Introduction

Sarcopenia is a disorder that primarily affects older persons and is characterized by a progressive loss of skeletal muscle mass and strength1. This condition leads to decreased physical function and mobility, increasing the risk of falls, fractures, and disability2,3. Recently, sarcopenia has been noted in younger populations, despite its usual association with older age4. Worldwide, the number of sarcopenia patients is projected to rise from 50 million to 200 million in the next 40 years5. Sarcopenia prevalence ranges from 8 to 36% in adults under 60 and from 10 to 27% in individuals aged 60 and above. Additionally, severe sarcopenia is found in 2–9% of adults with an average age of 68.5 years6. Sarcopenia can lead to a substantial rise in healthcare expenses. Hospitalization costs for patients under 65 years old escalate even more than for those over 65 years old7. Although numerous interventions have been suggested to combat sarcopenia, none have been successful in clinical trials thus far8.

The causes of sarcopenia are multifactorial, including age-related changes in muscle biology, hormonal changes, chronic diseases, and lifestyle factors such as physical inactivity and poor nutrition. Emerging evidence has confirmed a strong and intricate connection between obesity and sarcopenia9,10. Furthermore, obesity is marked by insulin resistance, chronic inflammation, disrupted glucose metabolism, and dyslipidemia11. Lipid abnormalities associated with obesity include decreased levels of high-density lipoprotein cholesterol (HDL-C) and higher levels of triglycerides (TG) and low-density lipoprotein cholesterol (LDL-C). Studies carried out reveal that non-HDL cholesterol is the most common dyslipidemia12,13.

There is growing recognition for the non-HDL-C to HDL-C ratio (NHHR) as a new and comprehensive marker for evaluating the lipid composition in atherosclerosis14. Moreover, research on US populations has revealed a possible link between a greater NHHR and a higher risk of type 2 diabetes15. Research has shown that when it comes to the diagnosis of insulin resistance (IR) and metabolic syndrome, NHHR is more useful than conventional lipid markers16. Together, the studies indicate NHHR could be a valuable tool for predicting diseases related to metabolic dysfunction. Thus, establishing NHHR as a novel marker that may lead to better sarcopenia prediction and treatment is crucial, and further research into the relationship between NHHR and sarcopenia is highly important from a scientific standpoint.

As a result, we carried out a thorough investigation to look at the relationship between NHHR and sarcopenia in the American population. Our aim was to give useful epidemiological insights using data from the National Health and Nutrition Examination Survey (NHANES) gathered between 2011 and 2018.

Methods

Study population

In order to provide thorough insights on the health and nutritional status of the American people, the Centers for Disease Control and Prevention’s multi-stage survey approach is used in the cross-sectional NHANES survey. The survey uses a sophisticated multi-stage probability sampling approach to guarantee the accuracy and representativeness of the sample. Each participant gave informed written agreement when they were enrolled in the NHANES, and the National Center for Health Statistics’ ethics review board approved the study.

This study made use of four cycles’ worth of data from the NHANES between 2011 and 2018. The criteria for inclusion were as follows: (1) individuals who were older than 20 years of age; (2) individuals who had complete data on sarcopenia; and (3) individuals who had complete data on the NHHR.

Initially, 39,156 participants were considered across the four waves. Exclusions were made for the following reasons: (1) participants younger than 20 years (n = 17,095); (2) missing sarcopenia data (n = 11,524); and (3) missing NHHR data (n = 450). Ultimately, the analysis comprised 10,087 participants (Fig. 1).

Fig. 1
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Include participants in the process.

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Assessment of sarcopenia

NHANES measured appendicular skeletal mass (ASM), which includes the non-bone and non-fat tissue in the limbs, using dual-energy X-ray absorptiometry (DEXA). The skeletal muscle index (SMI) was determined by the ratio of total ASM (kg) to BMI (kg/m²). Sarcopenia was defined by the Osteoarthritis Biomarkers Network of the National Institutes of Health as a SMI of less than 0.512 for women and 0.789 for males.

Assessment of NHHR

NHHR was employed as an independent variable in this investigation. The non-HDL to HDL cholesterol ratio, or NHHR, is computed based on the lipid profiles of the individuals.

