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NCT Number: NCT06320600

Precision Medicine of Diabetes Program in China

The prevalence of diabetes has been increasing year by year. According to the International Diabetes Federation (IDF) statistics in 2021, the global number of people with diabetes has reached 537 million. The patient population is large and heterogeneous, and precise diagnosis and treatment of diabetes patients have been a focus of attention. The objective of this study is to clarify the current status of precision classification and diagnosis of diabetes patients through retrospective and prospective cohort studies, and to establish a model for precision classification and diagnosis of diabetes, improving the accuracy of diabetes classification and diagnosis; based on big data of precision medicine for diabetes patients and follow-up cohorts, to establish precise diagnostic, therapeutic, and predictive methods for diabetes; and through multi-omics data analysis, to explore the potential pathogenesis of diabetes, explaining the heterogeneity of patients, and guiding individualized treatment for patients.

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Key information

Sex eligibility

All sexes

Study type

Observational

Primary location

the Second Xiangya Hospital

Changsha, Hunan, 410010, China

Location status: Recruiting

Location contact

Shuting Yang

CONTACT

[email protected]

15200863137

Who can participate

Healthy volunteers accepted: No

Only the study team can determine whether someone qualifies for participation.

Inclusion criteria

  • Patients diagnosed with diabetes according to WHO 1999 criteria

Exclusion criteria

  • with duration more than 1 year; Diabetes has not been diagnosed by WHO standards in 1999; Absence of medical history data

Treatment and study plan

No intervention

Other

It's an observational study, no intervention included

Primary outcomes

  1. The currency analysis of diabetes classification

    Time frame: July 1st 2023 to December 31th 2023

    To assess the accuracy rate of diabetes classification diagnoses according to "Classification of diabetes mellitus in China: an expert consensus statement" and explore variations across centers in different regions and hospital levels.

Secondary outcomes

  1. To establish a model for predicting diabetic retinopathy

    Time frame: January 1st 2024 to December 31th 2028

    To establish a model for diabetic retinopathy prediction, and calculate the sensitivity and specificity of the model were by comparing with the retinopathy outcome of patients.

  2. To establish a model for predicting diabetic kidney disease

    Time frame: January 1st 2024 to December 31th 2028

    To establish a model for diabetic kidney disease (DKD) prediction, and calculate the sensitivity and specificity of the model were by comparing with the DKD outcome of patients.

  3. To establish a model for predicting diabetic neuropathy

    Time frame: January 1st 2024 to December 31th 2028

    To establish a model for diabetic neuropathy prediction, and calculate the sensitivity and specificity of the model were by comparing with the outcome of patients.

Other outcomes

  1. To establish a model for hypoglycemic therapy

    Time frame: January 1st 2024 to December 31th 2028

    To establish a model for hypoglycemic therapy selection, and compare the rate of HbA1c<7% in patients who are in accordance with the model choice and those who aren't.

Sponsors and collaborators

Lead sponsor

Second Xiangya Hospital of Central South University

Other

Registry information

Acronym: PDC

Important dates

Study start
2023
Primary completion
2028
Study completion
2028
First posted
Mar 20, 2024
Registry last updated
Mar 20, 2024

OpenTrials presents study information sourced from ClinicalTrials.gov. The official registry record should be consulted for the latest information.

View the official ClinicalTrials.gov record (opens in a new tab)

This listing is for discovery and informational purposes only. It is not medical advice, does not guarantee that a study is recruiting, and does not determine eligibility. Contact the study team and a qualified healthcare professional when considering participation.

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