Innovative biomarkers for predicting type 2 diabetes mellitus: relevance to dietary management of frailty in older adults

Study on Influencing Factors of Frailty in Elderly Patients with Type II Diabetes

The purpose of this study was to analyze the influencing factors of frailty in elderly patients with type II diabetes. 332 elderly patients with type II diabetes admitted to our hospital from May 2018 to December 2019 were selected as the study subjects. The degree of frailty was evaluated by the Fried fragility phenotype scale, and the general information and the clinical data of patients were collected by inquiry and questionnaire survey. After that, all the data were analyzed by SPSS 20.0. Univariate analysis showed that there were significant differences in age, medication types, dietary habits, exercise tolerance, exercise capacity, body balance, coordination ability, urinary incontinence, anxiety, etc., in elderly patients with type II diabetes who suffered from different degrees of frailty (P < 0.01). In addition, there were significant differences in the presence of coronary heart disease, heart failure, atrial fibrillation, pulmonary diseases, osteoarticular diseases, anemia, and other blood diseases of elderly patients with type II diabetes who suffered from different degrees of frailty (P < 0.01). Multiple Logistic regression analyses indicated that weakened exercise tolerance, reduced exercise capacity, atrial fibrillation, anemia, and other blood diseases were the influencing factors of the frailty in elderly patients with type II diabetes. The degree of frailty in elderly patients with diabetes is affected by many factors, such as exercise tolerance, exercise capacity, atrial fibrillation, anemia, and other blood diseases; thus, reasonable intervention should be implemented for elderly patients with type II diabetes according to the influencing factors so as to effectively relieve frailty.

Semi supervised data mining model for the prognosis of pre-diabetic conditions in type 2 Diabetes Mellitus

Diabetic Mellitus is the leading disease in the world irrespective of age and geographical location. It is estimated that 43% of the overall population is affected by the disease. The reasons for the disease include inappropriate diet lifestyle with allied symptoms like obesity. Therefore, the prognosis and diagnosis of the disease are important for adequate combat and care. The prognosis related known symptoms of the disease include incontinence (inability to control urination) and frequent fatigue. Moreover, early prediction of the disease plays an important role in the prognosis of other associated conditions such as heart failure leading to chronic illness. Hence, it is of interest to describe a data mining based prediction model using known features (derived from epidemiological data collected from the public hospital using routine tests) to help in the prognosis of the disease. We used data pre-processing techniques for handling missing values and dimensionality reduction models to improve data quality. The Minimum Description Length principle (MDL) model for discretization (replacing a continuum with a finite set of points) is used to reduce high-level dimensionality of the dataset, which enabled to categorize the dataset into small groups in ordered intervals. Thus, we describe a semi-supervised learning technique (identifies promising attributes using clustering and classification methods) by combining data mining techniques for reasonable accuracy having adequate sensitivity and specificity for further discussion, cross-validation, revaluation, and application. Early prediction of the disease with improved accuracy by analysing specificity ranges in blood pressure and glucose levels will be useful to combat Diabetes Mellitus.