What this is
- This research investigates the link between ultra-processed food () consumption and the onset of .
- Using data from the French NutriNet-Santé cohort, the study analyzes dietary patterns and their potential impact on mental health.
- The findings suggest that higher intake is associated with an increased risk of developing over time.
Essence
- Higher consumption of is linked to an increased risk of in adults, suggesting dietary factors may influence mental health.
Key takeaways
- A 10% increase in ultra-processed food consumption correlates with a 21% higher risk of developing .
- The association is particularly significant for beverages and sauces or added fats, indicating specific food groups may have a greater impact.
Caveats
- The observational nature of the study limits the ability to establish causation between consumption and .
- Potential misclassification bias may arise from how foods are categorized within the NOVA classification system.
Definitions
- Ultra-processed foods (UPF): Manufactured food products containing numerous ingredients and additives, often energy-dense and low in micronutrients.
- Depressive symptoms: Indicators of depression assessed using the Center for Epidemiologic Studies Depression Scale (CES-D), with specific cut-off scores for diagnosis.
AI simplified
Introduction
Depression is a very common disorder, one of the five leading causes of years lived with disability in 2016 [1] and, according to WHO, the 1st leading cause of disease burden globally [2]. Depression etiology implies complex interactions between various factors including social, psychological, and biological factors.
Some treatments are effective but their limitations, as well as the detrimental effect of any depressive episode on the future course of the disease, make prevention crucial [3]. Among large-scale preventive interventions, acting on modifiable factors such as diet is a good candidate for public health action. Large-scale epidemiological studies have consistently documented an association between a healthy diet or dietary indexes reflecting the holistic quality of the diet and a lower risk of depression [4–7]. For instance, in the NutriNet-Santé study, we have observed that several dietary indexes reflecting nutritional recommendations were prospectively and inversely associated with the risk to develop depressive symptoms [8]. On the opposite, a western dietary pattern or pro-inflammatory diet characterized among other things by more processed foods has been associated with poor mental health [5, 6, 9]. Previous studies that reported associations between these diets and depression considered nutritional characteristics of the diet and interaction within the food matrix. However, some of those diets integrate a large part of ultra-processed food (UPF) (i.e., industrial recipes that are practical, ready to eat, and palatable [10]) which consumption has drastically increased over the past decades [11, 12]. For instance, a recent American study reported that, between 2007 and 2012, about 60% of the overall energy intake was provided by UPF [13]. In the French NutriNet-Santé study, UPF contributed to 35.9% of the daily energy intake and the proportion of UPF (%UPF) in the diet has been associated with a poor overall quality of the diet [14].
While processing ensures improvement of food availability, digestibility, short-term safety, transportability, and storage life [15], UPF are often energy-dense; mostly very rich in fat, sugar, and salt; and poor in micronutrients; thus, they may have a potential deleterious role on health. Beyond their unfavorable nutritional composition, they also contain other components generated during transformation such as neo-formed molecules produced during heating, food additives used in manufacturing, and molecules migrated from packaging, some of which might have a detrimental role for gut microbiota [16], involved in the development of several diseases characterized by an inflammatory component (including depression) [17]. The investigation of the association between UPF consumption and health is therefore important.
Recent studies on the link between UPF consumption and health have shown a positive association between UPF consumption and obesity [18], hypertension [19], metabolic disorders [20], and cancer [21]. To date, no study has focused on mental disorders.
The purpose of the present study was thus to investigate for the first time the prospective association between %UPF in the diet and the risk of depressive symptoms using the data of the NutriNet-Santé cohort study.
Methods
Study population
The data used in the current study are based on the web-based observational NutriNet-Santé cohort study, launched in France in 2009. The objective of the study is to investigate the link between nutrition and health, as well as determinants of dietary behaviors and nutritional status. Details on the design and method have been previously described [22]. Participants are adult volunteers (aged ≥ 18 years) recruited from the general population (all regions of France) with access to Internet by a vast multimedia campaign. Yearly, participants are asked to complete a set of self-administered web-based questionnaires related to sociodemographic data, economic conditions, physical activity, dietary intake, anthropometric data, and health status. The NutriNet-Santé study is conducted in accordance with the Declaration of Helsinki and was approved by the ethics committee of the French Institute for Health and Medical Research (IRB Inserm no. 0000388FWA00005831) and by the National Commission on Informatics and Liberty (CNIL no. 908450 and no. 909216). Electronic informed consent was obtained from all participants. The NutriNet-Santé study is registered in ClinicalTrials.gov↗ (NCT03335644↗).
