*Result*: Targeted and Untargeted GC-MS Metabolomics Coupled With Multivariate Chemometric Modeling Reveals Pioglitazone's Impact on Diabetic Rats.
Title:
Targeted and Untargeted GC-MS Metabolomics Coupled With Multivariate Chemometric Modeling Reveals Pioglitazone's Impact on Diabetic Rats.
Authors:
Abduljabbar MH; Department of Pharmacology and Toxicology, College of Pharmacy, Taif University, Taif, Saudi Arabia., Bafail DA; Department of Clinical Pharmacology, Faculty of Medicine, King Abdulaziz University, Jeddah, Saudi Arabia., Alnemari RM; Department of Pharmaceutics and Industrial Pharmacy, College of Pharmacy, Taif University, Taif, Saudi Arabia., Serag A; Pharmaceutical Analytical Chemistry Department, Faculty of Pharmacy, Al-Azhar University, Nasr City, Cairo, Egypt., Almalki AH; Addiction and Neuroscience Research Unit, Health Science Campus, Taif University, Taif, Saudi Arabia.; Department of Pharmaceutical Chemistry, College of Pharmacy, Taif University, Taif, Saudi Arabia.
Source:
Chemistry & biodiversity [Chem Biodivers] 2025 Dec; Vol. 22 (12), pp. e02006. Date of Electronic Publication: 2025 Aug 25.
Publication Type:
Journal Article
Language:
English
Journal Info:
Publisher: Verlag Helvetica Chimica Acta Country of Publication: Switzerland NLM ID: 101197449 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1612-1880 (Electronic) Linking ISSN: 16121872 NLM ISO Abbreviation: Chem Biodivers Subsets: MEDLINE
Imprint Name(s):
Original Publication: Zürich, Switzerland : Hoboken, NJ : Verlag Helvetica Chimica Acta ; Distributed in the USA by Wiley, c2004-
MeSH Terms:
Pioglitazone*/therapeutic use , Pioglitazone*/pharmacology , Metabolomics* , Diabetes Mellitus, Experimental*/drug therapy , Diabetes Mellitus, Experimental*/metabolism , Diabetes Mellitus, Experimental*/chemically induced , Hypoglycemic Agents*/therapeutic use , Hypoglycemic Agents*/pharmacology, Animals ; Gas Chromatography-Mass Spectrometry ; Rats ; Male ; Multivariate Analysis ; Principal Component Analysis ; Rats, Sprague-Dawley ; Discriminant Analysis ; Streptozocin ; Chemometrics
References:
R. Balaji, R. Duraisamy, and M. Kumar, “Complications of Diabetes Mellitus: A Review,” Drug Invention Today 12 (2019): 98–103.
American Diabetes Association. “Diagnosis and Classification of Diabetes Mellitus,” Diabetes Care 37 (2014): S81–S90.
H. Kolb and S. Martin, “Environmental/Lifestyle Factors in the Pathogenesis and Prevention of Type 2 Diabetes,” BMC Medicine 15 (2017): 1–11.
Y. Wu, Y. Ding, Y. Tanaka, and W. Zhang, “Risk Factors Contributing to Type 2 Diabetes and Recent Advances in the Treatment and Prevention,” International Journal of Medical Sciences 11 (2014): 1185–1200.
Y. Zheng, S. H. Ley, and F. B. Hu, “Global Aetiology and Epidemiology of Type 2 Diabetes Mellitus and Its Complications,” Nature Reviews Endocrinology 14 (2018): 88–98.
J. E. Lima, N. C. Moreira, and E. T. Sakamoto‐Hojo, “Mechanisms Underlying the Pathophysiology of Type 2 Diabetes: From Risk Factors to Oxidative Stress, Metabolic Dysfunction, and Hyperglycemia,” Mutation Research/Genetic Toxicology and Environmental Mutagenesis 874 (2022): 503437.
