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Abstract

Objective

Sarcopenia poses a significant global public health burden, underscoring the urgency of identifying reliable risk markers for early detection and intervention. This study aimed to elucidate the association between the neutrophil-to-high-density lipoprotein cholesterol ratio and sarcopenia as well as explore how various covariates modify this relationship.

Methods

A cross-sectional analysis was performed using data from 9040 individuals who participated in the National Health and Nutrition Examination Survey 2011–2018, of whom 721 were diagnosed with sarcopenia. Multivariable logistic regression models were employed to estimate the odds ratios and 95% confidence intervals for the association between the neutrophil-to-high-density lipoprotein cholesterol ratio and sarcopenia. Restricted cubic spline regression analysis was utilized to assess the nonlinear relationship between the neutrophil-to-high-density lipoprotein cholesterol ratio and sarcopenia risk. Subgroup analysis was performed to identify the relationship between different subgroups of neutrophil-to-high-density lipoprotein cholesterol ratio and sarcopenia. Considering the conflict between the lower age threshold and the diagnostic criteria for sarcopenia, we conducted sensitivity analysis among participants aged ≥40 years.

Results

Multivariable logistic regression analysis revealed that the neutrophil-to-high-density lipoprotein cholesterol ratio was positively associated with sarcopenia risk (odds ratio = 1.16, 95% confidence interval: 1.11–1.22, P < 0.0001). Restricted cubic spline regression analysis demonstrated a nonlinear relationship between the neutrophil-to-high-density lipoprotein cholesterol ratio and sarcopenia risk (nonlinear P < 0.05). Subgroup analyses indicated that sex, race, and hyperlipidemia significantly modified the association between the neutrophil-to-high-density lipoprotein cholesterol ratio and sarcopenia risk (P for interaction <0.05).

Conclusions

An elevated neutrophil-to-high-density lipoprotein cholesterol ratio is associated with a high risk of sarcopenia, indicating that the ratio can effectively assess the risk of sarcopenia and may contribute to early diagnosis and preventive intervention.

Keywords

Neutrophil-to-high-density lipoprotein cholesterol ratio sarcopenia skeletal muscle neutrophil high-density lipoprotein cholesterol National Health and Nutrition Examination Survey

Introduction

Sarcopenia, marked by the gradual loss of skeletal muscle mass, weakening of muscle strength, and diminished physical capacity, is a condition closely linked to the aging process. It can increase the risk of falls, lead to disability, cause metabolic disorders, and raise the all-cause mortality rate.¹ According to the definition of sarcopenia by the European Working Group on Sarcopenia in Older People (EWGSOP), the incidence of sarcopenia among Europeans aged 40–79 years is 1.6%, while that among Chinese individuals with an average age of 72 years is 3.4%.² Sarcopenia is often caused by factors such as genetics, environment, and lifestyle. Recent studies have shown that abnormalities in the immune system and body metabolism are associated with sarcopenia. Chronic low-grade inflammation and metabolic imbalance may contribute to sarcopenia development by promoting protein degradation and inhibiting muscle synthesis.^3,4

The neutrophil-to-high-density lipoprotein cholesterol ratio (NHR) is an emerging comprehensive inflammatory-metabolic biomarker that has garnered attention due to its predictive value in cardiovascular diseases and metabolic syndrome. Neutrophils are key effector cells of the innate immune system, and their elevation indicates systemic inflammation. In contrast, high-density lipoprotein cholesterol (HDL-C) maintains metabolic homeostasis through its anti-inflammatory, antioxidant, and reverse cholesterol transport functions. The NHR reflects inflammatory activity and perturbations in lipid metabolism. It is worth noting that muscle metabolism is closely related to the inflammatory microenvironment, and lipid metabolism disorders may accelerate muscle atrophy through mechanisms such as induction of insulin resistance and mitochondrial dysfunction.⁵ Therefore, exploring the relationship between NHR and sarcopenia may not only help clarify its potential pathophysiological mechanisms but also provide new targets for early screening and intervention.

