The Q-Score for Continuous Glucose Monitoring in Diabetes
There is a need for a metric to assess short-term glycaemic control, similar to the way in which measuring HbA1c allows the assessment of long-term glycaemic control. We aimed to develop a score-type metric that provides an overall, understandable assessment of the blood glucose profile. Ideally, this measure would be sensitive to chronic hyperglycaemia, glucose variability, and hypoglycaemia, and be applicable in routine diabetes care for the screening of profiles with insufficient metabolic control.
For the development of the metric, we first intended to identify the factors determining the quality of a CGM profile. We performed the first published factor analysis of CGM-variables. By definition, a factor represents a cluster of highly correlated variables. We identified four factors that described CGM profiles: hyperglycaemia, inter- and intra-daily variability, and hypoglycaemia. To verify our findings, we analysed the variables with positive loadings within each factor. For factor 1, we found that two variables were necessary to describe hyperglycaemia: MBG, and the time spent in the hyperglycaemic range. Thus, overall, five variables adequately described the CGM: central tendency, hyperglycaemia, intra-daily variability, hypoglycaemia, and inter-daily variability. These are equivalent to the key metrics (target range, glucose exposure, glucose variability, hypoglycaemia, and hyperglycaemia) suggested by an expert panel for the standardisation of glucose reporting, analysis, and clinical decision making. The new metric, which we called the Q-Score (Q = Quality), was constructed with parameters from these factors or key metrics.
Earlier studies have also sought to identify the most useful CGM-parameters for clinical use. Rodbard evaluated methods for assessment of glycaemic control and glycaemic variability. Consistent with our findings, Rodbard observed high correlations among MAGE, SD, IQR, and CONGA, and also concluded that these measures provided essentially the same information. The author defined four groups of methods for characterising glucose variability. Three groups contained parameters summarised by our factor analysis in the factor 'intra-day variability'. The fourth group, the MODD, was also identified in our study, belonging to factor 'inter-day variability'. In accordance with our findings, hypoglycaemia, hyperglycaemia, and euglycaemia were identified as parameters of glycemic control. Recently, Fabris et al. analysed a pool of 25 glucose variability indices using the Sparse principal component analysis in a study with 17 subjects diagnosed with type 1 diabetes. The authors identified a subset of 10 different glucose variability indices that are sufficient to preserve more than the 60% of the variance originally explained by all 25 variables.
CGM is increasingly being introduced into diabetes care; therefore, there is an increasingly urgent requirement for a metric summarising the quality of short-term glucose control. Like us, Rawlings et al. aimed to develop an integrated approach that provides a complete and consistent assessment of glycaemic control. However, these authors followed a different approach and published a graphical user interface for evaluation of CGM profiles based on glucose variability metrics [SD, MODD, CONGA(n), and MAGE] and glycaemic statistics (time spent within thresholds, time spent in hyperglycemic/hypoglycemic conditions, area under the curve, and mean glucose). The interface was tested in a small number of subjects with type 1 diabetes. Marling et al. developed a 'consensus perceived glycaemic variability metric' that captures the gestalt perceptions of experienced physicians using a machine learning algorithm. In tests on 250 CGM profiles from subjects with type 1 diabetes, this metric outperformed mean amplitude of glycaemic excursion, standard deviation, distance travelled, and excursion frequency. Thomas et al. also developed a measure for assessing glycaemic control using CGM profiles. In the 'Pentagon' model they included MBG, AUCG >160 mg/dl, tG >160 mg/dl, SDGlucose, and HbA1c; thus, that model included three of the factors identified in the present study; hyperglycaemia, central tendency and intra-daily variability. However, in contrast to the Q-Score, the Pentagon model included HbA1c. In another study, Thomas et al. reported that the Pentagon model was helpful for assessing individual glycaemic profiles of subjects with type 1 diabetes and for assessing the influence of therapeutic interventions. In addition, they showed that model predictions of the risk of developing late complications were more accurate than HbA1c predictions. Future studies are necessary to compare the Q-Score to the Pentagon model. These studies aimed to facilitate interpretations of blood glucose profiles and to allow the identification of 'weak points' in diabetes management, which is consistent with our approach in this study. However, these studies focused on type 1 diabetes, whereas we tested the Q-Score in a large set of CGM profiles obtained from subjects with type 1 and type 2 diabetes. It should be noted that the relatively small number of subjects with type 1 diabetes represents a limitation of our study.
