Bias in O-Information Estimation.

Higher-order relationships are a central concept in the science of complex systems. A popular method of attempting to estimate the higher-order relationships of synergy and redundancy from data is through the O-information. It is an information-theoretic measure composed of Shannon entropy terms that quantifies the balance between redundancy and synergy in a system. However, bias is not yet taken into account in the estimation of the O-information of discrete variables. In this paper, we explain where this bias comes from and explore it for fully synergistic, fully redundant, and fully independent simulated systems of n=3 variables. Specifically, we explore how the sample size and number of bins affect the bias in the O-information estimation. The main finding is that the O-information of independent systems is severely biased towards synergy if the sample size is smaller than the number of jointly possible observations. This could mean that triplets identified as highly synergistic may in fact be close to independent. A bias approximation based on the Miller-Maddow method is derived for the O-information. We find that for systems of n=3 variables the bias approximation can partially correct for the bias. However, simulations of fully independent systems are still required as null models to provide a benchmark of the bias of the O-information.

Predicting early-stage coronary artery disease using machine learning and routine clinical biomarkers improved by augmented virtual data.

Aims: Coronary artery disease (CAD) is a highly prevalent disease with modifiable risk factors. In patients with suspected obstructive CAD, evaluating the pre-test probability model is crucial for diagnosis, although its accuracy remains controversial. Machine learning (ML) predictive models can help clinicians detect CAD early and improve outcomes. This study aimed to identify early-stage CAD using ML in conjunction with a panel of clinical and laboratory tests. Methods and results: The study sample included 3316 patients enrolled in the Ludwigshafen Risk and Cardiovascular Health (LURIC) study. A comprehensive array of attributes was considered, and an ML pipeline was developed. Subsequently, we utilized five approaches to generating high-quality virtual patient data to improve the performance of the artificial intelligence models. An extension study was carried out using data from the Young Finns Study (YFS) to assess the results' generalizability. Upon applying virtual augmented data, accuracy increased by approximately 5%, from 0.75 to -0.79 for random forests (RFs), and from 0.76 to -0.80 for Gradient Boosting (GB). Sensitivity showed a significant boost for RFs, rising by about 9.4% (0.81-0.89), while GB exhibited a 4.8% increase (0.83-0.87). Specificity showed a significant boost for RFs, rising by ∼24% (from 0.55 to 0.70), while GB exhibited a 37% increase (from 0.51 to 0.74). The extension analysis aligned with the initial study. Conclusion: Accurate predictions of angiographic CAD can be obtained using a set of routine laboratory markers, age, sex, and smoking status, holding the potential to limit the need for invasive diagnostic techniques. The extension analysis in the YFS demonstrated the potential of these findings in a younger population, and it confirmed applicability to atherosclerotic vascular disease.

Association of inflammatory cytokines with lung function, chronic lung diseases, and COVID-19.

We investigated the effects of 35 inflammatory cytokines on respiratory outcomes, including COVID-19, asthma (atopic and non-atopic), chronic obstructive pulmonary disease (COPD), and pulmonary function indices, using Mendelian randomization and colocalization analyses. The emerging associations were further explored using observational analyses in the UK Biobank. We found an inverse association between genetically predicted macrophage colony stimulating factor (MCSF), soluble intercellular adhesion molecule-1 (sICAM), and soluble vascular cell adhesion molecule-1 with risk of COVID-19 outcomes. sICAM was positively associated with atopic asthma risk, whereas tumor necrosis factor-alfa showed an inverse association. A positive association was shown between interleukin-18 and COPD risk (replicated in observational analysis), whereas an inverse association was shown for interleukin-1 receptor antagonist (IL-1ra). IL-1ra and monocyte chemotactic protein-3 were positively associated with lung function indices, whereas inverse associations were shown for MCSF and interleukin-18 (replicated in observational analysis). Our results point to these cytokines as potential pharmacological targets for respiratory traits.

