Abstract
Objective
To examine the association between depression and chronic lung diseases (CLDs) among Chinese older adults.
Method
The data for this cross-sectional study were drawn from the 2015 wave of the China Health and Retirement Longitudinal Study. Depressive symptoms were assessed using the 10-item Center for Epidemiologic Studies Depression (CES-D) scale, with a CES-D score of 10 indicating depression. CLDs were determined by self-reported physician diagnosis. Multiple logistic regression models were used to evaluate the association between depression and CLDs. Smooth curve fitting was performed to explore potential dose–response relationships.
Results
A total of 6970 participants were included in this study. The median and interquartile age range was 66.0 (63.0–72.0) years, 3436 (49.3%) were female, and 1128 (16.2%) had CLDs. Depression was positively associated with CLDs (odds ratio [OR]: 1.20, 95% confidence interval [CI]: 1.02–1.41) after adjusting for age, sex, educational level, marital status, residence region, smoking status, drinking status, nighttime sleep duration, social participation, cooking fuel, body mass index, disability, and comorbidities. When CES-D scores were categorized into quintiles, compared to the quintile 1 group, the quintile 4 and quintile 5 groups showed increased CLDs odds of 41% (OR: 1.41, 95% CI: 1.10–1.82) and 42% (OR: 1.42, 95% CI: 1.09–1.84), respectively, after adjusting for all covariates. Smooth curve fitting indicated a positive linear relationship between the CES-D scores and CLDs. A series of sensitivity analyses supported this result.
Conclusions
Depression could be positively associated with CLDs in Chinese older adults. Future studies are warranted to test this association.
Supplementary Information
The online version contains supplementary material available at 10.1186/s12889-026-26746-1.
Keywords: Depression, Chronic lung disease, Older adults, CHARLS
Introduction
Population aging is a common global trend. By 2050, the global proportion of individuals aged ≥ 65 years is expected to rise to 22% [1]. In China, the population aged ≥ 65 years accounted for 12.0% of the total population in 2020, and it is expected to reach 26.1% by 2050 [2], representing a major public health problem. Among Chinese adults aged ≥ 60 years, the prevalence of chronic diseases is as high as 81.1%, affecting approximately 180 million people [3]. Chronic lung diseases (CLDs) affect the airways and other structures of the lungs. Some of the most common CLDs are chronic obstructive pulmonary disease (COPD), asthma, and interstitial lung disease [4, 5]. CLDs were the third most common cause of mortality, accounting for 4.0 million deaths globally and affecting approximately 454.6 million individuals in 2019 [4, 5]. In China, the burden of COPD remains substantial, and the affected population is still increasing [6]. The prognosis of CLDs is poor, with substantial impairment in quality of life and frequent exacerbations [7, 8].
Depression is a mental illness characterized by persistent sadness, loss of interest in activities, and impaired daily functioning [9]. In China, approximately 6.8% of the population experience lifetime depressive disorders [10], and the elderly are at high risk for depression [11]. Depression is a common comorbidity in chronic diseases, including COPD [12, 13]. Some studies [14–18] have found that depressive symptoms were associated with chronic respiratory diseases, such as COPD, asthma, and COPD mortality. Indeed, a meta-analysis [19] of 13 studies found that depression or anxiety was associated with a 43% increased risk of COPD outcomes in people aged ≤ 66 years. Two studies based on adults aged ≥ 40 years in the United States found that depression was associated with COPD [14, 15]. Two other studies based on Chinese middle-aged and elderly adults aged ≥ 45 years found that depressive symptoms were associated with the risk of CLDs [16, 17]. The association between depression and incident CLDs may be bidirectional [19]. Chronic respiratory diseases have also been found to be associated with depression or depressive symptoms [17, 20], although other studies have shown no such association [15, 21, 22]. These results indicate that further research is required to understand the relationship between the two. To the best of our knowledge, studies on the association between depression and CLDs remains scarce in older adults. Particularly, evidence on this association among older adults is a knowledge gap, and this association in the elderly Chinese population has not been comprehensively examined.
We hypothesized that depression would be associated with CLDs. In this study, we aimed to comprehensively explore the association between depression and CLDs in older Chinese adults based on the 2015 wave of the China Health and Retirement Longitudinal Study (CHARLS). We adjusted for a greater number of covariates, particularly the two covariates of cooking fuel and social participation. Multiple sensitivity analyses were conducted to verify the stability of the findings.
Methods
Study design and participants
The data for this study were obtained from the 2015 wave of the CHARLS, a nationally representative survey. The baseline survey, carried out in 2011, employed a multistage probability-proportional-to-size sampling strategy to recruit community-dwelling adults aged ≥ 45 years from 450 villages within 150 counties across 28 provinces, yielding a total of 17,708 participants. Follow-up interviews are conducted every 2–3 years, with dynamic entry and exit of individuals from the cohort. The CHARLS provides comprehensive information on health, social, and economic domains and supplies both household- and individual-level sampling weights that were directly derived from the selection probabilities [23]. Further details on the study are available on the CHARLS project website at http://charls.pku.edu.cn/. The CHARLS was approved by the Biomedical Ethics Review Committee of Peking University (ethical approval number: IRB00001052-11015). All participants involved provided written informed consent.
