Change in cognitive performance during seven-year follow-up in midlife is associated with sex, age, and education - The Cardiovascular Risk in Young Finns Study.

OBJECTIVE: Sex, age, and education are associated with the level of cognitive performance. We investigated whether these factors modulate the change in cognitive performance in midlife by leveraging the longitudinal data from the Cardiovascular Risk in Young Finns Study (YFS). METHODS: Participants of the YFS cohort performed a computer-based Cambridge Neuropsychological Test Automated Battery (CANTAB) in 2011 and 2018 (n = 1671, age 41-56 years in 2018). Overall cognitive performance and domains representing learning and memory, working memory, reaction time, and information processing were extracted by common principal component analysis from the longitudinal cognitive data. Linear models adjusted for baseline cognitive performance were used to study the association of sex, age, and education with changes in overall cognitive performance and in the cognitive domains. RESULTS: Cognitive performance decreased in all domains (overall cognition -0.56 SD, p < 0.001; working memory -0.81 SD, p < 0.001; learning and memory -0.70 SD, p < 0.001; reaction time -0.06 SD, p = 0.019; information processing -0.03 SD, p = 0.016). The decrease in working memory and information processing was greater in females compared to males. Cognitive performance decreased more in older participants in all domains. Education alleviated the decrease in cognitive performance in all domains except reaction time. The beneficial effect of education was greater for males. CONCLUSIONS: This study describes the natural course of aging-related changes in cognitive performance in midlife, the critical time window for early prevention of clinical cognitive decline. These findings provide a reference for studies focusing on determinants of pathological cognitive decline deviating from normal changes in cognitive performance.

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.

Personalized Intervention Based on Early Detection of Atherosclerosis: JACC State-of-the-Art Review.

Cardiovascular disease (CVD) remains the leading cause of morbidity and mortality worldwide and challenges the capacity of health care systems globally. Atherosclerosis is the underlying pathophysiological entity in two-thirds of patients with CVD. When considering that atherosclerosis develops over decades, there is potentially great opportunity for prevention of associated events such as myocardial infarction and stroke. Subclinical atherosclerosis has been identified in its early stages in young individuals; however, there is no consensus on how to prevent progression to symptomatic disease. Given the growing burden of CVD, a paradigm shift is required-moving from late management of atherosclerotic CVD to earlier detection during the subclinical phase with the goal of potential cure or prevention of events. Studies must focus on how precision medicine using imaging and circulating biomarkers may identify atherosclerosis earlier and determine whether such a paradigm shift would lead to overall cost savings for global health.

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.

Determinants of echocardiographic epicardial adipose tissue in a general middle-aged population - The Cardiovascular Risk in Young Finns Study.

Epicardial adipose tissue (EAT) is the cardiac visceral fat depot proposed to play a role in the etiology of various cardiovascular disease outcomes. Little is known about EAT determinants in a general population. We examined cardiometabolic, dietary, lifestyle and socioeconomic determinants of echocardiograpghically measured EAT in early adulthood. Data on cardiometabolic, dietary, lifestyle and socioeconomic factors were collected from participants of the Cardiovascular Risk in Young Finns Study (YFS; N = 1667; age 34-49 years). EAT thickness was measured from parasternal long axis echocardiograms. Multivariable regression analysis was used to study potential EAT determinants. Possible effect modification of sex was addressed. Mean EAT thickness was 4.07 mm (95% CI 4.00-4.17). Multivariable analysis [ß indicating percentage of change in EAT(mm) per one unit increase in determinant variable] indicated female sex (ß = 11.0, P < 0.0001), type 2 diabetes (ß = 14.0, P = 0.02), waist circumference (cm) (ß = 0.38, P < 0.0001), systolic blood pressure (mmHg) (ß = 0.18, P = 0.02) and red meat intake (g/day) (ß = 0.02, P = 0.05) as EAT determinants. Sex-specific analysis revealed age (year) (ß = 0.59, P = 0.01), alcohol intake (drinks/day) (ß = 4.69, P = 0.006), heavy drinking (yes/no) (ß = 30.4, P < 0.0001) as EAT determinants in women and fruit intake (g/day) (ß = -1.0, P = 0.04) in men. In the YFS cohort, waist circumference, systolic blood pressure and red meat intake were directly associated with EAT among all participants. In women, age, alcohol intake, heavy drinking and type 2 diabetes associated directly with EAT, while an inverse association was observed between fruit intake and EAT in men.

