Nutritional Intake and Depression: Hierarchical Multi-Factor Regression Analysis Using NHANES 2021–2023 Data

Abstract

Background: Depression is a serious and pervasive mental health illness that affects around 280 million people around the globe, causing psychological distress, physical health problems, and increased mortality rates. Conventional treatments are limited in accessibility and effectiveness, as relapse rates remain high. Emerging literature suggests that diet and nutrition play a role in influencing mental health, especially depression.

Methods: This study uses data from the National Health and Nutrition Examination Survey (NHANES) 2021–2023 cycle. Participants (n = 8,860; ages ≥12 years) completed two 24-hour dietary recalls to document nutrient intake and the Patient Health Questionnaire-9 (PHQ-9) to assess depressive symptoms. Demographics (e.g., gender, age, education, BMI, income, and marital status) and lifestyle factors (physical activity, sleep, and alcohol intake) were included in the analyses. Hierarchical multiple linear regression models were conducted using nutrient intake, demographic, and lifestyle factors as predictors with log-transformed PHQ-9 scores as the dependent variable.

Results: Model 1 (nutrients only) was not statistically significant overall (p = 0.060), but higher fiber intake (β = -0.236, p = 0.004) and lower carbohydrate intake (β = 0.203, p = 0.036) predicted lower depressive symptoms, while magnesium intake showed a marginal positive association (β = 0.137, p = 0.050). Model 2 (adding demographics) significantly improved the model (p < 0.001), and higher protein (β = -0.210, p = 0.008) and fiber intake (β = -0.193, p = 0.013) predicted lower symptoms of depression; from the demographics, higher BMI, being single/widowed, lower age, lower education, and lower income were associated with more severe symptoms. Model 3 (adding lifestyle factors) did not significantly improve the model (p = 0.341), and the lifestyle variables were not significant.

Conclusion: Higher dietary protein and fiber intake were independently associated with lower depressive symptoms after controlling for demographics and lifestyle factors. Demographic factors, including younger age, lower education, lower income, higher BMI, and being single/ widowed/separated, were significant predictors of depression.

Implications: These findings show a need for public health initiatives and policies to promote nutritional education and provide access to foods that reduce the risk of depression in low-income areas and low-education populations.

Limitations: This is a cross-sectional study that relied on self-reported data. Future longitudinal and experimental studies are needed to clarify the direction and mechanisms of these relationships.

Introduction

Depression, a mental health disorder characterized by persistent low mood or a loss of pleasure and interest in activities (World Health Organization, 2025), is one of the leading health challenges of the 21st century. In 2019, approximately 280 million individuals worldwide experienced depression (World Health Organization, 2025). Beyond its psychological burden, depression is strongly linked to physical health problems such as cardiovascular disease (CVD), cancer, diabetes, and respiratory diseases, and it is associated with increased all-cause mortality. Even mild symptoms can impair daily functioning (Steptoe, 2006) and work performance, with each one-point increase on the Patient Health Questionnaire-9 (PHQ-9) corresponding to a 1.65% decline in productivity (Beck et al., 2011). Furthermore, major depressive disorder (MDD) carries an estimated suicide risk of approximately 15% (Orsolini et al., 2020).

Longitudinal studies underscore the impact of depression on survival. For example, the Health, Alcohol and Psychosocial Factors In Eastern Europe (HAPIEE) project found that higher depressive symptoms (as measured by the Center for Epidemiologic Studies Depression [CES-D] scale) were linked to a 13–17% higher risk of all-cause mortality and a 20–23% higher risk of CVD-related death over seven years (Kozela et al., 2016). Additionally, depression increases the likelihood of multimorbidity: 17.7% of people with depression have two comorbid conditions (such as arthritis, asthma, diabetes, angina, chronic back pain, visual or hearing impairments, edentulism, and tuberculosis), 9.1% have three, and 4.9% have four or more, versus just 7.4%, 2.4%, and 0.9%, respectively, among non-depressed individuals (Stubbs et al., 2017).

