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Insulin sensitivity index (ISI0, 120) potentially linked to carbon isotopes of breath CO2 for pre-diabetes and type 2 diabetes.

Ghosh C, Mukhopadhyay P, Ghosh S, Pradhan M - Sci Rep (2015)

Bottom Line: Conversely, the strongest correlation was observed between 1/ISI0,120 and breath CO2 isotopes.Consequently, we determined several optimal diagnostic cut-off points of 1/ISI0,120 and (13)CO2/(12)CO2-isotope ratios to distinctively track the evolution of PD prior to the onset of T2D.Our findings suggest that isotopic breath CO2 is a novel method for accurate estimation of ISI0,120 and thus may open new perspectives into the isotope-specific non-invasive evaluation of insulin resistance for large-scale real-time diabetes screening purposes.

View Article: PubMed Central - PubMed

Affiliation: Department of Chemical, Biological and Macromolecular Sciences, S. N. Bose National Centre for Basic Sciences, Salt Lake, JD Block, Sector III, Kolkata-700098, India.

ABSTRACT
New strategies for an accurate and early detection of insulin resistance are important to delay or prevent the acute onset of type 2 diabetes (T2D). Currently, insulin sensitivity index (ISI0,120) is considered to be a viable invasive method of whole-body insulin resistance for use in clinical settings in comparison with other invasive sensitivity indexes like homeostasis model assessment (HOMA), and quantitative insulin sensitivity check index (QUICKI). To investigate how these sensitivity indexes link the (13)C/(12)C-carbon isotopes of exhaled breath CO2 to pre-diabetes (PD) and type 2 diabetes in response to glucose ingestion, we studied excretion dynamics of (13)C/(12)C-isotopic fractionations of breath CO2. Here, we show that (13)C/(12)C-isotope ratios of breath CO2 were well correlated with blood glucose, insulin, glycosylated-hemoglobin as well as with HOMA-IR and 1/QUICKI. Conversely, the strongest correlation was observed between 1/ISI0,120 and breath CO2 isotopes. Consequently, we determined several optimal diagnostic cut-off points of 1/ISI0,120 and (13)CO2/(12)CO2-isotope ratios to distinctively track the evolution of PD prior to the onset of T2D. Our findings suggest that isotopic breath CO2 is a novel method for accurate estimation of ISI0,120 and thus may open new perspectives into the isotope-specific non-invasive evaluation of insulin resistance for large-scale real-time diabetes screening purposes.

No MeSH data available.


Related in: MedlinePlus

Receiver operating characteristic (ROC) curves to determine optimal diagnostic cut-off points.a, δDOB13C‰ and b, 1/ISI0,120 for clinical diagnosis of normal (NDC), pre-diabetes (PD) and type 2 diabetes (T2D).
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f5: Receiver operating characteristic (ROC) curves to determine optimal diagnostic cut-off points.a, δDOB13C‰ and b, 1/ISI0,120 for clinical diagnosis of normal (NDC), pre-diabetes (PD) and type 2 diabetes (T2D).

Mentions: Finally, to investigate the precise metabolic transition from NDC to PD and then on to T2D, we determined the optimal diagnostic cut-off points of 1/ISI0,120 and δDOB13C‰ values in exhaled breath using receiver operating characteristics curve (ROC) analysis (Fig. 5a and b). ROC curves were generated by plotting the true positive rate (sensitivity) against the false positive rate (1-specficity) using the values of 1/ISI0,120 and δDOB13C‰. The highest values of sensitivity and specificity were used to calculate the optimal diagnostic cut-off points. A diagnostic cut-off point of 1/ISI0,120 = 0.0237 between individuals with PD and T2D, exhibited the sensitivity and specificity of 96.1% and 95.1%, respectively, whereas 1/ISI0,120 = 0.0149 accurately diagnosed individuals with NDC and PD. To apply breath analysis for the diagnosis of insulin resistant PD and T2D, we have calculated the optical diagnostic cut-off values of carbon-13 isotopes of exhaled breath CO2. We observed that individuals with δDOB13C‰ < 24.4 and δDOB13C‰ > 29.4 were considered as T2D and NDC respectively, whereas subjects with 29.4 > δDOB13C‰ > 24.4 were suggested to be PD. These cut-off points corresponded to the similar levels of diagnostic sensitivity and specificity as shown in Table 1. We have calculated these cut-off values with 95% confidence intervals. Thus the analyses of stable isotopes of the major metabolite of human breath CO2 establish a broad clinical feasibility as a sufficiently robust non-invasive detection method for an accurate diagnosis of PD and T2D with different metabolic states of insulin resistance. We also finally explored the positive and negative predictive values (PPV and NPV) for the diagnostic assessment. These two parameters essentially indicate the probabilities of getting diseases once the actual test results of the patients are known15. The present method demonstrated diagnostic PPV of 98% between NDC vs PD, and 96% between PD vs T2D. It also exhibited diagnostic NPV of 94% between NDC vs PD, and 95% between PD vs T2D, indicating an excellent diagnostic accuracy for the accurate evaluation of insulin resistance in different metabolic states. However, it is important to note that the cut-off values may depend on the food habits in the different populations of various countries and the isotopic compositions of labelled glucose. We have determined the cut-off values based on the Indian populations utilizing the mentioned labelled glucose. Therefore, it would be interesting to estimate the cut-off values within the subject-variability and also to elucidate the probable dietary effects on breath isotope analysis in future studies.


