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Contains fulltext : 296013.pdf (Publisher’s version ) (Open Access), Resting heart rate is associated with cardiovascular diseases and mortality in observational and Mendelian randomization studies. The aims of this study are to extend the number of resting heart rate associated genetic variants and to obtain further insights in resting heart rate biology and its clinical consequences. A genome-wide meta-analysis of 100 studies in up to 835,465 individuals reveals 493 independent genetic variants in 352 loci, including 68 genetic variants outside previously identified resting heart rate associated loci. We prioritize 670 genes and in silico annotations point to their enrichment in cardiomyocytes and provide insights in their ECG signature. Two-sample Mendelian randomization analyses indicate that higher genetically predicted resting heart rate increases risk of dilated cardiomyopathy, but decreases risk of developing atrial fibrillation, ischemic stroke, and cardio-embolic stroke. We do not find evidence for a linear or non-linear genetic association between resting heart rate and all-cause mortality in contrast to our previous Mendelian randomization study. Systematic alteration of key differences between the current and previous Mendelian randomization study indicates that the most likely cause of the discrepancy between these studies arises from false positive findings in previous one-sample MR analyses caused by weak-instrument bias at lower P-value thresholds. The results extend our understanding of resting heart rate biology and give additional insights in its role in cardiovascular disease development.

The development and diffusion of sustainable innovations are of interest to various public and private sector actors. The diffusion of sustainable innovations into value networks facilitates and is facilitated by the change of markets towards sustainability; however, this interaction needs further investigation. We examine how combinations of value network actors’ intentions and activities affect market change for a sustainable innovation We empirically explore market change related to introducing bioplastics into plastic food packaging value networks. We increase the understanding of market change for a sustainable innovation by showing how not only the actors with direct intentions to support the innovation but actors and activities indirectly related to the sustainable innovation contribute to market change. Such indirectly supportive activities focus on broader sustainability aims and can, for example, change market representations, practices, and norms in favor of the sustainable innovation. We propose sustainability layers to understand the diverse sustainability focuses of actors and their relation to the market change in question. Specifically, we contribute to market change literature by broadening the examination scope beyond the most active actors driving market change for an innovation.

Objective: To leverage large scale genetic association data to investigate the interplay between circulating cytokines and cardiometabolic traits, and thus identifying potential therapeutic targets. Design: Bi-directional Mendelian randomisation study. Setting: Genome-wide association studies from three Finnish cohorts (Northern Finland Birth Cohort 1966, Young Finns Study, or FINRISK study), and genetic association summary statistics pooled from observational studies for expression quantitative trait loci and cardiometabolic traits. Participants: Data for 47 circulating cytokines in 13 365 individuals from genome-wide association studies, summary statistic data for up to 21 735 individuals on circulating cytokines, summary statistic gene expression data across 49 tissues in 838 individuals, and summary statistic data for up to 1 320 016 individuals on cardiometabolic traits. Interventions: Relations between circulating cytokines and cardiovascular, anthropometric, lipid, or glycaemic traits (coronary artery disease, stroke, type 2 diabetes mellitus, body mass index, waist circumference, waist to hip ratio, systolic blood pressure, glycated haemoglobin, high density lipoprotein cholesterol, low density lipoprotein cholesterol, total cholesterol, triglycerides, C reactive protein, glucose, fasting insulin, and lifetime smoking). Main outcome methods: Genetic instrumental variables that are biologically plausible for the circulating cytokines were generated. The effects of cardiometabolic risk factors on concentrations of circulating cytokines, circulating cytokines on other circulating cytokines, and circulating cytokines on cardiometabolic outcomes were investigated. Results: Genetic evidence (mendelian randomisation P<0.0011) suggests that higher body mass index, waist circumference, smoking, higher concentrations of lipids, and systolic blood pressure increase circulating concentrations of several inflammatory cytokines and C reactive protein. Evidence

