ABSTRACT
Bleeding in early pregnancy and postpartum hemorrhage (PPH) bear substantial risks, with the former closely associated with pregnancy loss and the latter being the foremost cause of maternal death, underscoring the severe impact on maternal-fetal health. We identified five genetic loci linked to PPH in a meta-analysis. Functional annotation analysis indicated candidate genes HAND2, TBX3 and RAP2C/FRMD7 at three loci and showed that at each locus, associated variants were located within binding sites for progesterone receptors. There were strong genetic correlations with birth weight, gestational duration and uterine fibroids. Bleeding in early pregnancy yielded no genome-wide association signals but showed strong genetic correlation with various human traits, suggesting a potentially complex, polygenic etiology. Our results suggest that PPH is related to progesterone signaling dysregulation, whereas early bleeding is a complex trait associated with underlying health and possibly socioeconomic status and may include genetic factors that have not yet been identified.
Subject(s)
Genome-Wide Association Study , Polymorphism, Single Nucleotide , Postpartum Hemorrhage , Humans , Female , Postpartum Hemorrhage/genetics , Pregnancy , Genetic Predisposition to Disease , Genetic Loci , Receptors, Progesterone/genetics , Receptors, Progesterone/metabolismABSTRACT
Importance: Several reports suggest an increase in attention-deficit/hyperactivity disorder (ADHD) symptoms during the COVID-19 pandemic. This nationwide study assessed new ADHD diagnoses and ADHD prevalence before and during the pandemic. Objective: To investigate trends in new ADHD diagnoses, prevalence, and ADHD medication use from 2015 to 2022 in Finland. Design, Setting, and Participants: This longitudinal cohort study comprised the entire Finnish population. ADHD diagnoses and medication use were obtained from nationwide registers and assessed at 3 time points: in 2015 and before (2020) and after (2022) the pandemic. Data were analyzed from January 2015 to June 2022. Main outcomes and Measures: New ADHD diagnoses, ADHD lifetime prevalence, and ADHD medication use. Results: The cohort comprised 5â¯572â¯420 individuals (2â¯819â¯645 women [50.6%]). Lifetime prevalence of ADHD increased by 2.7-fold during 2015 to 2022; prevalence was 1.02% in 2015 (95% CI, 1.01%-1.03%), 1.80% in 2020 (95% CI, 1.79%-1.81%), and 2.76% in 2022 (95% CI, 2.75%-2.77%). Young men aged 13 to 20 years had the highest lifetime prevalence of 11.68% (95% CI, 11.56%-11.81%) in 2022. New ADHD diagnoses doubled during the pandemic, from 238 per 100â¯000 in 2020 to 477 per 100â¯000 in 2022. The pandemic-associated incremental increase in new diagnoses was 18.60% (95% CI, 16.47%-20.49%; 9482 per 50â¯897 cases). Young women aged 13 to 20 years had a 2.6-fold increase in new diagnoses during the pandemic, from 577 per 100â¯000 in 2020 to 1488 per 100â¯000 in 2022, and women aged 21 to 30 years had a 3.0-fold increase, from 361 per 100â¯000 to 1100 per 100â¯000. New diagnoses increased by 2.9-fold among those older than 55 years (from 5 per 100â¯000 to 13 per 100â¯000 in women and from 5 per 100â¯000 to 14 per 100â¯000 in men). Boys younger than 13 years had the highest absolute rate of new ADHD diagnoses in 2022 (1745 per 100â¯000), but boys and young men younger than 21 years did not show a significant incremental increase in new diagnoses. Lifetime prevalence of ADHD medication purchases was 0.57% (95% CI, 0.56%-0.58%) in 2015 (31â¯771 [55.62%] of those with ADHD diagnosis), 1.15% (95% CI, 1.14%-1.16%) in 2020 (64â¯034 [63.83%]), and 1.69% (95% CI 1.68%-1.70%) in 2022 (92â¯557 [61.43%]), respectively. Conclusions and Relevance: In this nationwide cohort study, new ADHD diagnoses and ADHD prevalence showed significant increase in Finland during the pandemic. ADHD medication use did not increase in relation to ADHD diagnoses. These results highlight potential adverse outcomes of pandemic-associated changes in living conditions.
