Comparative lipidomic profiling in adolescents with obesity and adolescents with type 1 diabetes
Bibliographic record
Abstract
OBJECTIVE: Both adolescents with obesity and those with type 1 diabetes (T1D) exhibit alterations in lipid profiles, but direct comparisons are limited. Comparing lipidomic profiles between obese individuals and those with T1D is crucial for identifying specific metabolic markers, informing tailored interventions, and advancing precision medicine strategies for these distinct populations. The aim of the study was to compare lipidomic profiles between adolescents with obesity and those with T1D, and to analyze associations between metabolites and clinical parameters. METHODS: We included 156 adolescents aged 11-18 years (59.6% girls) from the HEPAFIT (n=114, obesity) and Diactive-1 Cohort (n=42, T1D) studies. Clinical measures included anthropometrics, body composition, lipids, liver enzymes, glucose, and HbA1c. Lipidomic analysis of 277 serum/plasma metabolites used UHPLC-MS. RESULTS: Distinct lipid profiles were seen, with higher diglycerides, triglycerides, and certain phosphatidylinositols in the obesity group, while phosphatidylcholines, phosphatidylethanolamines, cholesterol esters, sphingomyelins, and ceramides were elevated in T1D. Triglycerides acyl chain lengths and saturation levels also varied. Multivariate analysis identified seven metabolites -PC(O-18:1/18:1), PC(O-18:1/22:4), PE(O-16:0/18:1), PE(18:2e/22:6), PC(40:1), PC(O-22:1/20:4), and PE(P-18:0/18:1)- significantly associated with clinical parameters. CONCLUSIONS: Distinct lipid profiles were observed among adolescents with obesity and T1D in the study, emphasizing the importance of understanding specific metabolite associations with clinical parameters for more precise health management.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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 | 0.001 | 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".