While standard treatments, such as antidepressant medications and psychotherapy, have shown effectiveness for some individuals, Rush et al. (2022) emphasized that the effects are highly variable, as many patients see little benefit, and others experience relapse. Over one-third of individuals with major depressive disorder (MDD) do not achieve sustained improvement despite multiple treatment attempts (Agency for Healthcare Research and Policy [AHQR], 2011). In the Sequenced Treatment Alternatives to Relieve Depression trial, researchers found that even after four treatment steps, merely two-thirds of patients reached remission. Moreover, among the individuals who saw improvement, between 35% and 70% of them experienced relapse, with the likelihood of lapse increasing with the increased number of treatment steps (Rush et al., 2006). These findings demonstrate the need for other strategies beyond repeated medication changes. Therefore, researchers are increasingly beginning to explore alternative and complementary approaches to mitigate depression.

Given the limitations in conventional treatments against depression and the rising emphasis on preventive healthcare, there is growing interest in the role of everyday lifestyle factors. The Biopsychosocial Model (Engel, 1977) proposes that mental health is influenced by a combination of biological, psychological, and social influences—domains in which nutrition plays a central role. Nutrition has been linked to biological pathways like inflammation (Stumpf et al., 2023), psychological factors like cognition (Stevenson et al., 2014) and mood (Strasser & Fuchs, 2015), and social determinants such as socioeconomic status (Darmon et al., 2008).

A growing body of research suggests a significant correlation between diet quality and common mental health disorders, particularly depression. Ekinci and Sanlier (2023) reported that poor dietary habits—characterized by low intake of fruits, vegetables, and essential nutrients such as omega-3 fatty acids, vitamin D, B vitamins, magnesium, selenium, zinc, and copper, but also processed unhealthy foods—are associated with higher rates of depression. Conversely, epidemiological evidence indicates that diets rich in fruits, vegetables, whole grains, fish, and nuts are associated with a lower risk of depression. Several biological mechanisms proposed to explain these associations include reduced inflammation, lower oxidative stress, improved neuroplasticity, and healthier gut microbiome profiles (Marx et al., 2017). Adherence to anti-inflammatory dietary patterns, such as the traditional Mediterranean diet—characterized by high consumption of fruits, vegetables, legumes, whole grains, nuts, fish, and olive oil—can reduce the risk of depressive symptoms and clinical depression by approximately 33% (Lassale et al., 2019). In addition, specific micronutrients, including folate (Alpert & Fava, 1997), vitamin D, vitamin B6, selenium, magnesium, zinc, and copper, have been found to be inversely associated with depression (Ekinci & Sanlier, 2023).

Despite these promising findings, much of the existing research is narrow in scope, focusing on small sample sizes or specific populations (e.g., community-dwelling welfare recipients aged 60–92 in a single town; German et al., 2011). Many studies also only focus on a specific nutrient, like the effects of a low-carbohydrate diet on anxiety and depression (Varaee et al., 2023). Few studies have assessed a broad range of macro- and micronutrients and their correlation with depression, consisting of data representing a large population size and a wide age range.

To address this gap, the present study uses data from the National Health and Nutrition Examination Survey (NHANES) collected between August 2021 and August 2023. The datasets from NHANES include dietary intake data and mental health-depression screener data from a large, nationally representative population of people 12 or older. This study aims to evaluate the relationship between depression and a wide range of macronutrients and micronutrients in this population.

Methods

Dataset and study population

This study uses publicly available data from the National Health and Nutrition Examination Survey (NHANES), a large-scale program run by the U.S. Centers for Disease Control and Prevention (CDC), to assess health and nutrition status in a nationally representative sample of the U.S. population. NHANES employs a data collection team composed of nurses, health technicians, and trained diet and health interviewers. The NHANES team travels to each community to collect data. To ensure that the survey participants are an accurate representation of people of all ages in the US, NHANES uses a multistage probability sampling design to ensure representativeness across age, gender, and racial/ethnic groups, with oversampling of children and adolescents, adults aged 60 and older, African Americans, Asians, and Hispanics.

Survey samples are drawn from smaller groups nested within larger population segments to ensure representativeness. In selected households, all individuals under 19 or over 60 were eligible to participate. For households with 1–3 adults, one adult was randomly selected; for households with four or more adults, two were randomly selected. Participants take part in a dietary interview covering food, beverage, and supplement intake. Blood samples are collected and dental examinations are performed for all but the youngest participants. Medical tests and procedures performed vary depending on each participant’s age (NHANES, 2024). NHANES includes demographic data, dietary data, examination data, laboratory data, questionnaire data, and limited access data. The current study focuses on dietary data, demographic data, and questionnaire data that demonstrate lifestyle choices and depression symptoms.

