Ultra-Processed Food Consumption and Mental Health: A Systematic Review and Meta-Analysis of Observational Studies
Abstract
:1. Introduction
2. Methods
2.1. Search Strategy
2.2. Study Selection, Inclusion and Exclusion Criteria
2.3. Data Extraction
2.4. Critical Appraisal Assessment
2.5. Data Analysis
3. Results
3.1. Search Results
3.2. Study Characteristics
3.3. Details of Exposure Variables and Average Ultra-Processed Food Consumption
3.4. Meta-Analyses and Narrative Syntheses
3.4.1. Common Mental disorders
Meta-Analysis
Narrative Synthesis
3.4.2. Depression
Meta-Analyses
Narrative Syntheses
3.4.3. Anxiety
Meta-Analysis
Narrative Syntheses
3.4.4. Trauma and Stress
Narrative Syntheses
3.4.5. Addiction
Narrative Syntheses
3.4.6. Eating Disorders
Narrative Synthesis
4. Discussion
4.1. Limitations and Future Directions
4.2. Implications
5. Conclusions
Supplementary Materials
Author Contributions
Funding
Conflicts of Interest
References
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Author/Year | Study Characteristics | Confounding Variables | Mental Disorder Parameters | Results | Overall Critical Appraisal |
---|---|---|---|---|---|
Adjibade et al., 2019 [48] | Study design: Prospective Sample size: 26,730 Country: France Population: Adults Dietary assessment: 3 × 24-h dietary records | Age, sex, body mass index, marital status, educational level, occupational categories, household income per consumption unit, residential area, number of 24-h dietary records, inclusion month, energy consumption without alcohol, alcohol consumption, smoking status, and physical activity |
| ↑ vs. ↓ UPF
| No concerns. |
Amadieu et al., 2021 [23] | Study design: Cross sectional Sample size: 48 Country: Belgium Population: Adults Dietary assessment: 3 × 24-h dietary records | Total energy intake |
| ↑ vs. ↓ UPF
| Potential bias: strategies to deal with confounding factors and statistical analysis domains. |
Ayton et al., 2021 [31] | Study design: Cross sectional Sample size: 73 Country: UK Population: Adults and adolescents Dietary assessment: Clinician documented dietary intake by asking the patient to describe “a typical food intake per day over the past 2 weeks” | None |
| No between-group difference in average ultra-processed food consumption (Chi-squared test: p = 0.19):
| Potential bias: inclusion criteria; measurement validity; strategies to deal with confounding factors and statistical analysis domains. |
Bonaccio et al., 2021 [22] | Study design: Cross sectional Sample size: 2741 Country: Italy Population: Adults Dietary assessment: Food-frequency questionnaire | Age, sex, geographical area, living area, educational level, household income, marital status, number of cohabitants, occupational class, history of chronic diseases, diagnosis of ≥1 disease during confinement, use of psychoactive drugs before and during lockdown |
| ↑ vs. ↓ UPF RISCOVID-19 sample
Moli-LOCK sample
| Potential bias: measurement validity domain. |
Coletro et al., 2021 [21] | Study design: Cross sectional Sample size: 1693 Country: Brazil Population: Adults Dietary assessment: Food-frequency questionnaire | Sex, age, marital status, educational background, family income and medical diagnosis of depression or anxiety disorders |
| ↑ vs. ↓ UPF
| Potential bias: measurement validity domain. |
Faisal-Cury et al., 2021 [17] | Study design: Cross sectional Sample size: 2680 Country: Brazil Population: Adolescents Dietary assessment: Food-frequency questionnaire | Sex, age, skin colour, indigenous mother schooling, school administrative dependency, physical activity practice and the habit of having meals with parents |
| ↑ vs. ↓ UPF
| No concerns. |
Filgueiras et al., 2019 [30] | Study design: Cross sectional Sample size: 33 Country: Brazil Population: Children Dietary assessment: Semi-quantitative food-frequency questionnaire | Sugar, salt and fat consumption |
| Food addiction vs. no food addiction
| Potential bias: strategies to deal with confounding factors domain. |
Gómez-Donoso et al., 2019 [49] | Study design: Prospective Sample size: 14,907 Country: Spain Population: Adults Dietary assessment: Semi-quantitative food-frequency questionnaire | Sex, stratified by age groups, and year of entrance to the cohort, baseline BMI, total energy consumption, physical activity, smoking status, marital status, living alone, employment status, working hours per week, health-related career, years of education, adherence to Trichopoulou’s MeDiet Score, and baseline self-perception of competitiveness, anxiety and dependence levels |
| ↑ vs. ↓ UPF
| No concerns. |
Lopes Cortes et al., 2021 [27] | Study design: Cross sectional Sample size: 1270 Country: Brazil Population: Adults Dietary assessment: Food-frequency questionnaire | Sex, age, educational level, socioeconomic status, marital status, smoking, high-risk alcohol consumption, physical activity status, BMI status, and self-rated health |
| High vs. low/moderate perceived stress
| Potential bias: measurement validity domain. |
Noll et al., 2022 [24] | Study design: Cross sectional Sample size: 225 Country: Brazil Population: Adults Dietary assessment: 3 × 24-h dietary records | Age, marital status, income, and early and late post-menopause |
| ↑ vs. ↓ UPF
| No concerns. |
