Abstract
Abstract
Purpose
The Tianjin Health and Chronic Disease Study (THCDS) is a longitudinal dynamic cohort study established in 2022, aiming to investigate risk factors and intervention targets of common non-communicable diseases (NCDs) in Tianjin, China.
Participants
A total of 14 324 participants (average age: 53.48, 34.8% females) were recruited for the baseline survey from July 2022 to November 2023. All participants underwent routine medical examination, including anthropometric (height, weight and blood pressure), ECG, colour Doppler ultrasound (thyroid, carotid artery, heart, abdominal and reproductive system), chest imaging measurements (X-ray or computerised tomographic scanning), and plasma, urine and faeces sample test and a standardised questionnaire, including demographic information, lifestyle factors (smoking, alcohol consumption, diet, sleep factors, physical activity, cognitive activity and social activity) and self-reported history of common chronic diseases. Participants older than 60 were also invited to perform cognitive function tests using the Montreal Cognitive Assessment scale. Follow-ups were tracked annually through routine medical examinations and standardised questionnaires to detect their health status.
Findings to date
Key baseline findings revealed sex disparities in disease prevalence and clinical characteristics, with males showing higher rates of hypertension (46.60% vs 34.23%), type 2 diabetes (17.39% vs 9.54%) and gout (33.47% vs 15.59%), while females had higher prevalence of hyperlipidaemia (15.47% vs 18.26%), insomnia (5.42% vs 10.00%) and cancer (1.75% vs 3.23%) (all p<0.05). Females demonstrated higher cognitive scores but lower scores in naming/abstraction compared with males. Furthermore, based on the baseline data, we also found that the combined elevation of fasting glucose and serum uric acid levels significantly correlates with non-alcoholic fatty liver disease, especially in individuals without self-reported diabetes status.
Future plans
THCDS is an ongoing prospective cohort with long-term follow-up (at least 15 years). Ongoing follow-ups will be used to investigate longitudinal trajectories of risk factors and chronic diseases and to identify modifiable determinants to inform NCD prevention strategies.
Trial registration number
ChiCTR2400083075; pre-result.
Keywords: Chronic Disease, EPIDEMIOLOGY, PUBLIC HEALTH, Physical Examination, Surveys and Questionnaires
STRENGTHS AND LIMITATIONS OF THIS STUDY.
This is a prospective dynamic cohort with detailed baseline data collection, integrating comprehensive medical examinations with a standardised questionnaire.
The annual follow-up, including repeated medical measurements and questionnaire interviews, enables the longitudinal assessment of risk trajectories for non-communicable diseases.
The representativeness of the cohort may be limited due to the recruitment strategy and study design, which were based on routine medical examinations in a defined urban population.
The use of self-reported data for health status and lifestyle factors is subject to inherent recall and reporting biases.
Introduction
On a global scale, chronic non-communicable diseases (NCDs) have emerged as the predominant threat to public health and account for 75% of all deaths worldwide, according to WHO estimates.1 This epidemiological transition from infectious diseases to chronic conditions manifests across both developed and developing countries, though with distinct patterns influenced by socioeconomic development. Particularly in rapidly developing regions such as China and other Asian countries, accelerated urbanisation and lifestyle modifications have driven a remarkable shift in disease burden.2
In China, the world’s largest developing nation, NCDs now contribute to 91.0% of total deaths according to the latest data from the Global Burden of Diseases Study 2021.3 The disease spectrum has undergone a significant transformation, with cardiovascular diseases (CVDs) being the leading cause of mortality, responsible for 46.74% of all-cause deaths in China.4 Type 2 diabetes mellitus (T2DM) prevalence surged from less than 1% in 1980 to 12.4% in 2024.5 Concurrently, China continues to face a significant cancer burden, with a consistently rising trend in both incidence and mortality. According to 2022 global cancer statistics, China represented approximately 24.2% of worldwide cancer cases.6 Notably, with the increasing ageing of the population in China, neurodegenerative disorders, especially Alzheimer’s disease (AD), are emerging as a critical health challenge, revealing a combined AD prevalence of 3.48% and an incidence rate of 7.90 per 1000 person-years in 2023.7 As a mega city of over 15 million people in northern China, Tianjin exhibits a higher prevalence of NCDs than the national average, which may be partly attributable to its dietary and lifestyle patterns, characterised by relatively high salt and fat intake and sedentary behaviours.8,10 Thus, it provides a suitable setting for investigating the development and progression of chronic diseases in an urban population. Given the escalating burden of NCDs and their profound impact on public health, there is an urgent need to prioritise preventive strategies and address modifiable risk factors.