Between 2021 and 2023, NHANES screened 22,660 individuals, of which 11,933 completed interviews, and 8,860 underwent examinations. The examined sample included 4,125 males (46.6%) and 4,735 females (53.4%). Participants encompassed a wide age range, with 133 infants younger than 1 year old (1.5%), 659 children between age 1 and 5 (7.4%), 869 between age 6 and 11 (9.8%), 584 between age 12 and 15 (6.6%), 551 between age 16 and 19 (6.2%), and 654 between age 20 and 29 (7.4%). Adult participants included 905 between age 30 and 39 (10.2%), 800 between age 40 and 49 (9.0%), 933 between age 50 and 59 (10.5%), 1,479 between age 60 and 69 (16.7%), 951 between age 70 and 79 (10.7%), and 342 participants aged 80 years and older (3.9%).

Measures

a) Depression

The depression screener data were obtained using the Patient Health Questionnaire-9 (PHQ-9; Kroenke et al., 2001). The PHQ-9 is a brief measure of depression as part of the full Patient Health Questionnaire, which is a validated self-administered inventory for screening, diagnosing, and measuring the severity of common mental health disorders in primary care and research settings. The depression scale consists of 9 items, based on DSM-IV criteria, with participants rating their symptoms on a scale from ‘0’ (not at all) to ‘3’ (nearly every day). A sum score was created, and higher scores indicate higher levels of depressive symptoms.

b) Dietary intake

For the dietary data, What We Eat in America (WWEIA), NHANES collaborates with the U.S. Department of Health and Human Services (DHHS) and the U.S. Department of Agriculture (USDA). All NHANES participants for the August 2021-August 2023 cycle were eligible to participate in the two 24-hour dietary recall interviews through telephone. 6,754 participants provided complete Day 1 intake data, and 5,879 of those participants also completed Day 2 recalls. For participants who completed both Day 1 and Day 2 dietary recalls, a composite score of average daily intakes was created by taking the mean for each nutrient across the two days. Dietary data were collected using the USDA’s Automated Multiple Pass Method (AMPM), a fully computerized five-step interview process developed to capture accurate food intake data in large national surveys. The five steps were 1) Quick List (participant recalls all foods and beverages consumed the day before the interview [midnight to midnight]), 2) Forgotten Foods (participant is asked about consumption of foods commonly forgotten during the Quick List step), 3) Time and Occasion (time and eating occasion are collected for each food), 4) Detail Cycle (for each food, a detailed description, amount eaten, and additions to the food are collected; eating occasions and times between eating occasions are reviewed to elicit forgotten foods), and 5) Final Probe (additional foods not remembered earlier are collected).

Two types of dietary intake data are available for the August 2021‑August 2023 survey cycle: Individual Foods files and Total Nutrient Intakes files. These files contain all the nutrients derived from foods, beverages, and water. These files exclude nutrients from supplements, antacids, or medication. The Individual Foods files contain detailed information about every food and beverage item reported by participants, including time of consumption, source, eating occasion, amount consumed, and nutritional content. Each item is coded using the USDA Food and Nutrient Database for Dietary Studies (FNDDS) 2023, which provides data on food energy and 64 specific nutrients or food components. The Total Nutrient Intakes files summarize each participant’s total daily intake of energy and nutrients derived from all reported foods and beverages (excluding supplements, antacids, or medications). These files also include responses to questions about salt use, special diet status, and fish/shellfish consumption (Day 1 only), as well as metadata on the reliability and completeness of each recall. This study uses the Dietary Interview-Total Nutrient Intakes files only, as the goal is to examine the relationship between specific nutrients’ total intake and depression scores.

c) Demographic and lifestyle

To have a more accurate and holistic view of the relationships between nutrient intake and depression symptoms, the study also considered demographic and lifestyle factors. Therefore, demographic variables (gender, age, education level, BMI, marital status, family poverty index) and lifestyle variables (physical activity, sleep hours, alcohol intake) were included in the analyses. Smoking data were not available in the datasets used for analysis, hence it was not included as a lifestyle factor.

