Abstract
This study aims to assess the associations between lifestyle behaviors – including smoking, alcohol consumption, tea consumption, and leisure-time physical activity – and traditional Chinese Medicine (TCM) body constitutions (BCs), Yin deficiency, Yang deficiency, and phlegm stasis, among Taiwanese adults participating in the Taiwan Biobank Study. A total of 15,665 participants from the Taiwan Biobank Study were included in the analysis. Comprehensive data were collected on lifestyle behaviors (including smoking, alcohol consumption, tea consumption, and leisure-time physical activity) and family medical history. TCM BC types – Yin deficiency, Yang deficiency, and phlegm stasis – were assessed using a well-validated BC questionnaire. Associations between lifestyle behaviors and BC types were examined using multivariate logistic regression models to estimate odds ratios (ORs). The average age was 46.55 years (standard deviation: 12.17), with 5611 males (35.82%). Compared with nonsmokers, current smokers had a significantly higher likelihood of having Yang deficiency (OR [95% confidence interval (CI)]: 1.62 [1.39–1.89]), Yin deficiency (1.64 [1.41–1.90]), and phlegm stasis (1.88 [1.59–2.22]) after multivariate adjustment. Compared with nondrinkers, current drinkers had a significantly higher likelihood of having Yin deficiency (1.21 [1.02–1.44]) and phlegm stasis (1.27 [1.04–1.56]) after multivariate adjustment. Tea consumption was significantly associated with phlegm stasis (1.18 [1.08–1.29]). Regarding leisure-time physical activity, this habit was negatively associated with Yang deficiency, Yin deficiency, and phlegm stasis with multivariate adjustment (0.71 [0.65–0.78], 0.74 [0.68–0.81], and 0.63 [0.58–0.70], respectively). This study demonstrates that lifestyle behaviors are associated with imbalanced TCM BCs, particularly Yin deficiency, Yang deficiency, and phlegm stasis.
Keywords: alcohol, body constitution, lifestyle behaviors, physical activity, smoking, tea
1. Introduction
Central to traditional Chinese Medicine (TCM) practice is the principle of “differentiation of syndromes and treatment based on the pattern” (辨證論治), which entails tailoring therapeutic interventions according to a holistic assessment of the patient’s constitution, disease progression, and various other factors. These constitutional traits significantly influence the presentation of diseases as well as the effectiveness of treatment.
TCM addresses different constitutional types through tailored treatments.[1] Yin deficiency treatments often include Yin-nourishing herbs like Liuwei Dihuang Wan and lily to relieve internal dryness. Such individualized approaches improve therapeutic precision while reducing the likelihood of over- or under-treatment. The theory of constitution also highlights the role of lifestyle: those with cold constitutions are advised to avoid cold exposure and consume warming foods, while those with hot constitutions should limit spicy or greasy foods. By aligning daily habits with one’s constitution, individuals can better maintain Yin-Yang balance and promote long-term health. Overall, TCM constitutional theory offers a comprehensive framework for personalized care and disease prevention.
The Body Constitution Questionnaire (BCQ) is a tool used in academic and clinical settings to assess an individual’s physical and physiological characteristics related to their body constitution (BC) in the concept of TCM. It refers to a person’s inherent physical traits and tendencies, such as metabolism, body type, and general health. The BCQ is often used in health and wellness studies, including fields like TCM and other holistic health practices.
The relationship between lifestyle behaviors – such as smoking, alcohol consumption, tea drinking, and leisure-time physical activity – and health outcomes has been extensively studied, revealing a significant impact on both chronic diseases and overall health and well-being. Cigarette smoking is a leading cause of preventable morbidity and mortality worldwide. It is strongly linked to a range of respiratory diseases, cardiovascular diseases, and various cancers.[2] Furthermore, smoking impairs immune function, rendering individuals more susceptible to infections and slowing the healing process of wounds.[2] Alcohol consumption demonstrates a complex relationship with health, exhibiting both beneficial and detrimental effects depending on the quantity, frequency, and patterns of consumption. Chronic excessive alcohol consumption is strongly correlated with numerous adverse health outcomes, including liver diseases (such as cirrhosis), cardiovascular disorders, neurological impairments, various cancers, and social and psychological issues related to addiction.[3] Tea, especially green tea, has long been recognized for its health-promoting properties. Epidemiological studies have suggested that tea consumption may be beneficial in preventing cardiovascular diseases, various cancers, weight management, and cognitive decline.[4] Leisure-time physical activity is widely acknowledged as a key component of healthy living, offering extensive benefits for both physical and mental health. Regular physical activity contributes to improved cardiovascular health, enhanced metabolic function, effective weight management, and better musculoskeletal health.[5] Furthermore, it has been shown to play a significant role in mental health, reducing the likelihood of depression and anxiety while enhancing overall psychological well-being.[5]
Although previous studies have reported associations between individual lifestyle factors and TCM constitutions, many involved small or selected populations and examined lifestyle factors separately. The joint associations of multiple lifestyle behaviors with different constitution patterns remain insufficiently characterized in large population-based samples. We hypothesized that lifestyle behaviors would be differentially associated with constitution types. This study extends previous research by examining multiple lifestyle factors simultaneously in a large standardized population, allowing assessment of their independent and joint associations while accounting for relevant comorbidities. We therefore assessed the associations of smoking, alcohol consumption, tea consumption, leisure-time physical activity, and a summative lifestyle behavior count with Yin deficiency, Yang deficiency, and phlegm stasis among Taiwanese adults in the Taiwan Biobank (TWB). We also examined the number of co-occurring constitution types (none, 1, 2, or 3) to provide additional insight into the associations of lifestyle behaviors with TCM constitutions.
2. Methods
2.1. Study participants
The participants in this study were drawn from the TWB database, which is based on Taiwan’s community population, primarily consisting of Han Chinese individuals aged 20 to 80 years, all of whom had no history of cancer. These participants were enrolled between 2012 and 2020, with a total of 189,132 individuals included (Fig. 1). After excluding individuals due to missing data on the Body Constitutions Questionnaire (N = 154,794) and covariates (N = 18,673), the final study sample consisted of 15,665 eligible individuals. The TWB, launched in 2012, is a large-scale, population-based resource designed to collect extensive data on lifestyle behaviors, family histories, and environmental risk factors of common complex diseases among individuals in Taiwan. The inclusion criteria for TWB participants are adults aged 20 to 80 who are able to provide informed consent. The exclusion criteria encompass individuals with a prior cancer diagnosis, as cancer is a major noncommunicable disease with a rising incidence post-midlife and is a leading cause of death in Taiwan.