Depressive symptoms
Two years after inclusion and every 2 years thereafter, depressive symptoms were assessed using the French version of the validated self-administered Center for Epidemiologic Studies Depression (CES-D) scale [23, 24]. The CES-D scale is composed of 20 items evaluating the frequency of depressive symptoms during the previous week. Response modalities are based on a four-point scale (0 = ‘less than 1 day’, 1 = ‘1–2 days’; 2 = ‘3–4 days’; and 3 = ‘5–7 days’). All sub-scores were summed to yield a total score ranging from 0 (no depressive symptoms) to 60 (elevated depressive symptoms). In our study, the internal consistency assessed by Cronbach’s alpha coefficient was high (> 0.80) at each CES-D scale assessment. In the present study, the presence of depressive symptoms was defined using the French validated cut-off values (CES-D ≥ 17 in men and ≥ 23 in women) [23, 24]. We defined ‘incident cases of depressive symptoms’ as participants who were free of depressive symptoms at the 1st CES-D assessment and then presenting depressive symptoms at least once during follow-up (i.e., based on one or multiple of the CES-D questionnaires completed after the initial CES-D assessment).
Dietary data and ultra-processed food consumption assessment
At inclusion and every 6 months thereafter, participants were invited to provide three non-consecutive 24-h dietary records. These were randomly assigned over a 2-week period (two weekdays and one weekend day) to cover intra-individual variability in intake. Consumption of all types of foods and beverages were reported on the web-based dietary record platform validated for self-administration [25]. The NutriNet-Santé web-based self-administered 24-h dietary records have also been validated against blood and urinary biomarkers [26, 27]. Portion sizes were determined using validated photographs [28] and household measures or directly by providing exact quantity (grams/milliliters). Energy and nutrient intakes were estimated using the published NutriNet-Santé food composition table including more than 3000 food items [29]. Composite home-made dishes were decomposed by using French recipes validated by nutrition professionals. Dietary under-reporters were identified using the method developed by Black [30]. The dietary data used in the present study are those collected during the first 2 years of follow-up (inclusion until the first CES-D assessment). Daily mean food intakes were calculated from all dietary records weighted according to the type of day (weekdays or weekend) with, on average, 7.98 (SD = 2.28) recorded days.
To account for the dietary profiles of participants, as a potentially strong confounder in the context of our study, we used principal component analysis (PCA) to extract ‘dietary pattern scores’ that are independent linear combinations of 22 pre-defined food groups, maximizing the explained variance. The number of dietary patterns retained was determined according to Cattel’s Scree plots and the interpretability of the principal components. Food groups with absolute loading coefficient > 0.3 were considered to be strongly associated with a pattern, and an individual pattern score was calculated by summing the intake of the 22 food groups, weighted by their loading coefficients. The first two dietary patterns accounted for about 18% of the initial variance (Additional file: Table S1). The first principal component, corresponding to a “healthy” dietary pattern, was strongly and positively correlated with intake of whole grains, olive oil, vegetables, and fruit. The second principal component, corresponding to a “western” dietary pattern, was strongly correlated with refined grains, potatoes, meat, and alcoholic beverages. 1
Classification of the level of processing
All foods and beverages were classified according to the four-group NOVA food classification system (un/minimally processed, culinary ingredient, processed food, and ultra-processed food) [12, 31]. The present study primarily focused on the ‘ultra-processed foods’ (UPF) category. The proportion (in weight, % grams/day) of UPF (%UPF) in the diet was calculated for each participant. UPF are manufactured food products containing numerous ingredients as well as additives such as hydrogenated oils, non-sugar sweeteners, modified starch, flavoring agents, emulsifiers, humectants, colors, and other additives used for cosmetic purpose. This food category includes among others: mass-produced packaged breads and buns; breakfast ‘cereals’, and ‘energy’ bars; sweet or savory packaged snacks; carbonated and ‘energy’ drinks; sweet fruit-based desserts with added sugars, artificial flavours and texturizing agents; flavoured milk drinks and cocoa drinks; industrial cookies, pastries, cakes, and cake mixes; confectionery (ice-cream, chocolate, candies); meat and chicken extracts and ‘instant’ sauces; margarines and spreads; cooked seasoned vegetables with ready-made sauces; ready-to-heat products (powdered and packaged ‘instant’ soups, noodles and desserts, pre-prepared pies, pasta and pizza dishes, poultry and fish ‘nuggets’, burgers, hot dogs, and other reconstituted meat products).
Covariates
Data on sex, date of birth, marital status (living alone, cohabiting, or separated/divorced/widowed), educational level (less than high school diploma, high school diploma, or university level), occupational categories (never-employed/other activity, self-employed, employee, intermediate profession, and managerial staff), residential area (rural or urban), smoking status (never, former or current smoker), household composition, and monthly household income (< 1200, 1200–1800, 1800–2700, > 2700 euros and a category of participants who refused to disclose their income) were collected at baseline using a self-administered web-based questionnaire [32].