Introduction: Helping others within and beyond the family has been related to living a healthy and long life. Compassion is a prosocial personality trait characterized by concern for another person who is suffering and the motivation to help. The current study examines whether epigenetic aging is a potential biological mechanism that explains the link between prosociality and longevity. Methods: We used data from the Young Finns Study that follows six birth-cohorts from age 3–18 to 19–49. Trait-like compassion for others was measured with the Temperament and Character Inventory in the years 1997 and 2001. Epigenetic age acceleration and telomere length were measured with five DNA methylation (DNAm) indicators (DNAmAgeHorvath, IEAA_Hannum, EEAA_Hannum, DNAmPhenoAge, and DNAmTL) based on blood drawn in 2011. We controlled for sex, socioeconomic status in childhood and adulthood, and body-mass index. Results and discussion: An association between higher compassion in 1997 and a less accelerated DNAmPhenoAge, which builds on previous work on phenotypic aging, approached statistical significance in a sex-adjusted model (n = 1,030; b = −0.34; p = 0.050). Compassion in 1997 predicted less accelerated epigenetic aging over and above the control variables (n = 843; b = −0.47; p = 0.016). There was no relationship between compassion in 2001 (n = 1108/910) and any of the other four studied epigenetic aging indicators. High compassion for others might indeed influence whether an individual’s biological age is lower than their chronological age. The conducted robustness checks partially support this conclusion, yet cannot rule out that there might be a broader prosocial trait behind the findings. The observed associations are interesting but should be interpreted as weak requiring replication.

Background/Objectives: This observational study dissects the complex temporal associations between body-mass index (BMI), waist-hip ratio (WHR) and circulating metabolomics using a combination of longitudinal and cross-sectional population-based datasets and new systems epidemiology tools. Subjects/Methods: Firstly, a data-driven subgrouping algorithm was employed to simplify high-dimensional metabolic profiling data into a single categorical variable: a self-organizing map (SOM) was created from 174 metabolic measures from cross-sectional surveys (FINRISK, n = 9708, ages 25–74) and a birth cohort (NFBC1966, n = 3117, age 31 at baseline, age 46 at follow-up) and an expert committee defined four subgroups of individuals based on visual inspection of the SOM. Secondly, the subgroups were compared regarding BMI and WHR trajectories in an independent longitudinal dataset: participants of the Young Finns Study (YFS, n = 1286, ages 24–39 at baseline, 10 years follow-up, three visits) were categorized into the four subgroups and subgroup-specific age-dependent trajectories of BMI, WHR and metabolic measures were modelled by linear regression. Results: The four subgroups were characterised at age 39 by high BMI, WHR and dyslipidemia (designated TG-rich); low BMI, WHR and favourable lipids (TG-poor); low lipids in general (Low lipid) and high low-density-lipoprotein cholesterol (High LDL-C). Trajectory modelling of the YFS dataset revealed a dynamic BMI divergence pattern: despite overlapping starting points at age 24, the subgroups diverged in BMI, fasting insulin (three-fold difference at age 49 between TG-rich and TG-poor) and insulin-associated measures such as triglyceride-cholesterol ratio. Trajectories also revealed a WHR progression pattern: despite different starting points at the age of 24 in WHR, LDL-C and cholesterol-associated measures, all subgroups exhibited similar rates of change in these measures, i.e. WHR progression was uniform regardless of the

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

Background: Childhood exposure to dyslipidemia is associated with adult atherosclerosis, but it is unclear whether the long‐term risk associated with dyslipidemia is attenuated on its resolution by adulthood. We aimed to address this question by examining the links between childhood and adult dyslipidemia on carotid atherosclerotic plaques in adulthood. Methods and Results: The Cardiovascular Risk in Young Finns Study is a prospective follow‐up of children that began in 1980. Since then, follow‐up studies have been conducted regularly. In 2001 and 2007, carotid ultrasounds were performed on 2643 participants at the mean age of 36 years to identify carotid plaques and plaque areas. For childhood lipids, we exploited several risk factor measurements to determine the individual cumulative burden for each lipid during childhood. Participants were categorized into the following 4 groups based on their childhood and adult dyslipidemia status: no dyslipidemia (reference), incident, resolved, and persistent. Among individuals with carotid plaque, linear regression models were used to study the association of serum lipids with plaque area. The prevalence of plaque was 3.3% (N=88). In models adjusted for age, sex, and nonlipid cardiovascular risk factors, the relative risk for carotid plaque was 2.34 (95% CI, 0.91–6.00) for incident adult dyslipidemia, 3.00 (95% CI, 1.42–6.34) for dyslipidemia resolved by adulthood, and 5.23 (95% CI, 2.57–10.66) for persistent dyslipidemia. Carotid plaque area correlated with childhood total, low‐density lipoprotein, and non–high‐density lipoprotein cholesterol levels. Conclusions: Childhood dyslipidemia, even if resolved by adulthood, is a risk factor for adult carotid plaque. Furthermore, among individuals with carotid plaque, childhood lipids associate with plaque size. These findings highlight the importance of primordial prevention of dyslipidemia in childhood to reduce atherosclerosis development.

