Abstract
Background
Malnutrition has been widely recognized as one of the critical modifiable determinants of adverse clinical outcomes in chronic obstructive pulmonary disease (COPD). However, the relationship between nutritional status and COPD across a broader population defined by lung function, as well as its association with acute exacerbations of COPD (AECOPD), remains unclear.
Methods
This study analyzed 3,591 participants with available lung function from the National Health and Nutrition Examination Survey 2007–2012. COPD was defined as a post-bronchodilator forced expiratory volume in one second (FEV1) to forced vital capacity (FVC) ratio below 0.70. Multivariable logistic and Cox regression were conducted to examine the relationship between nutritional status and COPD. Furthermore, cross-sectional data from consecutive AECOPD patients hospitalized at our institution from July 2024 to December 2024 were analyzed.
Results
In the NHANES study, participants with COPD exhibited poorer nutritional status than healthy controls. Body mass index (BMI) was associated with the presence of COPD (odds ratio [OR] 0.93, 95% confidence interval [CI] 0.929 to 0.931, p < 0.001) and all-cause mortality risk (OR 0.970, 95% CI 0.946 to 0.995, p = 0.019). When categorized by BMI levels, overweight had a lower risk of severe COPD (OR 0.394, 95%CI 0.307 to 0.507, p = 0.019) and all-cause mortality (OR 0.662, 95%CI 0.475 to 0.922, p = 0.015) compared to those with normal or underweight status. Other indicators, including malnutrition as defined by the geriatric nutritional risk index, as well as albumin and hemoglobin concentrations, were also associated with mortality in COPD participants. In the clinical research, the prevalence of malnutrition was approximately 48% in AECOPD. Malnourished patients not only presented with worse nutritional status but were also older, had more severe disease manifestations, and exhibited worse overall conditions. Moreover, nutritional indicators were significantly correlated with various clinical symptom scores in AECOPD.
Conclusion
Nutritional status may serve as a significant independent predictor of disease severity and clinical outcomes across the spectrum of COPD. Early implementation of systematic nutritional screening and nutritional interventions could potentially optimize clinical outcomes and modify disease progression in COPD.
Supplementary Information
The online version contains supplementary material available at 10.1186/s12890-025-03755-1.
Keywords: Chronic obstructive pulmonary disease, Nutrition, Body mass index, Prognosis
Background
Chronic obstructive pulmonary disease (COPD) is a progressive and irreversible condition characterized by airflow limitation, yet it is preventable and treatable. Currently, COPD ranks among the top three leading causes of death globally and imposes a significant burden on healthcare systems [1]. The presence of various comorbidities in COPD can further worsen the condition and lead to a poor prognosis. Among these comorbidities, malnutrition demonstrates particularly high epidemiological relevance, exhibiting a prevalence of approximately 30% in COPD populations [2]. The pathogenesis of malnutrition in COPD is complex and multifaceted, involving factors such as labored breathing and increased energy consumption, gastrointestinal congestion and reduced appetite leading to inadequate nutrient intake, oxidative stress caused by chronic hypoxia resulting in energy metabolic imbalance, and the impact of medications (Fig. 1) [3, 4]. Meanwhile, emerging evidence indicates that malnutrition is also closely associated with the risk and prognosis of COPD [5]. It not only negatively impacts exercise capacity, muscle function, and lung function but also increases the risk of exacerbations and mortality in COPD patients [6, 7]. However, most of these studies are based on COPD diagnoses made using clinical symptoms and lung function, defined by the Global Initiative for Chronic Obstructive Lung Disease (GOLD) criteria as a forced expiratory volume in one second (FEV1) to forced vital capacity (FVC) ratio of less than 0.7 [8], which may lead to underdiagnosis [9]. Similar to Alzheimer’s disease (AD), COPD is a heterogeneous condition that operates along a spectrum, with pathological changes occurring before clinical symptoms manifest [1, 10]. Further investigation into the link between malnutrition and COPD, particularly in the early stages or within broadly defined categories of the disease, such as COPD defined by lung function, is essential for guiding early prevention and treatment decisions.
Fig. 1.
Pathogenesis of malnutrition in COPD
The primary manifestations of malnutrition in COPD patients include reduced body mass index (BMI), decreased muscle mass, hypoalbuminemia, anemia, and others [11]. Nutritional status can be assessed using single and composite indicators, providing a comprehensive evaluation. Single indicators include lean body mass, BMI, muscle mass, albumin, and hemoglobin (Hb) levels, while composite indicators encompass the Nutritional Risk Screening 2002 (NRS 2002) and the Geriatric Nutritional Risk Index (GNRI), among others [12–14]. In this study, we comprehensively evaluated the relationship between nutrition, as represented by multiple nutritional assessment indicators, and broadly defined COPD, including disease severity, cognitive function, and mortality, using data from the National Health and Nutrition Examination Survey (NHANES). Additionally, few studies have explored the relationship between nutrition and acute exacerbations of chronic obstructive pulmonary disease (AECOPD). We also utilized the data from AECOPD patients hospitalized at our institution to evaluate the relationship between nutritional status and AECOPD.
