Tabriz University of Medical Sciences Journal of Cardiovascular and Thoracic Research 2008-5117 17 4 2025 12 17 Cardio-metabolic risk among healthcare providers: A latent profile study 265 273 10.34172/jcvtr.025.33541 EN Parya Esmaeili https://orcid.org/0009-0003-2599-2085 Sayyed M. Haybatollahi Neda Roshanravan https://orcid.org/0000-0002-8173-6532 Samad Ghaffari https://orcid.org/0000-0001-6806-9387 Naimeh Mesri Alamdari Saeed Mousavi Mohammad Asghari-Jafarabadi https://orcid.org/0000-0003-3284-9749 Journal Article 10.34172/jcvtr.025.33541 2025 03 25 2025 09 11 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.