Body Mass Index Categories and Hemogram-Derived Ratios and Inflammatory Indices in Children Aged 6 to 9 Years: A Single-Center Retrospective Study

Introduction

Childhood obesity is widely recognized as a major public health concern, similar to adult obesity. In recent decades, the number of children affected by excess weight has steadily increased. Among individuals aged 5 to 19 years, global obesity prevalence has risen from approximately 2% in 1990 to around 8% in recent years – an almost 4-fold increase. This trend indicates that childhood obesity is no longer an isolated phenomenon but a widespread issue requiring closer clinical and biological attention [1].

Childhood obesity is not a new phenomenon, yet its clinical consequences may emerge earlier than expected. Some children with excess weight already show slightly elevated blood pressure, early insulin resistance, or even hepatic steatosis [2]. These findings can persist into adolescence and adulthood, increasing the risk of type 2 diabetes and cardiovascular disease [3]. Being underweight also carries health risks, including slower growth and higher susceptibility to infections [4]. Therefore, maintaining a healthy weight in childhood is not only a clinical responsibility but also a broader concern for families, schools, and public health services [5].

In recent years, attention has turned to several simple indices that can be calculated from a standard complete blood count [6]. Ratios such as the neutrophil-to-lymphocyte ratio (NLR) and the platelet-to-lymphocyte ratio (PLR) have been examined in many clinical settings, and there is growing evidence that they reflect low-grade, systemic inflammatory activity [6,7]. Because these measures are inexpensive and routinely available, they have been used in research and clinical settings, although they are sometimes criticized for relying on database-mining approaches, which can yield statistically significant but clinically nonspecific associations [8]. In pediatrics, their low cost also gives these indices potential utility for population-level evaluation of obesity-related inflammatory risk. Notably, studies in both adults and children have shown differences in these ratios across weight groups, indicating that metabolic and inflammatory changes may be detectable even before overt disease develops [9,10].

Although hemogram-derived ratios offer a practical way to assess systemic inflammatory tone, the pediatric literature on their relationship with weight status is not fully consistent [10–12]. One key reason is that many studies group together children and adolescents across very broad age ranges. Hematologic profiles are not static throughout childhood; neutrophil–lymphocyte balance, platelet activity, and even overall leukocyte configuration shift naturally during periods of rapid growth and hormonal change. For example, the immune and hematologic patterns of a 6-year-old prepubertal child differ from those of a teenager undergoing pubertal maturation, and both differ from the patterns seen in late adolescence [13–15]. When these developmental stages are analyzed together, weight-related inflammatory differences may be blurred, weakened, or misinterpreted. Therefore, in this study, we did not consider childhood as a whole, but rather a small age group ratio, which we explain in detail below. These methodological variations make it difficult to draw firm conclusions, and they show the importance of examining more narrowly defined age ranges to better understand how weight status relates to inflammation in childhood.

Weight and inflammation can be difficult to interpret in children because blood cell counts and immune activity do not stay constant throughout growth. These values shift noticeably as puberty approaches, which can make it hard to see whether any changes are coming from weight itself or simply from developmental changes. For this reason, we chose to focus on children aged 6 to 9 years, a period in which growth is relatively steady and the hormonal changes of puberty have not yet begun. Studying this age group makes it more likely that any differences in hemogram-derived inflammatory ratios, if present, are linked to weight status rather than developmental stage. With this in mind, our retrospective study aimed to examine the relationship between BMI categories and hemogram-derived inflammatory ratios in children aged 6 to years, using BMI categories defined according to the WHO 2007 BMI-for-age percentile reference.

Material and Methods

STUDY DESIGN AND SETTING:

This study was designed as a retrospective cross-sectional analysis. Medical records of children who attended the Pediatric Outpatient Clinic of Bahçeşehir University Faculty of Medicine – Göztepe MedicalPark Hospital between January 2022 and August 2025 were reviewed. Ethics approval was obtained from the Institutional Clinical Research Ethics Committee (Approval No: 2025-15/02; Date: 01.10.2025). Because the study used retrospective and anonymized data, the requirement for informed consent was waived. All procedures were conducted in accordance with the Declaration of Helsinki.

DATA COLLECTION:

Data was retrieved from the hospital’s electronic medical record system (COMED hospital information system). Children were screened if they presented for a routine well-child/health supervision visit, with same-day measurements of height, weight, and complete blood count (CBC). Visits related to acute illness or clinical symptoms were excluded.