Figure 1.
Flowchart of procedures for defining eligible study subjects.
2.2. Data source: TWB database
Participants are recruited from over 30 sites across Taiwan, with these sites strategically selected to reflect the population density of various counties and cities. The aim is to ensure that the sample is representative of the Taiwanese population, incorporating demographic diversity in terms of age, gender, and regional distribution. Recruitment efforts encompass both urban and rural areas, enhancing the generalizability of the data. The TWB has a substantial sample size, with about 200,000 participants. To ensure representativeness and minimize selection bias, the recruitment process is designed to reach individuals from various backgrounds while also accounting for the genetic diversity within Taiwan. However, as with any biobank, there are inherent limitations in fully capturing all subgroups within the population.
2.3. Ethics approval and consent to participate
The present study was approved by the Institutional Review Board of Taichung Veterans General Hospital (approval number: CE24491A) and was conducted in accordance with the relevant regulations and guidelines. The requirement for informed consent was waived by the Institutional Review Board because of the retrospective nature of the study and the use of de-identified data.
2.4. Measurement of the BCQ
The BCQ, developed by Su et al,[6] consists of 44 items, each employing a 5-point Likert scale to assess the physiological states associated with 3 BC types: Yang deficiency (19 items), Yin deficiency (19 items), and phlegm stasis (16 items). Some items are common across the 3 scales. Respondents rate each item on a scale from 1 (indicating “never occurrence”) to 5 (indicating “always occurrence”). The resulting scores for each scale range as follows: both Yang deficiency and Yin deficiency span from 19 to 95, while the phlegm stasis scale ranges from 16 to 80. Higher scores on any of these scales indicate a greater deviation from the specific BC. Cutoff points for categorizing Yang deficiency, Yin deficiency, and phlegm stasis are set at 30.5, 29.5, and 26.5, respectively.[7,8] The BCQ was administered by a trained interviewer, who guided participants through self-reporting, ensuring that any ambiguities, discrepancies, or omissions were addressed to minimize potential measurement errors. The BCQ has demonstrated good factorial validity[8] and high internal consistency, with Cronbach’s α exceeding 0.88 for all 3 scales.[8] The scores for Yang-deficiency (1 vs 0), Yin-deficiency (1 vs 0), and phlegm-stasis/blood-stasis (1 vs 0) constitution status were summed to derive a new ordinal outcome variable representing the number of constitution types present. This variable had 4 possible categories: 0, indicating the absence of all 3 constitutions; 1, indicating the presence of any one constitution; 2, indicating the presence of any 2 constitutions; and 3, indicating the presence of all 3 constitutions. The BCQ questionnaire was not mandatory in the TWB data collection. Therefore, BCQ information was not available for all participants. However, the available dataset does not allow us to further distinguish whether the absence of BCQ information was attributable to individual nonresponse, differences in questionnaire administration, or other data-collection procedures.
2.5. Measurements for lifestyle behaviors
Lifestyle behaviors included smoking, alcohol consumption, tea consumption, and physical activity. Smoking status was classified as never, former, or current smoker based on smoking history. Never smokers had never smoked; former smokers had smoked regularly for at least 6 months but had quit; and current smokers had smoked regularly for at least 6 months and continued smoking at data collection. Alcohol consumption was classified as never/seldom, former, or current drinking based on reported drinking status. For current or former drinkers, alcohol intake was recorded as beverage volume, with >150 mL/wk for at least 6 months used to characterize regular consumption according to the study questionnaire. Because alcohol volume rather than ethanol content was available, exposure was expressed as mL/wk and was not converted to grams of ethanol or standard drinks. The duration of cessation among former drinkers was unavailable. Tea consumption was assessed by frequency, duration, and type and classified as nondrinkers or tea drinkers, including less-than-daily and daily consumption. Information on tea preparation, sugar, milk, and other additives was unavailable and therefore not considered. Physical activity was assessed by exercise frequency, duration, and type. Participants were classified as physically active if they exercised at least 3 times per week for more than 30 minutes per session; otherwise, they were classified as physically inactive. Smoking status was categorized into 3 mutually exclusive groups: current smokers, former smokers, and never smokers. All 3 categories were retained in the analyses without any merging or collapsing of categories. Similarly, alcohol consumption status was categorized into 3 mutually exclusive groups, with all categories retained without merging. These categorical variables were included in the regression models using the appropriate reference categories. For the construction of the lifestyle behavior count, former smokers and never smokers, as well as former drinkers and never drinkers, were combined based on their relatively similar associations with TCM BCs observed in the preliminary analyses.
Lifestyle behavior count: 4 lifestyle behaviors were assessed, including smoking, alcohol consumption, physical activity, and tea consumption. Each behavior was dichotomized and coded as 1 or 0 according to the study-specific classification used in the exploratory analysis. Specifically, current smoking, alcohol consumption, physical inactivity, and tea consumption were assigned a value of 1, whereas nonsmoking or former smoking, nondrinking or former drinking, physical activity, and no tea consumption were assigned a value of 0. The 4 binary indicators were then summed to generate a lifestyle behavior count ranging from 0 to 4, with higher scores indicating a greater number of the selected lifestyle behaviors. A score of 0 indicated the absence of all 4 coded behaviors, whereas a score of 4 indicated the presence of all 4 coded behaviors. This measure was developed for exploratory purposes and was not intended to represent a validated or prespecified index of unfavorable lifestyle behavior.
2.6. Measurements for covariates
The covariates consisted of sociodemographic factors such as age, sex, education, and marriage, anthropometric measurements of waist circumference and body mass index (BMI), and disease history obtained through personal interviews and physical checkups, including hypertension, diabetes, hyperlipidemia, gout, asthma, gastroesophageal reflux disease (GERD), and irritable bowel syndrome (IBS).
