Identification of gene networks jointly associated with depressive symptoms and cardiovascular health metrics using whole blood transcriptome in the Young Finns Study.

Background: Studies have shown that cardiovascular health (CVH) is related to depression. We aimed to identify gene networks jointly associated with depressive symptoms and cardiovascular health metrics using the whole blood transcriptome. Materials and methods: We analyzed human blood transcriptomic data to identify gene co-expression networks, termed gene modules, shared by Beck's depression inventory (BDI-II) scores and cardiovascular health (CVH) metrics as markers of depression and cardiovascular health, respectively. The BDI-II scores were derived from Beck's Depression Inventory, a 21-item self-report inventory that measures the characteristics and symptoms of depression. CVH metrics were defined according to the American Heart Association criteria using seven indices: smoking, diet, physical activity, body mass index (BMI), blood pressure, total cholesterol, and fasting glucose. Joint association of the modules, identified with weighted co-expression analysis, as well as the member genes of the modules with the markers of depression and CVH were tested with multivariate analysis of variance (MANOVA). Results: We identified a gene module with 256 genes that were significantly correlated with both the BDI-II score and CVH metrics. Based on the MANOVA test results adjusted for age and sex, the module was associated with both depression and CVH markers. The three most significant member genes in the module were YOD1, RBX1, and LEPR. Genes in the module were enriched with biological pathways involved in brain diseases such as Alzheimer's, Parkinson's, and Huntington's. Conclusions: The identified gene module and its members can provide new joint biomarkers for depression and CVH.

Exploring machine learning strategies for predicting cardiovascular disease risk factors from multi-omic data.

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.

Does the effect of adolescent health behaviours on adult cardiometabolic health differ by socioeconomic background? Protocol for a population-based cohort study.

INTRODUCTION: Adolescence is a sensitive period for cardiometabolic health. Yet, it remains unknown if adolescent health behaviours, such as alcohol use, smoking, diet and physical activity, have differential effects across socioeconomic strata. Adopting a life-course perspective and a causal inference framework, we aim to assess whether the effects of adolescent health behaviours on adult cardiometabolic health differ by levels of neighbourhood deprivation, parental education and occupational class. Gaining a better understanding of these social disparities in susceptibility to health behaviours can inform policy initiatives that aim to improve population health and reduce socioeconomic inequalities in cardiometabolic health. METHODS AND ANALYSIS: We will conduct a secondary analysis of the Young Finns Study, which is a longitudinal population-based cohort study. We will use measures of health behaviours-smoking, alcohol use, fruit and vegetable consumption, and physical activity-as exposure and parental education, occupational class and neighbourhood deprivation as effect modifiers during adolescence (ages 12-18 years). Eight biomarkers of cardiometabolic health (outcomes)-waist circumference, body mass index, blood pressure, low-density lipoprotein cholesterol, apolipoprotein B, plasma glucose and insulin resistance-will be measured when participants were aged 33-40. A descriptive analysis will investigate the clustering of health behaviours. Informed by this, we will conduct a causal analysis to estimate effects of single or clustered adolescent health behaviours on cardiometabolic health conditional on socioeconomic background. This analysis will be based on a causal model implemented via a directed acyclic graph and inverse probability-weighted marginal structural models to estimate effect modification. ETHICS AND DISSEMINATION: The Young Finns study was conducted according to the guidelines of the Declaration of Helsinki, and the protocol was approved by ethics committees of University of Helsinki, Kuopio, Oulu, Tampere and Turku. We will disseminate findings at international conferences and a manuscript in an open-access peer-reviewed journal.

The association between GRACE score at admission for myocardial infarction and the incidence of sudden cardiac arrests in long-term follow-up - the MADDEC study.