Methods
NHANES study
NHANES is a large cross-sectional population survey managed by the Centers for Disease Control and Prevention, designed to collect data on the health and nutritional status of the civilian noninstitutionalized U.S. population. Participants are selected through a complex, multistage probability sampling method, with oversampling of certain subgroups. Sample weights provide adjusted, unbiased data that can be generalized to the entire U.S. population. The survey was approved by the National Center for Health Statistics Institutional Review Board, which obtained informed consent from all participants [15, 16]. All data used in this study are publicly available and can be accessed through relevant keywords on the NHANES website (https://www.cdc.gov/nchs/nhanes/index.htm).
In this study, data from three cycles of the NHANES 2007–2008, 2009–2010, and 2011–2012 were integrated. Participants aged ≥ 50 years who had completed spirometry tests were included. However, participants with conditions that could potentially impact nutritional status, such as severe infections (white blood cell > 30,000/µL) or severe liver (alanine transaminase levels are elevated to more than 5 times the upper limit of normal) and renal dysfunction (chronic kidney disease stages 4–5), were excluded. Participants with an FEV1/FVC ratio ≥ 0.7 were included as healthy controls. Those with an FEV1/FVC ratio < 0.7 underwent further evaluation through a bronchodilator test. Participants with a post-bronchodilator FEV1/FVC ratio < 0.7 were classified into the COPD group, while those with a post-bronchodilator FEV1/FVC ratio ≥ 0.7 were excluded from the study.
The following information was collected: (1) Demographic information: age, sex, race, education level, and marital status. (2) Physical measurements: height, weight, and waist circumference (WC). BMI was calculated as weight in kilograms divided by height in square meters. Due to the small sample size of the underweight group (BMI < 18.5 kg/m²), the underweight and normal weight categories were combined into the “normal weight” group. Thus, BMI was classified into three levels: normal (< 25 kg/m²), overweight (25 to 30 kg/m²), and obesity (≥ 30 kg/m²). (3) Medical history: hypertension and diabetes mellitus were defined as self-reported physician-diagnosed conditions; participants were classified as former smokers if they had smoked at least 100 cigarettes in their lifetime, and as current smokers if they had smoked within the last month. (4) Laboratory tests: Hb concentrations, liver and kidney function, albumin levels. Anemia was defined as Hb concentrations below 12 g/dL for females and below 13 g/dL for males [17]. (5) Lung function parameters: FEV1, FVC, and FEV1/FVC ratio. The predicted FEV1 value was calculated using the NHANES III equations [18] and categorized into two groups: mild to moderate (≥ 50%) and severe (< 50%). (6) Cognitive function: cognitive assessment was conducted for participants aged 60 and above during the 2011–2012 cycle, including the Consortium to Establish a Registry for Alzheimer’s Disease (CERAD) Word Learning test, Animal Fluency test, and Digit Symbol Substitution test (DSST). The CERAD test aims to assess immediate and delayed learning abilities of new verbal information, the Animal Fluency Test primarily tests executive function, and the DSST evaluates processing speed, sustained attention, and working memory [19]. According to previous studies, cognitive impairment was defined as a score below the lowest quartile on cognitive tests (CERAD < 20; Word Fluency < 13; DSST < 36) [20]. (7) Mortality data: the NHANES public-use linked mortality files up to December 31, 2019. These files are linked to the National Death Index using a probabilistic matching algorithm by the National Center for Health Statistics [21]. The data collected included all-cause mortality and deaths due to chronic lower respiratory diseases, as defined by the International Statistical Classification of Diseases, 10th Revision. Nutritional status assessment indicators included BMI, WC-adjusted BMI, Hb, albumin, and the composite nutritional status index, namely the GNRI. GNRI was calculated using albumin levels, weight, and height with the formula: (1.489 * serum albumin) + (41.7 * (weight / ideal weight)), where ideal body mass = 22 × Height (m) × Height (m). A GNRI value of ≤ 98 was indicative of malnutrition [14].
Clinical research
Consecutive AECOPD patients hospitalized in the Department of Respiratory and Critical Care Medicine at the People’s Hospital of Leshan from July 2024 to December 2024 were enrolled in this study. The inclusion criteria comprised patients aged 50 years or older who were diagnosed with AECOPD according to the 2024 GOLD guidelines [1]. All patients in this study were in GOLD grades 2 to 4. The exclusion criteria included: (1) patients who received nutritional support during hospitalization; (2) those with concomitant bronchial asthma, interstitial lung disease, or other pulmonary diseases; and (3) those with hyperthyroidism, malignant tumors, tuberculosis, chronic renal failure, or other chronic wasting diseases. All procedures were approved by the Ethics Committee of Leshan People’s Hospital.
The following data were recorded: (1) Demographic data: age, sex, education level, residence (rural or urban), height, and weight; (2) Medical history: duration of illness, history of chronic diseases (self-reported previous diagnoses of hypertension, diabetes mellitus, etc.), smoking status (including former and current smokers), and use of inhaled medications; (3) Laboratory data: Hb, albumin, prealbumin, and C-reactive protein; (4) Lung function parameters: predicted FEV1 and FEV1/FVC. (5) Clinical evaluation: dyspnea was assessed using the Modified Medical Research Council (mMRC) scale; quality of life was assessed using the COPD Assessment Test (CAT); and frailty was assessed using the FRAIL scale [22]. Nutritional status was assessed using BMI, Hb concentration, albumin levels, NRS 2002, and GNRI. In the Chinese population, the NRS 2002 is more commonly used than the GNRI to assess nutritional status. Therefore, NRS 2002 was employed to evaluate nutritional status in the clinical research section. A score of ≥ 3 on the NRS 2002 indicated malnutrition. Patients were divided into two groups: the malnutrition group (NRS 2002 ≥ 3) and the normal nutrition group (NRS 2002 < 3).
