Abstract
Objective:
This study aims to investigate the relationship between nutritional status and quality of life (QOL) in patients with lung cancer, analyze the clinical application of nutritional support, and explore its association with biochemical markers and physical function.
Methods:
A total of 270 hospitalized lung cancer patients were enrolled. Demographic characteristics, cancer staging, and treatment details were collected. Nutritional and functional status were assessed using the Patient-Generated Subjective Global Assessment (PG-SGA), Nutritional Risk Screening 2002 (NRS-2002), and Karnofsky Performance Status (KPS) scores. Quality of life was evaluated with the EORTC QLQ-C30 questionnaire. The relationships between nutritional status, blood biochemical markers, body composition, and quality of life were analyzed.
Results:
Among the 270 hospitalized lung cancer patients analyzed, 74.81% were male, and 80.74% were aged over 65 years. PG-SGA scores indicated that 38.89% of patients were at high nutritional risk (PG-SGA ≥ 9), and 77.04% had not received nutritional support. PG-SGA scores were significantly correlated with several biochemical indicators (e.g., prealbumin, total bilirubin, alanine aminotransferase, and lymphocyte count) and nutritional parameters (e.g., NRS-2002, KPS scores, body weight, and mid-upper arm circumference). Patients with higher PG-SGA scores had significantly lower scores in physical functioning, role functioning, emotional functioning, and social functioning, alongside more severe symptoms such as fatigue, nausea, and pain. Further analysis revealed a negative correlation between PG-SGA scores and overall health status (r = −0.687, p < 0.001) and positive correlations with symptoms such as fatigue, nausea, pain, and insomnia (r > 0.5, p < 0.001).
Conclusion:
Nutritional status significantly impacts the quality of life in patients with lung cancer. PG-SGA scores are strongly associated with patients’ functional abilities and symptom burden. Despite the low utilization of nutritional support, particularly in high-risk groups, improving nutritional interventions may effectively enhance functional status and quality of life in these patients.
Introduction
Lung cancer, as one of the most common and deadliest cancers globally, has become a pressing concern in the field of public health (1, 2). Despite advances in early diagnosis and therapeutic strategies, the quality of life (QOL) of lung cancer patients remains a significant challenge (3–6). This is primarily because most patients are diagnosed at advanced stages and experience multiple complications during treatment. Many lung cancer patients not only face the progression of the tumor itself but also contend with malnutrition, functional impairment, and symptom burden, all of which collectively impact their overall quality of life (7). In recent years, the importance of nutritional status in cancer treatment has gained increasing recognition. Adequate nutrition not only enhances patients’ physical function and immunity but also improves treatment tolerance, reduces complications, and elevates quality of life (8, 9). However, lung cancer patients often face significant nutritional risks during treatment, likely due to advanced disease stages, treatment modalities, and overall health conditions (10). Consequently, assessing the nutritional status of lung cancer patients is critical for developing personalized treatment strategies and improving their quality of life. The Patient-Generated Subjective Global Assessment (PG-SGA) is a widely used clinical tool for nutritional assessment and has been proven effective in evaluating nutritional risk and functional status in cancer patients (11, 12). By integrating medical history, physical signs, and clinical symptoms, PG-SGA provides a comprehensive overview of patients’ nutritional status and functional impairments, enabling clinicians to implement appropriate nutritional interventions during treatment. Lung cancer patients, due to abnormal disease metabolism and treatment side effects, often experience significant weight loss, muscle wasting, and physical decline (13). Therefore, nutritional assessment plays a pivotal role in cancer management. Our study aims to explore the relationships between nutritional status, functional impairment, and quality of life in lung cancer patients. Specifically, it examines the associations of PG-SGA scores with blood biochemical markers, nutritional parameters, and quality-of-life metrics, shedding light on the impact of malnutrition and functional decline on patients’ quality of life.
Materials and methods
Materials
A single-center, cross-sectional observational study was conducted at the Affiliated Cancer Hospital of Zunyi Medical University, Zunyi City, Guizhou Province, China, from January 1, 2016, to January 1, 2019. A total of 270 hospitalized lung cancer patients were enrolled. Inclusion criteria included patients aged 18–80 years, fully conscious, without communication barriers, and able to cooperate with examinations. Participants also needed a histological diagnosis of lung cancer, complete medical history records, and follow-up data. Voluntary participation by both patients and their families was required. Exclusion criteria consisted of patients with AIDS or who had undergone organ transplantation, those in critical condition where accurate assessment was infeasible, and those who refused to participate or were unwilling to cooperate with the questionnaire. The study was approved by the ethics committees of all participating institutions and adhered to the principles of the Declaration of Helsinki.
Assessment method
The Nutritional Risk Screening 2002 (NRS-2002) is a standardized tool recommended by the European Society for Clinical Nutrition and Metabolism (ESPEN) for assessing nutritional risk in hospitalized patients (14, 15). It systematically evaluates three key factors: nutritional status, considering recent weight loss, body mass index (BMI), and dietary intake reduction; disease severity, accounting for increased metabolic demands due to acute or chronic illness; and age, with patients aged ≥70 years receiving an additional risk point. The total score ranges from 0 to 7, with a score of ≥3 indicating nutritional risk and the need for nutritional intervention. By facilitating early identification of at-risk patients, NRS-2002 plays a crucial role in guiding nutritional support strategies, improving clinical outcomes, and optimizing patient care, particularly in hospital and critical care settings.
