Letter to the Editor Regarding “Value of the Platelet-to-Lymphocyte Ratio and Platelet-to-Lymphocyte Product in the Evaluation of Systemic Lupus Erythematosus Disease Activity” [Letter]

Dear editor

We read with great interest the article by Li et al¹ recently published in the Journal of Inflammation Research, titled “Value of the Platelet-to-Lymphocyte Ratio and Platelet-to-Lymphocyte Product in the Evaluation of Systemic Lupus Erythematosus Disease Activity.” In this retrospective analysis of 418 patients with SLE, the authors compared the platelet-to-lymphocyte ratio (PLR) and the platelet-to-lymphocyte product (PLP) in assessing SLE disease activity. They found that PLP was significantly negatively correlated with SLEDAI 2000 and SLE-DAS, and was independently associated with glucocorticoid dose grading, whereas PLR was only correlated with disease activity in patients with normal platelet counts. As an exploratory study systematically evaluating the novel composite index PLP in the SLE population, this work is of considerable clinical relevance. Nevertheless, while fully acknowledging the value of this study, we would like to highlight several methodological issues that merit further discussion.

First, the mathematical properties of PLP as a product index and its incremental value warrant consideration. PLP, as the product of platelet and lymphocyte counts, is highly dependent on the concurrent fluctuations of these two parameters. In the present study, platelet count and lymphocyte count correlated with SLEDAI 2000 at –0.180 and –0.280, respectively, while PLP correlated at –0.278. The correlation of PLP does not appear to substantially exceed that of its individual components. Whether PLP truly provides independent information beyond platelet and lymphocyte counts, or merely serves as an amplified signal of the two, remains to be determined. In recent years, other haematological composite indices derived from routine blood counts—such as the systemic immune-inflammation index (SII) and the systemic inflammation response index (SIRI)—have been shown to be potential biomarkers of SLE disease activity. A meta-analysis of 11 studies demonstrated that SII was significantly positively correlated with SLEDAI scores (pooled correlation coefficient 0.322, 95% CI 0.146–0.478, p < 0.001), and that both SII and SIRI were significantly elevated in SLE patients compared with healthy controls.² Another study confirmed that SII and SIRI were positively correlated with SLEDAI-2K (rSII = 0.24, rSIRI = 0.25, p < 0.05) and were independent risk factors for lupus nephritis.³ The relative performance of PLP compared with these composite indices requires further investigation. Moreover, PLP and PLR both utilise platelet and lymphocyte counts mathematically, and may reflect different transformations of the same biological process in the same patient, raising the possibility of informational redundancy. If PLR provides additional information beyond PLP, the two may be complementary; otherwise, the more stable and interpretable index should be preferred.

Second, the clinical applicability of the optimal cut-off value warrants attention. The authors identified an optimal PLP cut-off of 128.25 (× 10¹⁸/L²). However, this non-integer value poses challenges for memorisation and application by frontline clinicians. Clinically used cut-offs are often integers or half-integers, which are easier to remember and apply. If the clinical value of PLP depends on such an irregular threshold, its widespread adoption may face a “memory barrier.” Future studies should explore simplification using clinically feasible approximate integers (eg, 100, 130, 150) and evaluate whether such simplified thresholds retain adequate predictive performance. In addition, the present cohort was predominantly composed of hospitalised patients with a high proportion of moderate-to-severe disease activity (median SLEDAI 2000 score: 13). The relationship between PLP and disease activity may be non-linear—for example, it may only show correlation beyond a certain threshold of disease activity. Previous studies have used restricted cubic spline analysis to reveal non-linear relationships between SLEDAI scores and clinical outcomes, highlighting the value of exploring threshold effects in SLE research.^4,5 Future studies employing restricted cubic spline analysis are warranted to examine the non-linear relationship and potential threshold effects between PLP and disease activity.

Third, the absence of head-to-head comparisons with classical activity biomarkers is a notable limitation. In clinical practice, the assessment of SLE disease activity often relies on combined biomarkers, such as anti-dsDNA antibodies in conjunction with complement C3 and C4. Previous studies have confirmed that complement C3a levels are significantly positively correlated with SLEDAI scores (r = 0.6134, p < 0.05), suggesting that complement activation products reflect disease severity.⁶ The present study only compared PLP with individual cell counts (platelets and lymphocytes), and did not perform head-to-head comparisons with these classical combined markers. If the AUC of the anti-dsDNA plus complement model significantly exceeds that of PLP, the incremental clinical value of PLP would be limited; conversely, if PLP performs comparably to or better than the classical marker combination, its clinical significance would be more substantial. Future studies directly comparing PLP with classical combined markers are warranted.

Fourth, the use of glucocorticoid dose grading as a validation indicator raises the issue of circular reasoning. The authors used GC dose grading as an indirect reflection of disease activity and found that PLP correlated with GC dose grading. However, GC dose itself is partly determined by disease activity—more severe disease typically prompts higher GC doses. The correlation between PLP and GC dose may essentially be an extension of the PLP–disease activity correlation, rather than an independent validation. Longitudinal data, such as changes in PLP following GC tapering, or analyses examining whether PLP independently predicts GC requirements after adjusting for disease activity, would help confirm the independent clinical value of PLP.

Fifth, comparisons with other emerging biomarkers are lacking. In recent years, numerous novel biomarkers have emerged in the SLE field, including serum interferon scores, CXCL13, and BAFF. The present study only compared PLP with platelets and lymphocytes, without benchmarking it against these more advanced or more specific biomarkers. Such comparisons would help clarify the specific position of PLP within the modern SLE biomarker landscape.

Sixth, the association between PLP and SLE-related organ damage has not been explored. Although the correlation between PLP and disease activity has been demonstrated, whether PLP is associated with the progression of SLE-related organ damage (eg, the SLICC/ACR Damage Index) remains unknown. A serum proteomics study based on 274 patients with newly diagnosed SLE identified six protein markers—including IL-18R1, NOS3, PD-L1, and SIRT2—significantly associated with cumulative organ damage at 5 years.⁷ If PLP could predict long-term organ damage, its clinical value would far exceed that of a mere activity indicator. The present study did not address the relationship between PLP and long-term organ damage, limiting its translation from an “activity assessment tool” to a “prognostic prediction tool.”

In summary, Li et al have provided an exploratory study of considerable clinical significance, offering preliminary evidence supporting the potential value of PLP in assessing SLE disease activity. We look forward to future prospective, multicentre studies that validate the incremental value of PLP over existing combined markers, explore the non-linear relationship and threshold effects between PLP and disease activity, evaluate its association with long-term organ damage, and perform direct comparisons with other emerging biomarkers. Such efforts would help to more precisely define the clinical role of PLP in SLE disease activity assessment and provide a more robust evidence base for clinical decision-making.

Funding Statement

No funding was received for this communication.

Data Sharing Statement

No datasets were generated or analysed during the current study.

Author Contributions

Rijuan Jin: Conceptualization, Writing -review & editing, Supervision. All authors gave final approval of the version to be published; have agreed on the journal to which the article has been submitted; and agree to be accountable for all aspects of the work.

Disclosure

The authors report no conflicts of interest in this communication.