The age variable records the age in years at the time of the screening interview for survey participants between the ages of 1 and 79 years old. All responses of participants 80 years and older are coded as “80.” In NHANES August 2021-August 2023, the weighted mean age for participants 80 years and older is 85 years. The education level variable recorded the highest grade or level of education completed by adults 20 years and older. BMI was calculated using weight in kilograms divided by height in meters squared (kg/m²), using standardized physical measurements collected in the NHANES Mobile Examination Center (MEC). All participants were eligible for the body measures examination, which was performed by trained health technicians following a uniform protocol. Weight was measured for all ages, and standing height was measured for participants aged 2 years and older. Measurements were typically taken on the right side of the body, unless a medical condition required otherwise. Marital status was collected from participants aged 14 and older. However, to protect participant confidentiality, marital status data are publicly released only for individuals aged 20 and older. The original six response categories were recorded into three groups: (1) married or living with a partner, (2) widowed, divorced, or separated, and (3) never married. The family poverty index was measured by the U.S. Department of Health and Human Services (HHS) poverty guidelines for the corresponding survey year.

The physical activity measure was derived from self-reported data for participants aged 18 years and older. Respondents were asked: “How long do you do these vigorous leisure-time physical activities each time?”. Participants provided either minutes or hours per session. Interviewers were prompted to confirm responses over 120 minutes per session, and entries under 0 or over 24 hours were not accepted. The final variable reflects the number of minutes typically spent per session engaging in vigorous-intensity leisure-time physical activities. The sleep hours measure records the usual number of sleep hours on weekdays or weekends. Alcohol consumption reflects the total grams of alcohol consumed by each participant during the 24-hour dietary recall. This variable reflects only alcohol obtained from dietary sources and does not include alcohol from medications or supplements.

Preliminary statistical analyses

The study took a comprehensive approach where no assumptions of a relationship between depression and a specific nutrient were made prior to analysis. To identify the potential contributors to depressive symptoms, bivariate correlation analyses were conducted between the PHQ-9 sum score and all available nutrient variables, lifestyle variables, and demographic variables. Only variables that showed statistically significant correlations with depression scores were selected for inclusion in further analysis.

Statistical analyses

All analyses were conducted using SPSS version 30 (IBM SPSS Statistics). Normality tests were conducted on the PHQ-9 outcome measure and showed that the data were not normally distributed based on skewness and kurtosis values; however, after log-transformation, these values were within the acceptable ranges (Skewness: 0.01; Kurtosis: -0.96) based on the criteria set by Kim et al. (2013) of values < +/-1. Data analyses were performed and are reported based on the log-transformed PHQ-9 total score. Variables that showed statistically significant correlations with PHQ-9 scores— including total nutrient intakes and relevant demographic or lifestyle factors—were entered into multiple linear regression models to determine which factors independently predicted depressive symptom severity.

Results

A hierarchical multiple linear regression analysis was conducted to examine whether dietary intake, demographic factors, and lifestyle variables predicted depressive symptoms (log-transformed). Predictors were entered in three sequential models: (1) dietary nutrients only, (2) dietary nutrients plus demographic variables, and (3) dietary nutrients, demographics, and lifestyle factors.

Model 1, which included dietary variables (e.g., sugar, protein, fiber, carbohydrates, vitamins, and minerals), did not significantly predict depression scores, F(13, 726) = 1.683, p = 0.060, although it approached significance. The model accounted for a small proportion of variance in depression scores, R² = 0.029. However, individual predictors were significant; fiber intake: β = -0.236, p = 0.004, carbohydrate intake: β = 0.203, p = 0.036, and magnesium intake was marginally significant: (β = 0.137, p = 0.050), suggesting that a higher fiber intake and a lower carbohydrate intake were related to lower levels of depressive symptoms.

