mestrado integrado em medicina development of an

MESTRADO INTEGRADO EM MEDICINA

Development of an application/software for analyzing glycemic variability in people with diabetes who use a flash interstitial glucose monitoring

Bruno Miguel Matos Soares

M

2020

Development of an application/software for analyzing glycemic variability

in people with diabetes who use a flash interstitial glucose monitoring

Estudante

Bruno Miguel Matos Soares

[email protected]

Mestrado Integrado em Medicina – Instituto de Ciências Biomédicas Abel Salazar

Centro Hospitalar Universitário do Porto

Orientadora

Sofia Monteiro de Moura Teixeira

Assistente Hospitalar do Serviço de Endocrinologia no Hospital Santo António, Centro Hospitalar Universitário do Porto

Assistente Convidada de Medicina II do MIM do ICBAS/CHUP

Coorientadora

Maria Helena Cardoso Pereira da Silva

Assistente Hospitalar Graduada Sénior e Diretora do Serviço de Endocrinologia no Hospital Santo António, Centro Hospitalar Universitário do Porto

Professora Catedrática Convidada com Agregação do MIM do ICBAS/CHUP

Junho de 2020

Junho de 2020

DEDICATÓRIA

À minha família por todo o amor e carinho e por todos os sacrifícios e incentivos no concretizar dos meus sonhos e objetivos.

Aos meus amigos por fazerem destes sonhos uma intensa alegria.

A Ana Maria Santos por estar sempre a meu lado e fazer dos meus, os nossos sonhos.

Volta ao tempo e o tempo volta então,

sopra ao vento que ele não destoa em vão,

se o tempo quiser o vento pode voltar,

deixa ver quem vem do outro lado do mar,

pode ser que seja o vento ou pode ser o luar,

só eu saberei quem nunca irá voltar

se nunca vier a soprar.

Bruno Matos Soares

i

AGRADECIMENTOS

Agradeço à Doutora Sofia Teixeira a orientação, a disponibilidade e o empenho no decorrer deste projeto. Uma inspiração, com um papel essencial na minha formação enquanto médico e enquanto pessoa.

À Professora Doutora Maria Helena Cardoso, gostaria de agradecer a revisão de conteúdos e das particularidades do trabalho, bem como, agradecer a sua visão otimista e vanguardista.

Ao Professor Paulo Pinho, gostaria de agradecer a genialidade, o profissionalismo e a colaboração na elaboração da aplicação/software - shp.Range (“Mensagem”).Value = “Obrigado”.

À Professora Doutora Carolina Lemos e à Professora Doutora Sandra Campelos gostaria de agradecer pela ajuda e disponibilidade na vertente estatística.

Agradeço a Helena Araújo a atenção e celeridade na revisão linguística.

Aos meus amigos e ao Grupo de Fados e Guitarradas de Biomédicas agradeço por me terem acompanhado ao longo destes anos, foi um privilégio.

Por fim, agradeço a Francisco Soares, Virgínia Matos, Francisco Matos Soares e a Ana Maria Santos todo o esforço, cumplicidade, amparo e apoio, os pilares do meu trajeto e da minha vida.

ii

RESUMO

Introdução e objetivos: A variabilidade glicémica é um reconhecido problema na gestão diária

da diabetes, contemplando uma representação das flutuações e dos padrões de hiper e

hipoglicemia. Com a definição, em 2019, das 10 métricas com utilidade clínica na gestão dos

dados obtidos pelos sistemas de monitorização da glicose intersticial, surgiu a necessidade de

superar as lacunas dos softwares existentes.

Este trabalho visa o desenvolvimento de uma aplicação/software que analise os dados obtidos

pela monitorização intermitente da glicose para as métricas propostas, verificando a utilidade

da interpolação linear neste tipo de sistemas e a adequabilidade das fórmulas do indicador de

gestão da glicose, por comparação com a hemoglobina glicada.

Métodos: O estudo foi dividido em dois passos, um para o desenvolvimento da

aplicação/software, usando o Visual basic numa folha de Excel, calculando as métricas/gráfico

com e sem interpolação linear; e outro para confirmar a validade através da comparação das

métricas obtidas pela nova aplicação com as disponíveis no programa do fabricante e com a

hemoglobina glicada.

Entre março de 2019 e janeiro de 2020 foram descarregados quarenta e oito ficheiros com os

dados da monitorização intermitente da glicose e os ficheiros PDF equivalentes, provenientes

de quarenta e quatro pacientes com diabetes tipo 1 seguidos no Serviço de Endocrinologia do

Centro Hospitalar Universitário do Porto.

