emerging evidence suggests that chronic wide glucose fluctuations increase the risk of severe hypoglycemia and may be instrumental for the development of chronic diabetes complications . therefore , development of sensitive tools to analyze blood glucose ( bg ) fluctuations and guide appropriate therapeutic changes to blunt wide bg excursions will have a critical role in preventing acute and chronic complications and improve quality of life in diabetic patients . continuous glucose monitors ( cgms ) that record glucose levels at short , regular intervals throughout the day afford patients and physicians the flexibility to track glucose trends , evaluate frequency and severity of hypoglycemia including during nocturnal patterns , and assess individualized response to exercise and various other stressors . as the use of cgms increases , conclusions previously drawn from single monitor blood glucose ( smbg ) data can be tested against this more robust data set to guide optimization in individualized insulin regimens , to effectively prevent severe hypoglycemia , to blunt hyperglycemic peaks in response to meals and other stressors , and to evaluate the longer term consequences of glucose variability on the development of diabetic complications . traditionally , hba1c is used to assess glycemic control and risk of complications in patients with diabetes . however , emerging evidence suggest that glucose variability may also play an important role in assessing the risk for hypoglycemia and/or in the development of microvascular complications and cardiovascular disease [ 3 - 5 ] via several mechanisms including its role in oxidative stress and vascular pathology [ 6 - 8 ] . several metrics to quantify glucose variability have been employed to date [ 9 - 12 ] . however , until recently the only data available for such studies were obtained through five- or seven - point single monitor profiles that provided only a restricted view of a patient s glucose dynamics over 24 hours . these discrete glucose measurements are limiting in both the amount of information available for the analysis of glycemic variability and the methods by which variability can be examined.metrics currently used to quantify glucose variability include , but are not limited to , standard deviation ( sd ) , mean amplitude of glycemic excursions ( mage ) , mean of daily differences ( modd ) , and continuous overall net glycemic action ( conga(n ) ) , and more recently standard deviation rate of change ( sdrc ) , average absolute rate of change ( aarc ) , glucose error grid analysis ( cg - ega ) , and prediction - error grid analysis ( pred - ega ) . none of these , however , fully address the issue of glucose volatility in the context of hypoglycemia . we have previously reported the development of the cgm - guide , an easy - to - use tool , that provides researchers and clinicians with a superior assessment of a patient s glucose landscape . the interface calculates and displays multiple metrics from inputted cgm data , offering not only a multifaceted approach to studying glucose variability , but also a means to investigate variability with more information - rich data sets . here , we report a new sensitive metric , dysf , which was developed with the cgm - guide , which performs superiorly in quantifying glucose volatility by taking into account the speed and magnitude of glycemic excursions between clinically - defined states . dysf employs the recently developed transition density profile from cgm - guide , which analyzes glucose excursions and transitions across different glycemic ranges , to predict the likelihood of onset of severe hypoglycemic episodes in a cohort of patients with type 1 diabetes . based on continuous glucose dynamics , dysf is thereby a measure of a patient s daily glucose  volatility  . we analyzed publicly archived cgm data from the juvenile diabetes research foundation ( jdrf ) continuous glucose monitoring randomized trial . briefly , the enrollment criteria were children and adults with type 1 diabetes mellitus ( t1 dm ) for more than 1 year ( aged 8 to 85 yrs ) , use of either an insulin pump or at least three daily insulin injections , and hba1c < 10.0% . this analysis used cgm data and hba1c levels collected at baseline from 441 t1 dm patients with complete demographic and cgm data . of the 441 patients analyzed , 32.4% were aged 8 - 14 yrs , 30.8% were aged 15 - 24 yrs and , 36.7% were  25 yrs . patients were stratified into approximately equal percentages of subjects with hba1c  7% or > 7% but can easily be stratified into any percentage based on clinical advise . a severe hypoglycemic event was defined as an event requiring the assistance of another person to actively administer carbohydrate , glucagon , or other resuscitative actions in the presence of seizure or coma . dysf is the daily weighted number of large monotonic glucose transitions that occur in less than one hour . dysf is derived from transition density profiles , described in , with the exception of employing equally - spaced bin thresholds for dysf analysis ( figure 1 ) . first , raw cgm data were partitioned into 40 mg / dl bins and exact transition points were established ( figure 1a , b ) . second , the magnitude of every continuous monotonic change in glucose bin levels was sorted into the number of thresholds crossed ( e.g. 2 , 4 , 3 , 5 ) ( figure 1c ) . third , transitions were separated into the time interval necessary to complete