This cross-sectional study was an analytical study focusing on associations, not a descriptive study; therefore, sampling weights and complex survey design were not applied. This study primarily included 21,097 participants in the 2015 wave. The exclusion criteria were as follows: (1) age < 60 years and missing age; (2) missing CLDs; (3) missing depressive scores; (4) nervous, emotional, or psychiatric problems; (5) memory-related disease; and (6) certain missing covariates. Participants with self-reported psychiatric or memory-related disorders were excluded because of potential recall bias [24]. Finally, the study included 6,970 participants (Fig. 1).
Fig. 1.
Flow chart of participants selection
Assessment of depression status
The 10-item Center for Epidemiologic Studies Depression (CES-D) scale was employed to evaluate depressive symptoms, with excellent reliability and validity in elderly Chinese community residents [25, 26]. Depressive symptoms in the past week were measured. The CES-D scale consists of 10 items: (1) I was bothered by things that do not usually bother me; (2) I had trouble keeping my mind on what I was doing; (3) I felt depressed; (4) I felt everything I did was an effort; (5) I felt hopeful about the future; (6) I felt fearful; (7) My sleep was restless; (8) I was happy; (9) I felt lonely; and (10) I could not get “going.” Each item was rated on a four-point scale: 0 (rarely or none of the time; < 1 day), 1 (some or a little of the time; 1–2 days), 2 (occasionally or a moderate amount of the time; 3–4 days), or 3 (most or all of the time; 5–7 days). Items 5 and 8 require reverse coding before scoring. The total CES-D score was computed by summing all 10 items (range: 0–30). A total score ≥ 10 indicated clinically significant depression [17, 26]. For brevity, “depression” refers to clinically significant depression.
Assessment of CLDs
Based on relevant literature [16, 17], CLDs were identified based on affirmative responses to the following question: “Have you been diagnosed with chronic lung diseases, such as chronic bronchitis or emphysema (excluding tumors, or cancer) by a doctor?” Asthma was not included in the definition.
Assessment of covariates
Using a standardized questionnaire, trained interviewers collected information on demographic characteristics and health-related status. Based on previous studies [16, 17, 22, 27], demographic variables, lifestyle factors, and health-associated factors were included. The demographic covariates were age, sex, educational level (below primary school, primary school, middle school, high school and above), marital status (married/living with partner, other), and region of residence (urban, rural), which were reported in previous literature [23].
Lifestyle factors included smoking status, drinking status, nighttime sleep duration, social participation, and use of cooking fuel. Smoking status was categorized as never, former, or current according to the following questions: “Have you ever chewed tobacco, smoked a pipe, smoked self-rolled cigarettes, or smoked cigarettes/cigars? Do you still have the habit or have you totally quit?” Drinking status was categorized as non-drinker, more than once a month, or less than once a month using the following question: “Did you drink any alcoholic beverages, such as beer, wine, or liquor in the past year?” “How often?” The nighttime sleep duration was derived from the following question: “During the past month, how many hours of actual sleep did you get at night (average hours for 1 night)?” The sleep duration was categorized as (< 7 h, 7–8 h, and > 8 h) [28]. Social participation included interacting with friends, playing Ma-jong, playing chess, playing cards, going to a community club, going to a sport, social, or other kind of club, taking part in a community-related organization, doing voluntary or charity work, and attending an educational or training course using the question “Have you done any of these activities in the last month?” Participants who reported engagement in any of these activities were categorized into the social participation group. Cooking fuel was classified as either a solid fuel (coal, crop residue, or wood burning for cooking) or a clean fuel (natural gas, marsh gas, liquefied petroleum gas, or electricity) [27].
Health-associated covariates included body mass index (BMI) and disability status. The following comorbidities associated with CLDs were also considered: hypertension, diabetes, cancer, heart diseases, stroke, arthritis, dyslipidemia, liver disease, kidney disease, digestive disease, and asthma. BMI was calculated as the ratio of weight to the square of height (kg/m2). The Activity of Daily Living scale in 2015 consists of 11 items: bathing, dressing, eating, getting in/out of bed, using the toilet, controlling urination, using the telephone, managing money, taking medications, shopping for groceries, and preparing a hot meal. If a respondent reported difficulty with any of the 11 items, they were defined as having a disability. A physician’s diagnosis of comorbidities was self-reported using the question “Have you been diagnosed with … by a doctor?”.
Statistical analysis
Table s1 summarizes the missing rates of covariates (Table s1 The missing number and rate of covariates (n = 7966)). The primary analysis was based on the following methods for handling missing values. Regarding variables with a missing rate of less than 5%, we used the deletion method. For the BMI with the highest rate, we set a dummy variable to indicate the missing values marked as “Missing.” Continuous variables are reported as the mean ± standard deviation (normal distribution) or median and interquartile range (skewed distribution). Categorical variables are presented as frequencies with percentages. Differences between participants with and without depression were compared using the analysis of variance or the Kruskal–Wallis test for continuous variables and the chi-square test for categorical variables.
