Umami: The Taste That Drives Purine Intake
Bibliographic record
Abstract
Rheumatologists commonly recommend low purine diets to gouty subjects based on studies linking the purine contents of food to increased risk for gout1. However, most rheumatologists, as well as physicians in general, do not know that one of the basic 5 human tastes is for purine-rich foods, a taste known as umami (oo-mah-mee). We present evidence of this revelation and why this may have occurred. Historically, an elevation in serum urate was considered benign except for carrying an increased risk for gout. Recently, soluble urate has been found to have myriad biologic effects, including raising blood pressure, increasing hepatic fat stores, and inducing insulin resistance2,3. Indeed, the uricase mutation that occurred in the mid-Miocene likely provided a survival advantage to our ancestors during a period of famine that occurred during that time in Europe4. If uric acid has an important role in human survival, then it is logical that there should be a taste receptor that would encourage the ingestion of foods that raise serum urate. In addition to the classic tastes of sour, bitter, sweet, and salt, there is a fifth basic taste, umami. Umami is the “savory” flavor and was first identified in traditional Japanese foods such as kombu (sea kelp), where it was found to result from the presence of free glutamate. The umami receptor in the taste buds is complex, and consists of the metabotropic glutamate receptor-4, the G-protein-coupled heterodimer of taste receptor type 1 member 1 (TAS1R1), and taste receptor type 1 receptor3 … Address correspondence to Dr. R.J. Johnson, Division of Kidney Diseases and Hypertension, University of Colorado Denver, Aurora, Colorado 80045, USA. E-mail: richard.johnson{at}ucdenver.edu
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".