Early identification of ambulatory persons at high short-term risk of death could benefit targeted prevention. To identify biomarkers for all-cause mortality and enhance risk prediction, we conducted ...high-throughput profiling of blood specimens in two large population-based cohorts.
106 candidate biomarkers were quantified by nuclear magnetic resonance spectroscopy of non-fasting plasma samples from a random subset of the Estonian Biobank (n = 9,842; age range 18-103 y; 508 deaths during a median of 5.4 y of follow-up). Biomarkers for all-cause mortality were examined using stepwise proportional hazards models. Significant biomarkers were validated and incremental predictive utility assessed in a population-based cohort from Finland (n = 7,503; 176 deaths during 5 y of follow-up). Four circulating biomarkers predicted the risk of all-cause mortality among participants from the Estonian Biobank after adjusting for conventional risk factors: alpha-1-acid glycoprotein (hazard ratio HR 1.67 per 1-standard deviation increment, 95% CI 1.53-1.82, p = 5×10⁻³¹), albumin (HR 0.70, 95% CI 0.65-0.76, p = 2×10⁻¹⁸), very-low-density lipoprotein particle size (HR 0.69, 95% CI 0.62-0.77, p = 3×10⁻¹²), and citrate (HR 1.33, 95% CI 1.21-1.45, p = 5×10⁻¹⁰). All four biomarkers were predictive of cardiovascular mortality, as well as death from cancer and other nonvascular diseases. One in five participants in the Estonian Biobank cohort with a biomarker summary score within the highest percentile died during the first year of follow-up, indicating prominent systemic reflections of frailty. The biomarker associations all replicated in the Finnish validation cohort. Including the four biomarkers in a risk prediction score improved risk assessment for 5-y mortality (increase in C-statistics 0.031, p = 0.01; continuous reclassification improvement 26.3%, p = 0.001).
Biomarker associations with cardiovascular, nonvascular, and cancer mortality suggest novel systemic connectivities across seemingly disparate morbidities. The biomarker profiling improved prediction of the short-term risk of death from all causes above established risk factors. Further investigations are needed to clarify the biological mechanisms and the utility of these biomarkers for guiding screening and prevention.
Ceramides are molecular lipids implicated in apoptosis, inflammation, obesity, and insulin resistance. An earlier study reported that ceramides were associated with fatal outcome among patients with ...coronary heart disease. Here, we examined whether ceramides are associated with major adverse cardiovascular events (MACEs) among apparently healthy individuals.
FINRISK 2002 is a population-based risk factor survey, which recruited men and women aged 25 to 74 years. The cohort was followed up until the end of 2014. We quantified 4 circulating ceramides, Cer(d18:1/16:0), Cer(d18:1/18:0), Cer(d18:1/24:0), and Cer(d18:1/24:1), in 8101 serum samples by a targeted liquid chromatography-tandem mass spectrometry assay. Primary outcome of interest was incident MACE (n=813). Secondary analyses were performed for MACE death (n=116) without previous nonfatal MACE and for recurrent MACE (n=226) among survivors of a previous incident MACE. We used Cox proportional hazard models adjusted for the Framingham covariates to determine the association of ceramides with the outcomes. Of the ceramide species, Cer(d18:1/18:0) had the strongest association with incident MACE and the highest unadjusted hazard ratio of 1.31 (95% confidence interval, 1.21-1.41), which remained significant at 1.21 (95% confidence interval, 1.11-1.33) after Framingham risk factor adjustments. The hazard ratios were generally stronger for recurrent and fatal events than for first events. Clinical net reclassification improvement was 7.5% (P=6.9×10
) for Cer(d18:1/18:0).
Distinct serum ceramides are associated with the risk of incident MACE in apparently healthy individuals. These results should encourage more detailed analyses of ceramides in cardiovascular pathobiology and suggest new biomarkers of MACE risk.
