We propose a cascaded 3D diffusion model framework to synthesize high-fidelity 3D PET/CT volume directly from demographic variables, addressing the growing need for realistic digital twins in oncologic imaging, virtual trials, and AI-driven data augmentation. Unlike deterministic phantoms, which rely on predefined anatomical and metabolic templates, our method employs a two-stage generative process: an initial score-based diffusion model synthesizes low-resolution PET/CT volumes from the demographic variables only, providing global anatomical structures and approximate metabolic activity, followed by a super-resolution residual diffusion model refining spatial resolution. Our framework was trained on 18-F FDG PET/CT scans from the AutoPET dataset and evaluated using organ-wise volume and standardized uptake value (SUV) distributions, comparing synthetic and real data between demographic subgroups. The organ-wise comparison demonstrated strong concordance between synthetic and real images. In particular, most of the deviations in metabolic uptake values remained within 3–5% of the ground truth in sub-group analysis. These findings highlight the potential of cascaded 3D diffusion models to generate anatomically and metabolically accurate PET/CT images, offering a robust alternative to traditional phantoms and enabling scalable, population-informed synthetic imaging for clinical and research applications. Codes can be found at https://github.com/siyeopyoon/TotalGen .