Os valores da hemoglobina glicada foram recolhidos da plataforma SClínico.

Resultados: As métricas obtidas por ambas as versões da nova aplicação/software e pelo

programa do fabricante apresentam excelente correlação. Esta correlação é, contudo, maior

entre a versão sem interpolação e o programa do fabricante.

O teste t e as diferenças standardizadas entre ambas versões da nova aplicação/software e o

programa do fabricante mostraram diferenças relevantes nos indicadores de gestão da glicose.

Quando os comparamos com a hemoglobina glicada, a menor diferença standardizada foi na

comparação com a versão sem interpolação (17.35%) e a maior na comparação com o programa

do fabricante (30.33%).

Conclusões: A aplicação/software desenvolvida é prática, intuitiva e atualizada, está validada

para pessoas com diabetes tipo 1 e é útil na clínica e na investigação, visto que não necessita de

internet ou instalação prévia.

Os resultados sugerem que a interpolação é um fator confundidor, contudo, são necessários

estudos com amostras maiores.

iii

A fórmula de Bergenstal para o cálculo do indicador de gestão da glicose, usada na nova

aplicação, aproxima-se mais da hemoglobina glicada que fórmula do Nathan, usado pelo

fabricante.

Palavras-chave: Variabilidade glicémica; Monitorização intersticial da glucose; Diabetes

mellitus; Indicador de gestão da glicose.

iv

ABSTRACT

Background and objectives: Glycemic variability is a recognized problem in the everyday

management of diabetes, contemplating an integrated picture of hyper and hypoglycemic

patterns. Since 2019, with the definition of the 10 metrics that may be useful to use in clinical

practice for interstitial glucose monitoring data, there is the need to overcome bibliographic and

computer-related shortcomings in existing applications/softwares.

This project aims to develop and validate an application/software that processes the data

obtained from flash glucose monitoring, calculating the proposed metrics and verifying the

usefulness of linear interpolation in this systems and the Glucose Management Indicators

formulae, comparing the latter with glycated hemoglobin.

Methods: The study was divided into two steps, one to develop the software using Visual Basic

in a Excel sheet, calculating the metrics/graphic with and without interpolation; and the other

to confirm its validity assessed through the comparison of the metrics obtained by the new

application with the ones that were already available in the manufacturer program and with

glycated hemoglobin.

From March 2019 to January 2020, forty-eight files with the raw data and the equivalent PDF

files were downloaded, from forty-four type 1 diabetic patients under the care of the

Endocrinology Department of Centro Hospitalar Universitário do Porto (Porto’s University

Hospital).

The glycated hemoglobin was collected from Sclínico platform.

Results: The glycemic metrics calculated by the both new application/software versions and the

manufacturer software showed great correlation. Nevertheless, this correlation is higher

between the without interpolation version and the manufacturer program.

The t-test and the standardized differences between both the new application/software

versions and the manufacturer program showed relevant differences for the Glucose

Management Indicator.

When comparing the Glucose Management Indicators with glycated hemoglobin, the

standardized differences was lowest when compared to the without interpolation version

(17.35%) and highest when compared to the manufacturer version (30.33%).

Conclusions: The developed application/software is practical, intuitive, updated to include all

the most recent glycemic metrics and validated in type 1 diabetic population. It is an excellent

tool to clinical practice and to promote research, attending that does not require nor internet

connection or previous installation.

v

The results suggest that interpolation may introduce an element of confounding, however,

further studies with larger samples are needed to confirm this.

Bergenstal’s Glucose Management Indicator formula, used in new application/software, is

closer to the glycated hemoglobin than Nathan’s formula, used in the manufacturer program.

Key words: Glycemic variability; Interstitial glucose monitoring; Diabetes mellitus; Glucose

Management Indicator.

vi

ABBREVIATIONS

%CV, Percentage of coefficient of variation

AGP, Ambulatory Glucose Profile

ATTD, Advanced Technologies & Treatments for Diabetes

CGM, Continuous glucose monitoring

FGM, Flash glucose monitoring

GV, Glycemic variability

GMI, Glucose Management Indicator

HbA1c, Glycated hemoglobin

IQR, Interquartile range

SD, Standard deviation

TA250, Time above 250 mg/dL - Percentage of readings and time > 250 mg/dL

TAR, Time above range - Percentage of readings and time > 180 mg/dL

TB54, Time below 54 mg/dL - Percentage of readings and time < 54 mg/dL

TBR, Time below range - Percentage of readings and time < 70 mg/dL

TIR, Time in range - Percentage of readings and time 70–180 mg/dL

WI, With interpolation

WOI, Without interpolation

vii

TABLE OF CONTENTS

AGRADECIMENTOS................................................................................................................. i

