurysmatic subarachnoid hemorrhage (aSAH) develop acute hydrocephalus requiring treatment with cerebrospinal fluid (CSF) drainage. CSF cell parameters are used in the diagnosis of nosocomial infections but also reflect sterile inflammation after aSAH. We aimed to study the temporal changes in CSF parameters and compare external ventricular drain (EVD)–derived and lumbar spinal drain–derived samples.We retrospectively identified consecutive patients with aSAH treated at our neurointensive care unit between January 2014 and May 2019. We mapped the temporal changes in CSF leucocyte count, erythrocyte count, cell ratio, and cell index during the first 19 days after aSAH separately for EVD-derived and spinal drain–derived samples. We compared the sample sources using a linear mixed model, controlling for repeated sampling.We included 1360 CSF samples from 197 patients in the analyses. In EVD-derived samples, the CSF leucocyte count peaked at days 4–5 after aSAH, reaching a median of 225 × 106 (interquartile range [IQR] 64–618 × 106). The cell ratio and index peaked at 8–9 days (0.90% [IQR 0.35–1.98%] and 2.71 [IQR 1.25–6.73], respectively). In spinal drain–derived samples, the leucocyte count peaked at days 6–7, reaching a median of 238 × 106 (IQR 60–396 × 106). The cell ratio and index peaked at 14–15 days (4.12% [IQR 0.63–10.61%]) and 12–13 days after aSAH (8.84 [IQR 3.73–18.84]), respectively. Compared to EVD-derived samples, the leucocyte count was significantly higher in spinal drain–derived samples at days 6–17, and the cell ratio as well as the cell index was significantly higher in spinal drain–derived samples compared to EVD samples at days 10–15.CSF cell parameters undergo dynamic temporal changes after aSAH. CSF samples from different CSF compartments are not comparable. [ABSTRACT FROM AUTHOR]