Statistical analysis
All statistical analyses were performed in R programming (version 4.2.2), with significance set at a two-sided p-value < 0.05. In the NHANES study, survey sampling weights were considered during data analysis. Data from three survey cycles (2007–2012) were combined, and the original 2-year sample weights (WTMEC2YR) were divided by 3. Demographic characteristics and examination indicators were compared between COPD participants and healthy controls. Propensity score matching at a 1:2 ratio (nearest neighbor matching algorithm) was performed to control for age differences between the two groups. Continuous variables were presented as mean (standard deviation), and categorical variables were presented as frequency (percentage). Continuous and categorical variables were analyzed using the Student’s t-test and chi-square test, respectively. Multivariate logistic regression analysis was conducted to evaluate the relationship between nutritional indicators (including continuous and categorical variables) and COPD (including its diagnosis, severity, and associated cognitive impairment), adjusting for age, sex, education level, race, hypertension, diabetes mellitus, and smoking status. Severe COPD was defined as GOLD grades 3 to 4. Multivariate Cox regression models were performed to examine the association between nutritional indicators and the risk of all-cause mortality or lower respiratory tract disease mortality, with adjustments for the same covariates. WC is a key cardiovascular risk marker, and BMI adjusted for WC better reflects muscle mass [23]. Therefore, in logistic and Cox regression models, WC was included as a covariate when BMI or BMI categories were considered. In the clinical research, clinical characteristics were compared between the malnutrition and normal nutrition groups. Spearman correlation analysis was conducted to assess the association between nutritional status and clinical evaluation indexes (GOLD grades, mMRC, CAT, and FRAIL scores).
Results
NHANES study
Characteristics of participants
The NHANES study enrolled 1197 cases of COPD, and the detailed data screening procedures are depicted in Fig. 2. After matching age using propensity score matching, 2394 healthy controls were included in the data analysis. Compared to the controls (Table 1), the COPD group had a higher proportion of males (64.41% vs. 46.16%, p = 0.039), lower BMI (27.19 ± 5.15 kg/m2 vs. 29.14 ± 6.01 kg/m2, p = 0.049), a higher proportion of smokers (both former and current, p < 0.05), and higher Hb concentration (14.55 ± 1.40 vs. 14.15 ± 1.40 g/dL, p = 0.041). However, there were no significant differences between the two groups in albumin levels and GNRI (p > 0.05).
Fig. 2.
Flowchart of the participants’ selection from NHANES 2007 to 2012
Table 1.
Baseline characteristics of participants
| Variables | COPD (n = 1197) | Controls (n = 2394) | p-value |
|---|---|---|---|
| Age (years) | 62.93 (7.94) | 63.32 (8.02) | 0.425 |
| Male (%) | 426 (64.41%) | 1289 (46.16%) | 0.039 |
| BMI (kg/m2) | 27.19 (5.15) | 29.14 (6.01) | 0.049 |
| Waistline circumference (cm) | 100.77 (14.77) | 102.79 (15.26) | 0.12 |
| Race | 0.04 | ||
| White | 90 (7.52%) | 329 (13.74%) | |
| African American | 89 (7.44%) | 282 (11.78%) | |
| Mexican American | 720 (60.15%) | 1100 (45.95%) | |
| Hispanic (other) | 232 (19.38%) | 538 (22.47%) | |
| Other | 66 (5.51%) | 145 (6.06%) | |
| Education beyond high school | 838 (70.01%) | 1683 (70.30%) | 0.681 |
| Material status | 0.807 | ||
| Married/living with partner | 775 (64.75%) | 1526 (63.74%) | |
| Other conditions | 422 (35.25%) | 868 (36.26%) | |
| Covered by health insurance | 1037 (86.63%) | 2080 (86.88%) | 0.918 |
| Former smoker | 878 (73.35%) | 1062 (44.36%) | 0.002 |
| Current smoker | 381 (31.83%) | 251 (10.48%) | < 0.001 |
| Hypertension | 616 (51.46%) | 1305 (54.51%) | 0.41 |
| Diabetes mellitus | 212 (17.71%) | 481 (20.09%) | 0.178 |
| Hemoglobin (g/dL) | 14.55 (1.40) | 14.15 (1.40) | 0.041 |
| ALT (U/L) | 23.93 (12.17) | 25.27 (13.66) | 0.347 |
| Albumin (g/L) | 42.56 (2.83) | 42.52 (2.99) | 0.735 |
| TC (mmol/l) | 5.21 (1.13) | 5.27 (1.09) | 0.553 |
| eGFR (ml/min) | 79.79 (16.07) | 79.46 (15.93) | 0.259 |
| UA (umol/l) | 337.49 (79.72) | 334.19 (80.60) | 0.089 |
| HbAlc % | 5.84 (0.86) | 5.91 (0.94) | 0.07 |
| GNRI | 104.70 (4.50) | 104.78 (4.51) | 0.672 |
Note: ALT, alanine transaminase; TC, total cholesterol; eGFR, estimated glomerular filtration rate; GNRI, Geriatric nutritional risk index. Data were presented as mean (standard deviation) or frequency (%). All statistics have been weighted to account for the NHANES complex survey design
Association between nutritional status and the risk and severity of COPD
As shown in Table 2, a higher BMI was associated with a lower risk of COPD (Odds ratio [OR] 0.930, 95%confidence interval [CI] 0.929 to 0.931, p < 0.001). Overweight (OR 0.664, 95%CI 0.617 to 0.714, p = 0.008) and obesity (OR 0.384, 95%CI 0.370 to 0.397, p < 0.001) were associated with a lower risk of COPD. After adjusting for WC, a higher BMI and obesity remained associated with a lower risk of COPD. GNRI-defined malnutrition was associated with a higher risk of COPD (OR 1.156, 95% CI 1.110 to 1.205, p = 0.02). Other indicators, including albumin levels, Hb concentration, anaemia, and GRNI, were not significantly associated with COPD risk (p > 0.05).