each change ( i.e. < 1 h , between 1 - 2 h , 2 - 3 h , etc . ) . finally , the frequency of each monotonic threshold crossing per day was plotted against the time interval needed to cross the indicated number of thresholds ( figure 1d ) . dysf calculation has been added to the analytical software cgm - guide ( patent - pending ) as the weighted daily number of ( > |40| mg / dl ) monotonic transitions that occur within one hour   ( figure 1d ) . to calculate dysf , a patient s raw cgm data is first analyzed by the transition density profile method outlined above , using glucose bin threshold intervals of 40 mg / dl . monotonic transitions ( i.e. periods of monotonic threshold crossings ) that occur within one hour and that exceed a magnitude of 1 threshold ( 40 mg / dl ) are identified . each transition is assigned a magnitude equal to the number of thresholds crossed during that transition ( for example , a monotonic decrease in glucose level across three thresholds would be given a magnitude of -3 ) . the sum of the absolute value of these scaled transition magnitudes is then divided by 24 to give the dysf units of weighted number of transitions per day ( figure 1d ) . robustness and sensitivity of dysf were evaluated for a range of choices in glucose threshold bin size ( 5 - 100 mg / dl ) without observed improvement above the standard 40 mg / dl in correlation to he . additionally , bin sizes of 40 mg / dl correspond to clinically important glycemic boundaries . the maximum glucose sampling interval at which dysf could be consistently measured was evaluated as 10 minutes using pair - wise t - tests for 1 min , 5 min , 10 min , 15 min , and 30 min intervals . a logistic regression model was used to predict the likelihood of observing a severe hypoglycemic episode in the 6 months prior to cgm monitoring . covariates considered were mean glucose , standard deviation of mean glucose , standard deviation of transition speeds , dysf , mage , modd , and conga . dysf was calculated at baseline for 441 t1 dm patients where 53.02% were female , mean age was 24.6 years , and mean hba1c was 7.4% . these patients had an average dysf of 7.14  5.39 with a cohort minimum at 0.33 and maximum at 54.89 . patients grouped by age and hba1c had an average dysf of 7.00  3.90 ( age 8 - 14 ) , 8.78  7.50 ( age 15 - 24 ) , 5.88  3.91 ( age  25 ) , 5.88  5.63 ( hba1c  7 ) , and 7.78  5.20 ( hba1c > 7 ) . in addition , to assess overall glycemic variability , cgm - guide profiles were created for all patients to calculate the most widely used glycemic metrics and statistics discussed in methods . a representative cgm - guide profile that includes most widely used glycemic metrics and statistics is shown in figure 2a . to predict the occurrence of hypoglycemic episodes , simple and multiple logistic regression models were fitted to the data and all glycemic metrics were compared ( figure 2 a - c ) . among the t1 dm patients analyzed who had a mean glucose  140 mg / dl at baseline , dysf divided by the mean glucose ( dysf / mean ) was the best predictor of the frequency of hypoglycemic episodes ( p - value = 0.13 ) ( figure 2b ) . the ratio dysf / mean was also the best predictor of frequency of hypoglycemic episodes in t1 dm patients with a baseline hba1c  6.5% ( p - value = 0.06 ) and in children aged 8 - 14 years ( p - value = 0.018 ) ( figure 2b ) . correlation between dysf and hba1c , mean glucose , and other glycemic variability metrics showed low overlap of information between metrics ( figure 2c ) . pearson correlation between dysf and each of the other measures of glycemic variability demonstrated that dysf provided additional information about glycemic variability with the exception of conga and standard deviation of slopes ( figure 2c ) . we also found that in this cohort of t1 dm patients , individuals within the same level of hba1c values demonstrated widely different dysfs , or volatilities , indicating patient - specific hba1c - independent variations in glucose transition times and/or dynamic ranges . in addition , individuals who exhibited similar hba1c levels ( including individuals with hba1c below 7.0% ) had highly variable glucose volatility as measured by variable dysf . individuals with the higher dysf values were those with poorer glycemic control as documented by hba1c values larger than 7% ( figure 3 ) . when comparing patients of any age who had zero , one , or multiple severe hypoglycemic episodes , average dysf values decreased incrementally with increased incidence of severe he , 7.27  5.6 , 6.63  4.23 , 5.61  3.47 , respectively . based on dysf values , a logistic model was used to predict the likelihood of children , 8 - 14 , experiencing severe hypoglycemia . children in the study who had the lowest dysf values ( close to zero ) had a 20% higher probability of having at least one severe hypoglycemic episode that required the assistance of another person for resuscitative actions ( data not shown ) . dysf was calculated at baseline for 441 t1 dm patients where 53.02% were female , mean age was 24.6 years , and mean hba1c was 7.4% . these patients had an average dysf of 7.14  5.39 with a cohort minimum at 0.33 and maximum at 54.89 . patients grouped by age and hba1c had an average dysf of 7.00  3.90 ( age 8 - 14 ) , 8.78  7.50 ( age 15 - 24 ) , 5.88  3.91 ( age  25 ) , 5.88  5.63 ( hba1c  7 ) , and 7.78  5.20 ( hba1c > 7 ) . in addition , to assess overall glycemic variability , cgm - guide