Table 2.
Ultra-processed food consumption and metabolic syndrome1and insulin resistance (n = 11).
| Author (year, location) | Source of data (mean/median follow-up time) | Population age - range and/or mean ± SD (% women) | Dietary questionnaire UPF assessment method | Adjustment | Main results |
|---|---|---|---|---|---|
| Prospective studies | |||||
| Canhada SL. (2023, Brazil)54 | Brazilian Longitudinal Study of Adult health (ELSA-Brazil) (7.9 y) |
n = 8,065 35-74 y (58.7%) |
Semi-quantitative 114-items FFQ NOVA classification (g/day) |
Age, gender, BMI, center, race/color, income level, school achievement, smoking status, physical activity, alcohol consumption, energy intake | Higher UPF consumption (4th quartile of >552 g/day vs. 1st quartile of <234 g/day) was associated with 19% increased risk of incident MetS (RR 1.19, 95% CI 1.07–1.32). A 150 g increase in UPF consumption a day was associated with a 4% higher risk of incident MetS (RR = 1.04, 95% CI 1.02–1.06) |
| Pan F. (2023, China)55 | China Nutrition and Health Survey (CNHS) (6 y) |
n = 5,147 >18 y (50.0%) |
24-hour dietary recall of 3 consecutive days at each survey Cumulative mean UPF intake NOVA classification (g/day) |
Gender, age, BMI, educational level, place of residence, regions, income level, smoking status, drinking status, metabolic equivalents, urbanicity, energy intake, and dietary factors (protein, total fat, carbohydrate, and sodium intake) | Higher UPF consumption (4th quartile of >36.1 g/day vs. 1st quartile of <6.5 g/day(was associated with 17% increased risk for MetS (HR 1.17, 95% CI 1.01–1.35; p for trend = 0.047) |
| Magalhães EIDS. (2022, Brazil)56 | The Ribeirão Preto birth cohort (14 y) |
n = 896 23-25 y (55.7%) |
Semi-quantitative 83-item FFQ (non-validated) NOVA classification (% of total kCal) NOVA classification (% of total food weight) |
Gender, age, skin color, educational level, marital status, household income, alcohol consumption, smoking status, physical activity, and energy intake | UPF consumption had no association with MetS (% of kCal RR 1.00, 95% CI 0.99-1.01; % of weight RR 1.00, 95% CI 0.99-1.01) |
| Cross-sectional studies | |||||
| Bezerra Barbosa L. (2023, Brazil)59 | Quilombos community-based survey | n = 895 19-59 y (100%) |
24-hour dietary recall NOVA classification (% of total kCal) NOVA score (ranging from 0 to 23) |
Model 3. Excess weight and neck circumference, plus variables from model 1 that showed p < 0.05 in the analysis for the aforementioned model - age and household income | Higher UPF consumption (4th quartile of 40.5% vs. 1st quartile of 0.0%) was not associated with a higher prevalence of MetS (PR 1.09, 95% CI 0.89-1.32). None of NOVA score categories were associated with higher prevalence of MetS |
| Liu Z. (2022, USA)50 | National Health and Nutrition Examination Survey (NHANES) | n = 6,545 >20 y mean 49.3 y (0.34 y SE) (53.5%) |
24-hour dietary recall NOVA classification (% of total food weight) |
Age, gender, race/ethnicity, educational level, family income to poverty ratio, marital status, smoking status, BMI, serum ALT, fasting triglycerides, total cholesterol, and uric acid | Higher UPF consumption (4th quartile of >68.3% vs. 1st quartile of <41.6%) was associated with higher odds for IR (OR 1.52, 95% CI 1.12–2.07), and a 10% increment in UPF consumption was associated with 11% higher odds for IR (OR 1.11, 95% CI 1.05–1.18; p for trend <0.002). IR was defined as the upper quartile (>Q4) of the study sample’s HOMA levels (>4.37) |
| Silva Meneguelli T. (2022, Brazil)62 | The Cardiovascular Health Care Program of the University Federal of Vicosa (PROCARDIO-UFV) | n = 325 ≥20 y (58.5%) |
24-hour dietary recall NOVA classification (% of total kCal) |