Nonetheless, current clinical data regarding the relationship between NHR and sarcopenia are relatively restricted, and their causal relationship and clinical application value need further research and validation. This study aimed to systematically explore the potential role of NHR in sarcopenia to provide theoretical support for risk stratification and precise intervention in patients with sarcopenia.

Methods and materials

Data extraction

This cross-sectional study employed data derived from the National Health and Nutrition Examination Survey (NHANES), a nationally representative health surveillance program.

There were 116,876 participants in the pooled cycles of NHANES. Participants aged ≤20 years (n = 52,563) and those with missing data on sarcopenia assessment (n = 39,232), HDL-C and neutrophil count (n = 11,245), and other covariates (n = 4796) were excluded.

Subsequently, individuals with missing data on educational level, race, poverty-to-income ratio (PIR), body mass index (BMI), healthy eating index (HEI), vigorous work activity, moderate work activity, smoking pattern, alcohol consumption, diabetes, hypertension, and hyperlipidemia (n = 4796) were also excluded.

The final analytical cohort comprised 9040 adults; written informed consent was obtained from each participant. Ethical waiver was obtained for this investigation from the National Center for Health Statistics Ethics Review Board (Protocols # 2011-18). The flowchart showing the selection of participants is illustrated in Figure 1. Our study was conducted in accordance with the Helsinki Declaration of 1975, as revised in 2024. We have de-identified all patient details. Our research followed the relevant EQUATOR guidelines, and the reporting of this research conformed to the Strengthening the Reporting of Observational studies in Epidemiology (STROBE) guidelines.⁶

Figure 1.

Flowchart illustrating the selection of study populations from the NHANES 2011–2018. NHANES: National Health and Nutrition Examination Survey.

Definition of NHR

The NHR is defined as the ratio of the neutrophil count (measured in units of 1000 cells/μL) to the HDL-C level (measured in mmol/L). Complete blood count (CBC) analysis was performed using the Beckman Coulter DxH 800 analyzer (Beckman Coulter, Miami, FL, USA). The NHANES Mobile Examination Center utilizes this device to collect detailed full blood count reports and cell distribution data from participants, thereby ensuring the accuracy and reliability of the neutrophil count. The HDL-C levels were accurately measured using the Roche Modular P (Roche Diagnostics, Mannheim, Germany) and Cobas 6000 systems (Roche Diagnostics) through enzymatic assays. All methods were performed in compliance with the NHANES CBC protocol, which provides standardized procedures for accurate and reproducible data collection.

Diagnosis of sarcopenia

According to the diagnostic criteria for sarcopenia established by the Foundation for the National Institutes of Health (FNIH), the appendicular skeletal muscle mass was measured using dual-energy X-ray absorptiometry, and the skeletal muscle index was calculated as the ratio of appendicular skeletal muscle mass (in kg) to BMI (in kg/m²). The cutoff values for diagnosing sarcopenia are different for men (0.789) and women (0.512).⁷

Covariates

Our investigation considered multiple covariates related to NHR and sarcopenia risk, which were divided into three essential categories: demographic data, lifestyle factors, and health status. Demographic data included age, sex, race (race and ethnicity data were recoded as Mexican American, non-Hispanic White, non-Hispanic Black, and other race), educational level (less than high school, high school, and more than high school), and PIR (ratio of the family income to the poverty threshold). Lifestyle factors included smoking pattern (never: fewer than 100 cigarettes in lifetime; former: more than 100 cigarettes in lifetime but currently not smoking; and now: more than 100 cigarettes in lifetime and currently smoking), alcohol consumption (never: fewer than 12 drinks in lifetime; former: 12 or more drinks in any year but no consumption in the past year or no consumption in the past year but 12 or more drinks in lifetime; mild: 1 drink/day for females and 2 drinks/day for males; moderate: 2 drinks/day for females and 3 drinks/day for males or binge drinking ≥2 times but <5 times; heavy: 3 drinks/day for females and 4 drinks/day for males or binge drinking ≥5 times), activity level (vigorous work activity: work entails vigorous physical effort that leads to considerable elevations in respiratory or cardiac rate; moderate work activity: work entails moderate-intensity tasks that result in a slight elevation in breathing or heart rate), and HEI (which consists of 10 subcomponents, such as grains, fruits, vegetables, dairy products, and meat, and each component is scored on a scale ranging from 0 to 10. Participants with a 2-day average score ≥60 were considered to be adhering to the dietary guidelines or consuming healthy foods). The health status of the participants was assessed based on BMI, HDL-C level (mmol/L), neutrophil count (1000 cells/μL), and medical history of hyperlipidemia, hypertension, diabetes, or Parkinson’s disease.