Tracking of serum lipid levels from childhood to adulthood: Systematic review and meta-analysis.

BACKGROUND AND AIMS: The utility of lipid screening in pediatric settings for preventing adult atherosclerotic cardiovascular diseases partly depends on the lifelong tracking of lipid levels. This systematic review aimed to quantify the tracking of lipid levels from childhood and adolescence to adulthood. METHODS: We systematically searched MEDLINE, Embase, Web of Science, and Google Scholar in March 2022. The protocol was registered with the International Prospective Register of Systematic Reviews (PROSPERO; ID: CRD42020208859). We included cohort studies that measured tracking of lipids from childhood or adolescence (<18 years) to adulthood (≥18) with correlation or tracking coefficients. We estimated pooled correlation and tracking coefficients using random-effects meta-analysis. Risk of bias was assessed with a review-specific tool. RESULTS: Thirty-three studies of 19 cohorts (11,020 participants) were included. The degree of tracking from childhood and adolescence to adulthood differed among lipids. Tracking was observed for low-density lipoprotein cholesterol (pooled r = 0.55-0.65), total cholesterol (pooled r = 0.51-0.65), high-density lipoprotein cholesterol (pooled r = 0.46-0.57), and triglycerides (pooled r = 0.32-0.40). Only one study included tracking of non-high-density lipoprotein cholesterol (r = 0.42-0.59). Substantial heterogeneity was observed. Study risk of bias was moderate, mostly due to insufficient reporting and singular measurements at baseline and follow-up. CONCLUSIONS: Early-life lipid measurements are important for predicting adult levels. However, further research is needed to understand the tracking of non-high-density lipoprotein cholesterol and the stability of risk classification over time, which may further inform pediatric lipid screening and assessment strategies.

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.

Subclinical atherosclerosis in young adults predicting cardiovascular disease: The Cardiovascular Risk in Young Finns Study.

BACKGROUND AND AIMS: Atherosclerosis is accompanied by pre-clinical vascular changes that can be detected using ultrasound imaging. We examined the value of such pre-clinical features in identifying young adults who are at risk of developing atherosclerotic cardiovascular disease (ASCVD). METHODS: A total of 2641 individuals free of ASCVD were examined at the mean age of 32 years (range 24-45 years) for carotid artery intima-media thickness (IMT) and carotid plaques, carotid artery elasticity, and brachial artery flow-mediated endothelium-dependent vasodilation (FMD). The average follow-up time to event/censoring was 16 years (range 1-17 years). RESULTS: Sixty-seven individuals developed ASCVD (incidence 2.5%). The lowest incidence (1.1%) was observed among those who were estimated of having low risk according to the SCORE2 risk algorithm (<2.5% 10-year risk) and who did not have plaque or high IMT (upper decile). The highest incidence (11.0%) was among those who were estimated of having a high risk (≥2.5% 10-year risk) and had positive ultrasound scan for carotid plaque and/or high IMT (upper decile). Carotid plaque and high IMT remained independently associated with higher risk in multivariate models. The distributions of carotid elasticity indices and brachial FMD did not differ between cases and non-cases. CONCLUSIONS: Screening for carotid plaque and high IMT in young adults may help identify individuals at high risk for future ASCVD.

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.

Non-High-Density Lipoprotein Cholesterol Levels From Childhood to Adulthood and Cardiovascular Disease Events.

