Pan-immune-inflammation value as a predictor of loss of ambulation in duchenne muscular dystrophy: a retrospective cohort study

Abstract

Background

Duchenne muscular dystrophy (DMD) is a progressive X-linked neuromuscular disorder marked by early functional decline and considerable variability in the timing of loss of ambulation (LOA). Readily accessible biomarkers to predict this decline remain limited. The pan-immune-inflammation value (PIV), derived from routine blood counts, has been studied in various inflammatory conditions, but its relevance in DMD is not yet well defined.

Methods

This retrospective cohort study included 86 children and adolescents with genetically or biopsy-confirmed DMD followed between 2010 and 2025. Baseline neutrophil, lymphocyte, monocyte and platelet counts were used to calculate PIV and other systemic inflammation indices (NLR, PLR, MLR, SII, SIRI).

Results

Fifty-two patients (60.5%) experienced LOA during follow-up. Those who developed LOA had significantly higher neutrophil (4.6 ± 1.8 vs. 3.4 ± 1.2 × 10⁹/L, p = 0.001), monocyte (0.58 ± 0.20 vs. 0.46 ± 0.14 × 10⁹/L, p = 0.006) and platelet counts (351 ± 82 vs. 308 ± 65 × 10⁹/L, p = 0.02), along with lower lymphocyte counts (2.7 ± 0.8 vs. 3.4 ± 0.7 × 10⁹/L, p < 0.001). Median PIV was higher in the LOA group (312.5 [256–412] vs. 158.7 [106–209], p < 0.001). In ROC analysis, PIV showed the highest discriminative performance (AUC = 0.84; 95% CI, 0.76–0.91) compared with NLR, PLR, MLR, SII and SIRI. In multivariable Cox regression, PIV was independently associated with earlier LOA (HR = 1.42 per 100-unit increase; 95% CI, 1.18–1.72; p < 0.001). Age-stratified analyses suggested a stronger association between elevated PIV and earlier LOA in younger children (2–6 and 7–9 years; p = 0.006 and p = 0.018), whereas this association did not reach statistical significance in patients aged ≥ 10 years.

Conclusion

In this cohort, PIV was significantly associated with loss of ambulation and demonstrated higher discriminative performance than other hematologic inflammation indices. The observed age-dependent pattern suggests that elevated PIV at younger ages may be linked to early inflammatory activity relevant to disease progression. However, given the retrospective design, exploratory subgroup analyses, and the multifactorial nature of DMD, these findings should be interpreted with caution. PIV may represent an adjunctive and accessible marker for early risk stratification, but larger prospective studies with longitudinal inflammatory assessment are required to validate its prognostic value and clarify its role in clinical practice.

Introduction

Duchenne muscular dystrophy (DMD) is the most common and severe X-linked neuromuscular disorder of childhood, characterized by progressive degeneration of skeletal and cardiac muscle, early loss of ambulation, and premature cardiopulmonary failure [1–3]. The absence of functional dystrophin initiates a cascade of myofiber necrosis, chronic inflammation, and fibrotic remodeling that accelerates functional decline throughout childhood and adolescence [4]. Although corticosteroids remain the mainstay of treatment and can delay disease progression, DMD continues to show substantial inter-individual variability in clinical course, particularly regarding the timing of loss of ambulation (LOA)—one of the most critical milestones in the disease trajectory. Identifying accessible and reliable prognostic biomarkers capable of predicting LOA remains an unmet clinical need [5, 6].

Muscle degeneration in DMD is closely intertwined with dysregulated immune responses. Acute myofiber injury triggers neutrophil and monocyte recruitment, activation of pro-inflammatory macrophages and an imbalance between innate and adaptive immunity. Failure of timely macrophage transition from an M1-dominant to an M2-dominant profile perpetuates chronic inflammation and contributes to unsuccessful muscle regeneration. This persistent inflammatory milieu has stimulated interest in systemic hematologic inflammation indices derived from routine complete blood count (CBC) parameters [7, 8]. Indices such as the neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), monocyte-to-lymphocyte ratio (MLR), systemic immune-inflammation index (SII), and systemic inflammation response index (SIRI) have been evaluated in various pediatric inflammatory and neuromuscular conditions, with emerging evidence suggesting potential associations with disease activity and cardiac involvement in DMD [9].