profiles were created for all patients to calculate the most widely used glycemic metrics and statistics discussed in methods . a representative cgm - guide profile that includes most widely used glycemic metrics and statistics is shown in figure 2a . to predict the occurrence of hypoglycemic episodes , simple and multiple logistic regression models were fitted to the data and all glycemic metrics were compared ( figure 2 a - c ) . among the t1 dm patients analyzed who had a mean glucose  140 mg / dl at baseline , dysf divided by the mean glucose ( dysf / mean ) was the best predictor of the frequency of hypoglycemic episodes ( p - value = 0.13 ) ( figure 2b ) . the ratio dysf / mean was also the best predictor of frequency of hypoglycemic episodes in t1 dm patients with a baseline hba1c  6.5% ( p - value = 0.06 ) and in children aged 8 - 14 years ( p - value = 0.018 ) ( figure 2b ) . correlation between dysf and hba1c , mean glucose , and other glycemic variability metrics showed low overlap of information between metrics ( figure 2c ) . pearson correlation between dysf and each of the other measures of glycemic variability demonstrated that dysf provided additional information about glycemic variability with the exception of conga and standard deviation of slopes ( figure 2c ) . we also found that in this cohort of t1 dm patients , individuals within the same level of hba1c values demonstrated widely different dysfs , or volatilities , indicating patient - specific hba1c - independent variations in glucose transition times and/or dynamic ranges . in addition , individuals who exhibited similar hba1c levels ( including individuals with hba1c below 7.0% ) had highly variable glucose volatility as measured by variable dysf . individuals with the higher dysf values were those with poorer glycemic control as documented by hba1c values larger than 7% ( figure 3 ) . when comparing patients of any age who had zero , one , or multiple severe hypoglycemic episodes , average dysf values decreased incrementally with increased incidence of severe he , 7.27  5.6 , 6.63  4.23 , 5.61  3.47 , respectively . based on dysf values , a logistic model was used to predict the likelihood of children , 8 - 14 , experiencing severe hypoglycemia . children in the study who had the lowest dysf values ( close to zero ) had a 20% higher probability of having at least one severe hypoglycemic episode that required the assistance of another person for resuscitative actions ( data not shown ) . dysf is a new metric for the measurement of glycemic variability that measures the volatility of a patient s glucose dynamics by weighting the daily average of glucose transitions that occur in less than one hour . usinglogistic regression models , dysf was found to be the most significant predictor of severe hypoglycemic episodes in children aged 8 - 14 years old , in patients with mean glucose less than or equal to 140 mg / dl ( figure 2b ) and in patients with hba1c < 6.5% . lower dysf values corresponded to higher risk of hypoglycemia and were indicative of smaller and/or slower glucose transitions over time . first , we found that dysf / mean is a sensitive tool that can more favorably assess and predict patients risk for experiencing severe hypoglycemic episodes compared to hba1c alone . we also confirm in a large sample of t1 dm patients that low hba1c levels can be misleading as an indicator of glycemic control . patients with hba1c below 6.5% are traditionally considered to have  well - controlled  diabetes . we demonstrate in this cohort , that a lower hba1c level (  6.5% ) may be the result of a high incidence of hypoglycemia as opposed to tighter glycemic control . we also show that by using dysf / mean , we are able to significantly enhance our ability to predict severe hypoglycemic events in patients with either low hba1c , low mean bg and in children . this has very important clinical significance as it helps create a patient specific phenotype that can be used by clinicians to preventsevere hypoglycemic events . second , the association between dysf and the occurrence of severe hypoglycemic episodes was found to be most significant in children ( aged 8 - 14 years ) across all levels of hba1c ( figure 2b ) . subjects were observed to vary widely in dysf values within the same class of hba1c , suggesting the degree of glucose fluctuations to be independent of hba1c level . this allows dysf to provide a different perspective on glycemic variability from what hba1c measures - namely , volatility . some studies have evaluated how children with t1 dm identify severe hypoglycemic episodes , which is critical for their prevention . gonder - fredrick et al . demonstrated that children with type 1 diabetes failed to recognize greater than 40% of hypoglycemic occurrences , and meltzer et al . observed that the average adolescent patient made irrelevant or inaccurate glucose estimations greater than 61% of the time . because he often occur during times when patients fail to recognize symptoms associated with hypoglycemia , dysf can be used to assess the severity and speed of glycemic excursions and therefore can be an effective clinical tool to prevent he and its serious consequences . lastly , the correlation between dysf and other glycemic variability metrics is relatively low , with often less than 50% of the variation in dysf being accounted for by other metrics ( figure 2c ) . this suggests that a combination of existing glycemic