Monthly household income was estimated per consumption unit (CU) using a weighting system: one CU attributed for the first adult in the household, 0.5 CU for other persons aged 14 or older, and 0.3 CU for children under 14 [33]. Weight and height data were collected by a validated self-administered anthropometric questionnaire [34]. Body mass index (BMI) was calculated as the ratio of weight to squared height (kg/m2). Physical activity was assessed using a short form of the French version of the International Physical Activity Questionnaire (IPAQ) [35]. Energy expenditure was classified as low physical activity (< 30 min of physical activity; equivalent to brisk walking/day), moderate physical activity (≥ 30 and < 60 min) or high physical activity (≥ 60 min). Prevalent and incident cases of cancer and cardiovascular diseases (strokes, myocardial infarctions, and acute coronary syndromes) were self-reported during follow-up; incident cases were validated by a medical committee based on medical records (diagnosis, hospitalization, radiological reports, electrocardiograms, etc.), and a link was made with medico-administrative databases of the French National Health insurance. Type 2 diabetes and hypertension were self-reported or identified using specific medication. In addition, subjective memory complaints were measured concomitantly with depressive symptoms scale using the French version of the validated self-administered Cognitive Difficulties Scale (CDS) [36, 37].
Statistical analysis
In the present study, data were missing for some covariates (n = 7 for marital status, n = 72 for occupational categories, n = 317 for residential area, n = 195 for educational level, and n = 435 for physical activity). As the proportion of missing values was < 1%, they were handled using the Hot Deck method, i.e., by replacing missing values with the value of respondents with similar characteristics [38] .
Participants included in the present study were compared with excluded eligible participants using chi-square tests or t tests. Participants’ characteristics and nutritional factors were compared across quartiles of %UPF using linear contrast or Cochran-Mantel-Haenszel tests. For descriptive purposes, nutrient intakes were energy-adjusted using the residual method [39].
The associations between %UPF (modeled as quartiles and as a continuous variable, while estimating coefficients associated with a 10% increase in UPF) and risk of depressive symptoms were assessed using Cox proportional hazards regression models for interval censored data. Hazard ratios (HR) and 95% confidence intervals (CI) were estimated. Linear trend tests across quartiles of %UPF were assessed by modeling these quartiles as ordinal variables. Age was used as the primary time scale variable. Entry time was defined as the age at the first CES-D measurement. Exit time was the age at last completed CES-D questionnaire, or the average of the age between the first occurrence of depressive symptoms and the age at the previous measurement for non-cases and cases respectively.
The first model was adjusted for age, sex, and BMI (continuous variable). The second model (main model) was additionally adjusted for marital status, educational level, occupational categories, monthly household income per consumption unit, residential area, energy intake without alcohol, number of 24 h records and inclusion month, smoking status, alcohol consumption, and physical activity. Five additional models were also performed to account for (a) PCA-extracted dietary patterns and intake of carbohydrates, lipids, and salt; (b) health events occurring during follow-up (cancer, type 2 diabetes, and cardiovascular diseases); (c) baseline CES-D score (continuous variable), and use of antidepressants during the follow-up; (d) CDS score (continuous variable), and (e) baseline CES-D score (continuous variable), use of antidepressants during the follow-up and CDS score (continuous variable).
A potential interaction between %UPF and the Western and the healthy dietary patterns on the risk of depressive symptoms was tested. We also considered a potential interaction between %UPF and the ratio between energy intake and energy needs calculated using the PAL (physical activity level) and basal metabolic rate (which was estimated using the Schofield equations accounting for age, sex, weight, and height [40]).
Another supplementary analysis was performed by considering the % of UPF within each food group. For this analysis, models were further adjusted for the intake of the considered food group. To account for the multiple testing, false discovery rate-corrected P values were estimated using the Benjamini-Hochberg procedure [41].
Flow chart of participant selection.Center for Epidemiologic Studies Depression Scale CES-D
Sensitivity analyses
A number of sensitivity analyses were performed to test the robustness of our findings. First, for comparison with international data, the proportion of total energy intake from UPF, usually used in other studies, in the diet was also calculated and the main analyses were rerun. Second, we tested the robustness of our findings when other CES-D cut-offs (a) 16 or (b) 19 were considered [23, 24]. We also repeated the analyses by considering as cases, only the participants who had depressive symptoms during follow-up (according to CES-D score) and also reported antidepressant treatment during follow-up. All statistical analyses were conducted using SAS (version 9.4; SAS institute Inc., Cary, NC, USA) with a significance level of 0.05 for two-sided tests.
