Prediction of type 1 diabetes with machine learning algorithms based on FTIR spectral data in peripheral blood mononuclear cells.

The incidence of autoimmunity is increasing, to ensure timely and comprehensive treatment, there must be a diagnostic method or markers that would be available to the general public. Fourier-transform infrared spectroscopy (FTIR) is a relatively inexpensive and accurate method for determining metabolic fingerprint. The metabolism, molecular composition and function of blood cells vary according to individual physiological and pathological conditions. Thus, by obtaining autoimmune disease-specific metabolic fingerprint markers in peripheral blood mononuclear cells (PBMC) and subsequently using machine learning algorithms, it might be possible to create a tool that will allow the diagnosis of autoimmune diseases. In this preliminary study, it was found that the peak shift at 1545 cm-1 could be considered specific for autoimmune disease type 1 diabetes (T1D), while the shifts at 1070 and 1417 cm-1 could be more attributed to the autoimmune condition per se. The prediction of T1D, despite the small number of participants in the study, showed an inverse AUC = 0.33 ± 0.096, n = 15, indicating a stable trend in the prediction of T1D based on FTIR metabolic fingerprint data in the PBMC.

Achievement of treatment targets predicts progression of vascular complications in type 1 diabetes.

BACKGROUND AND AIM: To study the association between achievement of guideline-defined treatment targets on HbA1c, low-density lipoproteins (LDL-C), and blood pressure with the progression of diabetic complications in patients with type 1 diabetes (T1D). METHODS: The study included 355 patients at baseline and 114 patients with follow-up data after 3-5 years. Outcome variables were the progression of diabetic kidney disease, retinopathy, or cardiovascular disease (CVD). We used logistic regression and other machine learning algorithms (MLA) to model the association of achievement of treatment targets and probability of progression of complications. RESULTS: Achievement of the target blood pressure was associated with 96% lower odds of a new CVD event (0.04 (95% CI 0.00, 0.53), p = 0.016), and 72% lower odds of progression of any complication (0.28 (95% CI 0.09, 0.89), p = 0.027. Achievement of HbA1c target was associated with lower odds of composite complication progression by 82% (0.18 (95% CI 0.04, 0.88), p = 0.034.) None of the patients who achieved HbA1c target progressed in CVD. MLA demonstrated good accuracy for the prediction of progression of CVD (AUC 0.824), and lower accuracy for other complications. CONCLUSION: The achievement of blood pressure and HbA1c treatment targets is associated with lower odds of vascular complication of T1D in a real life study.