Table 2.
Association between nutritional status and COPD
| Variables | Diagnosis of COPD | Severe COPD | ||
|---|---|---|---|---|
| OR (95%CI) | p | OR (95%CI) | p | |
| Continuous index | ||||
| BMI | 0.930 (0.929, 0.931) | < 0.001 | 1.015 (0.958, 1.075) | 0.667 |
| BMIa | 0.874 (0.838, 0.911) | 0.024 | 0.866 (0.763, 0.984) | 0.158 |
| Albumin | 0.991 (0.962, 1.022) | 0.628 | 0.907 (0.867, 0.918) | 0.004 |
| Hb | 1.050 (0.944, 1.167) | 0.464 | 0.99 (0.945, 1.037) | 0.705 |
| GNRI | 0.986 (0.963, 1.010) | 0.372 | 0.917 (0.883, 0.954) | 0.049 |
| Category index | ||||
| Normal weight | Reference | Reference | ||
| Overweight | 0.664 (0.617, 0.714) | 0.008 | 0.610 (0.222, 1.679) | 0.44 |
| Obesity | 0.384 (0.370, 0.397) | < 0.001 | 1.297 (0.825, 2.038) | 0.377 |
| Overweighta | 0.622 (0.499, 0.776) | 0.052 | 0.394 (0.307, 0.507) | 0.019 |
| Obesitya | 0.330 (0.215, 0.508) | 0.037 | 0.491 (0.139, 1.733) | 0.384 |
| Normal Hb | Reference | Reference | ||
| Anaemia | 0.878 (0.623, 1.238) | 0.536 | 1.927 (0.607, 6.118) | 0.382 |
| Normal GNRI | Reference | Reference | ||
| Malnutrition | 1.156 (1.110, 1.205) | 0.02 | 2.772 (2.431, 3.160) | 0.004 |
Note: OR, odds ratio; CI, confidence interval; BMI, body mass index; Hb, hemoglobin; GNRI, geriatric nutritional risk index. All models were adjusted for age, sex, race, education levels, hypertension, diabetes mellitus, and smoking status. Models additionally adjusted for waist circumference were marked with an “a”
Albumin levels (OR 0.907, 95%CI 0.867 to 0.918, p = 0.004), GNRI (OR 0.917, 95%CI 0.883 to 0.954, p = 0.049), and GNRI-defined malnutrition (OR 2.772, 95%CI 2.431 to 3.160, p = 0.004) were associated with the risk of more severe COPD. After adjusting for WC, overweight (OR 0.394, 95%CI 0.307 to 0.507, p = 0.019) was associated with a lower risk of severe COPD. BMI and other indicators were not significantly associated with the risk of more severe COPD (p > 0.05).
Association between nutritional status and cognitive function in COPD participants
In the NHANES 2011–2012 cycle, cognitive assessment data were available for 232 COPD participants, and the relationship between nutrition and cognitive function was analyzed. After adjusting for WC, a higher BMI was associated with a lower risk of cognitive impairment as defined by CREAD, and overweight was associated with higher odds of cognitive impairment as defined by DSST (p < 0.05, Table 3). No significant associations were observed between other nutritional assessment indicators and cognitive impairment (p > 0.05).
Table 3.
