Speaker
Description
Non-communicable diseases (NCDs) impose the largest global burden of morbidity, premature mortality, and healthcare expenditure. To shift from reactive to preventive care, early detection of pre-symptomatic molecular changes is essential. We propose a statistical framework for identifying the most sensitive and robust early molecular predictors of prevalent NCDs — including cardiovascular disease, cancer, chronic respiratory disorders, and diabetes — using longitudinal profiling of blood-based parameters. Many molecular variables in human blood are tightly regulated within narrow, person-specific ranges and react sensitively to health perturbations. Detecting early deviations from these individualized baselines may allow NCD identification years before symptom onset.
To enable such early detection, a study design is required that captures both individualized molecular stability and subtle deviations over extended periods. A new cohort design — repeated baselines, 10-year follow-up on 15000 healthy individuals — provides accurate personal reference ranges and sufficient temporal resolution to capture subtle pre-diagnostic molecular changes. What makes the cohort design truly novel is the integration of standard clinical laboratory parameters with high-throughput Fourier-transform infrared spectroscopy, offering broad, sensitive, and cost-efficient coverage of molecular signatures responsive to early pathophysiological changes. This combined framework captures both established biochemical markers and fine-grained spectral features, enabling the identification of early molecular deviation patterns that may be shared across diseases as well as those specific to individual NCDs.
Our approach focuses on identifying which molecular variables show the earliest, most consistent signals of deviation during the silent development of NCDs — even before diagnoses exist. To achieve this, we quantify molecular dynamics using linear mixed-effects models (LMM), modeling time as a continuous function, adjusting for relevant covariates to separate population-level trends, between-subject heterogeneity, and within-subject fluctuations. For each variable, standardized residuals are computed across all individuals to detect statistically significant and biologically relevant deviations from expected longitudinal trajectories. Variables with systematically elevated frequencies of such deviations are considered promising early indicators.
This knowledge will lead to the construction of a targeted, cost-effective NCD screening panel and enable optimized acquisition protocols. Ultimately, linking early molecular deviations with eventual clinical diagnoses will allow development of a multi-parametric screening algorithm capable of stratifying risk across multiple NCDs before symptoms arise. This research outlines a pathway toward population-level molecular precision prevention, combining statistical rigor, personalized baselines, and comprehensive molecular phenotyping to discover the most powerful pre-symptomatic predictors of prevalent NCDs.
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