We present a protocol for a cross-sectional study. This study will be conducted at the Chinese Medicine Clinics of Hong Kong Baptist University in Hong Kong Special Administrative Region, China. Data collection will be conducted over a 12-14 month period, commencing in November 2024.

This study has been approved by the Research Ethics Committee of Hong Kong Baptist University (REC/23-24/0564) and has been registered at ClinicalTrials.gov (NCT06703684). Volunteers will be required to provide written informed consent. The reporting of this study follows the recommendations of the STROBE (Strengthening the Reporting of Observational Studies in Epidemiology) guidelines [18].

We aim to recruit a cohort of 470 adult participants (age ≥18 years) diagnosed with both T2DM and MetS [19]. The inclusion and exclusion criteria are detailed in Textbox 1. Following screening, enrolled participants will proceed to complete the study assessments. Participants who complete all study procedures will receive a transportation allowance of HK$100.

The primary outcome will be the TCM syndrome differentiation in patients with T2DM and MetS, as assessed by the Syndrome Differentiation Questionnaire for T2DM and MetS (SDQTM). SDQTM is developed according to the Guideline for the Integrated Diagnosis and Treatment of Metabolic Syndrome [20], Guideline for the Integrated Diagnosis and Treatment of T2DM [21], and Guiding Principles for Clinical Research of New Chinese Medicine [22]. It includes 69 items (Textbox 2) that capture disease nature (eg, wind, dryness, yang deficiency) and location (eg, heart, kidney) [23]. Each item is a closed-ended question featuring a ranked scale with descriptors for symptom severity and frequency. Registered Chinese medicine practitioners will administer SDQTM in a face-to-face consultation integrating the 4 TCM diagnostic methods.

The secondary outcomes include demographic and anthropometric measurements, blood pressure, fasting glucose, glycosylated hemoglobin, lipid panel (total cholesterol, triglycerides, high-density lipoprotein, low-density lipoprotein), International Physical Activity Questionnaire-Short Form, Food Frequency Questionnaire, Pittsburgh Sleep Quality Index, Audit of Diabetes-Dependent Quality of Life, Framingham Stroke Risk Score (FSRS), retinal imaging, and concurrent medications.

Physical activity levels will be assessed using International Physical Activity Questionnaire-Short Form by recalling the time and days spent on 4 intensity levels (vigorous, moderate, walking, and sitting) over the previous 7 days [24]. Dietary intake will be assessed using a Food Frequency Questionnaire that is developed for older adults in Hong Kong and requires less than 30 minutes to complete [25]. The Pittsburgh Sleep Quality Index, a 19-item instrument, will be used to evaluate sleep quality over a 1-month interval, with its items generating 7 component scores that are summed into a global score ranging from 0 to 21, where higher scores indicate poorer sleep quality [26]. Audit of Diabetes-Dependent Quality of Life assesses diabetes-specific quality of life through 2 overview items and 19 items covering domains that include leisure, work, physical health, social relationships, and living conditions. A mean weighted impact score will be calculated, with lower scores indicating poorer diabetes-specific quality of life [27]. The FSRS will be used to estimate the individual risk of cardiovascular events, calculated based on age, smoking status, use of antihypertensive medications, blood pressure, diabetes status, high-density lipoprotein, and cholesterol levels [28]. Retinal microvasculature will be noninvasively visualized and quantified by fundus photography and computerized image analysis via a cloud-based artificial intelligence platform (Airdoc, Beijing Airdoc Technology Co, Ltd) to analyze its relation to cardiovascular events [29].

Participant confidentiality will be maintained using unique identifier codes, with de-identified data entered directly into the secure REDCap (Research Electronic Data Capture) database [30]. Electronic and hard-copy data will be stored on password-protected university servers and in locked cabinets, respectively, and retained for 7 years after the study. As this noninterventional study presents no anticipated risks, a data monitoring committee is not required, and monitoring will be performed by the principal investigator and the research team.

The minimum required sample size is calculated using the single-population proportion formula [31], based on T2DM with MetS prevalence of approximately 11.2% in Hong Kong [32]. With a 95% confidence level, a precision of SD 3%, and an assumed nonresponse rate of 10%, the study aims to recruit 470 participants.