Extensive experimental and epidemiological evidence has demonstrated that the development of chronic diseases is modulated by a complex interplay of environmental, psychosocial and economic determinants.11 12 These risk factors frequently coexist and exhibit synergistic interactions, amplifying their collective impact on disease pathogenesis. With the advancement of epidemiological research, there is growing recognition of the important role of early risk factor identification in mitigating the burden of chronic diseases.13 14 For the past few years, therapeutic education has emerged as a growing and complementary role of public health strategies, calling on the strengths of all healthcare professionals focusing on the reduction of risk factors such as poor dietary habits, physical inactivity and social disengagement.15
To address these critical public health challenges and advance the understanding of NCDs in a high-risk urban population, the Tianjin Health and Chronic Disease Study (THCDS) was established. This prospective cohort study aims to elucidate the complex interplay of modifiable risk factors—ranging from anthropometry to lifestyle behaviours—in the development and progression of NCDs within an urban population in Tianjin, China. By integrating comprehensive questionnaire data with standardised clinical measurements and longitudinal follow-up, THCDS seeks to identify early biomarkers and risk profiles for chronic diseases, thereby enabling targeted prevention and intervention strategies.
Cohort description
Who is in the cohort?
THCDS is a prospective dynamic cohort with baseline data collection completed from August 2022 to December 2023, enrolling employees and retirees from enterprises, institutions and universities in Tianjin, China. Participants were recruited using a non-probability sampling approach from individuals attending routine health check-ups at the Health Management Center of the Department of Geriatrics, Tianjin Medical University General Hospital, which provided health examination services for both working and retired individuals across a wide age range and was not restricted to elderly populations. Participants in the cohort were required to meet the following inclusion criteria: (1) aged ≥18 years; (2) had undergone at least one annual health examination at the centre in the past 3 years; and (3) signed an informed consent form and completed the baseline questionnaires survey and blood test. The exclusion criteria were as follows: (1) had severe physical or mental diseases, which may represent advanced disease stages and affect baseline comparability; and (2) refused to complete physical examination and face-to-face interview. All eligible individuals during the study period were consecutively invited to participate, resulting in a cohort based on a continuous sampling framework.
Overall, a total of 14 324 participants completed both the general physical examination and questionnaires. In cases where participants were unable to respond due to slight physical or mental limitations, family members were permitted to complete the questionnaire on their behalf. To ensure data accuracy, we strive to minimise such instances of proxy responses. All participants signed an informed consent prior to data collection. The study protocol was reviewed and approved by the Ethics Committee of Tianjin Medical University General Hospital (Ethical No. IRB2022-YX-135-01).
What has been measured?
The collection of this cohort consists of two parts: routine medical examination and a standardised questionnaire. The medical examination was carried out by professional physicians and nurses at the health examination centre, and the questionnaire was completed by trained researchers. All of whom received standardised training before launching the project.
Medical examination
Participants underwent anthropometric, blood pressure, ECG, colour Doppler ultrasound and imaging measurements and were instructed to provide plasma, urine and faeces samples at the time of physical examination. Anthropometric measurements, including height, weight, blood pressure, a 12-lead resting ECG involving heart rate, cardiac rhythm, PR interval, QRS interval, QT interval, and electrical axis, and eye examination, were conducted using standardised procedures and unified devices. After an overnight fast (at least 8 hours), 15 mL of venous blood was collected and taken to the hospital’s laboratory for immediate processing and testing, including liver function, kidney function, glucose profile, lipid profile, routine blood test, gastric function, electrolyte, thyroid function and immune function (table 1). Furthermore, a total of 12 serum tumour biomarkers were measured, including alpha-fetoprotein, carcinoembryonic antigen, prostate-specific antigen 600, carbohydrate antigen 19–9 XR, free prostate-specific antigen, ferritin, neuron-specific enolase, carbohydrate antigen 125/153/242, serum human chorionic gonadotropin and growth hormone. Midstream urine was collected for a routine urine test involving urine pH, nitrite, urobilinogen, specific gravity and bilirubin. Ultrasound examination was also performed to detect the thyroid, carotid artery, heart, abdominal (liver, gall bladder, pancreas, spleen and kidneys) and reproductive system (prostate and gynaecological). Imaging examination included either chest X-ray or computerised tomographic scanning. An overview of the measurements collected during the medical examination was provided in table 1.