2.7. Statistical analysis
Baseline characteristics of participants included and excluded from the analysis were summarized using means and standard deviations for continuous variables and frequencies and percentages for categorical variables. Standardized mean differences (SMDs) were used to evaluate the magnitude of differences between the 2 groups, with an absolute SMD <0.10 indicating a small or negligible difference. A two-sample t test was conducted for continuous variables, and a chi-square test was conducted for categorical variables. Odds ratios (ORs) and their 95% confidence intervals (CIs) were estimated from logistic regression models. The primary analyses evaluated the associations of the 4 individual lifestyle behaviors (smoking, alcohol consumption, tea consumption, and leisure-time physical activity) with each of the 3 TCM BCs (Yin deficiency, Yang deficiency, and phlegm stasis). Analyses using the composite lifestyle behavior index and TCM BCs were considered secondary analyses and were performed to evaluate the overall and cumulative associations of lifestyle behaviors with TCM BCs. For the count of TCM BCs, multinomial logistic regression models were used because the assumption of ordinal logistic regression was violated. Three progressively adjusted models were constructed. Model 1 adjusted for age and sex. Model 2 additionally adjusted for education, marital status, BMI, and other lifestyle behaviors. Model 3 further adjusted for disease history. This progressive adjustment strategy was used to examine the stability of the associations with increasing levels of covariate adjustment. Because some disease history variables could potentially be intermediates rather than confounders, estimates from model 3 were interpreted as additionally adjusted associations rather than as estimates of a fully confounder-controlled causal effect. Multicollinearity among disease history variables was assessed before their inclusion in model 3. For multiple-comparison control, the 4 prespecified lifestyle behaviors were treated as a hypothesis family separately for each of the 3 constitution outcomes (Yang deficiency, Yin deficiency, and phlegm stasis) in model 3. Thus, 3 prespecified hypothesis families were defined, with 4 hypothesis tests within each family. The Benjamini–Hochberg procedure was applied separately within each family to control the false discovery rate. The combined impact of lifestyle behaviors was assessed by summing the number of lifestyle behaviors reported by each participant. The associations between individual lifestyle factors, as well as lifestyle behavior count, and the number of constitution types were examined using multinomial logistic regression models. The ORs and 95% CIs were estimated to quantify the associations. Multiple imputation was therefore used for missing covariates under the missing at random assumption, conditional on the observed characteristics included in the imputation models. Single stochastic imputation via the Fully Conditional Specification method was performed using SAS PROC MI (version 9.4; SAS Institute, Cary). The imputation model incorporated all key continuous and categorical variables simultaneously, including demographic characteristics (age, sex, BMI, education, and marital status), lifestyle factors (smoking, alcohol, tea consumption, and sports habits), chronic comorbidities (hypertension, diabetes, hyperlipidemia, gout, asthma, GERD, and IBS), and TCM BC types (Yang deficiency, Yin deficiency, and phlegm stasis). Continuous variables were imputed using linear regression, while categorical variables were handled using logistic or discriminant function options within the Fully Conditional Specification framework. Sensitivity analyses included the primary analyses with the data imputed by multiple imputation. The imputation model included relevant demographic, lifestyle, exposure, and outcome variables. All analyses were performed using SAS version 9.4 (SAS). All P values were two-sided, and statistical significance was considered at P < .05.
3. Results
3.1. Distributions of characteristics
Table S1, Supplemental Digital Content 1, compares included and excluded participants. Included participants were slightly younger and had lower BMI, with generally similar sex and marital-status distributions. They were more likely to be never smokers, less likely to consume alcohol, and more likely to drink tea or engage in physical activity. Most disease prevalences were slightly lower among included participants. Overall, SMDs were small, indicating broadly comparable baseline characteristics.
A total of 15,665 participants were included in this study. The average age was 46.55 years (standard deviation: 12.17), with 5611 males (35.82%). The distributions of demographics, lifestyle behaviors, and disease history according to BC are shown in Table 1. Participants with Yang deficiency had a lower proportion of males (P < .001). The mean values of age and BMI were lower in the Yang deficiency group compared with those without Yang deficiency (both P < .001). Participants with Yang deficiency had a higher prevalence of being unmarried, no smoking, asthma, GERD, and IBS compared with those without (all P < .001), whereas they had a lower prevalence of alcohol consumption (both former and current, P = .03), leisure-time physical activity habits (P < .001), hypertension (P < .001), diabetes (P = .001), and gout (P < .001). As for Yin deficiency, participants with this BC had a lower proportion of males (P < .001). The mean values of age and BMI were lower in the Yin deficiency group compared with those without Yin deficiency (both P < .001). Participants with Yin deficiency had a higher prevalence of being unmarried (P < .001), no smoking (P < .001), hyperlipidemia (P = .008), asthma (P < .001), GERD (P < .001), and IBS (P < .001) compared with those without, whereas they had a lower prevalence of leisure-time physical activity habits (P < .001). As for phlegm stasis, participants with this BC had a lower prevalence of males (P < .001). The mean value of age was lower in the phlegm stasis group compared with those without phlegm stasis (P < .001). Participants with phlegm stasis had a higher probability of being unmarried (P < .001), no smoking habits (P < .001), and tea consumption (P = .008), whereas they had a lower prevalence of no leisure-time physical activity habits (P < .001), hypertension (P = .004), and gout (P = .01).
Table 1.
The distribution of demographics, lifestyle behaviors, and disease history according to body constitutions in participants.
