Association Between Body Composition and Risk of Primary Open-Angle Glaucoma.

Greater leg fat was associated with a significantly reduced risk of POAG.

• Leg fat index (LFI) showed HR of 0.85 (95% CI, 0.76–0.95; P = .006) for POAG incidence.
• Leg fat-to-muscle ratio also supported this association with HR of 0.35 (95% CI, 0.16–0.73; P = .005).
• The cohort analysis for POAG incidence included 291,983 participants from the UK Biobank.
• Fat and muscle mass were estimated using bioimpedance analysis and normalized for height.

No association was observed between muscle mass and the incidence of POAG.

• Muscle indices for arm, trunk, and leg were analyzed using covariate-adjusted Cox models.
• Neither arm muscle index, trunk muscle index, nor leg muscle index was significantly associated with POAG incidence.
• The cohort included 291,983 participants from the UK Biobank for POAG incidence analysis.

Greater fat mass in multiple body regions was associated with higher intraocular pressure (IOP) levels.

• Arm fat index was associated with higher IOP (β = 0.14; 95% CI, 0.07–0.22; P < .001).
• Leg fat index was associated with higher IOP (β = 0.15; 95% CI, 0.11–0.18; P < .001).
• Trunk fat index was associated with higher IOP (β = 0.07; 95% CI, 0.04–0.09; P < .001).
• The baseline IOP analysis included 88,123 participants from the UK Biobank.
• Associations with IOP were assessed using linear regression models.

Greater muscle mass in the leg and trunk was associated with lower IOP levels.

• Leg muscle index was associated with lower IOP (β = -0.24; 95% CI, -0.29 to -0.20; P < .001).
• Trunk muscle index was associated with lower IOP (β = -0.05; 95% CI, -0.08 to -0.01; P = .005).
• These associations were assessed using linear regression in 88,123 participants.

Body composition was measured using bioimpedance analysis with regional fat and muscle indices derived for arm, trunk, and leg.

• Fat and muscle mass in the arm, trunk, and leg were estimated using bioimpedance analysis.
• Measurements were normalized for height to derive the arm fat index, trunk fat index, leg fat index, arm muscle index, trunk muscle index, and leg muscle index.
• Fat-to-muscle ratios for each region were calculated as sensitivity analyses.
• The study used a combined cross-sectional and cohort design using UK Biobank data.
• The study design addressed the limitation that BMI does not differentiate fat from lean mass or capture body composition distribution.