variability metrics and dysf will be best suited to assess different populations and/or varying disease complications . a recent study by guerra et al . explored rates of change of glucose , using a deconvolution algorithm that introduces uncertainty in the model parameters and considers less than 30 minutes of glucose history to predict future risk . previous glucose variability metrics , such as modd , are considered to be a measure of daily glucose consistency , mage a measure of fluctuation severity , and conga a measure of glucose lability , or the likelihood of undergoing any change in glucose level over a defined length of time . by introducing dysf , a measure of glucose volatility , we can now explore over much longer historiesof bg data , the long - term effects of changes in glucose speed and magnitude on patient outcomes . in addition , we demonstrate that dysf offers tailored information about specific populations , such as children ( 8 - 14 years ) , or patients with various hba1c levels . despite clear differences in their defining properties , to date existing glucose variability metrics are used either interchangeably or individually with statistics such as sd to assess overall glycemic variability . cameron et al . demonstrated that glucose variability metrics , though correlated with each other in non - diabetic patients , are not correlated in diabetic populations . clarke and kovatchev   have applied metrics for studying hypoglycemic events in patients using single monitor blood glucose ( smbg ) measurements . their studies show some predictive measures of future hypoglycemic events but the metrics were strongly correlated to the past history of time spent in lower glycemic ranges and did not consider the entire course of patient data which includes hypo and hyper regions as well as normal ranges . thus , studies in diabetic populations that look at only one or two measures , or less sensitive measures of glucose variability , can not comprehensively assess glycemic variability because these do not take into account the full range of glycemic states a patient may encounter over shorter or longer periods of time , nor the spectrum of transition profiles from these states [ 26 - 28 ] , whereas glucose variability profiles such as those generated by cgm - guide overcome this challenge . dysf and cgm - guide profiles may also prove to be superior to current individual metrics in evaluating the role of glucose variability in the development of chronic diabetes complications . at present , low dysf is important in assessing trends in hypoglycemia ; however , high volatility may become important when assessing chronic diabetes complications and disease progression . rapid glucose fluctuations have been hypothesized to incorporate  stress  into a patient s system by increasing oxidative stress and contributing to the development of microvascular complicationsand cardiovascular disease [ 3 - 5,8,29,30 ] . the long - term effect of sustained volatility is the next pressing question in developing an improved picture of diabetes progression toward chronic conditions , especially in what are currently considered well - controlled populations . towards this effort , researchers and clinicians are now able to apply dysf , in conjunction with hba1c , as a tool to enhance their ability to understand type 1diabetes and to procure treatment options .
hemoglobin a1c ( hba1c ) is the current standard used in the clinical treatment of patients with diabetes . 
 however , it has been shown that patients with similar hba1c values may have widely different fluctuations in blood glucose values over the same period of time , including time spent in hyper- and/or hypo - glycemia . 
 hence , there exists a need for quantitative measures that can supplement hba1c in managing patients with diabetes . 
 we introduce and compare the dynamic stress factor , dysf , a newly developed metric that quantifies glycemic volatility based on patient - specific glucose transition density profiles with hba1c and with currently used glucose variability metrics in predicting severe hypoglycemia in children with type 1 diabetes . 
 dysf , the daily weighted number of large monotonic glycemic transitions that occur within one hour , was calculated for 441 total subjects with type 1 diabetes ( 146 children , aged 8 - 14 yrs ) to assess the magnitude and frequency of glucose transitions per day . 
 severe hypoglycemic episodes ( he ) were quantified for all subjects and evaluated against hba1c and existing measures of glucose variability , including sd , mage , modd , and conga using logistic regression models . dysf was found to be a predictor of severe he in children ( p = 0.018 ) with the likelihood of a child , aged 8 - 14 yrs , experiencing severe hypoglycemia increasing by up to 20% with decreasing values of up to 60% of dysf . 
 patients of any age who had one or multiple severe hypoglycemic episodes had on average a lower dysf when compared to those with no he . 
 additionally , when considering mean glucose levels , dysf / mean was a preliminary predictor of severe he in patients with hba1c  6.5% ( p = 0.062 ) . 
 dysf is a dynamic , quantitative , measure of daily glucose  volatility  that separates patients , within the same strata of hba1c , into visually distinct patient profiles . 
 dysf can be used as a preliminary predictor of clinically severe hypoglycemia in children and  well - controlled 
 patients with hba1c  6.5% .