Adding gender, age, educational level, marital status, family poverty index, and BMI in Model 2 significantly improved the model, F(19,720) = 7.311, p < 0.001 (ΔR² = 0.132, p < 0.001), accounting for R² = 0.162 of the total variance, indicating a large increase in explanatory power compared to Model 1. Significant predictors included protein intake: β=-0.210, p=0.008, fiber intake: β = -0.193, p = 0.013, magnesium intake: β = 0.134, p = 0.042, age: β = -0.160, p < 0.001, education level: β = -0.096, p = 0.014, BMI: β = 0.107, p = 0.002, marital status: β = 0.171, p < 0.001, and family poverty index: β = -0.142, p < 0.001.

Model 3 added lifestyle factors (i.e., physical activity, sleep hours, and alcohol intake). This model remained statistically significant, F(22,717) = 6.470, p < 0.001, with a slight increase in explained variance, R² = 0.166, but overall the model did not significantly improve (ΔR² = 0.004, p = 0.341) from Model 2. Significant predictors included protein intake: β = -0.197, p = 0.014, fiber intake: β = -0.179, p = 0.025, age: β = -0.165, p < 0.001, education level: β = -0.104, p = 0.008, BMI: β = 0.103, p = 0.004, marital status: β = 0.169, p < 0.001, and family poverty index: β = -0.141, p < 0.001. Lifestyle factors did not significantly predict depression scores (all p’s > 0.172), and magnesium intake became non-significant, although it remained marginal: β = 0.126, p = 0.056.

Discussion

The current study explored the intake of a broad range of nutrients and their correlation with depression symptoms by also taking into account demographic and lifestyle factors. The findings showed that certain nutrients, such as fiber and protein, were associated with depression scores, along with demographic factors like age, education level, BMI, marital status, and family poverty index.

Protein was found to be negatively associated with depression scores when accounting for demographic and lifestyle variables, suggesting that as individuals have a higher amount of protein in their daily intake, they report lower levels of depression. This finding is consistent with some prior studies that researched the relationship between protein intake and depression. In line with this, Li et al. (2020) found that protein intake, especially from milk and dairy products, had an inverse relationship with depressive symptoms, with the association remaining significant after a stratified analysis across various subgroups, including younger adults, men, individuals of different income levels, and all BMI categories. The dietary role in neurotransmission highlights a possible mechanism of how protein intake can influence mental health. Evidence supports that imbalances in some neurotransmitter levels are related to several mental illnesses, including depression. Since sufficient amounts of protein intake can provide the amino acids for a healthier brain and balanced neurotransmitter levels, higher protein intake may reduce the symptoms of depression (Gasmi et al., 2022). Another study found that tryptophan (a type of amino acid) intake was negatively correlated with self-reported levels of depression and positively associated with sleep duration (Lieberman et al., 2016). Other studies also highlight more complexity in the issue of protein intake and depression symptoms. Another NHANES study found a gender difference in the effects of increased protein intake on severely depressed mood, with higher protein intake being associated with lower risk of depressive symptoms in men, yet with a higher risk in women (Wolfe et al., 2011).

Fiber intake was one of the strongest predictors of depression symptoms in this study. Higher fiber intake was consistently correlated with lower depression scores, suggesting that as individuals have higher intakes of fiber, they experience fewer depressive symptoms. Our findings are in line with much of the prior research on fiber and depression symptoms. A study by Chrzastek et al. (2022) found that symptoms of depression were connected with higher consumption of sucrose, and greater fiber consumption was related to less frequent symptoms of depression. A systematic review and meta-analysis of epidemiologic studies found that higher fiber intake was associated with a 10% lower chance of depression in adults and a 57% lower chance in adolescents (Saghafian et al., 2023). This protective effect of fiber on depression could be attributed to a few mechanisms. A study has found that inflammation partially mediates the effects a high fiber diet has on depressive symptoms (Zhang et al., 2023). Another study on dietary fiber and the gut-brain axis suggests that dietary fiber influences affective and cognitive health by feeding the gut microbiome. The study found that individuals with depression had lower levels of beneficial bacteria like Bifidobacteria and Lactobacilli. These bacteria appear to mediate the connection between fiber intake, gut health, and psychological function (Torre et al., 2021).

Magnesium intake was marginally positively associated with depression in Model 1 and significantly in Model 2, suggesting that higher magnesium intake is a predictor of higher levels of depression. The correlation between magnesium and depressive symptoms became non-significant in Model 3 after accounting for lifestyle factors, indicating that magnesium is likely not independently predictive of depression. The initial positive trend between magnesium and depression in this study (Models 1 and 2) contradicts much of the current research on said topic (e.g., Derom et al., 2013; Tarleton et al., 2015). Derom et al. (2013) found that, although reverse causality cannot be excluded, a higher intake of dietary magnesium is associated with lower depression symptoms.