To develop a practical, readily interpreted metric for routine clinical use and screening, we intended that the Q-Score should allow categorisation of glycaemic control from very good to poor. To achieve categorisation in our study, three diabetes specialists independently classified CGM profiles into groups of very good, good, satisfactory, fair, and poor metabolic control. The majority of profiles were derived from subjects with type 2 diabetes. As expected, the results reflected subjective evaluations. However, the high Kendall's tau correlation indicated that the results were consistent. The groups of evaluated CGM profiles were used to define the Q-Score limits between categories of very good, good, satisfactory, fair, and poor glycaemic control. In addition, we conducted a proof-of-principle study to show that patients with diabetes could be stratified for treatment based on the Q-Score using the CGM profiles of 1,562 historical subjects. The category of good glycaemic control included the majority of subjects treated with diet, half of those treated with OHA, only a quarter of those treated with OHA + insulin, and less than 20% of those treated with insulin alone. These findings are in accordance with other studies that demonstrate that blood glucose profiles are worsened with increasing therapy complexity (from diet alone to insulin). Thus, the categorical Q-Score allows the identification of subjects with poor metabolic control who require therapy optimisation. We intend to verify the Q-Score in a larger-scale study that includes CGM profiles derived from hospitalised subjects and those with diabetes and coexistent chronic illness.
Patient-tailored diabetes therapy represents the state-of the art in diabetes care and management. Therefore, in addition to providing a general assessment of glycaemia, we demonstrate a method for identification of Q-Score parameters that require therapeutic attention and would provide a basis for personalised diabetes therapy. Profiles of people with diabetes categorised as very good or good were used to set the limit for the improvement potential of all Q-Score parameters. This method reveals the parameters that require therapeutic action; for example, the adjustment of the insulin therapy in the case of hypoglycaemia.
Discussion
There is a need for a metric to assess short-term glycaemic control, similar to the way in which measuring HbA1c allows the assessment of long-term glycaemic control. We aimed to develop a score-type metric that provides an overall, understandable assessment of the blood glucose profile. Ideally, this measure would be sensitive to chronic hyperglycaemia, glucose variability, and hypoglycaemia, and be applicable in routine diabetes care for the screening of profiles with insufficient metabolic control.
For the development of the metric, we first intended to identify the factors determining the quality of a CGM profile. We performed the first published factor analysis of CGM-variables. By definition, a factor represents a cluster of highly correlated variables. We identified four factors that described CGM profiles: hyperglycaemia, inter- and intra-daily variability, and hypoglycaemia. To verify our findings, we analysed the variables with positive loadings within each factor. For factor 1, we found that two variables were necessary to describe hyperglycaemia: MBG, and the time spent in the hyperglycaemic range. Thus, overall, five variables adequately described the CGM: central tendency, hyperglycaemia, intra-daily variability, hypoglycaemia, and inter-daily variability. These are equivalent to the key metrics (target range, glucose exposure, glucose variability, hypoglycaemia, and hyperglycaemia) suggested by an expert panel for the standardisation of glucose reporting, analysis, and clinical decision making. The new metric, which we called the Q-Score (Q = Quality), was constructed with parameters from these factors or key metrics.
Earlier studies have also sought to identify the most useful CGM-parameters for clinical use. Rodbard evaluated methods for assessment of glycaemic control and glycaemic variability. Consistent with our findings, Rodbard observed high correlations among MAGE, SD, IQR, and CONGA, and also concluded that these measures provided essentially the same information. The author defined four groups of methods for characterising glucose variability. Three groups contained parameters summarised by our factor analysis in the factor 'intra-day variability'. The fourth group, the MODD, was also identified in our study, belonging to factor 'inter-day variability'. In accordance with our findings, hypoglycaemia, hyperglycaemia, and euglycaemia were identified as parameters of glycemic control. Recently, Fabris et al. analysed a pool of 25 glucose variability indices using the Sparse principal component analysis in a study with 17 subjects diagnosed with type 1 diabetes. The authors identified a subset of 10 different glucose variability indices that are sufficient to preserve more than the 60% of the variance originally explained by all 25 variables.