INCLUSION AND EXCLUSION CRITERIA:

Children were included if:

Children were excluded if they had:

If more than 1 eligible visit existed, only the first was included.

MEASUREMENTS AND DEFINITIONS:

Body mass index (BMI) was calculated as weight (kg)/height (m²). BMI-for-age percentiles and z-scores were determined using the WHO 2007 (5–19 years) growth reference. Children were categorized into 3 BMI groups:

Because the sample size was limited, lower BMI percentiles (eg, <3^rd or <5^th) were combined into a single <15^th percentile group to preserve adequate subgroup sizes for statistical comparison. Similarly, children with BMI percentiles >85^th (overweight and obesity) were analyzed as a single “high BMI” group to ensure sufficient statistical power and reduce sparse subgroup counts; this terminology is used consistently throughout this report.

From CBC data, inflammatory indices were calculated as follows:

All CBC measurements in the study period were processed on the same automated hematology analyzer model, under routine internal and external quality control. Examples include platelet counts >900×10⁹/L and other hematologic measurements falling outside physiologically plausible limits, which were excluded after manual verification.

STATISTICAL ANALYSIS:

All statistical analyses were performed using SPSS version 22.0 (IBM Corp., Armonk, NY, USA). Data distribution was evaluated using histograms, Q-Q plots, and the Shapiro-Wilk test. Because inflammatory ratio variables were non-normally distributed, comparisons among BMI groups were conducted using the Kruskal-Wallis test. When significant differences were observed, Dunn’s post hoc pairwise comparisons with Bonferroni correction were applied. Associations between BMI z-scores and inflammatory indices (NLR, PLR, SII, SIRI) were examined using Spearman rank correlation. Statistical significance was defined as P<0.05 (2-tailed). Sample size was not predetermined because all eligible records within the study period were included.

Figures were generated separately from the statistical analyses using Numigo online analysis tools (numigo.com). The dataset was uploaded and the figures were created using the platform’s standard settings.

Results

Records of 338 children aged 6 to 9 years were reviewed. A total of 233 records were excluded due to incomplete height/weight or CBC data (n=102), visits for acute illness (n=58), recent infection within the past 30 days (n=32), recent corticosteroid or NSAID use (n=26), vaccination within the previous 2 weeks (n=14), or the presence of chronic inflammatory, metabolic, hematologic, hematologic or immunologic disease (n=1). Height and weight are routinely measured at each visit in our clinic; however, CBC results are not always available on the same day, as some children undergo blood sampling at a separate visit or provide previously obtained laboratory results. This workflow accounts for most of the missing data. After these exclusions, 105 records initially met the eligibility criteria. Five of these were excluded due to hematologic values considered physiologically implausible (eg, platelet count >900×109/L). The final analysis therefore included 100 children (Figure 1). Based on BMI-for-age percentiles, the study population consisted of 18 children in the <15th percentile group, 49 children in the 15th to 85th percentile group, and 33 children in the >85th percentile group. Laboratory characteristics for the 3 BMI groups are summarized in Table 1. The age distribution did not differ significantly among the BMI groups (median age: Group 1=86.5 months, Group 2=88 months, Group 3=94 months; P=0.104), and the female–male distribution was also similar across groups.

Apart from BMI classification, no significant differences were observed among the groups in standard hematologic parameters, including WBC, RBC, hemoglobin, hematocrit, MCV, MCH, platelet count, RDW, and differential leukocyte percentages (P>0.05 for all comparisons). Median WBC values were similar across groups (<15th percentile: 7.56×109/L, 15th–85th percentile: 7.18×109/L, >85th percentile: 7.75×109/L, P=0.669). Platelet counts also showed comparable distributions (317 vs 308 vs 303×109/L, P=0.536). Additionally, median absolute neutrophil (3.41 vs 3.45 vs 3.89×109/L; P=0.334) and absolute lymphocyte counts (3.03 vs 3.18 vs 2.92×109/L; P=0.620) did not differ significantly across the BMI percentile groups.

With respect to inflammatory ratios, NLR, PLR, and SII did not differ significantly across BMI groups (P=0.102, p=0.392, and P=0.127, respectively). Median NLR values were 1.12 (0.90–1.40), 1.02 (0.84–1.47), and 1.37 (0.95–1.62) for the <15th, 15th to 85th, and >85th percentile groups, respectively. Median PLR values were 104.97 (91.46–117.13), 96.31 (83.43–108.21), and 96.19 (86.25–119.52). SII values followed a similar pattern.