Identification and functional characterisation of DNA methylation differences between East- and West-originating Finns.

Eastern and Western Finns show a striking difference in coronary heart disease-related mortality; genetics is a known contributor for this discrepancy. Here, we discuss the potential role of DNA methylation in mediating the discrepancy in cardiometabolic disease-risk phenotypes between the sub-populations. We used data from the Young Finns Study (n = 969) to compare the genome-wide DNA methylation levels of East- and West-originating Finns. We identified 21 differentially methylated loci (FDR < 0.05; Δß >2.5%) and 7 regions (smoothed FDR < 0.05; CpGs ≥ 5). Methylation at all loci and regions associates with genetic variants (p < 5 × 10-8). Independently of genetics, methylation at 11 loci and 4 regions associates with transcript expression, including genes encoding zinc finger proteins. Similarly, methylation at 5 loci and 4 regions associates with cardiometabolic disease-risk phenotypes including triglycerides, glucose, cholesterol, as well as insulin treatment. This analysis was also performed in LURIC (n = 2371), a German cardiovascular patient cohort, and results replicated for the association of methylation at cg26740318 and DMR_11p15 with diabetes-related phenotypes and methylation at DMR_22q13 with triglyceride levels. Our results indicate that DNA methylation differences between East and West Finns may have a functional role in mediating the cardiometabolic disease discrepancy between the sub-populations.

Childhood family environment and systemic haemodynamics in adulthood: the Cardiovascular Risk in Young Finns Study.

AIMS: Childhood family environment is associated with adulthood health behaviours and cardiovascular health, but limited data are available concerning the relationship between childhood family environment and adulthood haemodynamic determinants of blood pressure. We evaluated how childhood family environment predicts adulthood systemic haemodynamics. METHODS: The sample came from the Cardiovascular Risk in Young Finns Study (n=1554-1620). Childhood family environment (1980) was assessed with four cumulative risk scores: socioeconomic family risk, risky emotional family atmosphere, stressful life events, and parents' risky health behaviours. Haemodynamic outcomes in 2007 (participants being 30-45 year-olds) included stroke volume index, systemic vascular resistance index, cardiac output index and heart rate. Analyses were adjusted for childhood (1980) cardiovascular risk factors (high-density lipoprotein and low-density lipoprotein cholesterol, triglycerides, insulin, body mass index and systolic blood pressure); and adulthood (2007) health behaviours (alcohol consumption, smoking, physical activity); and finally for adulthood cardiovascular risk factors. RESULTS: When adjusted for age and sex, high socioeconomic family risk predicted lower stroke volume index (P=0.001), higher heart rate (P=0.001) and higher systemic vascular resistance index (P=0.030). These associations remained after controlling for childhood cardiovascular covariates or adulthood health behaviours (P⩽0.02 for all) but diluted after controlling for adulthood cardiovascular risk factors. The other childhood cumulative risk scores (stressful life events, risky emotional atmosphere, or parents' risky health behaviour) did not predict adulthood haemodynamic outcomes. CONCLUSIONS: High childhood socioeconomic family risk predicted adulthood haemodynamic outcomes independently of childhood cardiovascular risk factors and adulthood health behaviours, while other childhood psychosocial adversities were not associated with cardiovascular function in adulthood.

Prediction of Sudden Cardiac Death With Ultra-Short-Term Heart Rate Fluctuations.