Patient-Generated Subjective Global Assessment (PG-SGA) is a nutritional assessment tool specifically developed by Ottery for cancer patients (16). It integrates both patient self-reporting and clinical evaluation to comprehensively assess the nutritional status and identify potential risks of malnutrition in cancer patients. The tool consists of two components: self-assessment and clinical assessment. Self-assessment includes four domains—weight, food intake, symptoms, and physical function—evaluating changes over specified periods and symptoms affecting eating. Clinical assessment involves the relationship between disease and nutritional needs, metabolic requirements, and physical examination, focusing on fat stores, muscle condition, and edema. The total score from these components categorizes patients into different risk levels: A, good nutritional status (0–1 points), B, possible malnutrition (2–3 points), C, moderate malnutrition (4–8 points), and severe malnutrition (≥9 points). For patients scoring 4–8, dietary intervention by a nutritionist and further clinical symptom assessment is recommended, while those scoring ≥9 require symptom management and nutritional intervention prior to cancer treatment initiation, reflecting more severe nutritional deficiencies in the patient population. For patients receiving total parenteral or enteral nutrition, PG-SGA scoring was adapted to reflect their artificial nutrition regimen. The food intake component was scored based on feeding tolerance rather than oral intake, considering factors such as gastrointestinal symptoms and metabolic complications. Weight assessment accounted for potential fluid retention. These modifications ensured an accurate evaluation of nutritional status in patients with artificial nutrition support.
The EORTC QLQ-C30 (Quality of Life Core Questionnaire) is a tool developed by the European Organization for Research and Treatment of Cancer (EORTC) to assess the quality of life in cancer patients. It is widely used in clinical research and treatment outcome evaluations for its reliability and validity. The QLQ-C30 covers 15 different domains, including 5 functional domains, 9 symptom domains, and 1 global quality of life domain. Each item is scored on a Likert scale with 4 levels (not at all, a little, quite a bit, very much), scored from 1 to 4, except for items 29 and 30, which are scored on a 7-point scale from very poor to very good (1–7). The global quality of life score is calculated using weighted averages and linear transformation, with a range from 0 to 100. High scores in the functional and global quality of life domains indicate better quality of life, while low scores reflect poorer quality. Conversely, high scores in the symptom domains suggest a higher burden of symptoms, and low scores indicate fewer symptoms.
Patients stood upright in the center of the scale with their arms extended laterally, barefoot, and dressed in light clothing. Body Mass Index (BMI) was calculated using the formula: BMI (kg/m2) = weight (kg)/height (m)2, based on the measurements of weight (W) and height (H) (17). Measurements of mid-arm circumference (MAC) and triceps skin-fold thickness (TSF) were performed on the non-dominant arm according to Leonard (18). Hand-grip strength (HGS) measurements were conducted in triplicate, with the final value being the average of these three measurements (19). Fasting blood samples were collected within 24 h of admission to assess the levels of white blood cells, neutrophils, lymphocytes, red blood cells, hemoglobin, platelets, serum albumin, prealbumin, total protein, blood urea nitrogen, transaminases, bilirubin, and creatinine. Laboratory data were measured using standard laboratory testing methods.
Statistical analysis
All statistical analyses were conducted using SPSS version 29.0. Descriptive statistics were employed to analyze the general patient demographics, results from nutritional assessments, quality of life scores, and laboratory test data, including means, standard deviations, frequencies, and percentages. For normally distributed continuous data, means and standard deviations were used; non-normally distributed data were expressed as median (Q25, Q75) and analyzed using non-parametric tests. Correlation analysis was performed using Spearman’s rank correlation to examine the relationship between PG-SGA screening results, quality of life scores, and laboratory test data. Multiple regression analysis was conducted with quality-of-life scores as the dependent variable and PG-SGA screening results as the independent variable, aiming to identify factors influencing the quality of life in lung cancer patients.
Results
Demographic and clinical characteristics of the study population
A total of 270 hospitalized lung cancer patients were analyzed, with a majority being male (74.81%) and over 65 years old (80.74%). The population was predominantly Han Chinese (87.78%). The smoking rate was high (73.33%), while the proportion of alcohol consumers was relatively low (22.96%). Among the patients, 51.11% had a habit of drinking tea, and their residential areas were mostly urban and rural (44.45 and 32.59%, respectively). The education level was predominantly high school (56.67%), and most patients were covered by rural cooperative medical insurance (65.56%). The majority of patients were diagnosed at an advanced stage (stage III-IV, 92.59%). Additionally, 90% of patients had no family history, 70.37% had not received radiotherapy or chemotherapy, and 34.44% had chronic diseases. These data highlight the high-risk characteristics and treatment status of lung cancer patients (Table 1).
Table 1
| Variables | N% |
|---|---|
| Age (years) | |
| ≤65 | 52 (19.26) |
| >65 | 218 (80.74) |
| Gender | |
| Female | 68 (25.19) |
| Male | 202 (74.81) |
| Nationality | |
| Han | 237 (87.78) |
| Ethnic minority | 33 (12.22) |
| Smoking | |
| Yes | 198 (73.33) |
| No | 72 (26.67) |
| Alcohol consumption | |
| Yes | 62(22.96) |
| No | 208(77.04) |
| Tea consumption | |
| Yes | 138(51.11) |
| No | 132(48.89) |
| Residence | |
| City | 120(44.45) |
| Town | 62(22.96) |
| Village | 88(32.59) |
| Education | |
| BS or above | 16(5.92) |
| High school | 153(56.67) |
| Primary school or no schooling | 101 (37.41) |
| Medical insurance | |
| Urban medical insurance | 93 (34.44) |
| Rural cooperative medical insurance | 177 (65.56) |
| Stage | |
| I-II | 20 (7.41) |
| III-IV | 250 (92.59) |
| Family history | |
| Yes | 27 (10.00) |
| No | 243 (90.00) |
| Radiotherapy or chemotherapy | |
| Yes | 80 (29.63) |
| No | 190 (70.37) |
| Complicated with chronic diseases | |
| Yes | 93 (34.44) |
| No | 177 (65.56) |
The characteristics of lung cancer patients.
