Dataset Information

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

ABSTRACT: 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.

SUBMITTER: Erawijantari PP

PROVIDER: S-EPMC10593042 | biostudies-literature | 2023 Oct

REPOSITORIES: biostudies-literature

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

Erawijantari Pande Putu PP Kartal Ece E Liñares-Blanco José J Laajala Teemu D TD Feldman Lily Elizabeth LE Carmona-Saez Pedro P Shigdel Rajesh R Claesson Marcus Joakim MJ Bertelsen Randi Jacobsen RJ Gomez-Cabrero David D Minot Samuel S Albrecht Jacob J Chung Verena V Inouye Michael M Jousilahti Pekka P Schultz Jobst-Hendrik JH Friederich Hans-Christoph HC Knight Rob R Salomaa Veikko V Niiranen Teemu T Havulinna Aki S AS Saez-Rodriguez Julio J Levinson Rebecca T RT Lahti Leo L

medRxiv : the preprint server for health sciences 20231012

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 us ...[more]

PMID: 37873403

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Project description:The lack of effective therapies for heart failure with preserved ejection fraction (HFpEF) reflects an incomplete understanding of its pathogenesis.We analysed baseline risk factors for incident HFpEF, heart failure with reduced ejection fraction (HFrEF) and valvular heart failure (VHF) in a community-based cohort.We recruited 2101 men and 1746 women ?60 years of age with hypertension, diabetes, ischaemic heart disease (IHD), abnormal heart rhythm, cerebrovascular disease or renal impairment. Exclusion criteria were known heart failure, left ventricular ejection fraction <50% or valve abnormality >mild in severity. Median follow-up was 5.6 (IQR 4.6-6.3) years.Median time to heart failure diagnosis in 162 participants was 4.5 (IQR 2.7-5.4) years, 73 with HFpEF, 53 with HFrEF and 36 with VHF. Baseline age and amino-terminal pro-B-type natriuretic peptide levels were associated with HFpEF, HFrEF and VHF. Pulse pressure, IHD, waist circumference, obstructive sleep apnoea and pacemaker were associated with HFpEF and HFrEF; atrial fibrillation (AF) and warfarin therapy were associated with HFpEF and VHF and peripheral vascular disease and low platelet count were associated with HFrEF and VHF. Additional risk factors for HFpEF were body mass index (BMI), hypertension, diabetes, renal dysfunction, low haemoglobin, white cell count and ?-blocker, statin, loop diuretic, non-steroidal anti-inflammatory and clopidogrel therapies, for HFrEF were male gender and cigarette smoking and for VHF were low diastolic blood pressure and alcohol intake. BMI, diabetes, low haemoglobin, white cell count and warfarin therapy were more strongly associated with HFpEF than HFrEF, whereas male gender and low platelet count were more strongly associated with HFrEF than HFpEF.Our data suggest a major role for BMI, hypertension, diabetes, renal dysfunction, and inflammation in HFpEF pathogenesis; strategies directed to prevention of these risk factors may prevent a sizeable proportion of HFpEF in the community.NCT00400257, NCT00604006 and NCT01581827.

Project description:Study Objectives:Heart failure has previously been linked to sleep disorders that are often associated with frequent disturbances to human rest/activity patterns. We tested whether fragmentation of sustained rest/activity patterns derived from actigraphic recordings at baseline predicts incident heart failure in community-based elderly individuals. Methods:We studied 1099 community-based elderly adults participating in the Rush Memory and Aging Project who had baseline motor activity monitoring up to 11 days and were followed annually for up to 14 years. Fragmentation was assessed using previously validated indexes, derived from the probability of transitions once sustained rest or activity has been established. Heart failure was recorded via a clinical interview during the annual follow-up. Cox proportional hazards models were constructed to examine the relationship between rest fragmentation index and incident heart failure. Covariates grouped in terms of demographics, lifestyle factors and co-morbidities and cardiovascular risk factors/diseases were included. Results:Increased rest fragmentation (but not activity fragmentation) was associated with higher risk for incident heart failure. Specifically, a subject with a rest fragmentation at the 90th percentile showed a 57% increased risk of developing incident heart failure compared to a subject at the 10th percentile in this cohort. This effect was equivalent to that of being over a decade older. These observations were consistent after adjusting for all covariates. Conclusion:Increased rest fragmentation, a potential surrogate for sleep fragmentation, is independently associated with a higher risk of developing heart failure in community-based elderly adults during up to 14 years of follow-up. Further work is required to examine the specific contributions from daytime napping versus nighttime sleep periods in the elderly, as well as the underlying autonomic and cardio-dynamic pathways that may explain the effects on heart function.

Project description:IntroductionHeart failure (HF) is common in chronic kidney disease (CKD); identifying patients with CKD at high risk for HF may guide clinical care. We assessed the prognostic value of cardiac biomarkers and echocardiographic variables for 10-year HF prediction compared with a published clinical HF prediction equation in a cohort of participants with CKD.MethodsWe studied 2147 Chronic Renal Insufficiency Cohort (CRIC) participants without prior HF with complete clinical, cardiac biomarker (N-terminal brain natriuretic peptide [NT-proBNP] and high sensitivity troponin-T [hsTnT]), and echocardiographic data (left ventricular mass [LVM] and left ventricular ejection fraction [LVEF] data). We compared the discrimination of the 11-variable Atherosclerosis Risk in Communities (ARIC) HF prediction equation with LVM, LVEF, hsTnT, and NT-proBNP to predict 10-year risk of hospitalization for HF using a Fine and Gray modeling approach. We separately evaluated prediction of HF with preserved and reduced LVEF (LVEF ≥50% and <50%, respectively). We assessed discrimination with internally valid C-indices using 10-fold cross-validation.ResultsParticipants' mean (SD) age was 59 (11) years, 53% were men, 43% were Black, and mean (SD) estimated glomerular filtration rate (eGFR) was 44 (16) ml/min per 1.73 m2. A total of 324 incident HF hospitalizations occurred during median (interquartile range) 10.0 (5.7-10.0) years of follow-up. The ARIC HF model with clinical variables had a C-index of 0.68. Echocardiographic variables predicted HF (C-index 0.70) comparably to the published ARIC HF model, while NT-proBNP and hsTnT together (C-index 0.73) had significantly better discrimination (P = 0.004). A model including cardiac biomarkers, echocardiographic variables, and clinical variables had a C-index of 0.77. Discrimination of HF with preserved LVEF was lower than for HF with reduced LVEF for most models.ConclusionThe ARIC HF prediction model for 10-year HF risk had modest discrimination among adults with CKD. NT-proBNP and hsTnT discriminated better than the ARIC HF model and at least as well as a model with echocardiographic variables. HF clinical prediction models tailored to adults with CKD are needed. Until then, measurement of NT-proBNP and hsTnT may be a low-burden approach to predicting HF in this population, as they offer moderate discrimination.

Dataset Information

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

Publications

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

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