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Author Spotlight: Assessing the Cardiovascular Profile of Patients with Metabolic Syndrome
Published on: September 27, 2024
Cardio-metabolic risk among healthcare providers: A latent profile study
Parya Esmaeili1,2, Sayyed M Haybatollahi3, Neda Roshanravan4
1Liver and Gastrointestinal Diseases Research Center, Tabriz University of Medical Sciences, Tabriz, Iran.
Insights
Healthcare professionals face cardio-metabolic disease (CMetD) risks. Latent Profile Analysis identified three risk groups, with older age, higher BMI, and abnormal lipid profiles indicating high risk for CMetD.
Area of Science:
- Cardiology
- Metabolic Health
- Public Health
Background:
- Cardio-metabolic disease (CMetD) poses a significant health challenge, particularly among healthcare professionals.
- Suboptimal management of metabolic disorders strains healthcare systems.
Purpose of the Study:
- To cluster healthcare providers into distinct risk profiles for CMetD using Latent Profile Analysis (LPA).
- To identify key predictors associated with each risk profile for targeted interventions.
Main Methods:
- Latent Profile Analysis (LPA) was applied to a cohort of 500 healthcare providers.
- Risk factors analyzed included age, blood pressure (BP), lipid profile, insulin, body mass index (BMI), and waist circumference.
Main Results:
- Three latent risk profiles were identified: low (42.4%), intermediate (21.8%), and high (35.8%).
- The high-risk profile was characterized by older age, higher BMI, elevated insulin, fasting blood glucose (FBS), and adverse lipid profiles (HDL, LDL, total cholesterol, triglycerides).
- Intermediate risk was linked to elevated BP and waist circumference, while hemoglobin and hematocrit levels predicted lower risk profiles.
Conclusions:
- LPA-derived profiles offer insights into CMetD risk stratification.
- Targeted screening and prevention strategies are crucial for older individuals with adverse metabolic and lipid profiles, elevated BP, and BMI.
Introduction:
Cardio-metabolic disease (CMetD) is a prevalent health issue among healthcare professionals, and suboptimal management of metabolic disorders places a burden on the healthcare system.
Methods:
The present study aimed to cluster the participants based on risk factors for the CMetDs using Latent Profile Analysis (LPA). This study was conducted on 500 healthcare providers, aged 18 to 75 years at Tabriz University of Medical Sciences, Tabriz, Iran. LPA was used to explore the latent risk profiles based on age, blood pressure (BP), lipid profile, insulin, body mass index (BMI), and waist circumference.
Results:
The individuals were classified into three LPA-driven profiles: low (42.4%), intermediate (21.8%), and high (35.8%). The high-risk profile found in older age and higher BMI, insulin, fasting blood glucose (FBS), as well as higher levels of high-density lipoprotein (HDL) cholesterol, low-density lipoprotein (LDL) cholesterol, total cholesterol, and triglyceride. Furthermore, in the intermediate risk profile, elevated levels of systolic/diastolic BP and waist circumference were associated with higher levels of risk. Haemoglobin and hematocrit levels were significant predictors of low and intermediate latent profiles. Higher levels of hemoglobin and hematocrit were associated with lower odds of being in low and intermediate latent profiles, compared to the high-risk profile (all P<0.05).
Conclusion:
LPA-derived latent profiles and the specific predictors of profiles help find control and prevention measures in CMetDs; older individuals with poorer lipid profiles, and, elevated insulin, triglyceride, FBS, BP, and BMI levels should be screened more carefully.
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