J.‐Y. Fang, C.‐H. Lin, T.‐H. Huang, and S.‐Y. Chuang, “In Vivo Rodent Models of Type 2 Diabetes and Their Usefulness for Evaluating Flavonoid Bioactivity,” Nutrients 11 (2019): 530.
S. N. Goyal, N. M. Reddy, K. R. Patil, et al., “Challenges and Issues With Streptozotocin‐Induced Diabetes—A Clinically Relevant Animal Model to Understand the Diabetes Pathogenesis and Evaluate Therapeutics,” Chemico‐Biological Interactions 244 (2016): 49–63.
J. W. F. Elte and J. F. Blicklé, “Thiazolidinediones for the Treatment of Type 2 Diabetes,” European Journal of Internal Medicine 18 (2007): 18–25.
B. Malhotra, P. Hiteshi, P. Khalkho, et al., “Bladder Cancer With Pioglitazone: A Case–Control Study,” Diabetes & Metabolic Syndrome: Clinical Research & Reviews 16 (2022): 102637.
R. E. J. Ryder and R. A. DeFronzo, “Pioglitazone: Inexpensive; Very Effective at Reducing HbA1c; no Evidence of Bladder Cancer Risk; Plenty of Evidence of Cardiovascular Benefit,” Diabetic Medicine 36 (2019): 1185–1186.
M. Ahmadian, J. M. Suh, N. Hah, et al., “PPARγ Signaling and Metabolism: The Good, the Bad and the Future,” Nature Medicine 19 (2013): 557–566.
P. S. Gillies and C. J. Dunn, “Pioglitazone,” Drugs 60 (2000): 333–343.
G. S. Papaetis, “Pioglitazone in Diabetic Kidney Disease: Forgotten But Not Gone,” Archives of Medical Science—Atherosclerotic Diseases 7 (2022): 78–93.
B. C. Muthubharathi, T. Gowripriya, and K. Balamurugan, “Metabolomics: Small Molecules That Matter More,” Molecular Omics 17 (2021): 210–229.
L. A. Filla and J. L. Edwards, “Metabolomics in Diabetic Complications,” Molecular BioSystems 12 (2016): 1090–1105.
M. H. Abu Bakar, M. R. Sarmidi, K.‐K. Cheng, et al., “Metabolomics—The Complementary Field in Systems Biology: A Review on Obesity and Type 2 Diabetes,” Molecular BioSystems 11 (2015): 1742–1774.
T. Cajka and O. Fiehn, “Toward Merging Untargeted and Targeted Methods in Mass Spectrometry‐Based Metabolomics and Lipidomics,” Analytical Chemistry 88 (2016): 524–545.
J. Zhou and Y. Yin, “Strategies for Large‐Scale Targeted Metabolomics Quantification by Liquid Chromatography‐mass Spectrometry,” Analyst 141 (2016): 6362–6373.
D. C. Sévin, A. Kuehne, N. Zamboni, and U. Sauer, “Biological Insights Through Nontargeted Metabolomics,” Current Opinion in Biotechnology 34 (2015): 1–8.
K. Segers, S. Declerck, D. Mangelings, Y. V. Heyden, and A. V. Eeckhaut, “Analytical Techniques for Metabolomic Studies: A Review,” Bioanalysis 11 (2019): 2297–2318.
M. Zeng, Z. Che, Y. Liang, et al., “GC–MS Based Plasma Metabolic Profiling of Type 2 Diabetes Mellitus,” Chromatographia 69 (2009): 941–948.
A. Lapolla, R. Flamini, A. D. Vedova, et al., “Glyoxal and Methylglyoxal Levels in Diabetic Patients: Quantitative Determination by a New GC/MS Method,” Clinical Chemistry and Laboratory Medicine 41 (2003): 1166–1173.
J.‐H. Huang, H.‐L. Xie, J. Yan, et al., “Interpretation of Type 2 Diabetes Mellitus Relevant GC–MS Metabolomics Fingerprints by Using Random Forests,” Analytical Methods 5 (2013): 4883–4889.