Association between nutritional status and cognitive impairment in COPD participants
| Variables | CERAD | Animal Fluency | DSST | |||
|---|---|---|---|---|---|---|
| OR (95%CI) | p | OR (95%CI) | p | OR (95%CI) | p | |
| Continuous index | ||||||
| BMI |
1.001 (0.995, 1.006) |
0.828 |
0.999 (0.989, 1.010) |
0.929 |
0.996 (1.978, 1.015) |
0.742 |
| BMIa |
0.985 (0.979, 0.991) |
0.044 |
0.999 (0.936, 1.066) |
0.970 |
1.021 (0.987, 1.056) |
0.356 |
| Albumin |
0.997 (0.980, 1.013) |
0.736 |
0.995 (0.986, 1.005) |
0.442 |
0.999 (0.997, 1.001) |
0.320 |
| Hb |
0.997 (0.985, 1.010) |
0.734 |
1.033 (0.989, 1.079) |
0.285 |
1.005 (0.936, 1.079) |
0.899 |
| GNRI |
1.0 (0.986, 1.014) |
0.993 |
0.997 (0.989, 1.007) |
0.615 |
0.998 (0.997, 1.001) |
0.218 |
| Category index | ||||||
| Normal weight | Reference | Reference | Reference | |||
| Overweight |
0.979 (0.824, 1.164) |
0.834 |
0.929 (0.812, 1.063) |
0.394 |
0.999 (0.930, 1.073) |
0.984 |
| Obesity |
1.025 (0.996, 1.054) |
0.233 |
0.978 (0.920, 1.040) |
0.559 |
0.947 (0.801, 1.120) |
0.59 |
| Overweighta |
0.922 (0.796, 1.067) |
0.389 |
0.913 (0.835, 0.997) |
0.181 |
1.111 (1.072, 1.152) |
0.029 |
| Obesitya |
0.908 (0.763, 1.081) |
0.391 |
0.944 (0.822, 1.086) |
0.507 |
1.170 (0.947, 1.446) |
0.282 |
| Normal Hb | Reference | Reference | Reference | |||
| Anaemia |
1.029 (0.989, 1.070) |
0.298 |
1.060 (0.697, 1.612) |
0.811 |
1.072 (0.674, 1.706) |
0.797 |
| Normal GNRI | Reference | Reference | Reference | |||
| Malnutrition |
1.039 (0.996, 1.085) |
0.219 |
0.909 (0.830, 0.995) |
0.173 |
0.940 (0.865, 1.021) |
0.281 |
Note: CERAD, the consortium to establish a registry for Alzheimer’s disease; DSST, digit symbol substitution test; OR, odds ratio; CI, confidence interval; BMI, body mass index; Hb, hemoglobin; GNRI, geriatric nutritional risk index. Models additionally adjusted for waist circumference were marked with an “a”
Association between nutritional status and mortality in COPD
Continuous variables, including BMI, Hb concentrations, and GNRI, as well as categorical variables, including overweight, anaemia, and malnutrition, were associated with all-cause mortality risk (p < 0.05, Table 4). After adjusting for WC, being overweight was still associated with all-cause mortality risk (p < 0.05). Additionally, GNRI and malnutrition were associated with lower respiratory tract disease mortality risk (p < 0.05).
Table 4.
Association between nutritional status and mortality in COPD participants
| Variables | All-cause mortality | Lower respiratory mortality | ||
|---|---|---|---|---|
| HR (95%CI) | p | HR (95%CI) | p | |
| Continuous index | ||||
| BMI | 0.970 (0.946, 0.995) | 0.019 | 0.927 (0.848, 1.012) | 0.091 |
| BMIa | 0.962 (0.914, 1.013) | 0.139 | 0.894 (0.747, 1.069) | 0.218 |
| Albumin | 0.969 (0.931, 1.009) | 0.127 | 0.920 (0.813, 1.041) | 0.187 |
| Hb | 0.950 (0.912, 0.989) | 0.013 | 1.316 (0.994, 1.742) | 0.110 |
| GNRI | 0.947 (0.923, 0.971) | < 0.001 | 0.902 (0.796, 1.024) | < 0.001 |
| Category index | ||||
| Normal weight | Reference | Reference | ||
| Overweight | 0.594 (0.449, 0.786) | < 0.001 | 0.320 (0.121, 0.845) | 0.021 |
| Obesity | 0.804 (0.596, 1.080) | 0.147 | 0.717 (0.291, 1.767) | 0.470 |
| Overweighta | 0.662 (0.475, 0.922) | 0.015 | 0.419 (0.134, 1.309) | 0.135 |
| Obesitya | 1.014 (0.627, 1.640) | 0.954 | 1.273 (0.277, 5.850) | 0.756 |
| Normal Hb | Reference | Reference | ||
| Anaemia | 1.762 (1.232, 2.521) | 0.002 | 1.168 (0.264, 5.169) | 0.838 |
| Normal GNRI | Reference | Reference | ||
| Malnutrition | 2.468 (1.726, 3.529) | < 0.001 | 5.138 (2.122, 12.443) | < 0.001 |
Note: HR, hazard ratio; CI, confidence interval; BMI, body mass index; Hb, hemoglobin; GNRI, geriatric nutritional risk index. Models additionally adjusted for waist circumference were marked with an “a”
Supplementary analysis
When COPD was diagnosed based on clinical symptoms and lung function, the results were similar to those of COPD defined solely by lung function, though the associations were less pronounced (Tables S1, S2, and S3). Additionally, nutritional status was associated with lung function parameters, including FEV1, FVC, and FEV1/FVC ratio (Tables S4 and S5).
After excluding underweight participants from the analysis, we found the relationship between different BMI categories and COPD to be consistent with our prior findings (Table S6).
The nonlinear association between BMI and COPD, along with its prognosis, was assessed through restricted cubic spline analysis. The restricted cubic spline analysis showed a nonlinear association between BMI and COPD risk (p = 0.03), severity (p < 0.01), and mortality (p < 0.001), but no such association was found between BMI and cognitive impairment (Figure S1, S2, S3).
Clinical research
Clinical characteristics of AECOPD patients with malnutrition
A total of 149 consecutive patients were included in the clinical study, with a malnutrition prevalence of approximately 48%. Compared to the normal nutritional group (Table 5), the malnutrition group was elderly (75.5 ± 8.90 years vs. 69.8 ± 9.11 years, p < 0.001 ), had a lower proportion of patients with high school education or above (14.1% vs. 41.0%, p = 0.001), and exhibited lower BMI (20.3 ± 3.23 kg/m2 vs. 22.5 ± 3.0 kg/m2, p < 0.001), GNRI (86.7 ± 6.86 vs. 93.6 ± 7.75, p < 0.001), and albumin levels (33.3 ± 3.80 g/L vs. 36.0 ± 4.81 g/L, p < 0.001). Additionally, they exhibited poorer kidney function, higher CAT, FRAIL, and mMRC scores, a higher proportion of more severe GOLD grades (grades 3 and 4), and prolonged hospital days.