Statistical analyses

This study used WTMEC2Y and WTMEC4YR as the weighting variables. Detailed information on example weighting code is available at https://www.cdc.gov/nchs/nhanes/search/default.aspx.⁸ Participants were grouped into two categories according to their sarcopenia status. The NHRs of all participants were divided into three groups, labeled Q1, Q2, and Q3. Baseline characteristics were compared using parametric (weighted Student’s t-tests) and nonparametric (χ²) approaches. Continuous measurements were expressed as mean ± standard error, and categorical variables were summarized using prevalence-weighted frequencies. The normality assumption of continuous data was verified using the Kolmogorov–Smirnov test in a pre-analysis. A multivariate logistic regression model was used to explore the relationship between NHR and sarcopenia. Model 1 was adjusted for age and sex. Model 2 was adjusted for race, educational level, and PIR in addition to the parameters in Model 1. Model 3 was adjusted for age, sex, race, educational level, PIR, smoking pattern, alcohol consumption, vigorous work activity, moderate work activity, hyperlipidemia, hypertension, diabetes, and Parkinson’s disease as covariates. All data were analyzed using R (version 4.2.3; R Foundation for Statistical Computing, Vienna, Austria) and RStudio. This study used two-sided statistical tests, with a significance level of P < 0.05. A restricted cubic spline (RCS) regression with 3 knots (10th, 50th, and 90th quartiles) was utilized to examine the correlation between NHR and the risk of sarcopenia, with nonlinearity assessed using analysis of variance. Considering that the lower age threshold conflicts with the diagnostic criteria for sarcopenia, we conducted sensitivity analyses in participants aged ≥40 years.

Results

Baseline characteristics of patients with and without sarcopenia

The baseline characteristics of patients diagnosed with sarcopenia from 2011 to 2018 are presented in Table 1. The final analysis comprised 9040 participants, including 721 individuals diagnosed with sarcopenia and 8319 without sarcopenia. The NHR quartiles (NHRQ) were Q1 (median = 1.835, n = 3017), Q2 (median = 3.072, n = 3010), and Q3 (median = 5.06, n = 3013). There were statistically significant differences (P < 0.05) in terms of age, race, educational level, alcohol consumption, PIR, hyperlipidemia, hypertension, diabetes, BMI, HEI, HDL-C, neutrophil count, and NHR between the sarcopenia and nonsarcopenia groups. In addition, the proportion of individuals with sarcopenia in Q1 was lower than that in Q3, demonstrating that participants with increased NHR had a higher incidence of sarcopenia (Table 1). We also assessed the relationship between different NHRQ and each variable (Supplementary Table 1).

Table 1.

Baseline characteristics of the participants with and without sarcopenia.

