Background
Cancer is a major contributor of the increasing global burden of disease, accounting for nearly 10 million deaths annually [ 1 , 2 ]. With the development of treatment and care, cancer has been recognized as a chronic disease and an increasing number of patients are now living as cancer survivors. However, these survivors face new challenges, as they have become a high-risk group for depression. For patients undergoing treatment, depression may stem from changes in body image during treatment, concerns about survival, and diminished quality of life [ 3 ]; for cancer survivors, it may be due to fear of recurrence, financial difficulties, comorbidities, and loneliness [ 4 ]. Affecting about 20% of the patients undergoing cancer treatment and cancer survivors (hereinafter referred to as “patients diagnosed with cancer”) [ 5 – 7 ], depression impairs patients’ ability to cope with the disease, reduces treatment adherence and quality of life, and may increase both mortality and the risk of suicide [ 8 – 11 ]. Therefore, implementing proactive intervening to alleviate depression in patients diagnosed with cancer is a clinical imperative.
Antidepressants are effective in alleviating depressive symptoms, but some medications have evident side effects [ 12 ]. Patients with certain types of cancer require maintenance medications following antitumor therapy. For example, patients with breast cancer often take oral endocrine therapy drugs (e.g., tamoxifen) to reduce the risk of recurrence [ 13 ]. Nevertheless, paroxetine and fluoxetine significantly affect the metabolism of tamoxifen, potentially resulting to treatment failure [ 14 , 15 ]. In addition to interacting with maintenance medications, antidepressants may interact adversely with chemotherapy agents [ 3 ]. There are also patients who do not respond to antidepressants [ 16 ]. In addition, among patients with mild or moderate depressive symptoms, antidepressants may offer limited or no benefit, and nonpharmacologic interventions are therefore recommended [ 17 , 18 ]. Randomized controlled trials (RCT) have demonstrated that cognitive-behavioral therapy effectively reduces depressive symptoms in patients diagnosed with cancer [ 19 – 22 ]. Thus, as complementary interventions to first-line antidepressants, nonpharmacologic treatments play a clinically important role in improving the management of depressive symptoms in patients diagnosed with cancer.
Lifestyle interventions provide a potentially safe and cost-effective adjunct to the standard first-line pharmacological and psychological treatments [ 18 , 23 ]. Clinical trials have shown that dietary and exercise interventions could mitigate depressive symptoms in the general population [ 24 – 26 ]. However, there are important gaps in our knowledge of the effects of lifestyle on depression in patients diagnosed with cancer, who face distinct psychological and physiological stressors. Observational studies suggested healthy lifestyles (e.g. exercise and quit smoking) were associated with a reduced risk of depression [ 27 , 28 ]. Nevertheless, the associations between lifestyles and depression among patients diagnosed with cancer have not been systematically investigated and remained unclear. Furthermore, the extent of benefit conferred by healthy lifestyle interventions in reducing their depression has not been quantified.
Recently, g-formula has been used to estimate and compare how changes in exposures influence changes in the distribution of outcomes under different hypothetical intervention strategies [ 29 – 31 ]. G-formula is a generalization of nonparametric standardization within the potential outcomes framework of causal inference [ 32 ]. When the assumptions of exchangeability (i.e., no unmeasured confounder), consistency (i.e., intervention is well-defined and all individuals receive the same version of intervention), and positivity (i.e., nonzero probability of receiving each intervention conditional on confounders) are satisfied, g-formula can yield marginal causal estimates of hypothetical intervention strategies. In other words, g-formula can answer the causal question directly: how would the PHQ-9 scores of patients diagnosed with cancer change if everyone adopted a healthy diet, quit smoking or did not drink alcohol [ 33 ].
To address the above research gaps, this study aimed to use NHANES 2005–2018 data to explore subgroup differences in depressive symptoms among different lifestyles in patients diagnosed with cancer, and assessed the effect of different lifestyle hypothesis interventions using the g-formula approach.
