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Investigating Lifestyle Data, microRNAs, and Immune Signals as Early Biomarkers of Pediatric Type 2 Diabetes

CSEF · 2026 Medicine & Physiology (Junior Division)

Myra Saxena

Overview

Type 2 Diabetes (T2D) is rapidly increasing among youth (2M), and projected to surge by 700% in 2060. Current medical strategies are reactive, focusing on post diagnosis treatment. Motivated by my personal loss, I sought to develop a tool to identify children at risk before symptoms appear. I hypothesised that at-risk youth exhibit coordinated changes in microRNAs, immune signals and lifestyle factors before glucose abnormalities appear. Using cross-sectional dataset NHANES 2013-2020 (ages 12-19), I developed 19 features including insulin resistance (HOMA-IR), beta-cell function (HOMA-B), with inactivity scores, nutritional stress and immune markers (CRP, NLR) as metabolic proxies. I trained Machine learning models with an AUC/ROC of 0.88 in distinguishing risk stages. The model revealed 30.7% of teenagers were in the reversible pre-compensatory phase characterized by normal glucose levels. Higher physical inactivity was identified as a key risk factor strongly associated with accelerated progression towards this phase. This study demonstrates that computational modeling of immune and lifestyle signals can identify critical and reversible pre-diabetes stages. Identification of 1 in 3 teenagers in the reversible pre-diabetes stage offers an opportunity to intervene and avert a public health crisis. To bridge the gap between data and intervention, I have published the model, accompanied by an UI application (on Github), that provides personalized recommendations and stage-specific interventions several years in advance. By utilizing "poor man's molecular marker" NLR, it's optimized for low cost real world usage. Future work includes validating these signals on comprehensive longitudinal datasets such as the TODAY study.

Competition history

  • CSEF 2026 Medicine & Physiology (Junior Division) · Entry J-15-07

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