Table 5.
Comparison of clinical characteristics between the malnutrition and normal nutrition groups in AECODP patients
| Variables | Malnutrition (N = 71) | Normal (N = 78) | p-value |
|---|---|---|---|
| Age (years) | 75.5 (8.90) | 69.8 (9.11) | < 0.001 |
| Male (%) | 48 (67.6%) | 59 (75.6%) | 0.365 |
| BMI (kg/m2) | 20.3 (3.23) | 22.5 (3.00) | < 0.001 |
| Education beyond high school | 10 (14.1%) | 32 (41.0%) | 0.001 |
| Hypertension (%) | 26 (36.6%) | 18 (23.1%) | 0.103 |
| Diabetes mellitus (%) | 13 (18.3%) | 8 (10.3%) | 0.24 |
| Somker (%) | 40 (56.3%) | 47 (60.3%) | 0.750 |
| Hemoglobin (g/L) | 126 (18.3) | 130 (19.1) | 0.153 |
| Lymphocyte (10^9) | 0.86 (0.42) | 1.21 (0.61) | < 0.001 |
| Albumin (g/L) | 33.3 (3.80) | 36.0 (4.81) | < 0.001 |
| Prealbumin (mg/L) | 169 (67.9) | 180 (64.0) | 0.328 |
| D-dimer | 3.79 (5.93) | 1.42 (2.08) | 0.002 |
| GFR (ml /min /1.73 m2) | 76.7 (21.5) | 85.2 (19.3) | 0.013 |
| C-reactive protein (mg/L) | 63.8 (75.6) | 39.2 (56.0) | 0.027 |
| NRS 2002 | 3 [3, 4] | 2 [1,2] | < 0.001 |
| GNRI | 86.7 (6.86) | 93.6 (7.75) | < 0.001 |
| CAT | 25 [22, 29] | 16 [12, 20] | < 0.001 |
| Frail | 3 [2, 3] | 1 [1, 2] | < 0.001 |
| mMRC | 4 [3, 4] | 3 [2, 3] | < 0.001 |
| use of inhaled drugs (%) | 57 (80.3%) | 54 (69.2%) | 0.175 |
| Gold grade | < 0.001 | ||
| Gold 2 | 0 (0.00%) | 17 (21.8%) | |
| Gold 3 | 16 (22.5%) | 35 (44.9%) | |
| Gold 4 | 55 (77.5%) | 26 (33.3%) | |
| hospital days | 10 [8, 12.5] | 8 [7, 10] | 0.011 |
Note: AECOPD, acute exacerbations of chronic obstructive pulmonary disease; BMI, body mass index; CAT, COPD Assessment Test; mMRC, Modified Medical Research Council. Data were presented as mean (standard deviation), median (interquartile range), or frequency (%)
Association between nutritional index and clinical symptom scores
As shown in Fig. 3, significant correlations were observed between various nutritional indices and clinical evaluation indicators, with NRS 2002, GNRI, and albumin levels showing the strongest associations.
Fig. 3.
Spearman correlation between nutritional indicators and clinical symptom scores. Note: BMI, body mass index; albumin; Hb, hemoglobin; GNRI, geriatric nutritional risk index; NRS, nutritional risk screening 2002; CAT, COPD Assessment Test; mMRC, Modified Medical Research Council. * denotes statistically significant correlations (p < 0.05) with effect sizes exceeding the threshold of|p| ≥ 0.2, where p represents the Spearman correlation coefficient
Discussion
In this study, we comprehensively investigated the relationship between nutritional status and COPD. Our study revealed two key findings: (1) In the broadly defined COPD population, nutritional status was found to be associated with disease severity, cognitive impairment, and mortality risk; (2) Malnutrition was common among AECOPD patients, and nutritional status was closely associated with disease severity and clinical conditions. Our findings further strengthen the link between malnutrition and COPD, highlighting the need for nutritional monitoring and clinical intervention throughout the disease progression process.
NHANES study
Early identification of risk factors or poor prognostic indicators, such as malnutrition, combined with proactive interventions, may contribute to disease prevention and management in early COPD [24]. Nevertheless, few studies have investigated the relationship between nutrition and early COPD. Establishing the relationship between nutrition and early COPD may provide scientific evidence for implementing early nutritional interventions in COPD patients, thereby facilitating earlier treatment and extending the window for effective intervention. Analogous to the use of Aβ in defining AD pathology, we defined COPD based on lung function (FEV1/FVC < 70%) to represent a broader or earlier-stage population and investigated the association between nutrition and COPD.
We compared the nutritional indicators of healthy controls and COPD participants and found that COPD individuals had a lower BMI compared to the healthy population, consistent with previous studies [25]. Evidence suggests that Hb levels in COPD may be either reduced or elevated [26, 27], and our results align with the latter. One possible explanation is that elevated Hb levels in COPD individuals may be linked to early-stage hypoxia, which triggers erythropoiesis as a compensatory response [28]. In COPD participants, other nutritional indicators, such as albumin levels and GNRI, did not exhibit significant differences when compared to those in the healthy population. These results may be attributed to the inclusion of a broader range of COPD participants, many of whom were in the early stages of the disease and only mildly malnourished, which could explain the lack of significant differences compared to the healthy population.
