Chronic Obstructive Pulmonary Disease (COPD) is an inflammatory condition, with gradual progression that involves the airway, alveoli, and microvasculature (1). The most recent Global Burden of Disease report has shown that COPD is the fourth cause of death globally, with a global mortality of 45.2/100,000 population (2). There are projected increases of 23% in the number of cases of COPD for those 25 years of age and older to 2050 amounting to 600 million cases globally. Low- and middle-income countries are estimated to have a prevalence over two times greater than that of high-income countries (3). Beyond localized lung inflammation, COPD can also induce systemic changes through systemic inflammation (4). These changes include reduced physical activity levels, as well as a loss of muscle mass and strength, all of which are closely associated with the development of sarcopenia (5, 6). Sarcopenia is significantly linked to increased risks of falls, hospitalization, and mortality (7, 8). The main characteristic of this muscle tissue disease is the progressive decrease of mass as well as strength (9). Global surveys reveal that the prevalence of sarcopenia ranges from 10 to 27%. Regionally, Oceania exhibits the highest prevalence at approximately 40%, followed by South America at 35% (10).

Over the last years, the pathophysiology of the coexistence between COPD and sarcopenia has received more and more attention, presenting among the most common COPD comorbidities. COPD patients contribute to the pathogenesis of sarcopenia by different mechanisms such as inflammation (4), oxidative stress (11), and disuse of skeletal muscles (12). Research has demonstrated that the prevalence of sarcopenia in stable COPD patients is 14.5%, and the prevalence increases gradually with age and disease severity (5). Another 124 patients with COPD, who were recruited in a cross-sectional study, reported prevalence of pre-sarcopenia of 46.3% and of sarcopenia of 12.4% (13). Moreover, in COPD patients sarcopenia strongly increases the chance of a bad prognosis. A prospective study conducted by the National Institute of Respiratory Diseases in Mexico among 240 COPD patients followed for 6.6 years, identified sarcopenia, low muscle strength and low grip strength as independent risk factors for poor prognosis in COPD patients (14). In addition, one study with data from the National Health and Nutrition Examination Survey (NHANES) reported an evidence that sarcopenia was significantly associated with greater all-cause mortality in US patients with COPD (15). The interaction of COPD and Sarcopenia induces a vicious cycle which aggravates the disease progress and increases both physical and mental burden to the patients, leading to a critically poor health status.

Although the prevalence of sarcopenia in patients with COPD is high, studies have shown that early identification and the use of nutritional supplements can significantly improve weight, muscle mass, and physical functions in COPD patients, such as the 6-min walk test, physical activity levels, and the 5-time sit-to-stand test (16). Thus, early identification, diagnosis, and treatment of sarcopenia in individuals with COPD may improve their prognosis and quality of life by recovering their functional ability and easing physical and mental health problems. However, according to the National Institute of Health (NIH) Foundation’s statement on the diagnosis of sarcopenia, current diagnostic methods require the measurement of appendicular lean body weight divided by body mass index (BMI). This diagnostic approach is not only cumbersome but also difficult to implement in some primary healthcare settings due to limited equipment. To date, nomogram models for the diagnosis of sarcopenia in COPD patients have not been widely reported. The advantage of nomograms lies in their ability to transform complex regression models into intuitive visual graphs, which help clinicians assess patient risk more conveniently and directly. As a result, nomograms have been widely applied in clinical settings for the assessment of disease risk and prognosis.

Building on this background, the purpose of this research is to create a nomogram model that can forecast a patient’s chance of developing sarcopenia if they have COPD. This model is designed to assist clinicians in early, accurate, and efficient identification of COPD patients with concurrent sarcopenia. By enabling timely detection, clinicians will be able to implement appropriate interventions, ultimately improving patient prognosis. This study holds significant clinical value, as it provides scientific evidence to guide the management of COPD patients and optimize treatment strategies.

This study developed a nomogram prediction model that uses simple demographic and biochemical factors to predict the risk of sarcopenia in COPD patients. Through regression analysis with LASSO and Analysis of logistic regression using multiple variables, the study identified four key predictive factors: gender, height, BMI, and WWI. The model demonstrated high predictive ability, with AUC values of 0.94 and 0.91 for the training and validation sets, respectively. Internal validation, along with the construction of calibration curves and DCA curves, further supported the model’s reliability and clinical applicability.

