Type of tea consumption and depressive symptoms in Chinese older adults

The eighth wave of the Chinese Longitudinal Healthy Longevity Survey (CLHLS) in 2018/2019 (briefly as 2018) was used to fulfill our research goals. The CLHLS was conducted in more than 630 counties/cities in 22 provinces of 31 provinces in mainland China plus on county in Hainan Province. The study sampled provinces roughly represents about 85% of the Chinese population in 2020. Given its research focus on longevity, the CLHLS oversampled long-lived populations. For example, in the sampled counties, for every 3 centenarians, 4 participants aged 80–89, 4 participants aged 90–99, and 5 participants aged 65–79 were recruited based on predesignated age and sex. If there was no such matched person, a person will be recruited in neighboring county with same predesignated age and sex. In other words, the CLHLS oversampled centenarians and the oldest-old (aged 80 years or older). Detailed information of the CLHLS can be found elsewhere [30, 31].

The 2018 wave of the CLHLS collected the self-reported types and frequencies of tea intake and assessed depressive symptoms by the Center for Epidemiologic Studies Depression Scale. After excluding 2469 participants with missing data on depressive symptoms, self-reported types of tea consumption, key covariables, the final analytical sample used in this study included 13,115 adults aged 65 years or older (5121 were aged 65–79, 6301 were aged 80–99, and 1693 were aged 100 years or older) (Fig. 1).

The Biomedical Ethics Committee, Peking University (IRB00001052–13074) approved the CLHLS. All participants or their legal representatives signed written consent forms to participate in the baseline and follow-up surveys.

The questionnaire in the 2018 wave of the CLHLS included items about the frequency of habitual consumption of 8 types of tea (green, black, Oolong, white, yellow, dark, compressed, and floral teas). The detailed types and classifications of tea consumption in this study are provided in supplements (Supplementary Table 1). In brief, we classified the type of tea into green tea, fermented tea (black, Oolong, white, yellow, dark, and compressed teas), and flower tea [32]. We grouped the frequency of tea consumption of each type of tea into 3 categories: daily (≥ 1 cup/day), occasionally (< 1 cup/day but ≥1 cup/month), and never or rarely (< 1 cup/month or never drink tea) [33].

We used the 10-item of the Center for Epidemiologic Studies Depression Scale (CES-D-10) to measure depressive symptoms in this study [34]. The answers were indicated in a four-scale metric, from “rarely” to “some days” (1–2 days), “occasionally” (3–4 days), or “most of the time” (5–7 days). For the two positive questions— “I was happy” and “I felt hopeful about the future”—answers were reversely coded before summation. We then coded all answers from 0 to 3 as “rarely” to “most of the time”, respectively. The total range of CES-D-10 scores in this study was 0–30, with higher scores indicating a greater severity of depressive symptoms. A person is considered to have depressive symptoms if he/she scored no less than 10 in the CES-D-10. This threshold of 10 has been widely used in previous studies [35] and well-validated in depression measurement in Chinese older populations, regardless of their age and dementia status [36, 37]. The internal consistency Cronbach’s alpha value of CES-D-10 is 0.807, which indicates a reasonable level of internal consistency.

To obtain more robust findings, we controlled a wide set of potential confounding factors such as demographics, socioeconomic status, family/social support, health behaviors, and health conditions. The demographic factors included age and sex. Socioeconomic conditions included education, socioeconomic status, rural residence, and geographical regions. Family/social support included marital status and living arrangement. Health behaviors included social and leisure activity index, smoking, alcohol drinking, BMI (as a proxy for unhealthy behaviors), and regular dietary (vegetable/fruit/fish/nut) intake. Health status were measured by self-rated health, cognitive impairment, medical illness, comorbidity, and disability in activities of daily living (ADL). All data were gathered by well-trained research teams through face-to-face home interviews. The sampled older adults were encouraged to answer as many questions as possible asked by the research team member. If the sampled older adult was not able to answer a question or do not the answer, the next-of-kin, the primary caregiver or other informants would be called to serve as proxy to provide the answer [38].