Methods
Study design and population
The NHANES has been conducted annually since 1999. It is a cross-sectional survey representing the civilian, non-institutionalized population of the United States. The NHANES employs a complex, multistage, stratified design with oversampling of minorities and older adults. It collects structured interview data, physical examination results (including urine and blood samples), and physiological measurements. When necessary, questions are answered by proxies on behalf of participants. More detailed information could be found at https://wwwn.cdc.gov/nchs/nhanes/ . To ensure an adequate sample size for the analyses, we combined data from the 2005 − 2006 through 2017–2018 survey cycles.
Of the 70,190 participants interviewed in NHANES between 2005 and 2018, 59,842 individuals without mental disorders were eligible. Those with PHQ-9 responses indicating “refused” or “do not know,” or lacking cancer-related data, were excluded from the analysis (Fig. 1 ). In the association analyses, we included 19,021 participants to explore lifestyles associated with declining PHQ-9 scores. The hypothetical intervention analyses focused on participants diagnosed with cancer. A total of 1,931 NHANES participants reported a cancer diagnosis by a doctor or other health professional between 2005 and 2018 (defined by the question “Have you ever been told by a doctor or other health professional that you had cancer or a malignancy of any kind?”).
Fig. 1 Flowchart of participant screening and inclusion
Outcome
PHQ-9 was used to evaluate the depressive symptoms over the past 2 weeks. It consists of 9 items with symptom score ranging from 0 (indicating not at all), 1(several days), 2 (more than half the days), and 3 (nearly every day). The items includes: [ 1 ] little interest or pleasure in doing things [ 2 ], feeling down, depressed, or hopeless [ 3 ], trouble falling or staying asleep, or sleeping too much [ 4 ], feeling tired or having little energy [ 5 ], Poor appetite or overeating [ 6 ], feeling bad about yourself — or that you are a failure or have let yourself or your family down [ 7 ], trouble concentrating on things, such as reading the newspaper or watching television [ 8 ], moving or speaking so slowly that other people could have noticed? Or the opposite — being so fidgety or restless that you have been moving around a lot more than usual [ 9 ], thoughts that you would be better off dead or of hurting yourself in some way. The total PHQ-9 scores range from 0 to 27, with higher score indicating more severe depressive symptoms.
Covariates
Based on the literature and prior knowledge, the covariates included gender, age, education level (less than 9th grade, 9-11th grade, high school graduate/general educational development or equivalent, some college or associate of arts degree, college graduate or above), marital status (married, widowed, divorced separated, never married, living with partner), ethnicity (Mexican American, other Hispanic, non-Hispanic White, non-Hispanic Black, other), ratio of family income to poverty, smoking status (never, previous, current), and alcohol status (drinker, non-drinker) [ 34 , 35 ]. Interviewers obtained birthdates and other demographic information through self-report in the initial screening questionnaire. Detailed information of covariates was provided in Supplementary File Table S1.
Health behaviors for hypothetical intervention
A total of six health behaviors which was previously shown to be associated with alleviating depressive symptom were chosen to evaluate the hypothetical intervention effect, including diet index, physical activity, smoking, alcohol, sleep duration, and waist circumference [ 23 , 24 , 28 ].
Diet index
The diet indices included HEI-2015 [ 36 ], MED [ 37 ], and DASH [ 38 ]. Calculation details were listed in Table S2-S4. These diet indexes were collected from two 24-hour recall interviews and derived after conversion to corresponding food equivalents in the Food Pattern Equivalence Database.
Physical activity
To assess physical activity levels, the Metabolic Equivalent of Task (MET) was utilized to calculate the quantify of leisure-time physical activity for each participant. The questionnaire included questions regarding daily and leisure-time activities. The activity information was collected via the Computer-Assisted Personal Interviewing (CAPI) system, prior to the physical examination at participants’ homes. Vigorous-intensity activities require substantial physical exertion and lead to significant increases in breathing and heart rate, whereas moderate-intensity activities involve moderate physical effort, resulting in slight increases in breathing and heart rate. Work-related physical activity consists of paid or unpaid work, study or training, household chores, and yard work. Leisure-time physical activity includes sports, fitness, and recreational activities. The recommended MET scores for vigorous and moderate work-related physical activity, walking or bicycling for transportation, vigorous and moderate leisure-time physical activity were 8.0, 4.0, 4.0, 8.0, and 4.0, respectively. To estimate MET-hours per week, the average hours spent on each activity per week were multiplied by the recommended MET scores. The MET-hours per week for vigorous and moderate work-related physical activity were summed to calculate total work-related physical activity, while the MET-hours per week for vigorous and moderate leisure-time physical activity were combined to calculate total leisure-time physical activity.