BACKGROUND: Conventional measures of heart rate variability (HRV) have shown only modest associations with sudden cardiac death (SCD). Detrended fluctuation analysis (DFA), with novel methodological developments to evaluate the short-term scaling exponent, is a potentially superior method compared to conventional HRV tools. OBJECTIVES: In this study, the authors studied the analysis of the association between DFA and SCD. METHODS: The investigators studied the predictive value of ultra-short-term heart rate fluctuations (1-minute electrocardiogram samples) with DFA at rest and during different stages of physical exertion for incident SCD among 2,794 participants undergoing clinical exercise testing in the prospective FINCAVAS (Finnish Cardiovascular Study). The novel key DFA measure, the short-scale scaling exponent computed with second-order detrending (DFA2 α1), was the main exposure variable. SCDs were defined by American Heart Association/European Society of Cardiology criteria using death certificates with written accounts of the events. RESULTS: During a median follow-up of 8.3 years (Q1-Q3: 6.4-10.5), 83 SCDs occurred. DFA2 α1 measured at rest (but not in exercise) associated highly significantly with the risk of SCD, with 1-SD lower values associating with a 2.4-fold (Q1-Q3: 2.0-3.0) risk (P < 0.001). The results persisted when adjusting for other major risk factors for SCD, including age, cardiovascular morbidities, cardiorespiratory fitness, heart rate reduction, and left ventricular ejection fraction. Associations between conventional HRV parameters (measured at any stage of exercise or at rest) and SCD were substantially weaker and statistically nonsignificant after adjusting for other risk factors. CONCLUSIONS: Ultra-short-term DFA2 α1, when measured at rest, is a powerful and independent predictor of SCD. The association between DFA2 α1 and SCD is modified by physical exertion.

Association of Serum Trimethylamine-N-Oxide Concentration from Childhood to Early Adulthood with Age and Sex.

BACKGROUND: Primary prevention is the cornerstone of cardiometabolic health. In the randomized, controlled Special Turku Coronary Risk Factor Intervention Project (STRIP), dietary counseling intervention was given to children from infancy to 20 years of age and a follow-up was completed at age 26 years. We investigated the associations of age, sex, gut microbiome, and dietary intervention with the gut metabolite and the cardiac biomarker trimethylamine-N-oxide (TMAO). METHODS: Overall, 592 healthy participants (females 46%) from STRIP were investigated. Compared to the control group, the intervention group had received dietary counseling between ages 7 months and 20 years focused on low intakes of saturated fat and cholesterol and the promotion of fruit, vegetable, and whole-grain consumption. TMAO serum concentrations were measured by a liquid chromatography-tandem mass spectrometry method at ages 11, 13, 15, 17, 19, and 26 years. Microbiome composition was assessed using 16S rRNA gene sequencing at 26 years of age. RESULTS: TMAO concentrations increased from age 11 to 26 years in both sexes. At all measurement time points, males showed significantly higher serum TMAO concentrations compared to females, but concentrations were similar between the intervention and control groups. A direct association between TMAO concentrations and reported fiber intake was found in females. Gut microbiome analysis did not reveal associations with TMAO. CONCLUSIONS: TMAO concentration increased from childhood to early adulthood but was not affected by the given dietary intervention. In females, TMAO concentrations could be directly associated with higher fiber intake suggesting sex-specific differences in TMAO metabolism.

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.

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.

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.

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.

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

BACKGROUND: Lifestyle factors may affect cancer risk. This study aimed to identify whether the American Heart Association ideal cardiovascular health (ICH) score and its individual variables in youth are associated with subsequent cancer incidence. METHODS: This study comprised participants of the Cardiovascular Risk in Young Finns Study free of cancer at the analysis baseline in 1986 (n = 1,873). The baseline age was 12 to 24 years, and the follow-up occurred between 1986 and 2018. RESULTS: Among 1,873 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% confidence interval, 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 the multivariable-adjusted model, including body mass index, smoking, diet, and socioeconomic status. A continuous PA index at ages 9 to 24 years and moderate-to-vigorous PA in youth were also related to decreased cancer incidence (P < 0.05). Body mass index, 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 are associated with a reduced subsequent cancer incidence, whereas the American Heart Association's ICH score in youth does not. IMPACT: This finding supports efforts to promote a healthy lifestyle and encourages PA during childhood, yielding a subsequent healthier life.

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.

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.

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.

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.

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.

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.