RESUMO ............................................................................................................................... ii

ABSTRACT............................................................................................................................. iv

ABBREVIATIONS................................................................................................................... vi

LIST OF TABLES ................................................................................................................... viii

LIST OF FIGURES ................................................................................................................... ix

INTRODUCTION..................................................................................................................... 1

METHODS.............................................................................................................................. 4

RESULTS ............................................................................................................................... 6

DISCUSSION.......................................................................................................................... 8

REFERENCES ....................................................................................................................... 11

viii

LIST OF TABLES

Table I - General characteristics of the sample studied, expressed as mean, variance and standard deviation for the variables proposed and obtained by the new application/software and the metrics available in the manufacturer program…………………………………………………………. 15 Table II - The comparison between the new application/software with and without interpolation and the manufacturer program………………………………………………………………………………………... 16, 17 Table III - The relationship between the Glucose Management Indicator from the new application/software with and without interpolation, the Glucose Management Indicator from the manufacturer program and the glycated hemoglobin………………………………….…………………… 18 Table IV - Analysis of the difference between the Glucose Management Indicator from the new application/software with and without interpolation, the Glucose Management Indicator from the manufacturer program and the glycated hemoglobin………………………………………………….…… 19

ix

LIST OF FIGURES

Figure 1 – Example of a report created by the manufacturer program…………………………………. 20

Figure 2 - The application/software menu presented in the programmed Excel sheet………….. 21

1

INTRODUCTION

The intensive glycemic control with the objective of achieving an glycated hemoglobin (HbA1c)

less than 7% is associated with the reduction of chronic diabetic complications.1–5

However, beside the levels of HbA1c, the fasting glucose levels, the post-prandial

hyperglycemia, the total glucose exposure and HbA1c variability through time correlate with the

risk of complications.6–8

Another element of discussion is glycemic variability (GV), a recognized issue in the everyday

management of diabetes and an area in this field of study. GV takes into account the intraday

glycemic excursions,9 encompassing patterns ranging from hyperglycemia to hypoglycemia.10,11

It can be divided into two types: short-term GV, represented by both within-day and between-

day GV; and long-term GV, over a longer period of time, usually involving HbA1c.12 There is a

great deal of literature on the management of GV, with numerous authors defending different

and complex metrics to monitor qualitatively and quantitatively this parameter.13–18 Mean

amplitude of glycemic excursions (MAGE, mean of daily excursions that exceeds one standard

deviation (SD) from the 24‐hour mean glucose), mean absolute daily differences (MODD, mean

of daily differences at the same time in glucose values separated by exactly 24 hours),

continuous overlapping net glycemic action (CONGA, measure of within-day GV – SD of

differences between any glucose value), interquartile range (IQR, measure of variability, ranking

data into four equal parts), SD and percentage of coefficient of variation (%CV) have been

proposed to quantify the short term GV. The visit-to-visit changes in HbA1c measure long term

GV. The lack of consensus on the metrics to describe both short-term and long-term GV and the

fact that most of these metrics are difficult to understand and cannot be determined with

available devices and thus requires additional computation are the major contributors to the

difficulties in establishing the relationship between these metrics and clinical outcomes.12

Nevertheless, GV seems to be an independent predictor of all complications and mortality in

type 1 and type 2 diabetes mellitus;6,8,13,19–25 and, interestingly it appears to have more harmful

effects than sustained chronic hyperglycemia in the pathogenesis of diabetic cardiovascular

complications.14 Thereby, the control of GV has an important role in the management of

diabetes.

Continuous and flash glucose monitoring (CGM and FGM) can successfully identify glucose

interstitial fluctuations10 and present an opportunity to address GV. There is a need for clearer

understanding of the impact of treatment on GV and diabetes complications, and this becomes

2

easier when we have standardized metrics, clear targets, and focused and up-to-date data

analysis tools.