Age was calculated according to self-reported date of birth with validations from various sources such as household registration record (hukou), personal identification card, genealogical records, ages of family members, and so forth. Dates were converted into Georgian calendar dates if they were based on Chinese lunar calendar dates. Levels of educational attainment were grouped into three categories according to years of schooling (0, 1–6, and ≥ 7 years). Current residence was dichotomized as “urban residence” or “rural residence”. Given its multidimensionality and complex of socioeconomic status and unavailability of an overall index in the CLHLS, we generated an overall socioeconomic status index by using a principal component analysis (PCA) based on four questions (primary occupation before retirement [white collar vs. others], family economic condition [a Likert scale with five points], retirement earnings, and living expenditure). Following some previous practice [35, 39], a compositional score based on the first component generated from PCA has been suggested to be a qualified measure of socioeconomic status and has been widely employed in previous studies. Marital status was divided as “currently married and living with spouse” or others (widowed, separated, divorced, or never married). Living arrangement was grouped into 3 categories: living with family members or others, living alone, and living in an institution. Social and leisure activity score was calculated by eight types of activities (whether a respondent did gardening, practiced Tai Chi, participated in square dance, raised poultry or pets, reading, playing Mahjong or cards, listening to the radio or watching TV, and participating in community social activities) and we scored each activity 1 for ‘never’, 2 for ‘sometimes’ 3 for ‘almost every day’; The score ranged from 8 to 24 with a higher score indicating more leisure activities, and the low social and leisure activity level was defined by the score less than 14. Smoking status was dichotomized as “non-current smoker or never-smoker” vs. “current smoker”, a similar approach was taken to define alcohol consumption and physical activity. Dietary intake, including vegetables, fruit, fish, and nut, were dichotomized as “regular intake” or “occasional or seldom intake”. The body mass index (BMI) was calculated as weight in kilograms divided by height in meters squared. Cognitive function was tested by using the Chinese version of the 30-point Mini-Mental State Examination (MMSE), and cognitive impairment was defined by an MMSE total score of < 24 [31]. The index of activity of daily living (ADL) was assessed by the Katz index [40], and we defined ADL disability as needing personal assistance in performing one or more of the five essential activities (bathing, transferring, dressing, eating, and toileting) or being incontinent [41]. We ascertained 14 self-reported medical illnesses, including hypertension, diabetes, dyslipidemia, heart disease, stroke, pneumonia (asthma/COPD), cataract or glaucoma, cancer, gastritis, arthritis, cholecystitis, rheumatism, nephritis, and hepatitis; we grouped the medical illness into 3 categories similar to some previous studies [34]: “chronic inflammatory disorders (heart disease, stroke, diabetes, pneumonia, gastritis, arthritis, cholecystitis, rheumatism, nephritis, and hepatitis)”, “other disorders”, and “none”. Comorbidity was defined as having 5 or more medical illnesses. Self-rated health was defined as “excellent or good” or “average or poor”. We considered geographical region on the basis of residential address to account for types of tea production areas [42] as well as differences in regional economic developments and social cultures in China: Northern China (Beijing, Tianjin, Hebei, Shanxi, Shaanxi, Shandong, Liaoning, Jilin, and Heilongjiang provinces), Eastern China (Shanghai, Jiangsu, Zhejiang, and Fujian provinces), Central China (Henan, Hubei, Jiangxi, Anhui, and Hunan provinces), Southwestern China (Guangdong, Guangxi, Chongqing, Sichuan, and Hainan provinces) (Supplementary Figure 1).

The subjects’ characteristics according to categories of type of tea consumption were compared by using analysis of variance or chi-square test, as appropriate. We used multivariate logistic regression analysis to calculate odds ratios (ORs) of depressive symptoms for the type of tea consumption, including green tea, fermented tea, and floral tea, with no habitual tea intake treated as the reference group. The base model (Model 1) included types of tea consumption plus demographic variables; Model 2 further controlled for socioeconomic variables: education, socioeconomic status, rural residence, and geographical regions; Model 3 additionally controlling for psychosocial and behavioral variables: marital status, living arrangement, social and leisure activity index, smoking, alcohol drinking, BMI, regular dietary (vegetable/fruit/fish/nut) intake; Model 4 added health variables in Model 3: self-rated health, cognitive impairment, medical illness, comorbidity, and ADL disability. In detailed analyses examining the dose-effect relation between the intake of green tea or fermented tea or floral tea with depressive symptoms, we classified the frequency of each type of tea consumption into 3 categories: daily (≥ 1 cup/day), occasionally (< 1 cup/day but ≥1 cup/month), and never or rarely (< 1 cup/month or never drink tea), and repeated multiple logistic regressions controlling for all covariates as above.

We conducted subgroup analyses to examine whether the associations between types and frequencies of tea intake and depressive symptoms differed by sex, age (< 80 years old vs. ≥80 years old), residence (urban residence vs. rural residence), and geographical regions (Northern China, Eastern China, Central China, and Southwestern China). We performed several steps of sensitivity analyses for the full model (Model 4) to assess the possible outcomes of the different thresholds used for the CES-D-10. First, we considered varied cut-off thresholds for the CES-D-10, such as 8 and 12, which are more sensitive (cut-off value = 8) or specific (cut-off value = 12) to discriminate the depressive symptoms, and used Model 4 to examine the associations. Second, the participants with severe cognitive impairment with scores of MMSE < 19 were excluded from the present study [43], because of a concerns of their possible recall biases in reporting types and frequencies of tea consumption. Moreover, we excluded older adults who were long bedridden or terminally ill for more robust estimates. We also tested our results by using a full sample after multiple imputations and by adjusting the sampling weight based on the age-sex-residence-specific distribution of the 2015 mini-census of China. STATA version 16.0 (Stata Corp, College Station, TX, USA) was used to perform all analyses. ArcGIS version 10.2 was used to perform map visualization of the geographical distribution of tea drinkers.

