To the Editor: Chronic pruritus of unknown origin (CPUO) is characterized by persistent itch lasting at least 6 weeks without any dermatologic cause or significant skin eruptions, posing a major diagnostic and therapeutic challenge.1 CPUO significantly affects the quality of life, importantly previous studies have highlighted associations between chronic pruritic disorders and renal dysfunction,2,3 yet the biological mechanisms underpinning these links remain poorly defined. Molecular evidence linking immune dysregulation to early end-organ involvement in CPUO is limited. We conducted a cross-sectional study investigating circulating biomarkers of renal, hepatic, and cardiovascular injury and systemic inflammation in adults with CPUO compared to healthy controls (HCs).
Plasma was collected using ethylenediaminetetraacetic acid (EDTA) tubes and processed uniformly across cohorts. Plasma cytokine concentrations were measured using multiplex Luminex assays. Patients with known kidney, liver and cardiac comorbidities were excluded to minimize confounding. Linear and logistic regression models were applied, adjusting for age, sex, and race, and P values were corrected for multiple testing using the Benjamini-Hochberg method. We analyzed plasma samples from patients with dermatologist-diagnosed CPUO (n = 10) and HCs (n = 9). CPUO cohort had a median age of 70.4 years versus 67.5 years in the HC cohort, with no significant difference, with similar sex and racial distributions (Table I). CPUO patients reported severe itch (mean Worst Itch Numeric Rating Scale 8.4) compared to no itch in HCs. CPUO patients exhibited significantly elevated levels of kidney injury associated biomarkers compared to HCs including α1-microglobulin, collagen IV, lipocalin-2, osteoactivin, tissue inhibitor of metalloprotease-1 (TIMP-1) and uromodulin (Fig 1, A P < .001). Heatmap visualization showed distinct clustering of CPUO patients from HCs, reflecting a reproducible biomarker signature (Fig 1, B). In multivariable logistic regression, each biomarker remained independently associated with CPUO, with adjusted odds ratios >1. (Fig 1, C).
Table I.
Patient demographics and multivariate linear regression analysis of kidney-associated cytokines in CPUO vs healthy controls
| HC (n = 9) | CPUO (n = 10) | P value | |
|---|---|---|---|
| Median age (IQR) | 67.5 (61-77) | 70.4 (62-77) | .3100 |
| Race | .5820 | ||
| Caucasian | 7 (77.8%) | 9 (90.0%) | |
| Other | 2 (22.2%) | 1 (10.0%) | |
| Sex (%) | .6499 | ||
| Female | 4 (44.4%) | 3 (30.0%) | |
| Male | 5 (55.6%) | 7 (70.0%) | |
| Comorbidities n (%) | |||
| Type 2 diabetes | 1 (11%) | 0 (0%) | |
| Hypertension | 0 (0%) | 3 (30%) | |
| ACEi/ARB use | 0 (0%) | 2 (20%) | |
| WI-NRS (SD) | 0 (0) | 8.4 (0.966) | 1.7E-6 |
| Cytokine | Effect size∗ | 95% CI | P value (adjusted)∗ |
|---|---|---|---|
| α1-Microglobulin | 1.842 | [1.51, 2.17] | 7.08E-09 |
| Collagen IV | 1.769 | [1.35, 2.19] | 2.44E-07 |
| TIMP-1 | 1.698 | [1.21, 2.19] | 2.45E-06 |
| Osteoactivin/GPNMB/DC-HIL | 1.539 | [0.93, 2.15] | 8.83E-05 |
| Lipocalin-2 | 1.498 | [0.86, 2.14] | 1.39E-04 |
| Uromodulin | 1.49 | [0.85, 2.13] | 1.39E-04 |
| HGF | 0.7021 | −0.2284 | .3868 |
| FGF.23 | −0.5324 | −1.492 | .3868 |
| FGF.21 | −0.2971 | −1.283 | .5333 |
| PF4 | −1.2863 | −2.0358 | .0134 |
| α 2 -Macroglobulin | −1.2535 | −2.0174 | .0134 |
| L-selectin | −1.0133 | −1.8654 | .0676 |
| Sap | −0.9554 | −1.8249 | .0746 |
| Fibrinogen | −0.8726 | −1.7646 | .0854 |
| AGP | −0.8648 | −1.7588 | .0854 |
| Fetuin A36 | −0.8375 | −1.7382 | .0854 |
| CRP | −0.5548 | −1.5111 | .2673 |
| Haptoglobin | 0.1803 | −0.8518 | .7161 |
AGP, Alpha-1-acid glycoprotein; CI, confidence interval; CPUO, chronic pruritus of unknown origin; CRP, C-reactive protein; DC-HIL, dendritic cell heparin integrin ligand; FGF21, Fibroblast Growth Factor 21; FGF23, Fibroblast Growth Factor 23; GPNMB, glycoprotein nonmetastatic melanoma protein; HC, healthy control; HGF, Hepatocyte Growth Factor; IGA, Investigator’s Global Assessment; IQR, interquartile range; PF4, platelet factor 4; SAP, serum amyloid P; SD, standard deviation; TIMP-1, Tissue Inhibitor of Metalloproteinase-1; WI-NRS, Worst Itch Numeric Rating Scale.