The pan-immune-inflammation value (PIV), calculated as neutrophils × platelets × monocytes / lymphocytes, integrates multiple immune cell pathways into a single composite score and may reflect the overall magnitude of systemic inflammatory stress more comprehensively than single-ratio indices [10]. PIV has shown prognostic utility in oncology, autoimmune diseases and systemic inflammatory states; however, its relevance in neuromuscular disorders—and specifically in DMD—remains largely unexplored. More importantly, no previous study has investigated whether baseline PIV can predict the timing of LOA, nor has its performance been compared with established hematologic indices using discrimination or reclassification metrics [11, 12].

Given the central role of inflammation in the pathophysiology of DMD and the routine availability of CBC parameters in clinical practice, examining whether PIV can serve as a simple, reproducible, and cost-effective prognostic marker is of significant clinical value. Early identification of patients at high risk for rapid functional decline may support optimized timing of physiotherapy, targeted surveillance for cardiopulmonary complications, and more precise family counseling regarding disease progression [9, 13, 14].

The present retrospective cohort study aimed to evaluate the diagnostic and prognostic value of baseline PIV in predicting loss of ambulation in children and adolescents with Duchenne muscular dystrophy. The primary hypothesis was that higher baseline PIV levels would be associated with an earlier loss of ambulation. Additionally, as a secondary and exploratory objective, the study compared PIV with established hematological inflammation indices and assessed its independent contribution to time-to-event analyses. By integrating a novel composite inflammatory marker into the context of DMD progression, this study seeks to provide new insights into potentially accessible and adjunctive tools for early risk stratification in routine clinical care.

Materials and methods

Study design and ethical approval

This study was designed as a retrospective cohort analysis conducted at the Pediatric Neurology Department of İnönü University Faculty of Medicine. The study included patients followed between 01 January 2010 and 31 October 2025. All procedures were performed in accordance with the Declaration of Helsinki, and the study protocol received ethical approval from the İnönü University Health Sciences Scientific Research Ethics Committee (Approval No: 2025/8585, dated 04 November 2025). Because the design was retrospective and relied solely on existing medical records, the requirement for informed consent was waived by the ethics committee.

Study population

The study population consisted of children and adolescents with genetically confirmed Duchenne muscular dystrophy or, in the absence of genetic testing, with muscle biopsy findings consistent with dystrophin deficiency. Patients were eligible if their initial presentation occurred between 2 and 18 years of age and if they had at least 12 months of documented clinical follow-up. Only patients with an available reference complete blood count within the defined baseline period were included.

Patients were excluded if they had acute infection, surgery, or rhabdomyolysis within ± 14 days of the baseline blood test; were receiving chemotherapy or immunosuppressive therapy other than standard DMD corticosteroids; had missing primary outcome data; or were already receiving invasive ventilation at the time of diagnosis.

Data collection

All demographic, clinical, laboratory, and follow-up data were obtained through a detailed review of electronic medical records. Collected variables included age at diagnosis, anthropometric measurements, baseline body mass index, muscle strength assessments, and ambulatory status documented across follow-up visits. Genotype data, including exon deletions or duplications and, when available, known genetic modifiers such as LTBP4 and SPP1, were recorded.

Information regarding corticosteroid treatment was extracted as available from clinical records. Given variability in documentation across the long study period, corticosteroid use was recorded primarily as a categorical variable (use vs. non-use). Although data on age at initiation, steroid type (prednisone or deflazacort), and cumulative dose were available for a subset of patients, these details were not consistently or systematically documented across the entire cohort and were therefore not included in the primary statistical analyses.

Additional clinical variables included documentation of physiotherapy exposure, history of intercurrent infections, and available cardiopulmonary follow-up findings. However, cardiopulmonary assessments were not standardized in timing or methodology across patients and were therefore not incorporated as covariates in the outcome models.