Variables	Total(N = 9040)	Sarcopenia group(N = 721)	Nonsarcopenia group(N = 8319)	P value
Age, years	39.42 ± 0.27	42.78 ± 0.57	39.18 ± 0.29	≤0.0001
Sex, n (%)				0.079
Male	4545 (49.46)	357 (45.41)	4188 (49.75)
Female	4495 (50.54)	364 (54.59)	4131 (50.26)
Race, n (%)				≤0.0001
Mexican American	1295 (9.90)	244 (25.12)	1051 (8.82)
Non-Hispanic Black	1880 (10.73)	52 (4.09)	1828 (11.20)
Non-Hispanic White	3411 (63.99)	206 (49.96)	3205 (64.99)
Other	2454 (15.37)	219 (20.83)	2235 (14.98)
Educational level, n (%)				≤0.0001
<High school	1477 (11.53)	206 (21.09)	1271 (10.85)
>High school	5602 (67.05)	323 (50.43)	5279 (68.23)
High school	1961 (21.42)	192 (28.47)	1769 (20.92)
PIR, n (%)				≤0.0001
<130%	2911 (22.73)	300 (32.78)	2611 (22.01)
≥350%	2888 (42.57)	156 (26.27)	2732 (43.73)
130%–349%	3241 (34.71)	265 (40.95)	2976 (34.26)
BMI, kg/m²	28.88 ± 0.14	35.66 ± 0.36	28.39 ± 0.13	≤0.0001
HEI				0.001
Healthy	2113 (23.47)	136 (17.47)	1977 (23.89)
Unhealthy	6927 (76.53)	585 (82.53)	6342 (76.11)
Smoking pattern, n (%)				0.083
Former	1537 (19.65)	144 (22.39)	1393 (19.45)
Never	5452 (59.02)	456 (60.34)	4996 (58.92)
Now	2051 (21.33)	121 (17.27)	1930 (21.62)
Alcohol consumption, n (%)				≤0.0001
Former	870 (8.65)	100 (13.47)	770 (8.31)
Heavy	2337 (26.94)	188 (28.49)	2149 (26.83)
Mild	2985 (34.36)	187 (26.43)	2798 (34.93)
Moderate	1706 (20.49)	90 (13.48)	1616 (20.99)
Never	1142 (9.56)	156 (18.13)	986 (8.95)
Vigorous work activity, n (%)				0.773
No	6861 (74.53)	558 (75.23)	6303 (74.48)
Yes	2179 (25.47)	163 (24.77)	2016 (25.52)
Moderate work activity, n (%)				0.072
No	5314 (55.47)	462 (60.24)	4852 (55.13)
Yes	3726 (44.53)	259 (39.76)	3467 (44.87)
Hypertension, n (%)				≤0.0001
No	6468 (72.81)	419 (57.83)	6049 (73.87)
Yes	2572 (27.19)	302 (42.17)	2270 (26.13)
Parkinson’s disease, n (%)				0.3
No	8982 (99.19)	714 (98.77)	8268 (99.23)
Yes	58 (0.80)	7 (1.23)	51 (0.77)
Diabetes, n (%)				≤0.0001
No	7381 (84.39)	468 (67.47)	6913 (85.59)
Yes	1659 (15.61)	253 (32.53)	1406 (14.41)
Hyperlipidemia, n (%)				≤0.0001
No	3340 (36.88)	161 (24.94)	3179 (37.73)
Yes	5700 (63.12)	560 (75.06)	5140 (62.27)
HDL-C, mmol/L	1.37 ± 0.01	1.25 ± 0.02	1.38 ± 0.01	≤0.0001
Neutrophil count, 1000 cells/μL	4.30 ± 0.03	4.93 ± 0.09	4.26 ± 0.03	≤0.0001
NHR	3.48 ± 0.04	4.29 ± 0.09	3.41 ± 0.04	≤0.0001
NHRQ				≤0.0001
Q1	3017 (32.90)	128 (17.95)	2889 (33.97)
Q2	3010 (33.82)	234 (28.99)	2776 (34.17)
Q3	3013 (33.28)	359 (53.06)	2654 (31.87)

Continuous variables are represented as means with standard error (SE), while categorical variables are shown as counts with corresponding percentages.

NHR: neutrophil-to-high-density lipoprotein cholesterol ratio; PIR: poverty-to-income ratio; BMI: body mass index; HEI: healthy eating index; HDL-C: high-density lipoprotein cholesterol; NHRQ: NHR quartiles.

Association between NHR and sarcopenia

Our study developed three models to assess the relationship between NHR and sarcopenia (Table 2). In the unadjusted model, compared with Q1, the odds ratios (ORs) for sarcopenia in Q2 and Q3 were 1.61 (95% confidence interval (CI): 1.18–2.18, P = 0.003) and 3.15 (95% CI: 2.27–4.37, P < 0.0001), respectively. Even after adjusting for all confounding factors in Model 3, compared with Q1, the OR for sarcopenia in Q3 was 2.19 (95% CI: 1.52–3.15, P < 0.0001). Compared with Q1, Q3 demonstrated a significant relationship with an elevated OR for sarcopenia (P for trend <0.0001) (Table 2).