Assessment of covariates

Numerous confounders were taken into account in this study, such as health status, lifestyle factors, and demographics. The demographic factors used in the study were age, sex, race, poverty index ratio (PIR), and level of education. Based on the household income to the poverty line ratio, PIR was divided into three groups: less than 1, 1 to less than 3, and 3 or more. Lifestyle variables comprised smoking behavior and physical activity levels. The findings of a questionnaire intended to assess smoking behavior were used to classify people as smokers if they had smoked more than 100 cigarettes throughout their lives. The Global Physical Activity Questionnaire was utilized to evaluate the degree of physical activity. The metabolic equivalent (MET) value was calculated using the formula: MET (minutes/week) = MET × frequency per week × duration per activity. Physical inactivity was defined as having a MET value below 600 min per week. Health status was determined by physician diagnoses or self-reports and included conditions such as diabetes, hypertension, and chronic kidney disease.

Statistical analysis

From 2011 to 2018, four survey cycles’ worth of data were gathered and entered into the NHANES database. Using descriptive analysis, the baseline characteristics of the final participants were categorized according to the presence or absence of sarcopenia. Continuous variables were presented as mean and standard deviation. Percentages are used to represent categorical variables. Logistic regression analysis was used to examine the relationship between NHHR and sarcopenia after a number of variables were taken into account. To increase the robustness of the findings and investigate potential associations between varying NHHR levels and sarcopenia, NHHR was transformed from a continuous to a categorical variable. To illustrate the dose-response relationship between NHHR and sarcopenia, restricted cubic spline (RCS) analysis was utilized. Additionally, threshold effect analysis was carried out to ascertain the critical point that exists between the two. A subgroup analysis was conducted in order to investigate the possible influence of age, sex, race, education level, and PIR on the association between sarcopenia and NHHR. To check the consistency and robustness of the findings, sensitivity analysis was done in the end, utilizing a statistical significance level of P < 0.05, all analyses were carried out utilizing R software (version 4.2.3).

Results

Baseline characteristics

The baseline characteristics data extracted from the NHANES database are shown in Fig. 1, with a total of 10,087 participants included, comprising 9,187 non-sarcopenic participants and 900 sarcopenic participants. The characteristics of the participants were categorized based on the presence of sarcopenia, as detailed in Table 1. Compared to those without sarcopenia, sarcopenic patients were generally older, predominantly of Mexican American descent, and had lower educational levels. In terms of lifestyle, these patients tended to be less active and had higher levels of total cholesterol and BMI, as well as lower HDL levels. Additionally, sarcopenic patients were more likely to suffer from diabetes, cardiovascular disease, chronic kidney disease, and hypertension. Notably, these patients had higher NHHR levels, indicating a correlation with sarcopenia.

Table 1 Baseline characteristics of the study population.
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Association between NHHR and Sarcopenia

In this work, logistic regression analysis was used to examine the association between NHHR and sarcopenia prevalence, as shown in Table 2. In model 1, no covariates were adjusted for, and we observed that the frequency of sarcopenia and NHHR showed a strong positive connection (OR = 1.16, 95% CI = 1.10–1.23). After adjusting for age, sex, and race (model 2), a strong positive association between sarcopenia and NHHR remained (OR = 1.11, 95% CI = 1.06–1.17). In Model 3, we further adjusted for additional variables, including educational level, PIR, smoking status, physical activity, hypertension, CAD, CKD, and diabetes. After adjusting for all these factors, the prevalence of sarcopenia increased by 7% (OR = 1.07, 95% CI = 1.02–1.13) for every unit rise in NHHR after other variables were gradually included. Higher NHHR levels were substantially related with an increased prevalence of sarcopenia compared to the lowest quartile (P-trend < 0.001), according to further examination into the relationship between NHHR levels and sarcopenia, when NHHR was converted to a categorical variable. This trend remained for the highest NHHR levels even after adjusting for all variables (OR = 1.77, 95% CI = 1.21–2.59), suggesting a strong positive connection between NHHR and sarcopenia.

