Abstract
Background
Sarcopenia is prevalent among hemodialysis patients and is associated with poor outcomes. The neutrophil-to-lymphocyte ratio (NLR), an easily obtainable marker of inflammation, may predict sarcopenia risk. This study aimed to investigate the association between NLR and sarcopenia risk in maintenance hemodialysis patients, examining this association in the context of obesity.
Methods
This cross-sectional study included 411 maintenance hemodialysis patients. Sarcopenia was diagnosed using the Asian Working Group for Sarcopenia criteria-2019 (AWGS 2019). Body composition was assessed using bioelectrical impedance analysis. Logistic regression models examined associations between NLR and sarcopenia risk, adjusting for potential confounders. Analyses were stratified by obesity status.
Results
The prevalence of sarcopenia was 51% (95% CI: 45.1-54.9%), with 37.2% classified as sarcopenic non-obese and 13.6% as sarcopenic obese. In fully adjusted models, each unit increase in NLR was associated with 10% higher odds of sarcopenia overall (OR 1.10, 95% CI: 1.00-1.21, p = 0.048). This association remained significant in sarcopenic obese patients (OR 1.15, 95% CI: 1.00-1.32, p = 0.049). Patients in the highest NLR tertile had 1.95 times higher odds of sarcopenia compared to the lowest tertile (95% CI: 1.12–3.40, p = 0.018), with a significant trend across tertiles (p-trend = 0.015).
Conclusions
NLR is independently associated with sarcopenia risk in hemodialysis patients, including those with obesity. These findings suggest NLR could serve as a simple, cost-effective tool for identifying hemodialysis patients at high risk of sarcopenia, potentially facilitating early intervention strategies.
Keywords: Sarcopenia, Hemodialysis, Neutrophil-to-lymphocyte ratio, Obesity, AWGS 2019, Prevalence
Introduction
Sarcopenia, characterized by progressive loss of skeletal muscle mass and function, is a prevalent and significant health concern among patients undergoing maintenance hemodialysis [1]. This condition is associated with numerous adverse outcomes, including decreased quality of life, increased risk of falls, higher hospitalization rates, and elevated mortality [2,3]. While traditionally associated with aging and malnutrition, sarcopenia in hemodialysis patients is a complex, multifactorial condition influenced by uremic toxicity, chronic inflammation, metabolic acidosis, and physical inactivity [4].
The diagnosis and management of sarcopenia in the hemodialysis population are complicated by the high prevalence of obesity, leading to the emerging concept of sarcopenic obesity [5]. This condition, where excess adiposity coexists with reduced muscle mass and function, poses unique challenges in clinical assessment and intervention strategies [6]. Traditional anthropometric measures like body mass index (BMI) may fail to accurately reflect body composition in these patients, potentially masking the presence of sarcopenia in overweight or obese individuals [7].
Chronic inflammation is a hallmark of end-stage renal disease and has been implicated as a key driver of muscle wasting in this population [8]. Various inflammatory markers have been associated with poor outcomes in hemodialysis patients, but many of these are not routinely measured in clinical practice [9]. In recent years, the neutrophil-to-lymphocyte ratio (NLR) has emerged as a simple, cost-effective marker of systemic inflammation that can be easily calculated from standard complete blood count results [10].
NLR has been shown to predict mortality and cardiovascular events in various populations, including hemodialysis patients [11,12]. However, its relationship with sarcopenia, particularly in the context of obesity, remains poorly understood. Given the complex interplay between inflammation, nutrition, and body composition in hemodialysis patients, investigating the association between NLR and sarcopenia risk could provide valuable insights into the pathophysiology of muscle wasting and potentially identify a clinically useful biomarker for sarcopenia risk assessment [13].
Furthermore, other hematological parameters, such as hemoglobin, hematocrit, and white blood cell counts, have been associated with nutritional status and outcomes in hemodialysis patients [14,15]. Given the complex interplay between inflammation, nutrition, and body composition in hemodialysis patients, investigating the association between NLR and sarcopenia risk could provide valuable insights into the pathophysiology of muscle wasting and potentially identify a clinically useful biomarker for sarcopenia risk assessment.