Smoking status, alcohol consumption, and waist circumference
Smoking status was determined by asking participants if they had smoked at least 100 cigarettes in their lifetime. Non-smokers answered “no”, while those who responded “yes” were further asked if they currently smoked. Alcohol consumption in the past 12 months was queried, with non-drinkers reporting less than 12 alcohol drinks each year. Sleep duration was recorded as number of hours by asking “How much sleep do you usually get at night on weekdays or workdays?” Waist circumference was measured by trained health technicians and was recorded in centimeters. Sleep duration was measured as the average sleep hours per night.
Statistical analysis
Data analysis and description were performed using R (Version 4.3.2; R Core Team, 2023). Descriptive statistics, including absolute and relative frequencies for discrete parameters, as well as the mean and standard deviation for continuous parameters, were computed.
In the association analysis, inverse probability weighting (IPW) was used to balance the correlations between covariates and exposures. For binary health behavior variables, propensity score (PS) was estimated using logistic regression (for categorical health behavior variables), and for continuous health behavior variables, generalized propensity score (GPS) was estimated using linear regression. In the outcome model, each participant was weighted by the inverse of PS or GPS. The average absolute standardized mean difference (SMD) for categorical variables and the absolute correlation for continuous variables were used to evaluate whether covariates were balanced after IPW. The average absolute SMD and absolute correlation less than or equal to 0.1 indicate balanced covariates [ 39 ]). Finally, weighted linear regression models were fitted to examine the association between health behaviors and PHQ-9 scores in the target population. Health behaviors significantly related with PHQ-9 scores were further used in the hypothetical intervention analysis.
In the hypothetical intervention analysis, we used g-formula method to estimate potential hypothetical intervention effects for reducing depressive symptoms among individuals diagnosed with cancer. First, we constructed linear regression models (e.g., outcome model) to estimate the expected value of PHQ-9 score, adjusted for the covariates described before (gender, age, education level, marital status, ethnicity, ratio of family income to poverty, smoking status, and alcohol status). Six single hypothetical intervention strategies were considered. Specifically, the distribution of six health behaviors among patients diagnosed with cancer was replaced with the distribution of the top 25% population from the association analysis by random sampling with replacement. We used resampling distributions for hypothetical interventions to ensure their realistic feasibility. For beneficial health behaviors (DASH score, MED score, HEI-2015 index, physical activity), top 25% population was defined as a subpopulation according to descending rank of each health behaviors. Since both excessive and insufficient sleep increase the risk of depression and other diseases, based on previous recommendations in the literatures, we selected 7–8 h of sleep duration and extracted its distribution in the general population [ 40 , 41 ]. For harmful behaviors (smoking, drinking, and waist circumference), top 25% population was defined as a subpopulation according to ascending rank of each health behaviors (Never smoking or drinking rank as the first ranks, current smoking and drinking rank as the last ranks). Hypothetical intervention effects (e.g., absolute difference) were then calculated as difference of expected values of PHQ-9 scores from empirical distribution and resampling distribution. Robust 95% confidence intervals (CIs) were obtained using standard errors from 5000 bootstrapped iterations. To compare the effectiveness of the hypothetical interventions on lifestyles between cancer and non-cancer group, we also performed the above steps in patients without a cancer diagnosis.
Given that the PHQ-9 includes a sleep item and improving sleep duration may alleviate other depressive symptoms in addition to the sleep dimension, we removed the PHQ-9 sleep entry, referring to it as the PHQ-8. We reanalyzed the association between sleep duration and the PHQ-8, as well as the effect of the hypothetical intervention.