The univariate logistic regression model was used to assess the relationship between variables and CLDs. Three logistic regression models were used to evaluate the association between depression and CLDs. The odds ratios (ORs) and 95% confidence intervals (CIs) were estimated. Model 1 was unadjusted; Model 2 was adjusted for age, sex, marital status, education level, and region of residence; and Model 3 was adjusted for all covariates. The selection of adjusted variables was primarily based on three key considerations: references from prior literature [16, 17, 27], clinical significance, and statistical results. To further examine the association between the severity of depression and CLDs, the CES-D scores were split into quintiles and then included in logistic regression models with quintile 1 as the reference group. Trend tests were conducted by treating the quintiles as continuous variables. The CES-D scores were also analyzed as a continuous variable to further validate our results. A generalized additive model with smoothing curve fitting was used to further explore potential nonlinear relationships. Subgroup analyses were conducted using a logistic regression model after adjusting for different covariates, except the effect modifier, to examine whether the potential association between depression and CLDs was moderated by the following characteristics: age, sex, educational level, marital status, region of residence, smoking status, drinking status, nighttime sleep duration, cooking fuel, BMI, hypertension, diabetes, heart disease, dyslipidemia, and disability. The interaction tests were evaluated using the likelihood ratio test.
Sensitivity analysis was performed as follows: (1) The differences in CLDs between the depression and non-depression groups were compared after 1:1 propensity score matching with a caliper width of 0.02 including all baseline covariates. Conditional logistic regression was performed to evaluate the association between depression and CLDs, adjusting for sleep duration and BMI, which remained statistically significant after propensity score matching. (2) Main regression analyses were repeated after the multiple imputation method (based on five replications in the R MI procedure) to address missing variables. (3) Main regression analyses were repeated excluding individuals with missing values. (4) The association between different depression states (no depression: 0–9, mild depression: 10–15, and moderate to severe depression: ≥ 16) [29] and CLDs were examined.
Data were sorted using STATA software (version 15.0). Statistical analysis was conducted using the Empower software (www.empowerstats.com; X and Y Solutions, Inc., Boston, MA, USA) and R version 4.2.0 (http://www.R-project.org; The R Foundation). A two-tailed P-value < 0.05 was considered statistically significant.
Results
Baseline characteristics of the participants
The study population screening process is illustrated in Fig. 1. A total of 6970 participants were included in this study. Table 1 presents the baseline characteristics of the study population according to depression status. Among the 6970 participants, the median and interquartile range of age was 66.0 (63.0–72.0) years, 3436 (49.3%) were female, 2366 (34.0%) had depression, and 1128 (16.2%) had CLDs. The prevalence of CLDs was higher in the depression group (19.9%) than in the non-depression group (14.3%) (P < 0.05). Almost all variables, including sex, educational level, marital status, region of residence, smoking status, drinking status, nighttime sleep duration, social participation, cooking fuel, BMI, disability, hypertension, diabetes, heart disease, stroke, arthritis, dyslipidemia, liver disease, kidney disease, digestive disease, asthma, and disability (all P < 0.05), significantly differed between the two groups, except for age and cancer (P > 0.05).
Table 1.
Baseline characteristics of participants
| Characteristics | Total n = 6970 |
Depression status | ||
|---|---|---|---|---|
| No depression n = 4604 |
Depression n = 2366 |
p-value | ||
| Age (years) | 66.0 (63.0–72.0) | 66.0 (63.0–72.0) | 66.0 (63.0–72.0) | 0.677 |
| Gender | < 0.001 | |||
| Female | 3436 (49.3%) | 2003 (43.5%) | 1433 (60.6%) | |
| Male | 3534 (50.7%) | 2601 (56.5%) | 933 (39.4%) | |
| Marital status | < 0.001 | |||
