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A Clinical Screening Tool Identifies Autoimmune Diabetes in Adults: Part 4

Clinical assessment

All subjects were interviewed by the same endocrinologist (S.F.) to determine the age at diabetes onset, presence of acute symptoms before diagnosis (polydipsia, polyuria, and unintentional loss of weight), weight and height at diagnosis, family history of diabetes, family or personal history of any HLA DR3/DQ2- and/or DR4/DQ8-associated autoimmune disease, i.e., autoimmune thyroid disease, celiac disease, Addison’s disease, vitiligo, rheumatoid arthritis, pernicious anemia, and autoimmune hepatitis. Details of the specific interview questions are provided in the appendix. Metabolic markers such as ketonuria, blood glucose level, and HbA1c (A1C) at diagnosis were not studied, as they were not routinely documented in these subjects.


Differences in age and BMI were analyzed with an unpaired t test. Differences in age at diagnosis according to decade category, BMI according to weight category, acute symptoms, personal and family history of autoimmune disease, and family history of diabetes were analyzed with Fisher’s exact tests. Statistical analyses were performed with GraphPad PRISM version 3.0 software.

The ability of a “LADA clinical risk score” to predict LADA was analyzed by a relative operating characteristic (ROC) plot using two different methods. The first method calculated a LADA clinical risk score based on the total number of “distinguishing” clinical features present in each subject. A distinguishing clinical feature was defined as a feature that was significantly more frequent in LADA compared with type 2 diabetes in the retrospective study. One point was scored for the presence of each distinguishing clinical feature, with a LADA clinical risk score of 5 being the maximum. In the second method, a LADA clinical risk score was calculated on the basis of a multivariate analysis of the distinguishing clinical features. Each clinical feature independently associated with LADA was weighted according to its odds ratio (OR) coefficient derived from a logistic regression model. The ability of the two clinical risk scores to predict LADA was assessed by calculating the area under the curve (AUC). Also, cutoff points with optimal sensitivity and specificity for both clinical scoring methods were determined to ascertain their ability to predict LADA in the prospective study.

POSTED ON March 9, 2011, ,