Background. Sudden cardiac arrest (SCA), often also leading to sudden cardiac death (SCD), is a common complication in coronary artery disease. Despite the effort there is a lack of applicable prediction tools to identify those at high risk. We tested the association between the validated GRACE score and the incidence of SCA after myocardial infarction. Material and methods. A retrospective analysis of 1,985 patients treated for myocardial infarction (MI) between January 1st 2015 and December 31st 2018 and followed until the 31st of December of 2021. The main exposure variable was patients' GRACE score at the point of admission and main outcome variable was incident SCA after hospitalization. Their association was analyzed by subdistribution hazard (SDH) model analysis. The secondary endpoints included SCA in patients with no indication to implantable cardioverter-defibrillator (ICD) device and incident SCD. Results. A total of 1985 patients were treated for MI. Mean GRACE score at baseline was 118.7 (SD 32.0). During a median follow-up time of 5.3 years (IQR 3.8-6.1 years) 78 SCA events and 52 SCDs occurred. In unadjusted analyses one SD increase in GRACE score associated with over 50% higher risk of SCA (SDH 1.55, 95% CI 1.29-1.85, p < 0.0001) and over 40% higher risk for SCD (1.42, 1.12-1.79, p = 0.0033). The associations between SCA and GRACE remained statistically significant even with patients without indication for ICD device (1.57, 1.30-1.90, p < 0.0001) as well as when adjusting with patients LVEF and omitting the age from the GRACE score to better represent the severity of the cardiac event. The association of GRACE and SCD turned statistically insignificant when adjusting with LVEF. Conclusions. GRACE score measured at admission for MI associates with long-term risk for SCA.

What is already known about this subject?Nearly 50% of cardiac mortality is caused by sudden cardiac death, often due to sudden cardiac arrest.Despite the effort, there is a lack of applicable prediction tools to identify those at high risk.What does this study add?This study shows that GRACE score measured at the point of admission for myocardial infarction can be used to evaluate patients' risk for sudden cardiac arrest in a long-term follow-up.How might this impact on clinical practice?Based on our findings, the GRACE score at the point of admission could significantly affect the patients' need for an ICD device after hospitalization for MI and should be considered as a contributing factor when evaluating the patients' follow-up care.

Association of Ideal Cardiovascular Health in Youth with Cancer Risk in Adulthood: Cardiovascular Risk in Young Finns Study.

BACKGROUND: Lifestyle factors may affect cancer risk. This study aimed to identify whether American Heart Association (AHA) Ideal Cardiovascular Health (ICH) score and its individual variables in youth associate with subsequent cancer incidence. METHODS: Study comprised of participants of the Cardiovascular Risk in Young Finns Study free of cancer at analysis baseline in 1986 (n=1873). Baseline age was 12-24 years and the follow-up occurred between 1986-2018. RESULTS: Among 1873 participants (mean age 17.3±4.1 years; 53.4% females at baseline), 72 incident cancer cases occurred during the follow-up (mean follow-up time 31.4±3.4 years). Baseline ICH score was not associated with future cancer risk (HR 0.96, 95% CI 0.78-1.12 per 1-point increment). Of individual ICH score variables, ideal physical activity (PA) was inversely associated with cancer incidence (age- and sex-adjusted HR 0.45 (0.23-0.88) per 1-category change [nonideal/ideal]), and remained significant in multivariable-adjusted model including also BMI, smoking, diet and socioeconomic status. A continuous physical activity index at ages 9-24 years and moderate to vigorous physical activity in youth were also related to decreased cancer incidence (p<0.05). BMI, smoking, diet, total cholesterol, glucose and blood pressure were not related to cancer risk. Of the dietary components, meat consumption was associated with cancer incidence (p=0.023). CONCLUSIONS: These findings indicate that higher PA levels in youth associate with a reduced subsequent cancer incidence whereas AHA´s ICH score in youth does not. IMPACT: This finding supports the efforts in promoting healthy lifestyle and encourages in physical activity during childhood yielding in subsequent healthier life.

Polymorphisms in ERBB4 and TACR1 associated with dry mouth in clozapine-treated patients.

