Towards personalised molecular feedback for weight loss.

Area of Science:

• Metabolic Health
• Dietary Science
• Biotechnology

Background:

• Dieting success varies greatly due to personal factors.
• Existing digital tools lack personalized biofeedback.
• A novel platform integrates lifestyle tracking with molecular biofeedback.

Purpose of the Study:

• To investigate the efficacy of a meal-skipping diet for weight loss.
• To explore the correlation between urinary lactate and insulin levels with weight loss.
• To develop a quantitative approach for monitoring dietary behavior.

Main Methods:

• Participants skipped one main meal daily, restricting caloric intake to approximately 1200KCal.
• A mobile health platform was utilized for tracking food intake, weight, and urine samples.
• A survey assessed participant motivation, sample collection willingness, and reasons for dropout.

Main Results:

• Meal skipping significantly promoted weight loss over 24 hours compared to a three-meal routine (p < 0.001).
• Urinary insulin and lactate levels differed significantly between healthy and obese individuals (p = 0.01 and 0.05, respectively).
• Participant motivation was primarily driven by weight loss goals and interest in their metabolic profile.

Conclusions:

• Meal skipping is an effective strategy for weight loss.
• Urinary insulin and lactate show potential as biomarkers for monitoring dietary intake and weight loss.
• The integrated platform offers a quantitative method for biofeedback in dieting individuals.