Results
In the NutriNet-santé cohort, participants who completed only 1 CES-D questionnaire (n = 24,154), compared to those who completed it at least two during follow-up (n = 40,831) were younger, less physically active, and more likely to be women, current smoker or living alone. They were also more likely to have a BMI value ≥ 30, a slightly higher baseline CES-D score, a household income per unit consumption < 1800, or not having provided their income and less likely to have a chronic disease and an intermediate profession or to be managerial staff (Additional file 2: Table S2). In addition, among eligible participants (n = 35,782), those included were more educated, more often managerial staff, and more often physically active and presented less often an obesity or chronic diseases than those excluded (Additional file 3: Table S3).
Association between ultra-processed food intake and incident depressive symptoms in population subgroups. Values are hazard ratios (HR) and 95% confidence intervals (95% CI).body max index;basal metabolic rate;Center for Epidemiologic Studies Depression Scale;energy intake. Model was adjusted for sex, age, marital status, educational level, occupational categories, household income per consumption unit, residential area, number of 24-h dietary records, inclusion month, energy intake without alcohol, alcohol intake, body max index, smoking status, and physical activity (main model) BMI BMR CES-D EI
| Baseline characteristics | Quartile 1 | Quartile 2 | Quartile 3 | Quartile 4 | trendPa |
|---|---|---|---|---|---|
| %UPF, range | 0%–10% | 10%–14% | 14%–19% | 19%–76% | |
| %UPF, median (IQR) | 7% (3%) | 12% (2%) | 16% (2%) | 23% (8%) | |
| n | 6682 | 6683 | 6683 | 6682 | |
| Age, year | 51.6 ± 12.2 | 48.9 ± 13.4 | 46.6 ± 14.2 | 42.0 ± 15.0 | < 0.0001 |
| Sex,(%)n | 0.43 | ||||
| Male | 1520 (22.7) | 1663 (24.9) | 1577 (23.6) | 1590 (23.8) | |
| Female | 5162 (77.3) | 5020 (75.1) | 5106 (76.4) | 5092 (76.2) | |
| Marital status,(%)n | < 0.0001 | ||||
| Living alone | 619 (9.2) | 777 (11.6) | 920 (13.8) | 1332 (19.9) | |
| Cohabiting | 5244 (78.5) | 5202 (77.9) | 5093 (76.2) | 4785 (71.6) | |
| Separated/divorced/widowed | 819 (12.3) | 704 (10.5) | 670 (10.0) | 565 (8.5) | |
| Educational level,(%)n | 0.29 | ||||
| < High school diploma | 1311 (19.6) | 1283 (19.2) | 1369 (20.5) | 1226 (18.3) | |
| High school diploma | 986 (14.8) | 964 (14.4) | 983 (14.7) | 1196 (17.9) | |
| University level | 4385 (65.6) | 4436 (66.4) | 4331 (64.8) | 4260 (63.8) | |
| Occupational categories, n (%) | < 0.0001 | ||||
| Never-employed/other activity | 103 (1.5) | 150 (2.2) | 227 (3.4) | 367 (5.5) | |
| Self employed | 338 (5.1) | 324 (4.9) | 316 (4.7) | 373 (5.6) | |
| Employee | 1369 (20.5) | 1479 (22.1) | 1680 (25.2) | 1975 (29.6) | |
| Intermediate profession | 1984 (29.7) | 2022 (30.3) | 1973 (29.5) | 1834 (27.4) | |
| Managerial staff | 2888 (43.2) | 2708 (40.5) | 2487 (37.2) | 2133 (31.9) | |
| Household income,(%)n | < 0.0001 | ||||
| Not answered | 664 (9.9) | 587 (8.8) | 654 (9.8) | 744 (11.1) | |
| < 1200 euros | 624 (9.3) | 722 (10.8) | 825 (12.3) | 1071 (16.0) | |
| 1200–1800 euros | 1349 (20.2) | 1516 (22.7) | 1623 (24.3) | 1698 (25.4) | |
| 1800–2700 euros | 1668 (25.0) | 1717 (25.7) | 1727 (25.8) | 1712 (25.6) | |
| ≥ 2700 euros | 2377 (35.6) | 2141 (32.0) | 1854 (27.7) | 1457 (21.8) | |
| Residential area,(%)n | 0.07 | ||||
| Rural | 1431 (21.4) | 1444 (21.6) | 1519 (22.7) | 1499 (22.4) | |
| Urban | 5251 (78.6) | 5239 (78.4) | 5164 (77.3) | 5183 (77.6) | |
| Smoking status,(%)n | < 0.0001 | ||||