M. E. Alosaimi, B. S. Alotaibi, M. H. Abduljabbar, R. M. Alnemari, A. H. Almalki, and A. Serag, “Therapeutic Implications of Dapagliflozin on the Metabolomics Profile of Diabetic Rats: A GC–MS Investigation Coupled With Multivariate Analysis,” Journal of Pharmaceutical and Biomedical Analysis 242 (2024): 116018.
H. Yang, D. H. Suh, D. H. Kim, et al., “Metabolomic and Lipidomic Analysis of the Effect of Pioglitazone on Hepatic Steatosis in a Rat Model of Obese Type 2 Diabetes,” British Journal of Pharmacology 175 (2018): 3610–3625.
T. J. Jönsson, H.‐L. Schäfer, A. W. Herling, and M. Brönstrup, “A Metabolome‐Wide Characterization of the Diabetic Phenotype in ZDF Rats and Its Reversal by Pioglitazone,” PLoS ONE 13 (2018): e0207210.
B. L. Furman, “Streptozotocin‐Induced Diabetic Models in Mice and Rats,” Current Protocols in Pharmacology 70 (2015): 5.47.41–45.47.20.
A. Ghasemi and S. Jeddi, “Streptozotocin as a Tool for Induction of Rat Models of Diabetes: A Practical Guide,” EXCLI Journal 22 (2023): 274.
K. Gothandam, V. S. Ganesan, T. Ayyasamy, and S. Ramalingam, “Antioxidant Potential of Theaflavin Ameliorates the Activities of Key Enzymes of Glucose Metabolism in High Fat Diet and Streptozotocin—Induced Diabetic Rats,” Redox Report 24 (2019): 41–50.
O. Fiehn, “Metabolomics by Gas Chromatography–Mass Spectrometry: Combined Targeted and Untargeted Profiling,” Current Protocols in Molecular Biology 114 (2016): 30.34.31–30.34.32.
Z. Pang, Y. Lu, G. Zhou, et al., “MetaboAnalyst 6.0: Towards a Unified Platform for Metabolomics Data Processing, Analysis and Interpretation,” Nucleic Acids Research 52 (2024): W398–W406.
Z. Bloomgarden, “Diabetes and Branched‐Chain Amino Acids: What is the Link?,” Journal of Diabetes 10 (2018): 350–352.
I. Ramzan, A. Ardavani, F. Vanweert, A. Mellett, P. J. Atherton, and I. Idris, “The Association between Circulating Branched Chain Amino Acids and the Temporal Risk of Developing Type 2 Diabetes Mellitus: A Systematic Review & Meta‐Analysis,” Nutrients 14 (2022): 4411.
Q. Zhou, W. W. Sun, J. C. Chen, et al., “Phenylalanine Impairs Insulin Signaling and Inhibits Glucose Uptake through Modification of IRβ,” Nature Communications 13 (2022): 4291.
M. Cruz, C. Maldonado‐Bernal, R. Mondragón‐Gonzalez, et al., “Glycine Treatment Decreases Proinflammatory Cytokines and Increases Interferon‐γ in Patients With Type 2 Diabetes,” Journal of Endocrinological Investigation 31 (2008): 694–699.
J. A. Bhat, S. R. Masoodi, M. H. Bhat, H. Bhat, P. O. Ahmad, and M. Sood, “Lactic Acidosis in Diabetic Ketoacidosis: A Marker of Severity or Alternate Substrate for Metabolism,” Indian Journal of Endocrinology and Metabolism 25 (2021): 59–66.
V. Stojanovic and S. Ihle, “Role of Beta‐Hydroxybutyric Acid in Diabetic Ketoacidosis: A Review,” Canadian Veterinary Journal 52 (2011): 426–430.
S. Chawla, N. Kaushik, N. P. Singh, R. K. Ghosh, and A. Saxena, “Effect of Addition of Either Sitagliptin or Pioglitazone in Patients With Uncontrolled Type 2 Diabetes Mellitus on Metformin: A Randomized Controlled Trial,” Journal of Pharmacology and Pharmacotherapeutics 4 (2013): 27–32.
