Multimodal Noninvasive Assessment of C-Reactive Protein for Systemic Inflammation in Adults: Cross-Sectional Study
Bibliographic record
Abstract
Background: Accurate and accessible measurements of inflammatory biomarkers are crucial for the diagnosis and monitoring of inflammatory diseases. The gold-standard C-reactive protein (CRP) requires venipuncture, which, despite providing high-quality samples, can cause discomfort, anxiety, and pain, particularly in vulnerable populations such as older patients. It is also resource-intensive, is unsuitable for remote or at-home use, and lacks continuous monitoring capability. These limitations limit patient autonomy and self-management, potentially leading to poorer prognosis due to delays in assessment and medical treatments. As digital health technologies advance, there is increasing interest in leveraging digital biomarkers for remote and real-time monitoring of systemic inflammation. Digital biomarkers derived from noninvasive biofluids could provide a scalable solution for tracking inflammatory status, offering a patient-centered alternative to traditional blood-based assessments. To date, however, there is no consensus on the most suitable modality for assessment or its digitization potential. Therefore, a comprehensive evaluation of the feasibility, reliability, and patient acceptability toward noninvasive, digital inflammatory biomarkers is needed. Objective: Our aim is to evaluate the feasibility of various noninvasive methods to assess inflammatory markers and identify the optimal modality for predicting serum CRP levels. Methods: Inflammatory biomarkers were assessed in 20 participants (10 patients with systemic inflammation defined as a CRP level >5 mg/L and 10 controls) using 6 noninvasive samples (urine, sweat, saliva, exhaled breath, core body temperature, and stool samples) alongside serum samples. Patient preferences were retrieved via a questionnaire. Mann-Whitney U test, Spearman correlation, and all-subset regression were conducted to assess the relationships between serum and nonserum biomarkers and identify optimal predictive models for serum CRP levels. Results: CRP levels were significantly elevated in the inflammation group compared to controls in urine (median 4.5, IQR 4.15-10.3 vs median 0.69, IQR 0.24-1.39 μg/mmol; P=.001) and saliva (median 4910, IQR 2735-13,275 vs median 473, IQR 309-700 pg/mL; P=.001). Urine and saliva CRP levels strongly correlated with serum CRP (rsp=0.886; P<.001; rsp=0.709; P<.001). The multimodal model using urine and saliva CRP predicted serum CRP levels with 76.1% outperforming single-modality models. Patients favored urine and saliva tests over blood tests. Conclusions: Urine and saliva represent promising noninvasive alternatives to traditional blood tests for assessing CRP, enabling more accessible and less invasive diagnostic and monitoring approaches.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.002 | 0.001 |
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".