Table 1. Summary of measurements in medical examination.
| Measurements | No. of variables | Variables |
|---|---|---|
| Administrative information | 1 | Date of medical examination |
| Anthropometric measurement | 3 | Height, weight, BMI |
| Blood pressure | 2 | Systolic blood pressure, diastolic blood pressure |
| ECG | 6 | Heart rate, cardiac rhythm, PR interval, QRS interval, QT interval, electrical axis |
| Eye examination | 2 | Cataract, arteriosclerosis of the retina |
| Laboratory test | ||
| Liver function | 10 | Alanine aminotransferase, bilirubin, direct bilirubin, total protein, albumin, globulin, gamma-glutamyl transpeptidase, aspartate aminotransferase, lactate dehydrogenase, alkaline phosphatase |
| Kidney function | 3 | Creatinine, urea and uric acid |
| Glucose profile | 2 | Fasting plasma glucose, glycated haemoglobin |
| Lipid profile | 4 | Total cholesterol, triglyceride, high-density lipoprotein cholesterol and low-density lipoprotein cholesterol |
| Routine blood | 26 | Red blood cells, haemoglobin, haematocrit, mean corpuscular volume, mean corpuscular haemoglobin concentration, red blood cell distribution width, white blood cells, lymphocytes, lymphocytes percentage, eosinophil percentage, basophils percentage, monocyte percentage, platelets, platelet volume distribution width, mean platelet volume, platelet crit, platelet-larger cell ratio, mean corpuscular haemoglobin concentration, neutrophil percentage, absolute neutrophil count, absolute eosinophils count, absolute basophils count, red blood cell distribution width, nucleated red blood cells, nucleated red blood cells percentage |
| Gastric function | 4 | Pepsinogen, helicobacter pylori testing |
| Electrolyte | 5 | Potassium, sodium, chlorine, calcium, phosphorus |
| Thyroid function | 3 | Free triiodothyronine, free thyroxine, thyroid-stimulating hormone |
| Immune function | 23 | Immunoglobulin, rheumatoid antibodies |
| Serum tumour markers | 12 | Alpha-fetoprotein, carcinoembryonic antigen, prostate-specific antigen 600, carbohydrate antigen 19–9 XR, free prostate-specific antigen, ferritin, neuron-specific enolase, carbohydrate antigen 125/153/242, serum human chorionic gonadotropin, growth hormone |
| Routine urine | 13 | pH, specific gravity, urinary albumin, urine glucose, urinary ketone body, urobilinogen, urinary bilirubin, urine nitrite, urine latent blood, urinary leucocyte esterase, urine creatinine, microalbuminuria, urine microalbuminuria/creatinine ratio |
| Stool examination | 5 | Red blood cells, white blood cells, egg count, faecal occult blood test (immune and chemical) |
| Colour Doppler ultrasound examination | ||
| Abdominal examination | 5 | Liver, gallbladder, spleen, pancreas and kidney |
| Thyroid examination | 2 | Length/width of a larger thyroid nodule |
| Carotid artery examination | 3 | Intima-media thickness of the left and right common carotid artery, maximum plaque thickness |
| Prostate examination | 3 | Prostate transverse diameter/longitudinal diameter/anterior-posterior diameter |
| Gynaecological examination | 2 | Uterine condition, bilateral adnexal condition |
| Cardiac echocardiography | 13 | Left ventricular end-diastolic diameter/posterior wall thickness, right ventricular anterior-posterior diameter, ventricular septal thickness, sinus of Valsalva diameter, anteroposterior diameter of the left atrium, lateral diameter of the right atrium, aortic/pulmonary/mitral/tricuspid valve blood flow velocity, ejection fraction |
| Chest examination | 2 | Chest X-ray, computerised tomographic scanning |
BMI, body mass index.
Questionnaire survey
The self-reported questionnaire was developed by professionals and supplemented or revised every year based on different study purposes. During or after the above medical examination, participants were invited to fill out the questionnaire on a voluntary basis. The main content of the questionnaire was shown in table 2 which includes demographic information (age, sex, education, marital status, occupation status, income level and proportion of medical expenditure); lifestyle factors (smoking/alcohol consumption status and amount, dietary frequency including coarse food grain, bean products, dairy products, eggs, meat, vegetables, fruit, tea, coffee and sugary beverages, sleep characteristics assessed by the Pittsburgh sleep quality index16 (PSQI, including sleep duration, sleep quality, hypnotic drug use, daytime sleepiness, napping and so on), physical exercise, cognitive activity including reading time and frequency of books, newspapers and magazines and social activity including frequency of travel, visiting friends, eating outside, etc); and self-reported history of common chronic diseases (T2DM, gout/hyperuricemia, hyperlipidaemia, hypertension, heart disease, stroke, insomnia, cancer, chronic obstructive pulmonary disease and others). The duration of disease, medication use and treatment time of the above various diseases will be recorded in the database in detail. Additionally, participants aged 60 and older were invited to voluntarily complete the Montreal Cognitive Assessment (MoCA),17 a brief cognitive screening tool with high sensitivity and specificity for detecting mild cognitive impairment, using a cut-off score of 26.