| Variables | Yang deficiency | Yin deficiency | Phlegm stasis | ||||||
|---|---|---|---|---|---|---|---|---|---|
| No (N = 11,816) | Yes (N = 3849) | P-value | No (N = 11,648) | Yes (N = 4017) | P-value | No (N = 12,787) | Yes (N = 2878) | P-value | |
| Demographics | |||||||||
| Age | 47.65 ± 12.08 | 43.19 ± 11.83 | <.001 | 47.34 ± 12.10 | 44.26 ± 12.06 | <.001 | 47.43 ± 12.16 | 42.67 ± 11.41 | <.001 |
| Sex | <.001 | <.001 | <.001 | ||||||
| Male | 4739 (40.11) | 872 (22.66) | 4527 (38.87) | 1084 (26.99) | 4965 (38.83) | 646 (22.45) | |||
| Female | 7077 (59.89) | 2977 (77.34) | 7121 (61.13) | 2933 (73.01) | 7822 (61.17) | 2232 (77.55) | |||
| Education | .70 | 1.00 | 1.00 | ||||||
| Illiterate | 8 (0.07) | 1 (0.03) | 7 (0.06) | 2 (0.05) | 8 (0.06) | 1 (0.03) | |||
| Literate | 11,808 (99.93) | 3848 (99.97) | 11,641 (99.94) | 4015 (99.95) | 12,779 (99.94) | 2877 (99.97) | |||
| Marriage | <.001 | <.001 | <.001 | ||||||
| Unmarried | 2124 (17.98) | 1115 (28.97) | 2166 (18.6) | 1073 (26.71) | 2379 (18.6) | 860 (29.88) | |||
| Married | 8454 (71.55) | 2360 (61.31) | 8269 (70.99) | 2545 (63.36) | 9077 (70.99) | 1737 (60.35) | |||
| Separated | 776 (6.57) | 266 (6.91) | 770 (6.61) | 272 (6.77) | 841 (6.58) | 201 (6.98) | |||
| Widowed | 462 (3.91) | 108 (2.81) | 443 (3.8) | 127 (3.16) | 490 (3.83) | 80 (2.78) | |||
| Lifestyle behaviors | |||||||||
| BMI | 24.14 ± 3.70 | 23.41 ± 3.95 | <.001 | 24.03 ± 3.71 | 23.77 ± 3.96 | <.001 | 23.96 ± 3.67 | 23.99 ± 4.21 | .67 |
| Smoking | <.001 | <.001 | <.001 | ||||||
| Never | 9637 (81.56) | 3296 (85.63) | 9585 (82.29) | 3348 (83.35) | 10,504 (82.15) | 2429 (84.4) | |||
| Former | 1223 (10.35) | 226 (5.87) | 1162 (9.98) | 287 (7.14) | 1288 (8.22) | 161 (5.59) | |||
| Current | 956 (8.09) | 327 (8.50) | 901 (7.74) | 382 (9.51) | 995 (7.78) | 288 (10.01) | |||
| Alcohol consumption | .03 | .83 | .13 | ||||||
| Never | 10,908 (92.32) | 3602 (93.58) | 10,792 (92.65) | 3718 (92.56) | 11,832 (92.53) | 2678 (93.05) | |||
| Former | 249 (2.11) | 71 (1.84) | 241 (2.07) | 79 (1.97) | 275 (2.15) | 45 (1.56) | |||
| Current | 659 (5.58) | 176 (4.57) | 615 (5.28) | 220 (5.48) | 680 (5.32) | 155 (5.39) | |||
| Tea consumption | .25 | .58 | .008 | ||||||
| Yes | 4179 (35.37) | 1322 (34.35) | 4076 (34.99) | 1425 (35.47) | 4429 (34.64) | 1072 (37.25) | |||
| No | 7637 (64.63) | 2527 (65.65) | 7572 (65.01) | 2592 (64.53) | 8358 (65.36) | 1806 (62.75) | |||
| Leisure-time physical activity | <.001 | <.001 | <.001 | ||||||
| Yes | 5029 (42.56) | 1148 (29.83) | 4908 (42.14) | 1269 (31.59) | 5411 (42.32) | 766 (26.62) | |||
| No | 6787 (57.44) | 2701 (70.17) | 6740 (57.86) | 2748 (68.41) | 7376 (57.68) | 2112 (73.38) | |||
| Disease history | |||||||||
| Hypertension | <.001 | .06 | .004 | ||||||
| Yes | 1185 (10.03) | 280 (7.27) | 1119 (9.61) | 346 (8.61) | 1236 (9.67) | 229 (7.96) | |||
| No | 10,631 (89.97) | 3569 (92.73) | 10,529 (90.39) | 3671 (91.39) | 11,551 (90.33) | 2649 (92.04) | |||
| Diabetes | .001 | .84 | .13 | ||||||
| Yes | 517 (4.38) | 123 (3.20) | 478 (4.10) | 162 (4.03) | 537 (4.2) | 103 (3.58) | |||
| No | 11,299 (95.62) | 3726 (96.80) | 11,170 (95.90) | 3855 (95.97) | 12,250 (95.8) | 2775 (96.42) | |||
| Hyperlipidemia | .64 | .008 | .06 | ||||||
| Yes | 721 (6.1) | 243 (6.31) | 682 (5.86) | 282 (7.02) | 765 (5.98) | 199 (6.91) | |||
| No | 11,095 (93.9) | 3606 (93.69) | 10,966 (94.14) | 3735 (92.98) | 12,022 (94.02) | 2679 (93.09) | |||
| Gout | <.001 | .41 | .01 | ||||||
| Yes | 427 (3.61) | 88 (2.29) | 391 (3.36) | 124 (3.09) | 442 (3.46) | 73 (2.54) | |||
| No | 11,389 (96.39) | 3761 (97.71) | 11,257 (96.64) | 3893 (96.91) | 12,345 (96.54) | 2805 (97.46) | |||
| Asthma | <.001 | <.001 | <.001 | ||||||
| Yes | 352 (2.98) | 238 (6.18) | 361 (3.1) | 229 (5.7) | 426 (3.33) | 164 (5.7) | |||
| No | 11,464 (97.02) | 3611 (93.82) | 11,287 (96.9) | 3788 (94.3) | 12,361 (96.67) | 2714 (94.3) | |||
| GERD | <.001 | <.001 | <.001 | ||||||
| Yes | 1182 (10.00) | 709 (18.42) | 1139 (9.78) | 752 (18.72) | 1327 (10.38) | 564 (19.6) | |||
| No | 10,634 (90.00) | 3140 (81.58) | 10,509 (90.22) | 3265 (81.28) | 11,460 (89.62) | 2314 (80.4) | |||
| IBS | <.001 | <.001 | <.001 | ||||||
| Yes | 208 (1.76) | 206 (5.35) | 225 (1.93) | 189 (4.71) | 269 (2.1) | 145 (5.04) | |||
| No | 11,608 (98.24) | 3643 (94.65) | 11,423 (98.07) | 3828 (95.29) | 12,518 (97.9) | 2733 (94.96) | |||
BMI = body mass index (kg/m2), GERD = gastroesophageal reflux disease, IBS = irritable bowel syndrome.
