| Literature DB >> 35845807 |
Mingming Deng1,2,3,4,5, Yiding Bian1,2,3,4,5, Qin Zhang6, Xiaoming Zhou7, Gang Hou1,2,3,4,5.
Abstract
Purpose: Sarcopenia is an important factor contributing to comorbidities in patients with chronic obstructive pulmonary disease (COPD) and is an independent risk factor for increased mortality. The diagnostic process for sarcopenia requires specific equipment and specialized training and is difficult procedurally. A previous study found that GDF15 levels are associated with skeletal muscle mass and function in patients with COPD. However, whether circulating GDF15 levels can be used for the prediction of sarcopenia in patients with COPD is unknown.Entities:
Keywords: GDF15; biomarker; chronic obstructive pulmonary disease; nomogram; sarcopenia
Year: 2022 PMID: 35845807 PMCID: PMC9282868 DOI: 10.3389/fnut.2022.897097
Source DB: PubMed Journal: Front Nutr ISSN: 2296-861X
Baseline characteristics of subjects.
| Variable | Total ( | With sarcopenia ( | Without sarcopenia ( | |
| Demographics | ||||
| Age, years | 64.4 ± 10.7 | 69.2 ± 7.2 | 62.5 ± 13.5 | 0.002 |
| Sex, male/female ( | 160/75 | 53/30 | 107/45 | 0.304 |
| Pulmonary function | ||||
| FEV1, % predicted | 60.5 ± 21.6 | 55.1 ± 22.4 | 64.2 ± 20.2 | 0.023 |
| FVC, % predicted | 82.1 ± 22.3 | 75.4 ± 25.1 | 85.6 ± 19.4 | 0.035 |
| FEV1/FVC, % | 54.2 ± 10.5 | 52.8 ± 11.1 | 57.7 ± 8.5 | 0.017 |
| GOLD stage | 0.006 | |||
| 1 + 2 | 156 | 43 | 113 | |
| 3 + 4 | 89 | 40 | 49 | |
| Physical function | ||||
| 6MWD, | 371.4 ± 74.4 | 336.0 ± 75.5 | 391.9 ± 65.9 | <0.001 |
| 5STS, | 8.2 ± 3.0 | 9.8 ± 3.7 | 7.1 ± 1.9 | 0.001 |
| Body composition | ||||
| BMI, kg/m2 | 23.8 ± 3.8 | 22.4 ± 3.5 | 24.8 ± 3.8 | 0.003 |
| FFMI (kg/m2) | 16.8 ± 2.3 | 15.6 ± 2.4 | 17.5 ± 2.1 | 0.001 |
| FFMI (male, kg/m2) | 17.5 ± 2.2 | 16.0 ± 2.3 | 18.2 ± 1.7 | <0.001 |
| FFMI (female, kg/m2) | 15.0 ± 1.9 | 13.4 ± 2.9 | 15.6 ± 1.2 | 0.03 |
| SMMI (kg/m2) | 6.3 ± 1.7 | 5.8 ± 0.9 | 6.8 ± 1.1 | <0.001 |
| SMMI (male, kg/m2) | 6.8 ± 1.0 | 6.2 ± 0.7 | 7.1 ± 1.0 | <0.001 |
| SMMI (female, kg/m2) | 5.4 ± 0.9 | 5.1 ± 0.9 | 5.7 ± 0.7 | 0.024 |
| HGS (kg) | 28.1 ± 6.9 | 23.9 ± 7.2 | 30.5 ± 6.9 | <0.001 |
| HGS (male, kg) | 28.9 ± 7.4 | 24.9 ± 6.0 | 30.8 ± 7.2 | <0.001 |
| HGS (female, kg) | 26.9 ± 9.2 | 22.7 ± 8.4 | 30.2 ± 8.6 | 0.016 |
FEV1, forced expiratory volume in the first second; FEV1% pred, FEV percentage predicted; FVC, forced vital capacity; FVC% pred, FVC percentage predicted; GOLD, Global Initiative for Chronic Obstructive Lung Disease; 6MWD, 6-min walk distance; 5STS, five-time chair stand test; BMI, body mass index; FFMI, fat-free mass index; SMMI, skeletal muscle mass index; HGS, handgrip strength.
