Can a urine test help diagnose chronic inflammatory diseases?
Urine chemistry differed in reproducible ways across six inflammatory diseases and could distinguish inflammatory bowel disease patients from healthy people quite accurately.
Source
Urine metabolome profiling of immune-mediated inflammatory diseases
Study at a glance
- Design
- Case-control — Patients with six IMIDs vs healthy controls; discovery cohort then independent validation cohort; urine 1H-NMR metabolomics with regression and classifiers
- N
- Two cohorts rather than one N: discovery 1210 patients and 100 controls (1180 and 93 after quality control); validation 1200 patients and 200 controls (1152 and 196 after quality control)
- Population
- Spanish patients with rheumatoid arthritis, psoriasis, psoriatic arthritis, lupus, Crohn's disease or ulcerative colitis, plus healthy controls
- Outcome
- Urine metabolite differences vs controls, diagnostic accuracy (AUC), and association with high vs low disease activity
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What they did
The researchers used nuclear magnetic resonance to measure small molecules in urine from patients with six immune-mediated inflammatory diseases (such as rheumatoid arthritis, lupus and Crohn's disease) and from healthy controls. They first searched for differences in a discovery cohort of 1210 patients and 100 controls, then checked the strongest candidates in a separate validation cohort of 1200 patients and 200 controls. Within each disease they compared patients with very high versus very low disease activity, and they adjusted for age, sex, BMI, diet, smoking and sampling time.
What they found
Of 28 patient-control differences found in discovery, 26 replicated in validation. Citrate was lower in the urine of most of the diseases, acting as a shared 'hub' marker, and the diseases clustered into clinically sensible groups (skin, bowel, and RA with lupus). Classifiers built in discovery and tested in validation gave AUCs of about 0.69 to 0.73 for the joint and skin diseases and 0.81 to 0.87 for Crohn's disease and ulcerative colitis. Lower citrate, hippurate and 3-hydroxyisovalerate tracked higher disease activity in Crohn's disease.
The limits
What it doesn't show
Patients were deliberately chosen at the extremes of disease activity and compared with healthy controls, which tends to overstate how well markers would work in a real clinic where the hard task is telling these diseases apart from similar conditions. Only three differential-diagnosis markers replicated, all for Crohn's versus ulcerative colitis. The design is cross-sectional, so it does not show whether the metabolites change before diagnosis or cause disease; the authors call for studies in people with pre-diagnostic symptoms and longitudinal follow-up. NMR also detected only 33 metabolites and missed molecules such as glycine.
Key terms
- Metabolomics
- Measuring many small molecules (metabolites) in a biological sample at once to profile body chemistry.
- Immune-mediated inflammatory disease (IMID)
- A chronic condition driven by abnormal, persistent immune activation, such as rheumatoid arthritis or Crohn's disease.
- Discovery and validation cohorts
- Finding candidate markers in one sample, then testing them in an independent sample to guard against chance findings.
- AUC (area under the ROC curve)
- A measure of how well a test separates cases from non-cases; 0.5 is chance and 1.0 is perfect.
- False discovery rate
- A correction that limits the expected proportion of false positives when many tests are run.
Flashcards
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Quiz yourself
Which metabolite was lower in the urine of most of the inflammatory diseases?
Common questions
Why use urine rather than blood or tissue?
Urine is easy and non-invasive to collect, and its chemistry reflects blood composition, making it attractive for routine testing.
Is an AUC of 0.7 good enough to diagnose someone?
It shows real signal but substantial overlap; on its own it would misclassify many people, so it is better seen as a possible aid than a stand-alone test.
Why does replication in a second cohort matter?
Testing many metabolites invites chance findings; confirming them in an independent group makes it much likelier they are real.
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