Context: Aging varies between individuals, with profound consequences for chronic diseases and longevity. One hypothesis to explain the diversity is a genetically regulated molecular clock that runs differently between individuals. Large human studies with long enough follow-up to test the hypothesis are rare due to practical challenges, but statistical models of aging are built as proxies for the molecular clock by comparing young and old individuals cross-sectionally. These models remain untested against longitudinal data. Objective: We applied novel methodology to test if cross-sectional modeling can distinguish slow vs accelerated aging in a human population. Methods: We trained a machine learning model to predict age from 153 clinical and cardiometabolic traits. The model was tested against longitudinal data from another cohort. The training data came from cross-sectional surveys of the Finnish population (n = 9708; ages 25–74 years). The validation data included 3 time points across 10 years in the Young Finns Study (YFS; n = 1009; ages 24–49 years). Predicted metabolic age in 2007 was compared against observed aging rate from the 2001 visit to the 2011 visit in the YFS dataset and correlation between predicted vs observed metabolic aging was determined. Results: The cross-sectional proxy failed to predict longitudinal observations (R2 = 0.018%, P = 0.67). Conclusion: The finding is unexpected under the clock hypothesis that would produce a positive correlation between predicted and observed aging. Our results are better explained by a stratified model where aging rates per se are similar in adulthood but differences in starting points explain diverging metabolic fates.

Background: Common pregnancy and perinatal complications are associated with offspring cardiometabolic risk factors. These complications may influence multiple metabolic traits in the offspring and these associations might differ with offspring age. Methods: We used data from eight population-based cohort studies to examine and compare associations of pre-eclampsia (PE), gestational hypertension (GH), gestational diabetes (GD), preterm birth (PTB), small (SGA) and large (LGA) for gestational age (vs. appropriate size for gestational age (AGA)) with up to 167 plasma/serum-based nuclear magnetic resonance-derived metabolic traits encompassing lipids, lipoproteins, fatty acids, amino acids, ketones, glycerides/phospholipids, glycolysis, fluid balance, and inflammation. Confounder-adjusted regression models were used to examine associations (adjusted for maternal education, parity age at pregnancy, ethnicity, pre/early pregnancy body mass index and smoking, and offspring sex and age at metabolic trait assessment), and results were combined using meta-analysis by five age categories representing different periods of the offspring life course: neonates (cord blood), infancy (mean ages: 1.1–1.6 years), childhood (4.2–7.5 years); adolescence (12.0–16.0 years), and adulthood (22.0–67.8 years). Results: Offspring numbers for each age category/analysis varied from 8925 adults (441 PTB) to 1181 infants (135 GD); 48.4% to 60.0% were females. Pregnancy complications (PE, GH, GD) were each associated with up to three metabolic traits in neonates (P≤0.001) with some evidence of persistence to older ages. PTB and SGA were associated with 32 and 12 metabolic traits in neonates respectively, which included an adjusted standardised mean difference of −0.89 standard deviation (SD) units for albumin with PTB (95% CI: −1.10 to −0.69, P=1.3×10⁻¹⁷) and −0.41 SD for total lipids in medium HDL with SGA (95% CI: −0.56 to −0.25, P=2.6×10⁻⁷), with some evidence of persistence to older a