Given the important role of diet, waist circumference, and other lifestyles in gastrointestinal and obesity-related cancers, we conducted a subgroup analysis. The analysis was stratified by obesity-related cancers, as defined by the International Agency for Research on Cancer (IARC). Obesity-associated cancers included esophageal adenocarcinoma, gastric cardia, colorectal, liver, gallbladder, pancreas, postmenopausal breast, uterus (corpus uteri), ovary, kidney (renal-cell), meningioma, thyroid, and multiple myeloma cancers (See Table S7 for specific codes in NHANES) [ 42 ].
To investigate whether the association between dietary patterns and PHQ-9 scores was mediated by hypertension and cardiovascular disease (CVD), we performed mediation analyses in the population used for association analyses. This analysis estimates both natural direct and indirect effects. Robust standard errors were calculated to construct 95% CIs.
A p-value of less than 0.05 was considered statistically significant. Data analyses were conducted from May 2023 to November 2023.
Results
Study characteristics
The final analysis contained 19,021 participants aged 20 to 80 years, with a mean (SD) age of 49.56 ± 17.52 years. Among those participants, 9,234 (48.5%) were male, and 1,931 (10.2%) had been diagnosed with cancer. The study flowchart is shown in Fig. 1 . The baseline characteristics of participants with missing data on PHQ-9 scores and covariates were highly similar to those with complete data (Supplemental File Table S5). Among participants diagnosed with cancer, the mean (SD) of PHQ-9 scores was 3.47 ± 4.89. Compared to participants without cancer, those diagnosed with cancer were older, had a higher education level, a higher ratio of family income to poverty, fewer current smokers, and elevated waist circumference. Additional baseline characteristics of the study population are provided in Table 1 . After IPW, the distributions of the covariates between groups were ideally balanced (Supplementary File Table S6 and Figure S1).
Table 1 Comparisons of characteristics between cancer and non-cancer groups Variables Cancer ( N = 1931) Non-cancer ( N = 17090) \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\:\varvec{t}/{\varvec{x}}^{2}$$\end{document} P value Age (years) 65.13 ± 13.76 47.80 ± 17.03 40.600 < 0.001 Sex 0.400 0.525 Male 924 (47.9) 8,308 (48.6) Female 1,007 (52.1) 8,782 (51.4) Race 535.84 < 0.001 Mexican American 111 (5.7) 2,613 (15.3) Other Hispanic 125 (6.5) 1,768 (10.3) Non-Hispanic White 1,359 (70.4) 7,369 (43.1) Non-Hispanic Black 258 (13.4) 3,628 (21.2) Other Race 78 (4.0) 1,712 (10.0) Education 20.760 < 0.001 Less than 9th grade 147 (7.6) 1,481 (8.7) 9-11th grade 221 (11.4) 2,389 (14.0) High school/educational development or equivalent 427 (22.1) 3,911 (22.9) Some college or associate of arts degree 590 (30.6) 5,115 (29.9) College graduate or above 546 (28.3) 4,194 (24.5) Marital status 502.730 < 0.001 Married 1,136 (58.8) 8,871 (51.9) Widowed 319 (16.5) 1,076 (6.3) Divorced 248 (12.8) 1,870 (10.9) Separated 53 (2.7) 547 (3.2) Never married 118 (6.1) 3,295 (19.3) Living with partner 57 (3.0) 1,431 (8.4) Ratio of family income to poverty 2.83 ± 1.61 2.51 ± 1.63 8.610 < 0.001 Smoking status 284.650 < 0.001 Never 785 (40.7) 3,959 (23.2) Previous 859 (44.5) 9,621 (56.3) Current 287 (14.9) 3,510 (20.5) Alcohol consumption 1.440 0.230 Yes a 1,380 (71.5) 12,433 (72.8) No 551 (28.5) 