Fig. 4: Precision analyses tested many features, most commonly age and beta cell function, infrequently corrected for multiple comparisons, and typically tested for differential impacts on a C-peptide-based measure. | Communications Medicine

Fig. 4: Precision analyses tested many features, most commonly age and beta cell function, infrequently corrected for multiple comparisons, and typically tested for differential impacts on a C-peptide-based measure.

From: Disease-modifying therapies and features linked to treatment response in type 1 diabetes prevention: a systematic review

Fig. 4: Precision analyses tested many features, most commonly age and beta cell function, infrequently corrected for multiple comparisons, and typically tested for differential impacts on a C-peptide-based measure.The alternative text for this image may have been generated using AI.

a Total number of features tested for association with each treatment response, with mean and SEM indicated, for all papers with precision analyses. b Stacked bar graph showing relative frequencies and percentages of papers that did or did not correct for multiple comparisons. c Frequencies of individual features tested for associations with treatment response. d Frequencies of outcomes utilized to assess for the presence of any features associated with differential treatment response. The C-peptide measure category was inclusive of any measure of beta cell function, including mixed meal area under the curve, stimulated C-peptide values, fasting C-peptide values, etc. F/u follow-up, fx function, Hba1c hemoglobin A1c, Aab autoantibody, HLA human leukocyte antigen, BMI body mass index, T1D type 1 diabetes, AGT abnormal glucose tolerance, CRP C-reactive Protein, DPTRS diabetes prevention trial-type 1 risk score, DKA diabetes ketoacidosis, Dx diagnosis.

Back to article page