3.2. Association of lifestyle behaviors with BCs
Table 2 presents the ORs for BCs based on smoking status across various hierarchical models. The effect of current smoking is significant after adjusting for age and sex, lifestyle behaviors, or comorbidities. Compared with nonsmokers, current smokers had a significantly higher likelihood of having Yang deficiency (OR [95% CI]: 1.62 [1.39–1.89]), Yin deficiency (1.64 [1.41–1.90]), and phlegm stasis (1.88 [1.59–2.22]) after multivariate adjustment. Among these models, other lifestyle habits and comorbidities had a greater impact on the association between smoking and phlegm stasis, as indicated by the largest change in magnitude of the OR across various hierarchical models for phlegm stasis. Table 3 shows the ORs for BCs based on alcohol consumption status across various hierarchical models. Compared with nondrinkers, both former and current drinkers had a significantly higher likelihood of having Yang deficiency (1.63 [1.24–2.16] and 1.26 [1.05–1.50], respectively) and Yin deficiency (1.46 [1.12–1.90] and 1.41 [1.19–1.66], respectively) after sex and age adjustment. After multivariate adjustment, former drinking was significantly associated with Yang deficiency (1.39 [1.04–1.86]), while current drinking was associated with Yin deficiency (1.21 [1.02–1.44]). Similarly, current drinking was associated with phlegm stasis (1.56 [1.29–1.89]) after age and sex adjustment. Following multivariate adjustment, the significant association between current drinking and phlegm stasis remained (1.27 [1.04–1.56]). Compared with the values of ORs across the various models, other lifestyle habits and comorbidities had a greater impact on the association between alcohol drinking and phlegm stasis, while the association between alcohol drinking and Yang deficiency is more sensitive, becoming nonsignificant after multivariate adjustment.
Table 2.
The odds ratios for body constitutions based on smoking status across various hierarchical models.
| Constitution | Smoking status | ||
|---|---|---|---|
| Never (N = 12,933) | Former (N = 1449) | Current (N = 1283) | |
| Yang deficiency | |||
| Sex, age-adjusted | 1.00 | 1.11 (0.94–1.31) | 1.69 (1.46–1.96)*** |
| Multivariate-adjusted* | 1.00 | 1.12 (0.95–1.32) | 1.63 (1.40–1.90)*** |
| Multivariate-adjusted†,‡ | 1.00 | 1.07 (0.90–1.26) | 1.62 (1.39–1.89)*** |
| Yin deficiency | |||
| Sex, age-adjusted | 1.00 | 1.19 (1.03–1.38)* | 1.77 (1.54–2.04)*** |
| Multivariate-adjusted* | 1.00 | 1.16 (1.00–1.35) | 1.65 (1.43–1.91)*** |
| Multivariate-adjusted†,‡ | 1.00 | 1.11 (0.95–1.29) | 1.64 (1.41–1.90)*** |
| Phlegm stasis | |||
| Sex, age-adjusted | 1.00 | 1.13 (0.94–1.37) | 2.16 (1.84–2.53)*** |
| Multivariate-adjusted* | 1.00 | 1.07 (0.89–1.30) | 1.90 (1.61–2.24)*** |
| Multivariate-adjusted†,‡ | 1.00 | 1.01 (0.83–1.22) | 1.88 (1.59–2.22)*** |
Data are presented as odds ratio (95% confidence interval).
BMI = body mass index.
Multivariate adjustment for sex, age, education, marriage, BMI, alcohol consumption, tea consumption, and leisure-time physical activity.
Multivariate-adjusted for sex, age, education, marriage, BMI, alcohol consumption, tea consumption, leisure-time physical activity, hypertension, diabetes, hyperlipidemia, gout, asthma, gastroesophageal reflux, and irritable bowel syndrome.
P values were adjusted using the Benjamini–Hochberg procedure separately within each constitution-specific hypothesis family, with the 4 lifestyle behaviors comprising each family.
P < .001.
Table 3.
The odds ratios for body constitutions based on alcohol consumption status across various hierarchical models.
| Constitution | Alcohol consumption status | ||
|---|---|---|---|
| Never (N = 14,510) | Former (N = 320) | Current (N = 835) | |
| Yang deficiency | |||
| Sex, age-adjusted | 1.00 | 1.63 (1.24–2.16)*** | 1.26 (1.05–1.50)* |
| Multivariate-adjusted† | 1.00 | 1.49 (1.13–1.98)** | 1.12 (0.93–1.34) |
| Multivariate-adjusted‡,‡ | 1.00 | 1.39 (1.04–1.86)* | 1.09 (0.90–1.32) |
| Yin deficiency | |||
| Sex, age-adjusted | 1.00 | 1.46 (1.12–1.90)** | 1.41 (1.19–1.66)*** |
| Multivariate-adjusted† | 1.00 | 1.30 (0.99–1.70) | 1.23 (1.04–1.46)* |
| Multivariate-adjusted‡,§ | 1.00 | 1.20 (0.92–1.59) | 1.21 (1.02–1.44)* |
| Phlegm stasis | |||
| Sex, age-adjusted | 1.00 | 1.35 (0.97–1.88) | 1.56 (1.29–1.89)*** |
| Multivariate-adjusted† | 1.00 | 1.11 (0.80–1.56) | 1.30 (1.07–1.58)** |
| Multivariate-adjusted‡,§ | 1.00 | 1.01 (0.71–1.42) | 1.27 (1.04–1.56)* |
Data are presented as odds ratio (95% confidence interval).
Multivariate adjustment for sex, age, education, marriage, BMI, smoking status, tea consumption, and leisure-time physical activity.
Multivariate-adjusted for sex, age, education, marriage, BMI, smoking status, tea consumption, leisure-time physical activity, hypertension, diabetes, hyperlipidemia, gout, asthma, gastroesophageal reflux, and irritable bowel syndrome.
P values were adjusted using the Benjamini–Hochberg procedure separately within each constitution-specific hypothesis family, with the 4 lifestyle behaviors comprising each family.
P < .05.
P < .01.
P < .001.
Table 4 shows the ORs of BCs based on tea consumption status across various hierarchical models. Tea consumption was significantly associated with phlegm stasis without adjustment (1.12 [1.03–1.22]), and this association remained significant after multivariate adjustment (1.18 [1.08–1.29]). In contrast, no associations were observed between tea consumption and Yang deficiency or Yin deficiency. Regarding leisure-time physical activity, this habit was negatively associated with Yang deficiency, Yin deficiency, and phlegm stasis, adjusting for age and sex, other lifestyle behaviors, or comorbidities (Table 5). Compared with participants with no leisure-time physical activity, those engaging in leisure-time physical activity showed significant associations with a reduced likelihood of Yang deficiency (0.71 [0.65–0.78]), Yin deficiency (0.74 [0.68–0.81]), and phlegm stasis (0.63 [0.58–0.70]) after multivariate adjustment.
Table 4.
