The aim is to establish an effective and practical early warning model for endocrine and metabolic diseases based on an environmental-gene-protein panoramic network, to uncover new mechanisms underlying the onset and progression of these diseases, and to screen for novel therapeutic targets.
This study aims to establish a specialized resource database for metabolic diseases-the Chongqing Environment, Inflammation, and Metabolic Diseases Study (EIMDS). The cohort will include approximately 8,000 community-based individuals who have been followed up for over 5 years, with clinical, biochemical, and endpoint event assessments conducted every two years, along with the collection of blood and urine samples. Based on the EIMDS cohort,this study will explore the risk factors for endocrine and metabolic diseases from a population perspective. Furthermore, by employing technologies such as whole-transcriptome sequencing, proteomics and phosphoproteomics, ATAC-seq, and single-cell sequencing,this study aim to elucidate the molecular mechanisms underlying endocrine and metabolic diseases and identify potential drug targets.
Study Type
OBSERVATIONAL
Enrollment
8,000
A single observational cohort of study participants undergoing baseline biochemical screening for autonomous aldosterone secretion (AAS). No experimental interventions or treatments are administered. All participants receive standard clinical care and undergo standardized baseline assessments, including measurement of plasma aldosterone concentration (PAC) and plasma renin concentration (PRC) for AAS classification, as well as collection of demographic, clinical, biochemical, anthropometric data and biospecimens for proteomic analysis.
The First Affiliated Hospital of Chongqing Medical University
Chongqing, China
RECRUITINGThe prevalence of autonomous secretion of aldosterone
The primary outcome is the prevalence of autonomous aldosterone secretion at baseline. Autonomous aldosterone secretion is defined by plasma aldosterone concentration (PAC) ≥100 pg/mL combined with plasma renin concentration (PRC) ≤15 uIU/mL in study participants at enrollment. Prevalence will be calculated as the number of participants meeting the above biochemical criteria divided by the total enrolled participants in the target population, presented as percentage with corresponding 95% confidence interval.
Time frame: The follow-up period from baseline enrollment to the completion of follow-up examinations was approximately 5 years
Identification of key risk factors associated with autonomous aldosterone secretion
To identify independent demographic, clinical, biochemical, and anthropometric risk factors associated with prevalent autonomous aldosterone secretion.Autonomous aldosterone secretion is biochemically defined as plasma aldosterone concentration ≥100 pg/mL and plasma renin concentration ≤15 uIU/mL. Multivariable regression analysis will be applied to evaluate significant associated factors; effect sizes (e.g., odds ratio) with 95% confidence intervals and P-values will be reported.
Time frame: The follow-up period from baseline enrollment to the completion of follow-up examinations was approximately 5 years
Association of autonomous aldosterone secretion with chronic kidney disease and metabolic disorders
To evaluate the cross-sectional association between biochemically defined autonomous aldosterone secretion and chronic kidney disease as well as metabolic abnormalities in study participants at baseline. AAS is defined as plasma aldosterone concentration ≥100 pg/mL combined with plasma renin concentration ≤15 uIU/mL. Chronic kidney disease and metabolic disorders will be defined according to current clinical guidelines; relevant correlation and regression analyses will be performed to report effect sizes with 95% confidence intervals and P-values.
Time frame: The follow-up period from baseline enrollment to the completion of follow-up examinations was approximately 5 years
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Screening of key proteins, signaling pathways and potential biomarkers associated with autonomous aldosterone secretion using proteomic analysis
To explore differentially expressed key proteins, enriched signaling pathways and candidate molecular biomarkers correlated with biochemical autonomous aldosterone secretion (AAS) via quantitative proteomic technology. AAS is defined as plasma aldosterone concentration ≥100 pg/mL and plasma renin concentration ≤15 uIU/mL. Bioinformatics analyses including protein differential expression profiling, functional enrichment analysis and pathway annotation will be performed to identify core molecular targets and potential diagnostic biomarkers for AAS.
Time frame: The follow-up period from baseline enrollment to the completion of follow-up examinations was approximately 5 years