Statistical analysis will be performed using SPSS (version 29.0; IBM Corp), Python (version 3.13.9), and R (version 4.5.2; R Foundation for Statistical Computing) software. Missing values will be imputed by multiple imputation using the chained equation method. Quantitative data will be summarized as mean (SD) if normally distributed or as median and interquartile range otherwise. Latent class analysis will be used to identify the TCM syndrome patterns. Factor analysis will be used to reduce the dimensions of TCM syndrome elements and explore their underlying structure. Data suitability will be evaluated using the Kaiser-Meyer-Olkin measure and Bartlett's test of sphericity. Principal component analysis with varimax rotation will be employed to extract the common factors. Based on the factor analysis results, hierarchical cluster analysis will be performed to classify TCM syndrome elements. Cluster boundaries will be determined by identifying large distances between successive fusion levels in the dendrogram. The adjusted Rand index will be used for measuring the agreement between classifications.

For the comparison of quantitative data, one-way analysis of variance will be used if the assumptions of normality and homogeneity of variance are met. Otherwise, the Kruskal-Wallis test will be used. Categorical data will be expressed as numbers and percentages, and comparisons will be conducted using the chi-square test or Fisher exact test, as appropriate. Spearman correlation analysis will be employed to evaluate correlations between continuous variables, and Kendall’s tau-b analysis will be used for ordinal variables. Logistic regression will be performed to examine the relationship between syndrome differentiation and MetS components. A 2-sided value of P<.05 will be considered statistically significant.

DM poses a critical global health challenge, with 65%-85% of individuals with T2DM also meeting the diagnostic criteria for MetS [5,6], underscoring a widespread profile of metabolic dysregulation. Given the high comorbidity, this large-scale cross-sectional study will be the first in Hong Kong to systematically characterize the distribution of TCM syndromes in this population. It will explore how TCM syndromes relate to important health-related behaviors such as physical activity, diet, and sleep quality, as well as quality of life. By clarifying the relationship between TCM syndromes and metabolic profiles, this study aims to combine TCM assessments with clinical indicators, support more tailored treatments, and underscore the value of TCM syndrome differentiation in managing and predicting outcomes in patients with T2DM and MetS.

Although TCM syndrome distributions in patients with T2DM and MetS have been previously examined separately [16,33,34], this study specifically focuses on their comorbidity. We anticipate that the TCM syndrome profile of T2DM-MetS comorbidity will demonstrate a distinct distribution pattern compared to either condition alone, exhibiting unique relationships with disease duration and severity. Given that both conditions represent independent cardiovascular risk factors, we further hypothesize that specific TCM syndrome patterns will correlate with particular metabolic abnormalities and varying risks of disease progression. Through the combined application of FSRS and retinal imaging, we will be able to quantitatively characterize cardiovascular risks across different syndrome patterns. Furthermore, we expect these TCM syndromes to be influenced by modifiable health behaviors and associated with differences in the quality of life. Collectively, these findings will provide evidence supporting the clinical value of TCM syndrome differentiation in prognostic stratification and personalized management of T2DM and MetS.

This study has several strengths, including its design as the first large-scale cross-sectional investigation systematically characterizing TCM syndromes in individuals with T2DM and MetS. This research innovatively integrates clinical indicators such as biochemical profiling and retinal imaging with TCM syndrome differentiation while also incorporating evaluations of health behaviors and quality of life. However, several limitations should be acknowledged. The cross-sectional design inherently prevents establishing causal relationships, and reliance on self-reported data may introduce recall bias. Additionally, potential confounding factors could affect result interpretation, and the generalizability of the findings beyond the studied population requires further validation.

The findings may provide practitioners with a practical framework for incorporating TCM syndrome differentiation into T2DM and MetS management. By identifying syndrome patterns associated with metabolic profiles and lifestyle factors, this approach supports personalized treatment strategies that combine biomedical and traditional diagnostic perspectives. The integration of retinal imaging and cardiovascular risk assessments with TCM diagnostics further provides a model for comprehensive evaluation in integrative medicine practice.

Beyond these clinical implications, this study underscores the need for validated TCM syndrome assessment tools to standardize future research. It also calls for longitudinal research to establish causal relationships between syndrome patterns and disease progression. Further investigation is warranted to explore the biological mechanisms underlying identified syndrome configurations, potentially through multi-omics approaches [35]. Intervention trials targeting specific syndrome patterns would advance evidence for personalized TCM therapies.

This study will first systematically characterize TCM syndromes in patients with T2DM and MetS by integrating clinical indicators with traditional diagnostic approaches. The findings are expected to reveal significant relationships between syndrome differentiation, metabolic parameters, lifestyle factors, and disease progression. This work may establish a framework for incorporating TCM syndrome differentiation into chronic disease management, thereby contributing to the development of personalized treatment strategies and improved patient outcomes in integrative medicine practice.

Results will be disseminated through peer-reviewed journals and presentations at international conferences focused on metabolic disorders, ensuring accessibility to clinicians, researchers, and policymakers.