Importance: Elevated non-high-density lipoprotein cholesterol (non-HDL-C; a recommended measure of lipid-related cardiovascular risk) is common in children and increases risk of adult cardiovascular disease (CVD). Whether resolution of elevated childhood non-HDL-C levels by adulthood is associated with reduced risk of clinical CVD events is unknown. Objective: To examine the associations of non-HDL-C status between childhood and adulthood with incident CVD events. Design, Setting, and Participants: Individual participant data from 6 prospective cohorts of children (mean age at baseline, 10.7 years) in the US and Finland. Recruitment took place between 1970 and 1996, with a final follow-up in 2019. Exposures: Child (age 3-19 years) and adult (age 20-40 years) non-HDL-C age- and sex-specific z scores and categories according to clinical guideline-recommended cutoffs for dyslipidemia. Main Outcomes and Measures: Incident fatal and nonfatal CVD events adjudicated by medical records. Results: Over a mean length of follow-up of 8.9 years after age 40 years, 147 CVD events occurred among 5121 participants (60% women; 15% Black). Both childhood and adult non-HDL-C levels were associated with increased risk of CVD events (hazard ratio [HR], 1.42 [95% CI, 1.18-1.70] and HR, 1.50 [95% CI, 1.26-1.78] for a 1-unit increase in z score, respectively), but the association for childhood non-HDL-C was reduced when adjusted for adult levels (HR, 1.12 [95% CI, 0.89-1.41]). A complementary analysis showed that both childhood non-HDL-C levels and the change between childhood and adulthood were independently associated with the outcome, suggesting that from a preventive perspective, both childhood non-HDL-C levels and the change into adulthood are informative. Compared with those whose non-HDL-C levels remained within the guideline-recommended range in childhood and adulthood, participants who had incident non-HDL-C dyslipidemia from childhood to adulthood and those with persistent dyslipidemia had increased risks of CVD events (HR, 2.17 [95% CI, 1.00-4.69] and HR, 5.17 [95% CI, 2.80-9.56], respectively). Individuals who had dyslipidemic non-HDL-C in childhood but whose non-HDL-C levels were within the guideline-recommended range in adulthood did not have a significantly increased risk (HR, 1.13 [95% CI, 0.50-2.56]). Conclusions and Relevance: Individuals with persistent non-HDL-C dyslipidemia from childhood to adulthood had an increased risk of CVD events, but those in whom dyslipidemic non-HDL-C levels resolve by adulthood have similar risk to individuals who were never dyslipidemic. These findings suggest that interventions to prevent and reduce elevated childhood non-HDL-C levels may help prevent premature CVD.

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.

Intergenerational continuity of loneliness and potential mechanisms: Young Finns Multigenerational Study.

Evidence on the intergenerational continuity of loneliness and on potential mechanisms that connect loneliness across successive generations is limited. We examined the association between loneliness of (G0) parents (859 mothers and 570 fathers, mean age 74 years) and their children (G1) (433 sons and 558 daughters, mean age 47 years) producing 991 parent-offspring pairs and tested whether these associations were mediated through subjective socioeconomic position, temperament characteristics, cognitive performance, and depressive symptoms. Mean loneliness across parents had an independent effect on their adult children's experienced loneliness (OR = 1.72, 95% CI 1.23-2.42). We also found a robust effect of mothers' (OR = 1.64, 95% CI 1.17-2.29), but not of fathers' loneliness (OR = 1.47, 95% CI 0.96-2.25) on offspring's experienced loneliness in adulthood. The associations were partly mediated by offspring depressive (41-54%) and anxiety (29-31%) symptoms. The current findings emphasize the high interdependence of loneliness within families mediated partly by offspring's mental health problems.

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.

Association of Long-Term Habitual Dietary Fiber Intake since Infancy with Gut Microbiota Composition in Young Adulthood.