Patient characteristics of the development set and validation set.
| Variable | Development set ( | Validation set ( | |
| Demographics | |||
| Age, years | 64.9 ± 12.0 | 63.9 ± 9.2 | 0.110 |
| Sex, m/f ( | 83/34 | 77/41 | 0.349 |
| Pulmonary function | |||
| FEV1,%predicted | 59.8 ± 21.0 | 56.2 ± 20.7 | 0.368 |
| FVC,% predicted | 82.5 ± 23.7 | 84.5 ± 20.0 | 0.700 |
| FEV1/FVC, % | 55.2 ± 9.9 | 53.1 ± 11.1 | 0.232 |
| GOLD stage | 0.927 | ||
| 1 + 2 | 78 | 78 | |
| 3 + 4 | 39 | 40 | |
| Physical function | |||
| 6MWD, | 369.0 ± 74.2 | 359.1 ± 78.1 | 0.370 |
| 5STS, | 7.8 ± 3.3 | 7.4 ± 2.3 | 0.624 |
| Body composition | |||
| BMI, kg/m2 | 23.9 ± 3.9 | 23.6 ± 3.8 | 0.532 |
| FFMI (kg/m2) | 16.8 ± 2.4 | 16.7 ± 2.3 | 0.750 |
| FFMI (male, kg/m2) | 17.5 ± 2.2 | 17.4 ± 2.0 | 0.956 |
| FFMI (female, kg/m2) | 15.0 ± 2.0 | 15.2 ± 2.4 | 0.798 |
| SMMI (kg/m2) | 6.4 ± 1.1 | 5.9 ± 1.4 | 0.143 |
| SMMI (male, kg/m2) | 6.8 ± 1.0 | 6.5 ± 1.1 | 0.470 |
| SMMI (female, kg/m2) | 5.4 ± 0.9 | 5.1 ± 1.4 | 0.436 |
| HGS (kg) | 28.2 ± 8.7 | 28.0 ± 7.6 | 0.787 |
| HGS (male, kg) | 29.4 ± 8.0 | 28.4 ± 6.9 | 0.699 |
| HGS (female, kg) | 26.1 ± 9.9 | 26.6 ± 9.8 | 0.817 |
FEV
FIGURE 1The relationships of serum GDF15 levels with the clinical features of patients with COPD. (A) Difference in serum GDF15 level in patients with GOLD A and GOLD B and in patients with GOLD C and GOLD D; (B) the relationship between serum GDF15 level and FEV1%predicted, FVC % predicted, FEV1/FVC (n = 117); (C) the relationship between serum GDF15 level and mMRC score, CAT score (n = 117); (D) the relationship between serum GDF15 level and 6MWD; (E) the relationship between serum GDF15 level and BMI, FFMI (n = 117). GOLD, global initiative for chronic obstructive lung disease; FEV1%predicted, forced expiratory volume in the first second percentage predicted; FVC% predicted, forced vital capacity percentage predicted; mMRC, the modified British Medical Research Council; CAT, COPD assessment test; 6MWD, 6-min walking distance; BMI, body mass index; FFMI, fat-free mass index.
FIGURE 2The relationships of serum GDF15 levels with skeletal muscle function in patients with COPD. (A) The relationship between serum GDF15 levels and SMMI (n = 117; (B) the relationship between serum GDF15 levels and 5STS (n = 117); (C) the relationship between serum GDF15 levels and HGS (n = 117); (D) the relationship between serum GDF15 levels and QMS (n = 111); (E,F) the relationship between serum GDF15 levels and HGS (n = 117); (D) the relationship between serum GDF15 levels and RFthick, RFcsa (n = 114). SMMI, Skeletal muscle mass index; 5STS, Five-time chair stand test; HGS, Handgrip strength; QMS, Quadriceps muscle strength; RFthick, The thickness of the rectus femoris, RFthick (E), RFcsa, The cross-sectional area of rectus femoris.
FIGURE 3The predictive value of the serum GDF15 level. (A) The serum GDF15 level differed between patients with sarcopenia and patients without sarcopenia; (B) receiver operating characteristic curve analysis of serum GDF15 level for the prediction of sarcopenia in the development set; (C) receiver operating characteristic curve analysis of serum GDF15 level for the prediction of sarcopenia in the validation set. ***p < 0.001.
FIGURE 4Construction of nomogram models. (A) A nomogram combining serum GDF15 level and clinical features was constructed based on the development set; (B) receiver operating characteristic curve analysis (left) and decision curve analysis (right) in the development set; (C) receiver operating characteristic curve analysis (left) and decision curve analysis (right) in the validation cohort.