Assessment of serum bile acid profiles as biomarkers of liver injury and liver disease in humans
Bibliographic record
Abstract
To assess the potential of individual bile acids (IBA) and their profiles as mechanistic biomarkers of liver injury for humans in real world situations, we interrogated samples collected under minimum controlled conditions (ie subjects were not fasted). Total bile acids (TBA) have been considered to be biomarkers of liver injury for decades, and more recently, monitoring of IBA has been proposed for differentiation of variety of etiologies of liver injury. We established a LC-MS/MS methodology to analyze nine IBA, generated reference ranges, and examined effects of age, gender, and ethnicity for each IBA. Furthermore, we evaluated the ability of IBA and their profiles to detect hepatic injury in subjects with a broad range of liver impairments. To date, our study utilized the largest total cohort of samples (N = 645) that were divided into 2 groups, healthy or liver impaired, to evaluate IBA as biomarkers. The TBA serum levels in the Asian ethnic group trended higher when compared to other ethnic groups, and the serum concentrations of IBA, such as glycocholic acid (GCA), glycochenodeoxycholic acid (GCDCA), chenodeoxycholic acid (CDCA), and taurochenoxycholic acid (TCDCA) were significantly increased. To our knowledge, this report is the first to describe ethnic differences in serum concentrations of IBAs. In patients with hepatic impairments, with the exception of deoxycholic acid (DCA), the concentrations of IBAs were significantly elevated when compared with healthy subjects. The conjugated bile acids displayed greater differences between healthy subjects and subjects with hepatic impairments than non-conjugated bile acids. Furthermore, the subjects with hepatic impairments exhibited distinct profiles (signatures) of IBAs that clustered subjects according the nature of their liver impairments. Although additional studies are needed, our data suggested that the analysis of IBA has the potential to become useful for differentiation of various forms of liver injury.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".