Elucidating the genetic risk of obesity through the human blood plasma proteome
Bibliographic record
Abstract
ABSTRACT Obesity is affecting an increasing number of individuals worldwide, but the complex interplay between genetic, environmental and lifestyle factors that control body weight is still poorly understood. Blood circulating protein are confounded readouts of the biological processes that occur in different tissues and organs of the human body. Many proteins have been linked to complex disorders and are also under substantial genetic control. Here, we investigate the associations between over 1,000 blood circulating proteins and body mass index (BMI) in three studies, including over 4,600 participants. We show that BMI is associated with widespread changes in the plasma proteome. We report 152 protein associations with BMI that replicate in at least one other study. 24 proteins also associate with a genome‐wide polygenic score (GPS) for BMI. These proteins are involved in lipid metabolism and inflammatory pathways impacting clinically relevant pathways of adiposity. Mendelian randomization suggests a bi‐directional causal relationship of BMI with three proteins (LEPR, IGFBP1, and WFIKKN2), a protein‐to‐BMI relationship for three proteins (AGER, DPT, and CTSA), and a BMI‐to‐protein relationship for 21 other proteins. Combined with animal model and tissue‐specific gene expression data, our findings suggest potential therapeutic targets and further elucidate the biological role of these proteins in pathologies associated with obesity.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".