Association between dietary sugar intake and depression in US adults: a cross-sectional study using data from the National Health and Nutrition Examination Survey 2011–2018

Data for this study were obtained from the NHANES database, a major program conducted by the Centers for Disease Control (CDC) and Prevention to assess the health and nutritional status of individuals in the US [14]. The NHANES contains demographic, dietary, examination, laboratory, and questionnaire data. All study participants provided informed consent and the study protocol was approved by the Ethics Review Board of the National Center for Health Statistics (NCHS). Information can be found on the NHANES website (https://​www.​cdc.​gov/​nchs/​nhanes/​participant.​htm).

Participants in our study were screened according to the following inclusion criteria: 1) age 20 years or above, and 2) sugar intake, which was assessed based on a 24-h recall. The exclusion criteria were: 1) incomplete Patient Health Questionnaire-9 (PHQ-9), and 2) no data on dietary sugar intake.

The PHQ-9 is considered an accurate and reliable tool to screen for depression. It contains nine items designed to capture the frequency of depressive symptoms, including appetite problems, fatigue, sleep difficulties, psychomotor retardation or agitation, concentration problems, lack of interest, low mood, feelings of worthlessness, and suicidal thoughts. Each question is scored from ‘0’ (not at all) to ‘3’ (nearly every day), with a total possible score of 0–27; a score ≥ 10 is considered clinically relevant depression (CRD) [15].

Dietary information was collected through interviews. Dietary intake data were used to estimate the types and amounts of foods and beverages (including all types of water) consumed during the 24 h before the interview (midnight to midnight). All NHANES participants were eligible for two 24-h dietary recall interviews. The first meal recall interview was conducted at a mobile screening center (MEC), and the second interview was conducted via telephone 3–10 days later. Interview data files were sent electronically from the field and imported into Survey Net, a computer-assisted food coding and data management system developed by the US Department of Agriculture (USDA) to calculate nutrient intakes. After the intake data were coded, various types of reviews and quality assurance procedures were conducted by the Food Survey Research Group (FSRG) scientists to ensure data quality. The study obtained the daily sugar intake based on the sum of the sugar content of all foods consumed in a single 24-h dietary review. Detailed information regarding the sugar intake assessment can be found at: https://​wwwn.​cdc.​gov/​Nchs/​Nhanes/​2013-2014/​DR2TOT_​H.​htm#DR2TSUGR.

This study extracted the mean sugar intake between the first and second dietary recall as the participants’ dietary sugar intake. For participants who only attended the 24-h dietary recall, sugar intake was defined as the day's sugar intake.

The covariates in this study—body mass index (BMI) and energy intake—were used as continuous variables. BMI was measured as weight (kg) divided by height (m) squared. Energy intake was determined by calculating the mean energy intake from the first and second dietary surveys. Categorical variables included age (20–44 years, 45–59 years, ≥ 60 years), sex (male or female), and race/ethnicity (non-Hispanic white, non-Hispanic black, Mexican American, other Hispanic, or other race/multiple races). Education level was categorized as high school not completed, high school completed, high school graduate, or college or associate degrees. Marital status was defined as married/living with a partner or widowed/divorced/separated/never married. Physical activity was self-reported by the participants as lacking, moderate, or vigorous. HTN (defined as systolic blood pressure ≥ 140 mmHg or diastolic blood pressure ≥ 90 mmHg) was determined using three blood pressure measurements at different times, an existing diagnosis, or evidence of an existing antihypertensive medication regimen. Participants were deemed to have diabetes mellitus (DM) if they used glucose-lowering therapies or anti-diabetic medications or had an HbA1c concentration of ≥ 6.5%, an oral glucose tolerance test (OGTT) resulting in ≥ 11.1 mmol/L, fasting plasma glucose ≥ 7.0 mmol/L, or random blood glucose ≥ 11.1 mmol/L. Cardiovascular disease (CVD) was deemed present for participants who experienced or had experienced coronary heart disease, congestive heart failure, heart attack, stroke, or angina. According to smoking habits, participants were categorized as non-smokers (smoked < 100 cigarettes over the lifetime), former smoker (not currently smoking but have consumed ≥ 100 cigarettes previously), and current smoker (smoking ≥ 100 cigarettes in life and smoke every day or some days). According to drinking habits, they were categorized as non-drinkers (drank < 12 drinks over the lifetime), former drinker (drank ≥ 12 drinks a year and has not drunk in the past year, or has not drunk in the past year but has drunk ≥ 12 drinks over the lifetime), and current drinker (has been drinking ≥ 12 drinks in the past year or smoke every day or some days). The poverty-income ratio (PIR) was defined as the ratio of family income to poverty threshold (< 1 indicating an income below the poverty threshold and ≥ 1 indicating an income above the poverty threshold, with the latter category further classified into two groups: 1.00 to < 2.00 and ≥ 2.00).