Gender, age, schooling, marital status, smoking status, and physical activity | No association was found between UPF and IR (PR 1.01, 95% CI 0.99-1.02). IR was defined as the upper quartile (>Q4) of the study sample’s TyG index (exact threshold not specified) |
| Hosseininasab D. (2022, Iran)61 | The community health center of the Tehran University of Medical Sciences (TUMS) | n = 391 18-48 y 36.7±9.1 y (100%) |
Semi-quantitative 147-items FFQ NOVA classification (g/day) |
Model 1. Age, BMI, physical activity, energy intake, supplement intake, job status | In adjusted linear regression models, an increase in one gram of UPF consumption was not significantly associated with QUICKI (β = -4.306, 95% CI -0.001-0.001) nor HOMA (β = -2.096, 95% CI -0.002-0.002) in the main multivariable model |
| Ivancovsky-Wajcman D. (2021, Israel)51 | Hepatic screening study | n = 789 40-70 y 58.8±6.6 y (47.4%) |
Semi-quantitative 117-items FFQ NOVA classification (% of total kCal) |
Age, gender, BMI, saturated fat intake, protein intake, physical activity, coffee consumption, and fiber intake | Higher UPF consumption (above median of 28%) was associated with higher odds for MetS (OR 1.88, 95% CI 1.31-2.71) |
| Martínez Steele E. (2019, USA)57 | National Health and Nutrition Examination Survey (NHANES) | n = 6,385 >20 y (50.8%) |
24-hour dietary recall NOVA classification (% of total kCal) |
Gender, age, race/ethnicity, family income to poverty ratio, educational attainment, smoking status, and physical activity | A 10% increase in UPF consumption was associated with a 4% higher prevalence of MetS (PR 1.04, 95% CI 1.02-1.07). Higher UPF consumption (5th quintile of >71% vs. 1st quintile of <40%) was associated with a higher prevalence of MetS (PR 1.28, 95% CI 1.09-1.50). The association was stronger in young adults (PR 1.94, 95% CI 1.39-2.72) and decreased with age |
| Lavigne-Robichaud M. (2018, Canada)58 | Nituuchischaayihititaau Aschii Environment-and-Health Study | n = 811 ≥18 y (58.7%) |
24-hour dietary recall NOVA classification (% of total kCal) |
Age, gender, area of residence, smoking status, alcohol consumption, and energy intake | Higher UPF consumption (5th quintile of 83% vs. 1st quintile of 21%) was associated with higher prevalence of MetS (OR 1.90, 95% CI 1.14-3.17; p for trend = 0.04) |
| Nasreddine L. (2018, Lebanon)60 | Community-based survey | n = 302 ≥18 y 39.4±13.8 y (61.2%) |
Semi-quantitative 80-items FFQ NOVA classification (% of total kCal) followed by dietary pattern analysis. The ultra-processed dietary pattern consisted mainly of fast foods, snacks, and sweets, while also including meat, roasted nuts, and liquor |
Age, gender, BMI, marital status, area of residence, educational level, income level, smoking status, physical activity, and energy intake | The ultra-processed dietary pattern had no association with MetS (OR 1.11, 95% CI 0.26-4.65) |
AHA, American Heart Association; ALT, alanine aminotransferase; FFQ, food frequency questionnaire; HOMA-IR, homeostatic model assessment for insulin resistance; HR, hazard ratio; IR, insulin resistance; MetS, metabolic syndrome; OR, odds ratio; PR, prevalence ratio; QUICKI, quantitative insulin-sensitivity check index; RR, relative risk; TyG, triglyceride-glucose (index); UPF, ultra-processed food.
1
MetS definition as accepted, recommended by several statements and guidelines of medical associations109,110 and with modification for use in the Asian population:111 the presence of at least three of five criteria; impaired fasting glucose (fasting glucose ≥100 mg/dl and/or medication), hypertension (systolic blood pressure/diastolic blood pressure ≥130/80-85 mmHg and/or medication), hypertriglyceridemia (triglycerides ≥150 mg/dl and/or medications), low levels of high-density lipoprotein cholesterol <40/50 mg/dl (among men and women, respectively), and abdominal obesity (elevated waist circumference among men and women, population- and country-specific definitions).