Table 2. Summary of data collected in the questionnaire.
| Characteristics | Indicators |
|---|---|
| Demographics | Age, sex, education, marital status, occupation status, income level, proportion of medical expenditure |
| Lifestyle | Smoking (including status and amount), alcohol consumption (including status and amount), dietary frequency (including coarse food grain, bean products, dairy products, eggs, meat, vegetables, fruit, tea, coffee and sugary beverages), sleep characteristics (assessed by the Pittsburgh Sleep Quality Index, PSQI; including sleep duration, sleep quality, hypnotic drug use, daytime sleepiness, napping, etc), physical activity (frequency, duration and intensity), cognitive activity (reading time and frequency of books, newspapers and magazines) and social activity (frequency of travel, visiting friends, eating outside, etc) |
| History of chronic diseases | Type 2 diabetes, gout/hyperuricemia, hyperlipidaemia, hypertension, heart disease, stroke, insomnia, cancer, COPD and others |
| Cognitive function | Visuospatial, naming, attention, language, abstraction, delayed recall and orientation (assessed by Montreal Cognitive Assessment, MoCA)* |
MoCA assessment was performed among participants aged 60 years and older.
COPD, chronic obstructive pulmonary disease; MoCA, Montreal Cognitive Assessment; PSQI, Pittsburgh Sleep Quality Index.
The whole questionnaire was shown in the online supplemental appendix 1. Specifically, the proportion of medical expenditure referred to the proportion of an individual’s total annual expenditure on healthcare-related costs, including outpatient visits, hospitalisation and medication expenses, in a family’s total annual income. Furthermore, physical activity was assessed based on self-reported questionnaire data on activity frequency, duration and intensity and categorised according to WHO guidelines,18 with participants engaging in ≥150 min/week of moderate-intensity or ≥75 min/week of high-intensity activity defined as active and others as inactive.
How often will the respondents be followed?
This cohort is designed as an ongoing prospective study to maintain long-term follow-up for as long as feasible (at least 15 years) to facilitate the investigation of the development and progression of chronic diseases and their modifiable risk factors. Participants are followed up annually through routine medical measurements and questionnaire interviews, allowing for repeated assessment of clinical indicators and lifestyle factors over time. The medical measurements are consistent with routine health check-ups, and the questionnaire will be revised every year based on different study purposes. To obtain changes in lifestyle factors and health status (disease history and medication use) over time, the modified information collected at baseline will be remeasured in the annual visits. As the cohort is embedded within a routine health examination system at our centre, follow-up can be conducted in a stable and continuous manner. In addition, the cohort is dynamic in nature, allowing newly eligible individuals to be continuously enrolled over time. Participants who die during follow-up will be treated as a distinct outcome and handled accordingly in outcome-specific analyses.
Statistical analyses
The normality of continuous variables was assessed using Shapiro-Wilk tests in combination with visual inspection of histograms and Q–Q plots. Continuous variables were presented as mean±SD or median (IQR), and categorical variables were presented as frequencies (percentages). Baseline characteristics of the study population between males and females were compared using independent-samples t-tests or Mann-Whitney U tests for continuous variables and χ2 tests for categorical variables. Because these comparisons were intended to provide descriptive summaries of the cohort rather than to test confirmatory hypotheses, no adjustment for multiple testing (eg, Bonferroni correction) was applied, and no multivariable adjustment for potential confounding factors was performed. In future longitudinal analyses, appropriate methods, including multiple imputation, sensitivity analyses and multivariate analysis, will be applied to assess the potential impact of missing data and confounders.
All statistical analyses were performed using Stata SE 15.0 for Windows (StataCorp, College Station, Texas), and two-tailed p values <0.05 were considered statistically significant.
Findings to date
Key findings from the baseline study
The main data from this database can be used to assess the influence of various lifestyle factors on chronic diseases and reveal under what circumstances may exert either a positive or negative effect on the onset and progression of chronic diseases. This section describes the characteristics of the baseline data and presents the findings of several pertinent themes within the database for illustrative purposes.
This cohort has now completed the collection of baseline information and is in the process of orderly follow-up. Table 3 presents the main baseline characteristics of the participants. A total of 14 324 participants (mean age: 53.48±15.31 years) were recruited at baseline. The median age was 53 years (IQR: 41–65 years), with an age range of 18 to 96 years. The cohort included 4990 women (34.8%, mean age: 53.16±14.74) and 9334 men (65.2%, mean age: 53.65±15.60). Participants had a relatively high level of education, with a mean of 17.7 years, and the majority were married. Most participants reported low levels of medical expenditure, and a large proportion had no history of smoking or alcohol consumption. Physical inactivity was common, while the median PSQI score indicated generally moderate sleep quality. In terms of clinical measurements, participants showed average levels of BMI and metabolic indicators within expected ranges for an urban adult population (table 3). Given the large sample size, many comparisons between males and females reached statistical significance; therefore, these findings were interpreted not only based on P values but also in consideration of the magnitude of differences and their potential clinical relevance. Notable sex differences were observed across demographic, lifestyle and clinical characteristics. Compared with males, females were more likely to be unmarried and reported a higher proportion of medical expenditure. In addition, females were less likely to smoke or consume alcohol and had a lower proportion meeting the recommended physical activity levels. Females also tended to have lower BMI and more favourable metabolic profiles, including lower levels of fasting glucose, uric acid and triglycerides, but higher levels of total cholesterol, HDL-C and LDL-C. Although many of these differences were statistically significant, the absolute differences in most continuous variables (eg, metabolic indicators) were relatively modest and generally within clinically comparable ranges. Overall, these findings suggest sex-specific patterns in lifestyle behaviours and cardiometabolic profiles.
