Diabetes Metab J.  2024 Jul;48(4):780-789. 10.4093/dmj.2023.0335.

Associations of Ultra-Processed Food Intake with Body Fat and Skeletal Muscle Mass by Sociodemographic Factors

• Jung S
• Seo J
• Kim JY
• Park S

• KMID: 2558045
• DOI: http://doi.org/10.4093/dmj.2023.0335

Abstract

Background
The effects of excessive ultra-processed food (UPF) consumption on body composition measures or sociodemographic disparities are understudied in Korea. We aimed to investigate the association of UPF intake with percent body fat (PBF) and percent appendicular skeletal muscle mass (PASM) by sociodemographic status in adults.
Methods
This study used data from the Korea National Health and Nutrition Examination Survey 2008–2011 (n=11,123 aged ≥40 years). We used a NOVA system to classify all foods reported in a 24-hour dietary recall, and the percentage of energy intake (%kcal) from UPFs was estimated. PBF and PASM were measured by dual-energy X-ray absorptiometry. Tertile (T) 3 of PBF indicated adiposity and T1 of PASM indicated low skeletal muscle mass, respectively. Multinomial logistic regression models were used to estimate odds ratios (OR) with 95% confidence interval (CI) after adjusting covariates.
Results
UPF intake was positively associated with PBF-defined adiposity (OR_{per 10% increase}, 1.04; 95% CI, 1.002 to 1.08) and low PASM (OR_{per 10% increase}, 1.05; 95% CI, 1.01 to 1.09). These associations were stronger in rural residents (PBF: OR_{per 10% increase}, 1.14; 95% CI, 1.06 to 1.23; PASM: OR_{per 10% increase}, 1.15; 95% CI, 1.07 to 1.23) and not college graduates (PBF: OR_{per 10% increase}, 1.06; 95% CI, 1.02 to 1.11; PASM: OR_{per 10% increase}, 1.07; 95% CI, 1.03 to 1.12) than their counterparts.
Conclusion
A higher UPF intake was associated with higher adiposity and lower skeletal muscle mass among Korean adults aged 40 years and older, particularly in those from rural areas and with lower education levels.

Keyword

Absorptiometry, photon; Adipose tissue; Food, processed; Muscle, skeletal; Sociodemographic factors

Figure

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  Fig. 1. Associations of ultra-processed food (UPF) intake with percent body fat defined adiposity and low skeletal muscle mass. Multinomial logistic regression models were used to estimate odds ratios (ORs) and their corresponding 95% confidence intervals (CIs) for the tertile (T) 3 of percent body fat and the T1 of percent appendicular skeletal muscle mass (ASM) comparing quartile (Q) 2, 3, and 4 to Q1 of UPF intake as the exposure variables (T3 of percent body fat: ≥24.5% for male, ≥36.0% for female; T1 of percent ASM: <30.7% for male, <24.1% for female). P for trends was determined by treating the median value of UPF intake as a continuous variable using multinomial logistic regression models. A 10% increase in UPF intake was used to estimate ORs for higher adiposity or lower ASM. A multivariable-adjusted model was adjusted for age, sex, residential area, education level, monthly household income level, marital status, current smoking, current drinking, walking exercise, weight training, and total energy intake.

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  Fig. 2. Associations of ultra-processed food (UPF) intake with percent body fat defined adiposity (A) and low skeletal muscle mass (B) by subgroups. Associations of ultra-processed food (UPF) intake with percent body fat defined adiposity and low skeletal muscle mass by subgroups. Multinomial logistic regression models were used to estimate odds ratios (ORs) and their corresponding 95% confidence intervals (CIs) for the tertile (T) 3 of percent body fat and the T1 of percent ASM comparing quartile (Q) 4 to Q1 of UPF intake as the exposure variables (T3 of percent body fat: ≥24.5% for male, ≥36.0% for female; T1 of percent ASM: <30.7% for male, <24.1% for female). P for trends was determined by treating the median value of UPF intake as a continuous variable using multinomial logistic regression models. A 10% increase in UPF intake was used to estimate ORs for higher adiposity or lower appendicular skeletal muscle mass (ASM). A multivariable-adjusted model was adjusted for age, sex, residential area, education level, monthly household income level, marital status, current smoking, current drinking, walking exercise, weight training, and total energy intake, except the corresponding subgroup variates.

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