Tianjin Diabetes and Health Cohort Study
Conditions: Diabetes Mellitus
Sponsor: Tianjin Medical University

 
 
Tianjin Diabetes and Health Cohort 
Study 
 
 
 
 
 
 
 
 
 
Document Date：2025-05-07 
ClinicalTrials.gov ID：NCT06913153 
 

Research Protocol： 
This study establishes a multidimensional dynamic cohort 
research platform leveraging comprehensive medical and 
healthcare data from approximately 400,000 diabetes patients in 
Tianjin during 2009-2023. Focused on addressing critical 
scientific challenges in diabetes prevention and management, 
the project employs advanced analytical methods to extract 
insights from multi-parametric data sources including electronic 
health records, medical imaging, and wearable device metrics. 
Diabetes mellitus represents a significant global public 
health challenge, with its prevalence exhibiting rapid growth in 
China. Tianjin, serving as the medical hub of the Beijing-
Tianjin-Hebei region, possesses a unique advantage through its 
network of secondary and tertiary hospitals that have 
systematically accumulated longitudinal medical data. This 
comprehensive dataset provides an invaluable resource for 
investigating 
the 
spatiotemporal 
patterns 
of 
diabetes 
epidemiology. In this study, we aim to establish a standardized 
data integration platform and develop innovative analytical 
algorithms. Leveraging the Tianjin Diabetes Population Cohort, 
we 
will 
construct 
an 
end-to-end 
research 
framework 
encompassing: disease risk prediction models, clinical decision 

support systems, and health policy evaluation methodologies. 
The research program comprises five synergistic modules 
designed to provide comprehensive insights into diabetes 
epidemiology.  
The prevalence trends module systematically analyzes the 
epidemiological characteristics of diabetes and its associated 
risk factors, while conducting comparative studies with global 
datasets to establish an evidence-based foundation for 
researchers worldwide. 
In the pathogenesis module, we will investigate the 
etiological mechanisms, risk factors, early biomarkers, and 
disease progression of diabetes mellitus and elucidate potential 
therapeutic targets for diabetes prevention and clinical 
intervention. 
In the risk prediction module, we will develop advanced 
machine learning ensemble models to identify high-risk 
populations for diabetes onset, predict the critical diabetic 
complications including diabetic nephropathy, retinopathy 
progression, cardiovascular diseases, and assess the life 
expectancy.  
In the personalized treatment and management module, we 
will systematically quantify the longitudinal effects of dynamic 

glycemic control fluctuations on disease-specific risks and all-
cause mortality, investigate medication dosing regimens and 
prescription patterns in relation to glycemic target achievement, 
and 
establish 
robust 
clinical 
evidence 
to 
standardize 
management protocols and optimize tiered care delivery systems 
for diabetes mellitus. 
In the health economics module, we conducted a 
comprehensive evaluation of diabetes-associated health and 
economic burdens in Tianjin and developed disease burden 
trajectories models for temporal trends projection. Furthermore, 
we performed rigorous health economic evaluations of diabetes 
interventions through Markov decision-analytic modeling and 
microsimulation techniques to identify cost-effective diabetes 
management strategies. 
Through the systematic integration of multi-dimensional 
data, this study will establish a comprehensive closed-loop 
research framework encompassing risk prediction, clinical 
intervention, and economic evaluation. This integrated approach 
will generate region-specific evidence to support precision 
diabetes prevention and control strategies in the local context.