Recent studies have demonstrated associations between NLR and sarcopenia in maintenance hemodialysis patients, including specific subgroups like overweight patients (18,33). However, these studies were limited by smaller sample sizes and insufficient consideration of sarcopenic obesity, a condition where excess adiposity coexists with reduced muscle mass and function. This distinction is crucial as sarcopenic obesity may have different inflammatory profiles and clinical implications compared to sarcopenia alone. Our study aims to address these gaps by examining the NLR-sarcopenia relationship across different body composition categories, including sarcopenic obesity, utilizing precise bioelectrical impedance analysis, and investigating interactions with multiple hematological parameters in a larger cohort of hemodialysis patients.
The primary objective of this study is to investigate the association between neutrophil-to-lymphocyte ratio (NLR) and sarcopenia risk in maintenance hemodialysis patients. Secondary objectives include examining the prevalence and characteristics of sarcopenia and sarcopenic obesity in this population, assessing whether the relationship between NLR and sarcopenia risk differs between non-obese and obese patients, and exploring associations between other hematological parameters and sarcopenia risk. An exploratory objective is to evaluate the potential of NLR as a simple, cost-effective screening tool for identifying hemodialysis patients at high risk of sarcopenia.
Methodology
Study design and participants
This Institutional-based Cross-sectional study was conducted at the Hemodialysis Unit of a tertiary care center in Gujarat, India. Patient recruitment and data collection occurred between June 2023 and June 2024. All assessments were performed on non-dialysis days, between 8 AM and 2 PM, to standardize the timing relative to dialysis sessions.
Eligibility criteria
We recruited adult patients (≥ 18 years) who had been on maintenance hemodialysis for at least three months. Exclusion criteria included acute infections, malignancies, and recent hospitalizations (within the past month).
Sample size calculation
The sample size was calculated using G*Power software (version 3.1.9.4). Based on previous studies examining the relationship between inflammatory markers and sarcopenia in chronic kidney disease patients, we anticipated a medium effect size (f2 = 0.15) for the association between NLR and sarcopenia. This effect size was chosen as it represents a clinically meaningful difference while being conservative enough to ensure adequate power for detecting smaller but potentially important associations. With an alpha level of 0.05 and a power of 0.80, the required sample size was calculated to be 392. We recruited 411 patients to account for potential dropouts or incomplete data. [[17,18]]
Sampling technique
We employed a consecutive sampling technique, approaching all eligible patients who attended the hemodialysis center during the study period. This non-probability sampling method was chosen to ensure a representative sample of our center’s hemodialysis population while maximizing recruitment efficiency.
Data collection
Demographic and clinical data were collected from electronic medical records and patient interviews. This included age, sex, dialysis vintage, and comorbidities. Anthropometric measurements (height, weight, BMI) were performed using standardized techniques [19].
Body composition assessment
Body composition was assessed using bioelectrical impedance analysis (BIA) with a multi-frequency device (InBody S10, Biospace, Seoul, Korea) [20]. Measurements were taken after a midweek dialysis session to ensure standardized fluid status.
Sarcopenia assessment and operational definitions
Sarcopenia was diagnosed based on the Asian Working Group for Sarcopenia (AWGS 2019) criteria, which requires low muscle mass (mandatory) plus either low muscle strength or poor physical performance. Low muscle mass was assessed using skeletal muscle mass index (SMI) derived from bioelectrical impedance analysis (BIA), with cut-offs of < 7.0 kg/m² for men and < 5.7 kg/m² for women. Muscle strength was evaluated using handgrip strength (cut-offs: <28 kg for men, < 18 kg for women). Physical performance was assessed via gait speed over a 4-meter course (cut-off: <1.0 m/s for both sexes). Obesity was defined based on body fat percentage measured by BIA (> 25% for men, > 35% for women). Participants were categorized into Normal (Non-sarcopenic), Sarcopenic Non-obese, and Sarcopenic Obesity [21,22]. Participants were categorized into Normal (Non-sarcopenic), Sarcopenic Non-obese, and Sarcopenic Obesity. The neutrophil-to-lymphocyte ratio (NLR) was calculated by dividing the absolute neutrophil count by the absolute lymphocyte count, with tertiles defined based on the distribution in our study population. Other operational definitions included hemodialysis adequacy (Kt/V ≥ 1.2), dialysis vintage (duration of maintenance hemodialysis in months), and comorbidities such as diabetes mellitus, hypertension, and cardiovascular disease, which were defined based on documented medical history.
