Predicting the risk of autoimmune thyroid disease in patients with vitiligo: Development and assessment of a new predictive nomogram

Abstract

BackgroundThis study aimed to develop an autoimmune thyroid disease (AITD) risk prediction model for patients with vitiligo based on readily available characteristics.MethodsA retrospective analysis was conducted on the clinical characteristics, demographics, skin lesions, and laboratory test results of patients with vitiligo. To develop a model to predict the risk of AITD, the Least Absolute Shrinkage and Selection Operator (LASSO) method was used to optimize feature selection, and logistic regression analysis was used to select further features. The C-index, Hosmer–Lemeshow test, and decision curve analysis were used to evaluate the calibration, discrimination ability and clinical utility of the model. Internally, the model was verified using bootstrapping; externally, two independent cohorts were used to confirm model accuracy.ResultsSex, vitiligo type, family history of AITD, family history of other autoimmune disease, thyroid nodules or tumors, negative emotions, skin involvement exceeding 5% of body surface area, and positive immune serology (IgA, IgG, IgM, C3, and C4) were predictors of AITD in the prediction nomogram. The model showed good calibration and discrimination (C-index: 0.746; 95% confidence interval: 0.701–0.792). The accuracy of this predictive model was 74.6%.In both internal validation (a C-index of 1000 times) and external validation, the C-index outperformed (0.732, 0.869, and 0.777). The decision curve showed that the AITD nomogram had a good guiding role in clinical practice.ConclusionThe novel AITD nomogram effectively evaluated the risk of AITD in patients with vitiligo.

Characteristics of vitiligo patients with versus without associated autoimmune thyroid disease

“…The current study expands upon a previous study, 13 including the 16 factors studied therein. Some of these factors were already mentioned and discussed in the literature or clinically, whereas others were newly discovered by our team in our clinical work.…”

Establishment of a promising vitiligo mouse model for pathogenesis and treatment studies

“…Vitiligo is an autoimmune skin disease affecting 0.5–2% of the global population, significantly impacting patients' quality of life and causing substantial psychosocial stress [ 1 ]. The etiology and pathogenesis of vitiligo remain unknown, with various hypotheses proposed to address its pathogenesis, including roles for innervation, microvascular anomalies, oxidative stress-induced melanocyte degeneration, defects in melanocyte adhesion, autoimmunity, somatic mosaicism, and genetic influences [ 2 ]. The autoimmunity theory suggests that self-reactive immune cells attack melanocytes, leading to their death and subsequent loss from the epidermis, resulting in depigmentation [ 3 ].…”

Deep Learning based Model for Detection of Vitiligo Skin Disease using Pre-trained Inception V3

“…As per the previous studies presented (Guo et al, 2022), vitiligo is a skin disease that affects 0.2% to 1.8% of people all over the world. The benign nature of vitiligo can be common, but with its increase in the visible areas of the body such as hands, face, mouth, etc., it becomes a disease that needs to be treated to avoid anxiety, low self-confidence, and depression issues (Ma et al, 2023). Vitiligo creates a lighter skin tone than the original skin colour.…”