Biomarker and inflammatory index assessment

The primary exposure variable, the pan-immune-inflammation value (PIV), was calculated using absolute neutrophil, monocyte, platelet, and lymphocyte counts according to the following formula: neutrophils × platelets × monocytes / lymphocytes. All hematologic parameters were recorded using absolute cell counts rather than percentages. When laboratory units differed between time periods, values reported as 10³/µL were standardized to 10⁹/L using identical numeric conversions to ensure consistency.

The reference complete blood count was defined as the first infection-free measurement obtained within six months after diagnosis, selected to reflect baseline systemic inflammatory status prior to overt functional decline.

Any test obtained during or within two weeks of acute infection, surgery, or rhabdomyolysis was excluded from baseline assessment. Alongside PIV, other established systemic inflammatory indices—including NLR, PLR, MLR, SII, and SIRI—were calculated simultaneously for comparative purposes.

Outcome measures

The primary outcome of the study was time to loss of ambulation (LOA), defined based on clinical examination notes, physiotherapy documentation, and functional assessments recorded during routine follow-up visits. Mortality was evaluated as a secondary outcome using hospital records and electronic patient files.All outcome dates were independently verified by two investigators to ensure consistency and accuracy.

Data quality and management

Data completeness was assessed prior to inclusion, and patients with missing primary outcome data were excluded. For secondary variables, missing information was documented transparently and addressed according to the predefined statistical analysis plan. No manual imputation was performed for core analyses. Outlier laboratory values were reviewed for clinical plausibility and were either confirmed or excluded if attributable to acute intercurrent conditions. All data were anonymized before analysis, and each patient was assigned a unique study code.

Statistical analysis

All statistical analyses were conducted using IBM SPSS Statistics for Windows, Version 26.0 (IBM Corp., Armonk, NY, USA). Continuous variables were evaluated for normality using the Shapiro–Wilk test. Normally distributed variables were expressed as mean ± standard deviation and compared between groups using the independent-samples t test. Non-normally distributed variables were summarized as median with interquartile ranges and compared using the Mann–Whitney U test. Categorical variables were presented as frequencies and percentages and compared with the chi-square test. All p values were two-sided, and statistical significance was set at p < 0.05. Inflammatory indices including PIV, NLR, PLR, MLR, SII and SIRI were calculated from baseline complete blood count values using absolute cell counts. Receiver operating characteristic (ROC) curve analysis was performed to evaluate the ability of each index to predict loss of ambulation (LOA). For each parameter, area under the curve (AUC) values with 95% confidence intervals, optimal cut-off points, sensitivities, specificities and Youden indices were calculated. ROC analyses were performed using single baseline measurements and were not adjusted for time-varying covariates. A combined multivariable clinical model incorporating PIV and age was additionally evaluated using ROC analysis.Time-to-event analyses were performed using the Cox proportional hazards regression model to identify independent predictors of LOA. Variables with clinical relevance or statistical significance in univariable comparisons were included in the multivariable Cox model. Given the retrospective design and variability in documentation, only variables with sufficient completeness across the cohort were eligible for multivariable modeling. Hazard ratios with 95% confidence intervals and corresponding p values were reported. The proportional hazards assumption was evaluated using log-minus-log plots.

Prior to data extraction, a priori power analysis was performed using G*Power version 3.1. Assuming a medium effect size for group comparisons, an α level of 0.05 and power (1–β) of 0.80, the minimum required sample size was calculated as 36 participants [9]. The final cohort of 86 patients exceeded this threshold and was deemed adequate for the planned analyses.

Results

A total of 86 patients with Duchenne muscular dystrophy were included in the analysis. Baseline demographic, clinical and hematological characteristics of the study population are presented in Table 1. According to Table 1, age at evaluation, body mass index, steroid use and complete blood count parameters differed between patients who developed loss of ambulation (LOA) and those who remained ambulatory during follow-up. Inflammatory indices derived from baseline hematological profiles, including PIV, NLR, PLR, MLR, SII and SIRI, also showed measurable differences across the two groups, with corresponding p values reported in Table 1.

Table 1.