Nutritional risk and functional status in lung cancer patients
In this study, Table 2 presents the results of nutritional risk screening (NRS2002) for lung cancer patients. The data show that among patients with NRS2002 scores ≥3 (high nutritional risk group), significantly more patients did not receive nutritional support (66 vs. 41, χ2 = 23.7, p = 0.001), indicating that patients at higher nutritional risk are more likely to lack nutritional intervention. Table 3 displays the results of PG-SGA and Karnofsky scores. The PG-SGA results show that the majority of patients fall into category C (malnourished, 105 patients), followed by category A (no change in nutritional status, 100 patients) and category B (at risk of malnutrition, 65 patients), suggesting that malnutrition is prevalent among lung cancer patients. Additionally, the Karnofsky scores indicate that most patients have a good functional status (score ≥ 70, 258 patients), though a small proportion of patients have severe functional impairment (score < 70, 12 patients). There are no significant gender differences in the PG-SGA and Karnofsky scores (p-values of 0.147 and 0.574, respectively), indicating that gender has little impact on nutritional risk and functional status in this sample of lung cancer patients.
Table 2
| NRS2002 | n | Nutritional support | No nutritional support | χ2 | P |
|---|---|---|---|---|---|
| ≥3 | 107 | 41 | 66 | 23.7 | 0.001 |
| <3 | 163 | 21 | 142 |
Results of nutritional risk screening for lung cancer patients(n).
Table 3
| Scores | n | Mean ± standard deviation | Males | Females | p |
|---|---|---|---|---|---|
| PG-SGA | 270 | 9.14 ± 7.11 | 0.147 | ||
| A(0–3) | 100 | 81 | 19 | ||
| B(4–8) | 65 | 51 | 14 | ||
| C(≥9) | 105 | 75 | 30 | ||
| Karnofsky | 270 | 84.85 ± 10.89 | 0.574 | ||
| ≥70 | 258 | 192 | 66 | ||
| <70 | 12 | 10 | 2 |
PG-SGA and Karnofsky scores of lung cancer patients.
A, no change in nutritional status; B, at risk of malnutrition and C, malnourished.
Nutritional support utilization in lung cancer patients: a gender and risk-based analysis
Among the 270 lung cancer patients, 208 (77.04%) did not receive any form of nutritional support. This included 153 males (73.56% of the total male population) and 55 females (26.44% of the total female population). Among patients with a PG-SGA score ≥ 4, 115 patients (67.65%) did not receive any nutritional support, with 78 males (45.88% of the male PG-SGA ≥4 group) and 37 females (21.76% of the female PG-SGA ≥4 group). For those who did receive nutritional support, the distribution was as follows: 46 patients (17.04%) received only parenteral nutrition (PN), including 36 males (78.26%) and 10 females (21.74%). Among the PG-SGA ≥4 group, this was 25.29%, with 33 males (76.74%) and 10 females (23.26%). Only 16 patients (5.93%) received enteral nutrition (EN), including 12 males (75.00%) and 4 females (25.00%). In the PG-SGA ≥4 group, this was 8.24%, with 10 males (71.43%) and 4 females (28.57%). Eight patients (2.96%) received both EN and PN support, including 6 males (75.00%) and 2 females (25.00%). In the PG-SGA ≥4 group, this was 4.71%, with equal gender representation (75.00%). This demonstrates that the use of nutritional support among lung cancer patients is low, particularly among those at higher nutritional risk (Table 4).
Table 4
| Nutrition support | Total (%) | Male (%) | Female (%) | PG-SGA ≥ 4 | ||
|---|---|---|---|---|---|---|
| Total (%) | Male (%) | Female (%) | ||||
| No | 208 (77.04) | 153 (73.56) | 55 (26.44) | 115 (67.65) | 78 (45.88) | 37 (21.76) |
| Yes | ||||||
| PN | 46 (17.04) | 36 (78.26) | 10 (21.74) | 43 (25.29) | 33 (76.74) | 10 (23.26) |
| EN | 16 (5.93) | 12 (75.00) | 4 (25.00) | 14 (8.24) | 10 (71.43) | 4 (28.57) |
| EN and PN | 8 (2.96) | 6 (75.00) | 2 (25.00) | 8 (4.71) | 6 (75.00) | 2 (75.00) |
The nutritional support in lung cancer patients.
PN, parenteral nutrition; EN, enteral nutrition.
Correlation between PG-SGA scores and biochemical markers in lung cancer patient
This study demonstrated significant associations between PG-SGA scores and various blood biochemical indicators in lung cancer patients. As the PG-SGA score increased, serum prealbumin (PA) levels significantly decreased (PA: 197.85 ± 7.42 in the PG-SGA ≥9 group vs. 223.76 ± 66.07 in the 0–3 group, p = 0.030). Total bilirubin (TBIL) levels were significantly elevated in higher PG-SGA score groups (11.02 ± 5.25 in the ≥9 group vs. 8.79 ± 3.72 in the 0–3 group, p = 0.000). Alanine aminotransferase (ALT) decreased significantly as PG-SGA scores increased (23.49 ± 15.77 in the ≥9 group vs. 37.75 ± 52.21 in the 0–3 group, p = 0.033). Lymphocyte count (LYMPH) was lower in higher PG-SGA score groups (1.16 ± 0.58 in the ≥9 group vs. 1.43 ± 0.97 in the 0–3 group, p = 0.027). Additionally, the PG-SGA score was positively correlated with the white blood cell/red blood cell ratio (WBC/RBC) (1.73 ± 0.94 in the ≥9 group vs. 1.45 ± 0.42 in the 0–3 group, p = 0.030). The total bilirubin/direct bilirubin ratio (TBIL/DBIL) was also elevated in patients with higher PG-SGA scores (4.91 ± 2.85 in the ≥9 group vs. 3.70 ± 1.02 in the 0–3 group, p = 0.000). These results suggest that poorer PG-SGA scores correlate with nutritional deficiencies and metabolic disturbances in lung cancer patients. Indicators such as prealbumin, total bilirubin, alanine aminotransferase, and lymphocyte count are critical for assessing the nutritional status of lung cancer patients (Table 5).
