Background and Aims To elucidate the genetic architecture of blood pressure (BP) and heart rate (HR) during early life and assess their potential relevance to adult health outcomes.Methods The largest genome-wide association study (GWAS) meta-analyses to date of childhood systolic BP, diastolic BP, pulse pressure, and mean arterial pressure (n = 28 425) and HR (n = 22 565) were conducted in children of European ancestry aged 4-17 years. Follow-up analyses included comparisons with adult GWAS results, polygenic risk score (PRS) analyses in independent cohorts of diverse ancestries, and a phenome-wide association study in the UK Biobank.Results Eight genome-wide significant loci were identified for childhood BP (KIAA2013, CACNB2, PLCE1, PAX2, COL4A2, RP11-236L14.1, CFDP1, TPX2) and three loci for childhood HR (CCDC141, ACHE, MYH6); all novel in children but previously reported in adults. Childhood PRSs explained up to 1.6% of BP variance and 5.2% of HR variance among children of European ancestry. Genetic correlations between childhood and adulthood BP traits were moderate (rg = 0.4-0.7), suggesting age-specific genetic effects on BP. In the UK Biobank, higher childhood BP PRS levels were significantly associated with a broad range of adult health outcomes, particularly cardiometabolic outcomes such as hypertension, angina, myocardial infarction, and cardiovascular disease-related mortality.Conclusions These findings advance the understanding of the genetic architecture of childhood BP and HR and provide compelling genetic evidence linking childhood BP to a broad spectrum of adult health outcomes-particularly cardiometabolic conditions-which may inform targeted prevention strategies from a young age.

AIMS:Numerous indices have been developed to quantify obesity and the distribution of body fat; however, none are sufficient alone, and combined usage is complicated by their potential intercorrelation. This study aims to quantify genetic and environmental influences on anthropometric measures, 12 derived obesity indices and the extent of their overlap. MATERIALS AND METHODS:We used four anthropometric measurements (height, weight, waist and hip circumference) from the baseline of the multi-generational Lifelines cohort study to calculate 12 indices of obesity and body fat distribution. Variance components attributable to genetic (h2), shared (c2) and unique environmental (e2) factors along with pairwise phenotypic (rP), genetic (rG), shared (rC), and unique environmental (rE) correlations were estimated using ASReml software. Genetic and environmental contributions to the phenotypic correlations were also quantified. RESULTS:A total number of 152 298 adult individuals (females = 89 091, 58.4%) were included. Strong correlations were observed among most indices. (rP, rG, rC, rE > 0.8). A body shape index (ABSI) and hip index (HI) were weakly correlated with other indices, largely independent of body mass index (BMI) ( r P < 0.10), and had the highest e 2 , accounting for 64.2% and 75.7% of their variance. Height showed the highest heritability ( h 2 = 91.7%), whereas most other traits were moderately heritable ( h 2 = 45%-55%). CONCLUSION:The high correlation between the majority of obesity indices implies their redundancy. In contrast, ABSI and HI were relatively independent of BMI and other indices and showed the greatest influence from individual-specific environmental factors, suggesting their potential utility as complementary tools in epidemiological research, clinical risk prediction, and monitoring of targeted interventions.

INTRODUCTION:Genome-wide association studies (GWAS) for kidney function have mainly focused on creatinine-based glomerular filtration rate (eGFRcrea), which is affected by variation in muscle mass. Moreover, the genetic basis of the sexual dimorphism of chronic kidney disease is underexplored. METHODS:We performed a GWAS meta-analysis for creatinine clearance (CrCl), a muscle mass-independent measure of kidney function, in 58,976 individuals of European ancestry from the Lifelines Cohort Study, including sex-specific analyses. RESULTS:We identified 16 independent loci with 21 genome-wide significant lead single-nucleotide polymorphisms (SNPs) associated with CrCl, two of which had not been reported previously in kidney function GWASs: rs146465192, located near the RP1-249F5.3 gene (effect allele frequency (EAF) 0.01, P = 3.38 × 10-9) and rs117014836, located near the AGPAT4 gene (EAF 0.02, P = 5.42 × 10-9). Both loci were also significantly associated with eGFRcrea in Lifelines, but not in previously published eGFR GWASs. In silico annotations revealed that rs146465192 was associated with plasma levels of IGF2R protein, whereas rs117014836 was associated with blood expression of AGPAT4 transcript. Furthermore, we identified two significant female-specific CrCl loci: rs8002366 (GPC6 locus) and rs12908437 (IGF1R locus), associated with GPC6 expression in kidney and IGF1R expression in blood, respectively. CONCLUSIONS:Our first large-scale GWAS of CrCl revealed two new genetic variants among both sexes and two female-specific variants influencing kidney function and highlighting the value of CrCl as a muscle mass-independent phenotype.