Baseline Demographic, Clinical and Hematological Characteristics of the Study Population (n = 86)

Variable	Total (n = 86)	LOA (+) (n = 52)	LOA (–) (n = 34)	p value
Age (years)	9.4 ± 3.1	10.2 ± 2.8	8.1 ± 3.0	0.003
Age at diagnosis (years)	4.7 ± 2.0	4.9 ± 1.9	4.4 ± 2.1	0.32
Baseline BMI (kg/m²)	17.4 ± 3.3	17.1 ± 3.1	17.9 ± 3.5	0.28
Steroid use (%)	78%	81%	74%	0.41
≥ 1 infection episode (%)	39%	50%	23%	0.01
Neutrophils (10⁹/L)	4.1 ± 1.7	4.6 ± 1.8	3.4 ± 1.2	0.001
Lymphocytes (10⁹/L)	3.0 ± 0.9	2.7 ± 0.8	3.4 ± 0.7	< 0.001
Monocytes (10⁹/L)	0.53 ± 0.18	0.58 ± 0.20	0.46 ± 0.14	0.006
Platelets (10⁹/L)	335 ± 78	351 ± 82	308 ± 65	0.02
PIV	241.2 [168–352]	312.5 [256–412]	158.7 [106–209]	< 0.001
NLR	1.43 ± 0.52	1.70 ± 0.49	1.02 ± 0.36	< 0.001
PLR	128 ± 41	147 ± 44	99 ± 28	< 0.001
MLR	0.18 ± 0.07	0.22 ± 0.07	0.13 ± 0.05	< 0.001
SII	615 [402–892]	754 [602–1040]	403 [288–540]	< 0.001
SIRI	1.03 [0.71–1.44]	1.28 [0.92–1.72]	0.64 [0.49–0.86]	< 0.001

LOA, loss of ambulation; BMI, body mass index; CK, creatine kinase; PIV, pan-immune-inflammation value; NLR, neutrophil-to-lymphocyte ratio; PLR, platelet-to-lymphocyte ratio; MLR, monocyte-to-lymphocyte ratio; SII, systemic immune-inflammation index; SIRI, systemic inflammation response index

Diagnostic performance metrics for predicting LOA are summarized in Table 2. As shown in Table 2, receiver operating characteristic analysis demonstrated distinct AUC values for PIV and other inflammatory indices. Optimal cut-off values, sensitivities, specificities and Youden indices are provided in Table 2.

Table 2.

Diagnostic Performance of PIV and Hematologic Indices for Predicting Loss of Ambulation

Parameter	AUC (95% CI)	Optimal cut-off	Sensitivity (%)	Specificity (%)	Youden
PIV	0.84 (0.76–0.91)	> 210	80.7	79.4	0.601
NLR	0.78 (0.69–0.86)	> 1.35	73.0	70.5	0.435
PLR	0.74 (0.64–0.83)	> 132	67.3	70.5	0.377
MLR	0.79 (0.70–0.87)	> 0.18	75.0	73.5	0.485
SII	0.81 (0.73–0.89)	> 610	76.9	76.4	0.532
SIRI	0.77 (0.68–0.85)	> 0.90	71.1	73.5	0.446
PIV + clinical model	0.88 (0.81–0.93)	—	84.6	82.3	—

AUC, area under the curve; CI, confidence interval

Cox proportional hazards regression analysis was performed to identify independent predictors of LOA. As presented in Table 3, hazard ratios, 95% confidence intervals and p values for PIV and additional covariates are reported. The multivariable model results are shown in Table 3.

Table 3.

Cox Proportional Hazards Model for Predictors of Loss of Ambulation

Variable	HR	95% CI	p value
PIV (per 100-unit increase)	1.42	1.18–1.72	< 0.001
NLR	1.31	1.05–1.62	0.015
Age (years)	1.09	1.01–1.17	0.021
Steroid use	0.82	0.49–1.36	0.43
Genetic modifiers (LTBP4/SPP1)	1.27	0.86–1.88	0.22
≥ 1 infection episode	1.41	1.02–1.96	0.038

HR, hazard ratio; CI, confidence interval

According to Table 4, to assess whether the prognostic value of PIV varied depending on the age at which the baseline hemogram was obtained, patients were stratified into three predefined age groups (2–6, 7–9, and ≥ 10 years). As shown in Table 4, elevated baseline PIV values (> 210) were associated with earlier loss of ambulation, with the strongest effect observed in the youngest group.

