Longitudinal Digital Phenotyping of Traditional Chinese Medicine Constitution From Routine Health Examination...

Background: Traditional Chinese Medicine (TCM) constitution is a structured health-state taxonomy used in preventive care, but its relationship with routinely collected health examination data, disease-related markers, and longitudinal change remains difficult to interpret in clinical informatics settings.

Objective: This study aimed to develop and evaluate a longitudinal clinical informatics framework for characterizing TCM constitution as a computable, explainable, record-based phenotype using routine health examination records.

Methods: We conducted a retrospective longitudinal analysis of 47,417 examination-constitution records from 11,355 older adults examined between 2017 and 2026. Baseline analyses used the first available record per participant, and longitudinal analyses used 32,648 adjacent annual visit pairs. The framework included bidirectional disease-constitution mapping, nonoverlapping multimarker burden modeling, lagged next-visit association models, constitution-state transition analysis, and temporal evaluation of routine-examination-based label prediction models. Additional sensitivity analyses evaluated participant overlap across calendar-year splits, participant-disjoint temporal evaluation, BMI-only and BMI-plus-waist baselines, exclusion of anthropometric predictors, adiposity adjustment of longitudinal models, and new-onset and persistence outcomes.

Results: Phlegm-dampness showed the clearest record-based signature and was associated with higher cardiometabolic marker burden than balanced constitution (incidence rate ratio 1.65, 95% CI 1.60-1.70). In lagged models, its associations with a subsequent abdominal ultrasound abnormality flag and cardiometabolic risk clustering persisted after BMI adjustment, whereas associations with diabetes-related markers, proteinuria, dyslipidemia, and glucose abnormalities were substantially attenuated. Of 11,355 participants, 8122 appeared in at least two original calendar-year splits. In a participant-disjoint temporal sensitivity analysis with 1054 new test participants, XGBoost identified phlegm-dampness label presence with an area under the receiver operating characteristic curve of 0.927 (95% CI 0.912-0.941). A BMI-only model achieved 0.923 (95% CI 0.907-0.938), whereas XGBoost without anthropometric predictors achieved 0.685 (95% CI 0.653-0.717).

Conclusions: Routine health examination data captured a reproducible, predominantly adiposity-centered phlegm-dampness label phenotype in this older adult cohort. High discrimination was retained in participant-disjoint evaluation but was nearly matched by BMI alone, and several longitudinal associations were explained by adiposity. The models should therefore be interpreted as decision-support and communication aids for an existing constitution assessment process, not as stand-alone diagnostic systems or comprehensive classifiers of TCM constitution.