Table 5
| Parameters | PG-SGA | F | P | ||
|---|---|---|---|---|---|
| A (0–3) | B (4–8) | C(≥9) | |||
| TP | 65.87 ± 9.08 | 64.75 ± 6.53 | 65.77 ± 7.21 | 0.953 | 0.387 |
| Cr | 74.74 ± 17.02 | 72.54 ± 15.76 | 72.15 ± 16.59 | 0.693 | 0.501 |
| ALB | 38.08 ± 6.83 | 36.50 ± 4.43 | 37.21 ± 5.63 | 1.615 | 0.201 |
| Urea | 4.73 ± 2.19 | 4.59 ± 2.84 | 4.55 ± 2.11 | 0.154 | 0.857 |
| PA | 223.76 ± 66.07 | 213.67 ± 65.69 | 197.85 ± 7.42 | 3.544 | 0.030 |
| TBIL | 8.79 ± 3.72 | 8.63 ± 3.17 | 11.02 ± 5.25 | 9.192 | 0.000 |
| DBIL | 2.82 ± 2.63 | 2.58 ± 1.46 | 3.18 ± 2.07 | 1.694 | 0.186 |
| GLU | 4.98 ± 0.72 | 5.43 ± 1.57 | 5.13 ± 0.90 | 2.533 | 0.082 |
| TG | 1.27 ± 0.70 | 2.19 ± 2.57 | 1.99 ± 1.51 | 0.578 | 0.569 |
| AST | 26.02 ± 12.77 | 28.94 ± 19.29 | 27.21 ± 13.89 | 0.743 | 0.477 |
| ALT | 37.75 ± 52.21 | 31.72 ± 37.99 | 23.49 ± 15.77 | 3.460 | 0.033 |
| HGB | 120.00 ± 16.80 | 117.37 ± 23.98 | 116.91 ± 20.21 | 0.678 | 0.050 |
| WBC | 5.86 ± 2.37 | 7.00 ± 4.02 | 6.52 ± 3.12 | 2.828 | 0.061 |
| NEUT | 3.88 ± 2.26 | 4.06 ± 2.36 | 4.55 ± 2.87 | 1.881 | 0.155 |
| LYMPH | 1.43 ± 0.97 | 1.44 ± 0.81 | 1.16 ± 0.58 | 3.645 | 0.027 |
| RBC | 4.14 ± 0.80 | 3.96 ± 0.69 | 3.91 ± 0.66 | 2.615 | 0.075 |
| PLT | 232.27 ± 112.06 | 234.34 ± 105.99 | 248 ± 87.34 | 0.726 | 0.485 |
| NEUT/WBC | 0.64 ± 0.20 | 0.66 ± 0.14 | 0.65 ± 0.18 | 0.967 | 0.381 |
| WBC/RBC | 1.45 ± 0.42 | 1.79 ± 1.15 | 1.73 ± 0.94 | 3.560 | 0.030 |
| TBIL/DBIL | 3.70 ± 1.02 | 3.78 ± 1.38 | 4.91 ± 2.85 | 10.870 | 0.000 |
| PA/ALB | 5.58 ± 1.90 | 5.52 ± 2.08 | 4.96 ± 1.38 | 1.685 | 0.187 |
| PA/TB | 3.38 ± 1.10 | 3.19 ± 1.22 | 2.90 ± 1.10 | 2.945 | 0.054 |
Association between the PG-SGA and blood biochemistry results.
A, no change in nutritional status; B, at risk of malnutrition and C, malnourished.
Impact of PG-SGA scores on nutritional parameters and body composition in lung cancer patients
We investigated the association between PG-SGA scores and various nutritional parameters, finding significant differences across different PG-SGA score groups (p < 0.05). NRS2002 scores, KPS scores, and mid-arm circumference (MAC) decreased with increasing PG-SGA scores, with significant differences observed for both NRS2002 and KPS scores (p < 0.0001) and MAC (p < 0.0001). Body weight (BW) and triceps skinfold thickness (TSF) also showed significant differences (p = 0.029 and p < 0.0001, respectively). Handgrip strength (HGS) was significantly higher in the PG-SGA 4–8 group compared to the ≥9 group (p < 0.0001). Mid-arm muscle circumference (MAMC) did not show significant differences between groups (p = 0.107). Additionally, the maximum circumference of the calves (MCC) decreased with increasing PG-SGA scores, with significant differences observed (p < 0.0001). These results suggest that with worsening nutritional status (higher PG-SGA scores), a decline in various nutritional parameters, particularly those related to body composition and functional capacity, was observed (Table 6).
Table 6
| Parameters | PG-SGA | F | p | ||
|---|---|---|---|---|---|
| 0–3 | 4–8 | ≥9 | |||
| NRS2002 (score) | 1.37 ± 0.75 | 2.43 ± 1.21 | 3.24 ± 1.28 | 90.556 | 0.000 |
| KPS (score) | 90.60 ± 5.83 | 85.85 ± 6.82 | 78.76 ± 13.28 | 39.283 | 0.000 |
| BW (Kg) | 61.26 ± 9.9 | 57.41 ± 9.18 | 58.51 ± 9.86 | 3.598 | 0.029 |
| BMI (kg/m4) | 22.87 ± 2.96 | 21.88 ± 2.78 | 22.06 ± 3.13 | 2.849 | 0.060 |
| MAC (cm) | 28.34 ± 4.65 | 25.92 ± 4.24 | 24.19 ± 2.33 | 30.747 | 0.000 |
| TSF (mm) | 23.36 ± 17.93 | 13.63 ± 11.56 | 6.75 ± 4.39 | 44.777 | 0.000 |
| HGS (kg) | 22.87 ± 9.40 | 24.42 ± 8.86 | 22.11 ± 8.39 | 15.126 | 0.000 |
| MAMC (cm) | 21.01 ± 4.78 | 21.64 ± 3.20 | 22.07 ± 3.61 | 2.253 | 0.107 |
| MCC-right (cm) | 33.50 ± 3.33 | 32.40 ± 2.91 | 31.75 ± 3.19 | 8.270 | 0.000 |
| MCC-left (cm) | 33.52 ± 3.27 | 32.41 ± 3.09 | 31.77 ± 2.88 | 8.415 | 0.000 |
Association between the PG-SGA and nutritional parameters results.