Table 2.

Association between NHR and sarcopenia.

Sarcopenia	Crude model		Model 1		Model 2		Model 3
Sarcopenia	OR (95% CI)	P	OR (95% CI)	P	OR (95% CI)	P	OR (95% CI)	P
NHR	1.23 (1.18, 1.27)	<0.0001	1.23 (1.19, 1.28)	<0.0001	1.19 (1.14, 1.24)	<0.0001	1.16 (1.11, 1.22)	<0.0001
Q1	Ref	–	Ref	–	Ref	–	Ref	–
Q2	1.61 (1.18, 2.18)	0.003	1.64 (1.23, 2.20)	0.001	1.43 (1.04, 1.95)	0.03	1.30 (0.95, 1.77)	0.1
Q3	3.15 (2.27, 4.37)	<0.0001	3.21 (2.33, 4.42)	<0.0001	2.56 (1.83, 3.59)	<0.0001	2.19 (1.52, 3.15)	<0.0001
P for trend		<0.0001		<0.0001		<0.0001		<0.0001

Model 1 was adjusted for age and, sex.

Model 2 was adjusted for age, sex, race, educational level, and PIR.

Model 3 was adjusted for age, sex, race, educational level, PIR, HEI, smoking pattern, alcohol consumption, vigorous work activity, moderate work activity, hypertension, diabetes, Parkinson’s disease, and hyperlipidemia.

OR: odds ratio; CI: confidence interval; NHR: neutrophil-to-high-density lipoprotein cholesterol ratio; PIR: poverty-to-income ratio; HEI: healthy eating index.

After adjusting for covariates, RCS regression demonstrated a significant nonlinear correlation between NHR and sarcopenia risk (nonlinear P < 0.05). Higher NHRs are associated with a progressive increase in sarcopenia risk (Figure 2).

Figure 2.

Nonlinear relationships between NHR and sarcopenia. The ORs, shown as solid lines, were adjusted for factors including age, sex, race, educational level, PIR, BMI, smoking pattern, alcohol consumption, vigorous work activity, moderate work activity, hypertension, diabetes, Parkinson’s disease, and hyperlipidemia. The corresponding 95% CIs are indicated by shaded regions. NHR: neutrophil-to-high-density lipoprotein cholesterol ratio; OR: odds ratios; PIR: poverty-to-income ratio; BMI: body mass index; CIs: confidence intervals.

Subgroup analyses

To examine the relationship between NHR and sarcopenia across different subpopulations, we performed stratified analyses by subgroups such as sex, age, race, educational level, PIR, BMI, activity, smoking pattern, alcohol consumption, diabetes, hypertension, hyperlipidemia, and Parkinson’s disease (Figure 3). Significant interaction was detected for sex (P for interaction = 0.01), with the correlation between NHR and sarcopenia being stronger in men (OR = 1.27 (1.22, 1.33), P < 0.001) than in women (OR = 1.16 (1.10, 1.22), P < 0.001). In terms of race (P for interaction = 0.006), compared with other races, non-Hispanic Black (OR = 1.42 (1.25, 1.60), P < 0.001) individuals showed a stronger correlation between NHR and sarcopenia risk. Significant interactions were observed between hyperlipidemia and sarcopenia (P for interaction = 0.006). The inverse correlation between NHR and sarcopenia was more pronounced in participants with hyperlipidemia (OR = 1.12 (1.05, 1.18), P < 0.001) than in those without (OR = 1.23 (1.18, 1.29), P < 0.001).

Figure 3.

Stratified analysis of NHR and sarcopenia. NHR: neutrophil-to-high-density lipoprotein cholesterol ratio.

Sensitivity analyses

Considering that the lower age threshold conflicted with the diagnostic criteria for sarcopenia, we conducted sensitivity analyses in participants aged ≥40 years, which revealed no differences compared with participants aged ≥20 years, as detailed in Table 3. In addition, we conducted a separate calculation of C-reactive protein (CRP) level; the results are shown in Supplementary Table 2. The results showed that the CRP levels were significantly higher in individuals with sarcopenia than in those without sarcopenia, which also indicated that the inflammatory level in individuals with sarcopenia was generally higher than that in individuals without sarcopenia. The collinearity diagnosis results of continuous variables are shown in Supplementary Table 3. It was found that the variance inflation factor (VIF) was <10, suggesting no obvious collinearity.