The study also found many demographic factors to be statistically associated with depression. Older age was consistently associated with lower depression scores, suggesting that younger individuals may be more vulnerable to experiencing depressive symptoms. Education level was inversely correlated to depression, indicating that people experience fewer depressive symptoms after achieving higher education. Family poverty index was negatively associated with depression, as participants from lower-income households were more likely to report depressive symptoms. BMI was positively associated with depression. Finally, the marital status variable was positively associated with depression, with widowed/divorced/separated individuals reporting higher depression scores than those who were partnered/married.

There are several limitations that should be taken into account when interpreting the current findings. First, the study bases its analysis on cross-sectional data from the NHANES (August 2021–August 2023) cycle. The results illustrate the relationship between nutrient intake and depressive symptoms at a single point in time and cannot determine causality. Therefore, the study is susceptible to reverse causality, where individuals with depressive symptoms have altered eating habits that lead to the increased or decreased nutrient intake that this study observes. Future longitudinal studies should be conducted to clarify whether the higher or lower nutrient intake is the result or cause of depressive symptoms. Second, this study relies on self-reported food intake from NHANES 24-hour dietary recall interviews. Although NHANES uses the validated AMPM to improve accuracy, self-reported intake may still deviate from true consumption. The PHQ-9, although validated, records only self-reported symptoms and lacks the accuracy of a clinical diagnostic assessment. Additionally, the study did not include smoking as a lifestyle factor due to its absence from the datasets. Since smoking habits could have an important role in depression, their importance should not be overlooked.

This study has important implications for public health. The observation of the associations of protein and fiber with depressive symptoms adds to the growing amount of evidence that diet and nutrition play an important role in mental health outcomes. These findings indicate that nutrition can potentially be a low-cost, low-risk, and medication-free way of preventing or minimizing depressive symptoms. There should be a focus on education, highlighting the effects of nutrition on mental health and promoting a nutrient-dense diet that includes high fiber intake through whole grains, vegetables, and fruits, and adequate protein intake. This education is especially important for low-income and low-education populations, as this study found them to be more at risk for depressive symptoms. Additionally, there is a need for government regulation through policy. Government agencies like the USDA and HHS should have dietary guidelines, food labeling regulations, and public health and nutrition programs that reflect the nutritional needs for depression prevention. The government should promote high fiber and high protein foods through policy, and ensure access to these nutrients to low-income communities through SNAP and WIC. In the food industry, a priority of including higher fiber and protein content in products would be beneficial in supporting psychological well-being. Clear package labeling can also allow the public to make informed decisions about their diet with mental health considerations. In research, there is a need for more interdisciplinary studies between nutrition, epidemiology, mental health, and public policy. Longitudinal studies that clarify the direction of effects between nutrient intake and depression and studies that explain the mechanism of how diet influences mental health outcomes are both essential in advancing the field. Ultimately, the study’s finding highlights the potential of nutrition in addressing mental health. Incorporating nutritional means in mental health prevention could be a key step in improving quality of life and reducing the effects of depression around the world.

To conclude, depression is currently one of the most prevalent and debilitating public health challenges in the world today, one without a reliable and universally available treatment. The present study looked at a wide range of micro and macronutrients’ relationship with depression, in a nationally representative U.S. sample, while factoring lifestyle and demographic differences, and found that protein and fiber intake are inversely correlated with depression symptoms. Magnesium intake is marginally positively correlated with depression symptoms but could not withhold adjustment for lifestyle factors. The findings of protein and fiber intake are largely in line with the findings of a lot of existing literature. Since this cross-sectional study cannot determine causality, further longitudinal or experimental studies should be conducted to clarify the direction of these relationships and the mechanisms by which nutrients and depression influence each other. Nevertheless, the findings build on existing literature highlighting the potential impact diet has on mental health. Therefore, it is crucial to consider nutrition, especially the nutrients that play a role in depression, when crafting public health policies, food industry practices, and general education.

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