BMI, body mass index; MAC, mid-arm diameter; TSF, triceps skin-fold; HGS, hand-grip strength; MAMC, Mid-Arm Muscle Circumference; MCC, Maximum calf circumference.
Impact of nutritional status on quality of life in lung cancer patients: a PG-SGA analysis
This study explored the relationship between nutritional status, assessed by PG-SGA, and quality of life (QOL) in lung cancer patients. Higher PG-SGA scores were significantly associated with declines in physical, role, emotional, and social function (e.g., physical function dropped from 93.33 in the 0–3 group to 40.00 in the ≥9 group, p < 0.0001). Symptoms like fatigue, nausea/vomiting, pain, insomnia, and loss of appetite worsened with increasing PG-SGA scores (e.g., fatigue increased from 32.69 in the 0–3 group to 49.69 in the 4–8 group, p < 0.0001). Economic issues also rose (p = 0.001), while diarrhea showed no significant differences (p = 0.978) (Tables 7). Correlation analysis revealed that PG-SGA scores were positively correlated with functional impairments (e.g., physical function, r = 0.605, p < 0.001) and symptoms like fatigue (r = 0.667, p < 0.001). Conversely, PG-SGA scores negatively correlated with overall QOL (r = −0.687, p < 0.001). No significant correlations were found for shortness of breath, appetite loss, constipation, diarrhea, or economic issues. These findings highlight the significant impact of poor nutritional status on QOL in lung cancer patients (Table 8).
Table 7
| Categories | PG-SGA | χ2 | p* | ||
|---|---|---|---|---|---|
| 0–3 | 4–8 | ≥9 | |||
| Physical functioning | 93.33 (86.67, 93.33) | 84.44 (77.78, 86.67) | 40.00 (20.00, 70.00) | 149.07 | 0.000 |
| Role functioning | 89.20 (74.07, 100) | 68.98 (52.60, 84.16) | 54.84 (31.09, 83.63) | 59.16 | 0.000 |
| Emotional functioning | 94.56 (85.15, 100) | 87.88 (72.82, 95.84) | 77.38 (64.84, 91.54) | 48.98 | 0.000 |
| Cognitive functioning | 81.36 (70.40, 92.69) | 71.57 (52.78, 87.90) | 83.94 (39.37, 83.94) | 30.89 | 0.000 |
| Social functioning | 57.78 (40.48, 76.52) | 38.61 (25.43, 53.43) | 50.75 (34.12, 69.82) | 32.28 | 0.000 |
| Global QOL | 87.32 (74.74, 100) | 73.21 (55.36, 85.63) | 48.99 (35.00, 65.00) | 113.69 | 0.000 |
| Fatigue | 32.69 (23.38, 43.92) | 49.69 (34.21, 61.91) | 18.71 (8.45, 29.33) | 95.21 | 0.000 |
| Nausea/Vomiting | 7.61 (00.00, 34.78) | 5.00 (00.00, 14.03) | 25.88 (6.33, 57.89) | 62.36 | 0.000 |
| Pain | 11.31 (00.00, 27.86) | 21.49 (6.13, 36.73) | 32.35 (14.70, 50.86) | 40.41 | 0.000 |
| Dyspnea | 1.83 (1.17, 2.59) | 17.26 (00.00, 40.12) | 21.59 (1.70, 47.11) | 11.91 | 0.003 |
| Insomnia | 16.67 (00.00, 36.29) | 28.98 (5.43, 59.32) | 45.40 (13.52, 76.60) | 31.25 | 0.000 |
| Appetite loss | 24.00 (00.00, 74.00) | 21.11 (3.06, 43.05) | 56.41 (27.66, 85.31) | 56.54 | 0.000 |
| Constipation | 9.09 (00.00, 34.34) | 7.53 (00.00, 25.00) | 25.78 (2.44, 61.51) | 38.17 | 0.000 |
| Diarrhea | 4.66 (00.00, 22.58) | 4.23 (00.00, 21.43) | 4.42 (00.00, 22.28) | 0.05 | 0.978 |
| Financial problems | 40.82 (19.05, 59.55) | 40.10 (21.25, 71.34) | 50.03 (33.00, 76.67) | 14.32 | 0.001 |
The correlation between nutritional status and quality of Life in lung cancer patients [Q50(Q25, Q75)].
*Kruskal-Wallis tests, p < 0.01.
Table 8
| EORTC QLQ-C30 | Correlation coefficient* | P |
|---|---|---|
| Functional scales | ||
| Physical functioning | 0.605 | 0.000 |
| Role functioning | 0.662 | 0.000 |
| Emotional functioning | 0.535 | 0.000 |
| Cognitive functioning | 0.379 | 0.000 |
| Social functioning | 0.551 | 0.000 |
| Global QOL | −0.687 | 0.000 |
| Symptom scales | ||
| Fatigue | 0.667 | 0.000 |
| Nausea/vomiting | 0.584 | 0.000 |
| Pain | 0.498 | 0.000 |
| Dyspnea | −0.059 | 0.332 |
| Insomnia | 0.364 | 0.000 |
| Appetite loss | −0.007 | 0.915 |
| Constipation | −0.016 | 0.798 |
| Diarrhea | 0.008 | 0.891 |
| Financial problems | 0.011 | 0.855 |
Correlation analysis between PG-SGA quantitative evaluation and quality of life in patients with lung cancer.
*Spearman rank correlation coefficient, p < 0.05.
