Related Experiment Video

Updated: Dec 27, 2025

Coronary Progenitor Cells and Soluble Biomarkers in Cardiovascular Prognosis after Coronary Angioplasty
10:03

Coronary Progenitor Cells and Soluble Biomarkers in Cardiovascular Prognosis after Coronary Angioplasty

Published on: January 28, 2020

5.7K

Association between C reactive protein and all-cause mortality in the ELSA-Brasil cohort

Chams B Maluf1, Sandhi Maria Barreto2, Luana Giatti2

  • 1Clinical Pathology, Universidade Federal de Minas Gerais, Belo Horizonte, Brazil chamsbm12@gmail.com.

Journal of Epidemiology and Community Health
|February 28, 2020
PubMed

Insights

High-sensitivity C reactive protein (hsCRP) is linked to increased mortality risk in a diverse Brazilian population. This association remained significant even after accounting for various health and lifestyle factors.

Area of Science:

  • Biomedical research
  • Epidemiology
  • Public health

Background:

  • High-sensitivity C-reactive protein (hsCRP) is a potential biomarker for cardiovascular and non-vascular mortality.
  • Existing evidence primarily originates from North American and European populations.
  • The role of hsCRP in mortality risk within multiethnic populations remains underexplored.

Purpose of the Study:

  • To investigate the association between hsCRP levels and all-cause mortality risk.
  • To examine this association in a multiethnic Brazilian cohort.
  • To determine if the association persists after adjusting for confounders.

Main Methods:

  • Utilized baseline data from 14,238 participants in the Brazilian Longitudinal Study of Adult Health (2008-2010).
  • Assayed hsCRP using immunochemistry.
  • Employed Cox regression analysis for univariate and adjusted models over a mean follow-up of 8.0±1.1 years.

Main Results:

  • Mortality risk increased progressively with higher hsCRP quartiles (Q2: HR 1.45, Q4: HR 1.95 vs. Q1).
  • The graded association persisted after excluding early deaths, suggesting it's not due to reverse causality.
  • Hazard ratios remained consistent after excluding participants with self-reported diabetes, cancer, or COPD.

Conclusions:

  • hsCRP levels are significantly associated with all-cause mortality in a highly admixed Brazilian population.
  • This association is independent of a wide range of lifestyle and clinical variables.
  • hsCRP may serve as a valuable mortality risk marker across diverse ethnic groups.
Abstract

Keywords:
cohort studiesepidemiologymortality

More Related Videos

Author Spotlight: Integrated Multi-Omics Analysis for Unveiling Multicellular Immune Signatures in Clinical Heart Attack Cohorts08:51

Author Spotlight: Integrated Multi-Omics Analysis for Unveiling Multicellular Immune Signatures in Clinical Heart Attack Cohorts

Published on: September 20, 2024

1.9K
High-resolution Melting PCR for Complement Receptor 1 Length Polymorphism Genotyping: An Innovative Tool for Alzheimer's Disease Gene Susceptibility Assessment07:26

High-resolution Melting PCR for Complement Receptor 1 Length Polymorphism Genotyping: An Innovative Tool for Alzheimer's Disease Gene Susceptibility Assessment

Published on: July 18, 2017

12.1K

Related Concept Videos

Blood Studies for Cardiovascular System II: CRP, Hcy, and Cardiac Natriuretic Peptide Markers01:19

Blood Studies for Cardiovascular System II: CRP, Hcy, and Cardiac Natriuretic Peptide Markers

Cardiac biomarkers are critical in diagnosing, prognosing, and managing cardiovascular diseases. Routine measurement of specific biomarkers such as B-type natriuretic peptide (BNP), C-reactive protein (CRP), and homocysteine (Hcy) is common practice in clinical settings to evaluate heart function and predict cardiovascular events.
These markers indicate stress or strain on the heart muscle:
Natriuretic Peptides (BNP)
Cardiac myocytes produce these hormones in response to ventricular stretching...
472
Bias in Epidemiological Studies01:29

Bias in Epidemiological Studies

Biases can arise at various stages of research, from study design and data collection to analysis and interpretation. Recognizing and addressing these biases is essential to ensure the validity and reliability of epidemiological findings.Broadly speaking, biases in epidemiology fall into three main categories: selection bias, information bias, and confounding. A more detailed description of possible biases is:  
1.2K
Assumptions of Survival Analysis01:15

Assumptions of Survival Analysis

Survival models analyze the time until one or more events occur, such as death in biological organisms or failure in mechanical systems. These models are widely used across fields like medicine, biology, engineering, and public health to study time-to-event phenomena. To ensure accurate results, survival analysis relies on key assumptions and careful study design.
327
Relative Risk01:12

Relative Risk

Relative risk (RR) is a statistical measure commonly used in epidemiology to compare the likelihood of a particular event occurring between two groups. This metric is important for evaluating the relationship between exposure to a specific risk factor and the probability of a particular outcome. It plays a crucial role in medical research, public health studies, and risk assessment. Relative risk quantifies how much more (or less) likely an event is to occur in an exposed group compared to an...
1.6K
Psychoneuroimmunology: Cardiovascular Disease01:27

Psychoneuroimmunology: Cardiovascular Disease

Psychoneuroimmunology (PNI) is a multidisciplinary field that examines how psychological factors, particularly stress, interact with the immune system and impact physical health. Research in PNI has shown that chronic or traumatic stress can disrupt both the hypothalamic-pituitary-adrenal axis and the sympathetic nervous system. These disruptions contribute to serious health conditions, including cardiovascular diseases.
A key area of focus in PNI is the relationship between stress and coronary...
288
Comparing the Survival Analysis of Two or More Groups01:20

Comparing the Survival Analysis of Two or More Groups

Survival analysis is a cornerstone of medical research, used to evaluate the time until an event of interest occurs, such as death, disease recurrence, or recovery. Unlike standard statistical methods, survival analysis is particularly adept at handling censored data—instances where the event has not occurred for some participants by the end of the study or remains unobserved. To address these unique challenges, specialized techniques like the Kaplan-Meier estimator, log-rank test, and...
500