However, a statistically significant difference was identified for SIRI (P=0.044). Median SIRI was 0.52 (0.41–0.78) in the <15th percentile group, 0.46 (0.35–0.66) in the 15th to 85th percentile group, and 0.64 (0.44–0.98) in the >85th percentile group. Post hoc testing indicated that this difference originated from the comparison between the 15th to 85th and >85th percentile groups (adjusted P=0.039. A statistically significant difference in SIRI was identified across BMI groups; however, post hoc analyses indicated that this difference was limited to a single pairwise comparison. This isolated finding should be interpreted with caution. No significant differences were observed in eosinophil-derived ratios (P>0.05 for all comparisons).

Distributions of NLR, PLR, SII, and SIRI across the 3 BMI groups are shown in Figure 2. NLR, PLR, and SII demonstrated broadly overlapping distributions, while SIRI showed a relatively higher distribution in the >85th percentile group. To examine this pattern in more detail, SIRI was also analyzed across 6 narrower BMI percentile strata (Figure 3). A gradual upward shift in SIRI values was observed toward higher percentiles, although interquartile ranges remained largely overlapping. Additionally, no significant correlations were found between BMI z-scores and any of the hematologic parameters or hemogram-derived inflammatory ratios, including WBC, differential cell counts, eosinophil-based ratios, NLR, PLR, SII, and SIRI (all Spearman r, P>0.05). In Spearman correlation analysis, age in months showed weak but statistically significant associations with neutrophil percentage, lymphocyte percentage and count, NLR, BMI, and the 6-level BMI category (ρ≈0.2–0.33; P<0.05), whereas no meaningful age-related correlations were observed for SII, SIRI, or other hemogram-derived indices.

In our clinic, height and weight are measured routinely at each visit; however, CBC testing is performed selectively based on clinical judgement. For this reason, the final cohort may modestly overrepresent children for whom clinicians requested additional laboratory evaluation.

Discussion

In this retrospective study, we examined whether complete blood count parameters and hemogram-derived inflammatory indices were associated with BMI categories in children aged 6 to 9 years SIRI was the only parameter that differed significantly across BMI groups. No significant differences were observed in WBC, HGB, PLT, NLR, PLR, SII, MPV, or RDW values. However, post hoc comparisons revealed that this difference was limited to the comparison between Group 3 (>85th percentile) and Group 2 (15–85th percentile) (Test statistic: 16.25; P=0.013; adjusted P=0.039), whereas no significant differences were found between Group 1 (<15th percentile) and the other groups (adjusted P>0.8). This suggests that the observed elevation in SIRI may reflect a mild neutrophil–monocyte shift associated with higher BMI, rather than a consistent inflammatory pattern across all BMI strata. In addition, no significant correlations were detected between BMI and any hemogram-derived indices. These findings suggest that systemic low-grade inflammation may not yet be fully established or detectable by routine hematologic inflammatory markers in children with higher BMI during early childhood. Importantly, interpretation is limited to a cohort of children undergoing clinician-requested complete blood count testing during routine visits, which may restrict generalizability to the broader pediatric population.

The extensive adult literature indicates that hemogram-derived ratios, including NLR and SII, are generally elevated in individuals with obesity compared with those of normal weight, although several studies have reported conflicting findings [9,16,17]. This pattern has been interpreted as a reflection of chronic low-grade inflammation associated with obesity. Elevated values of the same indices have also been reported in individuals who smoke, which further suggests that these markers tend to increase in the presence of persistent inflammatory stimuli [18–20].

Previous pediatric studies have reported inconsistent results regarding hemogram-derived inflammatory markers in childhood obesity. In a prospective study from Romania, leukocyte count, CRP, and platelet indices were higher in overweight/obese children, whereas NLR and PLR did not differ between BMI groups[12]. Similarly, a Brazilian cross-sectional study suggested that PLR may be more sensitive than NLR in reflecting central adiposity [11]. In contrast, research from Italy demonstrated that the positive association between NLR and metabolic syndrome seen in obese adults is not present in obese children and adolescents [21]. Large-scale data from the NHANES cohort further indicated that SII and SIRI increase with BMI, particularly in those with more pronounced excess weight [10]. Overall, these findings suggest that the inflammatory profile associated with obesity in childhood is variable and may depend on age, pubertal stage, and the specific inflammatory index assessed. Our negative findings, therefore, do not exclude the possibility of subtle inflammatory changes, which may be detectable through more specific biomarkers such as hs-CRP, interleukins, or adipokines, as demonstrated in other pediatric cohorts [3].

