ArticleMedicine2026
Dietary intake and hyperuricemia among US adults: A matched case-control analysis of NHANES 2001-2020.
Article in Medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Hyperuricemia, a chronic metabolic condition characterized by elevated serum uric acid levels, is a major risk factor for gout and has also been associated with cardiovascular disease, hypertension, chronic kidney disease, and metabolic syndrome. Although diet is widely recognized as an important modifiable factor in hyperuricemia, the associations between specific food groups and hyperuricemia remain incompletely understood. In this frequency-matched case-control analysis using data from the National Health and Nutrition Examination Survey 2001-2020, we included 886 individuals with hyperuricemia and 2171 matched controls. Dietary intake was assessed using 24-hour dietary recall data. Unconditional multivariable logistic regression models were applied to examine the associations between food group intake and hyperuricemia, and odds ratios (ORs) with 95% confidence intervals (CIs) were calculated by comparing the highest with the lowest intake quantiles. False discovery rate-adjusted P for trend values were additionally calculated to account for multiple comparisons. Higher intake of legumes, nuts, and seeds (OR, 0.74; 95% CI, 0.57-0.96), grain products (OR, 0.78; 95% CI, 0.62-0.96), whole-wheat bread, oats, and brown rice (OR, 0.76; 95% CI, 0.62-0.93), eggs (OR, 0.85; 95% CI, 0.77-0.94), and milk products (OR, 0.64; 95% CI, 0.51-0.79) were associated with lower odds of hyperuricemia. In contrast, higher intake of meat, poultry, and fish (OR, 1.22; 95% CI, 1.12-1.33), fish and seafood (OR, 1.09; 95% CI, 1.01-1.23), sugars, sweets, and beverages (OR, 1.60; 95% CI, 1.28-2.00), soft drinks (OR, 1.11; 95% CI, 1.03-1.21), sugar-sweetened soft drinks (OR, 1.13; 95% CI, 1.03-1.23), and sugar-sweetened tea (OR, 1.86; 95% CI, 1.11-3.12) were associated with higher odds of hyperuricemia. The overall directions of the observed associations were largely unchanged after false discovery rate adjustment, although fewer trend tests remained statistically significant. Several dietary factors were associated with hyperuricemia in this National Health and Nutrition Examination Survey-based frequency-matched case-control analysis. These findings may help inform dietary strategies for the prevention and management of hyperuricemia. However, the observed associations should be interpreted cautiously, particularly in light of multiple-comparison adjustment, and further studies are warranted to confirm the findings and clarify their clinical implications.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.