I.‐K. Lee, “The Role of Pyruvate Dehydrogenase Kinase in Diabetes and Obesity,” Diabetes & Metabolism Journal 38 (2014): 181–186.
American Diabetes Association. “Diagnosis and Classification of Diabetes Mellitus,” Diabetes Care 37 (2014): S81–S90.
H. Kolb and S. Martin, “Environmental/Lifestyle Factors in the Pathogenesis and Prevention of Type 2 Diabetes,” BMC Medicine 15 (2017): 1–11.
Y. Wu, Y. Ding, Y. Tanaka, and W. Zhang, “Risk Factors Contributing to Type 2 Diabetes and Recent Advances in the Treatment and Prevention,” International Journal of Medical Sciences 11 (2014): 1185–1200.
Y. Zheng, S. H. Ley, and F. B. Hu, “Global Aetiology and Epidemiology of Type 2 Diabetes Mellitus and Its Complications,” Nature Reviews Endocrinology 14 (2018): 88–98.
J. E. Lima, N. C. Moreira, and E. T. Sakamoto‐Hojo, “Mechanisms Underlying the Pathophysiology of Type 2 Diabetes: From Risk Factors to Oxidative Stress, Metabolic Dysfunction, and Hyperglycemia,” Mutation Research/Genetic Toxicology and Environmental Mutagenesis 874 (2022): 503437.
J.‐Y. Fang, C.‐H. Lin, T.‐H. Huang, and S.‐Y. Chuang, “In Vivo Rodent Models of Type 2 Diabetes and Their Usefulness for Evaluating Flavonoid Bioactivity,” Nutrients 11 (2019): 530.
S. N. Goyal, N. M. Reddy, K. R. Patil, et al., “Challenges and Issues With Streptozotocin‐Induced Diabetes—A Clinically Relevant Animal Model to Understand the Diabetes Pathogenesis and Evaluate Therapeutics,” Chemico‐Biological Interactions 244 (2016): 49–63.
J. W. F. Elte and J. F. Blicklé, “Thiazolidinediones for the Treatment of Type 2 Diabetes,” European Journal of Internal Medicine 18 (2007): 18–25.
B. Malhotra, P. Hiteshi, P. Khalkho, et al., “Bladder Cancer With Pioglitazone: A Case–Control Study,” Diabetes & Metabolic Syndrome: Clinical Research & Reviews 16 (2022): 102637.
R. E. J. Ryder and R. A. DeFronzo, “Pioglitazone: Inexpensive; Very Effective at Reducing HbA1c; no Evidence of Bladder Cancer Risk; Plenty of Evidence of Cardiovascular Benefit,” Diabetic Medicine 36 (2019): 1185–1186.
M. Ahmadian, J. M. Suh, N. Hah, et al., “PPARγ Signaling and Metabolism: The Good, the Bad and the Future,” Nature Medicine 19 (2013): 557–566.
P. S. Gillies and C. J. Dunn, “Pioglitazone,” Drugs 60 (2000): 333–343.
G. S. Papaetis, “Pioglitazone in Diabetic Kidney Disease: Forgotten But Not Gone,” Archives of Medical Science—Atherosclerotic Diseases 7 (2022): 78–93.
B. C. Muthubharathi, T. Gowripriya, and K. Balamurugan, “Metabolomics: Small Molecules That Matter More,” Molecular Omics 17 (2021): 210–229.
L. A. Filla and J. L. Edwards, “Metabolomics in Diabetic Complications,” Molecular BioSystems 12 (2016): 1090–1105.
M. H. Abu Bakar, M. R. Sarmidi, K.‐K. Cheng, et al., “Metabolomics—The Complementary Field in Systems Biology: A Review on Obesity and Type 2 Diabetes,” Molecular BioSystems 11 (2015): 1742–1774.
T. Cajka and O. Fiehn, “Toward Merging Untargeted and Targeted Methods in Mass Spectrometry‐Based Metabolomics and Lipidomics,” Analytical Chemistry 88 (2016): 524–545.