Univariate analysis of nutritional status
The results of the univariate binary Logistic regression analysis for severe malnutrition show that there are significant associations between severe malnutrition and alcohol consumption (p = 0.009) as well as tea consumption (p = 0.001). Non-drinkers have a 2.149-fold higher risk of developing severe malnutrition compared to drinkers (OR = 2.149, 95% CI: 1.210–3.819), and non-tea drinkers have a 2.334-fold higher risk compared to tea drinkers (OR = 2.334, 95% CI: 1.411–3.862). However, the p values of variables such as gender, age, nationality, smoking, disease stage, family history, and BMI are all greater than 0.05, indicating no statistical significance (Table 9).
Table 9
| Categories | P | OR (95%CI) |
|---|---|---|
| Gender | 0.307 | |
| Female | 1 | |
| Male | 1.337 (0.766 ~ 2.334) | |
| Age | 0.453 | |
| <65 | 1 | |
| ≥65 | 1.262 (0.687 ~ 2.320) | |
| Nationality | 0.116 | |
| Han | 1 | |
| Ethnic minority | 0.556 (0.267 ~ 1.156) | |
| Smoking | 0.573 | |
| Yes | 1 | |
| No | 0.854 (0.493 ~ 1.479) | |
| Alcohol consumption | 0.009 | |
| Yes | 1 | |
| No | 2.149 (1.210 ~ 3.819) | |
| Stage | 0.066 | |
| I–II | 1 | |
| III–IV | 0.532 (0.272 ~ 1.041) | |
| Family history | 0.301 | |
| Yes | 1 | |
| No | 1.524 (0.686 ~ 3.386) | |
| BMI (kg/m2) | 0.256 | 0.953 (0.878 ~ 1.035) |
| Tea consumption | 0.001 | |
| Yes | 1 | |
| No | 2.334 (1.411 ~ 3.862) |
Univariate analysis of severe malnutrition.
Discussion
This study analyzed the nutritional status, functional status, and quality of life (QOL) in 270 hospitalized lung cancer patients and investigated the relationship between PG-SGA scores and patient quality of life and nutritional parameters. Our data highlighted the high-risk characteristics of lung cancer patients, underscoring the importance of nutritional support in clinical treatment. Through analysis of patient demographic characteristics, we found that most patients were elderly males with a high smoking rate, reflecting the typical high-risk profile for lung cancer (20, 21). These findings are consistent with other similar studies, highlighting that smoking and aging are significant risk factors for lung cancer (22, 23). Furthermore, the majority of patients were diagnosed at an advanced stage (III-IV), further corroborating the commonality of late-stage diagnosis (24). Regarding nutritional status, although PG-SGA scores indicated that most patients experienced some degree of malnutrition, the majority of patients did not receive nutritional support (77.04%). This was particularly evident in the group with higher nutritional risk (PG-SGA ≥4), where 67.65% of patients did not receive any form of nutritional support. This highlights the insufficient use of nutritional intervention in lung cancer treatment, especially among those at high nutritional risk (25). Previous research has shown that malnutrition is linked to adverse clinical outcomes in cancer patients, emphasizing the crucial role of timely nutritional intervention in improving treatment outcomes and quality of life.
Moreover, an important finding of this study is the relationship between PG-SGA scores and various biochemical indicators. Patients with higher PG-SGA scores exhibited significant differences in prealbumin (PA), total bilirubin (TBIL), alanine transaminase (ALT), and lymphocyte count (LYMPH), indicating that PG-SGA scores can effectively link nutritional status with biochemical parameters. This is consistent with previous research, suggesting that PG-SGA scores not only reflect clinical nutritional status but also closely correlate with physiological indicators (26). The significant correlation between PG-SGA scores and multiple nutritional parameters, such as NRS2002 scores, KPS scores, mid-arm circumference, and body weight, further supports the validity of PG-SGA in assessing the nutritional status of lung cancer patients (27, 28). As PG-SGA scores increase, there is a noticeable decline in indicators related to body composition and functional capacity, particularly in changes observed in KPS scores and mid-arm circumference (29). Studies have shown that malnutrition directly impacts physical strength and functional status, which subsequently affects quality of life. Another important finding of this study is the significant association between PG-SGA scores and quality of life in lung cancer patients. As PG-SGA scores rise, there is a significant decrease in scores across multiple dimensions, including physical functioning, role functioning, emotional functioning, and social functioning. Symptoms such as fatigue, nausea/vomiting, pain, breathing difficulties, insomnia, and loss of appetite worsen with higher PG-SGA scores. These results align with existing research, demonstrating that malnutrition not only negatively affects patient functional status but also exacerbates the manifestation of cancer-related symptoms, significantly reducing quality of life (30, 31). Particularly, the significant decline in physical and role functioning suggests that, during lung cancer treatment, greater attention should be paid to patients’ physical health and daily living capabilities, and personalized nutritional interventions should be implemented to alleviate these symptoms.
Lastly, although PG-SGA scores did not show significant correlations with symptoms such as diarrhea, loss of appetite, and economic issues, their potential impact should not be overlooked. Symptoms like diarrhea are common among cancer patients, potentially linked to the medications used or the progression of the disease itself (32). Regarding economic issues, despite the lack of significant correlation, their prevalence among lung cancer patients highlights the financial stress patients may face during treatment, particularly in the absence of adequate insurance support (33).
Further, findings from the univariate binary Logistic regression analysis for severe malnutrition deserve attention. It showed significant associations between severe malnutrition and alcohol consumption (p = 0.009), as well as tea consumption (p = 0.001). Specifically, non-drinkers had a 2.149-fold higher risk of severe malnutrition than drinkers (OR = 2.149, 95% CI: 1.210–3.819), and non-tea drinkers faced a 2.334-fold higher risk compared to tea drinkers (OR = 2.334, 95% CI: 1.411–3.862).