BACKGROUND: Sialorrhea is a common and uncomfortable adverse effect of clozapine, and its severity varies between patients. The aim of the study was to select broadly genes related to the regulation of salivation and study associations between sialorrhea and dry mouth and polymorphisms in the selected genes. METHODS: The study population consists of 237 clozapine-treated patients, of which 172 were genotyped. Associations between sialorrhea and dry mouth with age, sex, BMI, smoking, clozapine dose, clozapine and norclozapine serum levels, and other comedication were studied. Genetic associations were analyzed with linear and logistic regression models explaining sialorrhea and dry mouth with each SNP added separately to the model as coefficients. RESULTS: Clozapine dose, clozapine or norclozapine concentration and their ratio were not associated with sialorrhea or dryness of mouth. Valproate use (p = 0.013) and use of other antipsychotics (p = 0.015) combined with clozapine were associated with excessive salivation. No associations were found between studied polymorphisms and sialorrhea. In analyses explaining dry mouth with logistic regression with age and sex as coefficients, two proxy-SNPs were associated with dry mouth: epidermal growth factor receptor 4 (ERBB4) rs3942465 (adjusted p = 0.025) and tachykinin receptor 1 (TACR1) rs58933792 (adjusted p = 0.029). CONCLUSION: Use of valproate or antipsychotic polypharmacy may increase the risk of sialorrhea. Genetic variations in ERBB4 and TACR1 might contribute to experienced dryness of mouth among patients treated with clozapine.

NMR metabolomic modeling of age and lifespan: A multicohort analysis.

Metabolomic age models have been proposed for the study of biological aging, however, they have not been widely validated. We aimed to assess the performance of newly developed and existing nuclear magnetic resonance spectroscopy (NMR) metabolomic age models for prediction of chronological age (CA), mortality, and age-related disease. Ninety-eight metabolic variables were measured in blood from nine UK and Finnish cohort studies (N ≈31,000 individuals, age range 24-86 years). We used nonlinear and penalized regression to model CA and time to all-cause mortality. We examined associations of four new and two previously published metabolomic age models, with aging risk factors and phenotypes. Within the UK Biobank (N ≈102,000), we tested prediction of CA, incident disease (cardiovascular disease (CVD), type-2 diabetes mellitus, cancer, dementia, and chronic obstructive pulmonary disease), and all-cause mortality. Seven-fold cross-validated Pearson's r between metabolomic age models and CA ranged between 0.47 and 0.65 in the training cohort set (mean absolute error: 8-9 years). Metabolomic age models, adjusted for CA, were associated with C-reactive protein, and inversely associated with glomerular filtration rate. Positively associated risk factors included obesity, diabetes, smoking, and physical inactivity. In UK Biobank, correlations of metabolomic age with CA were modest (r = 0.29-0.33), yet all metabolomic model scores predicted mortality (hazard ratios of 1.01 to 1.06/metabolomic age year) and CVD, after adjustment for CA. While metabolomic age models were only moderately associated with CA in an independent population, they provided additional prediction of morbidity and mortality over CA itself, suggesting their wider applicability.

Prostate Cancer Screening With PSA, Kallikrein Panel, and MRI: The ProScreen Randomized Trial.