| Former smoker | 2799 (41.9) | 2547 (38.1) | 2313 (34.6) | 2071 (31.0) | |
| Current smoker | 721 (10.8) | 809 (12.1) | 771 (11.5) | 905 (13.5) | |
| Never-smoker | 3162 (47.3) | 3327 (49.8) | 3599 (53.9) | 3706 (55.5) | |
| Physical activity,(%)nb | < 0.0001 | ||||
| Low | 1212 (18.1) | 1473 (22.1) | 1682 (25.2) | 2014 (30.1) | |
| Moderate | 1478 (22.1) | 1612 (24.1) | 1660 (24.8) | 1614 (24.2) | |
| High | 3992 (59.8) | 3598 (53.8) | 3341 (50.0) | 3054 (45.7) | |
| Body mass index,(%)cn | 0.001 | ||||
| Underweight | 294 (4.4) | 276 (4.1) | 272 (4.1) | 350 (5.2) | |
| Normal weight | 4517 (67.6) | 4459 (66.7) | 4417 (66.1) | 4323 (64.7) | |
| Overweight | 1448 (21.7) | 1477 (22.1) | 1522 (22.8) | 1414 (21.2) | |
| Obesity | 423 (6.3) | 471 (7.1) | 472 (7.0) | 595 (8.9) | |
| Chronic diseases,(%)dn | 741 (11.1) | 724 (10.9) | 683 (10.2) | 559 (8.4) | < 0.0001 |
| Baseline CES-D, mean score | 7.74 ± 5.38 | 7.94 ± 5.35 | 8.26 ± 5.46 | 8.90 ± 5.56 | < 0.0001 |
| Nutritional factors | Quartile 1 | Quartile 2 | Quartile 3 | Quartile 4 | trendPa |
|---|---|---|---|---|---|
| UPF, range | 0%–10% | 10%–14% | 14%–19% | 19%–76% | |
| UPF, median (IQR) | 7% (3%) | 12% (2%) | 16% (2%) | 23% (8%) | |
| n | 6682 | 6683 | 6683 | 6682 | |
| Total energy intake, Kcal/d | 1830 ± 434 | 1913 ± 446 | 1921 ± 448 | 1934 ± 459 | < 0.0001 |
| Alcohol intake, g/d | 9.5 ± 12.2 | 9.8 ± 12.2 | 8.3 ± 10.7 | 6.9 ± 9.9 | < 0.0001 |
| Energy intake without alcohol, Kcal/d | 1764 ± 413 | 1845 ± 421 | 1863 ± 427.4 | 1886 ± 442 | < 0.0001 |
| Carbohydrates,% energyb | 42.8 ± 6.5 | 43.1 ± 5.7 | 43.2 ± 5.7 | 43.6 ± 5.7 | < 0.0001 |
| Lipids, % energyb | 38.2 ± 6.1 | 38.7 ± 5.4 | 38.9 ± 5.1 | 39.0 ± 5.3 | < 0.0001 |
| Saturated fatty acids, g/dc | 31.6 ± 6.7 | 32.8 ± 6.5 | 33.4 ± 6.3 | 33.7 ± 6.4 | < 0.0001 |
| Monounsaturated fatty acids, g/dc | 30.8 ± 6.7 | 30.4 ± 5.8 | 30.3 ± 5.6 | 30.2 ± 5.5 | < 0.0001 |
| Polyunsaturated fatty acids, g/dc | 11.6 ± 3.9 | 11.4 ± 3.4 | 11.3 ± 3.3 | 11.4 ± 3.4 | 0.003 |
| Omega-3 fatty acids, g/dc | 1.6 ± 0.7 | 1.4 ± 0.6 | 1.4 ± 0.6 | 1.3 ± 0.6 | < 0.0001 |
| Protein, % energyb | 18.6 ± 3.8 | 17.8 ± 3.2 | 17.6 ± 3.3 | 17.1 ± 3.4 | < 0.0001 |
| Beta-carotene, μg/dc | 4031 ± 2233 | 3668 ± 1845 | 3502 ± 1927 | 3121 ± 1893 | < 0.0001 |
| Vitamin C, mg/dc | 132 ± 58.9 | 122 ± 63.0 | 116 ± 63.0 | 107 ± 64.1 | < 0.0001 |
| Vitamin D, μg/dc | 2.9 ± 1.8 | 2.8 ± 1.6 | 2.7 ± 1.5 | 2.5 ± 1.5 | < 0.0001 |
| Vitamin E, mg/dc | 12.1 ± 3.6 | 11.7 ± 3.2 | 11.6 ± 3.2 | 11.5 ± 3.3 | < 0.0001 |
| Folic acid, μg/dc | 356 ± 91.5 | 337.9 ± 79.7 | 330 ± 82.5 | 311 ± 86.3 | < 0.0001 |
| Vitamin B12, μg/dc | 5.8 ± 4.4 | 5.5 ± 3.7 | 5.3 ± 3.7 | 5.0 ± 3.5 | < 0.0001 |
| Magnesium, mg/dc | 367 ± 88.0 | 348 ± 80.3 | 334 ± 81.7 | 318 ± 86.8 | < 0.0001 |
| Fiber (g/d)c | 22.0 ± 5.7 | 20.6 ± 5.1 | 19.7 ± 5.1 | 18.2 ± 5.6 | < 0.0001 |
| Starchy foods | 213 ± 95.8 | 209 ± 92.2 | 199 ± 86.6 | 182 ± 83.8 | < 0.0001 |
| Fruit and vegetables | 579 ± 241 | 530 ± 215 | 503 ± 215 | 450 ± 225 | < 0.0001 |
| Meat, fish, eggs | 140 ± 64.1 | 134 ± 59.8 | 130 ± 59.8 | 120 ± 61.7 | < 0.0001 |
| Alcoholic drinks | 113 ± 151 | 118 ± 150 | 99.1 ± 129 | 82.9 ± 118 | < 0.0001 |
| Beverages | 1385 ± 580 | 1232 ± 481 | 1092 ± 438 | 945 ± 405 | < 0.0001 |
| Dairy products | 231 ± 152 | 233 ± 137 | 245 ± 139 | 256 ± 146 | < 0.0001 |
| Fatty / sweet products | 80.2 ± 51.1 | 99.0 ± 55.1 | 106 ± 58.7 | 116 ± 63.1 | < 0.0001 |
| snacks | 104 ± 66.4 | 125 ± 69.5 | 138 ± 75.4 | 156 ± 86.8 | < 0.0001 |
| Sauces/added fats | 28.7 ± 16.5 | 28.1 ± 16.1 | 27.4 ± 16.5 | 25.5 ± 16.7 | < 0.0001 |
| Quartile 1 | Quartile 2 | Quartile 3 | Quartile 4 | trendP | Continuousa | Pb | |