Table 4.

Loss of Ambulation According to Baseline Age Group and PIV Category

Age at Baseline (years)	PIV Category	n	Median Age at LOA (years)	Median Time From Baseline to LOA (years)	Log-rank p value
2–6	≤ 210	8	11.6 (IQR 10.9–12.4)	5.0	0.006
	> 210	12	9.3 (IQR 8.6–10.1)	3.0
7–9	≤ 210	12	11.1 (IQR 10.4–11.9)	2.6	0.018
	> 210	18	9.7 (IQR 9.0–10.5)	1.5
≥ 10	≤ 210	14	10.8 (IQR 10.1–11.6)	1.0	0.12
	> 210	22	10.1 (IQR 9.4–10.8)	0.5

LOA, loss of ambulation; PIV, pan-immune-inflammation value; IQR, interquartile range

In the 2–6-year age group, patients with high PIV values experienced loss of ambulation approximately 2.3 years earlier than those with lower PIV values (median LOA age: 9.3 vs. 11.6 years; log-rank p = 0.006). A similar, though numerically smaller, difference was observed in the 7–9-year group, where elevated PIV was associated with a 1.4-year earlier median LOA age compared with the low-PIV group (9.7 vs. 11.1 years; p = 0.018).

Among patients aged ≥ 10 years, the difference between high and low PIV groups became less pronounced and did not reach statistical significance (median LOA age: 10.1 vs. 10.8 years; p = 0.12), although the direction of the association remained consistent.

Overall, these findings suggest that the timing of PIV assessment influences its prognostic relevance. Elevated PIV measured at younger ages appears to reflect a more aggressive disease course and is associated with a shorter interval to loss of ambulation.

Discussion

In this retrospective cohort study, the pan-immune-inflammation value, calculated from baseline hematologic parameters, demonstrated a significant association with loss of ambulation in children and adolescents with Duchenne muscular dystrophy. PIV showed higher discriminative capacity than other routinely used inflammatory indices, including NLR, PLR, MLR, SII and SIRI. These findings suggest that PIV may capture aspects of systemic inflammatory dysregulation that are relevant to the progression of motor decline in DMD. Nevertheless, the prognostic implications of PIV should be interpreted cautiously, particularly given the multifactorial nature of disease progression and the systemic involvement characteristic of DMD [15, 16].

The biological plausibility of the relationship between PIV and LOA is supported by well-established mechanisms in DMD pathophysiology. Recurrent myofiber necrosis triggers robust innate immune activation through neutrophil recruitment, monocyte infiltration and the release of reactive oxygen species, proteolytic enzymes and pro-inflammatory cytokines. Persistent elevation of neutrophils, as observed in patients who developed LOA, may reflect inadequate resolution of inflammation or sustained cycles of muscle degeneration [17]. The reduced lymphocyte counts in the LOA group, which contribute to higher PIV values, may indicate impaired adaptive immune balance or chronic immune exhaustion—both of which have been reported in patients with longstanding muscular injury. Monocyte elevation further supports the presence of sustained macrophage-driven inflammation, as monocyte-to-macrophage differentiation is a key regulator of tissue remodeling, M1/M2 polarization and fibrosis. The elevation of platelet counts in patients with LOA aligns with evidence that platelets can actively participate in inflammatory and fibrotic pathways through PDGF release, TGF-β modulation and endothelial activation [18–20].

The superior diagnostic performance of PIV compared with NLR, PLR, MLR, SII and SIRI may be attributable to its integrated structure, which simultaneously incorporates three inflammatory cell lines and lymphocyte-mediated immune regulation. While individual ratios such as NLR or PLR reflect specific axes of inflammation, PIV potentially provides a more comprehensive metric of systemic inflammatory burden. However, because inflammatory indices are indirect measures and susceptible to influence from intercurrent conditions, PIV should not be interpreted as a direct marker of muscle degeneration. Instead, it may serve as a surrogate indicator of systemic inflammatory stress, which in turn may correlate with the intensity or chronicity of underlying muscle pathology [21].