According to the NIH (24) and the European Consensus on Sarcopenia Diagnosis (25), the diagnosis of sarcopenia, DXA is regarded as the gold standard. However, the European diagnostic guidelines point out several limitations of DXA: First, while DXA is a non-invasive testing method, its high cost limits its widespread use in community settings. Second, DXA may struggle to accurately distinguish between muscle and fat tissue in individuals with low muscle mass or edema, leading to reduced measurement accuracy. Finally, DXA involves low-dose radiation, and its prolonged or frequent use may pose potential health risks, particularly for older adults or individuals who require frequent monitoring. Therefore, the development of a simple and practical early prediction tool is of paramount importance.

Currently, various screening tools have been developed for sarcopenia in COPD patients, but their applicability and effectiveness remain challenged. A study on the risk factors for sarcopenia in COPD patients proposed a risk prediction model, expressed as: lgitP = −4.323 + 1.094 × X_age + 0.914 × X_BMI + 0.837 × X _{smoking history} + 0.846 × X_{annual acute exacerbation frequency} + 0.410 × X _{CAT score}−0.069 × X_FEV1/FVC% + 0.561 × X_{COPD duration}. However, the model is overly formulaic and complex, with an AUC of 0.756 and specificity of 0.632, indicating low predictive value (26).

In addition, another study investigated whether ultrasonographic evaluation of the rectus femoris muscle in COPD patients could predict sarcopenia and developed a corresponding nomogram model. The model indicated that rectus femoris thickness, cross-sectional area, age, and BMI could predict the occurrence of sarcopenia in COPD patients. However, this study did not consider important factors such as biochemical markers, nutritional status, and daily physical activity (27).

In our study, four key predictive factors were identified: gender, height, BMI, and WWI. From the gender perspective, as age increases, men typically experience a physiological decline in testosterone levels (28). In COPD patients, hypoxemia (29), obesity (30), and glucocorticoid therapy can lead to hypogonadism, resulting in reduced testosterone levels (31). Studies have shown that testosterone treatment increases both low and high concentrations of type I muscle fibers, as well as high concentrations of type II muscle fibers (32). Additionally, testosterone has a high rate of amino acid reutilization within cells, promoting protein synthesis and increasing muscle fiber size (33). In men with hypogonadism undergoing testosterone treatment, significant increases in muscle mass have been observed, with skeletal muscle hypertrophy driven by increased muscle protein synthesis rates (34). In a 3-year double-blind trial, older adult men receiving testosterone replacement therapy showed greater improvements compared to the placebo group (34). Although women experience muscle loss after menopause due to reduced estrogen levels, the impact is generally less pronounced than the effect of testosterone on sarcopenia in men (35). Furthermore, studies have suggested that as COPD progresses, male patients may experience a greater reduction in physical activity, which is a significant risk factor for sarcopenia (36). This finding is consistent with our results.

Our nomogram results indicate that the risk of sarcopenia in male COPD patients is significantly higher than that in females. Based on this finding, we recommend that clinical practice place more emphasis on male COPD patients and implement a range of effective interventions. In the early stages of the disease, it is crucial to regularly assess muscle mass and physical function in male patients. Therefore, male COPD patients should undergo regular physical assessments and receive personalized intervention strategies under the guidance of their healthcare providers. Early pharmacological treatments and non-pharmacological interventions can effectively delay further muscle loss and reduce the risk of developing sarcopenia (16).

Our study also found that as height decreases, the risk of sarcopenia in COPD patients increases. Aging generally leads to a gradual decrease in height (37), particularly around the age of 45, when a noticeable decline begins at a rate of approximately 0.09% per year (38). Significant height loss may indicate underlying skeletal health issues, such as osteoporosis (39) or vertebral fractures (40). Therefore, older adult individuals are at increased risk of height reduction and should be particularly mindful of their bone health. Ji et al. found that individuals with greater height loss are more likely to be diagnosed with sarcopenia (41). A nomogram prediction model for sarcopenia in diabetes was developed in a study with 1,246 diabetic patients, and the results showed that a decrease in height is associated with a higher risk of sarcopenia in diabetic patients (42). In a large sex-stratified study conducted in Korea, stature was identified as an independent clinical risk factor for sarcopenia in both men and women (43). Furthermore, another investigation demonstrated that a loss of more than 4 cm in height is associated with a markedly increased risk of developing sarcopenia (OR = 2.676; 95% CI = 1.122–6.284) (44). Our nomogram model also indicates that height is an important risk factor in the current study, which is consistent with previous research.

In addition, BMI is closely related to the occurrence of sarcopenia in COPD patients. A higher BMI often signifies obesity. Research has indicated that with increasing age, body fat content rises, resting metabolic rate decreases, and inflammation intensifies, all of which promote fat accumulation, increased waist circumference, and ultimately lead to sarcopenia, a phenomenon referred to as “sarcopenic obesity” (45). Moreover, obesity may further exacerbate the development of sarcopenia (46). Similarly, our study demonstrated a consistent increase in sarcopenia scores with rising body mass index (BMI), indicating a positive correlation between higher BMI and an elevated risk of developing sarcopenia.