4,657 (27.2) Waist Circumference (cm) 102.43 ± 15.66 99.56 ± 16.52 8.190 < 0.001 Sleep duration (hours) 7.12 ± 1.51 6.98 ± 1.46 4.180 < 0.001 Physical activity (METs) 39.93 ± 74.72 59.09 ± 98.77 -9.260 < 0.001 HEI-2015 index 53.77 ± 12.22 51.40 ± 11.97 8.290 < 0.001 MED index 3.60 ± 1.01 3.57 ± 1.05 1.580 0.113 DASH diet index 27.28 ± 3.02 26.68 ± 2.98 8.410 < 0.001 PHQ-9 score 3.47 ± 4.89 3.24 ± 4.38 1.420 0.157 Note: HEI-2015: Healthy Eating Index-2015; MED: mediterranean diet; DASH: Dietary Approaches to Stop Hypertension; a. indicating had at least 12 alcohol drinks/1 year
Table 1
Comparisons of characteristics between cancer and non-cancer groups
Note: HEI-2015: Healthy Eating Index-2015; MED: mediterranean diet; DASH: Dietary Approaches to Stop Hypertension; a. indicating had at least 12 alcohol drinks/1 year
| Variables | Cancer ( N = 1931) | Non-cancer ( N = 17090) | \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\:\varvec{t}/{\varvec{x}}^{2}$$\end{document} | P value |
| Age (years) | 65.13 ± 13.76 | 47.80 ± 17.03 | 40.600 | < 0.001 |
| Sex | 0.400 | 0.525 | ||
| Male | 924 (47.9) | 8,308 (48.6) | ||
| Female | 1,007 (52.1) | 8,782 (51.4) | ||
| Race | 535.84 | < 0.001 | ||
| Mexican American | 111 (5.7) | 2,613 (15.3) | ||
| Other Hispanic | 125 (6.5) | 1,768 (10.3) | ||
| Non-Hispanic White | 1,359 (70.4) | 7,369 (43.1) | ||
| Non-Hispanic Black | 258 (13.4) | 3,628 (21.2) | ||
| Other Race | 78 (4.0) | 1,712 (10.0) | ||
| Education | 20.760 | < 0.001 | ||
| Less than 9th grade | 147 (7.6) | 1,481 (8.7) | ||
| 9-11th grade | 221 (11.4) | 2,389 (14.0) | ||
| High school/educational development or equivalent | 427 (22.1) | 3,911 (22.9) | ||
| Some college or associate of arts degree | 590 (30.6) | 5,115 (29.9) | ||
| College graduate or above | 546 (28.3) | 4,194 (24.5) | ||
| Marital status | 502.730 | < 0.001 | ||
| Married | 1,136 (58.8) | 8,871 (51.9) | ||
| Widowed | 319 (16.5) | 1,076 (6.3) | ||
| Divorced | 248 (12.8) | 1,870 (10.9) | ||
| Separated | 53 (2.7) | 547 (3.2) | ||
| Never married | 118 (6.1) | 3,295 (19.3) | ||
| Living with partner | 57 (3.0) | 1,431 (8.4) | ||
| Ratio of family income to poverty | 2.83 ± 1.61 | 2.51 ± 1.63 | 8.610 | < 0.001 |
| Smoking status | 284.650 | < 0.001 | ||
| Never | 785 (40.7) | 3,959 (23.2) | ||
| Previous | 859 (44.5) | 9,621 (56.3) | ||
| Current | 287 (14.9) | 3,510 (20.5) | ||
| Alcohol consumption | 1.440 | 0.230 | ||
| Yes a | 1,380 (71.5) | 12,433 (72.8) | ||
| No | 551 (28.5) | 4,657 (27.2) | ||
| Waist Circumference (cm) | 102.43 ± 15.66 | 99.56 ± 16.52 | 8.190 | < 0.001 |
| Sleep duration (hours) | 7.12 ± 1.51 | 6.98 ± 1.46 | 4.180 | < 0.001 |
| Physical activity (METs) | 39.93 ± 74.72 | 59.09 ± 98.77 | -9.260 | < 0.001 |
| HEI-2015 index | 53.77 ± 12.22 | 51.40 ± 11.97 | 8.290 | < 0.001 |
| MED index | 3.60 ± 1.01 | 3.57 ± 1.05 | 1.580 | 0.113 |
| DASH diet index | 27.28 ± 3.02 | 26.68 ± 2.98 | 8.410 | < 0.001 |
| PHQ-9 score | 3.47 ± 4.89 | 3.24 ± 4.38 | 1.420 | 0.157 |
Propensity score weighted associations between health behaviors and depression symptoms