Table 3. Baseline characteristics of the study population (n=14 324).
| Total (n=14 324) | Male (n=9334) | Female (n=4990) | P value | |
|---|---|---|---|---|
| Age, years | 53.48±15.31 | 53.65±15.60 | 53.16±14.74 | 0.068 |
| Age range | (18, 96) | (18, 96) | (19, 95) | |
| Education | 17.70±3.28 | 17.96±3.20 | 17.22±3.39 | <0.001 |
| Junior high school and below | 279 (1.95) | 157 (1.68) | 122 (2.44) | |
| High school or vocational school | 771 (5.38) | 324 (3.47) | 447 (8.96) | |
| Undergraduate or college degree | 7132 (49.79) | 4600 (49.29) | 2532 (50.74) | |
| Graduate or above | 6141 (42.88) | 4252 (45.56) | 1889 (37.86) | |
| Marital status | <0.001 | |||
| Couple | 12 711 (88.75) | 8474 (90.79) | 4237 (84.93) | |
| Divorced/widowed/single | 1612 (11.25) | 860 (9.21) | 752 (15.07) | |
| Proportion of medical expenditure* | <0.001 | |||
| ≤10% | 9275 (57.81) | 5625 (60.30) | 2650 (53.17) | |
| 11–30% | 4600 (32.14) | 2811 (30.13) | 1789 (35.89) | |
| 31–50% | 1003 (7.01) | 588 (6.30) | 415 (8.33) | |
| ≥51% | 435 (3.04) | 305 (3.27) | 130 (2.61) | |
| Smoking | <0.001 | |||
| Never | 10 698 (74.71) | 5765 (61.78) | 4933 (98.88) | |
| Current/ever | 3622 (25.29) | 3566 (38.22) | 56 (1.12) | |
| Drinking | <0.001 | |||
| Never | 11 668 (81.53) | 6768 (72.59) | 4900 (98.22) | |
| Current/Ever | 2644 (18.47) | 2555 (27.41) | 89 (1.78) | |
| Physical activity level | <0.001 | |||
| Nonactive | 11 156 (79.36) | 7001 (76.26) | 4155 (85.21) | |
| Active | 2901 (20.64) | 2180 (23.74) | 721 (14.79) | |
| BMI, kg/m2 | 24.66±3.38 | 25.38±3.19 | 23.24±3.28 | <0.001 |
| <18.5 | 323 (2.52) | 108 (1.28) | 215 (4.94) | |
| 18.5–23.9 | 5262 (41.12) | 2763 (32.71) | 2499 (57.45) | |
| 24–29.9 | 6409 (50.09) | 4914 (58.18) | 1495 (34.37) | |
| ≥30 | 802 (6.27) | 661 (7.83) | 141 (3.24) | |
| PSQI | 5 (3, 7) | 5 (3, 7) | 5 (4, 8) | <0.001 |
| Fasting blood glucose, mmol/L | 5.34±1.25 | 5.42±1.29 | 5.19±1.16 | <0.001 |
| Uric acid, μmol/L | 351.77±90.18 | 383.73±83.02 | 291.86±70.38 | <0.001 |
| Total cholesterol, mmol/L | 5.09±1.02 | 4.98±0.98 | 5.30±1.04 | <0.001 |
| Triglyceride, mmol/L | 1.73±1.15 | 1.86±1.25 | 1.50±0.90 | <0.001 |
| HDL-C, mmol/L | 1.30±0.32 | 1.21±0.27 | 1.46±0.33 | <0.001 |
| LDL-C, mmol/L | 3.10±0.84 | 3.09±0.82 | 3.14±0.88 | 0.001 |
Data are presented as mean±SD or median (IQR) for continuous variables and n (%) for categorical variables.
Missing data: Education=1; Marital status=1; Smoking=4; Drinking=12; Physical activity level=267; BMI=1528; PSQI=312; Fasting blood glucose=23; Uric acid=25; Total cholesterol=21; Triglyceride=21; HDL-C=21; LDL-C=21.