| Other | 1322 (19.0%) | 753 (16.4%) | 569 (24.0%) | |
| Married/living with partner | 5648 (81.0%) | 3851 (83.6%) | 1797 (76.0%) | |
| Education level | < 0.001 | |||
| < Primary school | 3754 (53.9%) | 2210 (48.0%) | 1544 (65.3%) | |
| Primary school | 1645 (23.6%) | 1150 (25.0%) | 495 (20.9%) | |
| Middle school | 1025 (14.7%) | 796 (17.3%) | 229 (9.7%) | |
| ≥ High school | 546 (7.8%) | 448 (9.7%) | 98 (4.1%) | |
| Residence region | < 0.001 | |||
| Urban | 2647 (38.0%) | 1944 (42.2%) | 703 (29.7%) | |
| Rural | 4323 (62.0%) | 2660 (57.8%) | 1663 (70.3%) | |
| Smoking status | < 0.001 | |||
| Never | 3650 (52.4%) | 2261 (49.1%) | 1389 (58.7%) | |
| Former | 1361 (19.5%) | 981 (21.3%) | 380 (16.1%) | |
| Current | 1959 (28.1%) | 1362 (29.6%) | 597 (25.2%) | |
| Drinking Status | < 0.001 | |||
| Non-drinker | 4676 (67.1%) | 2957 (64.2%) | 1719 (72.7%) | |
| More than once a month | 1789 (25.7%) | 1307 (28.4%) | 482 (20.4%) | |
| Less than once a month | 505 (7.2%) | 340 (7.4%) | 165 (7.0%) | |
| Nighttime sleep duration | < 0.001 | |||
| 7–8 h | 2499 (35.9%) | 1854 (40.3%) | 645 (27.3%) | |
| < 7 h | 3706 (53.2%) | 2191 (47.6%) | 1515 (64.0%) | |
| > 8 h | 765 (11.0%) | 559 (12.1%) | 206 (8.7%) | |
| Social participation | < 0.001 | |||
| No | 3831 (55.0%) | 2389 (51.9%) | 1442 (60.9%) | |
| Yes | 3139 (45.0%) | 2215 (48.1%) | 924 (39.1%) | |
| Cooking fuel | < 0.001 | |||
| Clean fuel | 3844 (55.2%) | 2765 (60.1%) | 1079 (45.6%) | |
| Solid fuel | 3126 (44.8%) | 1839 (39.9%) | 1287 (54.4%) | |
| BMI status | < 0.001 | |||
| Normal (18.5–23.9 kg/m2) | 3066 (44.0%) | 2002 (43.5%) | 1064 (45.0%) | |
| Underweight (< 18.5 kg/m2) | 447 (6.4%) | 259 (5.6%) | 188 (7.9%) | |
| Overweight (24.0–27.9 kg/m2) | 1770 (25.4%) | 1219 (26.5%) | 551 (23.3%) | |
| Obese (≥ 28.0 kg/m2) | 660 (9.5%) | 446 (9.7%) | 214 (9.0%) | |
| Missing | 1027 (14.7%) | 678 (14.7%) | 349 (14.8%) | |
| Hypertension | < 0.001 | |||
| No | 4234 (60.7%) | 2882 (62.6%) | 1352 (57.1%) | |
| Yes | 2736 (39.3%) | 1722 (37.4%) | 1014 (42.9%) | |
| Diabetes | < 0.001 | |||
| No | 6203 (89.0%) | 4146 (90.1%) | 2057 (86.9%) | |
| Yes | 767 (11.0%) | 458 (9.9%) | 309 (13.1%) | |
| Cancer | 0.489 | |||
| No | 6848 (98.2%) | 4527 (98.3%) | 2321 (98.1%) | |
| Yes | 122 (1.8%) | 77 (1.7%) | 45 (1.9%) | |
| Heart disease | < 0.001 | |||
| No | 5531 (79.4%) | 3733 (81.1%) | 1798 (76.0%) | |
| Yes | 1439 (20.6%) | 871 (18.9%) | 568 (24.0%) | |
| Stroke | < 0.001 | |||
| No | 6690 (96.0%) | 4451 (96.7%) | 2239 (94.6%) | |
| Yes | 280 (4.0%) | 153 (3.3%) | 127 (5.4%) | |
| Arthritis | < 0.001 | |||
| No | 3743 (53.7%) | 2782 (60.4%) | 961 (40.6%) | |
| Yes | 3227 (46.3%) | 1822 (39.6%) | 1405 (59.4%) | |
| Dyslipidemia | 0.075 | |||
| No | 5652 (81.1%) | 3761 (81.7%) | 1891 (79.9%) | |
| Yes | 1318 (18.9%) | 843 (18.3%) | 475 (20.1%) | |
| Liver disease | 0.010 | |||
| No | 6547 (93.9%) | 4349 (94.5%) | 2198 (92.9%) | |
| Yes | 423 (6.1%) | 255 (5.5%) | 168 (7.1%) | |
| Kidney disease | < 0.001 | |||
| No | 6282 (90.1%) | 4228 (91.8%) | 2054 (86.8%) | |
| Yes | 688 (9.9%) | 376 (8.2%) | 312 (13.2%) | |
| Digestive disease | < 0.001 | |||
| No | 4832 (69.3%) | 3396 (73.8%) | 1436 (60.7%) | |
| Yes | 2138 (30.7%) | 1208 (26.2%) | 930 (39.3%) | |
| Asthma | < 0.001 | |||
| No | 6480 (93.0%) | 4324 (93.9%) | 2156 (91.1%) | |
| Yes | 490 (7.0%) | 280 (6.1%) | 210 (8.9%) | |
| Disability | < 0.001 | |||
| No | 4287 (61.5%) | 3305 (71.8%) | 982 (41.5%) | |
| Yes | 2683 (38.5%) | 1299 (28.2%) | 1384 (58.5%) | |
| Chronic lung diseases | < 0.001 | |||
| No | 5842 (83.8%) | 3946 (85.7%) | 1896 (80.1%) | |
| Yes | 1128 (16.2%) | 658 (14.3%) | 470 (19.9%) | |
| CES-D scores | 6.0 (3.0–12.0) | |||
Abbreviations: BMI Body Mass Index, CES-D Center for Epidemiologic Studies Depression
Association between depression and CLDs
Table 2 presents the associations between variables and CLDs. Depression was positively associated with CLDs (OR: 1.49, 95% CI: 1.30–1.69, P < 0.001), and CES-D scores were also positively associated with CLDs (OR: 1.04; 95% CI: 1.03–1.05, P < 0.001). Furthermore, variables such as age, sex, residence region, smoking status, nighttime sleep duration (< 7 h), BMI (underweight, overweight), heart disease, arthritis, liver disease, kidney disease, digestive disease, asthma, and disability were significantly associated with CLDs (all P < 0.05).