In order to do this, in June 2019, the Advanced Technologies & Treatments for Diabetes (ATTD)

International Consensus identified the 10 metrics considered most useful to clinical practice for

CGM and FGM data: number of days the CGM/FGM is worn, percentage of time the CGM/FGM

is active (sensor use), mean glucose, Glucose Management Indicator (GMI), time above range

(TAR, percentage of readings and time > 180 mg/dL), time above 250 mg/dL (TA250, Percentage

of readings and time > 250 mg/dL), time in range (TIR, percentage of readings and time 70–180

mg/dL), time below range (TBR, percentage of readings and time < 70 mg/dL), time below 54

mg/dL (TB54, Percentage of readings and time < 54 mg/dL) and %CV. It also established the

glycemic targets for these metrics.26

% CV and TIR may work as predictors of complications20,27,28, that is why their evaluation is crucial

in diabetes management. TAR and TA250 are markers of the time in hyperglycemia and TBR and

TB54 of the time in hypoglycemia. GMI is a delicate indicator of the mean glucose and works as

an instantaneous element of understanding the management of the disease and treatment

efficiency. The formula proposed to calculate GMI(%) is 3.31 + 0.02392 x mean glucose in

mg/dL29 while %CV (%) is 100 x ( SD / mean glucose).

In Portugal, there is a widespread use of FGM systems because they are supported by the

national health system.

The FGM system has two parts: a sensor inserted subcutaneously (5 mm), that measures

interstitial glucose every 15 minutes and stores it for up to 8 hours with a maximum lifespan of

14 days, after which it must be replaced; and a reader that reads the sensor on demand (NFC

technology) and displays the actual glucose value, a trend arrow and the glucose’s curve of the

previous 8 hours. It has the capacity to store the readings of 90 days. The patient and the health

care professionals can download the 90 days gathered data connecting the reader to a computer

with the manufacturer software30. The FGM reader also saves the raw data of 90 days in a

OneNote file format10 that can be extracted directly to an Excel format. Due to intermittent user

errors and sensor errors, some data will be missing when it is used long term but can be

estimated by linear interpolation.

The manufacturer software creates a report in a PDF format, as shown in Figure 1, that presents

percentage of time the FGM is active, mean glucose, GMI, TAR, TIR, TBR, number of

hypoglycemic events and the duration, the number of readings per day, the Ambulatory Glucose

Profile (AGP, a report that represents all CGM and FGM readings into an waveform graphic) and

the day-to-day glucose profile. It also presents GV in traffic light like graphic that allows the

health care professionals to assess GV in a qualitative way. This uses a different formula to

3

calculate GMI than the formula defined in the ATTD International Consensus. GMI is calculated

based on the formula developed by Nathan et al. 31 GMI (%)= (mean glucose in mg/dL + 46.7) /

28.7.30

Indeed, this manufacturer report misses some of the glycemic metrics most useful to clinical

practice for FGM data such as TB54, TA250 and %CV. In Portugal, the available

applications/softwares that could evaluate these metrics in a quantitative way are not available

in some medical centers (for example, LibreView32, that also calculates GMI using both formulae

or Tidepool33), or are cumbersome and difficult to use (for example, EasyGV34), making it

impossible to evaluate GV in clinical practice. So, there is a need to develop a new

application/software.

This project aims to overcome bibliographic and computer-related shortcomings in existing

applications/softwares, as well as streamlining and simplifying the work of medical practitioners

and the demands placed on them, through the development and validation of an

application/software that processes/uses the Excel data (raw data) obtained from FGM to

calculate the 10 metrics proposed by the ATTD International Consensus26 in a rapid and user-

friendly manner. It also pretends to verify the usefulness of linear interpolation in FGM systems

comparing the GMI obtained through the new application, considering two versions with and

without interpolation, with GMI obtained using the manufacturer program and HbA1c obtained

by blood testing.

4

METHODOLOGY

Methods

The study was divided into two steps: one for the development of the software and the second for its validation.

Part 1 – Development of the software

The application/software was developed using Visual Basic for applications, in this case, the

Excel sheet. There were created two versions, one that identifies and interpolates missing data

using piecewise linear interpolation to fill gaps in glucose readings (with interpolation (WI)

version); and another similar to the WI version except that it does not interpolate missing data

(without interpolation (WOI) version).

Each version uses the mathematical formulae set out in the ATTD International Consensus,

calculating those 10 metrics, the SD and a graphic representative of the glucose IQR by hour.

The piecewise linear interpolation method is similar to the one used in EasyGV34 and other

studies of glucose gaps.35–37

Part 2 – Validation of the software

From March 2019 to January 2020, at each clinical appointment, the FGM raw data were

downloaded from the FGM reader, and the equivalent PDF files were download from the

manufacturer program.

Forty-eight files, each containing 28 days worth of data, were collected from forty-four type 1

diabetic patients under the care of the Endocrinology Department of Centro Hospitalar

Universitário do Porto (Porto’s University Hospital). All forty-four patients were over 18 years

old and were using the FGM system for more than 1 year.