SUMMARY:Genome-wide association studies (GWASs) have identified thousands of genetic variants associated with complex traits and diseases. However, explaining the mechanisms underlying phenotypic variation remains challenging. Here, we introduce SNPannotator, an automated post-GWAS analysis software package designed to streamline the interpretation of GWAS findings. Our pipeline implements a multi-step process that identifies proxy variants in high linkage disequilibrium (LD) with associated lead variants, then queries comprehensive resources (including Ensembl, the GTEx Portal, the eQTL Catalog, and STRING DB) for genomic position, deleteriousness, regulatory annotations, clinical significance, trait associations, expression (eQTLs) and splicing quantitative trait loci (sQTLs), and functional enrichment analyses and compiles the results into user-friendly reports. This package is implemented in the R programming language and includes auxiliary functions for variant lookup and LD exploration. SNPannotator provides a practical framework for efficiently deriving biologically meaningful insights from GWAS data and for assisting researchers in prioritizing candidate variants for functional validation. AVAILABILITY AND IMPLEMENTATION:The SNPannotator package is available from the Comprehensive R Archive Network (CRAN) at https://cran.r-project.org/web/packages/SNPannotator. The development version and tutorial is available on GitHub (https://github.com/omicslaboratory/SNPannotator). The online version of the package is available at https://omicslab.org/snpannotator.

Background:While metabolic biomarkers are known to play a significant role in the development of ulcerative colitis (UC), the exact causal relationships between them remain uncertain and warrant further investigations. Here we report a bidirectional two-sample Mendelian randomization (MR) study to evaluate causal relationships between 503 blood metabolites and UC. Methods:We used genome-wide association study (GWAS) data on blood metabolite levels from two separate studies on European individuals (n = 8299 and 24,925). In addition, for UC, we utilized GWAS data from the same ancestry, including 417,932 participants, comprising 5371 UC cases and 412,561 controls. We employed the inverse variance weighted method for our discovery stage of MR analyses. Then, we used other methods, including MR-Egger, weighted median, weighted mode, simple mode, MR-pleiotropy residual sum and outlier, heterogeneity, and pleiotropy tests for sensitivity analyses to further validate our findings and assess the robustness of our results. Results:Our study suggests that total lipids in small high-density lipoprotein levels (S.HDL.L) are marginal significant positive associated with the development of UC (odds ratio = 1.167, 95% confidence interval: 0.998-1.364, P = 0.051). In addition, UC did not have a statistically significant effect on the metabolites. Conclusions:Total lipids in S.HDL.L may offer a potential trend as valuable circulating metabolic biomarkers for the screening and prevention of UC in clinical practice. In addition, they could serve as potential candidate molecules for elucidating the mechanisms underlying UC and for identifying suitable drug targets.

Background:Systems biology is an interdisciplinary approach, which will fundamentally transform the way biology is perceived and studied. Subsequently, biomedical knowledge, medical practice, health systems, and related industries will be changed. This change will ultimately lay the foundation for the new generation of medicine or high-performance medicine, so-called personalized medicine. The results of this renovation are already emerging at five levels: knowledge level, patient level, therapist level, health system level, and industry level. A national roadmap is the right way to shape the future in a conscious, effective, and preconceived way. Methods:Here, we provide a roadmap to expand systems biology approach in Iran, which can serve as a model for other countries with similar resources and strategic situation. We begin with field studies to map the current situation in the field and potential promoters and deterrents. We then identify key players and evaluate their power and benefit from expansion of systems biology approach. Finally, we provide strategies, key action areas, and feasible actions, as well as achievable goals and realistic vision and mission in a 10-year timeline, all in light of guidance from experts and pioneers in the field of systems biology. Results:We identified the strategic position of Iran at WO area, which means the need to focus on conservative strategies to minimize the weaknesses leveraging opportunities. Conclusions:Implementation of our suggestive 10-year roadmap will enhance the current situation of Iran in systems biology field to be the pioneer in west asia and a major player in the world.