Ruggiero et al., 2020 [28] | Study design: Cross sectional Sample size: 8569 Country: Brazil Population: Adults Dietary assessment: 1 × 24-h dietary record | Age, sex and energy intake, education, geographical area, place of residence, sport activity, occupation, marital status, smoking, BMI, CVD, cancer, hypertension, diabetes and hyperlipidaemia |
|
| Potential bias: measurement validity domain. |
Schulte et al., 2022 [29] | Study design: Cross sectional Sample size: 45 Country: USA Population: Adults Dietary assessment: Food-frequency questionnaire | Height and weight measurements considered biologically implausible values (height <44 inches (112 cm) or >90 inches (229 cm); weight <55 lb (24.95 kg) or >1000 lb (453.59 kg)), incorrectly answering “catch questions,” which have commonly-known answers (e.g., 2 + 2) designed to “catch” participants who respond without reading the questions carefully |
| Food addiction vs. no food addiction
| Potential bias: measurement validity domain. |
Silva et al., 2021 [18] | Study design: Cross sectional Sample size: 70,427 Country: Brazil Population: Adolescents Dietary assessment: 1 × 24-h dietary record | Chronological age, ethnicity, region of the country, type of city (capital or interior), and physical activity |
| ↑ vs. ↓ UPF
| Potential bias: measurement validity domain. |
Werneck et al., 2020 [26] | Study design: Cross sectional Sample size: 100,648 Country: Brazil Population: Adults Dietary assessment: Food-frequency questionnaire | Chronological age, ethnicity, region of the country, type of city (capital or interior), and physical activity |
| ↑ vs. ↓ UPF with high sedentary behaviour
↑ vs. ↓ UPF with high television viewing
| Potential bias: inclusion criteria and measurement validity domains. |
Werneck et al., 2020 COVID [20] | Study design: Cross sectional Sample size: 42,024 Country: Brazil Population: Adolescents Dietary assessment: Food-frequency questionnaire | Sex, age group, highest academic achievement, working status during the pandemic, skin colour, alcohol use, tobacco smoking, diagnoses of COVID-19 on a close friend, co-worker or relative and adherence to the quarantine |
| Depression vs. no depression
| Potential bias: inclusion criteria and measurement validity domains. |
Werneck et al., 2021 [25] | Study design: Cross sectional Sample size: 99,791 Country: Brazil Population: Adolescents Dietary assessment: Food-frequency questionnaire | Age group, ethnicity, food insecurity, country region, type of city and physical activity |
| ↑ vs. ↓ UPF
| Potential bias: inclusion criteria and measurement validity domains. |
Zheng et al., 2020 [19] | Study design: Cross sectional Sample size: 13,637 Country: USA Population: Adults Dietary assessment: 1 × 24-h dietary record | Age, sex, race, BMI, educational level, annual family income, marital status, physical activity, drinking, smoking, current hypertension, diabetes history, heart disease history, and chronic bronchitis. |
| ↑ vs. ↓ UPF
| Potential bias: measurement validity domain. |
Mental Disorder Parameters | Direct Association | Inverse Association | No Association |
---|---|---|---|
Meta-analyses (MA) | |||
Common mental disorders combined | 1 Cross-sectional MA: OR 1.53, 95%CI 1.43 to 1.63, p < 0.001, N = 185,773 | ||
Depression | 2 (a) Prospective MA: HR 1.22, 95%CI 1.16 to 1.28; p < 0.001, N = 41,637 (b) Cross-sectional MA: OR 1.44, 95%CI 1.14 to 1.82, p = 0.002, N = 15,555 | ||
Anxiety | 1 Cross-sectional MA: OR 1.48, 95%CI 1.37 to 1.59, p < 0.001, N = 101,709 | ||
Narrative synthesis of individual studies | |||
Common mental disorders combined | 1 (Faisal-Cury, Leite et al., 2021) [17] | ||
Depression | 3 (Werneck, Silva et al., 2020, Bonaccio, Costanzo et al., 2021) [20,22] | 1 (Amadieu, Leclercq et al., 2021) [23] | |
Anxiety | 4 (Werneck, Vancampfort et al., 2020, Bonaccio, Costanzo et al., 2021) [22,26] | 1 (Amadieu, Leclercq et al., 2021) [23] | |
Trauma and stress | 4 (Bonaccio, Costanzo et al., 2021, Lopes Cortes, Andrade Louzado et al., 2021) [22,27] | 3 (Ruggiero, Esposito et al., 2021) [28] | 3 (Bonaccio, Costanzo et al., 2021, Ruggiero, Esposito et al., 2021) [22,28] |
Addiction | 5 (Filgueiras, Pires de Almeida et al., 2019, Amadieu, Leclercq et al., 2021, Schulte, Kral et al., 2021) [23,29,30] | 1 (Amadieu, Leclercq et al., 2021) [23] |
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Lane, M.M.; Gamage, E.; Travica, N.; Dissanayaka, T.; Ashtree, D.N.; Gauci, S.; Lotfaliany, M.; O’Neil, A.; Jacka, F.N.; Marx, W. Ultra-Processed Food Consumption and Mental Health: A Systematic Review and Meta-Analysis of Observational Studies. Nutrients 2022, 14, 2568. https://doi.org/10.3390/nu14132568
Lane MM, Gamage E, Travica N, Dissanayaka T, Ashtree DN, Gauci S, Lotfaliany M, O’Neil A, Jacka FN, Marx W. Ultra-Processed Food Consumption and Mental Health: A Systematic Review and Meta-Analysis of Observational Studies. Nutrients. 2022; 14(13):2568. https://doi.org/10.3390/nu14132568
Chicago/Turabian StyleLane, Melissa M., Elizabeth Gamage, Nikolaj Travica, Thusharika Dissanayaka, Deborah N. Ashtree, Sarah Gauci, Mojtaba Lotfaliany, Adrienne O’Neil, Felice N. Jacka, and Wolfgang Marx. 2022. "Ultra-Processed Food Consumption and Mental Health: A Systematic Review and Meta-Analysis of Observational Studies" Nutrients 14, no. 13: 2568. https://doi.org/10.3390/nu14132568