Time‐based diets have gained popularity for their health benefits, but their effects on human longevity remain unclear, with most evidence from short‐term human trials and animal studies. We determined the associations between eating window and mortality among U.S. adults. We conducted a prospective cohort study using NHANES 2003–2018 data linked to mortality records through December 2019. The analytic sample included 33,052 adults (aged 20 and above) with two complete 24‐h dietary recalls collected at baseline. Eating window was defined as the time between first and last consumption of any food/beverage containing > 0 kcal within 24 h. We used survey‐weighted Cox regression with Restricted Cubic Splines ( RCS ) to model nonlinear associations, treating eating window as both a continuous and categorical variable (< 8.0–≥ 15.0 h/day). Models were adjusted for sociodemographic, lifestyle, health, and dietary factors. Subgroup analyses were conducted by age, sex, and race/ethnicity. Over a median follow‐up of 8 years, there were 4158 all‐cause, 1277 cardiovascular, and 989 cancer deaths. RCS models showed a U‐shaped association between eating window and mortality, with the lowest risk at ~11–12 h/day ( p = 0.004). Shorter windows (≤ 8 h) were linked to ≥ 30% higher all‐cause mortality, especially in older adults, and > 50% higher cardiovascular mortality in older adults, men, and Whites. Longer eating window categories (≥ 15 h/day) were associated with 25% higher all‐cause mortality (95% CI : 1.01–1.55). Moderate eating windows (~11–12 h/day) are linked to the lowest mortality risk, with deviations associated with higher risk. Differences across demographic groups highlight the need for personalized guidance.

Screening for colorectal cancer with blood-based testing should detect advanced adenomas (AA), facilitating more effective cancer prevention. We evaluated four different methods to detect AAs in plasma: (i) a machine learning algorithm, Signatures of fragment Length (SignaL), based on cell-free DNA (cfDNA) fragmentation; (ii) a “Protein-17” assay measuring 17 cancer-associated proteins; (iii) a global aneuploidy score (GAS); and (iv) cfDNA mutation analysis querying 15 genes commonly mutated in colorectal cancer. Existing data from study populations with and without cancer were utilized to determine 99.5% specificity thresholds. We studied 40 AA cases and 32 colonoscopy-negative controls. SignaL detected 9/40 AAs [22.5%, 95% confidence interval (CI), 12.3%–37.5%] at 100% specificity (95% CI, 89.3%–100%). Protein-17 detected 5/40 AAs (12.5%, 95% CI, 5.5%–26.1%) including three cases not identified by SignaL, at 100% specificity (95% CI, 89.3%–100%). GAS detected 11/40 AAs (27.5%, 95% CI, 16.1%–42.8%) but resulted in 2/32 positive controls (93.8% specificity, 95% CI, 79.9%–98.3%). Combining SignaL, the Protein-17 assay, and GAS at the 99.5% specificity thresholds resulted in detection of 16/40 AAs (40%, 95% CI, 26.3%–55.4%) at 93.8% specificity (95% CI, 79.9%–98.3%). Of 32/40 evaluable AA plasma samples, only one was cfDNA mutation positive at level of significance of 0.01. A blood-based assay based on the analysis of repeated sequence elements plus proteins seems to be able to detect a considerable fraction of patients with AA at relatively high specificity. Large, prospective studies are required to determine whether this approach can add to the options currently available for screening patients for premalignant lesions of the colon. Significance: Blood-based screening for colorectal cancer could improve testing uptake and outcomes. We propose novel methods to detect AAs in plasma using cfDNA fragmentation patterns, cancer-associated proteins, and aneuploidy with high specificity. Larger studies are needed to validate clinical utility.

Mutations detected, mutation calls at individual well level, and overall mutation score for every tested sample (N=32).

Performance of biomarker combinations at 99.5% specificity and comparison with FIT and mt-sDNA

Statistical Approach for Mutations

Cumulative barplot of the contributions of SignaL, Protein-17, and GAS to detection of AA at 99.5% specificity thresholds.