Evidence suggests that malnutrition is associated with an increased risk of clinically diagnosed COPD [2]. Similarly, we observed that nutritional status, including BMI, BMI categories, and GNRI-defined malnutrition, was also associated with the risk of COPD in a broad population. Furthermore, the association between BMI and COPD risk remained significant even after adjusting for WC. Notably, BMI adjusted for WC may more accurately reflect muscle mass [23], which is positively associated with lung function [29]. Therefore, a higher BMI adjusted for WC was associated with a lower risk of COPD. However, other nutritional indicators did not show a significant association with the risk of COPD, suggesting that BMI and GNRI may be more sensitive markers for predicting early COPD risk compared to other nutritional metrics in a broad COPD population.
We further explored the relationship between nutrition and the severity of COPD. The association between BMI and the risk of severe COPD was found to be statistically insignificant. However, when BMI was categorized, overweight adjusted for WC was associated with a lower risk of severe COPD, whereas obesity adjusted for WC was not significantly associated with the risk. These findings suggest that there may be a potential nonlinear relationship between BMI and COPD severity, and our supplemental restricted cubic spline analysis supports this possibility. Maintaining an overweight status may help preserve lung function in COPD patients, potentially due to the higher respiratory muscle mass associated with this BMI category [23]. Some other nutritional indicators, including albumin levels, GNRI, and GNRI-defined malnutrition, were also found to be significantly associated with the risk of severe COPD. Previous studies have also demonstrated an association between malnutrition and the severity of COPD [30]. The potential mechanisms by which malnutrition affects lung function include reduced respiratory muscle mass, weakened immune defenses, and increased susceptibility to lung infections, ultimately leading to a decline in lung function [31]. Although previous studies have indicated that nutritional support may have effects on improving lung function [32], its potential impact on lung function in the earlier stages of COPD remains uncertain. Further research is needed to explore this aspect.
Cognitive impairment is frequently observed in COPD patients, with a prevalence of approximately 20–30% [33]. Evidence suggests that both malnutrition and cognitive impairment are associated with a poorer prognosis in COPD [5, 34]. Few studies have investigated the relationship between malnutrition and cognitive function in COPD. In this study, we observed that a higher BMI was associated with a lower risk of cognitive impairment, as measured by the CERAD test, consistent with findings by Huang et al., who reported that malnutrition was linked to lower scores on the Mini-Mental State Examination [35], supporting the connection between nutritional status and cognitive function in COPD. However, we also found that being overweight was associated with a higher risk of cognitive impairment as assessed by the DSST test, which contrasts with previous findings. The limited sample size may have influenced these results, and the relationship between nutritional status and cognitive impairment may be complex, with moderate BMI levels potentially offering protective benefits while excessive adiposity contributes to cognitive decline. Future studies should incorporate more precise measures of body composition, longitudinal designs, and mechanistic investigations to better explore these relationships. The exact mechanisms linking malnutrition and cognitive impairment in COPD remain unclear. Based on the current research, this may be attributable to the following factors: (1) Malnutrition may reflect a reduction in respiratory muscle mass, leading to compromised respiratory function. This, in turn, can result in hypoxemia and hypercapnia, both of which may adversely affect cognitive function [36]; (2) Malnutrition signifies a global deficiency in essential nutrients, including trace elements, vitamin D3, glucose, and other substances critical for neuronal metabolism. Such deficiencies may disrupt neuronal function and contribute to cognitive decline [37, 38]; (3) Patients with COPD often experience cognitive impairments, such as AD, and the pathology of AD may, in turn, contribute to malnutrition [39]. These findings suggest that COPD patients with malnutrition are at a higher risk of cognitive impairment, highlighting the importance of improved nutritional management to help delay cognitive decline in this population.
COPD is the third leading cause of death globally after COVID-19. Various factors, including nutritional status, may influence COPD mortality [1]. Previous research suggests nutritional status is associated with a higher mortality risk in COPD [40, 41]. In this study, we further examined the relationship between nutrition and all-cause mortality within a broad COPD population. We demonstrated that BMI was associated with all-cause mortality, which is consistent with previous research [41]. Prior studies have shown a U-shaped relationship between BMI and mortality in patients with COPD [42]. Our analysis also confirms a nonlinear link between BMI and mortality in COPD. In this study, irrespective of adjustments for WC, being overweight was consistently associated with a lower risk of mortality. This finding suggests that maintaining a BMI of 25–30 kg/m² in COPD may be optimal, as it indicates adequate muscle mass while avoiding the increased cardiovascular risk associated with obesity [43]. We also found that both Hb decline and anemia were associated with a higher risk of mortality. Hb is the primary carrier of oxygen, and a decrease in Hb or the occurrence of anemia may further worsen hypoxia and exacerbate breathing difficulties [44], thereby being linked to an increased risk of mortality in COPD. We observed no significant association between albumin levels and mortality risk, which may be attributed to the broadly defined COPD population, where albumin levels showed minimal decline. In contrast, GNRI, which incorporates the combined effects of height, weight, and albumin levels, effectively predicts mortality. This makes it a simple yet reliable composite indicator of nutritional status in the elderly. It has been shown to predict mortality in both the general population and in individuals with COPD [5, 14]. The above evidence demonstrates that nutritional status was associated with mortality in a broad COPD population. Studies have shown that nutritional support can improve the nutritional condition of malnourished patients and their prognosis [45, 46]. Therefore, nutritional monitoring and intervention should be prioritized in clinical practice for the broadly defined COPD population.