A broader perspective on hemogram-derived indices and other data-mining–based approaches is equally essential when interpreting such findings. Many of these markers achieve statistical significance across different clinical settings; however, this does not necessarily translate into diagnostic utility or discriminative performance. For instance, a single ratio that has been associated with conditions as diverse as cancer diagnosis and treatment response, obesity, smoking status, COVID-19 severity, and cerebrovascular ischemia may be too nonspecific to function as a reliable biomarker in any one of these scenarios. Although such indices are attractive because they are low-cost and readily available, they are also influenced by a wide range of physiological and non-physiological variables, which limits their clinical applicability. In this regard, even though our study contributes to the growing literature in this field, the accumulated evidence suggests that hemogram-derived ratios should be interpreted with caution, and their use as standalone biomarkers in pediatric obesity is currently not justified.

The duration of exposure to obesity – along with its severity – may likewise influence the development of chronic inflammation and the extent to which hemogram-derived indices reflect such changes. Studies that reported significant alterations may have included older age groups, mixed pubertal stages, or heterogeneous sex distributions, which could partly explain the variability across findings. Establishing standardized age-, sex-, and BMI-specific criteria would therefore be beneficial for future research in this area. In our study, for example, SIRI was statistically higher in children with BMI ≥85th percentile, despite the relatively small sample size. Given the absence of correlations and the substantial overlap between groups, the isolated SIRI difference should be interpreted cautiously and may represent random variation rather than a meaningful inflammatory signal. SIRI incorporates both neutrophil and monocyte counts, making it a more complex index that potentially reflects broader inflammatory activity. Our data are insufficient to confirm or refute the presence of obesity-related low-grade inflammation in this narrow age group, and any such interpretations should remain speculative. For this reason, future investigations using SIRI and similar recently developed indices should include subgroup analyses based on BMI categories in both pediatric and adult populations to better clarify their clinical relevance.

This study has several limitations. First, its retrospective design may limit the ability to control for potential confounding variables. Second, the sample size was relatively small, which may have reduced the statistical power to detect subtle differences between BMI groups. Although most group comparisons were non-significant, several indices (including NLR, PLR, and SII) showed P values approaching significance. This pattern may indicate limited statistical power to detect small effect sizes, and larger cohorts will be needed to determine whether these trends reflect true biological differences. Third, we did not include cytokine profiles or more specific inflammatory biomarkers (eg, interleukins, TNF-α, adipokines), which could have provided a more detailed characterization of the inflammatory response associated with childhood obesity. In particular, the <15th percentile group included only 18 children, which does not preclude statistical comparison but may further limit the power to detect subtle differences within this subgroup. Similarly, it is also possible that the degree of obesity itself – particularly severe obesity above the 97th percentile – could influence inflammatory activity, although our sample was not sufficiently powered to evaluate this subgroup. The availability of anthropometric data was not random, and children who underwent height/weight measurement and CBC on the same day may represent a subgroup with suspected nutritional or clinical concerns. This selective inclusion limits generalizability and introduces the possibility of selection bias. Such clinician-driven testing may have led to an overrepresentation of children with suspected nutritional issues or subclinical health concerns, potentially inflating baseline inflammatory markers and attenuating true differences between BMI categories. Accordingly, this study should be considered exploratory, as the relatively small sample size limits its statistical power, and the absence of statistically significant differences should not be interpreted as definitive evidence of no association.

Conclusions

In this single-center retrospective cohort of children aged 6 to 9 years, most hemogram-derived indices did not differ across BMI categories, and only SIRI showed a modest elevation in those with BMI ≥85th percentile. These findings suggest that, in early childhood, routine hemogram-based indices alone may have limited utility for detecting low-grade inflammation associated with higher BMI, and that larger prospective studies with more specific biomarkers are needed. Given the limited sample size and potential selection bias, these results should be regarded as exploratory. The isolated difference in SIRI does not establish a consistent inflammatory signal, and more comprehensive biomarker profiling in larger cohorts will be required to clarify the biological relevance of these findings.