Laboratory measurements
Blood samples were collected before a midweek dialysis session after an overnight fast. Complete blood count, including differential white blood cell count, was performed using an automated hematology analyzer (Sysmex XN-3000, Sysmex Corporation, Kobe, Japan). NLR was calculated by dividing the absolute neutrophil count by the absolute lymphocyte count. Other biochemical parameters were measured using standard laboratory techniques.
Statistical analysis
Statistical analyses were performed using SPSS version 25.0 (IBM Corp., Armonk, NY, USA). The normality of continuous variables was assessed using the Shapiro-Wilk test. Differences between the three groups (Normal, Sarcopenic Non-obese, and Sarcopenic Obesity) were analyzed using one-way ANOVA or the Kruskal-Wallis test for continuous variables, and the chi-square test for categorical variables.
Logistic regression models were used to examine the association between hematological parameters and sarcopenia risk, adjusting for potential confounders including age, sex, diabetes mellitus, cardiovascular disease, albumin, and C-reactive protein. For the NLR analysis, we constructed three models: unadjusted, adjusted for demographic and clinical factors (Model 1), and further adjusted for other hematological parameters (Model 2).
The dose-response relationship between NLR tertiles and sarcopenia risk was assessed using trend analysis. We calculated odds ratios (ORs) and 95% confidence intervals (CIs) for each NLR tertile, using the lowest tertile as the reference group.
Interaction terms were included in the models to test for effect modification by patient category (Normal, Sarcopenic Non-obese, and Sarcopenic Obesity). A p-value < 0.05 was considered statistically significant for all analyses.
Missing data were addressed using multiple imputations with chained equations (MICE), assuming data were randomly missing. We created 20 imputed datasets, pooling results using Rubin’s rules. Sensitivity analyses comparing complete case analysis with imputed data analysis were performed to assess the impact of missing data.
Ethical consideration
This study was approved by the Institutional Review Board of Shri MP Shah Medical College and Guru Gobind Government Hospital (approval number: 03/01/2023). All participants provided written informed consent. For patients unable to provide consent due to cognitive impairment, consent was obtained from a legally authorized representative [16].
Results
High prevalence of sarcopenia and sarcopenic obesity in maintenance hemodialysis patients
Table 1 shows distinct characteristics across three patient groups: Normal (n = 202), Sarcopenic Non-obese (n = 153), and Sarcopenic Obesity (n = 56). Sarcopenic patients, particularly those with obesity, were significantly older (68.7 ± 9.7 years) compared to non-sarcopenic patients (58.5 ± 11.2 years, p < 0.001). BMI and body fat percentage were highest in the sarcopenic obesity group (33.2 ± 2.8 kg/m² and 38.6 ± 5.9%, respectively).
Table 1.
Characteristics of Hemodialysis patients by Sarcopenia and obesity status (N = 411)
| Characteristic | Normal (Non-sarcopenic) (n = 202) | Sarcopenic Non-obese (n = 153) | Sarcopenic Obesity (n = 56) | p-value |
|---|---|---|---|---|
| Demographics | ||||
| Age (years) | 58.5 ± 11.2 | 66.1 ± 10.7 | 68.7 ± 9.7 | < 0.001 |
| Sex (male), n (%) | 105 (52.0%) | 78 (51.0%) | 26 (46.4%) | 0.878 |
| Anthropometrics | ||||
| BMI (kg/m²) | 22.7 ± 1.6 | 24.5 ± 3.0 | 33.2 ± 2.8 | < 0.001 |
| Body Fat (%) | 24.5 ± 5.2 | 27.4 ± 6.4 | 38.6 ± 5.9 | < 0.001 |
| Hematological Parameters | ||||
| Hemoglobin (g/dL) | 11.2 ± 1.3 | 10.4 ± 1.6 | 10.1 ± 1.6 | < 0.001 |
| Hematocrit (%) | 33.6 ± 3.9 | 31.2 ± 4.7 | 30.3 ± 4.8 | < 0.001 |
| White Blood Cell Count (×10⁹/L) | 6.5 ± 2.1 | 7.0 ± 2.4 | 7.6 ± 2.5 | < 0.001 |
| Platelet Count (×10⁹/L) | 205.3 ± 69.8 | 197.5 ± 71.6 | 192.4 ± 72.6 | 0.403 |
| Neutrophil-to-Lymphocyte Ratio | 2.28 ± 1.12 | 2.71 ± 1.29 | 2.95 ± 1.35 | < 0.001 |
| Sarcopenia Indicators | ||||