While this study revealed significant associations between the nutritional status and quality of life in lung cancer patients, it also had several limitations. First, as a cross-sectional study, it cannot establish causality. Second, the sample was drawn from a single medical institution, which may limit the generalizability of the results. Third, the PG-SGA score might be subject to assessor bias, and it did not fully account for all potential influencing factors, such as psychosocial elements and medication use during treatment. The study also did not delve deeply into the specific impact of different types of nutritional interventions on patient quality of life. Future multi-center, longitudinal studies will help to further validate these associations and refine nutritional intervention strategies. Additionally, future prospective studies could further explore the association between nutritional status, quality of life, and survival outcomes in lung cancer patients.
Overall, this study underscores the importance of nutritional assessment and intervention in lung cancer treatment, particularly in patients at high nutritional risk. The association between PG-SGA and various parameters highlights its potential as a valuable tool for assessing nutritional status and predicting quality of life. Future research should focus on developing targeted interventions to improve nutritional support and ultimately enhance the quality of life and survival outcomes for lung cancer patients. Additionally, further exploration of the potential mechanisms linking nutritional status with disease progression is necessary.
Statements
Data availability statement
The original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to the corresponding author.
Ethics statement
The studies involving humans were approved by the Science and Technology Project of Guizhou Anti-Cancer Association. The studies were conducted in accordance with the local legislation and institutional requirements. The participants provided their written informed consent to participate in this study.
Author contributions
SS: Writing – original draft, Writing – review & editing. SX: Methodology, Writing – original draft, Writing – review & editing. TS: Data curation, Writing – original draft, Writing – review & editing. LN: Software, Writing – original draft, Writing – review & editing. XT: Investigation, Writing – original draft, Writing – review & editing. HM: Conceptualization, Writing – original draft, Writing – review & editing. XG: Funding acquisition, Resources, Writing – original draft, Writing – review & editing.
Funding
The author(s) declare that financial support was received for the research and/or publication of this article. This work was supported by the Science and Technology Project of Guizhou Anti-Cancer Association (Anti-Cancer Association Science and Technology No. 006 [2023]), the National Natural Science Foundation of China (82460465), the Natural Science Foundation of Guizhou Province (Qian Ke He Basic Project ZK [2024]346), Science and Technology Fund of Guizhou Provincial Health Commission (gzwkj2022-301 and gzwkj2025-221).
Acknowledgments
The authors would like to express their gratitude to all the patients who participated in this study and the medical staff who assisted in data collection. Special thanks to the Affiliated Cancer Hospital of Zunyi Medical University for providing support throughout the research process. We also acknowledge the contributions of the laboratory team for their assistance in processing biochemical data.
Conflict of interest
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.
Generative AI statement
The authors declare that no Gen AI was used in the creation of this manuscript.
Publisher’s note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
References
1.
LiaoWCouplandCACBurchardtJBaldwinDRGleesonFVHippisley-CoxJ. Predicting the future risk of lung cancer: development, and internal and external validation of the CanPredict (lung) model in 19·67 million people and evaluation of model performance against seven other risk prediction models. Lancet Respir Med. (2023) 11:685–97. doi: 10.1016/S2213-2600(23)00050-4
2.
SiegelRLGiaquintoANJemalA. Cancer statistics. CA Cancer J Clin. (2024) 74:12–49. doi: 10.3322/caac.21820
3.
DetterbeckFCWoodardGABaderASDacicSGrantMJParkHSet al. The proposed ninth edition TNM classification of lung cancer. Chest. (2024) 166:882–95. doi: 10.1016/j.chest.2024.05.026
4.
HoustonT. Screening for lung cancer. Med Clin North Am. (2020) 104:1037–50. doi: 10.1016/j.mcna.2020.08.005
5.
StylianouCKalemikerakisIKonstantinidisTKafaziAAlevizopoulosNParissopoulosSet al. Quality of life and symptom burden in non-small-cell lung Cancer patients receiving second-line chemotherapy compared with immunotherapy. Medicina. (2024) 60:1845. doi: 10.3390/medicina60111845
6.
StalkerMMarmarelisMLangerCCohenRBSinghAAggarwalCet al. Outcomes following treatment for progression in patients treated with Durvalumab consolidation in LA-NSCLC. Clin Lung Cancer. (2024) 26:124–130.e1. doi: 10.1016/j.cllc.2024.11.002
7.
LiuCAZhangQRuanGTShenLYXieHLLiuTet al. Novel diagnostic and prognostic tools for lung cancer cachexia: based on nutritional and inflammatory status. Front Oncol. (2022) 12:890745. doi: 10.3389/fonc.2022.890745
8.
MadedduCBusquetsSDonisiCLaiEPrettaALópez-SorianoFJet al. Effect of Cancer-related Cachexia and associated changes in nutritional status, inflammatory status, and muscle mass on immunotherapy efficacy and survival in patients with advanced non-small cell lung cancer. Cancers. (2023) 15:1076. doi: 10.3390/cancers15041076
9.
BergerotCDBergerotPGPhilipEJFerrariRPeixotoRMCraneTEet al. Enhancing Cancer supportive care: integrating psychosocial support, nutrition, and physical activity using telehealth solutions. JCO Glob Oncol. (2024) 10:e2400333. doi: 10.1200/GO-24-00333
10.
ChuaAVJrHernandezARBMendozaMJLSan JuanMD. Omega-3 fatty acids increase weight and quality of life scores in patients with advanced non-small cell lung cancer and cancer cachexia: a meta-analysis. Integr Cancer Ther. (2024) 23:15347354241275052. doi: 10.1177/15347354241275052
11.
MathiesVKippAPHammersenJSchrenkKGSchollSSchnetzkeUet al. Standardizing nutritional care for cancer patients: implementation and evaluation of a malnutrition risk screening. Oncol Res Treat. (2024) 48:26–36. doi: 10.1159/000542460
12.
TuYChenFYuQSongLChenM. Application of NRS2002 and PG-SGA in nutritional assessment for perioperative patients with head and neck squamous cell carcinoma: an observational study. Medicine. (2024) 103:e40025. doi: 10.1097/MD.0000000000040025
13.
