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Clinical prediction models combining routine clinical measures have high accuracy in identifying youth-onset type 2 diabetes defined by maintained endogenous insulin secretion: The SEARCH for Diabetes in Youth Study

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posted on 2024-01-22, 20:07 authored by Angus G. Jones, Beverley M Shields, Richard A Oram, Dana M Dabelea, William A Hagopian, Eva Lustigova, Amy S Shah, Julieanne Knupp, Amy K Mottl, Ralph B. D`Agostino Jr, Adrienne Williams, Catherine Pihoker, Jasmin Divers, Maria J Redondo

Objective

With high prevalence of obesity and overlapping features between diabetes subtypes, accurately classifying youth-onset diabetes can be challenging. We aimed to develop prediction models that, using characteristics available at diabetes diagnosis, can identify youth who will retain endogenous insulin secretion at levels consistent with type 2 diabetes (T2D).

Research Design and Methods

We studied 2,966 youth with diabetes in the prospective SEARCH study (diagnosis age ≤19 years) to develop prediction models to identify participants with fasting c-peptide ≥250 pmol/L (≥0.75ng/ml) after >3 years (median 74 months) diabetes duration. Models included clinical measures at baseline visit, at a mean diabetes duration of 11 months (age, BMI, sex, waist circumference, HDL-C), with and without islet autoantibodies (GADA, IA-2A) and a Type 1 Diabetes Genetic Risk Score (T1DGRS).

Results

Models using routine clinical measures with or without autoantibodies and T1DGRS were highly accurate in identifying participants with c-peptide ≥0.75 ng/ml (17% of participants; 2.3% and 53% of those with and without positive autoantibodies) (area under receiver operator curve [AUCROC] 0.95-0.98). In internal validation, optimism was very low, with excellent calibration (slope=0.995-0.999). Models retained high performance for predicting retained c-peptide in older youth with obesity (AUCROC 0.88-0.96), and in subgroups defined by self-reported race/ethnicity (AUCROC 0.88-0.97), autoantibody status (AUCROC 0.87-0.96), and clinically diagnosed diabetes types (AUCROC 0.81-0.92).

Conclusion

Prediction models combining routine clinical measures at diabetes diagnosis, with or without islet autoantibodies or T1DGRS, can accurately identify youth with diabetes who maintain endogenous insulin secretion in the range associated with type 2 diabetes.

Funding

The authors acknowledge the involvement of the Kaiser Permanente Southern California Marilyn Owsley Clinical Research Center (funded by Kaiser Foundation Health Plan and supported in part by the Southern California Permanente Medical Group), the South Carolina Clinical and Translational Research Institute at the Medical University of South Carolina (National Institutes of Health [NIH]/National Center for Advancing Translational Sciences [NCATS] grants UL1 TR000062 and UL1 TR001450), Seattle Children’s Hospital and the University of Washington (NIH/NCATS grant UL1 TR00423), University of Colorado Pediatric Clinical and Translational Research Center (NIH/NCATS grant UL1 TR000154), the Barbara Davis Center at the University of Colorado at Denver (Diabetes and Endocrinology Research Center NIH grant P30 DK57516), the University of Cincinnati (NIH/NCATS grants UL1 TR000077 and UL1 TR001425), and the Children with Medical Handicaps program managed by the Ohio Department of Health. The SEARCH 4 study is funded by the NIH NIDDK (grants 1R01DK127208-01 and 1UC4DK108173) and supported by the Centers for Disease Control and Prevention. The Population Based Registry of Diabetes in Youth Study is funded by the Centers for Disease Control and Prevention (DP-15-002) and supported by the NIH NIDDK (grants 1U18DP006131, U18DP006133, U18DP006134, U18DP006136, U18DP006138, and U18DP006139). The SEARCH 1–3 studies are funded by the Centers for Disease Control and Prevention (PA no. 00097, DP-05-069, and DP-10-001) and supported by NIDDK. NIH funding supported the Kaiser Permanente Southern California (grants U48/CCU919219, U01 DP000246, and U18DP002714), University of Colorado Denver (grants U48/CCU819241-3, U01 DP000247, and U18DP000247-06A1), Cincinnati Children’s Hospital Medical Center (grants U48/CCU519239, U01 DP000248, and 1U18DP002709), University of North Carolina at Chapel Hill (grants U48/CCU419249, U01 DP000254, and U18DP002708), Seattle Children’s Hospital (grants

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