Weekly Article Matching - 2024-05-19

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Below are the article matching results from the past week:

• Article Abstract:

  J Mass Spectrom. 2024 Jun;59(6):e5039. doi: 10.1002/jms.5039.

  ABSTRACT

  Utilizing a data-driven approach, this study investigates modifier effects on compensation voltage in differential mobility spectrometry-mass spectrometry (DMS-MS) for metabolites and peptides. Our analysis uncovers specific factors causing signal suppression in small molecules and pinpoints both signal suppression mechanisms and the analytes involved. In peptides, machine learning models discern a relationship between molecular weight, topological polar surface area, peptide charge, and proton transfer-induced signal suppression. The models exhibit robust performance, offering valuable insights for the application of DMS to metabolites and tryptic peptides analysis by DMS-MS.

  PMID:38747242 | DOI:10.1002/jms.5039

  
  Keywords: data-driven approach, compensation voltage, differential mobility spectrometry-mass spectrometry, signal suppression, small molecules, pinpoints, signal suppression mechanisms, analytes, machine learning models, molecular weight.
  Section Title: {'small molecules': '## MS Database for annotation'}
  One-sentence Summary: This text discusses the use of a data-driven approach to identify factors causing signal suppression in differential mobility spectrometry-mass spectrometry, with a focus on small molecules and peptides. Machine learning models are used to uncover relationships between various factors and signal suppression.
  DOI: doi:10.1002/jms.5039

• Article Abstract:

  Endocrinol Diabetes Metab. 2024 May;7(3):e00484. doi: 10.1002/edm2.484.

  ABSTRACT

  OBJECTIVE: This study investigates the metabolic differences between normal, prediabetic and diabetic patients with good and poor glycaemic control (GGC and PGC).

  DESIGN: In this study, 1102 individuals were included, and 50 metabolites were analysed using tandem mass spectrometry. The diabetes diagnosis and treatment standards of the American Diabetes Association (ADA) were used to classify patients.

  METHODS: The nearest neighbour method was used to match controls and cases in each group on the basis of age, sex and BMI. Factor analysis was used to reduce the number of variables and find influential underlying factors. Finally, Pearson's correlation coefficient was used to check the correlation between both glucose and HbAc1 as independent factors with binary classes.

  RESULTS: Amino acids such as glycine, serine and proline, and acylcarnitines (AcylCs) such as C16 and C18 showed significant differences between the prediabetes and normal groups. Additionally, several metabolites, including C0, C5, C8 and C16, showed significant differences between the diabetes and normal groups. Moreover, the study found that several metabolites significantly differed between the GGC and PGC diabetes groups, such as C2, C6, C10, C16 and C18. The correlation analysis revealed that glucose and HbA1c levels significantly correlated with several metabolites, including glycine, serine and C16, in both the prediabetes and diabetes groups. Additionally, the correlation analysis showed that HbA1c significantly correlated with several metabolites, such as C2, C5 and C18, in the controlled and uncontrolled diabetes groups.

  CONCLUSIONS: These findings could help identify new biomarkers or underlying markers for the early detection and management of diabetes.

  PMID:38739122 | PMC:PMC11090150 | DOI:10.1002/edm2.484

  
  Keywords: Endocrinol Diabetes Metab, normal, prediabetic, diabetic, good glycaemic control, poor glycaemic control, metabolites, tandem mass spectrometry, American Diabetes Association, biomarkers.
  Section Title: {'metabolites': '## Unsupervised methods', 'tandem mass spectrometry': '## ISfrag'}
  One-sentence Summary: This study investigated the metabolic differences between normal, prediabetic, and diabetic patients with good and poor glycaemic control, identifying potential biomarkers for early detection and management of diabetes.
  DOI: doi:10.1002/edm2.484