학술논문
Plasma Metabolite Profiling Identifies Nondiabetic Chronic Pancreatitis Patients With Metabolic Alterations Progressing to Prediabetes Before HbA1c.
Document Type
Academic Journal
Author
Ketavarapu V; Translational Research Center, Asian Healthcare Foundation, AIG Hospitals, Mindspace Road, Gachibowli, Hyderabad, Telangana, India.; Addipilli R; Centre for Mass Spectrometry, Department of Analytical & Structural Chemistry, CSIR-Indian Institute of Chemical Technology, Tarnaka, Hyderabad, Telangana, India.; Ragi N; Centre for Mass Spectrometry, Department of Analytical & Structural Chemistry, CSIR-Indian Institute of Chemical Technology, Tarnaka, Hyderabad, Telangana, India.; Pallerla P; Centre for Mass Spectrometry, Department of Analytical & Structural Chemistry, CSIR-Indian Institute of Chemical Technology, Tarnaka, Hyderabad, Telangana, India.; Simhadri V; Translational Research Center, Asian Healthcare Foundation, AIG Hospitals, Mindspace Road, Gachibowli, Hyderabad, Telangana, India.; Manne S; Translational Research Center, Asian Healthcare Foundation, AIG Hospitals, Mindspace Road, Gachibowli, Hyderabad, Telangana, India.; Sannapaneni K; Translational Research Center, Asian Healthcare Foundation, AIG Hospitals, Mindspace Road, Gachibowli, Hyderabad, Telangana, India.; Aslam M; Asian Institute of Gastroenterology, Somajiguda, Hyderabad, Telangana, India.; Talukadar R; AIG Hospitals, Mindspace Road, Gachibowli, Hyderabad, Telangana, India.; Ch VD; Department of Biochemistry, University College of Science, Osmania University, Hyderabad, Telangana, India.; Gv R; AIG Hospitals, Mindspace Road, Gachibowli, Hyderabad, Telangana, India.; Amanchy R; Department of Applied Biology, CSIR-Indian Institute of Chemical Technology, Tarnaka, Hyderabad, Telangana, India.; Reddy DN; AIG Hospitals, Mindspace Road, Gachibowli, Hyderabad, Telangana, India.; Sripadi P; Centre for Mass Spectrometry, Department of Analytical & Structural Chemistry, CSIR-Indian Institute of Chemical Technology, Tarnaka, Hyderabad, Telangana, India.; Sasikala M; Translational Research Center, Asian Healthcare Foundation, AIG Hospitals, Mindspace Road, Gachibowli, Hyderabad, Telangana, India.
Source
Publisher: Wolters Kluwer Health Country of Publication: United States NLM ID: 101532142 Publication Model: Electronic Cited Medium: Internet ISSN: 2155-384X (Electronic) Linking ISSN: 2155384X NLM ISO Abbreviation: Clin Transl Gastroenterol Subsets: MEDLINE
Subject
Language
English
Abstract
Introduction: Diabetes (T3cDM) secondary to chronic pancreatitis (CP) arises due to endocrine dysfunction and metabolic dysregulations. Currently, diagnostic tests are not available to identify patients who may progress from normoglycemia to hyperglycemia in CP. We conducted plasma metabolomic profiling to diagnose glycemic alterations early in the course of disease.
Methods: Liquid chromatography-tandem mass spectrometry was used to generate untargeted, targeted plasma metabolomic profiles in patients with CP, controls (n = 445) following TRIPOD guidelines. Patients were stratified based on glucose tolerance tests following ADA guidelines. Multivariate analysis was performed using partial least squares discriminant analysis to assess discriminatory ability of metabolites among stratified groups. COMBIROC and logistic regression were used to derive biomarker signatures. AI-ML tool (Rapidminer) was used to verify these preliminary results.
Results: Ceramide, lysophosphatidylethanolamine, phosphatidylcholine, lysophosphatidic acid (LPA), phosphatidylethanolamine, carnitine, and lysophosphatidylcholine discriminated T3cDM CP patients from healthy controls with AUC 93% (95% CI 0.81-0.98, P < 0.0001), and integration with pancreatic morphology improved AUC to 100% (95% CI 0.93-1.00, P < 0.0001). LPA, phosphatidylinositol, and ceramide discriminated nondiabetic CP with glycemic alterations (pre-diabetic CP); AUC 66% (95% CI 0.55-0.76, P = 0.1), and integration enhanced AUC to 74% (95% CI 0.55-0.88, P = 0.86). T3cDM was distinguished from prediabetic by LPA, phosphatidylinositol, and sphinganine (AUC 70%; 95% CI 0.54-0.83, P = 0.08), and integration improved AUC to 83% (95% CI 0.68-0.93, P = 0.05). CombiROC cutoff identified 75% and 78% prediabetes in validation 1 and 2 cohorts. Random forest algorithm assessed performance of integrated panel demonstrating AUC of 72% in predicting glycemic alterations.
Discussion: We report for the first time that a panel of metabolites integrated with pancreatic morphology detects glycemia progression before HbA1c in patients with CP.
(Copyright © 2024 The Author(s). Published by Wolters Kluwer Health, Inc. on behalf of The American College of Gastroenterology.)
Methods: Liquid chromatography-tandem mass spectrometry was used to generate untargeted, targeted plasma metabolomic profiles in patients with CP, controls (n = 445) following TRIPOD guidelines. Patients were stratified based on glucose tolerance tests following ADA guidelines. Multivariate analysis was performed using partial least squares discriminant analysis to assess discriminatory ability of metabolites among stratified groups. COMBIROC and logistic regression were used to derive biomarker signatures. AI-ML tool (Rapidminer) was used to verify these preliminary results.
Results: Ceramide, lysophosphatidylethanolamine, phosphatidylcholine, lysophosphatidic acid (LPA), phosphatidylethanolamine, carnitine, and lysophosphatidylcholine discriminated T3cDM CP patients from healthy controls with AUC 93% (95% CI 0.81-0.98, P < 0.0001), and integration with pancreatic morphology improved AUC to 100% (95% CI 0.93-1.00, P < 0.0001). LPA, phosphatidylinositol, and ceramide discriminated nondiabetic CP with glycemic alterations (pre-diabetic CP); AUC 66% (95% CI 0.55-0.76, P = 0.1), and integration enhanced AUC to 74% (95% CI 0.55-0.88, P = 0.86). T3cDM was distinguished from prediabetic by LPA, phosphatidylinositol, and sphinganine (AUC 70%; 95% CI 0.54-0.83, P = 0.08), and integration improved AUC to 83% (95% CI 0.68-0.93, P = 0.05). CombiROC cutoff identified 75% and 78% prediabetes in validation 1 and 2 cohorts. Random forest algorithm assessed performance of integrated panel demonstrating AUC of 72% in predicting glycemic alterations.
Discussion: We report for the first time that a panel of metabolites integrated with pancreatic morphology detects glycemia progression before HbA1c in patients with CP.
(Copyright © 2024 The Author(s). Published by Wolters Kluwer Health, Inc. on behalf of The American College of Gastroenterology.)