Background: Considering the increasing prevalence of adolescent smoking in recent years, this study proposes a machine learning (ML) approach for distinguishing adolescents who are prone to start smoking and those who do not directly confess to smoking. Methods: We used two repeated measures cross-sectional studies, including data from 7940 individuals as distinct training and test datasets. Utilizing the randomized least absolute shrinkage and selector operator (LASSO), the most influential factors were selected. We then investigated the performance of different ML approaches for the automatic classification of students into smoker/nonsmoker and low-risk/high-risk categories. Results: Randomized LASSO feature selection prioritized 15 factors, including peer influence, risky behaviors, attitude and school policy toward smoking, family factors, depression, and sex as the most influential factors in smoking. Applying different ML approaches to the three study plans yielded an AUC of up to 0.92, sensitivity of up to 0.88, PPV of up to 0.72, specificity of up to 0.98, and NPV of up to 0.99. Conclusions: The results showed the capability of our ML approach to distinguish between classes of smokers and nonsmokers. This model can be used as a brief screening tool for automated prediction of individuals susceptible to smoking for more precise preventive intervention plans focusing on adolescents.

Individuals with inflammatory bowel disease (IBD) are at a higher risk of developing mental disorders, such as anxiety and depression. The imbalance between the intestinal microbiota and its host, known as dysbiosis, is one of the factors, disrupting the balance of metabolite production and their signaling pathways, leading to disease progression. A metabolomics approach can help identify the role of gut microbiota in mental disorders associated with IBD by evaluating metabolites and their signaling comprehensively. This narrative review focuses on metabolomics studies that have comprehensively elucidated the altered gut microbial metabolites and their signaling pathways underlying mental disorders in IBD patients. The information was compiled by searching PubMed, Web of Science, Scopus, and Google Scholar from 2005 to 2023. The findings indicated that intestinal microbial dysbiosis in IBD patients leads to mental disorders such as anxiety and depression through disturbances in the metabolism of carbohydrates, sphingolipids, bile acids, neurotransmitters, neuroprotective, inflammatory factors, and amino acids. Furthermore, the reduction in the production of neuroprotective factors and the increase in inflammation observed in these patients can also contribute to the worsening of psychological symptoms. Analyzing the metabolite profile of the patients and comparing it with that of healthy individuals using advanced technologies like metabolomics, aids in the early diagnosis and prevention of mental disorders. This approach allows for the more precise identification of the microbes responsible for metabolite production, enabling the development of tailored dietary and pharmaceutical interventions or targeted manipulation of microbiota.

Irritable bowel syndrome (IBS) is a complicated gut-brain axis disorder that has typically been classified into subgroups based on the major abnormal stool consistency and frequency. The presence of components other than lower gastrointestinal (GI) symptoms, such as psychological burden, has also been observed in IBS manifestations. The purpose of this research is to redefine IBS subgroups based on upper GI symptoms and psychological factors in addition to lower GI symptoms using an unsupervised machine learning algorithm. The clustering of 988 individuals who met the Rome III criteria for diagnosis of IBS was performed using a mixed-type data clustering algorithm. Nine sub-groups emerged from the proposed clustering: (I) High diarrhea, pain, and psychological burden, (II) High upper GI, moderate lower GI, and psychological burden, (III) High psychological burden and moderate overall GI, (IV) High constipation, moderate upper GI, and high psychological burden, (V) moderate constipation and low psychological burden, (VI) High diarrhea and moderate psychological burden, (VII) moderate diarrhea and low psychological burden, (VIII) Low overall GI, and psychological burden, (IX) Moderate lower GI, and low psychological burden. The proposed procedure led to the discovery of new homogeneous clusters in addition to certain well-known Rome sub-types for IBS.

Background Functional gastrointestinal disorders (FGIDs), as a group of syndromes with no identified structural or pathophysiological biomarkers, are currently classified by Rome criteria based on gastrointestinal symptoms (GI). However, the high overlap among FGIDs in patients makes treatment and identifying underlying mechanisms challenging. Furthermore, disregarding psychological factors in the current classification, despite their approved relationship with GI symptoms, underlines the necessity of more investigation into grouping FGID patients. We aimed to provide more homogenous and well-separated clusters based on both GI and psychological characteristics for patients with FGIDs using an unsupervised machine learning algorithm.Methods Based on a cross-sectional study, 3765 (79%) patients with at least one FGID were included in the current study. In the first step, the clustering utilizing a machine learning algorithm was merely executed based on GI symptoms. In the second step, considering the previous step's results and focusing on the clusters with a diverse combination of GI symptoms, the clustering was re-conducted based on both GI symptoms and psychological factors.Results The first phase clustering of all participants based on GI symptoms resulted in the formation of pure and non-pure clusters. Pure clusters exactly illustrated the properties of most pure Rome syndromes. Re-clustering the members of the non-pure clusters based on GI and psychological factors (i.e., the second clustering step) resulted in eight new clusters, indicating the dominance of multiple factors but well-discriminated from other clusters. The results of the second step especially highlight the impact of psychological factors in grouping FGIDs.Conclusions In the current study, the existence of Rome disorders, which were previously defined by expert opinion-based consensus, was approved, and, eight new clusters with multiple dominant symptoms based on GI and psychological factors were also introduced. The more homogeneous clusters of patients could lead to the design of more precise clinical experiments and further targeted patient care.