| Handgrip Strength (kg) | 27.8 ± 8.7 | 22.7 ± 7.8 | 21.2 ± 7.5 | < 0.001 |
| Gait Speed (m/s) | 0.85 ± 0.22 | 0.74 ± 0.26 | 0.69 ± 0.27 | < 0.001 |
| Skeletal Muscle Mass Index (kg/m²) | 7.35 ± 1.20 | 6.52 ± 1.08 | 6.45 ± 1.05 | < 0.001 |
| Hemodialysis Parameters | ||||
| Dialysis Vintage (months) | 41.5 ± 33.8 | 52.4 ± 38.0 | 57.3 ± 39.2 | 0.002 |
| Kt/V | 1.40 ± 0.22 | 1.36 ± 0.25 | 1.31 ± 0.26 | 0.035 |
| Session duration (hours), mean ± SD | 3.9 ± 0.3 | 3.8 ± 0.4 | 3.8 ± 0.3 | 0.124 |
| Biochemical Parameters | ||||
| Albumin (g/dL) | 3.9 ± 0.4 | 3.5 ± 0.5 | 3.4 ± 0.5 | < 0.001 |
| C-reactive Protein (mg/L) | 5.5 ± 7.8 | 8.0 ± 9.3 | 9.5 ± 10.3 | 0.003 |
| Comorbidities | ||||
| Diabetes Mellitus, n (%) | 77 (38.0%) | 77 (50.3%) | 32 (57.1%) | 0.001 |
| Hypertension, n (%) | 167 (82.7%) | 132 (86.3%) | 48 (85.7%) | 0.182 |
| Cardiovascular Disease, n (%) | 58 (28.7%) | 57 (37.3%) | 23 (41.1%) | 0.047 |
| Membrane type | ||||
| Polysulfone, n (%) | 162 (80.2%) | 124 (81.0%) | 45 (80.4%) | 0.983 |
| Polyethersulfone, n (%) | 40 (19.8%) | 29 (19.0%) | 11 (19.6%) | |
| Dialysis modality | ||||
| Conventional HD, n (%) | 142 (70.3%) | 109 (71.2%) | 40 (71.4%) | 0.975 |
| Online HDF, n (%) | 60 (29.7%) | 44 (28.8%) | 16 (28.6%) |
Data are presented as mean ± standard deviation for continuous variables and n (%) for categorical variables. P-values are calculated using one-way ANOVA for continuous variables and chi-square tests for categorical variables. BMI: Body Mass Index
Hematological parameters revealed lower hemoglobin and higher NLR in sarcopenic groups, with the most pronounced differences in sarcopenic obesity (hemoglobin: 10.1 ± 1.6 g/dL, NLR: 2.95 ± 1.35). All sarcopenia indicators (handgrip strength, gait speed, and muscle mass index) showed significant decrements in both sarcopenic groups (p < 0.001).
Regarding dialysis parameters, sarcopenic patients had longer dialysis vintage (57.3 ± 39.2 months in sarcopenic obesity vs. 41.5 ± 33.8 months in the normal group, p = 0.002) and lower Kt/V (1.31 ± 0.26 vs. 1.40 ± 0.22, p = 0.035). However, dialysis modality distribution (conventional HD vs. online HDF) and membrane types were similar across groups. Session characteristics, including duration and membrane type, showed no significant differences.
Biochemical markers indicated poorer nutritional status and higher inflammation in sarcopenic groups, with lower albumin (3.4 ± 0.5 g/dL) and higher CRP (9.5 ± 10.3 mg/L) in sarcopenic obesity. Comorbidity burden was higher in sarcopenic groups, particularly diabetes mellitus (57.1% in sarcopenic obesity vs. 38.0% in normal group, p = 0.001).
Hematological parameters show strong association with sarcopenia risk
Table 2 presents a logistic regression analysis examining the association between various hematological parameters and the risk of sarcopenia across different patient categories. The analysis uses the Normal (Non-sarcopenic) group as the reference, allowing for direct comparisons. Decreased hemoglobin and hematocrit levels show strong associations with increased odds of sarcopenia across all categories. For instance, a 1 g/dL decrease in hemoglobin is associated with 40% higher odds of sarcopenia overall (OR 1.40, 95% CI 1.22–1.61, p < 0.001), with similar trends observed in both non-obese and obese sarcopenic groups. Increased white blood cell count significantly correlates with sarcopenia only in the obese category (OR 1.15, 95% CI 1.00-1.32, p = 0.048). Decreased platelet count and increased red cell distribution width are also associated with higher sarcopenia risk, particularly when considering all sarcopenic patients together. The platelet-to-lymphocyte ratio shows a borderline significant association with sarcopenia risk when considering all patients (OR 1.05, 95% CI 0.98–1.10, p = 0.051). These findings suggest that various hematological parameters, particularly those related to anemia and inflammation, may play important roles in the development or progression of sarcopenia in hemodialysis patients.