Table 2.
Associations between variables and chronic lung diseases
| Characteristics | OR (95% CI) | p-value |
|---|---|---|
| Age groups | ||
| < 75 | 1 | |
| ≥ 75 | 1.54 (1.31, 1.81) | < 0.001 |
| Gender | ||
| Female | 1 | |
| Male | 1.57 (1.38, 1.79) | < 0.001 |
| Marital status | ||
| Other | 1 | |
| Married/living with partner | 0.91 (0.78, 1.07) | 0.244 |
| Education level | ||
| < Primary school | 1 | |
| Primary school | 1.04 (0.89, 1.21) | 0.651 |
| Middle school | 0.85 (0.70, 1.03) | 0.101 |
| ≥ High school | 0.92 (0.72, 1.18) | 0.524 |
| Residence region | ||
| Urban | 1 | |
| Rural | 1.27 (1.11, 1.45) | 0.001 |
| Smoking status | ||
| Never | 1 | |
| Former | 2.54 (2.17, 2.97) | < 0.001 |
| Current | 1.50 (1.29, 1.75) | < 0.001 |
| Drinking Status | ||
| Non-drinker | 1 | |
| More than once a month | 0.89 (0.77, 1.04) | 0.135 |
| Less than once a month | 0.90 (0.70, 1.16) | 0.410 |
| Nighttime sleep duration | ||
| 7–8 h | 1 | |
| < 7 h | 1.25 (1.09, 1.44) | 0.002 |
| > 8 h | 1.01 (0.80, 1.27) | 0.959 |
| Social participation | ||
| No | 1 | |
| Yes | 0.95 (0.83, 1.08) | 0.395 |
| Cooking fuel | ||
| Clean fuel | 1 | |
| Solid fuel | 1.12 (0.98, 1.27) | 0.088 |
| BMI status | ||
| Normal (18.5–23.9 kg/m2) | 1 | |
| Underweight (< 18.5 kg/m2) | 2.33 (1.87, 2.91) | < 0.001 |
| Overweight (24.0–27.9 kg/m2) | 0.78 (0.66, 0.92) | 0.003 |
| Obese (≥ 28.0 kg/m2) | 0.82 (0.64, 1.04) | 0.104 |
| Missing | 0.97 (0.80, 1.17) | 0.724 |
| Hypertension | ||
| No | 1 | |
| Yes | 1.13 (0.99, 1.28) | 0.070 |
| Diabetes | ||
| No | 1 | |
| Yes | 1.06 (0.87, 1.30) | 0.542 |
| Cancer | ||
| No | 1 | |
| Yes | 1.14 (0.72, 1.82) | 0.576 |
| Heart disease | ||
| No | 1 | |
| Yes | 2.16 (1.87, 2.48) | < 0.001 |
| Stroke | ||
| No | 1 | |
| Yes | 1.07 (0.78, 1.48) | 0.657 |
| Arthritis | ||
| No | 1 | |
| Yes | 1.62 (1.42, 1.84) | < 0.001 |
| Dyslipidemia | ||
| No | 1 | |
| Yes | 1.00 (0.85, 1.17) | 0.980 |
| Liver disease | ||
| No | 1 | |
| Yes | 1.91 (1.52, 2.40) | < 0.001 |
| Kidney disease | ||
| No | 1 | |
| Yes | 1.73 (1.43, 2.08) | < 0.001 |
| Digestive disease | ||
| No | 1 | |
| Yes | 1.66 (1.46, 1.90) | < 0.001 |
| Asthma | ||
| No | 1 | |
| Yes | 19.19 (15.53, 23.71) | < 0.001 |
| Disability | ||
| No | 1 | |
| Yes | 1.54 (1.35, 1.75) | < 0.001 |
| CES-D scores | 1.04 (1.03, 1.05) | < 0.001 |
| Depression | ||
| No | 1 | |
| Yes | 1.49 (1.30, 1.69) | < 0.001 |
Abbreviations: OR Odds Ratio, CI Confidence Interval, BMI Body Mass Index, CES-D Center for Epidemiologic Studies Depression
The association between depression and CLDs using multiple logistic regression models is shown in Table 3. As a continuous variable, CES-D scores were positively associated with CLDs in the fully adjusted model. Depression was significantly associated with CLDs (OR: 1.20; 95% CI: 1.02–1.41) adjusted for all covariates. When CES-D scores were categorized into quintiles, compared to the Q1 group, the Q4 and Q5 groups showed increased CLDs odds of 41% (OR: 1.41, 95% CI: 1.10–1.82) and 42% (OR: 1.42, 95% CI: 1.09–1.84), respectively. A linear relationship was detected between CES-D scores and CLDs, and the odds of CLDs increased as the CES-D scores increased (Fig. 2).
Table 3.
