With the global aging trend intensifying, cancer presents a significant health challenge for the elderly. This study aims to assess the prevalence, mortality, and disability-adjusted life years (DALY) burden of cancer among individuals aged 60–94 worldwide from 1990 to 2021. Data on the prevalence, mortality, and DALY for 35 cancer types in the elderly were derived from the Global Burden of Disease Study (GBD) spanning 1990 to 2021. The analysis was performed at four stratified levels: global, 21 GBD regions, 204 countries, and five sociodemographic index (SDI) categories. Age-standardized rates for prevalence, mortality, and DALYs were computed. In 2021, the global elderly population (60–94 years) reported 691.13 × 10⁵ [627.13 × 10⁵, 773.82 × 10⁵] cancer cases and 71.86 × 10⁵ [65.07 × 10⁵, 77.31 × 10⁵] cancer deaths, with a DALY total of approximately 1394.17 × 10⁵ [1281.20 × 10⁵, 1495.54 × 10⁵]. Trends indicate a gradual increase in cancer cases and mortality over time, with DALY rates escalating with aging. Notably, high-income North America exhibited the highest age-standardized prevalence. High SDI regions faced the most substantial elderly cancer burden. The burden of cancer among the elderly exhibits considerable variability globally, with developed socioeconomic areas experiencing a greater prevalence than those in underdeveloped regions, necessitating urgent attention from health professionals.

Among adults with hypertension, modifiable risk factors differed substantially in their contributions to cardiovascular disease burden. More favorable cardiovascular health profiles were consistently associated with lower risks across eight cardiovascular outcomes. Nicotine exposure showed the largest population-attributable fractions across several diseases, followed by body mass index, highlighting the potential importance of risk factors beyond blood pressure control. Multi-omics analyses further identified proteins and metabolites that partially accounted for these associations and implicated inflammation-related pathways, including cytokine–cytokine receptor interactions, as potential biological correlates.

scMetaTraj is an R package that reconstructs single-cell trajectories from metabolic-module activity rather than whole-transcriptome similarity. In a 2,700-cell PBMC reference dataset it identified 10 metabolic subclusters. In an independent 14,771-cell dataset, monocyte-lineage cells shifted along metabolic pseudotime after 4-hour LPS stimulation. The method complements transcriptome-centered workflows and uses transcript-derived scores, not direct measurements of metabolic flux.