Insulin sensitivity index (ISI0, 120) potentially linked to carbon isotopes of breath CO2 for pre-diabetes and type 2 diabetes.

Ghosh C, Mukhopadhyay P, Ghosh S, Pradhan M - Sci Rep (2015)

Receiver operating characteristic (ROC) curves to determine optimal diagnostic cut-off points.a, δDOB13C‰ and b, 1/ISI0,120 for clinical diagnosis of normal (NDC), pre-diabetes (PD) and type 2 diabetes (T2D).
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4493706&req=5

f5: Receiver operating characteristic (ROC) curves to determine optimal diagnostic cut-off points.a, δDOB13C‰ and b, 1/ISI0,120 for clinical diagnosis of normal (NDC), pre-diabetes (PD) and type 2 diabetes (T2D).
Mentions: Finally, to investigate the precise metabolic transition from NDC to PD and then on to T2D, we determined the optimal diagnostic cut-off points of 1/ISI0,120 and δDOB13C‰ values in exhaled breath using receiver operating characteristics curve (ROC) analysis (Fig. 5a and b). ROC curves were generated by plotting the true positive rate (sensitivity) against the false positive rate (1-specficity) using the values of 1/ISI0,120 and δDOB13C‰. The highest values of sensitivity and specificity were used to calculate the optimal diagnostic cut-off points. A diagnostic cut-off point of 1/ISI0,120 = 0.0237 between individuals with PD and T2D, exhibited the sensitivity and specificity of 96.1% and 95.1%, respectively, whereas 1/ISI0,120 = 0.0149 accurately diagnosed individuals with NDC and PD. To apply breath analysis for the diagnosis of insulin resistant PD and T2D, we have calculated the optical diagnostic cut-off values of carbon-13 isotopes of exhaled breath CO2. We observed that individuals with δDOB13C‰ < 24.4 and δDOB13C‰ > 29.4 were considered as T2D and NDC respectively, whereas subjects with 29.4 > δDOB13C‰ > 24.4 were suggested to be PD. These cut-off points corresponded to the similar levels of diagnostic sensitivity and specificity as shown in Table 1. We have calculated these cut-off values with 95% confidence intervals. Thus the analyses of stable isotopes of the major metabolite of human breath CO2 establish a broad clinical feasibility as a sufficiently robust non-invasive detection method for an accurate diagnosis of PD and T2D with different metabolic states of insulin resistance. We also finally explored the positive and negative predictive values (PPV and NPV) for the diagnostic assessment. These two parameters essentially indicate the probabilities of getting diseases once the actual test results of the patients are known15. The present method demonstrated diagnostic PPV of 98% between NDC vs PD, and 96% between PD vs T2D. It also exhibited diagnostic NPV of 94% between NDC vs PD, and 95% between PD vs T2D, indicating an excellent diagnostic accuracy for the accurate evaluation of insulin resistance in different metabolic states. However, it is important to note that the cut-off values may depend on the food habits in the different populations of various countries and the isotopic compositions of labelled glucose. We have determined the cut-off values based on the Indian populations utilizing the mentioned labelled glucose. Therefore, it would be interesting to estimate the cut-off values within the subject-variability and also to elucidate the probable dietary effects on breath isotope analysis in future studies.

Bottom Line: Conversely, the strongest correlation was observed between 1/ISI0,120 and breath CO2 isotopes.Consequently, we determined several optimal diagnostic cut-off points of 1/ISI0,120 and (13)CO2/(12)CO2-isotope ratios to distinctively track the evolution of PD prior to the onset of T2D.Our findings suggest that isotopic breath CO2 is a novel method for accurate estimation of ISI0,120 and thus may open new perspectives into the isotope-specific non-invasive evaluation of insulin resistance for large-scale real-time diabetes screening purposes.

View Article: PubMed Central - PubMed

Affiliation: Department of Chemical, Biological and Macromolecular Sciences, S. N. Bose National Centre for Basic Sciences, Salt Lake, JD Block, Sector III, Kolkata-700098, India.

ABSTRACT
New strategies for an accurate and early detection of insulin resistance are important to delay or prevent the acute onset of type 2 diabetes (T2D). Currently, insulin sensitivity index (ISI0,120) is considered to be a viable invasive method of whole-body insulin resistance for use in clinical settings in comparison with other invasive sensitivity indexes like homeostasis model assessment (HOMA), and quantitative insulin sensitivity check index (QUICKI). To investigate how these sensitivity indexes link the (13)C/(12)C-carbon isotopes of exhaled breath CO2 to pre-diabetes (PD) and type 2 diabetes in response to glucose ingestion, we studied excretion dynamics of (13)C/(12)C-isotopic fractionations of breath CO2. Here, we show that (13)C/(12)C-isotope ratios of breath CO2 were well correlated with blood glucose, insulin, glycosylated-hemoglobin as well as with HOMA-IR and 1/QUICKI. Conversely, the strongest correlation was observed between 1/ISI0,120 and breath CO2 isotopes. Consequently, we determined several optimal diagnostic cut-off points of 1/ISI0,120 and (13)CO2/(12)CO2-isotope ratios to distinctively track the evolution of PD prior to the onset of T2D. Our findings suggest that isotopic breath CO2 is a novel method for accurate estimation of ISI0,120 and thus may open new perspectives into the isotope-specific non-invasive evaluation of insulin resistance for large-scale real-time diabetes screening purposes.

No MeSH data available.


Related in: MedlinePlus