Subject(s)
Attention Deficit Disorder with Hyperactivity , COVID-19 , SARS-CoV-2 , Humans , Attention Deficit Disorder with Hyperactivity/epidemiology , Attention Deficit Disorder with Hyperactivity/diagnosis , Finland/epidemiology , COVID-19/epidemiology , Female , Male , Adult , Adolescent , Prevalence , Longitudinal Studies , Young Adult , Child , Middle Aged , Pandemics , Registries , Child, PreschoolABSTRACT
BACKGROUND: Microbiota alterations are common in patients hospitalised for severe infections, and preclinical models have shown that anaerobic butyrate-producing gut bacteria protect against systemic infections. However, the relationship between microbiota disruptions and increased susceptibility to severe infections in humans remains unclear. We investigated the relationship between gut microbiota and the risk of future infection-related hospitalisation in two large population-based cohorts. METHODS: In this observational microbiome study, gut microbiota were characterised using 16S rRNA gene sequencing in independent population-based cohorts from the Netherlands (HELIUS study; derivation cohort) and Finland (FINRISK 2002 study; validation cohort). HELIUS was conducted in Amsterdam, Netherlands, and included adults (aged 18-70 years at inclusion) who were randomly sampled from the municipality register of Amsterdam. FINRISK 2002 was conducted in six regions in Finland and is a population survey that included a random sample of adults (aged 25-74 years). In both cohorts, participants completed questionnaires, underwent a physical examination, and provided a faecal sample at inclusion (Jan 3, 2013, to Nov 27, 2015, for HELIUS participants and Jan 21 to April 19, 2002, for FINRISK participants. For inclusion in our study, a faecal sample needed to be provided and successfully sequenced, and national registry data needed to be available. Primary predictor variables were microbiota composition, diversity, and relative abundance of butyrate-producing bacteria. Our primary outcome was hospitalisation or mortality due to any infectious disease during 5-7-year follow-up after faecal sample collection, based on national registry data. We examined associations between microbiota and infection risk using microbial ecology and Cox proportional hazards. FINDINGS: We profiled gut microbiota from 10 699 participants (4248 [39·7%] from the derivation cohort and 6451 [60·3%] from the validation cohort). 602 (5·6%) participants (152 [3·6%] from the derivation cohort; 450 [7·0%] from the validation cohort) were hospitalised or died due to infections during follow-up. Gut microbiota composition of these participants differed from those without hospitalisation for infections (derivation p=0·041; validation p=0·0002). Specifically, higher relative abundance of butyrate-producing bacteria was associated with a reduced risk of hospitalisation for infections (derivation cohort cause-specific hazard ratio 0·75 [95% CI 0·60-0·94] per 10% increase in butyrate producers, p=0·013; validation cohort 0·86 [0·77-0·96] per 10% increase, p=0·0077). These associations remained unchanged following adjustment for demographics, lifestyle, antibiotic exposure, and comorbidities. INTERPRETATION: Gut microbiota composition, specifically colonisation with butyrate-producing bacteria, was associated with protection against hospitalisation for infectious diseases in the general population across two independent European cohorts. Further studies should investigate whether modulation of the microbiome can reduce the risk of severe infections. FUNDING: Amsterdam UMC, Porticus, National Institutes of Health, Netherlands Organisation for Health Research and Development (ZonMw), and Leducq Foundation.
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PURPOSE: Population-based studies on the associations of plant-based foods, red meat or dairy with gut microbiome are scarce. We examined whether the consumption of plant-based foods (vegetables, potatoes, fruits, cereals), red and processed meat (RPM) or dairy (fermented milk, cheese, other dairy products) are related to gut microbiome in Finnish adults. METHODS: We utilized data from the National FINRISK/FINDIET 2002 Study (n = 1273, aged 25-64 years, 55% women). Diet was assessed with 48-hour dietary recalls. Gut microbiome was analyzed using shallow shotgun sequencing. We applied multivariate analyses with linear models and permutational ANOVAs adjusted for relevant confounders. RESULTS: Fruit consumption was positively (beta = 0.03, SE = 0.01, P = 0.04), while a dairy subgroup including milk, cream and ice-creams was inversely associated (beta=-0.03, SE 0.01, P = 0.02) with intra-individual gut microbiome diversity (alpha-diversity). Plant-based foods (R2 = 0.001, P = 0.03) and dairy (R2 = 0.002, P = 0.01) but not RPM (R2 = 0.001, P = 0.38) contributed to the compositional differences in gut microbiome (beta-diversity). Plant-based foods were associated with several butyrate producers/cellulolytic species including Roseburia hominis. RPM associations included an inverse association with R. hominis. Dairy was positively associated with several lactic producing/probiotic species including Lactobacillus delbrueckii and potentially opportunistic pathogens including Citrobacter freundii. Dairy, fermented milk, vegetables, and cereals were associated with specific microbial functions. CONCLUSION: Our results suggest a potential association between plant-based foods and dairy or their subgroups with microbial diversity measures. Furthermore, our findings indicated that all the food groups were associated with distinct overall microbial community compositions. Plant-based food consumption particularly was associated with a larger number of putative beneficial species.