Importance: Prostate-specific antigen (PSA) screening has potential to reduce prostate cancer mortality but frequently detects prostate cancer that is not clinically important. Objective: To describe rates of low-grade (grade group 1) and high-grade (grade groups 2-5) prostate cancer identified among men invited to participate in a prostate cancer screening protocol consisting of a PSA test, a 4-kallikrein panel, and a magnetic resonance imaging (MRI) scan. Design, Setting, and Participants: The ProScreen trial is a clinical trial conducted in Helsinki and Tampere, Finland, that randomized 61 193 men aged 50 through 63 years who were free of prostate cancer in a 1:3 ratio to either be invited or not be invited to undergo screening for prostate cancer between February 2018 and July 2020. Interventions: Participating men randomized to the intervention underwent PSA testing. Those with a PSA level of 3.0 ng/mL or higher underwent additional testing for high-grade prostate cancer with a 4-kallikrein panel risk score. Those with a kallikrein panel score of 7.5% or higher underwent an MRI of the prostate gland, followed by targeted biopsies for those with abnormal prostate gland MRI findings. Final data collection occurred through June 31, 2023. Main Outcomes and Measures: In descriptive exploratory analyses, the cumulative incidence of low-grade and high-grade prostate cancer after the first screening round were compared between the group invited to undergo prostate cancer screening and the control group. Results: Of 60 745 eligible men (mean [SD] age, 57.2 [4.0] years), 15 201 were randomized to be invited and 45 544 were randomized not to be invited to undergo prostate cancer screening. Of 15 201 eligible males invited to undergo screening, 7744 (51%) participated. Among them, 32 low-grade prostate cancers (cumulative incidence, 0.41%) and 128 high-grade prostate cancers (cumulative incidence, 1.65%) were detected, with 1 cancer grade group result missing. Among the 7457 invited men (49%) who refused participation, 7 low-grade prostate cancers (cumulative incidence, 0.1%) and 44 high-grade prostate cancers (cumulative incidence, 0.6%) were detected, with 7 cancer grade groups missing. For the entire invited screening group, 39 low-grade prostate cancers (cumulative incidence, 0.26%) and 172 high-grade prostate cancers (cumulative incidence, 1.13%) were detected. During a median follow-up of 3.2 years, in the group not invited to undergo screening, 65 low-grade prostate cancers (cumulative incidence, 0.14%) and 282 high-grade prostate cancers (cumulative incidence, 0.62%) were detected. The risk difference for the entire group randomized to the screening invitation vs the control group was 0.11% (95% CI, 0.03%-0.20%) for low-grade and 0.51% (95% CI, 0.33%-0.70%) for high-grade cancer. Conclusions and Relevance: In this preliminary descriptive report from an ongoing randomized clinical trial, 1 additional high-grade cancer per 196 men and 1 low-grade cancer per 909 men were detected among those randomized to be invited to undergo a single prostate cancer screening intervention compared with those not invited to undergo screening. These preliminary findings from a single round of screening should be interpreted cautiously, pending results of the study's primary mortality outcome. Trial Registration: ClinicalTrials.gov Identifier: NCT03423303.

Accumulation of Lewy-Related Pathology Starts in Middle Age: The Tampere Sudden Death Study.

When effective treatments against neurodegenerative diseases become a reality, it will be important to know the age these pathologies begin to develop. We investigated alpha-synuclein pathology in brain tissue of the Tampere Sudden Death Study-unselected forensic autopsies on individuals living outside hospital institutions in Finland. Of 562 (16-95 years) participants, 42 were positive for Lewy-related pathology (LRP). The youngest LRP case was aged 54 years, and the frequency of LRP in individuals aged ≥50 years was 9%. This forensic autopsy study indicates LRP starts already in middle age and is more common than expected in the ≥50 years-of-age non-hospitalized population. ANN NEUROL 2024;95:843-848.

Association of childhood socioeconomic status with adulthood maximal exercise blood pressure: the Cardiovascular Risk in Young Finns Study.

PURPOSE: Socioeconomic status has been related to resting blood pressure (BP) levels at different stages of life. However, the association of childhood socioeconomic status (SES) and adulthood exercise BP is largely unknown. Therefore, we studied the association of childhood SES with adulthood maximal exercise BP. MATERIALS AND METHODS: This investigation consisted of 373 individuals (53% women) participating in the Cardiovascular Risk in Young Finns Study who had data concerning family SES in childhood (baseline in 1980, at age of 6-18 years) and exercise BP response data in adulthood (follow-up in adulthood in 27-29 years since baseline). A maximal cardiopulmonary exercise test with BP measurements was performed by participants, and peak exercise BP was measured. RESULTS: In stepwise multivariable analysis including childhood risk factors and lifestyle factors (body mass index, systolic BP, low-density lipoprotein cholesterol, high-density lipoprotein cholesterol, triglycerides, insulin, fruit consumption, vegetable consumption, and physical activity), lower family SES in childhood was associated with higher maximal exercise BP in adulthood (ß value ± SE, 1.63 ± 0.77, p = 0.035). The association remained significant after further adjustment with participants SES in adulthood (ß value ± SE, 1.68 ± 0.65, p = 0.011) and after further adjustment with adulthood body-mass index, systolic BP, maximal exercise capacity, and peak heart rate in exercise (ß value ± SE, 1.25 ± 0.56, p = 0.027). CONCLUSIONS: These findings suggest that lower childhood family SES is associated with higher maximal exercise BP in adulthood.

