Association between vitamin A intake and depression: A cross-sectional study of NHANES from 2005 to 2020 and Mendelian randomization analysis

doi:10.21203/rs.3.rs-5264158/v1

Download PDF

Research Article

Association between vitamin A intake and depression: A cross-sectional study of NHANES from 2005 to 2020 and Mendelian randomization analysis

https://doi.org/10.21203/rs.3.rs-5264158/v1

This work is licensed under a CC BY 4.0 License

You are reading this latest preprint version

Background: The association of vitamin A intake with depression remains unclear. This study combined observational research and Mendelian randomization (MR) analysis to explore the relationship between vitamin A intake and depression.

Methods: First, we performed a cross-sectional study utilizing the National Health and Nutrition Examination Survey (NHANES) data from 2005 to 2020. Weighted multivariable logistic regressions and restricted cubic spline (RCS) regression were applied to explore the association between vitamin A intake and depression. We also conducted stratified and sensitivity analyses to assess the robustness of the results. Second, we conducted a MR analysis to assess the causal association between vitamin A and depression risk using the publicly available Genome-Wide Association Studies (GWAS) database. The inverse-variance weighted (IVW) method was our primary method for MR analysis. In addition, we performed multiple sensitivity analyses to evaluate the reliability of the results, including Cochran’s Q test, MR-Egger intercept regression, MR-Pleiotropy Residual Sum and Outlier (MR-PRESSO), and leave-one-out analysis.

Results: In the cross-sectional study, a total of 38,157 individuals were enrolled. The vitamin A intake was negatively associated with depression after adjusting for all covariates (OR=0.92, 95%CI: 0.88-0.97, P=0.001). Similar inverse associations were observed when vitamin A intake was converted into categorical variables. The RCS analysis found an L-shaped nonlinear relationship between dietary vitamin A intake and depression (P for non-linearity=0.010) after adjusting for all covariates. Vitamin A consumption was inversely associated with depression (OR=0.999, 95% CI: 0.999-1.000, P=0.002) for intakes below 492.00 μg. In contrast, no association was found between dietary vitamin A intake and depression (P=0.656) for intakes of 492.00 μg or higher. Furthermore, The inverse relationship between vitamin A intake and depression remained robust in both stratified and sensitivity analyses. In the MR analysis, a total of 15 single-nucleotide polymorphisms (SNPs) were identified as instrumental variables (IVs). The IVW method found no significant causal relationship between vitamin A and the risk of depression (OR=0.39, 95%CI: 0.10-1.58, P=0.188). Similar results were found in MR-Egger method, weighted median method, simple mode method, and weighted mode method. Sensitivity analyses confirmed the reliability of these results.

Conclusions: The observational study found that vitamin A intake was inversely associated with depression, regardless of whether vitamin A was measured as a continuous or categorical variable. However, findings from the MR analysis did not indicate a causal relationship between vitamin A and depression risk.

vitamin A

depression

association

NHANES

Mendelian randomization

Depression is a significant public health issue, affecting approximately 350 million people worldwide and projected to become the leading cause of disability by 2030.¹ Depression is a common mental disorder marked by symptoms including insomnia, self-deprecation, loss of interest, and suicidal thoughts.² The etiology and pathophysiology of depression remain incompletely understood. Several biological and neurological changes may be involved, including monoamine neurotransmitters, neuroendocrine regulation, immune-inflammatory responses, oxidative stress, neuroinflammation, and nitrosative pathways.^3,4 Currently, approximately 40% of depression cases do not respond effectively to pharmacological or psychosocial treatments,⁵ underscoring the necessity for improved prevention and treatment strategies.

Recent evidence indicates that food intake and dietary patterns may influence the development, prevention, and treatment of depression.^6,7 Diet and nutrition can influence brain health and mental illness through various mechanisms, as the optimal functioning of the central nervous system relies on a diverse array of nutrients.⁸ vitamin A, a generic term for compounds with retinol biological activity, can protect organisms from oxidative damage via removing reactive oxygen species (ROS) and other free radicals.⁹ In addition, vitamin A is a potent neural regulator involved in various physiological processes in the brain.¹⁰ To our knowledge, several observational studies have examined the relationship between vitamin A and depression.^11,12 However, no definitive conclusions have been reached.^11,12 For example, a cross-sectional study utilizing the 2009–2017 Korea National Health and Nutrition Examination Survey (NHANES) found that doubling daily vitamin A intake reduced the risk of depression by 8% (OR = 0.92, 95% CI: 0.85–0.99, P = 0.020).¹¹ In contrast, another cross-sectional study indicated that elevated serum vitamin A levels were associated with increased depression.¹² The inconsistent results may be due to sample size, residual bias, confounding factors, and reverse causality. Consequently, the causal relationship between vitamin A and the risk of depression remains unclear and requires further investigation.