Table 2 The relationship between NHHR and Sarcopenia.
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Nonlinear relationship and saturation effect analysis between NHHR and Sarcopenia

A nonlinear relationship between NHHR and the prevalence of sarcopenia was found using RCS analysis (P-non-linear < 0.0001), showing an inverted U-shaped positive correlation (Fig. 2). Threshold effect analysis identified a breakpoint at NHHR = 2.90 within the sarcopenia population. Further segmented logistic regression analysis (Table 3) indicated that on the left side of the breakpoint (NHHR < 2.90), each unit increase in NHHR significantly raised the probability of sarcopenia (OR = 1.62, 95% CI = 1.38–1.92). However, the rise in NHHR did not have a statistically significant influence on the prevalence of sarcopenia when NHHR > 2.90 (P = 0.60), with a log-likelihood ratio test result of less than 0.001.

Fig. 2
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RCS curve fits the Association of NHHR with Sarcopenia. Adjusted for age, Sex, Race, Educational level, PIR, Smoke, Activity status,Hypertension, CAD, CKD, Diabetes.

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Table 3 Two-stage logistic regression between NHHR and Sarcopenia.
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Subgroup analysis

To investigate any possible correlation between NHHR and sarcopenia, subgroup analyses and interaction tests were carried out. We conducted a subgroup analysis of Model 3 by incorporating demographic factors (Fig. 3). The findings showed that, in all populations, there was a positive correlation between NHHR and sarcopenia prevalence. The interaction test results were not statistically significant, further supporting the possibility of NHHR as an independent risk factor for sarcopenia.

Fig. 3
figure 3

Subgroup analysis of the association between NHHR and Sarcopenia. Adjusted for age, Sex, Race, Educational level, PIR, Smoke, Activity status,Hypertension, CAD, CKD, Diabetes.

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Sensitivity analysis

A sensitivity analysis was carried out in order to confirm the stability and coherence of the study findings, as shown in Table 4. After excluding extreme NHHR values (NHHR ± 3SD), 9,972 participants remained. Following full adjustment for covariates, the positive correlation between NHHR and sarcopenia remained stable (OR = 1.13, 95% CI = 1.04–1.23). The results showed a strong positive correlation between high NHHR levels and the prevalence of sarcopenia when NHHR was transformed into a categorical variable.

Table 4 Sensitivity analysis between NHHR and Sarcopenia.
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Discussion

This study investigated the connection between NHHR and sarcopenia in Americans. The correlation held well under several model modifications and statistical methods. A possible nonlinear link is suggested by analysis utilizing RCS regression models, wherein particular NHHR levels are associated with heightened risk indicated by inflection points. These findings provide credence to the theory that NHHR, a measure of lipid metabolic activity, may contribute to the onset of sarcopenia.

According to a European study, sarcopenia prevalence in people 60 years of age and older ranged from 10 to 27% worldwide, whereas severe sarcopenia prevalence varied from 2 to 9%6. According to recent studies, almost 40% of the world’s population suffers from dyslipidemia, with 53.65% of older people being affected17. Sarcopenia is linked to numerous negative health outcomes in older adults, such as increased mortality, disability, and a higher risk of falls supported by highly suggestive evidence18. In a similar way, the prevalence of obesity, defined as an excessive accumulation of fat mass, has been steadily rising. Recent statistics reveal that in the United States, obesity rates among adults over the age of 60 exceed 37.5% for males and 39.4% for females19. Additionally, scientists have found that sarcopenia is more common in people who are overweight or obese20. Sarcopenia obesity (SO) is characterized by the co-occurrence of poor muscle mass and function and excess adiposity. SO is a disorder that is becoming more widely recognized for its clinical and functional characteristics that may have a detrimental impact on significant patient-centered outcomes21. All this evidence suggests that there is a connection between sarcopenia and obesity.

However, the association between sarcopenia and obesity (or dyslipidemia) still lacks clarification. So far, data from a study indicated that females with sarcopenia had LDL-C levels that were 3.1 mg/dL higher compared to those without sarcopenia22. Nonetheless, a study showed that there was no discernible difference in HDL-C and LDL-C levels between individuals with sarcopenia and those without17. The ratio of non-HDL-C to HDL-C, two lipoproteins with different purposes, is represented by the acronym NHHR. A higher NHHR can signify imbalances in lipid metabolism which is typical character of dyslipidemia. Thus, this study primarily uses NHHR as a research indicator to elucidate the relationship between obesity and sarcopenia.