Proportion of medical expenditure refers to the proportion of individual’s total annual expenditure on healthcare-related costs in a family’s total annual income.
BMI, body mass index; HDL-C, high-density lipoprotein cholesterol; LDL-C, low-density lipoprotein cholesterol; PSQI, Pittsburgh Sleep Quality Index.
The baseline prevalence of major chronic diseases in this cohort was substantial, with hypertension (42.29%), gout/hyperuricaemia (27.24%) and hyperlipidaemia (16.45%) being the top three common conditions. Furthermore, notable sex differences were also observed in disease prevalence. Overall, males exhibited a higher prevalence of most cardiometabolic conditions, including T2DM, gout/hyperuricaemia, hypertension, heart disease and stroke, whereas females showed a higher prevalence of hyperlipidaemia, insomnia and cancer (table 4). These differences in disease prevalence may have clearer clinical implications, as they reflect variations in disease burden between males and females.
Table 4. The prevalence of self-reported chronic diseases at baseline (n=14 324).
| Total (n=14 324) | Male (n=9334) | Female (n=4990) | P value | |
|---|---|---|---|---|
| Type 2 diabetes | 2099 (14.65) | 1623 (17.39) | 476 (9.54) | <0.001 |
| Gout/hyperuricaemia | 3902 (27.24) | 3124 (33.47) | 778 (15.59) | <0.001 |
| Hyperlipidaemia | 2342 (16.45) | 1436 (15.47) | 906 (18.26) | <0.001 |
| Hypertension | 6052 (42.29) | 4345 (46.60) | 1707 (34.23) | <0.001 |
| Heart disease | 1060 (7.44) | 782 (8.43) | 278 (5.60) | <0.001 |
| Stroke | 268 (1.88) | 206 (2.22) | 62 (1.25) | <0.001 |
| Insomnia | 999 (7.01) | 503 (5.42) | 496 (10.00) | <0.001 |
| Cancer | 322 (2.26) | 162 (1.75) | 160 (3.23) | <0.001 |
| COPD | 60 (0.42) | 44 (0.47) | 16 (0.32) | 0.183 |
COPD, chronic obstructive pulmonary disease.
Cognitive function, assessed by the MoCA, showed that the mean score±SD among all participants was 26.66±2.30, and the specific domain scores were shown in table 5. In terms of sex differences, females had slightly higher total scores than males, particularly in delayed recall, while males performed better in naming and abstraction domains. There were no significant differences between females and males with respect to visuospatial, attention, language and orientation domains (table 5). However, although some differences reached statistical significance, the overall MoCA scores in both groups were close to the established clinical threshold, suggesting that these differences may have limited clinical significance.
Table 5. The score of cognitive function and specific domain of participants at baseline (n=2363).
| Total (n=2363) | Male (n=1325) | Female (n=1038) | P value | |
|---|---|---|---|---|
| Visuospatial | 4.41±0.84 | 4.39±0.87 | 4.44±0.81 | 0.215 |
| Naming | 2.95±0.24 | 2.98±0.19 | 2.92±0.29 | <0.001 |
| Attention | 5.85±0.47 | 5.86±0.44 | 5.84±0.47 | 0.391 |
| Language | 2.12±0.71 | 2.12±0.71 | 2.11±0.71 | 0.931 |
| Abstraction | 1.88±0.41 | 1.91±0.31 | 1.84±0.41 | <0.001 |
| Delayed recall | 3.36±1.40 | 3.17±1.44 | 3.61±1.31 | <0.001 |
| Orientation | 5.98±0.21 | 5.98±0.24 | 5.98±0.15 | 0.963 |
| Total | 26.66±2.30 | 26.48±2.33 | 26.90±2.24 | <0.001 |
Up to March 2025, we have completed the first-round follow-up with over 12 000 participants, with the second-round follow-up currently underway in an orderly manner. Hao et al explored the combined effect of fasting glucose and serum uric acid on nonalcoholic fatty liver disease (NAFLD) based on the baseline data and found that elevated fasting glucose levels are linked to NAFLD regardless of self-reported T2DM status. The combined elevation of fasting glucose and serum uric acid levels significantly correlates with NAFLD, especially in individuals without self-reported T2DM.19
Strengths and limitations
This cohort encompasses a diverse array of multidisciplinary content and provides a comprehensive panel dataset derived from an urban population in Tianjin, China. Several baseline characteristics, particularly sex-specific differences in metabolic indicators and the distribution of major chronic diseases, were broadly consistent with findings from other large-scale cohort studies in China and internationally.20,22 It offers an opportunity to evaluate the impact of social, economic, lifestyle, physical and mental health factors on health outcomes; the results underscore the necessity for early-life interventions to prevent various chronic diseases later on. Prior to commencing the study, we trained the relevant staff to ensure that each operational step was executed professionally, thereby minimising potential biases. Furthermore, attrition due to personal and social factors such as job changes and relocations is an unavoidable aspect of cohort establishment; such loss often introduces bias and diminishes researchers’ capacity to investigate research hypotheses as well as the reliability of their findings. Rather than assuming that such losses occur at random, we will assess differences between participants with complete follow-up and those lost to follow-up and conduct sensitivity analyses where appropriate.23