Limited data are available about the association of childhood socioeconomic status and adulthood exercise blood pressure.We prospectively examined whether childhood socioeconomic status is associated with adulthood exercise blood pressure in 373 participants aged 6­18 years at baseline (1980) from the longitudinal Cardiovascular Risk in Young Finns cohort study.In multivariable analysis, including childhood cardiovascular risk factors and lifestyle factors, lower family socioeconomic status in childhood was associated with higher maximal exercise blood pressure in adulthood.The association remained significant after further adjustment with participants socioeconomic status in adulthood and also after further adjustment with adulthood body mass index, systolic blood pressure, maximal exercise capacity and peak heart rate in exercise.Low childhood socioeconomic status predicted also higher risk of exaggerated exercise blood pressure response in adulthood, although this finding was diluted to non-significant after adjustment with adulthood body mass index and systolic blood pressure.These findings suggest that lower childhood family socioeconomic status is associated with higher maximal exercise blood pressure in adulthood.

Gene expression networks regulated by human personality.

Genome-wide association studies of human personality have been carried out, but transcription of the whole genome has not been studied in relation to personality in humans. We collected genome-wide expression profiles of adults to characterize the regulation of expression and function in genes related to human personality. We devised an innovative multi-omic approach to network analysis to identify the key control elements and interactions in multi-modular networks. We identified sets of transcribed genes that were co-expressed in specific brain regions with genes known to be associated with personality. Then we identified the minimum networks for the co-localized genes using bioinformatic resources. Subjects were 459 adults from the Young Finns Study who completed the Temperament and Character Inventory and provided peripheral blood for genomic and transcriptomic analysis. We identified an extrinsic network of 45 regulatory genes from seed genes in brain regions involved in self-regulation of emotional reactivity to extracellular stimuli (e.g., self-regulation of anxiety) and an intrinsic network of 43 regulatory genes from seed genes in brain regions involved in self-regulation of interpretations of meaning (e.g., production of concepts and language). We discovered that interactions between the two networks were coordinated by a control hub of 3 miRNAs and 3 protein-coding genes shared by both. Interactions of the control hub with proteins and ncRNAs identified more than 100 genes that overlap directly with known personality-related genes and more than another 4000 genes that interact indirectly. We conclude that the six-gene hub is the crux of an integrative network that orchestrates information-transfer throughout a multi-modular system of over 4000 genes enriched in liquid-liquid-phase-separation (LLPS)-related RNAs, diverse transcription factors, and hominid-specific miRNAs and lncRNAs. Gene expression networks associated with human personality regulate neuronal plasticity, epigenesis, and adaptive functioning by the interactions of salience and meaning in self-awareness.

Effect of weight on depression using multiple genetic instruments.

A striking global health development over the past few decades has been the increasing prevalence of overweight and obesity. At the same time, depression has become increasingly common in almost all high-income countries. We investigated whether body weight, measured by body mass index (BMI), has a causal effect on depression symptoms in Finland. Using data drawn from the Cardiovascular Risk in Young Finns Study (N = 1,523, mean age 41.9, SD 5), we used linear regression to establish the relationship between BMI and depression symptoms measured by 21-item Beck's Depression Inventory. To identify causal relationships, we used the Mendelian randomization (MR) method with weighted sums of genetic markers (single nucleotide polymorphisms, SNPs) as instruments for BMI. We employ instruments (polygenic risk scores, PGSs) with varying number of SNPs that are associated with BMI to evaluate the sensitivity of our results to instrument strength. Based on linear regressions, higher BMI was associated with a higher prevalence of depression symptoms among females (b = 0.238, p = 0.000) and males (b = 0.117, p = 0.019). However, the MR results imply that the positive link applies only to females (b = 0.302, p = 0.007) but not to males (b = -0.070, p = 0.520). Poor instrument strength may explain why many previous studies that have utilized genetic instruments have been unable to identify a statistically significant link between BMI and depression-related traits. Although the number of genetic markers in the instrument had only a minor effect on the point estimates, the standard errors were much smaller when more powerful instruments were employed.

Sex differences in coronary atherosclerosis during the pre- and postmenopausal period: The Tampere Sudden Death Study.

