The FTO rs9939609 variant is the most replicated common genetic variant associated with BMI and body fat percentage. The risk allele (A) associates with reduced satiety signaling, increased appetite, and 1.5–3 kg average weight difference compared to TT homozygotes — representing the single most studied gene in obesity genomics.
MC4R is the second most common monogenic cause of obesity and also carries common variants associated with appetite regulation. Reduced MC4R signaling impairs the satiety response to feeding — making caloric restriction disproportionately difficult compared to individuals with intact melanocortin signaling.
TCF7L2 is the strongest common genetic determinant of type 2 diabetes risk. Beyond diabetes, the risk allele impairs beta-cell function and glucose disposal — making carbohydrate-heavy diets systematically less effective for weight management in risk carriers. The primary variant for low-carb diet genotype matching.
PPARG is a master regulator of adipogenesis — the formation of new fat cells. The Pro12Ala variant alters PPARG activity, affecting fat storage efficiency, insulin sensitivity, and responsiveness to low-fat dietary interventions. A key variant for understanding why dietary fat intake affects different individuals so differently.
ADRB3 (beta-3 adrenergic receptor) mediates sympathetic nervous system control of lipolysis and thermogenesis in adipose tissue. The Trp64Arg variant reduces receptor activity — impairing fat mobilization during caloric restriction and reducing the thermogenic response to exercise.
ADRB2 variants affect exercise-induced lipolysis — how effectively fat is mobilized during aerobic exercise. Combined with training response genetics (ACTN3, PPARGC1A), this variant informs whether high-intensity or moderate steady-state exercise is more effective for body composition improvement in a given individual.
Structured recommendation for dietary approach — low-carbohydrate, low-fat, Mediterranean, or mixed — based on insulin sensitivity genetics, fat metabolism, and satiety signaling variants. The genotype-matched diet is not a theory; it's the approach most likely to produce consistent results for a given individual's biology.
ADRB3, ADRB2, and thermogenesis variants provide a genetic context for metabolic rate — explaining why caloric restriction produces different results across individuals and informing whether metabolic support interventions (exercise type, timing, cold exposure) are likely to be effective.
FTO and MC4R variants give a biological context for appetite patterns — validating the subjective experience of feeling hungry more often or struggling to stop eating. This context alone changes how users relate to behavioral interventions and adherence strategies.
Training genetics connected to metabolic profile — identifying whether resistance training, aerobic exercise, or high-intensity protocols best align with both the user's fitness genetics and their specific metabolic and fat storage profile for body composition goals.
Whether you're building a weight management program, a metabolic health app, or a nutrition platform — let's discuss what genomic intelligence adds to your personalization approach.