Mendelian randomization (MR) is an epidemiological method that employs single-nucleotide polymorphisms (SNPs) as genetic instrumental variables (IVs) to evaluate potential causal relationship between an exposure and a target outcome.¹³ This method effectively reduces bias in estimates of the relationship between exposure and outcome, enhances causal inference, and addresses the limitations of observational studies.¹⁴ In recent years, many studies have utilized the NHANES database and MR analysis to evaluate causal inference.^15,16 They have offered valuable analytical resources at both clinical and genetic levels, providing reliable evidence.^15,16 In the present study, we first assessed the association between vitamin A intake and depression through a cross-sectional analysis of NHANES data from 2005 to 2020. Then we performed a two-sample MR analysis using data from the genome-wide association study (GWAS) to further validate the association.

2.1 Study design

The research process in this study consisted of two parts. First, we performed weighted multivariable regression analyses to investigate the association between vitamin A intake and depression, utilizing publicly available data from the NHANES database covering the years 2005 to 2020. Second, we conducted a two-sample MR analysis using data from the GWAS datasets to assess the causal effect of genetically predicted vitamin A on depression risk.

2.2 Cross-sectional study of NHANES

2.2.1 Data sources and study population

The NHANES is a biennial cross-sectional health survey that assesses the health and nutritional status of Americans. The survey consists of household interviews, physical examinations, and laboratory tests. The National Center for Health Statistics at the Centers for Disease Control and Prevention (CDC) conducted the survey, and all participants provided informed consent. Data for this cross-sectional study were collected from the NHANES between 2005 and 2020. We included participants aged 18 and older who completed the interviews, excluding those with missing data on depression assessments and dietary vitamin A intake.

2.2.2 Dietary vitamin A intake in NHANES

Data on dietary intake were gathered using two 24-hour interview questionnaires. The first dietary recall interview was conducted in-person at the mobile examination center (MEC), and the second interview was conducted by telephone 3 to 10 days later. During the interviews, participants were asked to recall the types and quantities of foods and beverages consumed in the 24 hours prior to the interviews. The Food and Nutrient Database for Dietary Studies of the United States Department of Agriculture was utilized to calculate nutrient intakes. For the analysis, we calculated the total dietary intake of vitamin A by averaging the data from two dietary recalls if a participant completed both interviews. If only one reliable dietary recall was available, we used that single recall instead.

2.2.3 Depression assessment in NHANES

The NHANES assessed the severity of depression in participants over a two-week period using the Patient Health Questionnaire (PHQ-9). The PHQ-9 is a validated and reliable self-report tool consisting of nine items that align with the criteria for a major depressive episode as outlined in the American Psychiatric Association^’s Diagnostic and Statistical Manual of Mental Disorders, 4th edition. Each item on the PHQ-9 offers four response options, ranging from "0" (not at all) to "3" (almost every day). A total score of 10 or higher was classified as depression. Previous research indicated that a PHQ-9 score of 10 or higher has a sensitivity and specificity of 88% for identifying major depression.¹⁷

2.2.4 Covariates in NHANES

In this study, we adjusted for demographic and lifestyle covariates, physical examinations and physical activity, dietary factors, and comorbidities, drawing on previous studies and clinical practice.^18,19 Demographic and lifestyle covariates included age, sex, race/ethnicity, marital status, family income, education level, health insurance, smoking status and alcoholic drinks. Physical examinations and activity comprised body mass index (BMI) and work activity. Dietary factors included energy, protein, carbohydrate, and fat intakes. Comorbidities included diabetes, hypertension, hypercholesterolemia, stroke, coronary heart disease, and cancer or malignancy. Family income was assessed using the poverty income ratio (PIR). Race/ethnicity was classified as Mexican American, non-Hispanic White, non-Hispanic Black, other Hispanic, and other race. Education levels were categorized as less than high school, high school diploma, and more than high school. Marital status was classified into three categories: married or living with a partner, divorced, separated, or widowed, and never married. The smoking status was assessed through a series of questions about cigarette and tobacco use. Participants were classified as never smokers (fewer than 100 cigarettes), former smokers (more than 100 cigarettes but who have quit), and current smokers. Work activity was classified as moderate, vigorous, and other activity. Comorbidities were defined based on the doctor's prior knowledge of the condition, as indicated by a questionnaire.

2.2.5 Statistical analyses

All statistical analyses were performed using R software, version 4.2.2. A two-sided P-value < 0.05 was deemed statistically significant. According to NHANES guidelines, the sample weight must be recalculated when combining data from two or more cycles. We applied the recommended weighting methodology to the study data. Multiple imputation was employed to address missing covariate data, thereby reducing bias and enhancing precision and statistical power. Continuous variables were expressed as mean and standard deviation (SD) for normal distributions, or as median and interquartile range (IQR) for skewed distributions. Categorical variables were represented by counts and frequencies. The chi-square test, one-way ANOVA, and Kruskal-Wallis test were employed to compare categorical variables, normally distributed variables, and skewed variables, respectively. Weighted multivariable logistic regressions were applied to explore the association between vitamin A intake and depression by calculating the odds ratio (OR) and 95% confidence interval (CI). Additionally, we conducted restricted cubic spline (RCS) regression to evaluate the dose-response relationship between dietary vitamin A intake and depression. Moreover, stratified analyses were performed based on gender, age (< 60 years versus ≥ 60 years), race/ethnicity, marital status, education level, and health insurance. In sensitivity analysis, participants with missing covariate data were excluded to evaluate the stability of the results.

