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DNA methylation (DNAm) has been reported to be associated with many diseases and with mortality. We hypothesized that the integration of DNAm with clinical risk factors would improve mortality prediction. We performed an epigenome‐wide association study of whole blood DNAm in relation to mortality in 15 cohorts (n = 15,013). During a mean follow‐up of 10 years, there were 4314 deaths from all causes including 1235 cardiovascular disease (CVD) deaths and 868 cancer deaths. Ancestry‐stratified meta‐analysis of all‐cause mortality identified 163 CpGs in European ancestry (EA) and 17 in African ancestry (AA) participants at p < 1 × 10−7, of which 41 (EA) and 16 (AA) were also associated with CVD death, and 15 (EA) and 9 (AA) with cancer death. We built DNAm‐based prediction models for all‐cause mortality that predicted mortality risk after adjusting for clinical risk factors. The mortality prediction model trained by integrating DNAm with clinical risk factors showed an improvement in prediction of cancer death with 5% increase in the C‐index in a replication cohort, compared with the model including clinical risk factors alone. Mendelian randomization identified 15 putatively causal CpGs in relation to longevity, CVD, or cancer risk. For example, cg06885782 (in KCNQ4) was positively associated with risk for prostate cancer (Beta = 1.2, PMR = 4.1 × 10−4) and negatively associated with longevity (Beta = −1.9, PMR = 0.02). Pathway analysis revealed that genes associated with mortality‐related CpGs are enriched for immune‐ and cancer‐related pathways. We identified replicable DNAm signatures of mortality and demonstrated the potential utility of CpGs as informative biomarkers for prediction of mortality risk. DNA methylation (DNAm) is an important epigenetic regulatory mechanism and is associated with many diseases and with human aging. Our study demonstrated that inter‐individual variation in DNAm is associated with all‐cause mortality risk and with cause‐specific mortality. We built a prediction model by integrating DNAm with clinical risk factors and we show that doing so can improve mortality prediction.

Determining the time since deposition (TsD) of body fluid stains can provide crucial criminal information to forensic researchers. Although there are studies on inferring residual time through DNA and RNA markers, this requires high sample quality, and microorganisms, as a new type of marker with individual and tissue identification capabilities, have the potential for body fluid recognition and TsD inference. Blood and semen are the most common types of bodily fluid stains at crime scenes, but research on the inference of the TsD of these two types of stains through microorganisms still needs to be explored. Thus, this study collected samples of body fluid stains exposed indoors for up to 56 days and selected several microorganisms that were both liquid specific and related to residual time inference in blood (Methylobacterium and Sphingomonas) and semen (Gardnerella) stains via 16 S rRNA high-throughput sequencing. Furthermore, the microorganisms’ ability to infer TsD was verified using qPCR in validation group samples stored under the same conditions, and two multiple logistic regression models were constructed. The average absolute deviation of differences between the predicted and actual retention times of the three types of body fluids in the test set using two estimation methods was 2.15 and 2.06 days, respectively. In conclusion, this study has discovered four novel microorganisms related to the retention time of blood and semen and has preliminarily constructed the TsD prediction models, providing a new direction for future forensic research on the inference of TsD in blood and semen stains.