BACKGROUND AND AIMS: Women are believed to be protected from coronary heart disease (CHD) by the effects of estrogen but detailed studies on the vessel wall level are missing. We aimed to measure sex differences in atherosclerosis during the premenopausal and postmenopausal periods directly at the coronary arteries. METHODS: We analyzed statistics for sex differences in CHD mortality in Finland in 2020. Coronary atherosclerosis was measured using computer-assisted morphometry in 10-year age groups of 185 white Caucasian women and 515 men from the Tampere Sudden Death Study. RESULTS: CHD mortality was rare in both women and men before 50 years of age. After 50 years of age, male mortality increased rapidly, with women reaching equal levels in the oldest age groups. In the autopsy series, there were no differences in fatty streak, fibrotic or calcified plaque areas, nor in the plaque area or stenosis percentage in coronary arteries between premenopausal women and men in the same age group. The plaque area remained 25 % smaller in both coronaries in postmenopausal women aged 51-70 years compared to men. In the oldest postmenopausal group (≥70 years), plaque area reached the level of men. In the postmenopausal period, coronary stenosis in the left anterior descending (LAD) artery remained lower among women. CONCLUSION: We did not detect any major sex-difference in coronary atherosclerosis in the premenopausal period when women are considered to be protected from CHD. However, in line with CHD mortality statistics, postmenopausal women showed a slower speed of coronary atherosclerosis development compared to men.

Metabolic syndrome and epigenetic aging: a twin study.

BACKGROUND: Metabolic syndrome (MetS) is associated with premature aging, but whether this association is driven by genetic or lifestyle factors remains unclear. METHODS: Two independent discovery cohorts, consisting of twins and unrelated individuals, were examined (N = 268, aged 23-69 years). The findings were replicated in two cohorts from the same base population. One consisted of unrelated individuals (N = 1 564), and the other of twins (N = 293). Participants' epigenetic age, estimated using blood DNA methylation data, was determined using the epigenetic clocks GrimAge and DunedinPACE. The individual-level linear regression models for investigating the associations of MetS and its components with epigenetic aging were followed by within-twin-pair analyses using fixed-effects regression models to account for genetic factors. RESULTS: In individual-level analyses, GrimAge age acceleration was higher among participants with MetS (N = 56) compared to participants without MetS (N = 212) (mean 2.078 [95% CI = 0.996,3.160] years vs. -0.549 [-1.053,-0.045] years, between-group p = 3.5E-5). Likewise, the DunedinPACE estimate was higher among the participants with MetS compared to the participants without MetS (1.032 [1.002,1.063] years/calendar year vs. 0.911 [0.896,0.927] years/calendar year, p = 4.8E-11). An adverse profile in terms of specific MetS components was associated with accelerated aging. However, adjustments for lifestyle attenuated these associations; nevertheless, for DunedinPACE, they remained statistically significant. The within-twin-pair analyses suggested that genetics explains these associations fully for GrimAge and partly for DunedinPACE. The replication analyses provided additional evidence that the association between MetS components and accelerated aging is independent of the lifestyle factors considered in this study, however, suggesting that genetics is a significant confounder in this association. CONCLUSIONS: The results of this study suggests that MetS is associated with accelerated epigenetic aging, independent of physical activity, smoking or alcohol consumption, and that the association may be explained by genetics.

Polygenic risk for depression predicting temperament trajectories over 15 years - A general population study.

BACKGROUND: A great number of case-control and population-based studies have shown that depression patients differ from healthy controls in their temperament traits. We investigated whether polygenic risk for depression predicts trajectories of temperament traits from early adulthood to middle age. METHODS: Participants came from the population-based Young Finns Study (n = 2212). The calculation for Polygenic risk for depression (PRS) was based on the most recent genome-wide association study. Temperament traits of Harm Avoidance, Novelty Seeking, Reward Dependence, and Persistence were assessed with the Temperament and Character Inventory in 1997, 2001, 2007, and 2012 (participants being 24-50-year-olds). As covariates, we used depressive symptoms as assessed by a modified version of the Beck Depression Inventory, psychosocial family environment from parent-filled questionnaires, and socioeconomic factors from adulthood. RESULTS: High PRS predicted higher Persistence from early adulthood to middle age (p = 0.003) when controlling for depressive symptoms, psychosocial family environment, and socioeconomic factors. PRS did not predict trajectories of Novelty Seeking (p = 0.063-0.416 in different models) or Reward Dependence (p = 0.531-0.736). The results remained unaffected when participants with diagnosed affective disorders were excluded. Additionally, we found an interaction between PRS and depressive symptoms when predicting the Harm Avoidance subscale Anticipatory Worry, indicating that the association of Anticipatory Worry with depressive symptoms is stronger in individuals with higher (vs. lower) PRS. LIMITATIONS: There was some attrition due to the long follow-up. CONCLUSIONS: High polygenic risk for major depression may predict differences in temperament trajectories among those who have not developed any severe affective disorders.