2.3 MR analysis

2.3.1 Assumptions and data sources

The validity of the MR relies on three assumptions: (1) genetic IVs are associated with the exposure, (2) genetic IVs are independent of confounding factors, and (3) genetic IVs influence the outcome solely through exposure. Figure 1 outlines the study assumptions. Genetic data on vitamin A and depression were obtained from the IEU Open GWAS database (https://gwas.mrcieu.ac.uk). Table 1 presents detailed information about the GWAS datasets included in our study.

Table 1 Details of the GWAS datasets.

GWAS ID	Year	Trait	Population	Consortium	Sample size
ukb-a-458	2017	Vitamin A	European	Neale Lab	335,591
ieu-b-102	2019	Major depression	European	PGC	500,199

Abbreviations: PGC, Psychiatric Genomics Consortium.

2.3.2 Genetic IVs selection

We selected SNPs that were significantly associated with vitamin A (P<5×10^-8). If fewer than three SNPs are available for analysis, a more relaxed threshold of P<5×10^-6 is applied to include more SNPs. To minimize linkage disequilibrium (LD) bias, we conducted clumping on European samples from the 1000 Genomes Project, selecting SNPs within a 10,000 kb window with r²< 0.001. The F-statistic was calculated for each SNP to assess the strength of the association between SNP and exposure. Weak genetic instruments were excluded when the F-statistic was less than 10. We excluded SNPs absent from the outcome data. We harmonized the exposure and outcome data and removed palindromic SNPs with intermediate allele frequencies. Additionally, PhenoScanner V2 was utilized to assess the association of SNPs with potential confounding factors.

2.3.3 MR analysis

We performed a two-sample MR analysis to investigate the causal effect of vitamin A on the risk of depression. The inverse-variance weighted (IVW) method was our primary approach for MR results, while MR-Egger, weighted median, simple mode, and weighted mode methods served as complementary approaches. MR estimates were reported as OR with 95%CI, and a P-value of less than 0.05 was considered statistically significant. Furthermore, we conducted several sensitivity analyses to verify the robustness of the MR results. Cochran’s Q statistic was used to assess the heterogeneity among the different genetic IVs. MR-Egger regression intercept was applied to assess horizontal pleiotropy. MR-Pleiotropy Residual Sum and Outlier (MR-PRESSO) was employed to identify and rectify horizontal pleiotropy. Leave-one-out analysis was conducted to check for any single SNP that significantly drove the IVW estimates. All analyses were performed using the TwoSampleMR packages in R software (version 4.2.2).

3.1 Cross-sectional study of NHANES

3.1.1 Baseline characteristics of the included participants

Between 2005 and 2020, a total of 76,496 US residents participated in the NHANES survey. Among the 76,496 participants, 30,516 participants under 18 years, 5388 participants with missing dietary vitamin A intake data, and 2435 participants with missing depression assessment data were excluded from the study. Finally, 38,157 participants were eligible for analysis. The exclusion process was presented in Figure 2. Of the included participants, 3448 (9.04%) reported experiencing depression. Individuals with higher vitamin A intake were more likely to be men, Non-Hispanic White, better educated, married or living with a partner, insured, have a higher income, never smokers, have cancer or malignancy, have hypercholesterolemia, and free from hypertension, diabetes, coronary heart disease, or stroke. The baseline characteristics of the included participants were presented in Table 2.

Table 2 Baseline characteristics of the included participants.

