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AIDS. 2018 Nov 28;32(18):2767-2775. doi: 10.1097/QAD.0000000000002017.

Predicting diabetes risk among HIV-positive and HIV-negative women.

Author information

1
Hubert Department of Global Health, Rollins School of Public Health, Emory University, Atlanta, Georgia.
2
Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland.
3
Division of Infectious Diseases, Department of Medicine, University of California-San Francisco.
4
Department of Veterans Affairs, San Francisco, California.
5
Department of Biostatistics and Bioinformatics, Rollins School of Public Health, Emory University.
6
Division of Infectious Diseases, Department of Medicine, Emory University School of Medicine, Atlanta, Georgia.
7
Department of Sociomedical Sciences, Lerner Center for Public Health Promotion, Mailman School of Public Health at Columbia University, New York, New York.
8
Division of Infectious Disease, Department of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina.
9
Division of Infectious Diseases, University of Miami Miller School of Medicine, Miami, Florida.
10
Department of Medicine, Stroger Hospital and Rush University, Chicago, Illinois.
11
Department of Neurology (D.G.), SUNY-Downstate Medical Center, Brooklyn, New York.
12
Department of Preventive Medicine, University of Southern California, Los Angeles, California.
13
University of Mississippi Medical Center, Jackson, Mississippi.
14
Department of Family Medicine, Georgetown University Medical Center, Washington, District of Columbia.
15
Department of Medicine, Albert Einstein College of Medicine, Bronx, New York, USA.

Abstract

OBJECTIVE:

To assess the performance of an adapted American Diabetes Association (ADA) risk score and the concise Finnish Diabetes Risk Score (FINRISC) for predicting type 2 diabetes development in women with and at risk of HIV infection.

DESIGN:

Longitudinal analysis of the Women's Interagency HIV Study.

METHODS:

The women's Interagency HIV Study is an ongoing prospective cohort study of women with and at risk for HIV infection. Women without prevalent diabetes and 3-year data on fasting blood glucose, hemoglobin A1c, self-reported diabetes medication use, and self-reported diabetes were included. ADA and FINRISC scores were computed at baseline and their ability to predict diabetes development within 3 years was assessed [sensitivity, specificity and area under the receiver operating characteristics (AUROC) curve].

RESULTS:

A total of 1111 HIV-positive (median age 41, 60% African American) and 454 HIV-negative women (median age 38, 63% African-American) were included. ADA sensitivity did not differ between HIV-positive (77%) and HIV-negative women (81%), while specificity was better in HIV-negative women (42 vs. 49%, P = 0.006). Overall ADA discrimination was suboptimal in both HIV-positive [AUROC = 0.64 (95% CI: 0.58, 0.70)] and HIV-negative women [AUROC = 0.67 (95% CI: 0.57, 0.77)]. FINRISC sensitivity and specificity did not differ between HIV-positive (72 and 49%, respectively) and HIV-negative women (86 and 52%, respectively). Overall FINRISC discrimination was suboptimal in HIV-positive [AUROC = 0.68 (95% CI: 0.62, 0.75)] and HIV-negative women [AUROC = 0.78 (95% CI: 0.66, 0.90)].

CONCLUSION:

Model performance was suboptimal in women with and at risk of HIV, while greater misclassification was generally observed among HIV-positive women. HIV-specific risk factors known to contribute to diabetes risk should be explored in these models.

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