A higher HET-2015 index( β = -0.025, 95% CI, -0.030, -0.019), a higher MED diet index ( β = -0.172, 95% CI, -0.232, -0.112), an increased DASH diet index ( β = -0.077, 95% CI, -0.098, -0.057), greater metabolic equivalents of physical activity ( β =-0.004, 95% CI, -0.005, -0.003), reduced waist circumference ( β = 0.023, 95% CI, 0.019, 0.027), quit smoking ( β = -0.875, 95% CI, -0.715, -1.035) or never smoked ( β = -1.319, 95% CI, -1.478, -1.159), no alcohol( β = -0.237, 95% CI, -0.363, -0.111), and sleeping 7–8 h per night ( β = -1.179, 95% CI, -1.054, -1.303) were associated with lower PHQ-9 scores (Fig. 2 ). After removing the sleep item from the PHQ-9 scale, sleeping 7–8 h per night ( β = -0.862, 95% CI, -0.971, -0.755) remained associated with lower PHQ-9 scores (Table S8). Mediation analyses indicated that the majority of the association between dietary patterns and PHQ-9 scores was attributable to direct effects, with only a small proportion explained by indirect effects (Table S9).
Fig. 2 Propensity scores weighted association between health behaviors and depressive symptoms (Note: HEI-2015: Healthy Eating Index 2015; MED: mediterranean diet; DASH: Dietary Approaches to Stop Hypertension)
Hypothetical intervention effects for alleviating depression symptoms among participants diagnosed with cancer
For different hypothetical intervention strategies, reductions in PHQ-9 scores were as follows, ranked from highest to lowest: 1.117 (95% CI, 1.112, 1.122) under the smallest waist circumference, 0.878 (0.868, 0.888) under the 7–8 hours’ sleep duration, 0.743 (95% CI, 0.742, 0.744) under the highest MED Index, 0.690 (95% CI, 0.690, 0.690) under alcohol cessation, 0.683 (95% CI, 0.683, 0.684) under the best DASH Index, 0.650 (95% CI, 0.624, 0.676) under the highest physical activity equivalent, 0.378 (95% CI, 0.378, 0.378) under smoking cessation, and 0.125 (95% CI, 0.123, 0.128) under the highest HET-2015 Index (Fig. 3 ). The effects of hypothetical lifestyle interventions were different between individuals without a cancer diagnosis and those diagnosed with cancer (Figure S2). Specifically, with the exception of physical activity, alcohol consumption, and sleep duration, all other interventions led to greater improvements in PHQ-9 scores among individuals without cancer compared to those with cancer. After excluding the sleep item from the PHQ-9 scale, a hypothetical intervention on sleep duration can reduce PHQ-9 score by 0.757 (95% CI: 0.750, 0.764; Table S7). Among individuals diagnosed with obesity-related cancers, hypothetical interventions on MED diet, DASH diet, waist circumference, smoking cessation, and alcohol cessation were more effective in reducing PHQ-9 scores (Figure S3).
Fig. 3 Hypothetical intervention effects for alleviating depressive symptoms among participants diagnosed with cancer (HEI-2015: Healthy Eating Index 2015; MED: mediterranean diet; DASH: Dietary Approaches to Stop Hypertension. Absolute change indicates the value of the change in PHQ-9 under the hypothetical intervention.)
Discussion
In this cross-sectional study among individuals diagnosed with cancer, we found that healthier diets (including HEI-2015, MED, and DASH dietary), increased physical activity, smoking cessation, never having smoked, consuming fewer than 12 alcoholic drinks in the past 12 months, and 7–8 h sleeping duration were associated with less severe depressive symptoms. Additionally, our findings suggest that promoting healthy behaviors may help improve depressive symptoms in patients diagnosed with cancer, where interventions for health behaviors were ranked as waist circumference reduction, 7–8 hours’ sleep duration, MED diet, alcohol consumption, DASH diet, physical activity, smoking cessation, and HEL-2015 diet.