	vitamin A intake (μg)
Variables	Total	Q1 (<298.50)	Q2 (298.50-491.50)	Q3 (492.00-762.50)	Q4 (>762.50)	P-value
No.	38157	9524	9532	9551	9550
Depression, n (%)						< 0.001
No	34709 (90.96)	8424 (88.45)	8655 (90.8)	8767 (91.79)	8863 (92.81)
Yes	3448 ( 9.04)	1100 (11.55)	877 (9.2)	784 (8.21)	687 (7.19)
Gender, n (%)						< 0.001
Male	18796 (49.26)	4402 (46.22)	4438 (46.56)	4677 (48.97)	5279 (55.28)
Female	19361 (50.74)	5122 (53.78)	5094 (53.44)	4874 (51.03)	4271 (44.72)
Age (year), Mean ± SD	47.76 ± 18.70	45.27 ± 18.17	47.30 ± 18.59	48.84 ± 18.75	49.61 ± 18.97	< 0.001
Race/ethnicity, n (%)						< 0.001
Mexican American	5955 (15.61)	1702 (17.87)	1625 (17.05)	1474 (15.43)	1154 (12.08)
Other Hispanic	3651 ( 9.57)	1030 (10.81)	1007 (10.56)	860 (9)	754 (7.9)
Non-Hispanic White	16017 (41.98)	2952 (31)	3825 (40.13)	4341 (45.45)	4899 (51.3)
Non-Hispanic Black	8615 (22.58)	2861 (30.04)	2121 (22.25)	1895 (19.84)	1738 (18.2)
Other Race	3919 (10.27)	979 (10.28)	954 (10.01)	981 (10.27)	1005 (10.52)
Education level, n (%)						< 0.001
Less than high school	3504 ( 9.18)	1202 (12.62)	946 (9.92)	753 (7.88)	603 (6.31)
High school diploma	14129 (37.03)	4149 (43.56)	3672 (38.52)	3344 (35.01)	2964 (31.04)
More than high school	20524 (53.79)	4173 (43.82)	4914 (51.55)	5454 (57.1)	5983 (62.65)
Marital status, n (%)						< 0.001
Married /living with partner	22112 (57.95)	5064 (53.17)	5530 (58.02)	5767 (60.38)	5751 (60.22)
Divorced/separated/widowed	9556 (25.04)	2530 (26.56)	2418 (25.37)	2302 (24.1)	2306 (24.15)
Never married	6489 (17.01)	1930 (20.26)	1584 (16.62)	1482 (15.52)	1493 (15.63)
Family PIR, Median (IQR)	2.09 (1.09, 4.02)	1.62 (0.91, 3.18)	2.02 (1.04, 3.79)	2.28 (1.19, 4.45)	2.56 (1.26, 4.72)	< 0.001
Energy (kcal), Mean ± SD	2053.56 ± 866.82	1624.53 ± 695.16	1928.04 ± 703.30	2171.20 ± 785.27	2489.03 ± 1003.61	< 0.001
Protein (g), Mean ± SD	80.04 ± 36.67	61.10 ± 29.05	74.84 ± 29.82	84.92 ± 33.19	99.25 ± 42.02	< 0.001
Carbohydrate (g), Mean ± SD	248.11 ± 110.62	200.38 ± 92.41	231.61 ± 92.93	259.05 ± 100.59	301.23 ± 127.11	< 0.001
Total fat (g), Median (IQR)	72.00 (50.94, 98.68)	54.66 (37.88, 74.67)	69.54 (50.92, 91.38)	79.58 (58.15, 105.79)	88.59 (63.56, 120.27)	< 0.001
BMI (kg/m²), Mean ± SD	29.21 ± 7.10	29.48 ± 7.35	29.58 ± 7.13	29.21 ± 7.10	28.56 ± 6.77	< 0.001
Alcoholic drinks (drink/day), Median (IQR)	2.00 (1.00, 3.00)	2.00 (1.00, 3.00)	2.00 (1.00, 3.00)	2.00 (1.00, 3.00)	2.00 (1.00, 3.00)	< 0.001
Hypertension, n (%)						0.069
No	24959 (65.41)	6292 (66.06)	6201 (65.05)	6163 (64.53)	6303 (66)
Yes	13198 (34.59)	3232 (33.94)	3331 (34.95)	3388 (35.47)	3247 (34)
Hypercholesterolemia, n (%)						< 0.001
No	24863 (65.16)	6462 (67.85)	6214 (65.19)	6045 (63.29)	6142 (64.31)
Yes	13294 (34.84)	3062 (32.15)	3318 (34.81)	3506 (36.71)	3408 (35.69)
Diabetes, n (%)						0.016
No	33411 (87.56)	8337 (87.54)	8285 (86.92)	8345 (87.37)	8444 (88.42)
Yes	4746 (12.44)	1187 (12.46)	1247 (13.08)	1206 (12.63)	1106 (11.58)
Health insurance, n (%)						< 0.001
No	8081 (21.18)	2577 (27.06)	2127 (22.31)	1780 (18.64)	1597 (16.72)
Yes	30076 (78.82)	6947 (72.94)	7405 (77.69)	7771 (81.36)	7953 (83.28)
Coronary heart disease, n (%)						< 0.001
No	36666 (96.09)	9217 (96.78)	9164 (96.14)	9133 (95.62)	9152 (95.83)
Yes	1491 ( 3.91)	307 (3.22)	368 (3.86)	418 (4.38)	398 (4.17)
Stroke, n (%)						0.015
No	36741 (96.29)	9125 (95.81)	9172 (96.22)	9215 (96.48)	9229 (96.64)
Yes	1416 ( 3.71)	399 (4.19)	360 (3.78)	336 (3.52)	321 (3.36)
Cancer or malignancy, n (%)						< 0.001
No	34617 (90.72)	8867 (93.1)	8712 (91.4)	8562 (89.65)	8476 (88.75)
Yes	3540 ( 9.28)	657 (6.9)	820 (8.6)	989 (10.35)	1074 (11.25)
Work activity, n (%)						< 0.001
Moderate activity	8705 (22.81)	1938 (20.35)	2249 (23.59)	2181 (22.84)	2337 (24.47)
Vigorous activity	8571 (22.46)	2203 (23.13)	2096 (21.99)	2121 (22.21)	2151 (22.52)
Other	20881 (54.72)	5383 (56.52)	5187 (54.42)	5249 (54.96)	5062 (53.01)
Smoking status, n (%)						< 0.001
Never smokers	21468 (56.26)	5123 (53.79)	5342 (56.04)	5481 (57.39)	5522 (57.82)
Former smokers	8923 (23.38)	1849 (19.41)	2169 (22.75)	2382 (24.94)	2523 (26.42)
Current smokers	7766 (20.35)	2552 (26.8)	2021 (21.2)	1688 (17.67)	1505 (15.76)

Note: PIR, poverty income ratio.

