Anthropometric measurements and markers of renal function in adults and older adults

Phenotypic Characterization of Chronic Kidney Patients Through Hierarchical Clustering

Chronic kidney disease is a major public health problem around the world and this disease early diagnosis is still a great challenge as it is asymptomatic in its early stages. Thus, in order to identify variables capable of assisting CKD diagnosis and monitoring, machine learning techniques and statistical analysis use has shown itself to be extremely promising. For this work, unsupervised machine learning, statistical analysis techniques and discriminant analysis were used.Clinical Relevance - Discriminating variables characterization assist to differentiate groups of patients in different stages of Chronic Kidney Disease and it has important outcomes in the development of future models to aid clinical decision-making, as they can generate models with a greater predictive capacity for Chronic Kidney Disease, predominantly aiding the early diagnosis capacity of this pathology.

Dietary intake, anthropometric measurements, biochemistry profile and their associations with chronic kidney disease and diabetes mellitus

The chronic kidney disease (CKD) and diabetes mellitus (DM) are considered a serious public health problem. The objective was investigating the association of DM with the anthropometric measures, biochemical profile and dietary intake in patients with CKD. Is a cross-sectional study done in 2017, with 51 patients previously diagnosed with CKD. We collect socio-demographic, lifestyle variables, anthropometric measurements, biochemical profile and dietary intake. We using the Kolmogorov–Smirnov test, followed by Pearson's χ² test and Student's t test. Data were analysed using several multivariable logistic regression models, including the socio-demographic, anthropometric, dietary intake and biochemical variable. Variables with P ≤ 0⋅20 in the univariate analyses were selected and kept in the block in the simple and multiple logistic regression analysis, to determine the differences between the categories and the factors associated with the presence of DM or not, remaining in the model final, only the significant variables (P ≤ 0⋅05). Each variable was adjusted for all other variables included in the univariate analysis. The strength of the association was assessed by the odds ratio and 95% confidence intervals (CI). The multivariate logistic regression analysis evidenced that the increase of 1 cm in waist circumference and 1 mg/dl in VLDL-c values increases the chance of DM, respectively, by 8⋅4% (OR 1⋅076; P 0⋅05) and 8⋅8% (OR 1⋅102; P 0⋅01). In contrast, an increase of 1 mg/dl in total cholesterol decreases the chance of developing DM by 3⋅1% (OR 0⋅965; P 0⋅01), that is, it becomes a protective factor. The present study identified the associations between overweight, dietary intake and biochemical tests.