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Multiomics has shown promise in noninvasive risk profiling and early detection of various common diseases. In the present study, in a prospective population-based cohort with ~18 years of e-health record follow-up, we investigated the incremental and combined value of genomic and gut metagenomic risk assessment compared with conventional risk factors for predicting incident coronary artery disease (CAD), type 2 diabetes (T2D), Alzheimer disease and prostate cancer. We found that polygenic risk scores (PRSs) improved prediction over conventional risk factors for all diseases. Gut microbiome scores improved predictive capacity over baseline age for CAD, T2D and prostate cancer. Integrated risk models of PRSs, gut microbiome scores and conventional risk factors achieved the highest predictive performance for all diseases studied compared with models based on conventional risk factors alone. The present study demonstrates that integrated PRSs and gut metagenomic risk models improve the predictive value over conventional risk factors for common chronic diseases.
Subject(s)
Coronary Artery Disease , Diabetes Mellitus, Type 2 , Prostatic Neoplasms , Male , Humans , Diabetes Mellitus, Type 2/diagnosis , Prospective Studies , Risk Factors , Coronary Artery Disease/genetics , Genetic Risk ScoreABSTRACT
Hypoxia-inducible factor pathway genes are linked to adaptation in both human and nonhuman highland species. EPAS1, a notable target of hypoxia adaptation, is associated with relatively lower hemoglobin concentration in Tibetans. We provide evidence for an association between an adaptive EPAS1 variant (rs570553380) and the same phenotype of relatively low hematocrit in Andean highlanders. This Andean-specific missense variant is present at a modest frequency in Andeans and absent in other human populations and vertebrate species except the coelacanth. CRISPR-base-edited human cells with this variant exhibit shifts in hypoxia-regulated gene expression, while metabolomic analyses reveal both genotype and phenotype associations and validation in a lowland population. Although this genocopy of relatively lower hematocrit in Andean highlanders parallels well-replicated findings in Tibetans, it likely involves distinct pathway responses based on a protein-coding versus noncoding variants, respectively. These findings illuminate how unique variants at EPAS1 contribute to the same phenotype in Tibetans and a subset of Andean highlanders despite distinct evolutionary trajectories.
Subject(s)
Adaptation, Physiological , Altitude , Hematocrit , South American People , Humans , Adaptation, Physiological/genetics , Adaptation, Physiological/physiology , East Asian People , Hypoxia/genetics , Hypoxia/metabolism , Mutation, Missense/genetics , South American People/geneticsABSTRACT
Pulmonary arterial hypertension (PAH) is a rare and fatal vascular disease with heterogeneous clinical manifestations. To date, molecular determinants underlying the development of PAH and related outcomes remain poorly understood. Herein, we identify pulmonary primary oxysterol and bile acid synthesis (PPOBAS) as a previously unrecognized pathway central to PAH pathophysiology. Mass spectrometry analysis of 2,756 individuals across five independent studies revealed 51 distinct circulating metabolites that predicted PAH-related mortality and were enriched within the PPOBAS pathway. Across independent single-center PAH studies, PPOBAS pathway metabolites were also associated with multiple cardiopulmonary measures of PAH-specific pathophysiology. Furthermore, PPOBAS metabolites were found to be increased in human and rodent PAH lung tissue and specifically produced by pulmonary endothelial cells, consistent with pulmonary origin. Finally, a poly-metabolite risk score comprising 13 PPOBAS molecules was found to not only predict PAH-related mortality but also outperform current clinical risk scores. This work identifies PPOBAS as specifically altered within PAH and establishes needed prognostic biomarkers for guiding therapy in PAH.