J. Zhou and Y. Yin, “Strategies for Large‐Scale Targeted Metabolomics Quantification by Liquid Chromatography‐mass Spectrometry,” Analyst 141 (2016): 6362–6373.
D. C. Sévin, A. Kuehne, N. Zamboni, and U. Sauer, “Biological Insights Through Nontargeted Metabolomics,” Current Opinion in Biotechnology 34 (2015): 1–8.
K. Segers, S. Declerck, D. Mangelings, Y. V. Heyden, and A. V. Eeckhaut, “Analytical Techniques for Metabolomic Studies: A Review,” Bioanalysis 11 (2019): 2297–2318.
M. Zeng, Z. Che, Y. Liang, et al., “GC–MS Based Plasma Metabolic Profiling of Type 2 Diabetes Mellitus,” Chromatographia 69 (2009): 941–948.
A. Lapolla, R. Flamini, A. D. Vedova, et al., “Glyoxal and Methylglyoxal Levels in Diabetic Patients: Quantitative Determination by a New GC/MS Method,” Clinical Chemistry and Laboratory Medicine 41 (2003): 1166–1173.
J.‐H. Huang, H.‐L. Xie, J. Yan, et al., “Interpretation of Type 2 Diabetes Mellitus Relevant GC–MS Metabolomics Fingerprints by Using Random Forests,” Analytical Methods 5 (2013): 4883–4889.
M. E. Alosaimi, B. S. Alotaibi, M. H. Abduljabbar, R. M. Alnemari, A. H. Almalki, and A. Serag, “Therapeutic Implications of Dapagliflozin on the Metabolomics Profile of Diabetic Rats: A GC–MS Investigation Coupled With Multivariate Analysis,” Journal of Pharmaceutical and Biomedical Analysis 242 (2024): 116018.
H. Yang, D. H. Suh, D. H. Kim, et al., “Metabolomic and Lipidomic Analysis of the Effect of Pioglitazone on Hepatic Steatosis in a Rat Model of Obese Type 2 Diabetes,” British Journal of Pharmacology 175 (2018): 3610–3625.
T. J. Jönsson, H.‐L. Schäfer, A. W. Herling, and M. Brönstrup, “A Metabolome‐Wide Characterization of the Diabetic Phenotype in ZDF Rats and Its Reversal by Pioglitazone,” PLoS ONE 13 (2018): e0207210.
B. L. Furman, “Streptozotocin‐Induced Diabetic Models in Mice and Rats,” Current Protocols in Pharmacology 70 (2015): 5.47.41–45.47.20.
A. Ghasemi and S. Jeddi, “Streptozotocin as a Tool for Induction of Rat Models of Diabetes: A Practical Guide,” EXCLI Journal 22 (2023): 274.
K. Gothandam, V. S. Ganesan, T. Ayyasamy, and S. Ramalingam, “Antioxidant Potential of Theaflavin Ameliorates the Activities of Key Enzymes of Glucose Metabolism in High Fat Diet and Streptozotocin—Induced Diabetic Rats,” Redox Report 24 (2019): 41–50.
O. Fiehn, “Metabolomics by Gas Chromatography–Mass Spectrometry: Combined Targeted and Untargeted Profiling,” Current Protocols in Molecular Biology 114 (2016): 30.34.31–30.34.32.
Z. Pang, Y. Lu, G. Zhou, et al., “MetaboAnalyst 6.0: Towards a Unified Platform for Metabolomics Data Processing, Analysis and Interpretation,” Nucleic Acids Research 52 (2024): W398–W406.
Z. Bloomgarden, “Diabetes and Branched‐Chain Amino Acids: What is the Link?,” Journal of Diabetes 10 (2018): 350–352.
I. Ramzan, A. Ardavani, F. Vanweert, A. Mellett, P. J. Atherton, and I. Idris, “The Association between Circulating Branched Chain Amino Acids and the Temporal Risk of Developing Type 2 Diabetes Mellitus: A Systematic Review & Meta‐Analysis,” Nutrients 14 (2022): 4411.