Table 2.
Logistic Regression Analysis of Hematological Parameters Associated with Sarcopenia in different categories
| Variable | Normal (Non-sarcopenic) (n = 202) | Sarcopenic Non-obese (n = 153) | Sarcopenic Obesity (n = 56) | Total Sarcopenic (n = 209) |
|---|---|---|---|---|
| Hemoglobin (per 1 g/dL decrease) | ||||
| OR (95% CI) | 1.00 (Reference) | 1.40 (1.16–1.69) | 1.48 (1.19–1.84) | 1.40 (1.22–1.61) |
| p-value | - | < 0.001 | < 0.001 | < 0.001 |
| Hematocrit (per 3% decrease) | ||||
| OR (95% CI) | 1.00 (Reference) | 1.35 (1.12–1.63) | 1.42 (1.15–1.75) | 1.35 (1.18–1.55) |
| p-value | - | 0.002 | 0.001 | < 0.001 |
| White Blood Cell Count (per 1 × 10⁹/L increase) | ||||
| OR (95% CI) | 1.00 (Reference) | 1.10 (0.98–1.24) | 1.15 (1.00-1.32) | 1.12 (1.02–1.23) |
| p-value | - | 0.103 | 0.048 | 0.018 |
| Platelet Count (per 50 × 10⁹/L decrease) | ||||
| OR (95% CI) | 1.00 (Reference) | 1.18 (0.98–1.42) | 1.22 (0.98–1.52) | 1.18 (1.02–1.37) |
| p-value | - | 0.084 | 0.078 | 0.025 |
| Red Cell Distribution Width (per 1% increase) | ||||
| OR (95% CI) | 1.00 (Reference) | 1.21 (1.07–1.37) | 1.26 (1.08–1.47) | 1.22 (1.10–1.35) |
| p-value | - | 0.003 | 0.003 | < 0.001 |
| Platelet-to-Lymphocyte Ratio (per 10-unit increase) | ||||
| OR (95% CI) | 1.00 (Reference) | 1.04 (0.98–1.10) | 1.06 (0.99–1.13) | 1.05 (0.98–1.10) |
| p-value | - | 0.172 | 0.092 | 0.051 |
OR: Odds Ratio; CI: Confidence Interval. Models are adjusted for age, sex, diabetes mellitus, cardiovascular disease, albumin, Dialysis vintage (duration of hemodialysis), Kt/V, and C-reactive protein. P < 0.05-significant, P < 0.001-highly significant
Neutrophil-to-lymphocyte ratio independently predicts sarcopenia risk
Table 3 focuses on the association between the neutrophil-to-lymphocyte ratio (NLR) and sarcopenia risk across different patient categories. The analysis presents three models: unadjusted, adjusted model 1 (accounting for demographic and clinical factors), and adjusted model 2 (further accounting for other hematological parameters). In all models, higher NLR is consistently associated with increased odds of sarcopenia. In the fully adjusted model (Model 2), each unit increase in NLR is associated with 10% higher odds of sarcopenia overall (OR 1.10, 95% CI 1.00-1.21, p = 0.048). This association remains significant for the Sarcopenic Obesity group (OR 1.15, 95% CI 1.00-1.32, p = 0.049) but becomes non-significant for the Sarcopenic Non-obese group (OR 1.09, 95% CI 0.98–1.22, p = 0.109). The lack of significant interaction (p = 0.625 in Model 2) suggests that the relationship between NLR and sarcopenia risk is relatively consistent across BMI categories, although the association appears stronger in the obese group. These results indicate that NLR, a marker of systemic inflammation, may be an independent risk factor for sarcopenia in hemodialysis patients, particularly those with obesity.
Table 3.