The 13,115 study participants with a mean age of 83.7 years (13% aged 100 and above), were evenly distributed across the whole of China in four geographical regions and diversified by socioeconomic, lifestyle, and health-related characteristics (Table 1). Among them, the mean CES-D-10 score was 10.1 (SD: 4.7); 56.6% of the study participants showed a CES-D-10 score of ≥10 indicating depression. Overall, 70.3% of the study participants never or rarely consumed tea, 15.0% consumed green tea, 8.8% consumed fermented tea, and 5.9% consumed floral tea. They were widely distributed geographically, with fermented tea consumption relatively more concentrated in the Eastern tea production Region, and green tea in the Central Region (Fig. 2). Compared to non-drinkers, tea drinkers as a whole in the 2018 CLHLS sample were significantly younger, predominantly men, more likely to be married, urban rather than rural dwellers, more active in social and leisure activity, and had a higher socioeconomic status. However, tea drinkers were more likely to be smokers and alcohol drinkers as well. On the other hand, them were more likely to report regular intake of vegetables, fruits, nuts, and fish. Their prevalence rates of reported chronic diseases, including chronic inflammatory diseases and comorbidity, were higher.

Tea drinkers showed lower odds of having depressive symptoms in model controlling for demographic and socioeconomic variables: green tea, OR = 0.72 (95% CI: 0.65–0.80); fermented tea, OR = 0.79 (95% CI: 0.70–0.89); floral tea, OR = 0.62 (95% CI: 0.53–0.72) (Table 2). The magnitude of association was reduced after further controlling for psychological, lifestyle, behavioral, and health variables, but the final model controlling for all confounding risk factors showed that tea consumption remained associated with 15% (green tea) to 30% (floral tea) lower odds of having depressive symptoms. Stratified analyses showed some heterogeneity of associations by tea type, sex, age group, and geographical region: green tea in men (OR = 0.79) versus women (OR = 0.93), floral tea in men (OR = 0.77) versus women (OR = 0.61), fermented tea in the young-old group (OR = 0.81) versus the oldest-old group (OR = 0.96), green tea in Southwestern region (OR = 0.60) versus other regions (OR from 0.83 to 0.90), fermented tea in Central and Southwestern regions (OR = 0.70 and 0.76) versus Northern and Eastern regions (OR = 1.09 and 1.11), and floral tea in Eastern China (OR = 0.48) versus other regions (OR from 0.70 to 0.88).

The frequencies of intaking green, fermented, and floral tea all showed linear associations respectively with depressive symptoms, controlling for major potential confounding factors (Fig. 3). The associations were more marked for green tea (P = 0.001 for a linear trend) and floral tea (P = 0.001 for a linear trend). Daily drinking of one or more cups of tea of all three types of tea was significantly associated with 16% (fermented tea), 27% (green tea), and 47% (floral tea) lower odds of presence of depressive symptoms. Those associations were generally consistent in the subgroup analyses by sex and residence, while the associations of green tea and floral tea intake with depressive symptoms were more pronounced in the oldest-old group (≥ 80 years) compared to the young-old group (65–79 years). In the oldest-old subsample with never or rarely tea intake group as the reference, the ORs of depressive symptoms for daily green tea drinkers and daily floral tea drinkers were 0.72 (95% CI: 0.60, 0.86) and 0.47 (95% CI: 0.35, 0.64), respectively; yet the ORs were 0.82 (95% CI: 0.67, 1.01) and 0.60 (95% CI: 0.42, 0.86) for daily green tea drinkers and daily floral tea drinkers in the young-old groups. We also observed a geographical variation in the associations in green tea and fermented tea consumers, while the association seems more homogeneous in floral tea drinkers (Supplemental Table 2).

In sensitivity analysis using varied cut-off values of the CES-D-10 such as 8 and 12, we repeated the analysis for the full model (Model 4). The dose-effect relationship of frequencies of each type of tea intake with depressive symptom was only mildly altered in analyses using both cut-offs (Supplementary Figure 2). After excluding participants who were likely to have severe cognitive impairment (defined by a score of MMSE less than 19, n = 1432), the estimates or the significance levels of the observed associations were not altered in three types of tea drinkers. Moreover, we removed the participants who were long bedridden or terminally ill (n = 261), restricting the sample to non-bedridden and the results were identical to those we presented in the main text (Supplementary Figure 3). We also tested our result using a full sample after multiple imputations and further adjusting the sampling weight (Supplementary Figure 4). Those sensitivity analyses were all reasonably consistent with the final model in the main text.