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Related Concept Videos

Imaging Studies for Cardiovascular System V: CT01:28

Imaging Studies for Cardiovascular System V: CT

Cardiac computed tomography (CT) scanning is an advanced cardiac imaging technique that utilizes CT technology, with or without intravenous (IV) contrast, to produce accurate cross-sectional virtual slices of specific areas of the heart, coronary circulation, and major blood vessels such as the aorta, pulmonary veins, and arteries. The computer processes these slices to generate three-dimensional images. Multidetector CT (MDCT) is a rapid form of CT scanning that captures multiple slices...
Blood Studies for Cardiovascular System I: Cardiac Biomarkers01:20

Blood Studies for Cardiovascular System I: Cardiac Biomarkers

Cardiac biomarkers are enzymes, proteins, and hormones released into the blood when cardiac cells are injured. They are powerful tools for triaging.
The essential diagnostic tools for detecting myocardial necrosis and monitoring individuals suspected of having acute coronary syndrome (ACS) include:
Troponins
Troponins, particularly cardiac troponins I and T, are the most precise and sensitive markers of myocardial injury. They are detectable within 4-6 hours of myocardial injury and remain...
Obesity01:24

Obesity

The Body Mass Index (BMI) is a numerical value derived from a person's weight and height, used to categorize individuals into weight ranges. It is calculated using the formula: weight in kilograms divided by height in meters squared. Obesity is a health condition characterized by excessive accumulation of adipose tissue that poses health risks, often diagnosed with a BMI ≥ 30. This excess fat storage occurs when surplus dietary calories are converted into triglycerides and stored in adipocytes...
Blood Studies for Cardiovascular System III: Serum Lipid Profile01:25

Blood Studies for Cardiovascular System III: Serum Lipid Profile

Understanding serum lipids is crucial for maintaining cardiovascular health and preventing heart disease and stroke.
Serum lipids are fats and fatty substances in the blood and are crucial for various bodily functions, including energy storage, cellular structure, and hormone production. Serum lipids consist of cholesterol, triglycerides, and phospholipids.
Cholesterol is a soft, fat-like substance found in all body cells. It is crucial for producing hormones, vitamin D, and substances that aid...
Atherosclerosis III: Management01:26

Atherosclerosis III: Management

Management of atherosclerosis involves an integrated strategy encompassing pharmacological treatment, surgical interventions, lifestyle changes, and nutrition therapy to address the multifactorial nature of the disease.Pharmacological TherapyA cornerstone of atherosclerosis management is the use of pharmacological agents. Statins, such as atorvastatin, are pivotal in inhibiting HMG-CoA reductase, an enzyme that catalyzes an initial step in cholesterol synthesis in the liver. This reduction in...
Assessment of the Cardiovascular System I: Subjective Data01:23

Assessment of the Cardiovascular System I: Subjective Data

A thorough health history and physical assessment are essential for identifying cardiovascular disease (CVD) symptoms and distinguishing them from other health issues.
Initial Enquiry
Ask the patient about their primary concern and thoroughly explore all reported symptoms.
Medical History
Investigate past illnesses affecting the cardiovascular system, such as angina, anemia, rheumatic fever, congenital heart disease, stroke, thrombophlebitis, dysrhythmias, varicosities
Inquire about symptoms...

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Correction: Yalçın et al. Impact of SGLT2 Inhibitors on Cardiovascular Risk Scores, Metabolic Parameters, and Laboratory Profiles in Type 2 Diabetes. <i>Life</i> 2025, <i>15</i>, 722.

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Related Experiment Video

Updated: Jun 25, 2026

Pulse-Wave Velocity, Flow-Mediated Dilation, and Carotid Intima-Media Thickness to Assess Cardiovascular Risk in Population with Metabolic Syndrome
06:04

Pulse-Wave Velocity, Flow-Mediated Dilation, and Carotid Intima-Media Thickness to Assess Cardiovascular Risk in Population with Metabolic Syndrome

Published on: September 27, 2024

Model-Dependent Cardiovascular Risk Stratification in Obese Populations: Multicenter Study in Türkiye.

Nur Düzen Oflas1, Alihan Oral2, Ihsan Solmaz3

  • 1Department of Internal Medicine, Faculty of Medicine, Van Yuzuncu Yil University, Van, Türkiye.

International Journal of General Medicine
|June 24, 2026
PubMed
Summary
This summary is machine-generated.

Cardiovascular risk models vary significantly in obese Turkish populations, with substantial regional differences. Model choice and location impact risk estimates, necessitating careful interpretation and population-specific validation for preventive cardiology.

Keywords:
AHA PREVENTSCORE2SCORE2-DMTürkiyecardiovascular risk predictionmulticenter studyobesityrisk stratification

Related Experiment Videos

Last Updated: Jun 25, 2026

Pulse-Wave Velocity, Flow-Mediated Dilation, and Carotid Intima-Media Thickness to Assess Cardiovascular Risk in Population with Metabolic Syndrome
06:04

Pulse-Wave Velocity, Flow-Mediated Dilation, and Carotid Intima-Media Thickness to Assess Cardiovascular Risk in Population with Metabolic Syndrome

Published on: September 27, 2024

Area of Science:

  • Cardiology
  • Public Health
  • Epidemiology

Background:

  • Cardiovascular risk prediction models are crucial for preventive cardiology.
  • Existing models often lack representation of severely obese individuals.
  • Obesity is prevalent in Türkiye, increasing cardiometabolic disorder risk.

Purpose of the Study:

  • To compare cardiovascular risk estimates from SCORE2, SCORE2-DM, and AHA PREVENT models in a large obese cohort from Türkiye.
  • To evaluate inter-model agreement and regional differences in risk estimates.
  • To assess confounder-adjusted regional variations in cardiovascular risk.

Main Methods:

  • Retrospective analysis of 6,378 obese individuals from seven Turkish regions.
  • Estimation of 10-year cardiovascular risk using SCORE2, SCORE2-DM, and AHA PREVENT equations.
  • Statistical analysis including Kruskal-Wallis, chi-square, Spearman correlation, ICC, Bland-Altman, Cohen's kappa, and multivariable regression.

Main Results:

  • Significant regional heterogeneity observed in comorbidities and medication use.
  • Cardiovascular risk estimates varied by model: SCORE2 highest in Mediterranean/Southeastern Anatolia, PREVENT highest in Aegean.
  • SCORE2-DM showed homogeneous estimates; SCORE2 and PREVENT had strong rank-order but modest categorical agreement. Regional differences persisted after adjustment.

Conclusions:

  • Cardiovascular risk estimation in obese individuals is sensitive to the prediction model and regional factors.
  • Absolute risk estimates and classifications diverge significantly between models, especially in obese patients with diabetes.
  • Findings underscore the need for cautious interpretation and population-specific, outcome-based validation of risk prediction tools in high-risk obese populations.