The odds ratios for body constitutions based on tea consumption status across various hierarchical models.
| Constitution | Tea consumption status | |
|---|---|---|
| No (N = 10,164) | Yes (N = 5501) | |
| Yang deficiency | ||
| Sex, age-adjusted | 1.00 | 1.03 (0.96–1.12) |
| Multivariate-adjusted* | 1.00 | 1.03 (0.95–1.11) |
| Multivariate-adjusted†,‡ | 1.00 | 1.06 (0.97–1.14) |
| Yin deficiency | ||
| Sex, age-adjusted | 1.00 | 1.08 (1.00–1.17) |
| Multivariate-adjusted* | 1.00 | 1.05 (0.97–1.13) |
| Multivariate-adjusted†,‡ | 1.00 | 1.07 (0.99–1.16) |
| Phlegm stasis | ||
| Sex, age-adjusted | 1.00 | 1.21 (1.11–1.32)*** |
| Multivariate-adjusted* | 1.00 | 1.15 (1.05–1.25)** |
| Multivariate-adjusted†,‡ | 1.00 | 1.18 (1.08–1.29)** |
Data are presented as odds ratio (95% confidence interval).
BMI = body mass index.
Multivariate adjustment for sex, age, education, marriage, BMI, smoking status, alcohol consumption, and leisure-time physical activity.
Multivariate-adjusted for sex, age, education, marriage, BMI, smoking status, alcohol consumption, leisure-time physical activity, hypertension, diabetes, hyperlipidemia, gout, asthma, gastroesophageal reflux, and irritable bowel syndrome.
P values were adjusted using the Benjamini–Hochberg procedure separately within each constitution-specific hypothesis family, with the 4 lifestyle behaviors comprising each family.
P < .01.
P < .001.
Table 5.
The odds ratios for body constitutions based on leisure-time physical activity habit across various hierarchical models.
| Constitution | Leisure-time physical activity | |
|---|---|---|
| No (N = 9488) | Yes (N = 6177) | |
| Yang deficiency | ||
| Sex, age-adjusted | 1.00 | 0.73 (0.67–0.79)*** |
| Multivariate-adjusted* | 1.00 | 0.72 (0.67–0.79)*** |
| Multivariate-adjusted†,‡ | 1.00 | 0.71 (0.65–0.78)*** |
| Yin deficiency | ||
| Sex, age-adjusted | 1.00 | 0.74 (0.69–0.81)*** |
| Multivariate-adjusted* | 1.00 | 0.75 (0.69–0.81)*** |
| Multivariate-adjusted†,‡ | 1.00 | 0.74 (0.68–0.81)*** |
| Phlegm stasis | ||
| Sex, age-adjusted | 1.00 | 0.63 (0.57–0.69)*** |
| Multivariate-adjusted* | 1.00 | 0.64 (0.59–0.71)*** |
| Multivariate-adjusted†,‡ | 1.00 | 0.63 (0.58–0.70)*** |
Data are presented as odds ratio (95% confidence interval).
BMI = body mass index.
Multivariate adjustment for sex, age, education, marriage, BMI, smoking status, alcohol consumption, and tea consumption.
Multivariate-adjusted for sex, age, education, marriage, BMI, smoking status, alcohol consumption, tea consumption, hypertension, diabetes, hyperlipidemia, gout, asthma, gastroesophageal reflux, and irritable bowel syndrome.
P values were adjusted using the Benjamini–Hochberg procedure separately within each constitution-specific hypothesis family, with the 4 lifestyle behaviors comprising each family.
P < .001.
3.3. Association of lifestyle behavior count with BCs
We evaluated the association between lifestyle behavior count and susceptibility to TCM BC types. Individuals exhibiting 1 (1.29 [1.17–1.44]), 2 (1.56 [1.38–1.75]), 3 (2.01 [1.63–2.47]), and 4 (2.82 [1.89–4.22]) lifestyle behaviors were increasingly susceptible to Yang deficiency (Fig. 2). Similarly, individuals with 1 (1.23 [1.11–1.36]), 2 (1.56 [1.39–1.75]), 3 (1.96 [1.61–2.38]), and 4 (2.59 [1.76–3.79]) lifestyle behaviors showed greater susceptibility to Yin deficiency. For phlegm stasis constitution, the odds increased progressively with 1 (1.56 [1.38–1.77]), 2 (2.06 [1.79–2.36]), 3 (2.84 [2.27–3.55]), and 4 (3.75 [2.44–5.77]) lifestyle behaviors. The linear trends were all statistically significant (P < .001).
Figure 2.
Relationship of Yang deficiency, Yin deficiency, and phlegm stasis with lifestyle behavior count. ***P < .001. CI = confidence interval.
3.4. Association of individual lifestyle behaviors with the number of imbalanced BCs
The associations between individual lifestyle behaviors and the number of imbalanced TCM BCs, using multinomial logistic regression with participants having no imbalanced constitution as the reference group, are presented in Figure 3. Former smokers did not differ significantly from never smokers among participants with 1 or 2 imbalanced constitutions. However, former smoking was associated with lower odds of having 3 imbalanced constitutions (OR, 0.87; 95% CI, 0.76–0.99). Current smoking was associated with higher odds of having 1 imbalanced constitution (OR, 1.05; 95% CI, 1.01–1.09), but not with having 2 or 3 imbalanced constitutions. Former alcohol consumption was not significantly associated with the number of imbalanced constitutions. In contrast, current alcohol consumption was associated with higher odds of having 1 and 2 imbalanced constitutions, with ORs of 1.06 (95% CI, 1.02–1.11) and 1.07 (95% CI, 1.01–1.14), respectively, whereas the association with 3 imbalanced constitutions was not statistically significant (OR, 1.09; 95% CI, 0.94–1.25). Tea consumption was not significantly associated with having 1 or 2 imbalanced constitutions. However, tea consumption was associated with higher odds of having 3 imbalanced constitutions (OR, 1.11; 95% CI, 1.04–1.19). Leisure-time physical activity showed an inverse association with the number of imbalanced constitutions. Compared with participants without leisure-time physical activity, those who reported physical activity had lower odds of having 1 (OR, 0.96; 95% CI, 0.94–0.98), 2 (OR, 0.95; 95% CI, 0.92–0.99), or 3 (OR, 0.88; 95% CI, 0.82–0.95) imbalanced constitutions.
Figure 3.
