李香君

Abstract:

Dyslipidemia, a hallmark of metabolic syndrome (MetS), contributes to atherosclerotic and cardiometabolic disorders. Current clinical procedures for cardiotoxic blood lipid monitoring are unmet due to days-long analysis. In this study, AI-assisted attenuated total reflectance Fourier transform infrared (ATR-FTIR) spectroscopy was used to identify MetS and precisely quantify multiple blood lipid levels with a blood sample of 0.5 µl and the assaying time is approximately 10 minutes.

A total of 150 blood samples from 25 individuals without MetS and 25 with MetS yielded 491 spectral measurements. LDL-C, HDL-C, TG, VLDL-C, and cholesterol levels were defined as the predicted targets of lipid absorption profiles. Linear regression (LR), gradient boosting regression tree (GBT), and histogram-based gradient boosting regression tree (HGBTR) were used to train the models. In the regression models, HGBT best predicted the targets of TG, CHOL, HDL-C, LDL-C, and VLDL-C with R2 values of 0.854 (0.12), 0.684 (0.08), 0.758 (0.10), and 0.419 (0.11), respectively. The classification model with the greatest AUC was RF (0.978), followed by HGBT (0.972) and GBT (0.967).

This study concluded that predicting MetS and determining blood lipid levels with high R2 values and limited errors are feasible for monitoring during therapy and intervention.

Keywords – Attenuated total reflectance Fourier Transform infrared (ATR-FTIR) spectroscopy, metabolic syndrome, machine learning, lipids, triglycerides, cholesterol

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