Nonetheless, several limitations also warrant concern. Although this cohort was intended to reflect the general population, the recruitment through routine medical examinations in enterprises, institutions, and universities resulted in a study population largely composed of individuals with stable employment and higher educational attainment, which may introduce selection bias. Therefore, caution is needed when generalising our findings, and larger multi-centre studies with broader population coverage are warranted. Then, the questionnaires obtained in the current study contained self-reported information, such as disease status and lifestyle factors, which may inevitably lead to recall bias and reporting bias; however, standardised questionnaires and trained personnel were used to improve data quality. Potential bias due to loss to follow-up should also be considered, and its impact will be evaluated in future analyses. Furthermore, due to the recruitment framework based on employees and retirees from specific institutions, females were under-represented in the cohort. This sex imbalance may limit the generalisability of the findings and should be taken into account in the interpretation of sex-specific analyses. Finally, the size of this cohort was smaller than that typically observed in similar studies; however, it was designed as an ongoing prospective study without a predefined end date. We will continue to expand the sample size and maintain long-term follow-up as feasible, while remaining attentive to emerging health issues and exploring relevant research questions.
Collaboration
To maximise the use of data and biospecimens, we welcome and encourage collaborations from all over the world. Due to sensitive information, this dataset cannot be downloaded publicly. However, researchers with specific ideas and proposals are invited to contact the corresponding author (Prof. Qiang Zhang: zhangqiangyulv@163.com).
Supplementary material
Acknowledgements
The authors are grateful to all study participants and staff from medical examination institutions.
Footnotes
Funding: This work was supported by the National Key Research and Development Program of China (Grant No. 2023YFC3605200), the Major Research Plan of the National Natural Science Foundation of China (Grant No. 92163213) and the Tianjin Health Research Project (Grant No. TJWJ2024QN007).
prepub: Prepublication history and additional supplemental material for this paper are available online. To view these files, please visit the journal online (https://doi.org/10.1136/bmjopen-2025-114504).
Provenance and peer review: Not commissioned; externally peer reviewed.
Patient consent for publication: Consent obtained directly from patient(s).
Ethics approval: The study was approved by the Ethics Committee of Tianjin Medical University General Hospital (No. IRB2022-YX-135-01). Participants gave informed consent to participate in the study before taking part.
Data availability free text: To maximise the use of data and biospecimens, we welcome and encourage collaborations from all over the world. Due to sensitive information, this dataset cannot be downloaded publicly. However, researchers with specific ideas and proposals are invited to contact the corresponding author.
Patient and public involvement: Patients and/or the public were not involved in the design, or conduct, or reporting, or dissemination plans of this research.
Data availability statement
Data are available upon reasonable request.
References
- 1.World Health Organization Noncommunicable Diseases. 2025. https://www.who.int/news-room/fact-sheets/detail/noncommunicable-diseases Available.
- 2.Zhao P, Li K, Coyte PC. The impact of non-communicable chronic diseases on the earned income of working age Chinese residents. Humanit Soc Sci Commun . 2023;10:476. doi: 10.1057/s41599-023-01961-y. [DOI] [Google Scholar]
- 3.Liu H, Yin P, Qi J, et al. Burden of non-communicable diseases in China and its provinces, 1990–2021: Results from the Global Burden of Disease Study 2021. Chin Med J. 2024;137:2325–33. doi: 10.1097/CM9.0000000000003270. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 4.Wang L, Wang Y, Ji Z, et al. Trends of cause-specific cardiovascular disease mortality in China, 2009-2019: a nationwide longitudinal study. Ann Med. 2025;57:2455534. doi: 10.1080/07853890.2025.2455534. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 5.Xu Y, Lu J, Li M, et al. Diabetes in China part 1: epidemiology and risk factors. Lancet Public Health. 2024;9:e1089–97. doi: 10.1016/S2468-2667(24)00250-0. [DOI] [PubMed] [Google Scholar]