Association between depression and chronic lung diseases
| Variable | OR (95% CI), p-value | ||
|---|---|---|---|
| Model 1 | Model 2 | Model 3 | |
| CES-D scores | 1.04 (1.03, 1.05) < 0.001 | 1.05 (1.04, 1.06) < 0.001 | 1.03 (1.01, 1.04) < 0.001 |
| Depression | |||
| No | 1 | 1 | 1 |
| Yes | 1.49 (1.30, 1.69) < 0.001 | 1.59 (1.39, 1.82) < 0.001 | 1.20 (1.02, 1.41) 0.029 |
| CES-D scores quintile | |||
| Q1 (0-2) | 1 | 1 | 1 |
| Q2 (3-4) | 1.14 (0.89, 1.46) 0.297 | 1.14 (0.89, 1.46) 0.290 | 1.03 (0.78, 1.35) 0.830 |
| Q3 (5-7) | 1.48 (1.18, 1.85) 0.001 | 1.49 (1.19, 1.88) 0.001 | 1.16 (0.90, 1.50) 0.262 |
| Q4 (8-12) | 1.78 (1.42, 2.21) < 0.001 | 1.84 (1.47, 2.30) < 0.001 | 1.41 (1.10, 1.82) 0.007 |
| Q5 (13-30) | 2.10 (1.69, 2.61) < 0.001 | 2.33 (1.86, 2.93) < 0.001 | 1.42 (1.09, 1.84) 0.010 |
| p-value for trend | 1.04 (1.03, 1.06) < 0.001 | 1.05 (1.04, 1.06) < 0.001 | 1.02 (1.01, 1.04) 0.001 |
Abbreviations: 0R Odds Ratio, CI Confidence Interval, CES-D Center for Epidemiologic Studies Depression
Model 1 was not adjusted for covariates
Model 2 was adjusted for age, gender, marital status, education, residence region
Model 3 was adjusted for all covariates
Fig. 2.
Dose–response relationship between the CES-D scores and chronic lung diseases. The relationship was detected after all covariates were adjusted
Subgroup analyses and interaction tests showed that no significant interactions were found in any subgroups by age, sex, marital status, region of residence, smoking status, drinking status, nighttime sleep duration, cooking fuel, BMI, hypertension, diabetes, heart disease, dyslipidemia, and disability (all interaction P-value > 0.05) (Table 4). Considering multiple testing, a P-value < 0.05 for the interaction of educational level may not be statistically significant.
Table 4.
Subgroup analyses between depression and chronic lung diseases
| Subgroup | n | OR (95% CI), p-value | p for interaction |
|---|---|---|---|
| Age | 0.310 | ||
| < 75 | 5853 | 1.21 (1.01, 1.45) 0.042 | |
| ≥75 | 1117 | 1.08 (0.75, 1.57) 0.676 | |
| Gender | 0.721 | ||
| Female | 3436 | 1.21 (0.95, 1.53) 0.125 | |
| Male | 3534 | 1.20 (0.96, 1.49) 0.114 | |
| Marital status | 0.752 | ||
| Other | 1322 | 1.15 (0.81, 1.63) 0.426 | |
| Married/living with partner | 5648 | 1.19 (0.99, 1.43) 0.061 | |
| Education level | 0.005 | ||
| < Primary school | 3754 | 1.01 (0.81, 1.24) 0.955 | |
| Primary school | 1645 | 1.43 (1.03, 2.00) 0.034 | |
| Middle school | 1025 | 2.27 (1.43, 3.61) 0.001 | |
| ≥ High school | 546 | 0.76 (0.35, 1.65) 0.490 | |
| Residence region | 0.253 | ||
| Urban | 2647 | 1.36 (1.02, 1.81) 0.038 | |
| Rural | 4323 | 1.13 (0.93, 1.37) 0.232 | |
| Smoking status | 0.844 | ||
| Never | 3650 | 1.28 (1.01, 1.63) 0.044 | |
| Former | 1361 | 0.98 (0.69, 1.37) 0.891 | |
| Current | 1959 | 1.27 (0.94, 1.71) 0.120 | |
| Drinking status | 0.877 | ||
| Non-drinker | 4676 | 1.25 (1.03, 1.52) 0.024 | |
| More than once a month | 1789 | 1.12 (0.79, 1.59) 0.510 | |
| Less than once a month | 505 | 0.84 (0.43, 1.64) 0.605 | |
| Nighttime sleep duration | 0.221 | ||
| 7–8 h | 2499 | 1.12 (0.83, 1.50) 0.472 | |
| < 7 h | 3706 | 1.34 (1.08, 1.65) 0.007 | |
| > 8 h | 765 | 0.84 (0.48, 1.47) 0.542 | |
| Cooking fuel | 0.632 | ||
| Clean fuel | 3844 | 1.11 (0.88, 1.40) 0.364 | |
| Solid fuel | 3126 | 1.30 (1.03, 1.64) 0.025 | |
| BMI status | 0.678 | ||
| Normal (18.5–23.9 kg/m2) | 3066 | 1.09 (0.86, 1.39) 0.478 | |
| Underweight (< 18.5 kg/m2) | 447 | 2.07 (1.19, 3.61) 0.010 | |
| Overweight (24.0–27.9 kg/m2) | 1770 | 1.27 (0.88, 1.82) 0.201 | |
| Obese (≥ 28.0 kg/m2) | 660 | 1.37 (0.80, 2.36) 0.256 | |
| Missing | 1027 | 1.00 (0.65, 1.56) 0.984 | |
| Hypertension | 0.432 | ||
| No | 4234 | 1.15 (0.93, 1.42) 0.203 | |
| Yes | 2736 | 1.23 (0.96, 1.59) 0.101 | |
| Diabetes | 0.390 | ||
| No | 6203 | 1.22 (1.03, 1.45) 0.022 | |
| Yes | 767 | 1.04 (0.62, 1.73) 0.886 | |
| Heart disease | 0.109 | ||
| No | 5531 | 1.07 (0.89, 1.30) 0.467 | |
| Yes | 1439 | 1.60 (1.17, 2.18) 0.003 | |
| Dyslipidemia | 0.775 | ||
| No | 5652 | 1.23 (1.02, 1.47) 0.028 | |
| Yes | 1318 | 1.12 (0.77, 1.62) 0.557 | |
| Disability | 0.132 | ||
| No | 4287 | 1.30 (1.04, 1.64) 0.023 | |
| Yes | 2683 | 1.08 (0.86, 1.36) 0.514 |
Abbreviations: OR Odds Ratio, CI Confidence Interval, BMI Body Mass Index, CES-D Center for Epidemiologic Studies Depression
Model was adjusted for all covariates except effect modifier
Sensitivity analysis
