Related Experiment Video

Updated: May 13, 2026

Assessment of Child Anthropometry in a Large Epidemiologic Study
09:36

Assessment of Child Anthropometry in a Large Epidemiologic Study

Published on: February 2, 2017

Clinical implications of JUPITER in a contemporary European population: the EPIC-Norfolk prospective population study

Brigitte M Sondermeijer1, S Matthijs Boekholdt, Jamal S Rana

  • 1Department of Vascular Medicine, Academic Medical Center, Amsterdam, The Netherlands.

European Heart Journal
|March 2, 2013
PubMed

Insights

The JUPITER criteria identified 10.1% of European adults for statin therapy, showing a higher risk of coronary heart disease (CHD) events. These individuals had significantly higher CHD event rates than those not meeting the criteria.

Area of Science:

  • Cardiovascular disease prevention
  • Epidemiology
  • Clinical trial analysis

Background:

  • The Justification for the Use of statins in Prevention: an Intervention Trial Evaluating Rosuvastatin (JUPITER) criteria have been debated.
  • Understanding the prevalence and risk profile of individuals meeting JUPITER criteria in contemporary European populations is crucial for guiding statin therapy decisions.

Purpose of the Study:

  • To quantify the proportion of individuals meeting the JUPITER criteria in a European cohort.
  • To determine the risk profile and coronary heart disease (CHD) event risk for JUPITER-eligible individuals during long-term follow-up.
  • To compare the JUPITER criteria with existing risk assessment tools like SCORE and ATP III.

Main Methods:

  • Analysis of the EPIC-Norfolk population cohort, including 25,639 participants aged 45-79 years.
  • Long-term follow-up of 11.4 ± 2.8 years for coronary heart disease (CHD) events.
  • Quantification of individuals meeting JUPITER criteria (low-density lipoprotein cholesterol-C <3.4 mmol/L and C-reactive protein ≥ 2 mg/L) and comparison with SCORE and ATP III criteria.

Main Results:

  • Out of 8397 eligible individuals, 10.1% (846) met the JUPITER criteria.
  • JUPITER-eligible individuals had a significantly higher 10-person-year event rate (14.6%) compared to those with LDL-C <3.4 mmol/L and C-reactive protein <2 mg (7.0%).
  • The JUPITER criteria identified 18.1% and 18.9% of individuals not qualifying under SCORE or ATP III criteria, respectively.

Conclusions:

  • Individuals meeting JUPITER criteria in this European cohort exhibited significantly higher coronary heart disease event rates.
  • The JUPITER criteria identify a substantial proportion of the population for potential statin therapy who would not be candidates under SCORE or ATP III guidelines.
  • These findings highlight the potential impact of JUPITER criteria on statin eligibility in primary prevention within European populations.
Abstract

Keywords:
C-reactive proteinCoronary heart diseaseLow-density lipoprotein cholesterol

Related Concept Videos

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:
Prevalence and Incidence01:08

Prevalence and Incidence

In statistical epidemiology and health sciences, two essential metrics—prevalence and incidence—are fundamental for understanding disease dynamics within a population. These measures enable public health officials, epidemiologists, and researchers to assess the burden of diseases, allocate resources effectively, and design impactful public health policies and interventions.
Prevalence indicates the proportion of individuals in a population who have a specific disease or health condition at a...
Confounding in Epidemiological Studies01:27

Confounding in Epidemiological Studies

Confounding in statistical epidemiology represents a pivotal challenge, referring to the distortion in the perceived relationship between an exposure and an outcome due to the presence of a third variable, known as a confounder. This variable is associated with both the exposure and the outcome but is not a direct link in their causal chain. Its presence can lead to erroneous interpretations of the exposure's effect, either exaggerating or underestimating the true association. This phenomenon...
Longitudinal Studies01:26

Longitudinal Studies

Longitudinal studies are also widely used in other medical and social science fields. For instance, in cardiovascular research, they can monitor patients' health over decades to identify risk factors for heart disease, such as high cholesterol or smoking, and evaluate the long-term effectiveness of preventive measures. Similarly, in mental health studies, researchers might follow individuals from adolescence into adulthood to understand the development and progression of conditions like...
Bioavailability Study Design: Healthy Subjects Versus Patients01:15

Bioavailability Study Design: Healthy Subjects Versus Patients

Bioavailability studies are essential for evaluating a drug's therapeutic efficacy and understanding its absorption patterns under various physiological conditions. Conducting such studies on target patient populations provides more relevant data by simulating real-world disease states. However, practical challenges often necessitate the use of young, healthy adult volunteers as study subjects.Patients may exhibit altered drug absorption patterns due to the effects of the disease itself,...
Study Designs in Epidemiology01:20

Study Designs in Epidemiology

Epidemiological study designs are fundamental tools for investigating the distribution, determinants, and control of health conditions in populations. They help researchers understand the relationships between exposures and outcomes, and they broadly fall into two categories: "observational" and "experimental" studies.
Observational studies are those where the researcher does not intervene but rather observes natural variations. They include cross-sectional, cohort, and case-control studies.