|---|---|---|---|---|---|---|---|
| UPF, range | 0%–10% | 10%–14% | 14%–19% | 19%–76% | |||
| UPF, median (IQR) | 7% (3%) | 12% (2%) | 16% (2%) | 23% (8%) | |||
| n | 6682 | 6683 | 6683 | 6682 | 26,730 | ||
| Number of cases | 491 | 459 | 557 | 714 | 2221 | ||
| Person years | 21,597 | 21,097 | 20,468 | 19,918 | 83,080 | ||
| Model 1c | 1 (ref) | 0.90 (0.79; 1.02) | 1.07 (0.94; 1.21) | 1.31 (1.16; 1.47) | < 0.0001 | 1.23 (1.17; 1.29) | < 0.0001 |
| Model 2d | 1 (ref) | 0.91 (0.80; 1.04) | 1.09 (0.96; 1.23) | 1.30 (1.15; 1.47) | < 0.0001 | 1.21 (1.15; 1.27) | < 0.0001 |
| Model 3e | 1 (ref) | 0.91 (0.80; 1.04) | 1.08 (0.95; 1.23) | 1.29 (1.13; 1.47) | < 0.0001 | 1.22 (1.16; 1.29) | < 0.0001 |
| Model 4f | 1 (ref) | 0.92 (0.81; 1.04) | 1.09 (0.97; 1.24) | 1.31 (1.16; 1.48) | < 0.0001 | 1.21 (1.15; 1.27) | < 0.0001 |
| Model 5g | 1 (ref) | 0.88 (0.77; 1.00) | 1.00 (0.88; 1.13) | 1.13 (1.00; 1.28) | 0.01 | 1.14 (1.09; 1.20) | < 0.0001 |
| Model 6h | 1 (ref) | 0.88 (0.78; 1.00) | 1.06 (0.94; 1.20) | 1.27 (1.13; 1.44) | < 0.0001 | 1.21 (1.15; 1.27) | < 0.0001 |
| Model 7i | 1 (ref) | 0.86 (0.76; 0.98) | 1.00 (0.88; 1.13) | 1.13 (1.00; 1.28) | 0.01 | 1.15 (1.09; 1.21) | < 0.0001 |
| Quartile 1 | Quartile 2 | Quartile 3 | Quartile 4 | trendPa | |
|---|---|---|---|---|---|
| Starchy foods | 1 (ref) | 0.97 (0.86; 1.10) | 0.97 (0.86; 1.10) | 1.01 (0.89; 1.14) | 0.98 |
| Fruit and vegetables | 1 (ref) | 0.92 (0.81; 1.03) | 0.97 (0.86; 1.10) | 1.08 (0.95; 1.22) | 0.57 |
| Meat, fish, eggs | 1 (ref) | 1.08 (0.96; 1.22) | 0.97 (0.86; 1.10) | 1.04 (0.92; 1.17) | 0.98 |
| Beverages | 1 (ref) | 1.19 (0.91; 1.54) | 1.00 (0.89; 1.12) | 1.25 (1.13; 1.38) | 0.002 |
| Dairy products | 1 (ref) | 1.03 (0.91; 1.16) | 1.06 (0.94; 1.20) | 1.13 (1.00; 1.27) | 0.2 |
| Fatty/sweet products | 1 (ref) | 1.02 (0.90; 1.16) | 1.05 (0.93; 1.18) | 1.08 (0.96; 1.22) | 0.57 |
| Snacks | 1 (ref) | 0.97 (0.85; 1.10) | 1.10 (0.98; 1.25) | 1.10 (0.98; 1.24) | 0.18 |
| Sauces/added fats | 1 (ref) | 1.05 (0.93; 1.19) | 0.96 (0.85; 1.09) | 1.23 (1.10; 1.39) | 0.02 |
Sensitivity analyses
Sensitivity analyses using other CES-D cut-offs (16 or 19) to identify cases of depressive symptoms or %UPF weighted on energy intake rather than on quantity consumed in gram yielded similar associations (Additional file 5: Table S4 and Additional file 6: Table S5). In addition, the analyses considering as cases only participants who had depressive symptoms and also used antidepressant treatment during follow-up showed stronger associations (Additional file 7: Table S6). However, the associations were not significant when the %UPF was modeled as quartiles, mainly due to low statistical power because of the small number of cases (n = 113 cases). In the main model, the estimated HR for the analysis with a 10% increase in UPF consumption was 1.43 (95%CI = 1.18–1.73).
Discussion
In this large cohort study of adults, the %UPF in the diet was positively associated with the risk of incident depressive symptoms even after extensive adjustment in particular for dietary patterns correlated to %UPF. Indeed, in coherence with previous studies, we found that the %UPF in the diet varied according to the socioeconomic profile and lifestyle of individuals [42, 43].