After adjusting for other factors that may influence depression, the main focus was whether dietary sugar intake was associated with depression. Continuous variables were expressed as mean ± standard deviation, and categorical variables as percentages. The weighted χ2 test was used to compare categorical variables between groups, a one-way analysis of variance to compare normally distributed variables between groups, and the Kruskal–Wallis H test was employed to compare variables with a skewed distribution between groups. A multivariate logistic regression analysis was used to evaluate the independent association between dietary sugar intake and depression. The participants were categorized into four groups based on dietary sugar intake: < 57.47g/d, 57.47 to < 93.42g/d, 93.42 to < 141.76g/d, and ≥ 141.76g/d. The study utilized three levels of adjustment: Model 1 was adjusted for age, sex, and race/ethnicity; Model 2 was adjusted for the variables in Model 1 plus educational level, marital status, and PIR; and Model 3 was adjusted for the variables in Model 2 plus HTN, DM, CVD, drinking status, smoking status, physical activity level, and energy intake. The imputation of missing data was used to impute the missing R package. This random-forest-based technique is computationally highly efficient for high-dimensional data on categorical and continuous predictors [16].

This study included 18,439 participants (Fig. 1).

Table 1 presents the quartiles of participants according to their dietary sugar intake. There were statistically significant differences in age, sex, educational level, race/ethnicity, marital status, PIR, smoking status, alcohol status, HTN, DM, CVD, energy intake, and physical activity among the different dietary sugar intake groups (P < 0.05). No significant differences occurred in BMI (P > 0.05). Covariates with P < 0.05 in univariate analysis were included for further analysis.

Participants in the lowest dietary sugar intake in quartile (Q1, < 57.47g/d) were generally older, of female gender, highly educated, non-Hispanic white, living with a partner, rather wealthy, smoked less, drank more, had no HTN, DM, or CVD, had a lower energy intake, and were less active. Conversely, participants with the highest dietary sugar intake in Q4 (≥ 141.76g/d) were likely to be younger, of male gender, highly educated, non-Hispanic white, married or cohabitating, had a lower income, consumed more alcohol, never smoked, had no HTN, DM, or CVD, a higher energy intake and did not engage in physical activity.

A comparison of the characteristics between the participants included and excluded from the analysis is presented in Table S1 in the Supplementary Material (except for participants who were excluded because of their age). The missing data are listed in Table S2.

In the comprehensively adjusted model, a linear relationship was observed between dietary sugar intake and depression (Fig. 2).

Table 2 presents the results of the multiple regression analysis. In the crude model, dietary sugar intake was positively correlated with depression (odds ratio [OR] = 1.17, 95% confidence interval [CI]:1.10–1.24, P < 0.001). After adjusting for confounders, a significant relationship was observed between dietary sugar intake and depression in Models 1–3. In Model 3, adjustment for all covariates revealed that the incidence of depression increased by 28% for every 100 g/d increase in dietary sugar intake (OR = 1.28, 95% CI = 1.17–1.40, P < 0.001).

After adjusting for age, sex, race/ethnicity, PIR, educational level, marital status, HTN, DM, CVD, drinking status, smoking status, physical activity level, and energy intake compared with participants in the first quartile (dietary sugar intake 57.47 g/d), the second group (≥ 57.47 to < 93.42 g/d, OR = 0.87, 95% CI = 0.73–1.03, P = 0.096), the third group (≥ 93.42 to < 141.76 g/d, OR = 1.01, 95% CI = 0.85–1.20, P = 0.945), and the fourth group (≥ 141.76 g/d, OR = 1.33, 95% CI = 1.10–1.61, P = 0.003) had an increased prevalence of depression (P for trend was significant in all the models).

Additionally, we performed subgroup and threshold effect analyses. Subgroup analyses according to age, sex, race/ethnicity, PIR, educational level, marital status, HTN, DM, CVD, drinking status, smoking status, physical activity, and energy intake revealed results similar to those of the main analysis (Fig. 3).

No significant interactions were observed between dietary sugar intake and any of the variables. Table 3 presents the results of the threshold effect, indicating that dietary sugar intake was linearly associated with depression (log-likelihood ratio (LLR) = 0.051).