Our nomogram also includes the WWI as a factor. Previous studies have clearly indicated a significant negative correlation between WWI and ASM (r = −0.511, P < 0.001) (47). In a Korean study involving 5,983 individuals, a high WWI was associated with high fat mass, low muscle mass, and low bone mass, suggesting that WWI could serve as a comprehensive indicator of body composition (48). In another study examining the correlation between WWI and sarcopenia, it was found that after adjusting for confounders, for each increase of 1 unit in WWI, the risk of sarcopenia in men increased by 14.55 times (49). Moreover, in individuals with diabetes, a higher weight-adjusted waist index (WWI) has been identified as an independent risk factor for sarcopenia (OR = 1.836; 95% CI: 1.216–2.772) (50). Consistently, our multivariate logistic regression analysis revealed that for each unit increase in WWI, the risk of developing sarcopenia increased by 4.83 times.

Based on the nomogram model we developed, we recommend early identification and intervention for COPD patients in clinical practice to reduce the risk of sarcopenia (51). First, regular aerobic exercise and resistance training have been shown to play a significant role in maintaining and enhancing muscle strength (52). It is recommended that patients engage in appropriate activities such as walking, cycling, or light resistance training, which not only help improve muscle mass but also enhance endurance and daily functional capacity. Second, nutritional support is another key factor in preventing sarcopenia. Studies have demonstrated that vitamin D plays a crucial role in maintaining muscle function, with vitamin D deficiency being linked to increased muscle weakness and sarcopenia (53). Therefore, ensuring adequate vitamin D levels through supplements or sunlight exposure is essential. Lastly, weight management is particularly important for male COPD patients (54). Maintaining a healthy weight not only reduces the risk of sarcopenia but also improves overall health. Through a balanced diet and moderate exercise, patients can avoid excessive weight loss or obesity. Additionally, avoiding prolonged sitting and bed rest is an effective strategy to prevent muscle wasting (55).

The nomogram prediction model for sarcopenia in COPD patients developed in this study assists clinicians in quickly identifying high-risk COPD patients with sarcopenia under non-invasive and low-cost conditions. The model performs exceptionally well, with an AUC of 0.94 for the training set and 0.91 for the validation set, providing strong evidence for early intervention. The main advantages of this model include: (1) all the indicators used are based on physical measurements, which do not require complex equipment; (2) compared to existing methods that heavily rely on imaging examinations, this model clearly identifies the relevant risks of sarcopenia in COPD patients through simple physical measurements; (3) the nomogram provides an intuitive, convenient, and personalized risk prediction tool with strong potential for wide application.

The nomogram developed in this study demonstrates good clinical usability and potential for broader implementation. Based on four simple, non-invasive indicators (gender, height, BMI, and WWI), it requires no laboratory or imaging tests and is suitable for use in primary care, rehabilitation, and community health settings. Its intuitive interface allows healthcare providers to rapidly assess sarcopenia risk in COPD patients, enabling early detection and stratification. This supports the development of individualized nutritional, physical, and therapeutic interventions and holds significant value in chronic disease management. Despite the high predictive accuracy of the proposed nomogram, several limitations should be acknowledged. First, the retrospective design may introduce selection bias; therefore, large, multi-centre prospective studies are warranted to verify the model’s generalizability and stability. Second, all data were obtained from a single source (NHANES), underscoring the need for external validation using independent, multi-centre cohorts. Third, although age, smoking status, serum albumin level and diabetes history were included in the initial variable pool, they were excluded during LASSO selection because of partial information overlap with other predictors or limited incremental predictive value in the training set. This statistical exclusion does not negate their biological relevance, and their prognostic contribution should be clarified in future prospective or multi-centre investigations. Finally, this study did not differentiate COPD phenotypes, which limits the ability to assess subtype-specific risk patterns. Moreover, data on participants’ use of systemic corticosteroids, inhaled corticosteroids, and GOLD classification were incomplete or unavailable, further restricting the granularity of COPD-related risk stratification. Future studies should address these factors to refine the model’s applicability and predictive accuracy.

An effective and practical technique for the early detection of sarcopenia in COPD patients was created by this study using a nomogram model based on gender, height, BMI, and WWI. The model demonstrates excellent predictive ability and clinical utility, offering valuable guidance for early intervention and improving patient prognosis.