The first hypothesis which may explain our findings relies on the fact that ultra-processed foods are often part of generally “unhealthy”/western dietary patterns. Although not entirely composed of UPF, western diet is marked by elevated consumption of UPF and has been associated with depressive outcomes in epidemiologic study. Indeed, in a previous investigation based on data from the NutriNet-Santé study, the diet of high consumers of UPF was relatively ‘unhealthy’ [14], i.e., characterized by a low consumption of fruit and vegetables and a high intake of sweet products or soft drinks. Similar findings were observed in a study conducted within the NHANES, a representative survey conducted in the American population [44]. This is of high importance since ‘western’-style dietary patterns have been previously related to depression [5, 6]. In particular, a recent meta-analysis including 21 studies conducted in 10 countries reported that a diet rich in red meat, processed meat, refined grains, sweets, high-fat dairy products, butter, potatoes, and high-fat gravy was associated with an elevated risk of depression: presenting a high versus a low ‘Western-type diet’ score was associated with an 18% (95%CI = 5%–34%) increased risk [6].
In the Whitehall study, which included middle-aged UK adults, a diet rich in some types of UPF foods such as sweetened desserts, fried food, and processed meat but also refined grains and high-fat dairy products was also associated with higher odds of depressive symptoms (ORtertile3 vs. tertile1 = 1.58, 95%CI = 1.11–2.23) [45]. An increased risk of depression was also observed among the participants included in the Seguimiento Universidad de Navarra—University of Navarra Follow-up (SUN) Project, who were in the highest quintile of fast food (hamburgers, sausages, pizza) and processed pastries (muffins, doughnuts, croissants) compared with those in the lowest quintile (HRquintiles 5 vs. quintiles 1 = 1.37, 95%CI = 1.02–1.83) [46]. In addition, in the Personality and Total Health (PATH) Through Life Study—a longitudinal community study including 3663 Australian participants from 3 age cohorts (20+; 40+; 60+ years), a higher score concerning an unhealthy dietary pattern characterized by a high consumption of roast meat, sausages, hamburgers, steak, chips, crisps, and soft drinks was an independent predictor of the risk depressive symptoms over time [47]. Anyway these studies do not allow to distinguish the specific role of nutritional profile versus non-nutritional components, part of the western diet, implied in the association with depression.
Then, when stratifying analysis on ‘adequate energy intake’ reflected by the ratio between energy intake and energy needs, a stronger association was observed among participants with lower energy intakes. This may suggest that a limited energy intake associated with a large part of UPF in the diet could limit the intakes of bioactive micronutrients that are beneficial for depression prevention.
Importantly, the link between UPF consumption and depression could be at least partly explained by effect of some non-nutrient components used for or produced during processing. Indeed, UPF often contain products additives (in particular emulsifiers) or molecules resulting from high-temperature heating which may among others cause alterations to the gut microbiota [16], which has been suggested to show important interrelations with mental health [48]. To the best of our knowledge, no investigation in humans has been conducted to explore the specific role of food additives for the risk of depression except concerning artificial sweeteners. Some experimental studies argue for a modulating role of artificial sweeteners, such as aspartame, on neurotransmitters regulation which may lead to symptoms such as mood or depression [49]. However, a recent review based on more than 370 scientific papers reported that data are currently insufficient to conclude [50].