Contains fulltext : 283506.pdf (Publisher’s version ) (Open Access), Background: The knowledge of factors influencing disease progression in patients with established coronary heart disease (CHD) is still relatively limited. One potential pathway is related to peroxisome proliferator-activated receptor gamma coactivator-1 alpha (PPARGC1A), a transcription factor linked to energy metabolism which may play a role in the heart function. Thus, its associations with subsequent CHD events remain unclear. We aimed to investigate the effect of three different SNPs in the PPARGC1A gene on the risk of subsequent CHD in a population with established CHD. Methods: We employed an individual-level meta-analysis using 23 studies from the GENetIcs of sUbSequent Coronary Heart Disease (GENIUS-CHD) consortium, which included participants (n = 80,900) with either acute coronary syndrome, stable CHD, or a mixture of both at baseline. Three variants in the PPARGC1A gene (rs8192678, G482S; rs7672915, intron 2; and rs3755863, T528T) were tested for their associations with subsequent events during the follow-up using a Cox proportional hazards model adjusted for age and sex. The primary outcome was subsequent CHD death or myocardial infarction (CHD death/myocardial infarction). Stratified analyses of the participant or study characteristics as well as additional analyses for secondary outcomes of specific cardiovascular disease diagnoses and all-cause death were also performed. Results: Meta-analysis revealed no significant association between any of the three variants in the PPARGC1A gene and the primary outcome of CHD death/myocardial infarction among those with established CHD at baseline: rs8192678, hazard ratio (HR): 1.01, 95% confidence interval (CI) 0.98-1.05 and rs7672915, HR: 0.97, 95% CI 0.94-1.00; rs3755863, HR: 1.02, 95% CI 0.99-1.06. Similarly, no significant associations were observed for any of the secondary outcomes. The results from stratified analyses showed null results, except for significant inverse associations between rs7672915 (intro

Background: Low education, low cognitive abilities, and certain cognitive styles are suggested to predispose to social intolerance and prejudices. Evidence is, however, restricted by comparatively small samples, neglect of confounding variables and genetic factors, and a narrow focus on a single sort of prejudice. We investigated the relationships of education, polygenic cognitive potential, cognitive performance, and cognitive styles with social intolerance in adulthood over a 15-year follow-up. Methods: We used data from the prospective population-based Young Finns Study (n = 960‒1679). Social intolerance was evaluated with the Social Intolerance Scale in 1997, 2001, and 2011; cognitive performance with the Cambridge Neuropsychological Test Automated Battery in 2011; cognitive styles in 1997; and socioeconomic factors in 1980 (childhood) and 2011 (adulthood); and polygenic cognitive potential was calculated based on genome-wide association studies. Results: We found that nonrational thinking, polygenic cognitive potential, cognitive performance, or socioeconomic factors were not related to social intolerance. Regarding cognitive styles, low flexibility (B = –0.759, p < .001), high perseverance (B = 1.245, p < .001), and low persistence (B = –0.329, p < .001) predicted higher social intolerance consistently in the analyses. Discussion: When developing prejudice-reduction interventions, it should be considered that educational level or cognitive performance may not be crucial for development of social intolerance. Adopting certain cognitive styles may play more important roles in development of social intolerance.

Only 3–5% of heavy alcohol users develop acute alcohol pancreatitis (AAP). This suggests that additional triggers are required to initiate the inflammatory process. Genetic susceptibility contributes to the development of AAP, and SPINK1 mutation is a documented risk factor. We investigated the prevalence of the SPINK1(N34S) mutation in patients with AAP compared to heavy alcohol users who had never suffered an episode of pancreatitis. Blood samples for the mutational analysis from patients with first episode (n = 60) and recurrent AAP (n = 43) and from heavy alcohol users without a history of AAP (n = 98) as well as from a control population (n = 1914) were obtained. SPINK1 mutation was found in 8.7% of the patients with AAP. The prevalence was significantly lower in healthy controls (3.4%, OR 2.72; 1.32–5.64) and very low in alcoholics without pancreatitis (1.0%, OR 9.29; 1.15–74.74). In a comparison adjusted for potential cofounders between AAP patients and alcoholics, SPINK1 was found to be an independent marker for AAP. The prevalence of the SPINK1 mutation is overrepresented in AAP patients and very low in alcoholics without pancreatitis. This finding may play a role in understanding the variable susceptibility to AAP found in heavy alcohol users.