3.1.2 Association between dietary vitamin A intake and depression

In weighted multivariable logistic regression analyses, dietary vitamin A intake was evaluated as both continuous and categorical variables (Table 3). After controlling for potential confounding factors, the vitamin A intake (as a continuous variable) was negatively associated with depression (OR=0.92, 95%CI: 0.88-0.97, P=0.001). Similar inverse associations were found when vitamin A intake was converted into categorical variables. Compared with individuals with lower vitamin A intake in Q1, the adjusted OR values for vitamin A intake and depression in Q2, Q3, and Q4 were 0.89 (95%CI: 0.80-0.98, P=0.020), 0.89 (95%CI: 0.80-0.99, P=0.031), and 0.88 (95%CI: 0.78-0.99, P=0.032), respectively. Furthermore, the inverse associations remained strong across all models that adjusted for various covariates.

The RCS analysis found an L-shaped nonlinear relationship between dietary vitamin A intake and depression (P for non-linearity=0.010) after adjusting for all covariates (Figure 3). The results of the two-stage linear regression model revealed that the threshold effect inflection point between vitamin A intake and depression was 492.00 μg (Table 4). The results showed a different trend before and after the threshold point. Vitamin A intake was inversely associated with depression (OR=0.999, 95%CI: 0.999-1.000 P=0.002) on the left side of the inflection point. While on the right side, no significant association was found between vitamin A intake and depression (P=0.656).

The stratified analyses revealed that the trends of ORs remained steady across all stratifications (Figure 4), implying that the relationship of dietary vitamin A intake with depression was robust. In sensitivity analysis, the results remained robust despite the exclusion of individuals with missing covariate data. Dietary vitamin A intake was inversely associated with depression (OR =0.89, 95%CI: 0.83-0.95, P=0.001) after adjusting for all covariates. When dietary vitamin A intake was categorized, the adjusted ORs for Q2, Q3, and Q4 compared to Q1 were 0.83 (95% CI: 0.71-0.96, P=0.012), 0.85 (95% CI: 0.73-1.00, P=0.049), and 0.79 (95%CI: 0.66-0.94, P= 0.009), respectively.

Table 3 Association between dietary vitamin A intake and depression.

Dietary vitamin A intake (μg)	Event (%)	Non-adjusted model		Model 1		Model 2		Model 3
Dietary vitamin A intake (μg)	Event (%)	OR (95%CI)	P-value	OR (95%CI)	P-value	OR (95%CI)	P-value	OR (95%CI)	P-value
ln (vitamin A)	3448 (9.04)	0.80 (0.77-0.83)	<0.001	0.91 (0.87-0.94)	<0.001	0.92 (0.88-0.96)	<0.001	0.92 (0.88-0.97)	0.001
Quartiles
Q1 (<298.50)	1100 (11.55)	1 (Ref)		1 (Ref)		1 (Ref)		1 (Ref)
Q2 (298.50-491.50)	877 (9.20)	0.78 (0.71-0.85)	<0.001	0.87 (0.79-0.96)	0.005	0.89 (0.81-0.98)	0.020	0.89 (0.80-0.98)	0.020
Q3 (492.00-762.50)	784 (8.21)	0.68 (0.62-0.75)	<0.001	0.86 (0.78-0.95)	0.003	0.89 (0.80-0.99)	0.034	0.89 (0.80-0.99)	0.031
Q4 (>762.50)	687 (7.19)	0.59 (0.54-0.66)	<0.001	0.83 (0.74-0.92)	<0.001	0.87 (0.77-0.98)	0.022	0.88 (0.78-0.99)	0.032
Trend test			<0.001		<0.001		0.024		0.032

Note: CI, confidence interval; OR, odds ratio; ln (vitamin A), vitamin A intake values were natural logarithmised. Model 1 was adjusted for age, gender, race/ethnicity, marital status, education level, family income, health insurance, BMI, work activity, alcoholic drinks, and smoking status. Model 2 was adjusted for age, gender, race/ethnicity, marital status, education level, family income, health insurance, BMI, work activity, alcoholic drinks, smoking status, energy, protein, carbohydrate, and total fat. Model 3 was was fully adjusted, including age, gender, race/ethnicity, marital status, education level, family income, health insurance, BMI, work activity, alcoholic drinks, smoking status, energy, protein, carbohydrate, total fat, diabetes, hypertension, hypercholesterolemia, stroke, coronary heart disease, and cancer or malignancy.

Table 4 Threshold effect analysis of the relationship between vitamin A intake and depression.

Inflection point	OR (95%CI)	P-value
Vitamin A intake<492.00 μg	0.999 (0.999-1.000)	0.002
Vitamin A intake≥492.00 μg	1.000 (1.000-1.000)	0.656
Likelihood ratio test		0.001

Note: CI, confidence interval; OR, odds ratio. Adjustment covariates included age, gender, race/ethnicity, marital status, education level, family income, health insurance, BMI, work activity, alcoholic drinks, smoking status, energy, protein, carbohydrate, total fat, diabetes, hypertension, hypercholesterolemia, stroke, coronary heart disease, and cancer or malignancy.