Associations of individual lifestyle behaviors with the number of imbalanced TCM body constitutions using multinomial logistic regression. ***P < .001; *P < .05. TCM = traditional Chinese Medicine.
For sensitivity analyses, Figure S1, Supplemental Digital Content 2 illustrates the relationships of Yang deficiency, Yin deficiency, and phlegm stasis with lifestyle behavior count after multiple imputation. Overall, mid-range lifestyle scores (count 2 and 3) were significantly associated with slightly higher odds of all 3 constitutions compared with a score of 0, with ORs peaking at lifestyle behavior count 3 (Yang deficiency: OR = 1.06; Yin deficiency: OR = 1.06; phlegm stasis: OR = 1.11). However, at the highest level of lifestyle behavior count 4, the associations for all 3 constitutions lost statistical significance, as evidenced by wider 95% CIs crossing the reference line. In contrast to the complete-case analysis, the associations substantially attenuated after multiple imputation, with several associations no longer apparent. Notably, the associations of lifestyle behavior count (4 vs 0 and 1 vs 0) with Yang deficiency were no longer apparent. Similarly, the link between a lifestyle behavior count of 4 versus 0 and both Yin deficiency and phlegm stasis became statistically nonsignificant.
Figure S2, Supplemental Digital Content 3 shows the associations between lifestyle behaviors and the count of TCM BCs after multiple imputation. Among 189,132 participants, current smoking was associated with higher odds of having 1, 2, or 3 imbalanced constitutions compared with 0 constitutions (OR: 1.36, 95% CI, 1.11–1.66; 1.50, 1.18–1.90; 2.09, 1.72–2.56), tea consumption with higher odds of having 3 imbalanced constitutions compared with 0 constitutions (OR: 1.17, 95% CI, 1.05–1.30), and leisure-time physical activity with lower odds of having 1, 2, or 3 imbalanced constitutions compared with 0 constitutions (OR: 0.83, 95% CI, 0.75–0.93; 0.77, 0.68–0.88; 0.59, 0.52–0.66). Alcohol consumption was not significantly associated with the count of TCM BC. Compared with the original analysis, the protective effect of leisure-time physical activity became more pronounced, with the ORs decreasing progressively as the number of imbalanced constitutions increased. Conversely, the risk estimates for smoking status changed substantially after accounting for missing data. In the primary model, current smoking was only weakly associated with having 1 imbalanced constitution and showed no significant association with higher counts. However, in the multiple imputation model, current smoking became a highly significant and powerful risk factor, demonstrating a clear dose–response relationship with the severity of imbalance (1 vs 0: OR = 1.36, P < .01; 2 vs 0: OR = 1.50, P < .001; 3 vs 0: OR = 2.09, P < .001). In the complete-case analysis, current drinking was significantly associated with constitution count 1 versus 0 (OR, 1.06; 95% CI, 1.02–1.11) and constitution count 2 versus 0 (OR, 1.07; 95% CI, 1.01–1.14). However, these associations were no longer statistically significant in the multiple imputation analysis. Tea consumption remained significantly associated with constitution count 3 versus 0 in both the complete-case and multiple imputation analyses, with ORs of 1.17 (95% CI, 1.05–1.30) and 1.11 (95% CI, 1.04–1.19), respectively. The direction and statistical significance of the association were consistent across the 2 analyses.
4. Discussion
This study considered TCM BCs of Yang deficiency, Yin deficiency, and phlegm stasis with lifestyle factors such as smoking, alcohol consumption, tea consumption, and leisure-time physical activity and found that persons who smoked, consumed alcohol, and did not regularly engage in leisure-time physical activity were more likely to have TCM BCs of Yin deficiency and phlegm stasis; persons who smoked and did not regularly engage in leisure-time physical activity were more likely to have a TCM BC of Yang deficiency. In addition, persons who drank tea were associated with a higher likelihood of phlegm stasis.
Among all lifestyle factors examined, our results indicate that smoking exhibits the strongest association with an increased likelihood of the TCM BCs Yin deficiency, Yang deficiency, and phlegm stasis. In contrast, leisure-time physical activity demonstrates a protective effect against having these TCM BCs. Smoking remains a major public health concern and is a leading cause of preventable morbidity and mortality worldwide. Individuals who smoke are at a significantly higher likelihood of chronic obstructive pulmonary disease, emphysema, interstitial lung diseases, lung fibrosis, cardiovascular diseases, lung cancer, and other serious health conditions, compared with nonsmokers.[9–11] Conversely, physical activity plays a critical role in the prevention of noncommunicable chronic diseases and mortality.[12] It is widely recognized that higher levels of physical activity are associated with a reduced likelihood of various adverse health outcomes, including neurodegenerative diseases, cancer, cardiometabolic syndrome, weight gain, all-cause mortality, cardiovascular mortality, and incident cardiovascular disease. Our findings reveal that smoking is associated with a 62%, 64%, and 88% increased likelihood of Yang deficiency, Yin deficiency, and phlegm stasis, respectively. In contrast, leisure-time physical activity is associated with a 29%, 26%, and 37% reduced likelihood of these respective TCM BCs.
These findings are significant both from a preventive health and a TCM diagnostic perspective. The findings suggest that regular physical activity may be associated with a lower prevalence of imbalanced TCM constitutions such as Yin deficiency, Yang deficiency, and phlegm stasis. Clinicians, especially those integrating TCM principles, might emphasize regular leisure-time physical activity as a nonpharmacologic intervention to maintain constitutional balance and overall health. As guidance for TCM diagnosis and treatment, if a patient is sedentary, a TCM practitioner might proactively screen for signs of these constitution types. Yin deficiency may present with symptoms like dryness, heat sensations, and insomnia. Yang deficiency often involves fatigue, cold limbs, and frequent urination. Phlegm stasis can manifest as heaviness, brain fog, or digestive issues. Understanding this correlation can help tailor herbal prescriptions, acupuncture treatments, and lifestyle advice. The interpretation of the findings regarding the associations between other lifestyle patterns and TCM BCs is also similar.
