Abstract
Early diagnosis is important to reduce the incidence and mortality rate of diabetes. The feasibility of early diagnosis of diabetes was studied via near-infrared spectra (NIRS) combined with a support vector machine (SVM) and aquaphotomics. Firstly, the NIRS of entire blood samples from the population of healthy, pre-diabetic, and diabetic patients were obtained. The spectral data of the entire spectra in the visible and near-infrared region (400–2,500 nm) were used as the research object of the qualitative analysis. Secondly, several preprocessing steps including multiple scattering correction, variable standardization, and first derivative and second derivative steps were performed and the best pretreatment method was selected. Finally, for the early diagnosis of diabetes, models were established using SVM. The first overtone of water (1,300–1,600 nm) was used as the research object for an aquaphotomics model, and the aquagram of the healthy group, pre-diabetes, and diabetes groups were drawn using 12 water absorption patterns for the early diagnosis of diabetes. The results of SVM showed that the highest accuracy was 97.22% and the specificity and sensitivity were 95.65 and 100%, respectively when the pretreatment method of the first derivative was used, and the best model parameters were c = 18.76 and g = 0.008583.The results of the aquaphotomics model showed clear differences in the 1,400–1,500 nm region, and the number of hydrogen bonds in water species (1,408, 1,416, 1,462, and 1,522 nm) was evidently correlated with the occurrence and development of diabetes. The number of hydrogen bonds was the smallest in the healthy group and the largest in the diabetes group. The suggested reason is that the water matrix of blood changes with the worsening of blood glucose metabolic dysfunction. The number of hydrogen bonds could be used as biomarkers for the early diagnosis of diabetes. The result show that it is effective and feasible to establish an accurate and rapid early diagnosis model of diabetes via NIRS combined with SVM and aquaphotomics.
Highlights
Pre-diabetes refers to abnormal fasting glucose or impaired glucose tolerance that has not yet reached the diagnostic criteria for diabetes
After the second-order derivative was used to improve the spectral resolution, significant differences were found in the 1,400–1,500 nm region, which shows that water absorption patterns could be used for the early diagnosis of diabetes
near-infrared spectra (NIRS) combined with machine learning and aquaphotomics were used for the early diagnosis of diabetes in this paper
Summary
Pre-diabetes refers to abnormal fasting glucose or impaired glucose tolerance that has not yet reached the diagnostic criteria for diabetes. It is the only reversible stage in the course of type 2 diabetes (Yu et al, 2013). One study has indicated that there will be 472 million people in the world with diabetes by the year 2025 (Xiaomin et al, 2016). Patients have no specific symptoms in the early stages of type 2 diabetes. It is important to develop methods for the early diagnosis of type 2 diabetes so that appropriate diet and lifestyle interventions can be provided at an early stage to reduce the incidence of diabetes and control the condition of pre-diabetes
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