After propensity score matching, participants with depression had a higher prevalence of CLDs than those without depression (19.2% vs. 16.5%, P < 0.05) (Table s2 Comparison between propensity score matched group). In addition, the results of conditional logistic regression adjusted for sleep duration and BMI indicated that the direction of the effect size was consistent with the main findings (OR: 1.11, 95% CI: 0.95–1.29) (Table s3 Association between depression and chronic lung diseases after propensity score matching). After the multiple imputation method for addressing missing variables, depression was still significantly associated with CLDs (OR: 1.21, 95% CI: 1.05–1.40) adjusted for all covariates (Table s4 Association between depression and chronic lung diseases after multiple imputation). Similar results persisted both after removing all missing values (Table s5 Association between depression and chronic lung diseases after removing all missing values) and various depressive states (Table s6 Association between depressive states and chronic lung diseases), further supporting the association between depression and CLDs.
Discussion
In this cross-sectional study, we examined the association between depression and CLDs in 6970 Chinese adults aged ≥ 60 years. Our findings indicated that depression was associated with a 20.0% increased odds of CLDs (OR: 1.20, 95% CI: 1.02–1.41) adjusted for multiple covariates, particularly social participation and cooking fuel type. Additionally, a positive linear relationship was observed, in that the likelihood of CLDs increased with higher depressive scores. To the best of our knowledge, this study is the first comprehensive exploration of the relationship between depression and CLDs in older Chinese adults.
Several studies have shown that depression is positively associated with chronic respiratory diseases. Sun et al. [14] found that severe depression was associated with cough (OR: 3.32, 95% CI: 1.57–7.05), wheeze (OR: 2.84, 95% CI: 1.52–5.31), and COPD (OR: 2.57 95% CI: 1.24–4.92) among the population aged ≥ 40 years in the United States based on the National Health and Nutrition Examination Survey (NHANES). Another study based on the NHANES database revealed a significant association between mild depression and COPD (OR: 1.81, 95% CI: 1.30–2.52) among adults aged ≥ 40 years in the United States [15]. Wang et al. [16] found that depressive symptoms were a significant new-onset risk for lung disease (HR: 1.49, 95% CI: 1.17–1.89) among Chinese middle-aged and elderly populations using the CHARLS data. Another study based on the CHARLS data showed that elevated depressive symptoms significantly increased the risk of CLDs (HR: 1.68, 95% CI: 1.46–1.93) in middle-aged and older Chinese adults [17]. Our study found that depression was positively associated with CLDs (OR: 1.20; 95% CI: 1.02–1.41) in the elderly Chinese population aged ≥ 60 years, which is consistent with previous studies [14–17]. An OR of 1.20 suggested a relatively weak effect size, which may be influenced by the selected cutoff value of 10. However, the curve-fitting plot indicated that the likelihood of CLDs increased as depressive scores increased. Specifically, as presented in Table 3, the OR for depressive scores within the highest quintile (Q5) was 1.42, whereas the OR for moderate to severe depression was 1.40 (Table S5). These findings suggest a potential association between depression and CLDs, with a stronger association observed in patients with more severe depression.
Although previous studies have examined the association between depression or depressive symptoms and CLDs, evidence in older adults remains scarce. Our study focusing on older Chinese adults revealed a relatively modest yet robust effect size, with an OR of 1.20 (95% CI: 1.02–1.41), which is lower in magnitude compared to earlier estimates. Furthermore, a linear relationship existed between depressive scores and CLDs, which is consistent with previous studies [14, 16] but differed from the results of a nonlinear association [15]. The inconsistent findings across studies can be explained by several factors. First, the study populations differed significantly, characterized by variations in ethnic backgrounds and age stratification. Our study focused on elderly Chinese adults (≥ 60 years), whereas a previous study [15] analyzed the United States population (≥ 40 years). Second, our study adjusted for a broader range of potential confounding factors, particularly cooking fuel types and social participation.
