Association between serum creatinine and type 2 diabetes in the Chinese population: a retrospective cohort study

Antidiabetic Properties of Caffeoylmalic Acid, a Bioactive Natural Compound Isolated from Urtica dioica

The uncontrolled hyperglycemia that characterizes diabetes mellitus (DM) causes several complications in the organism. DM is among the major causes of deaths, and the limited efficacy of current treatments push the search for novel drug candidates, also among natural compounds. We focused our attention on caffeoylmalic acid, a phenolic derivative extracted from Urtica dioica, a plant investigated for its potential against type 2 DM. This compound was tested for its antidiabetic activity in vitro through a glucose uptake assay, in vivo in a mouse DM model and through molecular docking towards α-amylase and α-glucosidase. The effects on glucose blood level, liver enzymes, insulin and creatinine levels as well as on lipid and blood parameters, considered biochemical markers of diabetes, were also evaluated. The results showed an antidiabetic activity in vitro and in vivo, as the compound stimulates glucose absorbtion and reduces blood glucose levels. Moreover, it ameliorates lipid profile, liver and blood parameters, with moderate effect on insulin secretion. Taken together, these findings pave the way for the compounds from this class of caffeoylmalic acid as potential antidiabetic compounds.

Comprehensive machine learning models for predicting therapeutic targets in type 2 diabetes utilizing molecular and biochemical features in rats

Introduction

With the increasing prevalence of type 2 diabetes mellitus (T2DM), there is an urgent need to discover effective therapeutic targets for this complex condition. Coding and non-coding RNAs, with traditional biochemical parameters, have shown promise as viable targets for therapy. Machine learning (ML) techniques have emerged as powerful tools for predicting drug responses.

Method

In this study, we developed an ML-based model to identify the most influential features for drug response in the treatment of type 2 diabetes using three medicinal plant-based drugs (Rosavin, Caffeic acid, and Isorhamnetin), and a probiotics drug (Z-biotic), at different doses. A hundred rats were randomly assigned to ten groups, including a normal group, a streptozotocin-induced diabetic group, and eight treated groups. Serum samples were collected for biochemical analysis, while liver tissues (L) and adipose tissues (A) underwent histopathological examination and molecular biomarker extraction using quantitative PCR. Utilizing five machine learning algorithms, we integrated 32 molecular features and 12 biochemical features to select the most predictive targets for each model and the combined model.

Results and discussion

Our results indicated that high doses of the selected drugs effectively mitigated liver inflammation, reduced insulin resistance, and improved lipid profiles and renal function biomarkers. The machine learning model identified 13 molecular features, 10 biochemical features, and 20 combined features with an accuracy of 80% and AUC (0.894, 0.93, and 0.896), respectively. This study presents an ML model that accurately identifies effective therapeutic targets implicated in the molecular pathways associated with T2DM pathogenesis.

Association between N-terminal pro-B-type natriuretic peptide and clinical outcomes in bedridden patients with stroke: a cross-sectional study

OBJECTIVES

Patients with stroke often remain bedridden despite rehabilitation. Serum N-terminal pro-B-type natriuretic peptide (NT-pro-BNP) levels increase after stroke. Our study aimed to investigate the difference in NT-pro-BNP levels between bedridden and non-bedridden patients with stroke and to explore the factors influencing NT-pro-BNP levels in bedridden patients.

DESIGN

A single-centre, cross-sectional study.

SETTING

This study was conducted in a hospital, Shenzhen, China.

PARTICIPANTS

Between January 2019 and December 2022, 465 participants were included in this study.

OUTCOME MEASURES

The collected data included basic information, laboratory data and echocardiographic parameters. Binary logistic regression analysis and receiver operating characteristic curves were used to identify factors associated with high NT-pro-BNP levels.

RESULTS

Bedridden patients with stroke had higher levels of NT-pro-BNP, D-dimer, high-sensitivity C reactive protein (hs-CRP) and lower levels of creatinine, high-density lipoprotein cholesterol, albumin and haemoglobin, as well as lower left ventricular ejection fraction, fractional shortening and the ratio between the peak velocities of early and late diastolic filling than non-bedridden patients. In bedridden patients, age ≥75 years, high levels of hs-CRP and creatinine, and low levels of albumin were associated with high NT-pro-BNP levels. In non-bedridden patients, age ≥75 years and high creatinine levels were associated with high NT-pro-BNP levels. In bedridden patients with stroke, the area under the curve (AUC) of hs-CRP was 0.700 (p<0.001, 95% CI 0.638 to 0.762) with a cut-off value of 5.12 mg/L. The AUC of albumin was 0.671 (p<0.001, 95% CI 0.606 to 0.736) with a cut-off value of 37.15 g/L.

CONCLUSIONS

NT-pro-BNP levels were higher in bedridden patients with stroke than in non-bedridden patients. Decreased albumin and elevated hs-CRP levels were associated with high levels of NT-pro-BNP in bedridden patients. Further studies are needed to explore the risk stratification and potential treatments for elevated NT-pro-BNP in bedridden patients with stroke.