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Risk Analysis for Diabetic Cardiovascular Autonomic Neuropathy

NCT02461472 · Status: COMPLETED · Type: OBSERVATIONAL · Enrollment: 2000

Last updated 2019-03-20

No results posted yet for this study

Summary

A large-scale, community-based, cross-sectional study was conducted to explore the extent to which risk factors associated with diabetic cardiovascular autonomic neuropathy (DCAN) in general Chinese population. A total of more than 2000 diabetic participants were recruited by using multiple stages sampling (first cluster sampling and then simply sampling). Data involved in demographic information, clinical biomarkers such as glucose and lipids profiles, medical and therapy history were collected. Every participants was complete DNA extracted and genotyped. Diabetic Cardiovascular autonomic functions were measured by using short-term heart rate variability (HRV) to evaluate the outcome of DCAN. Univariate and multiple variables analysis have been performed to examine potential environmental and genetic risk factors of CAN. In addition, clinical risk model, simply screening model and nonlinear system model such as artificial neural network was created, respectively.

Conditions

  • Diabetic Cardiovascular Autonomic Neuropathy

Interventions

OTHER

not intervention

This is a cross-sectional study, no intervention was performed.

Sponsors & Collaborators

  • Huashan Hospital

    collaborator OTHER

  • Shanghai Tongji Hospital, Tongji University School of Medicine

    lead OTHER

Principal Investigators

  • Zihui Tang, M.D and Ph.D · Huashan Hospital

Eligibility

Min Age
30 Years
Max Age
90 Years
Sex
ALL
Healthy Volunteers
Yes

Timeline & Regulatory

Start
2013-01-31
Primary Completion
2018-08-31
Completion
2019-02-28

Countries

  • China

Study Locations

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Read the full study record

This page highlights key information. For complete eligibility criteria, study locations, investigator contacts, and the full protocol, visit the original record on ClinicalTrials.gov.

View NCT02461472 on ClinicalTrials.gov