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BACKGROUND: Dyslipidemia is treated effectively with statins, but treatment has the potential to induce new-onset type-2 diabetes. Gut microbiota may contribute to this outcome variability. We assessed the associations of gut microbiota diversity and composition with statins. Bacterial associations with statin-associated new-onset type-2 diabetes (T2D) risk were also prospectively evaluated. METHODS: We examined shallow-shotgun-sequenced fecal samples from 5755 individuals in the FINRISK-2002 population cohort with a 17+-year-long register-based follow-up. Alpha-diversity was quantified using Shannon index and beta-diversity with Aitchison distance. Species-specific differential abundances were analyzed using general multivariate regression. Prospective associations were assessed with Cox regression. Applicable results were validated using gradient boosting. RESULTS: Statin use associated with differing taxonomic composition (R2, 0.02%; q=0.02) and 13 differentially abundant species in fully adjusted models (MaAsLin; q<0.05). The strongest positive association was with Clostridium sartagoforme (ß=0.37; SE=0.13; q=0.02) and the strongest negative association with Bacteroides cellulosilyticus (ß=-0.31; SE=0.11; q=0.02). Twenty-five microbial features had significant associations with incident T2D in statin users, of which only Bacteroides vulgatus (HR, 1.286 [1.136-1.457]; q=0.03) was consistent regardless of model adjustment. Finally, higher statin-associated T2D risk was seen with [Ruminococcus] torques (ΔHRstatins, +0.11; q=0.03), Blautia obeum (ΔHRstatins, +0.06; q=0.01), Blautia sp. KLE 1732 (ΔHRstatins, +0.05; q=0.01), and beta-diversity principal component 1 (ΔHRstatin, +0.07; q=0.03) but only when adjusting for demographic covariates. CONCLUSIONS: Statin users have compositionally differing microbiotas from nonusers. The human gut microbiota is associated with incident T2D risk in statin users and possibly has additive effects on statin-associated new-onset T2D risk.
Subject(s)
Diabetes Mellitus, Type 2 , Dyslipidemias , Gastrointestinal Microbiome , Hydroxymethylglutaryl-CoA Reductase Inhibitors , Humans , Hydroxymethylglutaryl-CoA Reductase Inhibitors/adverse effects , Cross-Sectional Studies , Diabetes Mellitus, Type 2/diagnosis , Diabetes Mellitus, Type 2/epidemiology , Dyslipidemias/diagnosis , Dyslipidemias/drug therapy , Dyslipidemias/epidemiologyABSTRACT
BACKGROUND: Centrally collected Finnish national health register data on adverse pregnancy outcomes are available for research, but the validity of the data is largely unknown. Our aim was to compare the diagnoses of preeclampsia (PE), gestational diabetes (GDM), and preterm delivery from hospital records with the registry based diagnoses from the Finnish Care Register for Health Care (FCR). Data on gestational age at delivery from the Medical Birth Registry (MBR) was also studied. METHODS: The Finnish Genetics of Pre-eclampsia Consortium (FINNPEC) Study cohort was used as a data source. Each diagnosis was ascertained from electronic hospital records. The validity of diagnoses obtained by record linkage of FCR and MBR was assessed against the classification previously confirmed independently by a research nurse and a study physician. RESULTS: Sensitivity of PE diagnoses in FCR was 80.3 % (95 % CI 78.3 % to 82.2 %) andspecificity 95.3 % (95 % CI 93.9 % to 96.4 %). Sensitivity for GDM was 64.1 % (95 % CI: 58.7 % - 69.3 %) and specificity 98.5 % (95 % CI: 97.9 % - 98.9 %), whereas sensitivity and specificity for preterm delivery were 32.4 % (95 % CI: 29.0 % - 36.0 %) and 99.7 % (95 % CI: 99.3 % - 99.9 %). Sensitivity of preterm delivery in the MBR was 99.1 % and specificity 99.9 %. CONCLUSIONS: FCR registry diagnoses for PE have satisfactory sensitivity and high specificity. Diagnoses for GDM and preterm delivery have lower sensitivity limiting their use in studies, and data from MBR should be preferred when studying preterm deliveries.