Accumulating evidence indicates that the relationship between depression and CLD progression is mediated through biological mechanisms. First, depression is strongly associated with chronic systemic inflammation [30, 31]. Pro-inflammatory cytokine levels, including IL-6 and TNF-α, are significantly elevated in individuals with depression [31, 32]. These cytokines drive airway inflammation and remodeling in COPD, contributing to its pathology and progression [33]. Additionally, chronic inflammation may compromise pulmonary immune defenses, increasing susceptibility to infections, which accelerate disease exacerbations [8]. Second, depression induces dysregulation of the hypothalamic-pituitary-adrenal axis, leading to abnormal cortisol secretion [34]. In addition, heightened oxidative stress in depression serves as an important link to the pathophysiology of COPD [35, 36]. Oxidative stress causes chronic inflammation, impairs autophagy, and reduces the activity of antioxidant enzymes, further accelerating the decline in lung function [37, 38]. Our results provide further evidence for the positive association between depression and CLDs among the elderly population. These findings provide evidence for the incorporation of psychological assessment into geriatric care, supporting the establishment of an integrated healthcare model.
Our study has several strengths. First, this is the first study to investigate the association between depression and CLDs among older adults (≥ 60 years) using a large sample in China. Second, we adjusted for multiple covariates, with particular attention to cooking fuel exposure, which is a potential environmental risk factor for CLDs based on existing literature [27]. Third, multiple sensitivity analysis methods were employed to verify the robustness of the results.
Limitations
Despite its strengths, several limitations of this study should also be acknowledged. First, a cross-sectional study can only establish association rather than causation, and the temporal sequence between depression and CLDs was difficult to determine. More longitudinal or experimental studies are warranted in the future to elucidate the causal relationships. Second, depression was defined using a self-reported scale, which might introduce misclassification. In addition, our study used the CES-D scale to screen for depressive symptoms, rather than relying on the gold standard for depression diagnosis (the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition, DSM-V) [39], so the results may include some false-positives. Third, CLDs were defined by self-reported physician diagnosis, which might lead to information bias. In addition, the definition of CLDs focused on chronic bronchitis and emphysema; therefore, the results may not be applicable to other chronic respiratory diseases. Fourth, our study population was restricted to Chinese adults aged ≥ 60 years, which may limit the generalizability of our findings to other populations and age groups. Fifth, despite comprehensive adjustment for known confounders, residual confounding from unmeasured variables may bias the observed associations. Finally, this study did not use sampling weights or adjust for the complex survey design in the analysis, which may introduce bias in estimating descriptive statistics such as population prevalence rates. However, our primary objective was to examine the associations between depression and CLDs, not to precisely estimate population parameters.
Conclusion
In this cross-sectional study, we found a positive association between depression and CLDs among Chinese older adults. The causal direction of this relationship requires further validation through future longitudinal studies.
Supplementary Information
Supplementary Material 2. Table s1 The missing number and rate of covariates (n = 7966).
Supplementary Material 3. Table s2 Comparison between propensity score matched group.
Supplementary Material 4. Table s3 Association between depression and chronic lung diseases after propensity score matching.
Supplementary Material 5. Table s4 Association between depression and chronic lung diseases after multiple imputation.
Supplementary Material 6. Table s5 Association between depression and chronic lung diseases after removing all missing values.
Supplementary Material 7. Table s6 Association between depressive states and chronic lung diseases.
Acknowledgements
We express profound gratitude to Peking University for providing access to the CHARLS dataset, and to all contributors engaged in data collection and management.
We thank LetPub (www.letpub.com.cn) for its linguistic assistance during the preparation of this manuscript.
Authors’ contributions
CY and LX contributed equally to this work. All authors reviewed the manuscript.
Funding
This study had no funding.
Data availability
The raw data for this study is available from: http://charls.pku.edu.cn/en.
Declarations
Ethics approval and consent to participate
The CHARLS project was approved by the Biomedical Ethics Review Committee of Peking University (ethical approval number: IRB00001052-11015). All participants provided written informed consent. All procedures were conducted in accordance with the Declaration of Helsinki.
Consent for publication
Not applicable.
Competing interests
The authors declare no competing interests.
Footnotes
Publisher’s Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Supplementary Material 2. Table s1 The missing number and rate of covariates (n = 7966).
Supplementary Material 3. Table s2 Comparison between propensity score matched group.
Supplementary Material 4. Table s3 Association between depression and chronic lung diseases after propensity score matching.
Supplementary Material 5. Table s4 Association between depression and chronic lung diseases after multiple imputation.
Supplementary Material 6. Table s5 Association between depression and chronic lung diseases after removing all missing values.
Supplementary Material 7. Table s6 Association between depressive states and chronic lung diseases.
Data Availability Statement
The raw data for this study is available from: http://charls.pku.edu.cn/en.