Background: Resistance training (RT) is an effective intervention for glycemic control and cardiometabolic health in individuals with type 2 diabetes (T2D). However, the use of RT in individuals at risk for T2D to prevent or delay the onset of T2D, and RT program characteristics that are most effective are still unknown. The purpose of this review is to determine the effects of RT on cardiometabolic risk factors in those at risk for T2D and to examine RT program characteristics associated with intervention effectiveness.
Methods: PubMed, Cochrane, Web of Science, and Embase databases were systematically searched for published controlled trials that compared cardiometabolic outcomes in adults with cardiometabolic risk for those that underwent an RT intervention with those that did not. A systematic review and meta-analysis was conducted to determine the effect of RT on glycosylated hemoglobin (HbA1c), fasting plasma glucose (FPG), body fat percentage (BF%), total cholesterol (TC), high-density lipoprotein (HDL), low-density lipoprotein (LDL), and triglycerides (TG). Additional analyses examined effects of intervention duration and dietary intervention on FPG and TG.
Results: Fourteen trials with 668 participants were included. For RT compared to controls, the standardized mean difference (SMD) was -1.064 for HbA1c (95% confidence interval [CI] -1.802 to -0.327; p=0.005), -0.99 for FPG (95% CI -1.798 to -0.183; p=0.016), -0.933 for TC (95% CI -1.66 to -0.206; p=0.012), -0.840 for BF% (95% CI -1.429 to -0.251; p=0.005), -0.693 for HDL (95% CI -1.230 to -0.156; p=0.011), -1.03 for LDL (95% CI -2.03 to -0.050; p=0.039), and -0.705 for TG (95% CI -1.132 to -0.279; p=0.001).
Conclusions: RT is beneficial for improving glycemic control, BF%, and blood lipids in those at risk for diabetes. The addition of a dietary component did not result in larger reductions in FPG and TG than RT alone.
Prospero Registration Id: CRD42019122217.
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http://dx.doi.org/10.1186/s40798-021-00321-x | DOI Listing |
PLoS One
January 2025
Health Research and Social Development Forum (HERD) International, Lalitpur, Nepal.
Introduction: Sexual behavior among youth is a public health concern, particularly in contexts where cultural norms, socio-economic factors, and access to comprehensive sexual education play pivotal roles. This paper aims to examine the determinants of sexual behavior among Nepali youths.
Methods: This study analyzed data from 7,122 individuals aged 15-24 years from the Nepal Demographic and Health Survey (NDHS) 2022, focusing on a nationally representative sample.
J Head Trauma Rehabil
January 2025
Author Affiliations: Department of Rehabilitation Medicine, University of Washington, Seattle, Washington (Drs Bale and Hoffman); and Craig Hospital Research Department, Englewood, Colorado (Mr Sevigny).
Objective: To determine whether there are differences in healthcare utilization for chronic pain based on location (rural vs urban/suburban) or healthcare system (civilians vs Military Service Members and Veterans [SMVs]) after moderate-severe TBI.
Setting: Eighteen Traumatic Brain Injury Model Systems (TBIMS) Centers.
Participants: A total of 1,741 TBIMS participants 1 to 30 years post-injury reporting chronic pain at their most recent follow-up interview.
PLoS One
January 2025
Department of Medicine Epidemiology and Population Sciences, Baylor College of Medicine, Houston, Texas, United States of America.
Objectives: It is significant to know how much early detection and screening could reduce the proportion of occult metastases and benefit NSCLC patients.
Methods: We used previously designed and validated mathematical models to obtain the characteristics of LC in the population including undetectable metastases at the time of diagnosis. The survival was simulated using the survival functions from Surveillance, Epidemiology and End Results (SEER) data stratified by stage.
Bioinformatics
January 2025
Infectious Disease and Microbiome Program, Broad Institute of MIT and Harvard, Cambridge, MA 02142, United States.
Motivation: Partial order alignment is a widely used method for computing multiple sequence alignments, with applications in genome assembly and pangenomics, among many others. Current algorithms to compute the optimal, gap-affine partial order alignment do not scale well to larger graphs and sequences. While heuristic approaches exist, they do not guarantee optimal alignment and sacrifice alignment accuracy.
View Article and Find Full Text PDFAm J Health Syst Pharm
January 2025
Department of Pharmacy, Dell Seton Medical Center at the University of Texas, Austin, TX, USA.
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