Drinking tea has been regarded as a health-promoting habit, and scientific evidence supporting its benefits continues to grow.[13–22] Tea consumption has been associated with the prevention of various debilitating conditions, particularly in maintaining cardiovascular and metabolic health.[19,20] Several studies suggest that polyphenolic compounds found in both green and black tea contribute to cardiovascular protection, especially against atherosclerosis and coronary heart disease.[21,22] Furthermore, tea intake has been linked to anti-aging, antidiabetic, and other health-promoting effects.[17–20] However, this study found that tea consumption may be associated with the TCM BC of phlegm stasis. The underlying mechanisms of this association remain unclear. Although differences in tea preparation and consumption patterns may potentially contribute to this association, information on added sugar, milk, and other preparation characteristics was not collected in the TWB and therefore could not be evaluated in the present study. Further studies incorporating detailed information on tea type, preparation methods, quantity, and accompanying ingredients are warranted to clarify the observed association.
According to TCM theory, the association between current alcohol consumption and an increased likelihood of Yin deficiency and phlegm-stasis constitution types can be understood through the fundamental principles governing the body’s internal balance of Yin-Yang, Qi, Blood, and Body Fluids.[23,24] Alcohol is considered warm or hot in nature and possesses a dispersing quality. When consumed regularly or in excess, it generates internal heat, which gradually depletes Yin fluids (including body fluids and essence) and dries the internal environment. This process contributes to the development of Yin deficiency, typically manifested by symptoms such as dry mouth, night sweats, irritability, and a red tongue with little coating. Similarly, the link between alcohol consumption and phlegm stasis constitution is attributed to alcohol’s tendency to promote dampness and heat. When dampness accumulates and combines with internal heat or stagnation, it may congeal into phlegm, ultimately contributing to a phlegm stasis pattern.
This study has several strengths. It is the first to explore the relationship between lifestyle behaviors and TCM BCs in a large sample of Taiwanese individuals. It also examined the combined effects of various lifestyle behaviors. Furthermore, the study employed a standardized and well-validated tool to assess both lifestyle behaviors and TCM BCs, helping to minimize measurement error.
Several limitations of this study should be considered. First, although the TWB is a community-based, population-based research resource, participation is voluntary, and the cohort was not established through probability-based sampling of the general Taiwanese population. Therefore, the study sample may differ from the broader Taiwanese population in terms of demographic, health, and lifestyle characteristics. Accordingly, caution is warranted when generalizing our findings to the wider Taiwanese population. Second, the cross-sectional design of this observational study precludes the establishment of causal relationships and is susceptible to reverse causality and the influence of unmeasured or unknown confounding variables. Although efforts were made to adjust for potential confounders through covariate control, the possibility of residual confounding cannot be fully excluded. Third, given the cross-sectional nature of the TWB data, the temporal ordering between lifestyle behaviors and TCM BCs could not be established. Therefore, the observed associations should not be interpreted as causal relationships. Reverse causation is possible, whereby individuals with particular TCM BCs may have modified their lifestyle behaviors. In addition, residual confounding due to unmeasured or imperfectly measured factors cannot be excluded. Longitudinal studies are needed to establish temporality and further clarify the direction and potential causal nature of these associations. Fourth, because BCQ information was unavailable for a substantial proportion of the source cohort, the possibility of selection bias due to differential availability or completion of the BCQ questionnaire cannot be completely excluded. Last, alcohol consumption was recorded as the volume of alcoholic beverages rather than grams of ethanol or standard drinks, and information on beverage-specific alcohol concentration was unavailable. Thus, the alcohol exposure measure may not fully capture the amount of ethanol consumed. In addition, tea consumption was characterized by frequency, duration, and type, but information on preparation methods and additives such as sugar or milk was unavailable. Therefore, the observed associations with tea consumption should not be interpreted in relation to these unmeasured characteristics.
This study demonstrates that lifestyle behaviors – such as smoking, alcohol consumption, and lack of regular leisure-time physical activity – are associated with imbalanced TCM BCs, particularly Yin deficiency, Yang deficiency, and phlegm stasis. In addition, tea consumption was found to be positively associated with phlegm stasis.
Acknowledgments
This research has been conducted using the Taiwan Biobank resource. We thank all the participants and investigators of the Taiwan Biobank.
Author contributions
Data curation: Chia-I Tsai, Yi-Ming Chen, Jeng-Sen Tseng, Pin-Kuei Fu, Jun-Sing Wang, Jiun-Long Wang, Hsi-Wen Hsu, Tsai-Chung Li.
Funding acquisition: Chia-I Tsai.
Methodology: Chia-I Tsai, Tsai-Chung Li.
Project administration: Chia-I Tsai.
Formal analysis: Hsi-Wen Hsu, Tsai-Chung Li.
Investigation: Tsai-Chung Li.
Writing – original draft: Chia-I Tsai, Tsai-Chung Li.
Writing – review & editing: Chia-I Tsai, Yi-Ming Chen, Jeng-Sen Tseng, Pin-Kuei Fu, Jun-Sing Wang, Jiun-Long Wang, Tsai-Chung Li.
Abbreviations:
- BC
- body constitution
- BCQs
- body constitution questionnaires
- BMI
- body mass index
- CIs
- confidence intervals
- GERD
- gastroesophageal reflux disease
- IBS
- irritable bowel syndrome
- ORs
- odds ratios
- SMDs
- standardized mean differences
- TCM
- traditional Chinese Medicine
- TWB
- Taiwan Biobank
This work was supported by the Taichung Veterans General Hospital (grant number: TCVGH-1137501E).
The authors have no conflicts of interest to declare.
The data that support the findings of this study are available from a third party, but restrictions apply to the availability of these data, which were used under license for the current study, and so are not publicly available. Data are available from the authors upon reasonable request and with permission of the third party.
Supplemental Digital Content is available in the online version of this article (http://dx.doi.org/10.1097/MD.0000000000051060).
How to cite this article: Tsai C-I, Chen Y-M, Tseng J-S, Fu P-K, Wang J-S, Wang J-L, Hsu H-W, Li T-C. Associations between lifestyle behaviors and traditional Chinese Medicine body constitutions: A cross-sectional study. Medicine 2026;105:41(e51060).
Contributor Information
Chia-I Tsai, Email: citsai777@vghtc.gov.tw.
Yi-Ming Chen, Email: ymchen1@vghtc.gov.tw.
Jeng-Sen Tseng, Email: tzeng64@gmail.com.
Pin-Kuei Fu, Email: yetquen@gmail.com.
Jun-Sing Wang, Email: wangjilong1217@gmail.com.
Jiun-Long Wang, Email: wangjilong1217@gmail.com.
Hsi-Wen Hsu, Email: hsiwen0614@gmail.com.
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