Intensive lifestyle modification programs are intended to stabilize or promote regression of coronary artery disease; however, clinical response is often nonuniform, complicating appropriate utilization of resources and prediction of outcome. This study assessed physiological and psychological benefits to 72 persons participating in a prospective, nonrandomized, four-component lifestyle change program and compared response between patients with clinical cardiovascular disease (CVD) and patients with elevated risk factors for CVD but without clinical manifestations of disease. Subjects entering the program due to elevated risk factor levels alone demonstrated equal or greater benefit, in terms of improvement in primary CVD risk factors and reduction in measures of coronary disease risk developed in the Framingham Heart Study, than those with clinical CVD. These findings suggest that intensive lifestyle change programs may be important for primary prevention in individuals at increased risk of CVD.
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http://dx.doi.org/10.1111/j.1520-037x.2004.3332.x | DOI Listing |
J Gen Intern Med
January 2025
Department of Medicine, Division of General Internal Medicine, Johns Hopkins School of Medicine, Baltimore, MD, USA.
Objective: To assess the influence of neighborhood socioeconomic deprivation on the effectiveness of an intensive lifestyle intervention (ILI) in the Look AHEAD trial.
Research Design And Methods: Look AHEAD randomized adults with overweight/obesity and type 2 diabetes to ILI for weight loss, or Diabetes Support and Education (DSE). We linked participant data from four study sites to the 2000 United States Census to generate a neighborhood socioeconomic deprivation score.
Front Public Health
January 2025
Brigham and Women's Hospital, Harvard Medical School, Boston, MA, United States.
Objectives: Type 2 diabetes (T2D) and prediabetes are associated with poor walking endurance, a marker of physical function. We aimed to examine the long-term effects of metformin or intensive lifestyle intervention in adults at high risk of T2D on their 6-min walk test (6MWT) performance.
Methods: Participants were randomized in the 3-year Diabetes Prevention Program (DPP) to one of the three groups: lifestyle intervention, metformin, or placebo, and were subsequently followed in the DPP Outcomes Study.
Cureus
December 2024
Internal Medicine, Coimbatore Medical College, Coimbatore, IND.
Pancreatogenic diabetes also known as type 3c diabetes mellitus (DM) is a distinct entity often overlooked and misdiagnosed as type 2 diabetes. It results from exocrine pancreatic dysfunction involving both insulin and glucagon deficiencies due to damage to pancreatic beta and alpha cells. This case highlights a 46-year-old male presenting with diabetic ketoacidosis (DKA), a rare but severe complication of type 3c DM.
View Article and Find Full Text PDFJ Diabetes Res
January 2025
Human Potential Centre, Faculty of Health and Environmental Sciences, Auckland University of Technology, Auckland, New Zealand.
This study explores a novel healthcare model employed in the primary care setting integrating a carbohydrate-reduction dietary approach and health coaching for managing prediabetes (PD) and Type 2 diabetes (T2D) in New Zealand. Using qualitative methods, we conducted focus groups with 46 patients and individual interviews with health coaches and general practitioners across two regions. Five major themes emerged from inductive thematic analysis: reduced carbohydrate lifestyles, health coaching, implementation, empowerment, and sustainability.
View Article and Find Full Text PDFBrief Bioinform
November 2024
Hubei Provincial Key Laboratory of Artificial Intelligence and Smart Learning, Central China Normal University, Wuhan 430079, China.
Identifying phage-host interactions (PHIs) is a crucial step in developing phage therapy, which is the promising solution to addressing the issue of antibiotic resistance in superbugs. However, the lifestyle of phages, which strongly depends on their host for life activities, limits their cultivability, making the study of predicting PHIs time-consuming and labor-intensive for traditional wet lab experiments. Although many deep learning (DL) approaches have been applied to PHIs prediction, most DL methods are predominantly based on sequence information, failing to comprehensively model the intricate relationships within PHIs.
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