Effect sizes and 95% CIs are from linear regression models relating CPUO status to standardized (z-score) cytokine levels, controlling for age, sex, and race. Positive effect sizes indicate higher mean cytokine levels in CPUO compared to healthy controls. P values were adjusted for multiple comparisons using the Benjamini–Hochberg method.
Fig 1.
Circulating kidney biomarkers in CPUO compared to HC. A, Box-and-whisker plots show plasma concentrations of α1-microglobulin, collagen IV, lipocalin-2/NGAL, osteoactivin, TIMP-1, and uromodulin (ng/mL) in participants with CPUO and HC (HC = 9; CPUO = 10). Each dot represents an individual participant. Horizontal lines denote medians, boxes indicate IQR, and whiskers show the full range. Statistical comparisons were performed using Mann–Whitney U-test; significance levels are indicated (∗∗∗∗P < .0001). B, Heatmap of standardized (z-score) circulating kidney biomarker levels showing hierarchical clustering of participants (rows) and biomarkers (columns). Color intensity reflects relative expression (blue = low, red = high). The condition annotation bar distinguishes CPUO from HC samples, illustrating clustering patterns based on overall biomarker profiles. C, Forest plot showing adjusted ORs with 95% CIs for kidney injury biomarkers associated with CPUO, derived from logistic regression models for a 1-unit increase in log-transformed biomarker levels. An odds ratio >1 indicates higher odds of CPUO. CI, Confidence interval; CPUO, chronic pruritus of unknown origin; IQR, interquartile range; ORs, odds ratios.
α1-Microglobulin and lipocalin-2 indicate tubular injury, while collagen IV and TIMP-1 are associated with extracellular matrix accumulation and fibrosis. Osteoactivin contributes to inflammatory and fibrotic responses.4,5 Uromodulin was increased in CPUO, in contrast to its decline in chronic kidney disease (CKD), suggesting early renal stress rather than nephron loss. While being often described as kidney-specific, these markers can also reflect broader systemic stress. Collagen IV and TIMP-1 are linked to hepatic fibrosis, Osteoactivin is expressed in skin and upregulated in inflammation, and lipocalin-2 acts as an acute-phase reactant. Together, they capture tubular damage, structural changes, inflammation, and fibrosis, providing a complete view of renal dysfunction.
No significant differences were observed in the liver/metabolic injury biomarkers. Cardiac biomarker α-2 macroglobulin and platelet factor 4 were significantly reduced in CPUO compared to HCs. (Table I). These unexpected reductions warrant caution interpretation.
Collectively, our findings showed elevated kidney injury markers in CPUO suggesting systemic inflammation and subclinical renal stress. Given that pruritus is common in chronic kidney disease, these findings suggest that CPUO and renal dysfunction may share biological pathways. Renal stress markers may enable early detection before irreversible injury occurs.
Limitations include the small sample size and cross-sectional design. CPUO group had higher hypertension and angiotensin-converting enzyme inhibitor (ACEi)/angiotensin II receptor blocker (ARB) use, but the study was not powered to adjust for these differences; residual confounding cannot be excluded. Longitudinal studies are warranted to establish CPUO and renal dysfunction.
Conflicts of interest
Dr Kwatra is an advisory board member/consultant for Abbvie, Amgen, Arcutis Biotherapeutics, Aslan Pharmaceuticals, Cara Therapeutics, Castle Biosciences, Celldex Therapeutics, Eli Lilly, Galderma, Genzada Pharmaceuticals, Incyte Corporation, Johnson & Johnson, Leo Pharma, Novartis Pharmaceuticals Corporation, Pfizer, Regeneron Pharmaceuticals, and Sanofi and has served as an investigator for Galderma, Regeneron, Incyte, Pfizer, and Sanofi. All other authors report no conflicts of interest.
Acknowledgments
The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.
Footnotes
Funding sources: S.G.K. is supported by the National Institute of Arthritis and Musculoskeletal and Skin Diseases of the National Institutes of Health under Award Number K23AR077073. Part of this work was also funded by grants to S.G.K. from the Dermatology Foundation.
Patient consent: Not applicable; this study involved retrospective analysis of deidentified blood samples collected under IRB-approved protocols.
IRB approval status: Approved.
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