Association of Continuous Neutrophil-to-Lymphocyte Ratio (NLR) with Sarcopenia Risk in different categories
| NLR Analysis | Normal (Non-sarcopenic) (n = 202) | Sarcopenic Non-obese (n = 153) | Sarcopenic Obesity (n = 56) | p-value for interaction |
|---|---|---|---|---|
| Unadjusted Model | ||||
| OR (95% CI) | 1.20 (1.10–1.31) | 1.19 (1.08–1.32) | 1.26 (1.11–1.43) | 0.492 |
| p-value | < 0.001 | < 0.001 | < 0.001 | |
| Adjusted Model 1* | ||||
| OR (95% CI) | 1.15 (1.05–1.26) | 1.14 (1.03–1.27) | 1.21 (1.06–1.38) | 0.538 |
| p-value | 0.003 | 0.013 | 0.004 | |
| Adjusted Model 2** | ||||
| OR (95% CI) | 1.10 (1.00-1.21) | 1.09 (0.98–1.22) | 1.15 (1.00-1.32) | 0.625 |
| p-value | 0.048 | 0.109 | 0.049 |
OR: Odds Ratio; CI: Confidence Interval. P < 0.05-significant, P < 0.001-highly significant. * Adjusted Model 1: Adjusted for age, sex, diabetes mellitus, cardiovascular disease, albumin, C-reactive protein, Dialysis vintage (duration of hemodialysis), Kt/V, and body mass index. ** Adjusted Model 2: Adjusted for all factors in Model 1 plus hemoglobin, hematocrit, white blood cell count, platelet count, and red cell distribution width
Dose-dependent relationship between nlr levels and sarcopenia risk
Table 4 expands on the NLR analysis by categorizing NLR into tertiles and examining their association with sarcopenia risk. The results show a dose-response relationship between NLR levels and sarcopenia risk, which persists even after full adjustment for confounding factors (Model 2). Compared to the lowest NLR tertile, patients in the highest NLR tertile have significantly higher odds of sarcopenia across all categories, even after full adjustment. For all sarcopenic patients, those in the highest NLR tertile have 1.95 times higher odds of sarcopenia compared to those in the lowest tertile (95% CI 1.12–3.40, p = 0.018) in the fully adjusted model. This trend is consistent across BMI categories, with the strongest association observed in the Sarcopenic Obesity group (OR 2.45, 95% CI 1.01–5.94, p = 0.047). The significant p-trend values (p = 0.015 for all sarcopenic patients in Model 2) indicate a linear trend in the association between NLR tertiles and sarcopenia risk. The lack of significant interaction (p = 0.754 in Model 2) again suggests that the relationship between NLR tertiles and sarcopenia risk is consistent across BMI categories. These findings further support the potential utility of NLR as a marker for sarcopenia risk in hemodialysis patients, emphasizing the importance of inflammation in the pathogenesis of sarcopenia, particularly in the context of obesity.
Table 4.
Association of Neutrophil-to-lymphocyte ratio (NLR) tertiles with Sarcopenia Risk in different categories
| NLR Tertiles | Normal (Non-sarcopenic) (n = 202) | Sarcopenic Non-obese (n = 153) | Sarcopenic Obesity (n = 56) | p-value for interaction |
|---|---|---|---|---|
| Tertile 1 (Low NLR) | ||||
| Range | ≤ 1.85 | ≤ 1.81 | ≤ 1.95 | |
| n (% Sarcopenic) | 67 (33.2%) | 50 (32.9%) | 17 (30.3%) | |
| Adjusted OR (95% CI)* | 1.00 (Reference) | 1.00 (Reference) | 1.00 (Reference) | |
| Adjusted OR (95% CI)** | 1.00 (Reference) | 1.00 (Reference) | 1.00 (Reference) | |
| Tertile 2 (Medium NLR) | ||||
| Range | 1.86–2.80 | 1.82–2.74 | 1.96–2.95 | |
| n (% Sarcopenic) | 67 (33.2%) | 50 (32.9%) | 17 (30.3%) | |
| Adjusted OR (95% CI)* | 1.42 (0.84–2.40) | 1.40 (0.78–2.51) | 1.70 (0.73–3.95) | 0.865 |
| p-value | 0.191 | 0.259 | 0.218 | |
| Adjusted OR (95% CI)** | 1.30 (0.76–2.22) | 1.28 (0.70–2.34) | 1.55 (0.65–3.69) | 0.891 |
| p-value | 0.338 | 0.422 | 0.321 | |
| Tertile 3 (High NLR) | ||||
| Range | > 2.80 | > 2.74 | > 2.95 | |
| n (% Sarcopenic) | 68 (33.7%) | 52 (34.2%) | 16 (28.6%) | |
| Adjusted OR (95% CI)* | 2.35 (1.39–3.97) | 2.25 (1.25–4.05) | 3.00 (1.29–6.98) | 0.698 |
| p-value | 0.001 | 0.007 | 0.011 | |
| Adjusted OR (95% CI)** | 1.95 (1.12–3.40) | 1.85 (0.99–3.45) | 2.45 (1.01–5.94) | 0.754 |
| p-value | 0.018 | 0.053 | 0.047 | |
| p-trend* | < 0.001 | 0.005 | 0.007 | |
| p-trend** | 0.015 | 0.045 | 0.041 | |