Recent RCTs have shown the benefits of the MED in reducing depressive symptoms [ 43 – 45 ]. Furthermore, an RCT evaluating a DASH dietary intervention for postmenopausal women reported improvements in depression scores within the DASH and healthy diet groups, which included lean red meat, whole grains, and fruits and vegetables. However, no significant differences between the groups were observed [ 46 ]. Current evidence regarding dietary interventions is mainly for patients without malignant tumors and is based on a small sample of around 100. Simultaneously, the association between HEI-2015 score and depression has thus far been established solely in observational studies [ 47 – 50 ]. No studies to date have investigated the effects of HEI-2015 dietary interventions on depression. We conducted hypothetical interventions involving nearly two thousand patients diagnosed with cancer and found that adherence to the top 25% of the HEI-2015, MED, and DASH dietary pattern index alleviated depressive symptoms. Furthermore, we observed that the MED and DASH dietary patterns appeared to be more effective in reducing depressive symptoms compared to the HEI-2015. The HEI-2015 recommends a diet abundant in fruits, vegetables, legumes, seafood and plant proteins, grains, dairy products and fatty acids, with moderate intake of sodium, added sugars and saturated fats [ 51 ]. Adherence to HEI-2015 diet represents the quality of diets that align with recommendations of the United States Department of Agriculture and the Dietary Guidelines of Americans and is linked to balance and nutrient-specific effects of a healthy diet [ 52 ]. The MED diet is characterized by plant-based foods (fruits, vegetables, legumes and grains), olive oil as the main cooking fat, moderate intake of fish, eggs, poultry and dairy products, low intake of red meat and moderate consumption of red wine [ 53 ]. The DASH diet is rich in fruits, vegetables and low-fat dairy products, low in saturated fats [ 54 ]. Higher DASH scores are associated with improved blood pressure, lipids, and reduced inflammation. Although the link between diet and depression is not fully understood, brain damage caused by oxidative stress, insulin resistance, inflammation, and alterations in blood vessel formation have implicated in the development of depression, and all of these factors can be modified by dietary intake [ 55 ]. A common feature of these dietary patterns is the higher recommended intake of fruits, vegetables, and nuts and a lower intake of pro-inflammatory foods such as processed meats and trans fats. The beneficial components of these dietary patterns, such as oleate found in olive oil and omega-3 fatty acids, possess antioxidant and anti-inflammatory properties that mitigate systemic inflammation and oxidative stress, preventing these factors from impairing brain function and mood regulation [ 56 , 57 ]. People can choose the dietary pattern that best aligns with their personal preferences.
In our study, increasing physical activity MET significantly relieved depressive symptoms. This finding is consistent with a meta-analysis of RCTs, which showed that exercise has a significant effect on depression compared to usual care [ 58 , 59 ]. However, a meta-analysis of patients with colorectal cancer indicated that physical activity had no effect on depression [ 60 ], potentially due to differences in cancer types. The questionnaire items limited our ability to define more detailed exercise interventions (e.g., type of exercise).
Our study demonstrates a significant reduction in depressive symptoms under the reduction of waist circumference, which can be achieved by increasing physical activity and modifying dietary patterns.
Observational studies and a Mendelian randomization study have highlighted the adverse effects of smoking on mental health [ 61 , 62 ]. We observed that intervention to achieve non-smoking status in patients diagnosed with cancer had a significant improvement on depressive symptoms. A meta-analysis indicated that smoking cessation was associated with a reduction in depressive symptoms from baseline to follow-up, although evidence from RCTs is limited [ 63 ].
Our results also suggest that abstinence from alcohol was beneficial for depression. Alcohol consumption has been shown to be associated with higher levels of depression among patients diagnosed with cancer [ 64 , 65 ], while other studies claimed that greater alcohol intake is associated with lower depression in colorectal cancer survivors [ 66 ]. Previous studies have mostly defined exposure in terms of alcohol abuse, and further research is needed to investigate the effectiveness of alcohol-related intervention.
