Integration of polygenic and gut metagenomic risk prediction for common diseases.

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.

Functional EPAS1/HIF2A missense variant is associated with hematocrit in Andean highlanders.

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.

Pulmonary primary oxysterol and bile acid synthesis as a predictor of outcomes in pulmonary arterial hypertension.

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.

Validation of the Finnish Care register for Health Care diagnoses for preeclampsia, gestational diabetes and preterm delivery.

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.

Role of Gut Microbiota in Statin-Associated New-Onset Diabetes-A Cross-Sectional and Prospective Analysis of the FINRISK 2002 Cohort.

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.

Evidence from Finland and Sweden on the relationship between early-life diseases and lifetime childlessness in men and women.

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.

A toolbox of machine learning software to support microbiome analysis.

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.

Advancing microbiome research with machine learning: key findings from the ML4Microbiome COST action.

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.

Microbiome-based risk prediction in incident heart failure: a community challenge.

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.

Pregnancy-Associated Bleeding and Genetics: Five Sequence Variants in the Myometrium and Progesterone Signaling Pathway are associated with postpartum hemorrhage.

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.

Greengenes2 unifies microbial data in a single reference tree.

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.

Gut microbiome and atrial fibrillation-results from a large population-based study.

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.

Inflammatory proteomics profiling for prediction of incident atrial fibrillation.

OBJECTIVE: Atrial fibrillation (AF) has emerged as a common condition in older adults. Cardiovascular risk factors only explain about 50% of AF cases. Inflammatory biomarkers may help close this gap as inflammation can alter atrial electrophysiology and structure. This study aimed to determine a cytokine biomarker profile for this condition in the community using a proteomics approach. METHODS: This study uses cytokine proteomics in participants of the Finnish population-based FINRISK cohort studies 1997/2002. Risk models for 46 cytokines were developed to predict incident AF using Cox regressions. Furthermore, the association of participants' C reactive protein (CRP) and N-terminal pro B-type natriuretic peptide (NT-proBNP) concentrations with incident AF was examined. RESULTS: In 10 744 participants (mean age of 50.9 years, 51.3% women), 1246 cases of incident AF were observed (40.5% women). The main analyses, adjusted for participants' sex and age, suggested that higher concentrations of macrophage inflammatory protein-1ß (HR=1.11; 95% CI 1.04, 1.17), hepatocyte growth factor (HR=1.12; 95% CI 1.05, 1.19), CRP (HR=1.17; 95% CI 1.10, 1.24) and NT-proBNP (HR=1.58; 95% CI 1.45, 1.71) were associated with increased risk of incident AF. In further clinical variable-adjusted models, only NT-proBNP remained statistically significant. CONCLUSION: Our study confirmed NT-proBNP as a strong predictor for AF. Observed associations of circulating inflammatory cytokines were primarily explained by clinical risk factors and did not improve risk prediction. The potential mechanistic role of inflammatory cytokines measured in a proteomics approach remains to be further elucidated.

The gut microbiome is a significant risk factor for future chronic lung disease.

BACKGROUND: The gut-lung axis is generally recognized, but there are few large studies of the gut microbiome and incident respiratory disease in adults. OBJECTIVE: We sought to investigate the association and predictive capacity of the gut microbiome for incident asthma and chronic obstructive pulmonary disease (COPD). METHODS: Shallow metagenomic sequencing was performed for stool samples from a prospective, population-based cohort (FINRISK02; N = 7115 adults) with linked national administrative health register-derived classifications for incident asthma and COPD up to 15 years after baseline. Generalized linear models and Cox regressions were used to assess associations of microbial taxa and diversity with disease occurrence. Predictive models were constructed using machine learning with extreme gradient boosting. Models considered taxa abundances individually and in combination with other risk factors, including sex, age, body mass index, and smoking status. RESULTS: A total of 695 and 392 statistically significant associations were found between baseline taxonomic groups and incident asthma and COPD, respectively. Gradient boosting decision trees of baseline gut microbiome abundance predicted incident asthma and COPD in the validation data sets with mean area under the curves of 0.608 and 0.780, respectively. Cox analysis showed that the baseline gut microbiome achieved higher predictive performance than individual conventional risk factors, with C-indices of 0.623 for asthma and 0.817 for COPD. The integration of the gut microbiome and conventional risk factors further improved prediction capacities. CONCLUSIONS: The gut microbiome is a significant risk factor for incident asthma and incident COPD and is largely independent of conventional risk factors.

Genome-wide screen of otosclerosis in population biobanks: 27 loci and shared associations with skeletal structure.

Otosclerosis is one of the most common causes of conductive hearing loss, affecting 0.3% of the population. It typically presents in adulthood and half of the patients have a positive family history. The pathophysiology of otosclerosis is poorly understood. A previous genome-wide association study (GWAS) identified a single association locus in an intronic region of RELN. Here, we report a meta-analysis of GWAS studies of otosclerosis in three population-based biobanks comprising 3504 cases and 861,198 controls. We identify 23 novel risk loci (p < 5 × 10-8) and report an association in RELN and three previously reported candidate gene or linkage regions (TGFB1, MEPE, and OTSC7). We demonstrate developmental stage-dependent immunostaining patterns of MEPE and RUNX2 in mouse otic capsules. In most association loci, the nearest protein-coding genes are implicated in bone remodelling, mineralization or severe skeletal disorders. We highlight multiple genes involved in transforming growth factor beta signalling for follow-up studies.

Inflammatory and infectious upper respiratory diseases associate with 41 genomic loci and type 2 inflammation.

Inflammatory and infectious upper respiratory diseases (ICD-10: J30-J39), such as diseases of the sinonasal tract, pharynx and larynx, are growing health problems yet their genomic similarity is not known. We analyze genome-wide association to eight upper respiratory diseases (61,195 cases) among 260,405 FinnGen participants, meta-analyzing diseases in four groups based on an underlying genetic correlation structure. Aiming to understand which genetic loci contribute to susceptibility to upper respiratory diseases in general and its subtypes, we detect 41 independent genome-wide significant loci, distinguishing impact on sinonasal or pharyngeal diseases, or both. Fine-mapping implicated non-synonymous variants in nine genes, including three linked to immune-related diseases. Phenome-wide analysis implicated asthma and atopic dermatitis at sinonasal disease loci, and inflammatory bowel diseases and other immune-mediated disorders at pharyngeal disease loci. Upper respiratory diseases also genetically correlated with autoimmune diseases such as rheumatoid arthritis, autoimmune hypothyroidism, and psoriasis. Finally, we associated separate gene pathways in sinonasal and pharyngeal diseases that both contribute to type 2 immunological reaction. We show shared heritability among upper respiratory diseases that extends to several immune-mediated diseases with diverse mechanisms, such as type 2 high inflammation.