During the past few years, various novel statistical methods have been developed for fine-mapping with the use of summary statistics from genome-wide association studies (GWASs). Although these ...approaches require information about the linkage disequilibrium (LD) between variants, there has not been a comprehensive evaluation of how estimation of the LD structure from reference genotype panels performs in comparison with that from the original individual-level GWAS data. Using population genotype data from Finland and the UK Biobank, we show here that a reference panel of 1,000 individuals from the target population is adequate for a GWAS cohort of up to 10,000 individuals, whereas smaller panels, such as those from the 1000 Genomes Project, should be avoided. We also show, both theoretically and empirically, that the size of the reference panel needs to scale with the GWAS sample size; this has important consequences for the application of these methods in ongoing GWAS meta-analyses and large biobank studies. We conclude by providing software tools and by recommending practices for sharing LD information to more efficiently exploit summary statistics in genetics research.
The goal of fine-mapping in genomic regions associated with complex diseases and traits is to identify causal variants that point to molecular mechanisms behind the associations. Recent fine-mapping ...methods using summary data from genome-wide association studies rely on exhaustive search through all possible causal configurations, which is computationally expensive.
We introduce FINEMAP, a software package to efficiently explore a set of the most important causal configurations of the region via a shotgun stochastic search algorithm. We show that FINEMAP produces accurate results in a fraction of processing time of existing approaches and is therefore a promising tool for analyzing growing amounts of data produced in genome-wide association studies and emerging sequencing projects.
FINEMAP v1.0 is freely available for Mac OS X and Linux at http://www.christianbenner.com
: christian.benner@helsinki.fi or matti.pirinen@helsinki.fi.
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.
Clinical laboratory tests are a critical component of the continuum of care. We evaluate the genetic basis of 35 blood and urine laboratory measurements in the UK Biobank (n = 363,228 individuals). ...We identify 1,857 loci associated with at least one trait, containing 3,374 fine-mapped associations and additional sets of large-effect (>0.1 s.d.) protein-altering, human leukocyte antigen (HLA) and copy number variant (CNV) associations. Through Mendelian randomization (MR) analysis, we discover 51 causal relationships, including previously known agonistic effects of urate on gout and cystatin C on stroke. Finally, we develop polygenic risk scores (PRSs) for each biomarker and build 'multi-PRS' models for diseases using 35 PRSs simultaneously, which improved chronic kidney disease, type 2 diabetes, gout and alcoholic cirrhosis genetic risk stratification in an independent dataset (FinnGen; n = 135,500) relative to single-disease PRSs. Together, our results delineate the genetic basis of biomarkers and their causal influences on diseases and improve genetic risk stratification for common diseases.
Although genetic risk scores have been used to predict hypertension, their utility in the clinical setting remains uncertain. Our study comprised N=218 792 FinnGen participants (mean age 58 years, ...56% women) and N=22 624 well-phenotyped FINRISK participants (mean age 50 years, 53% women). We used public genome-wide association data to compute polygenic risk scores (PRSs) for systolic and diastolic blood pressure (BP). Using time-to-event analysis, we then assessed (1) the association of BP PRSs with hypertension and cardiovascular disease (CVD) in FinnGen and (2) the improvement in model discrimination when combining BP PRSs with the validated 4- and 10-year clinical risk scores for hypertension and CVD in FINRISK. In FinnGen, compared with having a 20 to 80 percentile range PRS, a PRS in the highest 2.5% conferred 2.3-fold (95% CI, 2.2–2.4) risk of hypertension and 10.6 years (95% CI, 9.9–11.4) earlier hypertension onset. In subgroup analyses, this risk was only 1.6-fold (95% CI, 1.5–1.7) for late-onset hypertension (age ≥55 years) but 2.8-fold (95% CI, 2.6–2.9) for early-onset hypertension (age <55 years). Elevated systolic BP PRS also conferred 1.3-fold (95% CI, 1.2–1.4) risk of CVD and 2.3 years (95% CI, 1.6–3.1) earlier onset. In FINRISK, systolic and diastolic BP PRSs improved clinical risk prediction of hypertension (but not CVD), increasing the C statistics by 0.7% (95% CI, 0.3–1.1). We demonstrate that genetic information improves hypertension risk prediction. BP PRSs together with traditional risk factors could improve prediction of hypertension and particularly early-onset hypertension, which confers substantial CVD risk.
