The Effect of Biologic Therapies on Serum Metabolic Biomarkers in Patients with Psoriatic Arthritis
Bibliographic record
Abstract
Objectives Since biologic therapies such as TNF inhibitors (TNFi) and IL-17 inhibitors (IL-17i) may affect the cardio-metabolic profile of patients with psoriatic arthritis (PsA),[1-3] we assessed their short-term effects on serum metabolites in patients with PsA, and determined whether these metabolite changes differed across the 2 drug classes. Methods A nested cohort study was conducted among participants with available serum samples from a longitudinal PsA cohort who initiated TNFi or IL-17i therapy. Serum samples prior to initiation of therapy, and 3 to 6 months after initiation of therapy, were used to quantify 64 metabolic biomarkers using a Nuclear Magnetic Resonance targeted metabolomics panel, which comprised lipid particles, amino acids and various other metabolites. T tests were used to compare differences in metabolite levels before versus after therapy within each drug class. Linear mixed effects models assessed the effect of each drug class on changes in metabolite levels adjusting for age, sex, lipid lowering drugs, diabetes, hypertension and menopause. Results 163 patients were analyzed between 2013 and 2021 (mean age 51 ± 12.6 years, 45.5% female). Among TNFi users, levels of alanine, glycine, citrate and creatinine significantly increased post-treatment, whereas levels of glycoprotein acetyls (GlycA), a marker of systemic inflammation, decreased. Among IL-17i users, concentrations of citrates significantly increased post-treatment, whereas alanine, glycine, histidine, GlycA and creatinine decreased. When comparing biomarkers between classes of medications, post- and pre-treatment levels differed significantly for alanine, glycine, histidine, citrate, GlycA and creatinine. In models adjusted for age and sex involving TNFi users, levels of alanine (Estimate [EST] 0.034; 95% CI 0.008, 0.06), glycine (EST 0.029; 95% CI 0.02, 0.04), phenylalanine (EST 0.006; 95% CI 0.001, 0.01), citrate (EST 0.008; 95% CI 0.005, 0.01) and creatinine (EST 3.87; 95% CI 0.41, 7.4) increased post-treatment, whereas acetate (EST −0.03; 95% CI −0.03,−0.02) and GlycA (EST −0.05; 95% CI −0.09,−0.01) decreased (Table 1). In models adjusted for age and sex involving IL-17i users, changes were observed among fewer biomarkers, with low-density lipoprotein (LDL) particle size (EST 0.04; 95% CI 0.009, 0.07) increasing post-treatment, and levels of histidine (EST−0.005; 95% CI −0.008,−0.003) and acetone (EST −0.005; 95% CI −0.009,−0.001) decreasing. Conclusion TNFi and IL-17i appear to differentially affect the metabolic profile of patients with PsA. Treatment with TNFi was associated with more changes in metabolite profiles than IL-17i, including changes associated with systemic inflammation (GlycA) and amino acids. The implication of these changes on long-term cardio-metabolic risk needs further research. [1.] Roubille C. Ann Rheum Dis 2015;74(3):480-9. [2.] Sattar N. Arthritis Rheum 2007;56(3):831-9. [3.] Merola JF. Rheumatol Ther 2022;9(3):935-55.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.001 |
| Open science | 0.000 | 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".