THE study consists of a meta-analysis of three surveys involving over 400,000 individuals.
The scientists found that even though drinking milk leads to higher body mass index (BMI) and body fat, it still lowers the risk of coronary heart disease.
Dairy milk is a complex substance. For example, it contains 18 out of 20Trusted Source essential proteins and amino acidsTrusted Source, but it also contains saturated fats.
Perhaps this is why attempts to definitively identify its role in cardiometabolic diseases and its effect on cholesterol levels have produced conflicting results.
A newly published study from the University of Reading in the United Kingdom attempts to resolve such contradictions. The study is based on a meta-analysis of three existing large population studies.
The authors conclude that people who consume dairy milk have lower levels of both types of cholesterol and a lower risk of coronary heart disease than people who do not drink milk.
Despite this, people who do drink milk have higher BMI and more body fat. These are typically considered risk factors for cardiovascular issues.
Lead study author Vimal Karani, a professor of nutrigenetics and nutrigenomics at the University of Reading, summarizes the study’s findings:
“We found that among participants with a genetic variation that we associated with higher milk intake, they had higher BMI [and] body fat, but importantly had lower levels of good and bad cholesterol. We also found that those with the genetic variation had a significantly lower risk of coronary heart disease.
All of this suggests that reducing the intake of milk might not be necessary for preventing cardiovascular diseases.”
The study was a collaboration involving researchers from the University of Reading, the University of South Australia in Adelaide, the Southern Australian Health and Medical Research Institute, also in Adelaide, University College London in the U.K., and the University of Auckland in New Zealand.
The study authors note that the contradictory results of earlier studies may have to do with unknown confounding factors, or confounders that studies have not measured well enough.