3.2 Causal association in MR analysis

3.2.1 Genetic IVs

We selected SNPs using a P threshold of 5×10^-6, resulting in 101 SNPs that met the criteria. Out of the 101 SNPs, 84 were excluded during the clumping process. The F statistics for the identified SNPs surpassed the empirical threshold of 10, with values between 20.90 and 31.35. Two SNPs were not included in the outcome data. The harmonized process revealed no SNPs classified as palindromic SNPs. No SNPs were excluded using PhenoScanner V2. Finally, 15 eligible SNPs were selected as genetic IVs to evaluate the causal relationship between vitamin A and depression risk. The selection process for genetic IVs was illustrated in Figure 5.

3.2.2 MR Analyses

The IVW method found no significant causal relationship between vitamin A and the risk of depression (OR=0.39, 95%CI: 0.10-1.58, P=0.188). Similar estimates were found in MR-Egger method (OR=0.26, 95%CI: 0.02-4.18, P=0.357), weighted median method (OR=0.75, 95%CI: 0.11-4.93, P=0.763), simple mode method (OR=1.76, 95% CI: 0.06-49.39, P=0.745), and weighted mode method (OR=1.76, 95%CI: 0.05-56.69, P=0.755). The results of the MR analyses were listed in Table 5. The scatter plot of the MR analyses was presented in Figure 6. In sensitivity analyses, the Cochran's Q test revealed no significant heterogeneity (P=0.570). Both the MR-Egger intercept (P for intercept=0.739) and MR-PRESSO analysis (global test P=0.550) did not find any horizontal pleiotropy. Furthermore, the results of leave-one-out analysis demonstrated that no single SNP drove the MR estimate (Figure 7).

Table 5 Results of MR estimates.

Method	Number of SNP	OR (95%CI)	P-value	Heterogeneity (P-value)	Pleiotropy (P-value)	MR-PRESSO (P-value)
IVW	15	0.39 (0.10-1.58)	0.188	0.570	0.739	0.550
MR-Egger	15	0.26 (0.02-4.18)	0.357
Weighted median	15	0.75 (0.11-4.93)	0.763
Simple mode	15	1.76 (0.06-49.39)	0.745
Weighted mode	15	1.76 (0.05-56.69)	0.755

Abbreviations: IVW, inverse-variance weighted; CI, confidence interval; OR, odds ratio; SNP, single-nucleotide polymorphisms.

The present study provided a comprehensive investigation of the relationship between vitamin A and depression based on a combination of large-scale observational study data and MR analysis of large-scale genetic data. The results of the observational study found an inverse relationship between vitamin A intake and depression. However, in contrast to the observational findings, the results of the MR analysis do not support an association between vitamin A and the risk of depression. More specifically, our results indicated that increases in vitamin A intake were not associated a reduced risk of depression.

Vitamin A refers to a group of compounds with retinol biological activity, typically found in animal-derived foods.²⁰ It is related to several physiological processes, such as immune-inflammatory responses, oxidative stress, and energy metabolism.²¹ Given that oxidative stress significantly contributes to the pathophysiology of depression, it seems naturally that dietary vitamin A intake is associated with depression. Previous studies have shown an inconsistent relationship between vitamin A intake and depression.^11,12,22-24 A meta-analysis of observational studies demonstrated that dietary vitamin A intake was inversely associated with depression (RR=0.83, 95%CI: 0.70-1.00, P=0.05).²² But the results may be influenced by the significant heterogeneity and the diverse classifications of exposure and depression.²² In a cross-sectional study using the NHANES data between 2007 and 2020, vitamin A intake ≥731.38 μg was associated with lower incidence of depression in patients with heart failure.²³ Moreover, a cross-sectional study utilizing the 2016-2018 Korea NHANES data found that lower serum vitamin A levels were associated with a higher risk of depression in premenopausal women.²⁴ However, a cross-sectional study utilizing the NHANES data from 2005 to 2006 found a positive relationship between vitamin A concentration and depression.¹² In this study, we conducted a RCS analysis to investigate the dose-response relationship between vitamin A intake and depression. Before the inflection point, the depression risk gradually decreased along with the increase of vitamin A intake. However, after the inflection point, there was no association between dietary vitamin A intake and depression. This nonlinear relationship may explain the inconsistent findings regarding the association between vitamin A and depression in previous observational studies.

However, across all MR methods, we found no evidence of a causal relationship between vitamin A and depression risk. We performed several sensitivity analyses to ensure the validity of MR results, which indicated no significant heterogeneity or horizontal pleiotropy. In this study, the MR results were inconsistent with those of observational study. One possible reason is that depression can alter appetite and food intake.²⁵ Consequently, it remains unclear whether lower vitamin A intake is a cause of depression or a result of the condition. Another reason is that the interactions and antagonisms among nutrients in various diets complicate the relationship between dietary nutrients and depression.^6,7 MR analysis offered more robust evidence than the observational study, indicating that no causal relationship can be established between vitamin A intake and depression risk.