Subject(s)
Diabetes, Gestational , Pre-Eclampsia , Premature Birth , Pregnancy , Infant, Newborn , Female , Humans , Pre-Eclampsia/diagnosis , Pre-Eclampsia/epidemiology , Diabetes, Gestational/diagnosis , Diabetes, Gestational/epidemiology , Premature Birth/diagnosis , Premature Birth/epidemiology , Finland/epidemiology , Pregnancy Outcome/epidemiologyABSTRACT
The percentage of people without children over their lifetime is approximately 25% in men and 20% in women. Individual diseases have been linked to childlessness, mostly in women, yet we lack a comprehensive picture of the effect of early-life diseases on lifetime childlessness. We examined all individuals born in 1956-1968 (men) and 1956-1973 (women) in Finland (n = 1,035,928) and Sweden (n = 1,509,092) to the completion of their reproductive lifespan in 2018. Leveraging nationwide registers, we associated sociodemographic and reproductive information with 414 diseases across 16 categories, using a population and matched-pair case-control design of siblings discordant for childlessness (71,524 full sisters and 77,622 full brothers). The strongest associations were mental-behavioural disorders (particularly among men), congenital anomalies and endocrine-nutritional-metabolic disorders (strongest among women). We identified new associations for inflammatory and autoimmune diseases. Associations were dependent on age at onset and mediated by singlehood and education. This evidence can be used to understand how disease contributes to involuntary childlessness.
Subject(s)
Mental Disorders , Reproduction , Male , Child , Humans , Female , Aged , Finland/epidemiology , Sweden/epidemiology , Educational StatusABSTRACT
The human microbiome has become an area of intense research due to its potential impact on human health. However, the analysis and interpretation of this data have proven to be challenging due to its complexity and high dimensionality. Machine learning (ML) algorithms can process vast amounts of data to uncover informative patterns and relationships within the data, even with limited prior knowledge. Therefore, there has been a rapid growth in the development of software specifically designed for the analysis and interpretation of microbiome data using ML techniques. These software incorporate a wide range of ML algorithms for clustering, classification, regression, or feature selection, to identify microbial patterns and relationships within the data and generate predictive models. This rapid development with a constant need for new developments and integration of new features require efforts into compile, catalog and classify these tools to create infrastructures and services with easy, transparent, and trustable standards. Here we review the state-of-the-art for ML tools applied in human microbiome studies, performed as part of the COST Action ML4Microbiome activities. This scoping review focuses on ML based software and framework resources currently available for the analysis of microbiome data in humans. The aim is to support microbiologists and biomedical scientists to go deeper into specialized resources that integrate ML techniques and facilitate future benchmarking to create standards for the analysis of microbiome data. The software resources are organized based on the type of analysis they were developed for and the ML techniques they implement. A description of each software with examples of usage is provided including comments about pitfalls and lacks in the usage of software based on ML methods in relation to microbiome data that need to be considered by developers and users. This review represents an extensive compilation to date, offering valuable insights and guidance for researchers interested in leveraging ML approaches for microbiome analysis.
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The rapid development of machine learning (ML) techniques has opened up the data-dense field of microbiome research for novel therapeutic, diagnostic, and prognostic applications targeting a wide range of disorders, which could substantially improve healthcare practices in the era of precision medicine. However, several challenges must be addressed to exploit the benefits of ML in this field fully. In particular, there is a need to establish "gold standard" protocols for conducting ML analysis experiments and improve interactions between microbiome researchers and ML experts. The Machine Learning Techniques in Human Microbiome Studies (ML4Microbiome) COST Action CA18131 is a European network established in 2019 to promote collaboration between discovery-oriented microbiome researchers and data-driven ML experts to optimize and standardize ML approaches for microbiome analysis. This perspective paper presents the key achievements of ML4Microbiome, which include identifying predictive and discriminatory 'omics' features, improving repeatability and comparability, developing automation procedures, and defining priority areas for the novel development of ML methods targeting the microbiome. The insights gained from ML4Microbiome will help to maximize the potential of ML in microbiome research and pave the way for new and improved healthcare practices.