BACKGROUND: Dietary fiber is an important health-promoting component of the diet, which is fermented by the gut microbes that produce metabolites beneficial for the host's health. OBJECTIVES: We studied the associations of habitual long-term fiber intake from infancy with gut microbiota composition in young adulthood by leveraging data from the Special Turku Coronary Risk Factor Intervention Project, an infancy-onset 20-y dietary counseling study. METHODS: Fiber intake was assessed annually using food diaries from infancy ≤ age 20 y. At age 26 y, the first postintervention follow-up study was conducted including food diaries and fecal sample collection (N = 357). Cumulative dietary fiber intake was assessed as the area under the curve for energy-adjusted fiber intake throughout the study (age 0-26 y). Gut microbiota was profiled using 16S ribosomal ribonucleic acid amplicon sequencing. The primary outcomes were 1) α diversity expressed as the observed richness and Shannon index, 2) ß diversity using Bray-Curtis dissimilarity scores, and 3) differential abundance of each microbial taxa with respect to the cumulative energy-adjusted dietary fiber intake. RESULTS: Higher cumulative dietary fiber intake was associated with decreased Shannon index (ß = -0.019 per unit change in cumulative fiber intake, P = 0.008). Overall microbial community composition was related to the amount of fiber consumed (permutational analysis of variation R2 = 0.005, P = 0.024). The only genus that was increased with higher cumulative fiber intake was butyrate-producing Butyrivibrio (log2 fold-change per unit change in cumulative fiber intake 0.40, adjusted P = 0.023), whereas some other known butyrate producers such as Faecalibacterium and Subdoligranulum were decreased with higher cumulative fiber intake. CONCLUSIONS: As early-life nutritional exposures may affect the lifetime microbiota composition and disease risk, this study adds novel information on the associations of long-term dietary fiber intake with the gut microbiota. This trial was registered at clinicaltrials.gov as NCT00223600.

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.

Association of tobacco smoke exposure with metabolic profile from childhood to early adulthood: the Special Turku Coronary Risk Factor Intervention Project.

AIMS: To investigate the associations between passive tobacco smoke exposure and daily smoking with a comprehensive metabolic profile, measured repeatedly from childhood to adulthood. METHODS AND RESULTS: Study cohort was derived from the Special Turku Coronary Risk Factor Intervention Project (STRIP). Smoking status was obtained by questionnaire, while serum cotinine concentrations were measured using gas chromatography. Metabolic measures were quantified by nuclear magnetic resonance metabolomics at 9 (n = 539), 11 (n = 536), 13 (n = 525), 15 (n = 488), 17 (n = 455), and 19 (n = 409) years. Association of passive tobacco smoke exposure with metabolic profile compared participants who reported less-than-weekly smoking and had serum cotinine concentration <1 ng/mL (no exposure) with those whose cotinine concentration was ≥10 ng/mL (passive tobacco smoke exposure). Associations of daily smoking with metabolic profile in adolescence were analysed by comparing participants reporting daily smoking with those reporting no tobacco use and having serum cotinine concentrations <1 ng/mL. Passive tobacco smoke exposure was directly associated with the serum ratio of monounsaturated fatty acids to total fatty acids [ß = 0.34 standard deviation (SD), (0.17-0.51), P < 0.0001] and inversely associated with the serum ratios of polyunsaturated fatty acids. Exposure to passive tobacco smoke was directly associated with very-low-density lipoprotein particle size [ß = 0.28 SD, (0.12-0.45), P = 0.001] and inversely associated with HDL particle size {ß = -0.21 SD, [-0.34 to -0.07], P = 0.003}. Daily smokers exhibited a similar metabolic profile to those exposed to passive tobacco smoke. These results persisted after adjusting for body mass index, STRIP study group allocation, dietary target score, pubertal status, and parental socio-economic status. CONCLUSION: Both passive and active tobacco smoke exposures during childhood and adolescence are detrimentally associated with circulating metabolic measures indicative of increased cardio-metabolic risk.

A substantial proportion of children are affected by tobacco smoke exposure worldwide, and early life exposure to passive tobacco smoke may be even more harmful than active smoking in terms of cardiovascular disease risk. Our study suggests the following: Passive tobacco smoke exposure during childhood is associated with metabolic measures indicative of increased cardio-metabolic risk and that the association profile is similar with active daily smoking during adolescence.Reducing both active and passive tobacco smoke exposures during childhood and adolescence could reduce the risk of future cardio-metabolic disease.