OR: Odds Ratio; CI: Confidence Interval. P < 0.05-significant, P < 0.001-highly significant.*Adjusted Model 1: Adjusted for age, sex, diabetes mellitus, cardiovascular disease, albumin, C-reactive protein, Dialysis vintage (duration of hemodialysis), Kt/V, and body mass index. ** Adjusted Model 2: Adjusted for all factors in Model 1 plus hemoglobin, hematocrit, white blood cell count, platelet count, and red cell distribution width. p-trend: p-value for trend across tertiles. p-value for interaction: tests whether the association between NLR and sarcopenia risk differs among categories
Discussion
This cross-sectional study investigated the association between hematological parameters, particularly the neutrophil-to-lymphocyte ratio (NLR), and sarcopenia risk in hemodialysis patients across different categories based on sarcopenia and obesity status. Our findings reveal significant associations between various hematological markers and sarcopenia, with NLR emerging as a robust predictor of sarcopenia risk across all categories.
Our study found a high prevalence of sarcopenia among hemodialysis patients (51%), which is higher compared to the systematic review with previous reports (26 to 35%) [23]. This discrepancy can be attributed to several factors. Firstly, our use of the Asian Working Group for Sarcopenia (AWGS) criteria, specifically tailored for Asian populations, may have led to higher detection rates than European or North American guidelines. The characteristics of our study population, including age distribution, dialysis vintage, nutritional status, and comorbidity burden, could also contribute to the higher prevalence. Additionally, our use of bioelectrical impedance analysis (BIA) for muscle mass assessment may offer higher sensitivity compared to older techniques used in some previous studies. Notably, sarcopenia was present in both non-obese and obese patients, highlighting the phenomenon of sarcopenic obesity [24]. Specifically, out of 209 sarcopenic patients, 56 (26.8%) were classified as having sarcopenic obesity, representing 13.6% of the total study population. This underscores the importance of assessing muscle mass and function in dialysis patients, regardless of their BMI or body fat percentage.
Our results consistently show that lower hemoglobin and hematocrit levels are associated with higher sarcopenia risk across all categories. For instance, a 1 g/dL decrease in hemoglobin is associated with 40% higher odds of sarcopenia overall, with similar trends in both non-obese and obese sarcopenic groups. This aligns with previous studies linking anemia to reduced muscle strength and physical performance in chronic kidney disease patients [25]. The mechanisms underlying this association may involve reduced oxygen delivery to muscles, impaired mitochondrial function, and decreased physical activity due to fatigue [26].
The association between increased white blood cell count and sarcopenia risk, particularly in obese patients (OR 1.15, 95% CI 1.00-1.32, p = 0.048), suggests a potential role of chronic inflammation in muscle wasting. This is further supported by the elevated C-reactive protein levels observed in sarcopenic patients. Chronic inflammation has been implicated in the pathogenesis of sarcopenia through various mechanisms, including increased protein catabolism and impaired muscle regeneration [27].
Our findings extend beyond previous studies that examined NLR as a sarcopenia predictor in hemodialysis patients [18,33] by demonstrating differential associations across body composition categories and establishing a clear dose-response relationship through tertile analysis. Notably, we found a stronger association in sarcopenic obesity (OR 1.15, 95% CI 1.00-1.32) compared to non-obese sarcopenia, suggesting that inflammation may play a more significant role in muscle wasting when excess adiposity is present. Additionally, our comprehensive analysis of multiple hematological parameters provides new insights into the complex interplay between inflammation, nutritional status, and body composition in this population [12,28].
