Hypothesis and Research Question

• Population - Adults with dysglycemic levels (prediabetes) recruited from a private hospital in Sector 51, Gurgaon.

• Intervention - Continuous biofeedback tools (BIA scales, CGMs, AI-based dietary tracking) combined with MRI/MRS scans and personalized dietary intervention strategies.

• Comparison - Standard prediabetes management per ICMR guidelines (educational materials, six monthly lab and MRI/MRS assessments).

• Outcome - Improved glycemic control, MRI/MRS parameters, and favourable shifts in multi-omics and mitochondrial health.

• Research Question - Can integrating continuous biofeedback devices with personalized dietary management strategies enhance glycemic control, improve multi-omics markers and mitochondrial health indicators related to glycemic control, and reduce diabetes progression more effectively than standard ICMR guidelines?

Study Design

Study Type and Recruitment

This is a prospective, stratified randomized controlled trial targeting adults with prediabetes. Participants will be recruited through preventive health checkups, corporate employee screenings, and targeted outreach programs. Eligibility will be confirmed with blood and stool tests, including hemogram, HbA1c, fasting glucose tolerance test, and metabolomics screening. Following describes the stratification strategy for the study.

Study Groups

• Control Group: Follows standard ICMR prediabetes guidelines (balanced diet, physical activity, sleep hygiene) with follow-up assessments every six months.

• Intervention Group: Follows the same guidelines plus staged integration of biofeedback and dietary tools:

  • Bioelectrical Impedance Analysis (BIA) scale

  • Continuous Glucose Monitors (CGM)

  • Smart calorie-tracking application

  • Three structured dietary consultations

Assessment Package Details

• Baseline Package

  • Anthropometrics: weight, height, waist circumference, blood pressure

  • Body composition (BIA + MRI for fat distribution)

  • Blood tests: HbA1c, fasting glucose, insulin, hemogram

  • Stool tests: ova and cysts

  • Multi-omics profiling: metabolomics, lipidomics, genomics (blood), microbiome (stool)

  • Lifestyle and demographic history

• 6-Monthly Package

  • Repeats all baseline health assessments

  • Reinforcement of ICMR guidelines

  • Multi-omics repeated only at endpoint (genomics done once)

• Intervention Package (intervention group only)

  • BIA scale for continuous body composition tracking

  • Two CGMs (for baseline monitoring + post-diet plan validation)

  • Smart calorie tracker for real-time food logging

  • Three diet consultations (baseline, mid, final)

Intervention Group Setup Stages

Stage 1: Receive BIA scale + baseline assessments

Stage 2: Weekly monitoring & compliance checks

Stage 3: First CGM + Smart calorie tracker for 15 days (normal diet logging)

Stage 4: Personalized dietary plan created using CGM + food log data

Stage 5: Second CGM + diet plan validation

Stage 6: Return to BIA scale tracking + ongoing monitoring

Stage 7: Six-monthly follow-ups until study completion (2 years)

Outcomes Expected for the Study

• Primary Outcomes

  • Progression or regression of glycemic status (prediabetes → normal / diabetes)

  • Changes in obesity category (based on BMI and body composition)

• Secondary Outcomes

  • Associations between biofeedback data (CGM, BIA, calorie logs) and multi-omics profiles

  • Changes in body composition, metabolic markers, and lifestyle adherence

  • Insights into the biological impact of using continuous, consumer-friendly health monitoring tools

Sample Size and Recruitment Strategy

This study will use a prospective, stratified randomized controlled trial (RCT) with one control and one intervention group, observed in parallel with no crossover. Participants will be recruited from preventive health checkups (urban), employee wellness programs, and rural outreach initiatives.

To minimize bias and ensure balance, we will use stratified randomization based on three key factors:

• Age: ≤40 years (pre-catabolic) vs. >40 years (post-catabolic)

• Gender: male vs. female

• Residency: urban vs. rural

These variables are important because they influence glycemic control, metabolic health, and response to interventions. This creates 8 strata (2 × 2 × 2), ensuring equal representation across groups.

Sample Size Estimation

• Using a parallel-group design with α = 0.05, 80% power, and effect size from prior literature, the base requirement was 63 participants per group (126 total).

• To maintain at least 10 participants per group in each stratum, the adjusted total was 160.

• With a 10% attrition buffer, the final target is 178 participants (89 per group).

Feasibility
The medical center diagnoses ~50–70 new prediabetes cases per month. Assuming one in three consents, recruitment should reach 15–20 participants per month, allowing completion within 8–10 months.

This structured approach ensures statistical validity, balanced groups, and results that are generalizable across diverse prediabetic populations.

Statistical Analysis Strategy

Longitudinal Analysis (Tracking Health Over Time)

• Key outcomes fasting glucose, HbA1c, and body composition will be measured at baseline, midpoint, and endpoint.

• We will use linear mixed-effects models (LMMs), a method designed for repeated measures on the same individuals. This accounts for both fixed effects (like time, group, age, sex, BMI) and random effects (differences between individuals).

• A time × group interaction will test whether the intervention leads to greater improvements compared to standard care.

• If some data are missing, the model can handle it under the “Missing At Random” (MAR) assumption.

• For variables that are not normally distributed, data transformations (e.g., log-scale) or non-parametric tests will be applied.

• Statistical significance will be set at p < 0.05 (meaning less than 5% chance the result is random).

Translational Analysis (Understanding Biological Mechanisms)
To explore why health changes occur, we will analyze multi-omics data:

• Microbiome: Gut bacterial diversity (alpha and beta diversity) and changes in specific taxa will be tested with methods like DESeq2 or ANCOM. Associations with health outcomes will be explored using mixed-effects models.

• Metabolomics & Lipidomics: Both single-variable tests (with False Discovery Rate [FDR] correction) and broader pattern-recognition techniques (e.g., PCA, PLS-DA) will be used. Correlation networks and machine learning models (e.g., random forest) may help identify predictive biomarkers.

• Genomics: Gene–diet interactions will be examined using regression models with interaction terms. Key SNPs linked to glycemic traits and insulin resistance will be assessed for their role in intervention response.

• Multiple testing will be corrected using the Benjamini–Hochberg FDR method to reduce false positives.

Connecting the Data - Integrated Interpretation
We will combine clinical results and omics data to get a complete picture of how biofeedback tools and personalized diets affect blood sugar and weight in prediabetes. Pathway and systems biology tools (e.g., KEGG, Reactome) will help link the different data layers.

Actions following the completion of project

To validate reproducibility across varied populations, a multicentric follow-up will be launched. In parallel, pilot implementation will begin in collaboration with hospitals and public health programs, with systems biology assessment evaluating integration and responsiveness of multi-dimensional data (clinical, behavioral, metabolic) in real-world settings. Policy briefs will be developed, and stakeholder dialogues with ICMR and the Ministry of Health conducted to advocate for inclusion of biofeedback-driven, personalized interventions in national diabetes prevention frameworks. These efforts aim to catalyze clinical adoption and support evidence-based updates to prediabetes management guidelines.