Plasmonic Alloys Enhanced Metabolic Fingerprints for the Diagnosis of COPD and Exacerbations

Su, Haiyang; Song, Yuanlin; Yang, Shouzhi; Zhang, Ziyue; Shen, Yao; Yu, Lan; Chen, Shujing; Gao, Lei; Chen, Cuicui; Hou, Dongni; Wei, Xinping; Ma, Xuedong; Huang, Pengyu; Sun, Dejun; Zhou, Jian; Qian, Kun

doi:10.1021/acscentsci.3c01201

Cited by 4 publications

(1 citation statement)

References 92 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…To validate the reliability of serum GDF15, we need to conduct more prospective, multi-center investigations in the future. Furthermore, a recent study has shown that constructing the mesoporous PdPt-assisted LDI MS for biomarker analysis results in a more accurate early diagnosis of COPD and AECOPD ( 47 ). In the future, we may be able to use this platform to detect serum biomarkers, such as GDF15 and ALB to identify malnutrition in patients with AECOPD to provide early nutritional therapy.…”

Section: Discussionmentioning

confidence: 99%

Serum growth differentiation factor 15 as a biomarker for malnutrition in patients with acute exacerbation of chronic obstructive pulmonary disease

Shi,

Yue,

Tang

et al. 2024

Front. Nutr.

View full text Add to dashboard Cite

BackgroundChronic obstructive pulmonary disease (COPD) is a common respiratory disease that often coexists with malnutrition during acute exacerbation (AECOPD) and significantly affects the prognosis. Previous studies have shown that growth differentiation factor 15 (GDF15) levels promote appetite suppression, weight loss, and muscle weakness, and are markedly high in peripheral blood following inflammatory stimulation. However, it is still unknown whether serum GDF15 levels can be used to predict malnutrition in patients with AECOPD.MethodsA total of 142 patients admitted to the Department of Respiratory Medicine at Anshun People’s Hospital between December 2022 and August 2023 were selected for this study. The participants were divided into two groups: malnutrition group (n = 44) and non-malnutrition group (n = 98) based on a body mass index (BMI) < 18.5 kg/m2, according to the Global Leadership Initiative on Malnutrition (GLIM) criteria. Serum GDF15 levels were measured using the enzyme-linked immunosorbent assay (ELISA) and compared between the two groups. Spearman correlation analysis was used to examine the association between serum GDF15 levels, baseline data, and clinical indicators. Binary logistic regression was used to identify the independent risk factors for AECOPD combined with malnutrition. The predictive value of serum GDF15, albumin (ALB), and a combination of these was evaluated to identify malnutrition in patients with AECOPD using a receiver operating characteristic (ROC) curve.ResultsSerum GDF15 levels in patients with malnutrition and AECOPD were significantly higher than those in patients without malnutrition, whereas the serum ALB levels were significantly lower than those in patients without malnutrition (p < 0.001). Moreover, serum GDF15 levels were negatively correlated with BMI (r = −0.562, p < 0.001), mid-arm circumference (r = −0.505, p < 0.001), calf circumference (r = −0.490, p < 0.001), total protein (r = −0.486, p < 0.001), ALB (r = −0.445, p < 0.001), and prognostic nutritional index (r = −0.276, p = 0.001), and positively correlated with C-reactive protein (r = 0.318, p < 0.001), COPD assessment test score (r = 0.286, p = 0.001), modified medical research council classification (r = 0.310, p < 0.001), and global initiative for chronic obstructive pulmonary disease grade (r = 0.177, p = 0.035). Furthermore, serum GDF15 levels were an independent risk factor for malnutrition in patients with AECOPD (OR = 1.010, 95% CI, 1.003∼1.016). The optimal cut-off value of serum GDF15 level was 1,092.885 pg/mL, with a sensitivity of 65.90% and a specificity of 89.80%, while the serum ALB level was 36.15 g/L, with a sensitivity of 86.40% and a specificity of 65.00%, as well as a combined sensitivity of 84.10% and a specificity of 73.90%. Serum GDF15 and serum ALB levels had a good predictive ability (AUC = 0.856, AUC = 0.887), and the ROC revealed a greater combined prediction value for the two (AUC = 0.935).ConclusionSerum GDF15 levels could be used as a potential biomarker in the prediction of malnutrition in patients with AECOPD, offering a guidance for future clinical evaluation of malnutrition.

show abstract

Section: Discussionmentioning

confidence: 99%

Serum growth differentiation factor 15 as a biomarker for malnutrition in patients with acute exacerbation of chronic obstructive pulmonary disease

Shi,

Yue,

Tang

et al. 2024

Front. Nutr.

View full text Add to dashboard Cite

show abstract

Serum and Urine Metabolic Fingerprints Characterize Renal Cell Carcinoma for Classification, Early Diagnosis, and Prognosis

Xu,

Fang,

Wang

et al. 2024

Advanced Science

View full text Add to dashboard Cite

Renal cell carcinoma (RCC) is a substantial pathology of the urinary system with a growing prevalence rate. However, current clinical methods have limitations for managing RCC due to the heterogeneity manifestations of the disease. Metabolic analyses are regarded as a preferred noninvasive approach in clinics, which can substantially benefit the characterization of RCC. This study constructs a nanoparticle‐enhanced laser desorption ionization mass spectrometry (NELDI MS) to analyze metabolic fingerprints of renal tumors (n = 456) and healthy controls (n = 200). The classification models yielded the areas under curves (AUC) of 0.938 (95% confidence interval (CI), 0.884–0.967) for distinguishing renal tumors from healthy controls, 0.850 for differentiating malignant from benign tumors (95% CI, 0.821–0.915), and 0.925–0.932 for classifying subtypes of RCC (95% CI, 0.821–0.915). For the early stage of RCC subtypes, the averaged diagnostic sensitivity of 90.5% and specificity of 91.3% in the test set is achieved. Metabolic biomarkers are identified as the potential indicator for subtype diagnosis (p < 0.05). To validate the prognostic performance, a predictive model for RCC participants and achieve the prediction of disease (p = 0.003) is constructed. The study provides a promising prospect for applying metabolic analytical tools for RCC characterization.

show abstract

Quaternary phosphonium AIEgens nanoparticles as innovative agents for developing latent fingerprints

Huang,

Chen,

Chien

et al. 2024

Analytica Chimica Acta

View full text Add to dashboard Cite

Plasmonic Alloys Enhanced Metabolic Fingerprints for the Diagnosis of COPD and Exacerbations

Cited by 4 publications

References 92 publications

Serum growth differentiation factor 15 as a biomarker for malnutrition in patients with acute exacerbation of chronic obstructive pulmonary disease

Serum growth differentiation factor 15 as a biomarker for malnutrition in patients with acute exacerbation of chronic obstructive pulmonary disease

Serum and Urine Metabolic Fingerprints Characterize Renal Cell Carcinoma for Classification, Early Diagnosis, and Prognosis

Quaternary phosphonium AIEgens nanoparticles as innovative agents for developing latent fingerprints

Contact Info

Product

Resources

About