The present study has several strengths. First, we combined a large sample observational study based on the NHANES with two-sample MR analysis. Utilizing observational studies alone is susceptible to the impact of unmeasured confounding factors and reverse causality, while MR studies can mitigate these issues. Second, we performed a RCS analysis to explore the dose-response relationship between vitamin A intake and depression. Third, we performed multiple sensitivity analyses in both the observational study and MR analysis to validate the results. However, this study also has several limitations that should be acknowledged. First, our observational study showed that there was an L-shaped nonlinear relationship between vitamin A intake and depression. The MR study assessed only linear causal associations, leaving the possibility of nonlinear causality unaddressed. Second, although our study did not detect directional pleiotropy, potential pleiotropies may still exist. This is a common issue in MR analysis and can introduce bias. Third, Our MR study focused on individuals of European descent, whereas the observational study involved a multiethnic US population. Future studies should focus on a single ethnicity to eliminate potential confounding factors related to population heterogeneity. Fourth, we used a relaxed P-value threshold to select genetic IVs, which may introduce weak instrument bias. Nevertheless, we evaluated the strength of the instruments and found that all had F statistics exceeding 10, the standard threshold for strong instruments.

The MR analysis did not find a causal relationship between vitamin A intake and depression risk among individuals of European descent, despite an observational study indicating a significant association between vitamin A intake and depression in a multiethnic US population. The observational results may be influenced by uncontrolled confounding factors and reverse causality. Therefore, based on the current findings, there is no evidence that increased vitamin A intake is beneficial for preventing depression. This finding requires validation through well-designed prospective cohort studies, and the underlying mechanisms should also be investigated.

Funding

No funding was received.

Author contributions

Study concept and design: Dehua Zhao, Jisheng Wang. Acquisition of data: Dehua Zhao, Xiaoqing Long. Analysis and interpretation of data: Dehua Zhao, Xiaoqing Long. Drafting of the manuscript: Dehua Zhao. Revising it for intellectual content: Dehua Zhao, Xiaoqing Long, Jisheng Wang. Final approval of the completed manuscript: Dehua Zhao, Xiaoqing Long, Jisheng Wang.

Acknowledgements

None.

Conflict of interest

The authors declare that they have no competing interests.

Data availability statement

The data that support the findings of this study are available from the corresponding author upon reasonable request.

Ethics approval and consent to participate

Not applicable.

Patient consent for publication

Not applicable.