McLeanJBMoylanJSHorrellEMAndradeFH. Proteomic analysis of media from lung cancer cells reveals role of 14-3-3 proteins in cachexia. Front Physiol. (2015) 6:136. doi: 10.3389/fphys.2015.00136
14.
KondrupJAllisonSPEliaMVellasBPlauthM. ESPEN guidelines for nutrition screening 2002. Clin Nutr. (2003) 22:415–21. doi: 10.1016/S0261-5614(03)00098-0
15.
KondrupJRasmussenHHHambergOStangaZ. Nutritional risk screening (NRS 2002): a new method based on an analysis of controlled clinical trials. Clin Nutr. (2003) 22:321–36. doi: 10.1016/s0261-5614(02)00214-5
16.
OtteryFD. Rethinking nutritional support of the cancer patient: the new field of nutritional oncology. Semin Oncol. (1994) 21:770–8. PMID:
17.
GutinI. In BMI we trust: reframing the body mass index as a measure of health. Soc Theory Health. (2018) 16:256–71. doi: 10.1057/s41285-017-0055-0
18.
LeonardWR. Contributions of A. Roberto Frisancho to human population biology: an introduction. Am J Hum Biol. (2009) 21:599–605. doi: 10.1002/ajhb.20916
19.
SchlüsselMMdos AnjosLAde VasconcellosMTKacG. Reference values of handgrip dynamometry of healthy adults: a population-based study. Clin Nutr. (2008) 27:601–7. doi: 10.1016/j.clnu.2008.04.004
20.
DubinSGriffinD. Lung cancer in non-smokers. Mo Med. (2020) 117:375–9. PMID:
21.
ToumazisIBastaniMHanSSPlevritisSK. Risk-based lung cancer screening: a systematic review. Lung Cancer. (2020) 147:154–86. doi: 10.1016/j.lungcan.2020.07.007
22.
PustylnyakVOPerevalovaAMGulyaevaLF. Mechanistic insights of hsa-mir-301a regulation by tobacco smoke in lung squamous cell carcinoma: evidence from bioinformatics analysis. Bioinform Biol Insights. (2024) 18:11779322241302168. doi: 10.1177/11779322241302168
23.
WuZXiaFLinR. Global burden of cancer and associated risk factors in 204 countries and territories, 1980-2021: a systematic analysis for the GBD 2021. J Hematol Oncol. (2024) 17:119. doi: 10.1186/s13045-024-01640-8
24.
NooreldeenRBachH. Current and future development in lung Cancer diagnosis. Int J Mol Sci. (2021) 22:8661. doi: 10.3390/ijms22168661
25.
BossiPDelrioPMascheroniAZanettiM. The Spectrum of malnutrition/Cachexia/sarcopenia in oncology according to different cancer types and settings: a narrative review. Nutrients. (2021) 13:1980. doi: 10.3390/nu13061980
26.
CaoJXuHLiWGuoZLinYShiYet al. Nutritional assessment and risk factors associated to malnutrition in patients with esophageal cancer. Curr Probl Cancer. (2021) 45:100638. doi: 10.1016/j.currproblcancer.2020.100638
27.
GuoZQYuJMLiWFuZMLinYShiYYet al. Survey and analysis of the nutritional status in hospitalized patients with malignant gastric tumors and its influence on the quality of life. Support Care Cancer. (2020) 28:373–80. doi: 10.1007/s00520-019-04803-3
28.
ZhouXLiuJZhangQRaoSWuXZhangJet al. Comparison of the suitability between NRS2002 and MUST as the first-step screening tool for GLIM criteria in hospitalized patients with GIST. Front Nutr. (2022) 9:864024. doi: 10.3389/fnut.2022.864024
29.
HuoZChongFYinLLiNLiuJZhangMet al. Comparison of the performance of the GLIM criteria, PG-SGA and mPG-SGA in diagnosing malnutrition and predicting survival among lung cancer patients: a multicenter study. Clin Nutr. (2023) 42:1048–58. doi: 10.1016/j.clnu.2023.04.021
30.
SalasSCottetVDossusLFassierPGinhacJLatino-MartelPet al. Nutritional factors during and after Cancer: impacts on survival and quality of life. Nutrients. 14:2958. doi: 10.3390/nu14142958
31.
JainRCossCWhooleyPPhelpsMOwenDH. The role of malnutrition and muscle wasting in advanced lung Cancer. Curr Oncol Rep. (2020) 22:54. doi: 10.1007/s11912-020-00916-9
32.
MoschenARSammyYMarjenbergZHeptinstallABPooleyNMarczewskaAM. The underestimated and overlooked burden of diarrhea and constipation in cancer patients. Curr Oncol Rep. (2022) 24:861–74. doi: 10.1007/s11912-022-01267-3
33.
AbramsHRDurbinSHuangCXJohnsonSFNayakRKZahnerGJet al. Financial toxicity in cancer care: origins, impact, and solutions. Transl Behav Med. (2021) 11:2043–54. doi: 10.1093/tbm/ibab091
Summary
Keywords
lung cancer, nutritional risk, nutritional status, quality of life, PG-SGA, functional status
Citation
Shen S, Xing S, Shen T, Niu L, Tian X, Ma H and Gou X (2025) Nutritional risk, functional decline, and symptom burden in lung cancer: a study based on PG-SGA scores and biochemical data. Front. Nutr. 12:1545177. doi: 10.3389/fnut.2025.1545177
Received
17 December 2024
Accepted
24 March 2025
Published
03 April 2025
Volume
12 - 2025
Edited by
William Kwame Amakye, South China University of Technology, China
Reviewed by
Yelin Shao, Zhejiang University, China
Marisa Cebola, Escola Superior de Tecnologia da Saúde de Lisboa (ESTeSL), Portugal
Updates
Copyright
© 2025 Shen, Xing, Shen, Niu, Tian, Ma and Gou.
This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
*Correspondence: Hu Ma, mahuab@163.com; Xiaoxia Gou, gouxx2020@163.com
Disclaimer
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.