Fingerprinting hyperglycemia using predictive modelling approach based on low-cost routine CBC and CRP diagnostics
Abstract Hyperglycemia is an outcome of dysregulated glucose homeostasis in the human body and may induce chronic elevation of blood glucose levels. Lifestyle factors such as overnutrition, physical inactivity, and psychosocials coupled with systemic low-grade inflammation have a strong negative imp...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2024-01-01
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| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-023-44623-4 |
| _version_ | 1827382196181139456 |
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| author | Amna Tahir Kashif Asghar Waqas Shafiq Hijab Batool Dilawar Khan Omar Chughtai Safee Ullah Chaudhary |
| author_facet | Amna Tahir Kashif Asghar Waqas Shafiq Hijab Batool Dilawar Khan Omar Chughtai Safee Ullah Chaudhary |
| author_sort | Amna Tahir |
| collection | DOAJ |
| description | Abstract Hyperglycemia is an outcome of dysregulated glucose homeostasis in the human body and may induce chronic elevation of blood glucose levels. Lifestyle factors such as overnutrition, physical inactivity, and psychosocials coupled with systemic low-grade inflammation have a strong negative impact on glucose homeostasis, in particular, insulin sensitivity. Together, these factors contribute to the pathophysiology of diabetes (DM) and expanding landscape of its prevalence regionally and globally. The rapid rise in the prevalence of type 2 diabetes, therefore, underscores the need for its early diagnosis and treatment. In this work, we have evaluated the discriminatory capacity of different diagnostic markers including inflammatory biomolecules and RBC (Red Blood Cell) indices in predicting the risk of hyperglycemia and borderline hyperglycemia. For that, 208,137 clinical diagnostic entries obtained over five years from Chugtai Labs, Pakistan, were retrospectively evaluated. The dataset included HbA1c (n = 142,011), complete blood count (CBC, n = 84,263), fasting blood glucose (FBG, n = 35,363), and C-reactive protein (CRP, n = 9035) tests. Our results provide four glycemic predictive models for two cohorts HbA1c and FBG) each having an overall predictive accuracy of more than 80% (p-value < 0.0001). Next, multivariate analysis (MANOVA) followed by univariate analysis (ANOVA) was employed to identify predictors with significant discriminatory capacity for different levels of glycemia. We show that the interplay between inflammation, hyperglycemic-induced derangements in RBC indices, and altered glucose homeostasis could be employed for prognosticating hyperglycemic outcomes. Our results then conclude a glycemic predictor with high sensitivity and specificity, employing inflammatory markers coupled with RBC indices, to predict glycemic outcomes (ROC p-value < 0.0001). Taken together, this study outlines a predictor of glycemic outcomes which could assist as a prophylactic intervention in predicting the early onset of hyperglycemia and borderline hyperglycemia. |
| first_indexed | 2024-03-08T14:17:08Z |
| format | Article |
| id | doaj.art-5caecaf6748c44b6a64703c0aed493e0 |
| institution | Directory Open Access Journal |
| issn | 2045-2322 |
| language | English |
| last_indexed | 2024-03-08T14:17:08Z |
| publishDate | 2024-01-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj.art-5caecaf6748c44b6a64703c0aed493e02024-01-14T12:21:28ZengNature PortfolioScientific Reports2045-23222024-01-0114111810.1038/s41598-023-44623-4Fingerprinting hyperglycemia using predictive modelling approach based on low-cost routine CBC and CRP diagnosticsAmna Tahir0Kashif Asghar1Waqas Shafiq2Hijab Batool3Dilawar Khan4Omar Chughtai5Safee Ullah Chaudhary6Biomedical Informatics and Engineering Research Laboratory, Department of Life Sciences, Syed Babar Ali School of Science and Engineering, Lahore University of Management SciencesBasic Science Department, Shaukat Khanum Memorial Cancer Hospital and Research CentreDepartment of Internal Medicine, Shaukat Khanum Memorial Cancer Hospital and Research CentreChughtai Institute of PathologyChughtai Institute of PathologyChughtai Institute of PathologyBiomedical Informatics and Engineering Research Laboratory, Department of Life Sciences, Syed Babar Ali School of Science and Engineering, Lahore University of Management SciencesAbstract Hyperglycemia is an outcome of dysregulated glucose homeostasis in the human body and may induce chronic elevation of blood glucose levels. Lifestyle factors such as overnutrition, physical inactivity, and psychosocials coupled with systemic low-grade inflammation have a strong negative impact on glucose homeostasis, in particular, insulin sensitivity. Together, these factors contribute to the pathophysiology of diabetes (DM) and expanding landscape of its prevalence regionally and globally. The rapid rise in the prevalence of type 2 diabetes, therefore, underscores the need for its early diagnosis and treatment. In this work, we have evaluated the discriminatory capacity of different diagnostic markers including inflammatory biomolecules and RBC (Red Blood Cell) indices in predicting the risk of hyperglycemia and borderline hyperglycemia. For that, 208,137 clinical diagnostic entries obtained over five years from Chugtai Labs, Pakistan, were retrospectively evaluated. The dataset included HbA1c (n = 142,011), complete blood count (CBC, n = 84,263), fasting blood glucose (FBG, n = 35,363), and C-reactive protein (CRP, n = 9035) tests. Our results provide four glycemic predictive models for two cohorts HbA1c and FBG) each having an overall predictive accuracy of more than 80% (p-value < 0.0001). Next, multivariate analysis (MANOVA) followed by univariate analysis (ANOVA) was employed to identify predictors with significant discriminatory capacity for different levels of glycemia. We show that the interplay between inflammation, hyperglycemic-induced derangements in RBC indices, and altered glucose homeostasis could be employed for prognosticating hyperglycemic outcomes. Our results then conclude a glycemic predictor with high sensitivity and specificity, employing inflammatory markers coupled with RBC indices, to predict glycemic outcomes (ROC p-value < 0.0001). Taken together, this study outlines a predictor of glycemic outcomes which could assist as a prophylactic intervention in predicting the early onset of hyperglycemia and borderline hyperglycemia.https://doi.org/10.1038/s41598-023-44623-4 |
| spellingShingle | Amna Tahir Kashif Asghar Waqas Shafiq Hijab Batool Dilawar Khan Omar Chughtai Safee Ullah Chaudhary Fingerprinting hyperglycemia using predictive modelling approach based on low-cost routine CBC and CRP diagnostics Scientific Reports |
| title | Fingerprinting hyperglycemia using predictive modelling approach based on low-cost routine CBC and CRP diagnostics |
| title_full | Fingerprinting hyperglycemia using predictive modelling approach based on low-cost routine CBC and CRP diagnostics |
| title_fullStr | Fingerprinting hyperglycemia using predictive modelling approach based on low-cost routine CBC and CRP diagnostics |
| title_full_unstemmed | Fingerprinting hyperglycemia using predictive modelling approach based on low-cost routine CBC and CRP diagnostics |
| title_short | Fingerprinting hyperglycemia using predictive modelling approach based on low-cost routine CBC and CRP diagnostics |
| title_sort | fingerprinting hyperglycemia using predictive modelling approach based on low cost routine cbc and crp diagnostics |
| url | https://doi.org/10.1038/s41598-023-44623-4 |
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