Identification of Inflammatory Biomarkers for Predicting Peripheral Arterial Disease Prognosis in Patients with Diabetes
Bibliographic record
Abstract
Background: Peripheral arterial disease (PAD) is known to be strongly linked to major adverse limb events, ultimately leading to an increased risk of limb-threatening conditions. We developed a predictive model using five identified biomarkers to predict major adverse limb events, limb loss, diabetic (DM) foot ulcers, and vascular intervention in patients with underlying PAD and DM over 2 years. Methods: A single-center prospective case control study with was conducted with 2 years’ follow up. In the discovery phase the cohort was randomly split into a 70:30 ratio, and proteins with a higher mean level of expression in the DM PAD group compared to the DM non-PAD group were identified. Next, a random forest model was trained using (1) clinical characteristics, (2) a five-protein panel, and (3) clinical characteristics combined with the five-protein panel. Demographic data were analyzed by independent t-test and chi-square test. The importance of predictive features was calculated using the variable importance (gain) score. The model was used and assessed for its ability to diagnose PAD, predict limb loss, predict major adverse limb events (MALEs), predict diabetic foot ulcers, and predict the need for vascular surgery. The model was evaluated using area under the receiver operating characteristic curve and net reclassification index. Results: The cohort of 392 patients was matched for age, sex, and comorbidities. Five proteins were identified (TNFa: tumor necrosis factor alpha, BMP-10: bone morphogenic protein 10, CCL15/MIP1 delta: chemokine (c-c motif) ligand 15/macrophage inflammatory protein 1 delta, MMP-10: matrix metalloprotease 10, and HTRA2/Omi: HTRA2, also known as Omi) as having a significantly higher level of expression in the DM PAD group. HTRA/Omi had the highest contribution to the model’s ability to diagnose PAD in diabetic patients. Model performance was best when combined with clinical characteristics to predict limb loss (AUROC 0.86, 0.76, 0.80), foot ulcer (AUROC 0.87, 0.82, 0.67), MALE (AUROC 0.81, 0.78, 0.67), and the need for vascular surgery (AUROC 0.82, 0.81, 0.61). Conclusions: In this study, we describe a biomarker panel that can be used in combination with clinical characteristics to create an accurate prediction model for diagnosis and prognostication of PAD in the setting of DM.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.001 |
| 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".