According to the results of this cross-sectional study, which included 10,087 American participants-9,187 of whom were non-sarcopenic and 900 of whom were sarcopenic-sarcopenic patients were typically older, more likely to be of Mexican American descent, and less educated. These patients tended to lead less active lifestyles, with higher BMI, total cholesterol, and HDL levels among their health outcomes. A meta-analysis also suggested that vegetable and fruit intake significantly reduce the risk of sarcopenia23. Additionally, sarcopenic patients were more likely to suffer from diabetes, cardiovascular disease, chronic kidney disease, and hypertension. Researchers have also noted that sarcopenia, an age-related condition, is strongly linked to hypertension due to the vascular damage it causes at both the macrovascular and microvascular levels24,25. Additionally, a review revealed that the most frequent factors linked to sarcopenic obesity are inflammation, hypertension, insulin resistance, dyslipidemia, and inactivity26.

In this study, notably, individuals with elevated NHHR may be more likely to experience sarcopenia, as sarcopenic patients exhibited higher NHHR levels. Subgroup analysis provided more proof that this favorable association is stable. Notably, this link was unaffected by PIR, age, gender, ethnicity, or educational attainment, suggesting that NHHR may function as a separate risk factor for sarcopenia. Furthermore, we discovered that there is a crucial point at an NHHR of 2.90 in the nonlinear association between NHHR and sarcopenia. This suggests that NHHR is positively associated with the prevalence of sarcopenia when the NHHR value is below 2.90. Sensitivity and additional analyses further confirmed the stability of the correlation. Our hypothesis for this observation is that, beyond a certain threshold of NHHR, other factors may come into play that could influence sarcopenia in a non-linear manner. Specifically, at higher levels of NHHR (greater than 2.9), it is possible that the relationship between NHHR and sarcopenia may be confounded by factors such as metabolic dysregulation, systemic inflammation, or other underlying comorbidities that tend to emerge at these higher NHHR levels. Additionally, very high NHHR values could indicate a shift toward a more complex pathophysiological mechanism where the direct association between NHHR and sarcopenia becomes less clear or is attenuated. An experimental study demonstrated that a high-fat diet can reduce muscle mass, strength, and fiber cross-sectional area, while increasing muscle fatty infiltration in naturally aging rats, eventually leading to sarcopenic obesity27. The results of this study indicate that NHHR is a highly valuable tool for early prevention and diagnosis in high-risk populations, as it functions as an independent and early indicator of the likelihood of developing sarcopenia.

Study strengths and limitations

This study has a number of strengths. Above all, by examining a large sample size of 10,087 individuals, this study provides epidemiological evidence of the relationship between NHHR and sarcopenia in the American population. This study also made use of the large NHANES database, which includes a representative sample of people from various parts of the United States. This large dataset offered a solid foundation for investigating the connection between NHHR and sarcopenia risk. The results of this study can be efficiently extended to the total population of the United States by using appropriate sampling weights. Finally, by carefully controlling a variety of confounding variables, this study efficiently minimizes potential biases and improves the study’s robustness and interpretability.

It is crucial to recognize some restrictions. First, due to the cross-sectional nature of the study, a causal relationship between NHHR and sarcopenia cannot be established. Second, depending solely on self-reported information may result in recall bias, especially when it comes to lifestyle choices and sarcopenia history. Furthermore, even after taking into consideration a number of possible confounders, unaccounted for factors may still have an impact on the outcomes. Finally, it is advised to use caution when extrapolating the findings to other groups because the study focused on the U.S. demography. In light of these drawbacks, it will be imperative to conduct additional longitudinal research and develop better techniques for gathering data in order to further our comprehension of the connection between NHHR and the risk of sarcopenia. Additionally, this will help in the advancement of more potent medicinal and preventive approaches.

Conclusion

In conclusion, the significance of NHHR as a predictive biomarker for sarcopenia is highlighted by this study’s result, providing chances for improved risk assessment, focused therapies, individualized management plans, and preventive medicine. By incorporating NHHR tests into clinical practices, healthcare providers can improve patient care by detecting patients who may be at risk for sarcopenia and taking preventative action to reduce this risk, which in turn improves overall health outcomes.