Migraine affects over a billion individuals worldwide but its genetic underpinning remains largely unknown. Here, we performed a genome-wide association study of 102,084 migraine cases and 771,257 controls and identified 123 loci, of which 86 are previously unknown. These loci provide an opportunity to evaluate shared and distinct genetic components in the two main migraine subtypes: migraine with aura and migraine without aura. Stratification of the risk loci using 29,679 cases with subtype information indicated three risk variants that seem specific for migraine with aura (in HMOX2, CACNA1A and MPPED2), two that seem specific for migraine without aura (near SPINK2 and near FECH) and nine that increase susceptibility for migraine regardless of subtype. The new risk loci include genes encoding recent migraine-specific drug targets, namely calcitonin gene-related peptide (CALCA/CALCB) and serotonin 1F receptor (HTR1F). Overall, genomic annotations among migraine-associated variants were enriched in both vascular and central nervous system tissue/cell types, supporting unequivocally that neurovascular mechanisms underlie migraine pathophysiology.

We investigated (a) whether psychosocial factors (experienced stress, anticipatory worry, social detachment, sleeping disturbances, alcohol use) predict the course of paranoid ideation between the ages of 24 to 50 years and (b) whether the predictive relationships are more likely to proceed from the psychosocial factors to paranoid ideation, or vice versa. The participants (N = 1534–1553) came from the population-based Young Finns study. Paranoid ideation and psychosocial factors were assessed by reliable self-report questionnaires in 2001, 2007, and 2011/2012. The data were analyzed using growth curve and structural equation models. High experienced stress, anticipatory worry, social detachment, frequent sleeping disturbances, and frequent alcohol use predicted more paranoid ideation. More risk factors predicted increasing paranoid ideation. There were bidirectional predictive relationships of paranoid ideation with experienced stress, anticipatory worry, social detachment, and sleeping disturbances. The link between alcohol use and paranoid ideation was only correlative. In conclusion, paranoid ideation increases by reciprocal interactions with stress, worry, social detachment, and sleeping disturbances. The findings support the threat–anticipation model of paranoid ideation, providing important implications for treatment of paranoia.

We investigated whether temperament modifies an association between polygenic intelligence potential and cognitive test performance in midlife. The participants (n = 1647, born between 1962 and 1977) were derived from the Young Finns Study. Temperament was assessed with Temperament and Character Inventory over a 15-year follow-up (1997, 2001, 2007, 2012). Polygenic intelligence potential was assessed with a polygenic score for intelligence. Cognitive performance (visual memory, reaction time, sustained attention, spatial working memory) was assessed with CANTAB in midlife. The PGSI was significantly associated with the overall cognitive performance and performance in visual memory, sustained attention and working memory tests but not reaction time test. Temperament did not correlate with polygenic score for intelligence and did not modify an association between the polygenic score and cognitive performance, either. High persistence was associated with higher visual memory (B = 0.092; FDR-adj. p = 0.007) and low harm avoidance with higher overall cognitive performance, specifically better reaction time (B = −0.102; FDR-adj; p = 0.007). The subscales of harm avoidance had different associations with cognitive performance: higher “anticipatory worry,” higher “fatigability,” and lower “shyness with strangers” were associated with lower cognitive performance, while the role of “fear of uncertainty” was subtest-related. In conclusion, temperament does not help or hinder one from realizing their genetic potential for intelligence. The overall modest relationships between temperament and cognitive performance advise caution if utilizing temperament-related information e.g. in working-life recruitments. Cognitive abilities may be influenced by temperament variables, such as the drive for achievement and anxiety about test performance, but they involve distinct systems of learning and memory.

The genetic basis for the emergence of creativity in modern humans remains a mystery despite sequencing the genomes of chimpanzees and Neanderthals, our closest hominid relatives. Data-driven methods allowed us to uncover networks of genes distinguishing the three major systems of modern human personality and adaptability: emotional reactivity, self-control, and self-awareness. Now we have identified which of these genes are present in chimpanzees and Neanderthals. We replicated our findings in separate analyses of three high-coverage genomes of Neanderthals. We found that Neanderthals had nearly the same genes for emotional reactivity as chimpanzees, and they were intermediate between modern humans and chimpanzees in their numbers of genes for both self-control and self-awareness. 95% of the 267 genes we found only in modern humans were not protein-coding, including many long-non-coding RNAs in the self-awareness network. These genes may have arisen by positive selection for the characteristics of human well-being and behavioral modernity, including creativity, prosocial behavior, and healthy longevity. The genes that cluster in association with those found only in modern humans are over-expressed in brain regions involved in human self-awareness and creativity, including late-myelinating and phylogenetically recent regions of neocortex for autobiographical memory in frontal, parietal, and temporal regions, as well as related components of cortico-thalamo-ponto-cerebellar-cortical and cortico-striato-cortical loops. We conclude that modern humans have more than 200 unique non-protein-coding genes regulating co-expression of many more protein-coding genes in coordinated networks that underlie their capacities for self-awareness, creativity, prosocial behavior, and healthy longevity, which are not found in chimpanzees or Neanderthals.

