Bridging Longitudinal Health Records and 3D Human Generators with Biomarker-Guided Signed Choquet Forecasting

Longitudinal health records contain compact, structured measurements, while pretrained 3D human generators operate on high-dimensional visual conditions. We study how to connect these representations when the health cohort has no participant-specific 3D scans. We introduce the Signed Choquet Twin (SCT), a health digital twin that forecasts body and blood measurements from diet records and turns those forecasts into 3D bodies. SCT predicts each person's expected change from their age, sex and baseline measurements, then adds a diet term built from the 11 components of the Alternative Healthy Eating Index. This term uses a symmetric Choquet integral that assigns a weight to each component and to each pair of components, so every forecast breaks down exactly into the contributions of individual foods and their interactions. A key result is that this diet term is linear in its weights, and the valid weights form two convex cones, which makes estimation clean and exact. Cross-fitted networks first remove the influence of each person's background, and small convex quadratic programs then give the exact weights, with cross-validated shrinkage toward the total diet score. To generate bodies without real scans, SCT learns from generated reference bodies how changes in measurements should shift the input to a frozen image-to-3D generator. On 7,876 participants from two long-running studies, SCT forecasts ten measurements more accurately than regression, gradient boosting and a Choquet baseline. These accuracy gains come from the trajectory networks, while the diet term contributes interpretability. Better diet quality is consistently linked to lower body weight and a smaller waist across all training runs. The total diet score captures most of this effect, and a synthetic cohort with known effects confirms that the model recovers the overall diet effect reliably. The 3D trajectories remain qualitative because measured longitudinal 3D scans are unavailable, so we present SCT as a measurable pipeline from longitudinal health records to explainable diet effects and 3D visualizations, together with a practical route for generating temporal body updates without paired 3D supervision.