Category Ranking

98%

Total Visits

921

Avg Visit Duration

2 minutes

Citations

20

Article Abstract

Background And Aims: Restless legs syndromes (RLS) are intrinsic sleeping disorder and its prevalence rate is 10-15% in general population but it is observed that prevalence rate is different in diabetes patients. Current study aims to find prevalence and determinants of RLS in people living with type 2 diabetes mellitus in Pakistan.

Method: A multicenter cross-sectional observational study was conducted in 388 diabetes patients attending daily diabetes clinics and teaching hospitals in Pakistan's twin city between August 2019 and February 2020. The chi-square test and linear regression were used to detect RLS-related factors in type 2 diabetes mellitus.

Results: The prevalence of RLS found was; 3.1% patients with diabetes were suffering from very severe RLS, 23.5% from severe RLS, 34% from moderate RLS, 21.1% from mild RLS and 18.3% from non-RLS. Gender, age, education, blood glucose fasting (BSF), blood glucose random (BSR) and HBA1c were found to be significant predictors of RLS in patients with diabetes.

Conclusion: Policy makers can develop local interventions to curb the growing RLS prevalence by keeping in control the risk factors of RLS in people living with type 2 diabetes.

Download full-text PDF

Source
http://dx.doi.org/10.1016/j.dsx.2021.02.013DOI Listing

Publication Analysis

Top Keywords

type diabetes
16
rls
10
prevalence determinants
8
diabetes
8
diabetes mellitus
8
prevalence rate
8
diabetes patients
8
rls people
8
people living
8
living type
8

Similar Publications

Background: We investigated circulating protein profiles and molecular pathways among various chronic kidney disease (CKD) etiologies to study its underlying molecular heterogeneity.

Methods: We conducted a proteomic biomarker analysis in the DAPA-CKD trial recruiting adults with and without type 2 diabetes with an eGFR of 25 to 75 mL/min/1.73m2 and a UACR of 200 to 5000 mg/g.

View Article and Find Full Text PDF

Importance: The cardiovascular benefits of glucagon-like peptide-1 receptor agonists (GLP-1 RAs) may vary by body mass index (BMI), but evidence on BMI-specific outcomes remains limited.

Objective: To investigate the associations of GLP-1 RA use with cardiovascular and kidney outcomes across BMI categories in patients with type 2 diabetes.

Design, Setting, And Participants: This retrospective cohort study used the Chang Gung Research Database, a clinical dataset covering multiple hospitals in Taiwan.

View Article and Find Full Text PDF

Importance: Youth living with type 1 diabetes (T1D) are increasingly choosing automated insulin delivery (AID) systems to manage their blood glucose. Few systematic reviews meta-analyzing results from randomized clinical trials (RCTs) are available to guide decision-making.

Objective: To study the association of prolonged AID system use in an outpatient setting with measures of glucose management and quality of life in youth with T1D.

View Article and Find Full Text PDF

Artificial intelligence (AI) is increasingly applied in nutrition science to support clinical decision-making, prevent diet-related diseases such as obesity and type 2 diabetes, and improve nutrition care in both preventive and therapeutic settings. By analyzing diverse datasets, AI systems can support highly individualized nutritional guidance. We focus on machine learning applications and image recognition tools for dietary assessment and meal planning, highlighting their potential to enhance patient engagement and adherence through mobile apps and real-time feedback.

View Article and Find Full Text PDF

To date, the closed-loop system represents the best commercialized management of type 1 diabetes. However, mealtimes still require carbohydrate estimation and are often associated with postprandial hyperglycemia which may contribute to poor metabolic control and long -term complications. A multicentre, prospective, non-interventional clinical trial was designed to determine the effectiveness of a novel algorithm to predict changes in blood glucose levels two hours after a usual meal.

View Article and Find Full Text PDF