A specific role of UPF on depression, beyond nutritional aspects, may, among others, also rely on changes in microbiota induced by non-nutritive components, in particular by emulsifiers which may provoke gut dysbiosis and mediate inflammatory processes in the gut [51]. In addition, a specific nanoparticle used as, TiO2whitening agent, has been related to neuroinflammation in an animal model [52]. Findings from animal studies have suggested that some food additives (e.g., monosodium glutamate) may induce anxiety and depression symptoms [53] or increase susceptibility to the depressor stimuli [54].
The association reported in this study is of interest in terms of public health namely for prevention of depression. In this context, it should be noted that the benefit of decreasing %UPF in diet may be even stronger in other populations than in our sample of French volunteers included in a diet-related study. Indeed, while UPF (as % of energy) accounted for 32% in our population, a higher proportion has been documented in other studies. For instance, in the UK national diet and Nutrition Survey, 53% of the energy intake [55] was provided through UPF. In North America, %UPF was even higher as evaluated by the representative survey (NHANES), with an average of 57.5% of calories coming from ultra-processed foods [44]. Such elevated consumption of UPF may be an important lever in terms of public health strategy for the prevention of depression. Our results showing that the association between %UPF and the risk of depressive symptoms vary across food groups may help guiding future research toward the non-nutrient components that are most likely to convey an increased risk of depression. Should ultra-processed beverages, dairy products, snacks, and fats share common food additives that are less present in other food groups, these food additives might warrant further scrutiny.
Some limitations of our study should be noted. First, the allocation of foods to the categories defined by the NOVA may have led to misclassification bias—particularly since the food composition table used so far in our study is based on generic foods, and not foods as sold. Thus, for food which can be more or less processed, the most frequent level of processing for a food item was applied. Second, given the observational design of our study, we cannot entirely exclude reverse causality, although our study is of prospective nature. Moreover, despite the fact that we accounted for a wide range of confounders in our statistical models, unmeasured factors related to depression such as life events might have led to potential residual confounding; thus, causality of the observed associations is not established. Third, participants of the NutriNet-Santé study were volunteers in a nutrition-related cohort and thus more interested in nutritional issues and healthy lifestyles than the general population. In particular, their consumption of UPF may be lower than in the general population which may have led to an underestimation of the associations investigated in our study. In addition, excluding participants who completed only one CES-D questionnaire and participants with depressive symptoms at baseline might have resulted in excluding those most likely to have depressive symptoms. Similar analysis in this specific population should deserve further investigations. All this might have led to a selection bias and thus a potential bias in the risk estimates. As a result, any generalization of our findings should be done with caution. Important strengths of this study include its prospective design, the large sample, and the repeated assessment of depressive symptoms using a validated tool, as well as the quality of the dietary data based on repeated dietary records allowing to assess usual dietary intakes. Finally, the wide range of confounding factors contributed to improve the validity of our findings.
Conclusions
In this prospective study, we found a positive association between the %UPF in the overall diet and the risk of incident depressive symptoms. Positive associations were also found for beverages and sauces or added fats, when %UPF in the food groups was investigated.
This study highlights a potential role of non-nutritional aspects of the diet in the depression development. Overall, there is a need to collect more detailed data on the degree of food processing and additive or contaminant contents in food surveys to better explore UPF consumption and its potential impact on health.
Additional files
Additional file 1:Table S1. Loading coefficients of the PCA-extracted dietary patterns. (PDF 13 kb)Additional file 2:Table S2. Comparison of participants who completed one CES-D questionnaire to those who completed it at least two during follow-up, NutriNet-Santé study. (PDF 301 kb)Additional file 3:Table S3. Comparison of included and excluded participants, NutriNet-Santé study. (PDF 132 kb)Additional file 4:Figure S1. Dose-response association between ultra-processed food intake and incident depressive symptoms using Restricted Cubic Spline. (PDF 190 kb)Additional file 5:Table S4. Association between ultra-processed food intake and incident depressive symptoms using other cut-off values to define depressive symptoms, NutriNet-Santé study. (PDF 134 kb)Additional file 6:Table S5. Association between ultra-processed food intake (% of energy) and incident depressive symptoms, NutriNet-Santé study. (PDF 125 kb)Additional file 7:Table S6. Association between ultra-processed food intake and incident depressive symptoms (considering as cases, only the participants who had depressive symptoms and also reported antidepressant treatment during follow-up), NutriNet-Santé study. (PDF 125 kb)