Multimorbidity profiles in patient population from Central China: a study based on electronic health records

Comprehensive, life-course multimorbidity data derived from linked outpatient and inpatient electronic health records (EHRs) remain scarce globally. We analyzed integrated EHRs (2016-2023) from approximately 3.2 million individuals in Yichang, a prefecture-level city in Central China, to characterize disease co-occurrence during this observation window by identifying both the most frequent combinations and significant non-random associations across all ages. Multimorbidity was defined as the presence of ≥ 2 distinct lifetime conditions recorded for an individual. We identified the 50 most common disease triads and constructed disease networks using partial correlation analysis, ranking hub conditions with the Multimorbidity Coefficient (MMC). Overall, 74.5% of the population experienced multimorbidity (mean 5.29 conditions; women 5.59, men 4.98), with the burden rising steeply with age. Triad analysis revealed a clear life-course pattern, beginning with respiratory clusters in childhood and diverging by sex in young adulthood, female gynaecological versus male musculoskeletal/urological clusters, followed by cardiometabolic and cardiovascular dominance in mid-to-late life. Gastritis (K29) and sleep disorders (G47) were notably frequent components in adult triads. Network analysis identified K29, heart failure (I50), hypoproteinaemia (E88), anaemia (D64), and dermatitis (L30) as the top five hubs. Hub importance also varied by sex, with conditions such as osteoporosis (M81) being more central for women and benign prostatic hyperplasia (N40) for men. This study details a high multimorbidity burden and reveals a distinctive architecture characterized by a diverse, multi-system core where digestive, cardiometabolic, and systemic conditions co-dominate. Mapping these constellations provides critical insights for clinical anticipation, public health prevention, and research into shared pathways.