Related Experiment Video

Updated: Aug 8, 2026

Making Record-efficiency SnS Solar Cells by Thermal Evaporation and Atomic Layer Deposition
14:01

Making Record-efficiency SnS Solar Cells by Thermal Evaporation and Atomic Layer Deposition

Published on: May 22, 2015

PROVE-IT proves that lower is better: a contrary view

Steven A Grover1

  • 1Centre for the Analysis of Cost-Effective Care and the Division of General Internal Medicine, The Montreal General Hospital, Montreal, Quebec. steven.grover@mcgill.ca

The Canadian Journal of Cardiology
|February 25, 2006
PubMed

Insights

Lowering low-density lipoprotein (LDL) cholesterol and C-reactive protein (CRP) together improves cardiovascular outcomes more than lowering LDL alone. Statins show varied effects on CRP, influencing overall cardiovascular benefit.

Area of Science:

  • Cardiovascular Medicine
  • Pharmacology

Background:

  • Low-density lipoprotein (LDL) cholesterol and C-reactive protein (CRP) are established independent risk factors for cardiovascular events.
  • Statin therapy, primarily for lipid modification, exhibits pleiotropic effects, including CRP reduction.

Discussion:

  • Conflicting results from recent clinical trials on intensive LDL lowering highlight the importance of pleiotropic effects.
  • Specific statins' differential impact on CRP concentrations may significantly influence observed therapeutic benefits.

Key Insights:

  • Lowering both LDL cholesterol and CRP offers superior cardiovascular protection compared to LDL reduction alone.
  • The pleiotropic effects of statins, particularly on CRP, are crucial determinants of their efficacy in preventing cardiovascular events.

Outlook:

  • Future cardiovascular event prevention strategies should consider targeting both LDL cholesterol and CRP.
  • Further research into statin-specific pleiotropic mechanisms could optimize cardiovascular risk management.

More Related Videos

Probing the Limits of Egg Recognition Using Egg Rejection Experiments Along Phenotypic Gradients07:34

Probing the Limits of Egg Recognition Using Egg Rejection Experiments Along Phenotypic Gradients

Published on: August 22, 2018

Methods for Presenting Real-world Objects Under Controlled Laboratory Conditions06:54

Methods for Presenting Real-world Objects Under Controlled Laboratory Conditions

Published on: June 21, 2019

Related Concept Videos

Regression Toward the Mean01:52

Regression Toward the Mean

Regression toward the mean (“RTM”) is a phenomenon in which extremely high or low values—for example, and individual’s blood pressure at a particular moment—appear closer to a group’s average upon remeasuring. Although this statistical peculiarity is the result of random error and chance, it has been problematic across various medical, scientific, financial and psychological applications. In particular, RTM, if not taken into account, can interfere when researchers try to extrapolate results...
Confirmation Biases01:31

Confirmation Biases

The confirmation bias is the tendency to focus on information that confirms our existing beliefs and ignore information that is inconsistent with our expectations. For example, if you think that your professor is not very nice, you notice all of the instances of rude behavior exhibited by the professor while ignoring the countless pleasant interactions he is involved in on a daily basis. Have you ever fallen prey to the confirmation bias, either as the source or target of such bias?
Null and Alternative Hypotheses01:16

Null and Alternative Hypotheses

The actual hypothesis testing begins by considering two hypotheses. They are termed  the null hypothesis and the alternative hypothesis. These hypotheses contain opposing viewpoints.
The null hypothesis, denoted by H0 is a statement of no difference between the variables—they are not related. This can often be considered the status quo. As  a result if you cannot accept the null, it requires some action.
The alternative hypothesis, denoted by H1 or Ha, is a claim about the population that is...
Testing a Claim about Population Proportion01:24

Testing a Claim about Population Proportion

A complete procedure for testing a claim about a population proportion is provided here.
There are two methods of testing a claim about a population proportion: (1) Using the sample proportion from the data where a binomial distribution is approximated to the normal distribution and (2) Using the binomial probabilities calculated from the data.
The first method uses normal distribution as an approximation to the binomial distribution. The requirements are as follows: sample size is large...
Testing a Claim about Standard Deviation01:19

Testing a Claim about Standard Deviation

A complete procedure to test a claim about population standard deviation or population variance is explained here.
The hypothesis testing for the claim of population standard deviation (or variance) requires the data and samples to be random and unbiased. The population distribution also must be normal. There is no specific requirement on the sample size as the estimation is based on the chi-square distribution.
As a first step, the hypothesis (null and alternative) concerning the claim about...
Bonferroni Test01:10

Bonferroni Test

The Bonferroni test is a statistical test named after Carlo Emilio Bonferroni, an Italian mathematician best known for Bonferroni inequalities. This statistical test is a type of multiple comparison test to determine which means are different than the rest. Bonferroni test can minimize the Type 1 error by reducing the significance level alpha, which otherwise increases with sample pairs.
The means of different samples are first paired in all possible combinations.
The null hypothesis of the...