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Heart failure (HF) is a major public health problem. Early identification of at-risk individuals could allow for interventions that reduce morbidity or mortality. The community-based FINRISK Microbiome DREAM challenge (synapse.org/finrisk) evaluated the use of machine learning approaches on shotgun metagenomics data obtained from fecal samples to predict incident HF risk over 15 years in a population cohort of 7231 Finnish adults (FINRISK 2002, n=559 incident HF cases). Challenge participants used synthetic data for model training and testing. Final models submitted by seven teams were evaluated in the real data. The two highest-scoring models were both based on Cox regression but used different feature selection approaches. We aggregated their predictions to create an ensemble model. Additionally, we refined the models after the DREAM challenge by eliminating phylum information. Models were also evaluated at intermediate timepoints and they predicted 10-year incident HF more accurately than models for 5- or 15-year incidence. We found that bacterial species, especially those linked to inflammation, are predictive of incident HF. This highlights the role of the gut microbiome as a potential driver of inflammation in HF pathophysiology. Our results provide insights into potential modeling strategies of microbiome data in prospective cohort studies. Overall, this study provides evidence that incorporating microbiome information into incident risk models can provide important biological insights into the pathogenesis of HF.
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Bleeding in early pregnancy and postpartum hemorrhage (PPH) bear substantial risks, with the former closely associated with pregnancy loss and the latter being the foremost cause of maternal death, underscoring the severity of these complications in maternal-fetal health. Here, we investigated the genetic variation underlying aspects of pregnancy-associated bleeding and identified five loci associated with PPH through a meta-analysis of 21,512 cases and 259,500 controls. Functional annotation analysis indicated candidate genes, HAND2, TBX3, and RAP2C/FRMD7, at three loci and showed that at each locus, associated variants were located within binding sites for progesterone receptors (PGR). Furthermore, there were strong genetic correlations with birth weight, gestational duration, and uterine fibroids. Early bleeding during pregnancy (28,898 cases and 302,894 controls) yielded no genome-wide association signals, but showed strong genetic correlation with a variety of human traits, indicative of polygenic and pleiotropic effects. Our results suggest that postpartum bleeding is related to myometrium dysregulation, whereas early bleeding is a complex trait related to underlying health and possibly socioeconomic status.
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Studies using 16S rRNA and shotgun metagenomics typically yield different results, usually attributed to PCR amplification biases. We introduce Greengenes2, a reference tree that unifies genomic and 16S rRNA databases in a consistent, integrated resource. By inserting sequences into a whole-genome phylogeny, we show that 16S rRNA and shotgun metagenomic data generated from the same samples agree in principal coordinates space, taxonomy and phenotype effect size when analyzed with the same tree.
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BACKGROUND: Atrial fibrillation (AF) is an important heart rhythm disorder in aging populations. The gut microbiome composition has been previously related to cardiovascular disease risk factors. Whether the gut microbial profile is also associated with the risk of AF remains unknown. METHODS: We examined the associations of prevalent and incident AF with gut microbiota in the FINRISK 2002 study, a random population sample of 6763 individuals. We replicated our findings in an independent case-control cohort of 138 individuals in Hamburg, Germany. FINDINGS: Multivariable-adjusted regression models revealed that prevalent AF (N = 116) was associated with nine microbial genera. Incident AF (N = 539) over a median follow-up of 15 years was associated with eight microbial genera with false discovery rate (FDR)-corrected P < 0.05. Both prevalent and incident AF were associated with the genera Enorma and Bifidobacterium (FDR-corrected P < 0.001). AF was not significantly associated with bacterial diversity measures. Seventy-five percent of top genera (Enorma, Paraprevotella, Odoribacter, Collinsella, Barnesiella, Alistipes) in Cox regression analyses showed a consistent direction of shifted abundance in an independent AF case-control cohort that was used for replication. INTERPRETATION: Our findings establish the basis for the use of microbiome profiles in AF risk prediction. However, extensive research is still warranted before microbiome sequencing can be used for prevention and targeted treatment of AF. FUNDING: This study was funded by European Research Council, German Ministry of Research and Education, Academy of Finland, Finnish Medical Foundation, and the Finnish Foundation for Cardiovascular Research, the Emil Aaltonen Foundation, and the Paavo Nurmi Foundation.