Table 3.

Baseline characteristics of patients aged ≥40 years.

Variables	Total(N = 4420)	Sarcopenia group(n = 460)	Nonsarcopenia group(n = 3960)	P value
Age, years	49.64 ± 0.13	50.99 ± 0.38	49.52 ± 0.15	0.001
Sex, n (%)				0.61
Male	2123 (49.12)	212 (50.66)	1911 (48.98)
Female	2297 (50.88)	248 (49.34)	2049 (51.02)
Race, n (%)				≤0.0001
Mexican American	617 (7.84)	154 (22.34)	463 (6.56)
Non-Hispanic Black	961 (10.18)	35 (4.29)	926 (10.71)
Non-Hispanic White	1676 (68.55)	134 (54.18)	1542 (69.83)
Other	1166 (13.42)	137 (19.19)	1029 (12.91)
Educational level, n (%)				≤0.0001
<High school	807 (11.89)	143 (21.22)	664 (11.07)
>High school	2647 (66.04)	208 (53.38)	2439 (67.16)
High school	966 (22.06)	109 (25.41)	857 (21.77)
PIR, n (%)				≤0.0001
<130%	1233 (17.16)	183 (28.78)	1050 (16.13)
≥350%	1644 (50.39)	115 (30.79)	1529 (52.13)
130%–349%	1543 (32.45)	162 (40.43)	1381 (31.74)
BMI, kg/m²	29.47 ± 0.16	35.33 ± 0.49	28.95 ± 0.15	≤0.0001
HEI				≤0.001
Healthy	1171 (25.68)	89 (17.10)	1067 (26.07)
Unhealthy	3249 (74.32)	371 (82.89)	2893 (73.94)
Smoking pattern, n (%)				0.125
Former	930 (23.74)	111 (28.64)	819 (23.31)
Never	2478 (54.79)	271 (54.01)	2207 (54.86)
Now	1012 (21.47)	78 (17.35)	934 (21.84)
Alcohol consumption, n (%)				≤0.0001
Former	584 (11.68)	82 (18.46)	502 (11.08)
Heavy	910 (21.80)	93 (23.26)	817 (21.67)
Mild	1576 (38.02)	130 (28.50)	1446 (38.87)
Moderate	785 (19.77)	55 (12.06)	730 (20.45)
Never	565 (8.73)	100 (17.71)	465 (7.93)
Vigorous work activity, n (%)				0.781
No	3439 (76.19)	365 (77.01)	3074 (76.12)
Yes	981 (23.81)	95 (22.99)	886 (23.88)
Moderate work activity, n (%)				0.361
No	2759 (58.80)	308 (62.49)	2451 (58.48)
Yes	1661 (41.19)	152 (37.51)	1509 (41.52)
Hypertension, n (%)				≤0.0001
No	2576 (60.70)	211 (45.03)	2365 (62.09)
Yes	1844 (39.30)	249 (54.97)	1595 (37.91)
Parkinson’s disease, n (%)				0.998
No	4376 (98.71)	455 (98.71)	3921 (98.71)
Yes	44 (1.29)	5 (1.29)	39 (1.292)
Diabetes, n (%)				≤0.0001
No	3203 (77.36)	258 (59.45)	2945 (78.95)
Yes	1217 (22.64)	202 (40.55)	1015 (21.05)
Hyperlipidemia, n (%)				0.128
No	1142 (24.68)	83 (20.46)	1059 (25.06)
Yes	3278 (75.32)	377 (79.55)	2901 (74.94)
HDL-C, mmol/L	1.39 ± 0.01	1.29 ± 0.03	1.40 ± 0.01	0.002
Neutrophil count, 1000 cells/μL	4.29 ± 0.04	4.84 ± 0.11	4.24 ± 0.04	≤0.0001
NHR	3.42 ± 0.05	4.14 ± 0.13	3.36 ± 0.05	≤0.0001
NHRQ				≤0.0001
Q1	1472 (32.92)	92 (21.90)	1380 (33.89)
Q2	1474 (33.22)	156 (27.93)	1318 (33.69)
Q3	1474 (33.86)	212 (50.17)	1262 (32.42)

Continuous variables are represented as means with standard error (SE), while categorical variables are shown as counts with corresponding percentages.

