Identifying individuals at high risk of chronic diseases via easily measured biomarkers could improve public health efforts to prevent avoidable illness and death. Here we present nuclear magnetic resonance blood metabolomics from half a million samples from three national biobanks. We built metabolomic risk scores that identify a high-risk group for each of 12 diseases that cause the most morbidity in high-income countries and show consistent cross-biobank replication of the relative risk of disease for these groups. We show that these metabolomic risk scores are more strongly associated with future disease onset than polygenic scores for most of these diseases. In a subset of 18,000 individuals with metabolomic biomarkers measured at two time points we show that people whose scores change have dramatically different future risk of disease, suggesting that repeat measurements capture the benefits of lifestyle change. We show cross-biobank calibration of our scores. Since metabolomics can be measured from a standard blood sample, we propose such tests can be feasibly implemented today in preventative health programs.
SARS-CoV-2 vaccination is currently the mainstay in combating the COVID-19 pandemic. However, there are still people among vaccinated individuals suffering from severe forms of the disease. We conducted a retrospective cohort study based on data from nationwide e-health databases. The study included 184,132 individuals who were SARS-CoV-2 infection-naive and had received at least a primary series of COVID-19 vaccination. The incidence of BTI (breakthrough infection) was 8.03 (95% CI [confidence interval] 7.95⎼8.13/10,000 person-days), and for severe COVID-19 it was 0.093 (95% CI 0.084⎼ 0.104/10,000 person-days). The protective effect of vaccination against severe COVID-19 remained constant for up to six months, and the booster dose offered an additional pronounced benefit (hospitalization aHR 0.32, 95% CI 0.19⎼0.54). The risk of severe COVID-19 was higher among those ≥ 50 years of age (aHR [adjusted hazard ratio] 2.06, 95% CI 1.25⎼3.42) and increased constantly with every decade of life. Male sex (aHR 1.32, 95% CI 1.16⎼1.45), CCI (The Charlson Comorbidity Index) score ≥ 1 (aHR 2.09, 95% CI 1.54⎼2.83), and a range of comorbidities were associated with an increased risk of COVID-19 hospitalization. There are identifiable subgroups of COVID-19-vaccinated individuals at high risk of hospitalization due to SARS-CoV-2 infection. This information is crucial to driving vaccination programs and planning treatment strategies.
Survival analysis in clinical trials has been extensively researched, but its applicability to large omics-based biobanks requires further investigation. This study addresses two important issues in time-to-event data analysis in biobank settings. First, there is the need to pay attention to left-truncation of some outcomes that would prevent individuals joining the biobank, if the event of interest occurs before possible recruitment time. To avoid biases due to left-truncation, it has been suggested to use age as time scale in the analysis, whereas an individual is considered at risk only after recruitment to the biobank. Second, one needs to address the computational burden due to relative slowness of the conventional algorithm to maximize the partial likelihood function for the Cox model. This study examines the impact of the choice of timescale on the bias and power, while varying effect size and censoring rate in the range realistically seen in a typical biobank setting. Moreover, the study explores a computationally fast two-step martingale residual (MR) based approach for Cox modeling in high-dimensional omics data. The findings indicate that the choice of timescale has minimal impact on accuracy for small hazard ratios (HR), but for larger HRs, accounting for left-truncation is crucial to reduce bias. When maximizing power for association discovery, using participant age as the time scale (whereas individual is considered to be at risk immediately after birth) yields the highest power. The two-step MR approach is recommended for genome-wide association studies due to acceptable precision and retained power for small effect sizes. However, for predictions and polygenic risk score calculations, effect sizes should be recalculated using the conventional Cox proportional hazards model while accounting for left-truncation. The conclusions are based on simulations and illustrated with survival data from the Estonian Biobank cohort.
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