Trajectories of cardiovascular risk predict pregnancy outcomes: The Bogalusa Heart Study and the Cardiovascular Risk in Young Finns Study.

BACKGROUND: Life course patterns of change in risk-trajectories-affect health. OBJECTIVES: To examine how trajectories of cardiovascular risk factors are associated with pregnancy and birth outcomes. METHODS: Data from two cohort studies participating in the International Childhood Cardiovascular Consortium-The Bogalusa Heart Study (BHS; started in 1973, N = 903 for this analysis) and the Cardiovascular Risk in Young Finns Study (YFS; started in 1980, N = 499) were used. Both followed children into adulthood and measured cardiovascular risk factors, including body mass index (BMI), systolic and diastolic blood pressure (SBP/DBP), total, lipoprotein (LDL)- and high density lipoprotein (HDL)-cholesterol and serum triglycerides. Discrete mixture modelling was used to divide each cohort into distinct trajectories according to these risk factors from childhood to early adulthood, and these groups were then used to predict pregnancy outcomes including small for gestational age (SGA; <10th study-specific percentile of gestational age by sex), preterm birth (PTB; <37 weeks' gestation), hypertensive disorders of pregnancy (HDP) and gestational diabetes mellitus (GDM), with control for age at baseline and at first birth, parity, socioeconomic status, BMI and smoking. RESULTS: The models created more trajectories for BMI, SBP and HDL-cholesterol in the YFS than in BHS, for which three classes generally seemed to be sufficient to represent the groups in the population across risk factors. In BHS, the association between the higher and flatter DBP trajectory and PTB was aRR 1.77, 95% confidence interval [CI] 1.06, 2.96. In BHS the association between consistent total cholesterol and PTB was aRR 2.16, 95% CI 1.22, 3.85 and in YFS the association between elevated high trajectory and PTB was aRR 3.35, 95% CI 1.28, 8.79. Elevated-increasing SBP was associated with a higher risk of GH in BHS and increasing or persistent-obese BMI trajectories were associated with GDM in both cohorts (BHS: aRR 3.51, 95% CI 1.95, 6.30; YFS: aRR 2.61, 95% CI 0.96, 7.08). CONCLUSIONS: Trajectories of cardiovascular risk, particularly those that represent a consistent or more rapid worsening of cardiovascular health, are associated with a higher risk of pregnancy complications.

Clinical and biochemical associations of urinary metabolites: quantitative epidemiological approach on renal-cardiometabolic biomarkers.

BACKGROUND: Urinary metabolomics has demonstrated considerable potential to assess kidney function and its metabolic corollaries in health and disease. However, applications in epidemiology remain sparse due to technical challenges. METHODS: We added 17 metabolites to an open-access urinary nuclear magnetic resonance metabolomics platform, extending the panel to 61 metabolites (n = 994). We also introduced automated quantification for 11 metabolites, extending the panel to 12 metabolites (+creatinine). Epidemiological associations between these 12 metabolites and 49 clinical measures were studied in three independent cohorts (up to 5989 participants). Detailed regression analyses with various confounding factors are presented for body mass index (BMI) and smoking. RESULTS: Sex-specific population reference concentrations and distributions are provided for 61 urinary metabolites (419 men and 575 women), together with methodological intra-assay metabolite variations as well as the biological intra-individual and epidemiological population variations. For the 12 metabolites, 362 associations were found. These are mostly novel and reflect potential molecular proxies to estimate kidney function, as the associations cannot be simply explained by estimated glomerular filtration rate. Unspecific renal excretion results in leakage of amino acids (and glucose) to urine in all individuals. Seven urinary metabolites associated with smoking, providing questionnaire-independent proxy measures of smoking status in epidemiological studies. Common confounders did not affect metabolite associations with smoking, but insulin had a clear effect on most associations with BMI, including strong effects on 2-hydroxyisobutyrate, valine, alanine, trigonelline and hippurate. CONCLUSIONS: Urinary metabolomics provides new insight on kidney function and related biomarkers on the renal-cardiometabolic system, supporting large-scale applications in epidemiology.

