Associations of Plasma Lipidomic Profiles with Uric Acid and Hyperuricemia Risk in Middle-Aged and Elderly Chinese
Bibliographic record
Abstract
Abstract Little is known about the links of disturbed lipid metabolism with hyperuricemia (HUA). We aimed to investigate the associations of lipidomic profiles with uric acid (UA)/HUA and their modifying factors in middle-aged and elderly Chinese. A total of 350 lipids were quantified in 2247 community-based Chinese aged 50–70 years by high-coverage targeted lipidomics. HUA was defined by plasma UA > 420 μmol/L in men or > 360 μmol/L in women. The prevalence of HUA in this population was 10.4%. After multivariable adjustment including BMI and lifestyle, 123 lipids were significantly associated with UA, predominantly glycerolipids (GLs) and glycerophospholipids (GPs). Specifically, diacylglycerol [DAG (16:0/22:5), DAG (16:0/22:6), DAG (18:1/20:5), DAG (18:1/22:6)], phosphatidylcholine [PC (16:0/20:5)), and triacylglycerol (TAG (53:0)] were the most significant lipid signatures positively associated with HUA risk, while lysophosphatidylcholine (LPC (20:2)) was inversely associated with HUA risk ( p < 0.05). Network analysis also showed a positive association between TAGs/PCs/DAGs contained module and HUA risk ( p < 0.01). Notably, HUA-related lipids were associated with de novo lipogenesis fatty acids, especially 16:1n-7 (Spearman correlation coefficients = 0.32–0.41, p < 0.001). Reduced rank regression showed that increased aquatic products intake was correlated to elevated HUA risk and HUA-associated lipids; while high dairy consumption was correlated with low level of HUA-associated lipids (|factor loadings| ≥ 0.2). Moreover, mediation analyses suggested that the lipid-HUA associations were partially mediated by retinol-binding protein 4 (RBP4, mediation proportion 5–14%), an adipokine linked with dyslipidemia and insulin resistance. In conclusion, disturbed specific metabolisms of GLs and GPs were associated with high prevalent HUA, partially mediated by RBP4 and/or influenced by certain dietary factors.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".