Discussion

To clarify the relationship between NHR and sarcopenia, we performed a cross-sectional analysis of 9040 individuals from the NHANES database. Our research revealed a notable association between NHR and sarcopenia risk. Moreover, even after considering potential confounders, the high NHR group presented a significantly increased probability of sarcopenia compared with the low NHR group. The link was especially strong in men, non-Hispanic Black individuals, and those with diabetes or hyperlipidemia. In summary, higher NHRs are linked to an increased likelihood of sarcopenia.

Sarcopenia pathogenesis is closely linked to chronic, low-grade inflammation. With aging, the body may develop a low-grade chronic inflammatory state without obvious infection. This inflammatory state interferes with the balance of muscle protein synthesis and decomposition through the continuous expression of proinflammatory cytokines (such as tumor necrosis factor-α and interleukin-6) and eventually leads to progressive reduction in skeletal muscle mass, strength, and physical capacity.⁹ Previous NHANES studies have found that the markers of systemic inflammation, such as systemic immune-inflammation index and neutrophil-to-lymphocyte ratio (NLR), are demonstrably associated with an elevated risk of sarcopenia, particularly among individuals with inflammatory conditions, such as chronic kidney disease and hepatocellular carcinoma.^10–12 Nonetheless, no research has yet examined the correlation between NHR and sarcopenia.

Neutrophils, which are key innate immune effector cells, play a dual role in inflammation–sarcopenia interaction. They exacerbate muscle damage through the release of reactive oxygen species (ROS) and proteolytic enzymes.¹³ In contrast, single-cell RNA sequencing has identified neutrophil subpopulations that promote inflammation resolution, which may influence sarcopenia progression.¹⁴ In obesity-related sarcopenia, neutrophil infiltration interacting with adipose tissue inflammation accelerates muscle atrophy.¹⁵ Moreover, neutrophil-derived circular RNA may participate in diabetes-related inflammatory muscle loss via microRNA regulatory networks.¹⁶

Abnormal lipid metabolism is closely linked to sarcopenia. Research has demonstrated that the individuals with sarcopenia often have higher total cholesterol, triglycerides (TG), and residual cholesterol but lower HDL-C levels.^17,18 A Chinese study showed that each 15.4 mg/dL drop in the HDL-C increases the sarcopenia risk.¹⁹ Low HDL-C can cause chronic inflammation and insulin resistance, accelerating muscle breakdown.²⁰ Additionally, HDL-C has antioxidant properties. Low HDL-C can cause ROS accumulation, damaging mitochondrial energy metabolism.²¹ Metabolic disorders such as obesity and diabetes often coexist with low HDL-C and may confound their relationship. However, extremely high HDL-C levels can increase the risk of muscle strength reduction and sarcopenia in older adults, suggesting a nonlinear association between HDL-C and muscle health.²² Therefore, it is crucial to monitor HDL-C levels in older adults to maintain muscle health.

Subgroup analysis revealed that the relationship between NHR and sarcopenia incidence was significantly different between the two sexes. Estrogen has a protective effect on muscle and lipid metabolism, which may partially offset the risk caused by elevated NHR in women.²³ Conversely, androgen’s proinflammatory properties may strengthen the association of NHR with sarcopenia in men.²⁴ Consistently, the effect size of NHR was significantly higher in men than in women, indicating that males with sarcopenia are more sensitive to the risk caused elevated NHR. Thus, closer NHR monitoring is required in men. For men with high NHR, focusing on therapies that alleviate inflammation and improve lipid metabolism, such as healthy lifestyle interventions and statins, is essential to lower the sarcopenia risk.

