Search citation statements
Paper Sections
Citation Types
Year Published
Publication Types
Relationship
Authors
Journals
IntroductionHeart failure is a complex syndrome characterized by impaired cardiac function. Despite improvements in treatment, the prevalence of heart failure continues to rise. The Cardiometabolic Index (CMI), a novel measure combining abdominal obesity and lipid levels, has emerged as a potential predictor of cardiac metabolic risk.MethodsWe analyzed data from the National Health and Nutrition Examination Survey (NHANES) involving 22,586 participants to investigate the association between CMI and heart failure. Multivariable logistic regression models and RCS analysis were used to explore the association between heart failure and CMI after adjusting for potential confounders. Subgroup analyses were performed among populations with different demographic and clinical characteristics.ResultsOur results revealed a significant positive correlation between CMI and heart failure, with odds ratios of 2.77 and 1.87 for the highest quartile after adjusting for confounders. Subgroup analyses indicated heightened risks among older adults and those with hypertension or diabetes. ROC curve analysis demonstrated that CMI offers good diagnostic value for heart failure, surpassing traditional measures like BMI.DiscussionOur findings suggest that CMI is a valuable tool for assessing the risk of heart failure, particularly in individuals with increased abdominal obesity or abnormal lipid profiles. This highlights the importance of addressing cardiac metabolic health in both prevention and treatment strategies for heart failure. Future research should focus on exploring causal relationships and refining predictive models that incorporate CMI to enhance early detection and intervention.
IntroductionHeart failure is a complex syndrome characterized by impaired cardiac function. Despite improvements in treatment, the prevalence of heart failure continues to rise. The Cardiometabolic Index (CMI), a novel measure combining abdominal obesity and lipid levels, has emerged as a potential predictor of cardiac metabolic risk.MethodsWe analyzed data from the National Health and Nutrition Examination Survey (NHANES) involving 22,586 participants to investigate the association between CMI and heart failure. Multivariable logistic regression models and RCS analysis were used to explore the association between heart failure and CMI after adjusting for potential confounders. Subgroup analyses were performed among populations with different demographic and clinical characteristics.ResultsOur results revealed a significant positive correlation between CMI and heart failure, with odds ratios of 2.77 and 1.87 for the highest quartile after adjusting for confounders. Subgroup analyses indicated heightened risks among older adults and those with hypertension or diabetes. ROC curve analysis demonstrated that CMI offers good diagnostic value for heart failure, surpassing traditional measures like BMI.DiscussionOur findings suggest that CMI is a valuable tool for assessing the risk of heart failure, particularly in individuals with increased abdominal obesity or abnormal lipid profiles. This highlights the importance of addressing cardiac metabolic health in both prevention and treatment strategies for heart failure. Future research should focus on exploring causal relationships and refining predictive models that incorporate CMI to enhance early detection and intervention.
Background: Shift work is essential in health care because of the need for 24-hour services but it is associated with adverse health outcomes, including disrupted circadian rhythms, poor sleep, unhealthy dietary habits, and increased stress. These effects may differ across job categories, such as nursing officers and hospital support staff, owing to varying physical and psychological demands. Limited research exists on how shift work impacts these groups differently, particularly regarding readiness to change unhealthy lifestyle behaviors. Objectives: This study aims to assess and compare lifestyle factors across six domains - nutrition, physical activity, sleep, stress, social relationships, and addictions - between hospital support staff and nursing officers with rotating shifts versus fixed daytime duties. It also aims to evaluate the association between readiness to change lifestyle patterns and work type and determine the influence of job category and shift type on lifestyle parameters after adjusting for confounders such as demographics and body composition. Methodology: A case-control study was conducted at All India Institute of Medical Sciences (AIIMS) Nagpur from December 2023 to June 2024. The study involved 327 participants (165 cases and 162 controls) comprising nurses and hospital support staff, aged 21-45 years. The case group included 83 nurses and 84 hospital support staff working rotating shifts for at least three years, while the control group consisted of 81 nurses and 81 staff members with fixed daytime schedules. General assessments, including demographics, body composition (InBody 770), and lifestyle assessments across nutrition, physical activity, sleep (Pittsburgh Sleep Quality Index; PSQI), stress (Perceived Stress Scale; PSS-10), social connectivity (Social Support Questionnaire), and alcohol use (a modified version of the 10-item Alcohol Use Disorders Identification Test (AUDIT-C)) were performed. Readiness to change lifestyle behaviors was assessed using the stages of the change model. Results: Shift workers had a significantly higher body weight (p = 0.030), larger waist circumference (p = 0.029), and higher calorie intake (p = 0.043) than non-shift workers. They also exhibited lower cardiovascular fitness (p = 0.021) and reduced water intake (p = 0.043). Among the nursing officers, shift workers had significantly poorer sleep quality (p = 0.003) and higher calorie intake (p = 0.046). Stress levels were paradoxically lower among shift nurses (p = 0.025) but not among support staff. Readiness to change lifestyle behaviors did not differ significantly between shift and non-shift workers across all domains. Logistic regression showed that sleep quality was significantly associated with shift work among nursing officers (odds ratio (OR): 6.503, p = 0.038), while no significant associations were found for other lifestyle parameters. Conclusion: This study highlights the adverse effects of shift work on body composition, calorie intake, car...
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
customersupport@researchsolutions.com
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.