One data-derived diet pattern tracked with lower visceral-adiposity odds in 850 adults. The two more familiar ‘healthy’ and ‘mixed’ patterns tracked with nothing measured

A Tehran study asked whether the statistical method used to find a dietary pattern changes what appears to matter for metabolic health. Researchers analysed food-frequency questionnaires from 850 adults aged 20–59 and compared patterns built by principal component analysis with one built by reduced-rank regression.

The reduced-rank pattern — marked by fruits, organ meat, eggs, sweets, nuts and refined grains — was associated with lower odds of a high visceral adiposity index. People in its top third had an adjusted odds ratio of 0.53 compared with the bottom third. The confidence interval was 0.28 to 0.97 and the trend p-value was 0.041. The ‘healthy’ and ‘mixed’ patterns identified by principal components were not clearly associated with the insulin-resistance or adiposity indices.

The awkward food mixture is a warning not to turn a model into a diet prescription. Reduced-rank regression is guided by selected intermediate variables, so it is more likely to find a pattern connected with them. All food and metabolic measurements were taken in the same cross-sectional window, which cannot establish direction or causation. The study shows how analytic choices can reveal different signals; it does not show that sweets or organ meat reduce visceral fat.