How reliable is the statistical evidence for limiting saturated fat intake? A fresh look at the influential Hooper meta-analysis

Intern Med J. 2019 Nov;49(11):1418-1424. doi: 10.1111/imj.14325.


Background: Evidence from meta-analyses has been influential in deciding whether or not limiting saturated fat intake reduces the incidence of cardiovascular disease. Recently, random effects analyses have been criticised for exaggerating the influence of publication bias and an alternative proposed which obviates this issue: 'inverse-variance heterogeneity'.

Aims: We re-analysed the influential Hooper meta-analysis that supports limiting saturated fat intake to decide whether or not the results of the study were sensitive to the method used.

Methods: Inverse-variance heterogeneity analysis of this summary study was carried out, and the results contrasted with standard methods. Publication bias was also considered.

Results: Inverse variance heterogeneity analysis of the Hooper combined cardiovascular disease end point results returned a pooled relative risk of 0.93 (95% confidence interval: 0.74-1.16). This finding contrasts with the traditional random effects analysis with the corresponding statistic of 0.83 (95% confidence interval: 0.72-0.96). Egger tests, funnel and Doi plots along with recently published suppressed trial results suggest that publication bias is present.

Conclusions: This study questions the use of the Hooper study as evidence to support limiting saturated fat intake. Our re-analysis, together with concordant results from other meta-analyses of trials indicate that routine advice to reduce saturated fat intake in people with (or at risk for) cardiovascular disease be reconsidered.

Keywords: cardiovascular disease; epidemiological method; meta-analysis; saturated fatty acid.

MeSH terms

  • Cardiovascular Diseases / epidemiology*
  • Cardiovascular Diseases / etiology
  • Dietary Fats / adverse effects*
  • Fatty Acids / adverse effects*
  • Humans
  • Linear Models
  • Meta-Analysis as Topic
  • Publication Bias*
  • Risk
  • Statistics as Topic*


  • Dietary Fats
  • Fatty Acids