Polygenic risk for schizophrenia, social dispositions, and pace of epigenetic aging: Results from the Young Finns Study.

Schizophrenia is often regarded as a disorder of premature aging. We investigated (a) whether polygenic risk for schizophrenia (PRSsch ) relates to pace of epigenetic aging and (b) whether personal dispositions toward active and emotionally close relationships protect against accelerated epigenetic aging in individuals with high PRSsch . The sample came from the population-based Young Finns Study (n = 1348). Epigenetic aging was measured with DNA methylation aging algorithms such as AgeAccelHannum , EEAAHannum , IEAAHannum , IEAAHorvath , AgeAccelHorvath , AgeAccelPheno , AgeAccelGrim , and DunedinPACE. A PRSsch was calculated using summary statistics from the most comprehensive genome-wide association study of schizophrenia to date. Social dispositions were assessed in terms of extraversion, sociability, reward dependence, cooperativeness, and attachment security. We found that PRSsch did not have a statistically significant effect on any studied indicator of epigenetic aging. Instead, PRSsch had a significant interaction with reward dependence (p = 0.001-0.004), cooperation (p = 0.009-0.020), extraversion (p = 0.019-0.041), sociability (p = 0.003-0.016), and attachment security (p = 0.007-0.014) in predicting AgeAccelHannum , EEAAHannum , or IEAAHannum . Specifically, participants with high PRSsch appeared to display accelerated epigenetic aging at higher (vs. lower) levels of extraversion, sociability, attachment security, reward dependence, and cooperativeness. A rather opposite pattern was evident for those with low PRSsch . No such interactions were evident when predicting the other indicators of epigenetic aging. In conclusion, against our hypothesis, frequent social interactions may relate to accelerated epigenetic aging in individuals at risk for psychosis. We speculate that this may be explained by social-cognitive impairments (perceiving social situations as overwhelming or excessively arousing) or ending up in less supportive or deviant social groups.

Genetic risk scores associated with temperament clusters in Finnish depression patients.

OBJECTIVE: Cloninger's temperament dimensions have been studied widely in relation to genetics. In this study, we examined Cloninger's temperament dimensions grouped with cluster analyses and their association with single nucleotide polymorphisms (SNPs). This study included 212 genotyped Finnish patients from the Ostrobothnia Depression Study. METHODS: The temperament clusters were analysed at baseline and at six weeks from the beginning of the depression intervention study. We selected depression-related catecholamine and serotonin genes based on a literature search, and 59 SNPs from ten different genes were analysed. The associations of single SNPs with temperament clusters were studied. Using the selected genes, genetic risk score (GRS) analyses were conducted considering appropriate confounding factors. RESULTS: No single SNP had a significant association with the temperament clusters. Associations between GRSs and temperament clusters were observed in multivariate models that were significant after permutation analyses. Two SNPs from the DRD3 gene, two SNPs from the SLC6A2 gene, one SNP from the SLC6A4 gene, and one SNP from the HTR2A gene associated with the HHA/LRD/LP (high harm avoidance, low reward dependence, low persistence) cluster at baseline. Two SNPs from the HTR2A gene were associated with the HHA/LRD/LP cluster at six weeks. Two SNPs from the HTR2A gene and two SNPs from the COMT gene were associated with the HP (high persistence) cluster at six weeks. CONCLUSION: GRSs seem to associate with an individual's temperament profile, which can be observed in the clusters used. Further research needs to be conducted on these types of clusters and their clinical applicability.