Q. Zhou, W. W. Sun, J. C. Chen, et al., “Phenylalanine Impairs Insulin Signaling and Inhibits Glucose Uptake through Modification of IRβ,” Nature Communications 13 (2022): 4291.
M. Cruz, C. Maldonado‐Bernal, R. Mondragón‐Gonzalez, et al., “Glycine Treatment Decreases Proinflammatory Cytokines and Increases Interferon‐γ in Patients With Type 2 Diabetes,” Journal of Endocrinological Investigation 31 (2008): 694–699.
J. A. Bhat, S. R. Masoodi, M. H. Bhat, H. Bhat, P. O. Ahmad, and M. Sood, “Lactic Acidosis in Diabetic Ketoacidosis: A Marker of Severity or Alternate Substrate for Metabolism,” Indian Journal of Endocrinology and Metabolism 25 (2021): 59–66.
V. Stojanovic and S. Ihle, “Role of Beta‐Hydroxybutyric Acid in Diabetic Ketoacidosis: A Review,” Canadian Veterinary Journal 52 (2011): 426–430.
S. Chawla, N. Kaushik, N. P. Singh, R. K. Ghosh, and A. Saxena, “Effect of Addition of Either Sitagliptin or Pioglitazone in Patients With Uncontrolled Type 2 Diabetes Mellitus on Metformin: A Randomized Controlled Trial,” Journal of Pharmacology and Pharmacotherapeutics 4 (2013): 27–32.
I.‐K. Lee, “The Role of Pyruvate Dehydrogenase Kinase in Diabetes and Obesity,” Diabetes & Metabolism Journal 38 (2014): 181–186.
Grant Information:
TU-DSPP-2024-154 Taif University
Contributed Indexing:
Keywords: biomarkers; metabolomics; multivariate analysis streptozotocin; thiazolidinediones
Substance Nomenclature:
X4OV71U42S (Pioglitazone)
0 (Hypoglycemic Agents)
5W494URQ81 (Streptozocin)
0 (Hypoglycemic Agents)
5W494URQ81 (Streptozocin)
Entry Date(s):
Date Created: 20250825 Date Completed: 20251219 Latest Revision: 20260126
Update Code:
20260130
DOI:
10.1002/cbdv.202502006
PMID:
40853505
Database:
MEDLINE
*Further Information*
*Diabetes mellitus is a chronic metabolic disorder characterized by hyperglycemia and alterations in metabolic pathways. Herein, gas chromatography-mass spectrometry (GC-MS) metabolomics was employed to investigate the changes in serum metabolites of streptozotocin-induced diabetic rats in response to pioglitazone treatment. Both untargeted and targeted analyses were conducted to identify and quantify the key metabolic perturbations. Multivariate analysis modeling such as principal component analysis (PCA), partial least squares-discriminant analysis (PLS-DA), and orthogonal projections to latent structures DA (OPLS-DA) revealed significant differences in the metabolic profiles among the diabetic, pioglitazone-treated, and control groups. A panel of 15 differentially altered metabolites was identified, of which a targeted quantification was performed of 6 key metabolites that were significantly modulated by pioglitazone treatment. These metabolites include two branched-chain amino acids (l-isoleucine, l-valine), two organic acids (lactic, beta-hydroxybutyric), and two fatty acids (palmitic, stearic). Interestingly, pathway analysis revealed the inability of pioglitazone to restore the levels of some metabolites, suggesting possible persistent metabolic alterations in diabetes even after treatment. This metabolic signature offers significant potential for clinical translation, serving as a biomarker panel for monitoring diabetes progression and therapeutic efficacy. Such comprehensive GC-MS metabolomics platform provides a robust analytical framework for accelerating diabetes drug discovery, enabling precise therapeutic monitoring, and facilitating personalized medicine approaches in diabetes management.
(© 2025 Wiley‐VHCA AG, Zurich, Switzerland.)*