Circulating cytokines and growth factors are regulators of inflammation and have been implicated in autoimmune and metabolic diseases. In this genome-wide association study (GWAS) of up to 8,293 ...Finns we identified 27 genome-widely significant loci (p < 1.2 × 10−9) for one or more cytokines. Fifteen of the associated variants had expression quantitative trait loci in whole blood. We provide genetic instruments to clarify the causal roles of cytokine signaling and upstream inflammation in immune-related and other chronic diseases. We further link inflammatory markers with variants previously associated with autoimmune diseases such as Crohn disease, multiple sclerosis, and ulcerative colitis and hereby elucidate the molecular mechanisms underpinning these diseases and suggest potential drug targets.
Genetics plays an important role in coronary heart disease (CHD) but the clinical utility of genomic risk scores (GRSs) relative to clinical risk scores, such as the Framingham Risk Score (FRS), is ...unclear. Our aim was to construct and externally validate a CHD GRS, in terms of lifetime CHD risk and relative to traditional clinical risk scores.
We generated a GRS of 49 310 SNPs based on a CARDIoGRAMplusC4D Consortium meta-analysis of CHD, then independently tested it using five prospective population cohorts (three FINRISK cohorts, combined n = 12 676, 757 incident CHD events; two Framingham Heart Study cohorts (FHS), combined n = 3406, 587 incident CHD events). The GRS was associated with incident CHD (FINRISK HR = 1.74, 95% confidence interval (CI) 1.61-1.86 per S.D. of GRS; Framingham HR = 1.28, 95% CI 1.18-1.38), and was largely unchanged by adjustment for known risk factors, including family history. Integration of the GRS with the FRS or ACC/AHA13 scores improved the 10 years risk prediction (meta-analysis C-index: +1.5-1.6%, P < 0.001), particularly for individuals ≥60 years old (meta-analysis C-index: +4.6-5.1%, P < 0.001). Importantly, the GRS captured substantially different trajectories of absolute risk, with men in the top 20% of attaining 10% cumulative CHD risk 12-18 y earlier than those in the bottom 20%. High genomic risk was partially compensated for by low systolic blood pressure, low cholesterol level, and non-smoking.
A GRS based on a large number of SNPs improves CHD risk prediction and encodes different trajectories of lifetime risk not captured by traditional clinical risk scores.
Genome-wide association studies have identified several genetic variants associated with coronary heart disease (CHD). The aim of this study was to evaluate the genetic risk discrimination and ...reclassification and apply the results for a 2-stage population risk screening strategy for CHD.
We genotyped 28 genetic variants in 24 124 participants in 4 Finnish population-based, prospective cohorts (recruitment years 1992-2002). We constructed a multilocus genetic risk score and evaluated its association with incident cardiovascular disease events. During the median follow-up time of 12 years (interquartile range 8.75-15.25 years), we observed 1093 CHD, 1552 cardiovascular disease, and 731 acute coronary syndrome events. Adding genetic information to conventional risk factors and family history improved risk discrimination of CHD (C-index 0.856 versus 0.851; P=0.0002) and other end points (cardiovascular disease: C-index 0.840 versus 0.837, P=0.0004; acute coronary syndrome: C-index 0.859 versus 0.855, P=0.001). In a standard population of 100 000 individuals, additional genetic screening of subjects at intermediate risk for CHD would reclassify 2144 subjects (12%) into high-risk category. Statin allocation for these subjects is estimated to prevent 135 CHD cases over 14 years. Similar results were obtained by external validation, where the effects were estimated from a training data set and applied for a test data set.
Genetic risk score improves risk prediction of CHD and helps to identify individuals at high risk for the first CHD event. Genetic screening for individuals at intermediate cardiovascular risk could help to prevent future cases through better targeting of statins.
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