Hyperlipidemia typically accelerates sarcopenia via persistent inflammation and oxidative stress.²⁵ However, our research shows that in patients with hyperlipidemia, NHR is linked to a lower risk of muscle mass reduction. This may be attributed to the fact that this population adopted a healthier lifestyle, including exercise and healthy diet, following the diagnosis of hyperlipidemia, resulting in biased results. In addition, metabolic disorders caused by the low-quality diet among Hispanic Black individuals may be responsible for the increased NHR and accelerated muscle loss.

This study leveraged the NHANES database, which has a key advantage of a large sample size, resulting in enhanced generalizability and reliability of the results. Moreover, the study introduces a novel approach by combining neutrophil counts and HDL-C levels to create a composite biomarker, NHR. This biomarker integrates inflammation and lipid metabolism, offering a holistic assessment of sarcopenia risk factors and mechanisms. By evaluating these processes jointly in a unified model, rather than separately, the study elucidates their interactive contributions to sarcopenia.

There are certain limitations in this study. First, the cross-sectional study design precludes definitive inferences about causality. An elevated NHR may either contribute to sarcopenia or simply be a marker commonly observed in individuals with sarcopenia. Second, the limitations of the NHANES database prevented a focused analysis of the high-risk older population. Third, our study did not consider the effect of drugs that affect lipid metabolism, such as statins and antipsychotic drugs, which may confound the association between NHR and sarcopenia risk through insulin resistance or dyslipidemia. Multi-center longitudinal studies that integrate muscle imaging (such as ultrasound and magnetic resonance imaging) and molecular testing and conduct a comparison with established markers (such as NLR and TG-glucose index) are needed in the future to extensively clarify the unique relationship between NHR and sarcopenia.

Conclusion

This study used the NHANES database to examine the relationship between NHR and sarcopenia risk. The results indicate that an elevated NHR is correlated with a higher risk of sarcopenia. This suggests that NHR can help evaluate the sarcopenia risk, potentially aiding in early diagnosis and preventive interventions.

Supplemental Material

sj-pdf-1-imr-10.1177_03000605251367610 - Supplemental material for Relationship of the neutrophil-to-high-density lipoprotein cholesterol ratio with sarcopenia: A cross-sectional study

Supplemental material, sj-pdf-1-imr-10.1177_03000605251367610 for Relationship of the neutrophil-to-high-density lipoprotein cholesterol ratio with sarcopenia: A cross-sectional study by Tao Zhang, Ya Zhou, Long-Fei Luo, Deng-Jun Ji, Liang Wang, Jun Xie, An-Ning Zhu, Jun-Tao Yan, Zhen Yan, Li Gong and Wu-Quan Sun in Journal of International Medical Research

Footnotes

Acknowledgements

We extend our deepest gratitude to the dedicated professionals at the National Center for Health Statistics (NCHS) for their invaluable contributions to this study. Their meticulous efforts in the design and implementation of the National Health and Nutrition Examination Survey (NHANES), rigorous data collection protocols, and sustained commitment to maintaining the NHANES database have been instrumental in advancing public health research. We are profoundly grateful for their generosity in sharing this critical resource.

Author contributions

Tao Zhang and Ya Zhou conceived the study framework, coordinated the implementation, performed the statistical analyses, and supervised data validation. Long-Fei Luo, Liang Wang, and Deng-Jun Ji prepared the initial manuscript draft and conducted subsequent refinements. Jun-Tao Yan, Zhen Yan, and Li Gong critically reviewed the content and enhanced the methodological rigor. An-Ning Zhu, Jun Xie, and Wu-Quan Sun provided technical expertise and cross-checked interpretations. All authors participated in manuscript revisions, endorsed intellectual content, and approved the publication-ready version of the manuscript.

Data availability statement

The datasets for this study can be found at .

Declaration of conflicting interests

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Funding

This research work was supported by the National Natural Science Foundation of China [grant number 82305425], the Science and Technology Development Project of Shanghai University of Traditional Chinese Medicine [grant numbers 23KFL119 and 24KFL089], and the Traditional Rehabilitation Medicine Research Project of China [Grant No. SMC2013].

ORCID iD

Tao Zhang

Supplemental material

Supplemental material for this article is available online.

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