The biological plausibility of this association lies in the dual role of NLR as a marker of both inflammation and nutritional status. Elevated neutrophil counts reflect ongoing inflammation, while lymphopenia may indicate poor nutritional status and impaired immune function [29]. Both chronic inflammation and malnutrition are key contributors to the development of sarcopenia in dialysis patients [30].
The consistency of the NLR-sarcopenia association across categories is particularly noteworthy. Patients in the highest NLR tertile have significantly higher odds of sarcopenia compared to those in the lowest tertile, even after full adjustment (OR 1.95, 95% CI 1.12–3.40, p = 0.018 for all sarcopenic patients). This suggests that NLR may be a valuable tool for identifying sarcopenia risk in both non-obese and obese dialysis patients, addressing the diagnostic challenge posed by sarcopenic obesity [31].
Clinical implications
Our findings have several important clinical implications. First, they highlight the need for routine assessment of muscle mass and function in all hemodialysis patients, regardless of BMI or body fat percentage. Second, they suggest that NLR, an easily obtainable and cost-effective marker, could be used as a screening tool to identify patients at high risk of sarcopenia. This could facilitate early intervention strategies to prevent or mitigate muscle loss.
The strong association between anemia and sarcopenia underscores the importance of optimal anemia management in dialysis patients. While current guidelines focus on cardiovascular outcomes, our results suggest that maintaining adequate hemoglobin levels may also have benefits for muscle health [32].
Limitations and future directions
Several limitations of our study should be acknowledged. The cross-sectional design precludes inference of causality. Longitudinal studies are needed to establish whether changes in NLR predict the development or progression of sarcopenia. Additionally, residual confounding cannot be ruled out while we adjusted for several potential confounders, including other hematological parameters.
Future research should explore the mechanisms underlying the NLR-sarcopenia association, potentially through the measurement of specific inflammatory cytokines and muscle-specific biomarkers. Intervention studies targeting inflammation reduction and nutritional optimization could help elucidate whether modifying NLR leads to improvements in muscle mass and function.
Conclusion
The present study demonstrates a strong, consistent association between NLR and sarcopenia risk in hemodialysis patients across different categories of sarcopenia and obesity. These findings suggest that NLR could serve as a valuable tool for sarcopenia risk assessment in this population. Further research is warranted to validate these findings and explore the potential of NLR-guided interventions to prevent or manage sarcopenia in dialysis patients.
Acknowledgements
We acknowledge and are grateful to all the patients who contributed to the data collection for this study. We are also thankful to Dr. Nandini Desai (Dean and Chairperson of MDRU), Dr. Dipesh Parmar (Professor and Head, Department of Community Medicine), and Shri M P Shah Government Medical College, Jamnagar, India.
Author contributions
YM contributed to the conceptualization, data curation, formal analysis, investigation, methodology, resources, supervision, validation, writing (original draft), and writing (review and editing). YM, PP, SS, JK and DL contributed to the conceptualization, data curation, formal analysis, investigation, writing (original draft), and writing (review and editing). YM, PP, SS, JK and DL contributed to the methodology, resources, supervision, validation, and writing (review and editing). YM, PP, SS, JK and DL contributed to the formal analysis, investigation, writing (original draft), and writing (review and editing). All the authors read and approved the final manuscript.
Funding
None.
Data availability
The datasets generated and/or analysed during the current study are not publicly available to protect the privacy of the study participants but are available from the corresponding author upon reasonable request.
Declarations
Ethics approval and consent to participate
Good clinical care guidelines were followed, and the guidelines were established as per the Helsinki Declaration 2008. All the participants were given clear instructions about the study before the start of the study. Written informed consent was obtained from the patients in the vernacular language for study participation. No identifying information or images have been included in the original article, which was submitted for publication in an online open-access publication. The entire methodology and protocol were approved by the Institutional Ethical Committee of Shri M P Shah Government Medical College, Jamnagar, Gujarat, India. The study protocol was reviewed and approved by the institutional review board or ethics committee. (approval number: 03/01/2023).Our study is not a clinical trial(so clinical trial registration number is not applicable).
Consent for publication
Not Applicable.
Competing interests
The authors declare no competing interests.
Footnotes
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Data Availability Statement
The datasets generated and/or analysed during the current study are not publicly available to protect the privacy of the study participants but are available from the corresponding author upon reasonable request.