Association between Metabolite Profiles, Metabolic Syndrome and Obesity Status
Bibliographic record
Abstract
Underlying mechanisms associated with the development of abnormal metabolic phenotypes among obese individuals are not yet clear. Our aim is to investigate differences in plasma metabolomics profiles between normal weight (NW) and overweight/obese (Ov/Ob) individuals, with or without metabolic syndrome (MetS). Mass spectrometry-based metabolite profiling was used to compare metabolite levels between each group. Three main principal components factors explaining a maximum of variance were retained. Factor 1's (long chain glycerophospholipids) metabolite profile score was higher among Ov/Ob with MetS than among Ov/Ob and NW participants without MetS. This factor was positively correlated to plasma total cholesterol (total-C) and triglyceride levels in the three groups, to high density lipoprotein -cholesterol (HDL-C) among participants without MetS. Factor 2 (amino acids and short to long chain acylcarnitine) was positively correlated to HDL-C and negatively correlated with insulin levels among NW participants. Factor 3's (medium chain acylcarnitines) metabolite profile scores were higher among NW participants than among Ov/Ob with or without MetS. Factor 3 was negatively associated with glucose levels among the Ov/Ob with MetS. Factor 1 seems to be associated with a deteriorated metabolic profile that corresponds to obesity, whereas Factors 2 and 3 seem to be rather associated with a healthy metabolic profile.
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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".