Mathers CD, Loncar D. Projections of global mortality and burden of disease from 2002 to 2030. PLoS Med. 2006;3(11):e442.
Ferriani LO, Silva DA, Molina MDCB, Mill JG, Brunoni AR, da Fonseca MJM, Moreno AB, Benseñor IM, de Aguiar OB, Barreto SM, Viana MC. Associations of depression and intake of antioxidants and vitamin B complex: Results of the Brazilian Longitudinal Study of Adult Health (ELSA-Brasil). J Affect Disord. 2022;297:259-268.
Das A, Cumming RG, Naganathan V, Ribeiro RV, Le Couteur DG, Handelsman DJ, Waite LM, Hirani V. The association between antioxidant intake, dietary pattern and depressive symptoms in older Australian men: the Concord Health and Ageing in Men Project. Eur J Nutr. 2021; 60(1):443-454.
Wigner P, Czarny P, Galecki P, Su KP, Sliwinski T. The molecular aspects of oxidative & nitrosative stress and the tryptophan catabolites pathway (TRYCATs) as potential causes of depression. Psychiatry Res. 2018;262:566-574.
Aljabali AAA, Alkaraki AK, Gammoh O, Tambuwala MM, Mishra V, Mishra Y, Hassan SS, El-Tanani M. Deciphering Depression: Epigenetic Mechanisms and Treatment Strategies. Biology (Basel). 2024;13(8):638.
Owczarek M, Jurek J, Nolan E, Shevlin M. Nutrient deficiency profiles and depression: A latent class analysis study of American population. J Affect Disord. 2022;317:339-346.
Gibson-Smith D, Bot M, Brouwer IA, Visser M, Giltay EJ, Penninx BWJH. Association of food groups with depression and anxiety disorders. Eur J Nutr. 2020;59(2):767-778.
Al-Fartusie FS, Al-Bairmani HK, Al-Garawi ZS, Yousif AH. Evaluation of Some Trace Elements and Vitamins in Major Depressive Disorder Patients: a Case-Control Study. Biol Trace Elem Res. 2019;189(2):412-419.
Hu B, Lin ZY, Zou RP, Gan YW, Ji JM, Guo JX, Li WG, Guo YJ, Xu HQ, Sun DL, Yi M. Dietary Zinc Intake Affects the Association Between Dietary Vitamin A and Depression: A Cross-Sectional Study. Front Nutr. 2022; 9:913132.
Hu P, van Dam AM, Wang Y, Lucassen PJ, Zhou JN. Retinoic acid and depressive disorders: Evidence and possible neurobiological mechanisms. Neurosci Biobehav Rev. 2020;112:376-391.
Nguyen HD, Oh H, Hoang NHM, Jo WH, Kim MS. Environmental science and pollution research role of heavy metal concentrations and vitamin intake from food in depression: a national cross-sectional study (2009-2017). Environ Sci Pollut Res Int. 2022;29(3):4574-4586.
Huang X, Fan Y, Han X, Huang Z, Yu M, Zhang Y, Xu Q, Li X, Wang X, Lu C, Xia Y. Association between Serum Vitamin Levels and Depression in U.S. Adults 20 Years or Older Based on National Health and Nutrition Examination Survey 2005⁻2006. Int J Environ Res Public Health. 2018;15(6):1215.
Hemani G, Zheng J, Elsworth B, Wade KH, Haberland V, Baird D, Laurin C, Burgess S, Bowden J, Langdon R, Tan VY, Yarmolinsky J, Shihab HA, Timpson NJ, Evans DM, Relton C, Martin RM, Davey Smith G, Gaunt TR, Haycock PC. The MR-Base platform supports systematic causal inference across the human phenome. Elife. 2018;7:e34408.
Skrivankova VW, Richmond RC, Woolf BAR, Yarmolinsky J, Davies NM, Swanson SA, VanderWeele TJ, Higgins JPT, Timpson NJ, Dimou N, Langenberg C, Golub RM, Loder EW, Gallo V, Tybjaerg-Hansen A, Davey Smith G, Egger M, Richards JB. Strengthening the Reporting of Observational Studies in Epidemiology Using Mendelian Randomization: The STROBE-MR Statement. JAMA. 2021;326(16):1614-1621.
Guo Y, Yan J. Association between tobacco smoke exposure and depression: the NHANES 2005-2018 and Mendelian randomization study. Arch Public Health. 2024; 82(1):100.
Wu S, Yuan G, Wu L, Zou L, Wu F. Identifying the association between depression and constipation: An observational study and Mendelian randomization analysis. J Affect Disord. 2024; 359:394-402.
Jackson SE, Smith L, Firth J, Grabovac I, Soysal P, Koyanagi A, Hu L, Stubbs B, Demurtas J, Veronese N, Zhu X, Yang L. Is there a relationship between chocolate consumption and symptoms of depression? A cross-sectional survey of 13,626 US adults. Depress Anxiety. 2019;36(10):987-995.
Iranpour S, Sabour S. Inverse association between caffeine intake and depressive symptoms in US adults: data from National Health and Nutrition Examination Survey (NHANES) 2005-2006. Psychiatry Res. 2019;271:732-739.
Li Z, Wang W, Xin X, Song X, Zhang D. Association of total zinc, iron, copper and selenium intakes with depression in the US adults. J Affect Disord. 2018; 228:68-74.
Xu X, Liu N, Yu W. No Evidence of an Association between Genetic Factors Affecting Response to Vitamin A Supplementation and Myopia: A Mendelian Randomization Study and Meta-Analysis. Nutrients. 2024; 16(12):1933.
Xue Y, Zeng M, Zheng YI, Wang L, Cao S, Wu C, Tang M, Liu S. Gender difference in vitamin A levels in first-episode drug-naïve depression patients: a case-control and 24-weeks follow-up study. Pharmazie. 2020; 75(1):32-35.
Zhang Y, Ding J, Liang J. Associations of Dietary Vitamin A and Beta-Carotene Intake With Depression. A Meta-Analysis of Observational Studies. Front Nutr. 2022; 9:881139.
Wang L, Wang X, Su H, Xu J. Association between vitamin A intake and depression among patients with heart failure. ESC Heart Fail. 2024.
Lee SM, Baek JC. Serum Vitamin Levels, Cardiovascular Disease Risk Factors, and Their Association with Depression in Korean Women: A Cross-Sectional Study of a Nationally Representative Sample. Medicina (Kaunas). 2023; 59(12):2183.
Piccolo M, Belleau EL, Holsen LM, Trivedi MH, Parsey RV, McGrath PJ, Weissman MM, Pizzagalli DA, Javaras KN. Alterations in resting-state functional activity and connectivity for major depressive disorder appetite and weight disturbance phenotypes. Psychol Med. 2023; 53(10):4517-4527.

No competing interests reported.

Download PDF

Editor assigned by journal
01 Nov, 2024
Submission checks completed at journal
16 Oct, 2024
First submitted to journal
14 Oct, 2024

You are reading this latest preprint version

Association between vitamin A intake and depression: A cross-sectional study of NHANES from 2005 to 2020 and Mendelian randomization analysis

Status:

Version 1

Abstract

Figures

1. Introduction

2. Materials and methods

2.1 Study design

2.2 Cross-sectional study of NHANES

2.2.1 Data sources and study population

2.2.2 Dietary vitamin A intake in NHANES

2.2.3 Depression assessment in NHANES

2.2.4 Covariates in NHANES

2.2.5 Statistical analyses

2.3 MR analysis

2.3.1 Assumptions and data sources

3. Results

4. Discussion

5. Conclusion

Declarations

References

Additional Declarations

Status:

Version 1