Background: Observational findings for high-density lipoprotein (HDL)-mediated cholesterol efflux capacity (HDL-CEC) and coronary heart disease (CHD) appear inconsistent, and knowledge of the genetic architecture of HDL-CEC is limited. Objectives: A large-scale observational study on the associations of HDL-CEC and other HDL-related measures with CHD and the largest genome-wide association study (GWAS) of HDL-CEC. Participants/Methods: Six independent cohorts were included with follow-up data for 14,438 participants to investigate the associations of HDL-related measures with incident CHD (1,570 events). The GWAS of HDL-CEC was carried out in 20,372 participants. Results: HDL-CEC did not associate with CHD when adjusted for traditional risk factors and HDL cholesterol (HDL-C). In contradiction, almost all HDL-related concentration measures associated consistently with CHD after corresponding adjustments. There were no genetic loci associated with HDL-CEC independent of HDL-C and triglycerides. Conclusions: HDL-CEC is not unequivocally associated with CHD in contrast to HDL-C, apolipoprotein A-I, and most of the HDL subclass particle concentrations.

Background: Epidemiological and experimental evidence has linked chronic inflammation to cancer aetiology. It is unclear whether associations for specific inflammatory biomarkers are causal or due to bias. In order to examine whether altered genetically predicted concentration of circulating cytokines are associated with cancer development, we performed a two-sample Mendelian randomisation (MR) analysis. Methods: Up to 31,112 individuals of European descent were included in genome-wide association study (GWAS) meta-analyses of 47 circulating cytokines. Single nucleotide polymorphisms (SNPs) robustly associated with the cytokines, located in or close to their coding gene (cis), were used as instrumental variables. Inverse-variance weighted MR was used as the primary analysis, and the MR assumptions were evaluated in sensitivity and colocalization analyses and a false discovery rate (FDR) correction for multiple comparisons was applied. Corresponding germline GWAS summary data for five cancer outcomes (breast, endometrial, lung, ovarian, and prostate), and their subtypes were selected from the largest cancer-specific GWASs available (cases ranging from 12,906 for endometrial to 133,384 for breast cancer). Results: There was evidence of inverse associations of macrophage migration inhibitory factor with breast cancer (OR per SD = 0.88, 95% CI 0.83 to 0.94), interleukin-1 receptor antagonist with endometrial cancer (0.86, 0.80 to 0.93), interleukin-18 with lung cancer (0.87, 0.81 to 0.93), and beta-chemokine-RANTES with ovarian cancer (0.70, 0.57 to 0.85) and positive associations of monokine induced by gamma interferon with endometrial cancer (3.73, 1.86 to 7.47) and cutaneous T-cell attracting chemokine with lung cancer (1.51, 1.22 to 1.87). These associations were similar in sensitivity analyses and supported in colocalization analyses. Conclusions: Our study adds to current knowledge on the role of specific inflammatory biomarker pathways in cancer aetiolog

Background: Low‐grade inflammation in the young may contribute to the early development of cardiovascular disease. We assessed whether circulating levels of glycoprotein acetyls (GlycA) were better able to predict the development of adverse cardiovascular disease risk profiles compared with the more commonly used biomarker high‐sensitivity CRP (C‐reactive protein). Methods: A total of 3306 adolescents and young adults from the Avon Longitudinal Study of Parents and Children (mean age, 15.4±0.3; n=1750) and Cardiovascular Risk in Young Finns Study (mean age, 32.1±5.0; n=1556) were included. Baseline associations between inflammatory biomarkers, body composition, cardiovascular risk factors, and subclinical measures of vascular dysfunction were assessed cross‐sectionally in both cohorts. Prospective risk of developing hypertension and metabolic syndrome during 9‐to‐10‐year follow‐up were also assessed as surrogate markers for future cardiovascular risk. GlycA showed greater within‐subject correlation over 9‐to‐10‐year follow‐up in both cohorts compared with CRP, particularly in the younger adolescent group (r=0.36 versus 0.07). In multivariable analyses, GlycA was found to associate with multiple lifestyle‐related cardiovascular disease risk factors, cardiometabolic risk factor burden, and vascular dysfunction (eg, mean difference in flow‐mediated dilation=−1.2 [−1.8, −0.7]% per z‐score increase). In contrast, CRP levels appeared predominantly driven by body mass index and showed little relationship to any measured cardiovascular risk factors or phenotypes. In both cohorts, only GlycA predicted future risk of both hypertension (risk ratio [RR], ≈1.1 per z‐score increase for both cohorts) and metabolic syndrome (RR, ≈1.2–1.3 per z‐score increase for both cohorts) in 9‐to‐10‐year follow‐up. Conclusions: Low‐grade inflammation captured by the novel biomarker GlycA is associated with adverse cardiovascular risk profiles from as early as adolescence and predicts f