- 6.Wu Y, He S, Cao M, et al. Comparative analysis of cancer statistics in China and the United States in 2024. Chin Med J. 2024;137:3093–100. doi: 10.1097/CM9.0000000000003442. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 7.Ji Q, Chen J, Li Y, et al. Incidence and prevalence of Alzheimer’s disease in China: a systematic review and meta-analysis. Eur J Epidemiol. 2024;39:701–14. doi: 10.1007/s10654-024-01144-2. [DOI] [PubMed] [Google Scholar]
- 8.Jiao H, Zhang Y, Guo Z. Prevalence and Associated Factors of Self-Reported Coronary Heart Disease: A Population-Based Cross-Sectional Survey in Tianjin. Risk Manag Healthc Policy. 2024;17:3137–45. doi: 10.2147/RMHP.S497216. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 9.Zheng W, Wang X, Xue X, et al. Characteristics of hypertension in the last 16 years in high prevalence region of China and the attribute ratios for cardiovascular mortality. BMC Public Health. 2023;23:114. doi: 10.1186/s12889-022-14974-0. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 10.Xue X-D, Li W, Xie M-Q, et al. High sodium diet intake and cardiovascular diseases: An attributable death study in Tianjin, China. J Clin Hypertens (Greenwich) 2023;25:30–7. doi: 10.1111/jch.14599. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 11.Global Burden of Disease Cancer Collaboration. Fitzmaurice C, Abate D, et al. Global, Regional, and National Cancer Incidence, Mortality, Years of Life Lost, Years Lived With Disability, and Disability-Adjusted Life-Years for 29 Cancer Groups, 1990 to 2017: A Systematic Analysis for the Global Burden of Disease Study. JAMA Oncol. 2019;5:1749–68. doi: 10.1001/jamaoncol.2019.2996. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 12.Helman TJ, Headrick JP, Stapelberg NJC, et al. The sex-dependent response to psychosocial stress and ischaemic heart disease. Front Cardiovasc Med. 2023;10:1072042. doi: 10.3389/fcvm.2023.1072042. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 13.Lagraauw HM, Kuiper J, Bot I. Acute and chronic psychological stress as risk factors for cardiovascular disease: Insights gained from epidemiological, clinical and experimental studies. Brain Behav Immun. 2015;50:18–30. doi: 10.1016/j.bbi.2015.08.007. [DOI] [PubMed] [Google Scholar]
- 14.Jackowska B, Wiśniewski P, Noiński T, et al. Effects of lifestyle-related risk factors on life expectancy: A comprehensive model for use in early prevention of premature mortality from noncommunicable diseases. PLoS One. 2024;19:e0298696. doi: 10.1371/journal.pone.0298696. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 15.Sun Q, Yu D, Fan J, et al. Healthy lifestyle and life expectancy at age 30 years in the Chinese population: an observational study. Lancet Public Health. 2022;7:e994–1004. doi: 10.1016/S2468-2667(22)00110-4. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 16.Buysse DJ, Reynolds CF, III, Monk TH, et al. The Pittsburgh sleep quality index: A new instrument for psychiatric practice and research. Psychiatry Res. 1989;28:193–213. doi: 10.1016/0165-1781(89)90047-4. [DOI] [PubMed] [Google Scholar]
- 17.Nasreddine ZS, Phillips NA, Bédirian V, et al. The Montreal Cognitive Assessment, MoCA: a brief screening tool for mild cognitive impairment. J Am Geriatr Soc. 2005;53:695–9. doi: 10.1111/j.1532-5415.2005.53221.x. [DOI] [PubMed] [Google Scholar]
- 18.WHO Guidelines Approved by the Guidelines Review Committee . WHO guidelines on physical activity and sedentary behaviour. Geneva: World Health Organization; 2020. [Google Scholar]
- 19.Hao Y, Li X, Zhang Y, et al. Combined effect of fasting blood glucose and serum uric acid on nonalcoholic fatty liver disease. Lipids Health Dis. 2025;24:168. doi: 10.1186/s12944-025-02538-1. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 20.Wu S, Huang Z, Yang X, et al. Prevalence of ideal cardiovascular health and its relationship with the 4-year cardiovascular events in a northern Chinese industrial city. Circ Cardiovasc Qual Outcomes. 2012;5:487–93. doi: 10.1161/CIRCOUTCOMES.111.963694. [DOI] [PubMed] [Google Scholar]
- 21.Kelly RK, Harris K, Muntner P, et al. Variations in sex differences in major cardiometabolic risk factors by age and menopause status: results from the UK Biobank. Open Heart. 2025;12:e003182. doi: 10.1136/openhrt-2025-003182. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 22.Kelly RK, Harris K, Carcel C, et al. Sex Differences in Blood Pressure and Cardiovascular Disease in the UK Biobank: A Prospective Cohort Study. Am J Hypertens. 2026;39:98–107. doi: 10.1093/ajh/hpaf214. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 23.Kristman V, Manno M, Côté P. Loss to follow-up in cohort studies: how much is too much? Eur J Epidemiol. 2004;19:751–60. doi: 10.1023/b:ejep.0000036568.02655.f8. [DOI] [PubMed] [Google Scholar]