Background: Birth cohorts are essential for developing evidence-based policies and advancing knowledge on different aspects of the concept of developmental origins of health and diseases (DOHaD). The Prospective Epidemiological Research Studies in IrAN (PERSIAN) is a multicentre cohort in Iran. It is one of the pioneers of DOHaD research in the Middle East and North Africa (MENA) region. This profile provides a brief overview of this birth cohort, focusing on the objectives and design of the study. The main objective of this birth cohort is to evaluate the associations of socio-economic characteristics, lifestyle, diet, environmental exposures and epigenetic factors with outcomes of: pregnancy; mother and child mental and physical health and well-being; child neurodevelopment; and the establishment of chronic disease risk factors. Methods: The enrolment of PERSIAN Birth Cohort participants is currently ongoing in five Iranian cities (Isfahan, Yazd, Semnan, Sari and Rafsanjan). We plan to recruit 15,000 mother–offspring pairs, and to follow them for at least ten years. Data collection consists of three consecutive phases: (1) periconception until birth; (2) infancy (0–2 years); and (3) childhood (3–11 years). We are collecting data on both ‘determinants of health’ and ‘health outcomes’. In addition to questionnaires and physical examination, various biological samples, including blood, urine, hair, nail, cord blood and breastmilk are being collected. Growth and neurodevelopment of children will be monitored. Appropriate data analysis schemes will be employed to assess the role of early life factors in health and disease that would facilitate international comparisons.

High blood pressure is the foremost heritable global risk factor for cardiovascular disease. We report the largest genetic association study of blood pressure traits to date (systolic, diastolic, pulse pressure) in over one million people of European ancestry. We identify 535 novel blood pressure loci that not only offer new biological insights into blood pressure regulation but also reveal shared loci influencing lifestyle exposures. Our findings offer the potential for a precision medicine strategy for future cardiovascular disease prevention.

To dissect the genetic architecture of blood pressure and assess effects on target organ damage, we analyzed 128,272 SNPs from targeted and genome-wide arrays in 201,529 individuals of European ancestry, and genotypes from an additional 140,886 individuals were used for validation. We identified 66 blood pressure-associated loci, of which 17 were new; 15 harbored multiple distinct association signals. The 66 index SNPs were enriched for cis-regulatory elements, particularly in vascular endothelial cells, consistent with a primary role in blood pressure control through modulation of vascular tone across multiple tissues. The 66 index SNPs combined in a risk score showed comparable effects in 64,421 individuals of non-European descent. The 66-SNP blood pressure risk score was significantly associated with target organ damage in multiple tissues but with minor effects in the kidney. Our findings expand current knowledge of blood pressure-related pathways and highlight tissues beyond the classical renal system in blood pressure regulation.

Many disorders are associated with altered serum protein concentrations, including malnutrition, cancer, and cardiovascular, kidney, and inflammatory diseases. Although these protein concentrations are highly heritable, relatively little is known about their underlying genetic determinants. Through transethnic meta-analysis of European-ancestry and Japanese genome-wide association studies, we identified six loci at genome-wide significance (p < 5 × 10(-8)) for serum albumin (HPN-SCN1B, GCKR-FNDC4, SERPINF2-WDR81, TNFRSF11A-ZCCHC2, FRMD5-WDR76, and RPS11-FCGRT, in up to 53,190 European-ancestry and 9,380 Japanese individuals) and three loci for total protein (TNFRS13B, 6q21.3, and ELL2, in up to 25,539 European-ancestry and 10,168 Japanese individuals). We observed little evidence of heterogeneity in allelic effects at these loci between groups of European and Japanese ancestry but obtained substantial improvements in the resolution of fine mapping of potential causal variants by leveraging transethnic differences in the distribution of linkage disequilibrium. We demonstrated a functional role for the most strongly associated serum albumin locus, HPN, for which Hpn knockout mice manifest low plasma albumin concentrations. Other loci associated with serum albumin harbor genes related to ribosome function, protein translation, and proteasomal degradation, whereas those associated with serum total protein include genes related to immune function. Our results highlight the advantages of transethnic meta-analysis for the discovery and fine mapping of complex trait loci and have provided initial insights into the underlying genetic architecture of serum protein concentrations and their association with human disease.