Background: Prediction of clinical outcomes for individual cancer patients is an important step in the disease diagnosis and subsequently guides the treatment and patient counseling. In this work, we develop and evaluate a joint outcome and biomarker supervised (estrogen receptor expression and ERBB2 expression and gene amplification) multitask deep learning model for prediction of outcome in breast cancer patients in two nation-wide multicenter studies in Finland (the FinProg and FinHer studies). Our approach combines deep learning with expert knowledge to provide more accurate, robust, and integrated prediction of breast cancer outcomes. Materials and Methods: Using deep learning, we trained convolutional neural networks (CNNs) with digitized tissue microarray (TMA) samples of primary hematoxylin-eosin-stained breast cancer specimens from 693 patients in the FinProg series as input and breast cancer-specific survival as the endpoint. The trained algorithms were tested on 354 TMA patient samples in the same series. An independent set of whole-slide (WS) tumor samples from 674 patients in another multicenter study (FinHer) was used to validate and verify the generalization of the outcome prediction based on CNN models by Cox survival regression and concordance index (c-index). Visual cancer tissue characterization, i.e., number of mitoses, tubules, nuclear pleomorphism, tumor-infiltrating lymphocytes, and necrosis was performed on TMA samples in the FinProg test set by a pathologist and combined with deep learning-based outcome prediction in a multitask algorithm. Results: The multitask algorithm achieved a hazard ratio (HR) of 2.0 (95% confidence interval [CI] 1.30–3.00), P < 0.001, c-index of 0.59 on the 354 test set of FinProg patients, and an HR of 1.7 (95% CI 1.2–2.6), P = 0.003, c-index 0.57 on the WS tumor samples from 674 patients in the independent FinHer series. The multitask CNN remained a statistically independent predictor of survival in bot

Highlights: Adults with MONW have a lower BMI during youth until young adulthood, but higher BMI after this than adults with metabolically healthy normal weight. Adults with MONW have a greater decrease in physical activity from youth to adulthood than other adults. Healthy lifestyle is important in the prevention of metabolic disorders, particularly in individuals who are slim in childhood. Background: Individuals with metabolically obese normal-weight (MONW) have higher risk of cardiovascular events than those with obesity but a metabolically healthy status. Etiological factors leading to MONW are not well known. We hypothesized distinct trajectories of changes in BMI and physical activity may modify metabolic risk and distinguish individuals with MONW from those who remain healthy. Methods: We compared the mean levels of BMI and physical activity at eight time points (1980, 1983, 1986, 1989, 1992, 2001, 2007, 2011) between MONW and healthy normal-weight adults using linear mixed-model analysis. The analyses included 1180 participants of the Cardiovascular Risk in Young Finns study, a population-based study that represents six different age cohorts 3, 6, 9, 12, 15 and 18 years of age at baseline. Results: Individuals with adult MONW had significantly lower BMI in childhood and young adulthood, but their BMI increased more than in other adults after this age (p<0.001for interaction between time and MONW status). Physical activity decreased relatively more since youth in individuals with adult MONW (p<0.001). Conclusions: Relative leanness in youth and subsequent weight gain in young adulthood, and a gradual decrease in physical activity levels from youth to adulthood, predispose normal-weight individuals to metabolic impairments. The results highlight the importance of a healthy lifestyle in the prevention of metabolic disorders, particularly in individuals who are slim in childhood.