Serum protein signature of coronary artery disease in type 2 diabetes mellitus
Abstract Background Coronary artery disease (CAD) is the leading cause of morbidity and mortality in patients with type 2 diabetes mellitus (T2DM). The purpose of the present study was to discriminate the Indian CAD patients with or without T2DM by using multiple pathophysiological biomarkers. Metho...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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BMC
2019-01-01
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| Series: | Journal of Translational Medicine |
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| Online Access: | http://link.springer.com/article/10.1186/s12967-018-1755-5 |
| _version_ | 1818216860551741440 |
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| author | Ramu Adela Podduturu Naveen Chander Reddy Tarini Shankar Ghosh Suruchi Aggarwal Amit Kumar Yadav Bhabatosh Das Sanjay K. Banerjee |
| author_facet | Ramu Adela Podduturu Naveen Chander Reddy Tarini Shankar Ghosh Suruchi Aggarwal Amit Kumar Yadav Bhabatosh Das Sanjay K. Banerjee |
| author_sort | Ramu Adela |
| collection | DOAJ |
| description | Abstract Background Coronary artery disease (CAD) is the leading cause of morbidity and mortality in patients with type 2 diabetes mellitus (T2DM). The purpose of the present study was to discriminate the Indian CAD patients with or without T2DM by using multiple pathophysiological biomarkers. Methods Using sensitive multiplex protein assays, we assessed 46 protein markers including cytokines/chemokines, metabolic hormones, adipokines and apolipoproteins for evaluating different pathophysiological conditions of control, T2DM, CAD and T2DM with CAD patients (T2DM_CAD). Network analysis was performed to create protein-protein interaction networks by using significantly (p < 0.05) altered protein markers in each disease using STRING 10.5 database. We used two supervised analysis methods i.e., between class analysis (BCA) and principal component analysis (PCA) to reveals distinct biomarkers profiles. Further, random forest classification (RF) was used to classify the diseases by the panel of markers. Results Our two supervised analysis methods BCA and PCA revealed a distinct biomarker profiles and high degree of variability in the marker profiles for T2DM_CAD and CAD. Thereafter, the present study identified multiple potential biomarkers to differentiate T2DM, CAD, and T2DM_CAD patients based on their relative abundance in serum. RF classified T2DM based on the abundance patterns of nine markers i.e., IL-1β, GM-CSF, glucagon, PAI-I, rantes, IP-10, resistin, GIP and Apo-B; CAD by 14 markers i.e., resistin, PDGF-BB, PAI-1, lipocalin-2, leptin, IL-13, eotaxin, GM-CSF, Apo-E, ghrelin, adipsin, GIP, Apo-CII and IP-10; and T2DM _CAD by 12 markers i.e., insulin, resistin, PAI-1, adiponectin, lipocalin-2, GM-CSF, adipsin, leptin, Apo-AII, rantes, IL-6 and ghrelin with respect to the control subjects. Using network analysis, we have identified several cellular network proteins like PTPN1, AKT1, INSR, LEPR, IRS1, IRS2, IL1R2, IL6R, PCSK9 and MYD88, which are responsible for regulating inflammation, insulin resistance, and atherosclerosis. Conclusion We have identified three distinct sets of serum markers for diabetes, CAD and diabetes associated with CAD in Indian patients using nonparametric-based machine learning approach. These multiple marker classifiers may be useful for monitoring progression from a healthy person to T2DM and T2DM to T2DM_CAD. However, these findings need to be further confirmed in the future studies with large number of samples. |
| first_indexed | 2024-12-12T06:58:41Z |
| format | Article |
| id | doaj.art-e214426bfd9e4026aa6221cbe0a1d97d |
| institution | Directory Open Access Journal |
| issn | 1479-5876 |
| language | English |
| last_indexed | 2024-12-12T06:58:41Z |
| publishDate | 2019-01-01 |
| publisher | BMC |
| record_format | Article |
| series | Journal of Translational Medicine |