Clinical research
Few studies have investigated the relationship between malnutrition and AECOPD. In the present study, we found that the prevalence of malnutrition was higher in AECOPD patients (about 48%), aligning with findings from previously published literature [47]. Evidence suggests that malnutrition is closely associated with the risk of acute exacerbations in COPD [8], and there may be a bidirectional relationship between the two. On the one hand, AECOPD patients may experience increased energy expenditure due to respiratory effort, reduced appetite, insufficient intake, and inflammatory effects, all of which can worsen malnutrition. On the other hand, malnutrition is often accompanied by reduced muscle mass and weakened immune function, which in turn increases the risk of acute exacerbations in COPD patients [47]. Compared to patients with normal nutritional status, those with malnutrition were found to be in poorer overall condition. In addition to having poorer nutritional status, they were older, had lower education levels, higher D-dimer and C-reactive protein levels, and more severe lung function grading. These worsened conditions made their treatment more complex, extended their hospital stays, and led to higher medical costs [48]. Additionally, malnourished AECOPD patients exhibited higher clinical symptoms and prognostic assessment scores, reflecting a worse prognosis. These findings underscore the urgent need to strengthen nutritional support for AECOPD patients.
Limitations
Several limitations of this study should be noted: (1) Due to the cross-sectional design of this clinical study, it was unable to assess the association between nutritional status and prognosis in AECOPD patients. (2) In the NHANES study, defining COPD based on lung function may lead to underdiagnosis in younger participants. However, by including only participants aged 50 and above, we likely minimized the potential for this bias. (3) In the NHANES study, underweight participants are relatively rare. Consequently, we merged the underweight and normal weight groups. This approach might influence the analysis of overweight and obese participants. However, our supplementary analyses showed consistent results. (4) Preserved ratio impaired spirometry is increasingly recognized as a potential precursor stage of COPD. Nevertheless, this study did not evaluate the relationship between nutritional status and such cases. Future research needs to investigate the link between nutrition and COPD progression in preserved ratio impaired spirometry and AECOPD patients to provide valuable clinical insights.
Conclusion
Our study suggests that nutritional status is associated with disease severity, cognitive function, and mortality risk in a broad COPD population. Additionally, malnutrition was frequently observed in AECOPD patients, with nutritional status associated with the severity of clinical symptoms. These findings underscore the critical role of nutrition in COPD management, highlighting its potential impact on disease progression, symptom burden, and prognosis. Our study reinforces the importance of incorporating nutritional assessment and intervention into the treatment of patients with COPD.
Electronic supplementary material
Below is the link to the electronic supplementary material.
Acknowledgements
We sincerely acknowledge the NHANES for providing invaluable data resources that made this research possible. We are deeply grateful to all participants whose contributions were essential to the success of this study.
Abbreviations
- AECOPD
Acute exacerbations of chronic obstructive pulmonary disease
- BMI
Body mass index
- CAT
The COPD Assessment Test
- CERAD
The Consortium to Establish a Registry for Alzheimer’s Disease
- CI
Confident interval
- COPD
Chronic obstructive pulmonary disease
- DSST
Digit Symbol Substitution test
- FEV1
Forced expiratory volume in one second
- FVC
Forced vital capacity
- GNRI
Geriatric Nutritional Risk Index
- GOLD
Global Initiative for Chronic Obstructive Lung Disease
- Hb
Hemoglobin
- mMRC
The Modified Medical Research Council
- NHANES
The National Health and Nutrition Examination Survey
- NRS 2002
Nutritional Risk Screening 2002
- OR
Odds ratio
- WC
Waist circumference
Author contributions
YY, WL, and HLW conceived and designed the study. YY and MQY performed data acquisition and investigation. YY, MQY, YX, and LCL conducted statistical analysis, interpreted the results, and generated visualizations. YY, MQY, WL, and HLW were involved in critical revision, data curation, and reviewing the final manuscript. All authors contributed to drafting the manuscript, reviewed multiple versions, provided intellectual input, and approved the final version.
Funding
This work was supported by the Sichuan Medical Association No. S20019.
Data availability
The datasets generated and analyzed during the current study are available in the NHANES repository. Please see the https://www.cdc.gov/nchs/nhanes/index.htm for more details. The data of the clinical study utilized in this research are accessible from the corresponding authors upon a reasonable request.
Declarations
Ethics approval and consent to participate
The experimental protocol was established according to the ethical guidelines of the Helsinki Declaration and was approved by the Human Ethics Committee of Leshan People’s Hospital. Written informed consent was obtained from individual or guardian participants.
Consent for publication
Not applicable.
Competing interests
The authors declare no competing interests.
Clinical trial number
Not applicable.
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.
Supplementary Materials
Data Availability Statement
The datasets generated and analyzed during the current study are available in the NHANES repository. Please see the https://www.cdc.gov/nchs/nhanes/index.htm for more details. The data of the clinical study utilized in this research are accessible from the corresponding authors upon a reasonable request.