Predictive value of 1-hour postprandial SPARC levels on metabolic outcomes from Mediterranean diet adherence: results from a randomized controlled feeding study
Bibliographic record
Abstract
Abstract Precision nutrition is pivotal to preventing cardiometabolic diseases. However, almost no single blood biomarker capable of predicting the metabolic benefits of specific dietary patterns has yet been identified. Here, we revealed the associations of plasma levels of the secreted protein acidic and rich in cysteine (SPARC), an inflammatory factor highly expressed in fat tissues, and insulin sensitivity improvement in a 6-month randomized controlled, calorie-restricted feeding trial recruiting 235 Chinese adults with overweight/obesity and prediabetes: the Mediterranean diet (MD) group (n = 81), the traditional Jiangnan diet (TJD) group (n = 81), and the control diet (CD) group (n = 73). The 1-h post-glucose loading plasma SPARC levels (SPARC-1H) decreased significantly from baseline to 3 months and 6 months in the MD group, whereas no significant changes were observed in the TJD or CD groups. Further analyses revealed that the individuals with higher baseline SPARC-1H levels were associated with fewer improvements in fasting insulin (β ± SE: 1.54 ± 0.43; P = 0.002), fasting glucose (0.10 ± 0.04; P = 0.049), the homeostasis model assessment of insulin resistance (HOMA-IR, 0.47 ± 0.12; P = 0.0005), and the homeostasis model assessment of β-cell function (HOMA-β, 7.63 ± 3.27; P = 0.047) after 6 months in the MD group. Moreover, baseline SPARC-1H levels were positively correlated with changes in lipidomic profiles, including three alkenylphosphatidylethanolamines, which potentially mediate the cardiometabolic benefits of MD. No significant associations were observed in the other two diet groups. Our findings suggest postprandial SPARC as a predictor for the metabolic benefits of MD, offering a potential biomarker for individualized nutrition intervention against cardiometabolic diseases.
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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 | 0.005 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".