Sleep disorders are prevalent in patients diagnosed with cancer and are a major source of emotional distress, depression, and pain [ 67 , 68 ]. Previous studies have reported insufficient evidence of an association between sleep duration and depression, and the present study contributes additional evidence that prolonged sleep duration alleviates depressive symptoms.
This study warrants the implementation of further clinical actions. For patients diagnosed with cancer, lifestyle interventions effectively mitigate depressive symptoms. Although interventions on waist circumference and sleep appear to be the most effective based on the ranking of effectiveness, such interventions are relatively less specific compared to others, as waist circumference and sleep represent health-related attributes rather than specific actions [ 33 ]. Therefore, dietary, alcohol/smoking cessation, and physical activity interventions can be prioritized. Primary clinical priorities should address modifiable behaviors: [ 1 ] personalized dietary optimization following MED or DASH patterns (superior to HEI-2015 in symptom alleviation); [ 2 ] graded exercise regimens adapted to functional status (e.g., low-intensity activity for sedentary individuals, the prevention for fall and injuries in elderly patients) [ 69 , 70 ]; [ 3 ] smoking and alcohol cessation, given 15% smoking and 70% alcohol use prevalence among participants. Waist circumference reduction and adequate sleep time (7–8 h nightly) could be achieved through other behavioral changes. For example, dietary interventions and physical activity promotion both benefit the reduction of waist circumference.
The strength of this study is the selection of population-based distributions of intervening variables rather than the guideline-recommended criteria, thereby enhancing the feasibility of our intervention strategy. While certain interventions are challenging to implement in randomized controlled trials (RCTs), g-formula offers a viable method for estimating their effects. In addition, the dietary, exercise, smoking cessation, alcohol abstinence, and sleep interventions showed preliminary validity based on analysis of nationally representative data. Moreover, g-formula also allows for direct ranking of interventions based on effect size, which can help in clinical decision-making.
There are also some limitations of this study; First, we acknowledge cross-sectional design are the major limitation of this study, and although lifestyle is a long-term habit and the outcome is a measure of depressive symptoms over the last two weeks, reverse causality cannot be ruled out. Second, model misspecification and residual confounding may have interfered with the validity of our results. Finally, the study population for this study was drawn from U.S. adults, which restricts the generalizability of the findings.
Conclusions
In conclusion, lifestyle interventions can reduce depressive symptoms in the U.S. population diagnosed with cancer. Clinicians can tailor dietary and exercise recommendations to suit individual patient needs and characteristics. Given the nearly 15% and 70% of patients reported smoking and alcohol consumption, the importance of smoking and alcohol cessation should be further emphasized. In addition, reducing waist circumference and adequate sleep time also serve as effective intervention strategies.
Electronic supplementary material
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Supplementary Material 1
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Acknowledgements
We are grateful to all the staff and participants of NHANES.
Author contributions
Li Yang: Conceptualization, formal analysis, writing– original draft, writing– review and editing.Zhiping Yuan: Conceptualization, writing– review and editing. Qing Pan: Software, Methodology, Visualisation，writing– original draft.
Funding
This study was supported by a project of The First People’s Hospital of Yibin (Grant No. 2021-KYY-01).
Data availability
The data are publicly available and details are provided on the website ( https://wwwn.cdc.gov/nchs/nhanes/ ).
Declarations
Ethics approval and consent to participate
The NHANES study was approved by the local ethics committee as well as informed consent from the participants. Ethical consent was waived for the use of publicly available data.
Consent for publication
Not applicable.
Competing interests
The authors declare no competing interests.
Abbreviations
National Health and Nutrition Examination Survey data
Patient Health Questionnaire-9
Mediterranean Diet
Dietary Approaches to Stop Hypertension
Healthy Eating Index-2015
Randomized controlled trials
Metabolic Equivalent of Task
Computer-Assisted Personal Interviewing
Inverse probability weighting
Propensity Score
Generalized Propensity Score
Standardized Mean Difference
Confidence Intervals
International Agency for Research on Cancer
Cardiovascular Disease
References
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