| spelling | doaj.art-e214426bfd9e4026aa6221cbe0a1d97d2022-12-22T00:33:54ZengBMCJournal of Translational Medicine1479-58762019-01-0117111710.1186/s12967-018-1755-5Serum protein signature of coronary artery disease in type 2 diabetes mellitusRamu Adela0Podduturu Naveen Chander Reddy1Tarini Shankar Ghosh2Suruchi Aggarwal3Amit Kumar Yadav4Bhabatosh Das5Sanjay K. Banerjee6Drug Discovery Research Center, Translational Health Science and Technology Institute (THSTI)Mediciti Institute of Medical SciencesCentre for Human Microbial Ecology, Translational Health Science and Technology InstituteDrug Discovery Research Center, Translational Health Science and Technology Institute (THSTI)Drug Discovery Research Center, Translational Health Science and Technology Institute (THSTI)Centre for Human Microbial Ecology, Translational Health Science and Technology InstituteDrug Discovery Research Center, Translational Health Science and Technology Institute (THSTI)Abstract Background Coronary artery disease (CAD) is the leading cause of morbidity and mortality in patients with type 2 diabetes mellitus (T2DM). The purpose of the present study was to discriminate the Indian CAD patients with or without T2DM by using multiple pathophysiological biomarkers. Methods Using sensitive multiplex protein assays, we assessed 46 protein markers including cytokines/chemokines, metabolic hormones, adipokines and apolipoproteins for evaluating different pathophysiological conditions of control, T2DM, CAD and T2DM with CAD patients (T2DM_CAD). Network analysis was performed to create protein-protein interaction networks by using significantly (p < 0.05) altered protein markers in each disease using STRING 10.5 database. We used two supervised analysis methods i.e., between class analysis (BCA) and principal component analysis (PCA) to reveals distinct biomarkers profiles. Further, random forest classification (RF) was used to classify the diseases by the panel of markers. Results Our two supervised analysis methods BCA and PCA revealed a distinct biomarker profiles and high degree of variability in the marker profiles for T2DM_CAD and CAD. Thereafter, the present study identified multiple potential biomarkers to differentiate T2DM, CAD, and T2DM_CAD patients based on their relative abundance in serum. RF classified T2DM based on the abundance patterns of nine markers i.e., IL-1β, GM-CSF, glucagon, PAI-I, rantes, IP-10, resistin, GIP and Apo-B; CAD by 14 markers i.e., resistin, PDGF-BB, PAI-1, lipocalin-2, leptin, IL-13, eotaxin, GM-CSF, Apo-E, ghrelin, adipsin, GIP, Apo-CII and IP-10; and T2DM _CAD by 12 markers i.e., insulin, resistin, PAI-1, adiponectin, lipocalin-2, GM-CSF, adipsin, leptin, Apo-AII, rantes, IL-6 and ghrelin with respect to the control subjects. Using network analysis, we have identified several cellular network proteins like PTPN1, AKT1, INSR, LEPR, IRS1, IRS2, IL1R2, IL6R, PCSK9 and MYD88, which are responsible for regulating inflammation, insulin resistance, and atherosclerosis. Conclusion We have identified three distinct sets of serum markers for diabetes, CAD and diabetes associated with CAD in Indian patients using nonparametric-based machine learning approach. These multiple marker classifiers may be useful for monitoring progression from a healthy person to T2DM and T2DM to T2DM_CAD. However, these findings need to be further confirmed in the future studies with large number of samples.http://link.springer.com/article/10.1186/s12967-018-1755-5Type 2 diabetes mellitusCoronary artery diseasesCytokines/chemokinesApolipoproteinsAdipokinesMetabolic hormones and biomarkers |
| spellingShingle | Ramu Adela Podduturu Naveen Chander Reddy Tarini Shankar Ghosh Suruchi Aggarwal Amit Kumar Yadav Bhabatosh Das Sanjay K. Banerjee Serum protein signature of coronary artery disease in type 2 diabetes mellitus Journal of Translational Medicine Type 2 diabetes mellitus Coronary artery diseases Cytokines/chemokines Apolipoproteins Adipokines Metabolic hormones and biomarkers |
| title | Serum protein signature of coronary artery disease in type 2 diabetes mellitus |
| title_full | Serum protein signature of coronary artery disease in type 2 diabetes mellitus |
| title_fullStr | Serum protein signature of coronary artery disease in type 2 diabetes mellitus |
| title_full_unstemmed | Serum protein signature of coronary artery disease in type 2 diabetes mellitus |
| title_short | Serum protein signature of coronary artery disease in type 2 diabetes mellitus |
| title_sort | serum protein signature of coronary artery disease in type 2 diabetes mellitus |
| topic | Type 2 diabetes mellitus Coronary artery diseases Cytokines/chemokines Apolipoproteins Adipokines Metabolic hormones and biomarkers |
| url | http://link.springer.com/article/10.1186/s12967-018-1755-5 |
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