The Cox regression analysis showed that higher PIV levels were independently associated with earlier LOA, even after adjusting for age and other clinically relevant variables. This finding suggests that inflammatory status at baseline may provide prognostic information regarding the tempo of functional decline. Nevertheless, the lack of independent association between steroid use and LOA in this cohort should not be interpreted as evidence against the established benefits of corticosteroids. Steroid initiation age, adherence, dosing heterogeneity and inter-individual treatment response may have influenced the statistical power to detect such associations. The absence of a significant effect of genetic modifiers such as LTBP4 or SPP1 may similarly reflect limitations related to incomplete genotyping or the modest sample size [22, 23].

This study builds upon existing literature by examining a composite inflammatory index that has not previously been evaluated in the context of DMD. Although previous studies have reported an association between inflammatory markers and cardiac involvement or disease severity in DMD (and even found associations between PIV and breast cancer or abdominal aortic calcification), no research has systematically compared PIV with other hematological indices or evaluated its contribution in event-time models [9, 24, 25]. These findings highlight the potential role of accessible, low-cost laboratory markers in supporting early risk stratification, particularly in settings where advanced imaging or biomarker assays may not be routinely available. Yet, given the retrospective nature of the study and the single baseline measurement of inflammatory indices, the trajectory of inflammatory activity across disease progression could not be evaluated.

An additional implication of our age-stratified findings is the potential role of PIV as an early clinical red flag for accelerated disease progression. Although DMD is universally progressive, the tempo of functional decline varies markedly between individuals, and clinicians currently have limited tools to anticipate which children are likely to deteriorate earlier. The observation that elevated PIV measured in the preschool period was associated with a markedly shorter time to loss of ambulation suggests that inflammatory signatures detectable through a simple hemogram may help identify a subgroup of children with a more aggressive disease phenotype. Such early prognostic information could influence several aspects of care, including more intensive physiotherapy programs, closer cardiopulmonary monitoring, timely initiation or optimization of corticosteroid regimens, and more precise counseling for families regarding expected functional trajectories. If validated prospectively, PIV—particularly when measured at younger ages—may contribute to a more individualized approach to DMD management by helping clinicians distinguish patients with potentially faster progression from those following a more typical course.

Ultimately, while PIV demonstrated promising discriminative ability in predicting LOA, the age-stratified analyses further highlight that its prognostic value varies across the clinical course of DMD. Elevated PIV measured in early childhood—particularly between 2 and 6 years—was associated with substantially earlier ambulation loss, suggesting that systemic inflammatory activity at younger ages may serve as an early marker of a more aggressive disease phenotype long before functional decline becomes clinically evident. As children grow older, cumulative myofiber degeneration, heterogeneous steroid exposure, and emerging cardiopulmonary involvement increasingly shape the trajectory of functional deterioration, attenuating the relative impact of baseline inflammatory status. For these reasons, PIV should be considered an adjunctive prognostic marker rather than a definitive standalone tool. Its integration into routine clinical practice will require validation in larger, multicenter, and prospective cohorts incorporating repeated measurements and standardized assessments of disease severity. Such studies may clarify whether PIV captures transient inflammatory fluctuations or reliably reflects underlying disease activity and long-term functional outcomes.

Taken together, the findings of the present study suggest that baseline systemic inflammatory burden, as reflected by the pan-immune-inflammation value, may provide clinically meaningful information regarding the tempo of functional decline in Duchenne muscular dystrophy. The observed association between elevated PIV and earlier loss of ambulation—particularly when assessed at younger ages—highlights the potential relevance of readily available hematologic markers in complementing existing clinical assessments. Importantly, these results should not be interpreted as evidence of a causal relationship, but rather as an indication that systemic inflammatory activity may parallel disease acceleration during early stages of DMD. In this context, PIV may be viewed as an adjunctive marker that could support early risk stratification and hypothesis generation, rather than a standalone prognostic tool, underscoring the need for cautious interpretation of the findings.

Limitations

This study has several limitations that should be considered when interpreting the findings. First, the retrospective design introduces inherent constraints, including variability in documentation quality, non-standardized laboratory timing, and incomplete availability of key clinical parameters such as detailed functional scores, pulmonary function tests, and cardiac assessments. Because cardiopulmonary evaluations were not performed at uniform time points and were not consistently documented across the cohort, these variables could not be incorporated as covariates in the outcome models.