CGM is increasingly being introduced into diabetes care; therefore, there is an increasingly urgent requirement for a metric summarising the quality of short-term glucose control. Like us, Rawlings et al. aimed to develop an integrated approach that provides a complete and consistent assessment of glycaemic control. However, these authors followed a different approach and published a graphical user interface for evaluation of CGM profiles based on glucose variability metrics [SD, MODD, CONGA(n), and MAGE] and glycaemic statistics (time spent within thresholds, time spent in hyperglycemic/hypoglycemic conditions, area under the curve, and mean glucose). The interface was tested in a small number of subjects with type 1 diabetes. Marling et al. developed a 'consensus perceived glycaemic variability metric' that captures the gestalt perceptions of experienced physicians using a machine learning algorithm. In tests on 250 CGM profiles from subjects with type 1 diabetes, this metric outperformed mean amplitude of glycaemic excursion, standard deviation, distance travelled, and excursion frequency. Thomas et al. also developed a measure for assessing glycaemic control using CGM profiles. In the 'Pentagon' model they included MBG, AUCG >160 mg/dl, tG >160 mg/dl, SDGlucose, and HbA1c; thus, that model included three of the factors identified in the present study; hyperglycaemia, central tendency and intra-daily variability. However, in contrast to the Q-Score, the Pentagon model included HbA1c. In another study, Thomas et al. reported that the Pentagon model was helpful for assessing individual glycaemic profiles of subjects with type 1 diabetes and for assessing the influence of therapeutic interventions. In addition, they showed that model predictions of the risk of developing late complications were more accurate than HbA1c predictions. Future studies are necessary to compare the Q-Score to the Pentagon model. These studies aimed to facilitate interpretations of blood glucose profiles and to allow the identification of 'weak points' in diabetes management, which is consistent with our approach in this study. However, these studies focused on type 1 diabetes, whereas we tested the Q-Score in a large set of CGM profiles obtained from subjects with type 1 and type 2 diabetes. It should be noted that the relatively small number of subjects with type 1 diabetes represents a limitation of our study.
To develop a practical, readily interpreted metric for routine clinical use and screening, we intended that the Q-Score should allow categorisation of glycaemic control from very good to poor. To achieve categorisation in our study, three diabetes specialists independently classified CGM profiles into groups of very good, good, satisfactory, fair, and poor metabolic control. The majority of profiles were derived from subjects with type 2 diabetes. As expected, the results reflected subjective evaluations. However, the high Kendall's tau correlation indicated that the results were consistent. The groups of evaluated CGM profiles were used to define the Q-Score limits between categories of very good, good, satisfactory, fair, and poor glycaemic control. In addition, we conducted a proof-of-principle study to show that patients with diabetes could be stratified for treatment based on the Q-Score using the CGM profiles of 1,562 historical subjects. The category of good glycaemic control included the majority of subjects treated with diet, half of those treated with OHA, only a quarter of those treated with OHA + insulin, and less than 20% of those treated with insulin alone. These findings are in accordance with other studies that demonstrate that blood glucose profiles are worsened with increasing therapy complexity (from diet alone to insulin). Thus, the categorical Q-Score allows the identification of subjects with poor metabolic control who require therapy optimisation. We intend to verify the Q-Score in a larger-scale study that includes CGM profiles derived from hospitalised subjects and those with diabetes and coexistent chronic illness.
Patient-tailored diabetes therapy represents the state-of the art in diabetes care and management. Therefore, in addition to providing a general assessment of glycaemia, we demonstrate a method for identification of Q-Score parameters that require therapeutic attention and would provide a basis for personalised diabetes therapy. Profiles of people with diabetes categorised as very good or good were used to set the limit for the improvement potential of all Q-Score parameters. This method reveals the parameters that require therapeutic action; for example, the adjustment of the insulin therapy in the case of hypoglycaemia.
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