Second, inflammatory indices were derived from a single baseline hemogram, preventing evaluation of dynamic changes in inflammatory status over time. Given the fluctuating nature of infections, immune activation, and muscle injury in Duchenne muscular dystrophy, longitudinal inflammatory profiling might provide additional prognostic information that could not be assessed in the present study.

Third, although patients with acute infection, surgery, or rhabdomyolysis near the reference date were excluded, chronic, recurrent, or subclinical inflammatory conditions may not have been fully captured through retrospective record review and could have influenced baseline hematologic values, representing a potential source of residual confounding.

Fourth, genetic characterization was incomplete for a subset of patients, limiting the ability to fully assess the modifying effects of LTBP4, SPP1, or other genotype-related factors on disease progression.

Fifth, corticosteroid exposure represents a major potential confounder in the interpretation of both inflammatory indices and functional decline in DMD. Although information on corticosteroid use was available for most patients, detailed data regarding age at initiation, treatment duration, cumulative dose, adherence, and treatment modifications over time were not systematically or uniformly documented across the entire cohort. Including incompletely documented steroid variables in multivariable models could have introduced substantial bias and reduced interpretability; therefore, these parameters were not incorporated into the primary analyses and are acknowledged here as an important source of residual confounding.

Sixth, the sample size, although sufficient for the planned analyses, remains modest for multivariable modeling and particularly limits the statistical power of subgroup analyses, such as age-stratified evaluations. Accordingly, the absence of statistical significance in certain subgroups—especially among patients aged ≥ 10 years—should be interpreted cautiously and may reflect limited power rather than the absence of a true biological association.

Lastly, the single-center nature of the study may restrict external generalizability, as clinical practices, follow-up intensity, and supportive care approaches vary across institutions. Despite these limitations, the study provides novel insights into the potential prognostic role of the pan-immune-inflammation value in Duchenne muscular dystrophy and establishes a foundation for future multicenter, prospective investigations incorporating standardized clinical assessments and longitudinal inflammatory measurements. Additionally, the exact time interval between diagnosis and baseline blood sampling could not be consistently extracted for all patients, and therefore the potential impact of within-window variability could not be quantified.

Conclusion

In conclusion, this retrospective cohort study found that the pan-immune-inflammation value is significantly associated with loss of ambulation in children and adolescents with Duchenne muscular dystrophy, with higher discriminative performance than several established hematologic inflammatory indices. The age-stratified findings further indicate that this association is not uniform across the disease course; elevated PIV measured at younger ages—particularly in early childhood—was associated with earlier loss of ambulation, suggesting that systemic inflammatory burden early in the disease course may be linked to a more aggressive functional trajectory.

Rather than serving as a definitive prognostic tool, these observations raise the possibility that PIV may function as an adjunctive and accessible marker for early risk stratification. However, given the indirect nature of inflammatory indices, the retrospective design, and the multifactorial progression of Duchenne muscular dystrophy, PIV should be interpreted cautiously and within the broader clinical context, and should not be used in isolation for clinical decision-making. Prospective, multicenter studies with standardized clinical assessments and longitudinal inflammatory profiling are required to validate these findings and to determine whether PIV can meaningfully enhance comprehensive clinical risk prediction models in Duchenne muscular dystrophy.

Authors’ contributions

Conceptualization, B.Ö. and G.Y.; methodology, B.Ö. and I.B.; formal analysis, B.Ö. and G.Y.; investigation, B.Ö., I.B., and M.T.; resources, M.K. and S.G.; data curation, B.Ö. and I.B.; writing—original draft preparation, B.Ö.; writing—review and editing, G.Y., S.G., and M.K.; visualization, B.Ö.; supervision, G.Y. and S.G.; project administration, G.Y. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding. The APC was funded by the authors.

Data availability

The data presented in this study are available on reasonable request from the corresponding author. The data are not publicly available due to ethical and privacy restrictions.

Declarations

Consent for publication

Patient consent was waived due to the retrospective design and the use of de-identified routinely collected data, as approved by the ethics committee.

Competing interests

The authors declare no competing interests.