The HbA1c was obtained through DCA Vantage Analyzer siemens and it was available on the

SClínico platform. The collected values were the ones available on the last day of the 28 days

time range of the equivalent FGM raw data and PDF file.

The study protocol was approved by the Centro Hospitalar Universitário do Porto (Porto’s

University Hospital) Research Ethics Board.

5

Statistical analysis

Difference between continuous variables was assessed through a Student t-test and the degree

of consistency between both applications (the new application/software and the manufacturer

one) was evaluated through Pearson correlation.

All p-values are two-tailed and were considered statistically significant if <0.05.

Standardized differences between the metrics from the two programs were also calculated as a

quality control measure and a standardized difference less than 10% was considered

satisfactory.38,39 The standardized differences were calculated in Microsoft Excel using the

following formula:

𝑑 = (𝑥1−𝑥2

√𝑠12+𝑠22

2

) , 𝑥 being the average of the variable to be assessed and s being its variance.

Statistical analysis was performed using IBM® SPSS® Statistics 26.0.

6

RESULTS

Part 1 – development of the application / software

It was created an Excel sheet capable of reading OneNote files, selecting the date range to be

analyzed (and in the WI version identifying and interpolating missing data), and calculating the

proposed metrics. The menu of this Excel sheet is shown in Figure 2.

To proceed with the analysis, one should enter the name of the OneNote file to analyze,

followed by .txt, in the space that reads ‘’nameonenotefile.txt’’. Next, one should select the date

range in the relevant space, date format dd/mm/yyyy and click the “Import & Analyze” button

to import the data (and, in the WI version, also perform the linear interpolation of FGM gaps),

and calculate the metrics/graphic.

At the end, one could export the results to an PDF file, by clicking in the “Export” button.

Part 2 – Validation of the application/software

The forty-eight OneNote files were analyzed using both versions of the new

application/software and the results obtained were compared with the forty-eight

manufacturer reports for the corresponding 28-day period.

The glycemic metrics obtained expressed as mean, variance and standard deviation, are shown

in Table I.

The comparison between both versions (WI and WOI) of the new application/software and the

manufacturer program are shown in Table II, assessed through Pearson correlation, Student T-

test and standardized differences. Both versions of the new application/software present good

correlation with the manufacturer program in all the evaluated metrics, however there is a

higher correlation coefficient between the metrics obtained from the WOI version and the

manufacturer program than between the WI version and the manufacturer program.

GMI obtained from both versions of the new application/software and manufacturer program

was significantly different with a standardized difference of 19.93% (WOI version) and 14.04%

(WI version). There were no significant differences for any of the remaining metrics compared

between both WI and WOI versions and the manufacturer program as the standardized

differences were lower than 10%.

The comparison between GMI calculated with both versions of the new application/software,

GMI from the manufacturer program and the HbA1c are shown in Table III.

7

The lowest correlation was observed between WI version and HbA1c (0.813); while the

correlation between WOI GMI and manufacturer GMI and HbA1c were similar (0.867 and 0.869,

respectively). The difference between HbA1c and GMI obtained with all softwares, including the

manufacturer program, was significantly different. The standardized difference was lowest

when compared to the WOI version (17.35%) and highest when compared to the manufacturer

version (30.33%).

Analysis of the difference between GMI values from both versions of the new

application/software (WI and WOI), the manufacturer program GMI and the HbA1c, shown in

Table IV, revealed that this difference between HbA1c and GMI was greater than 0.5% in 22.9%

of the cases in the WOI version, in 25.0% of the cases in WI version and in 37.5% of the cases in

the manufacturer program.

8

DISCUSSION

Given the unquestionable significance of GV, several authors over the years have stressed the

need for standardized metrics and targets, with various metrics being developed, tested and

advocated. 10,15–18,40–45 From simple to complex mathematics, and supported by wide-ranging

and lengthy explanations, the standardized metrics proposed by the ATTD are based on the most

user-friendly and easiest to interpret, for healthcare professionals, patients and carers.26 In

Portugal, the FGM use is widely spread in type 1 diabetic patients, but unfortunately, the FGM

reports lacks some of this standardized metrics, especially the %CV, TB54 and TA250.

The primary objective of this work was to develop an application/software that solved this

problem ensuring reliable data.

The new application/software validity was accessed through Pearson correlation coefficient,

also known as the linear correlation coefficient. It revealed an excellent degree of correlation,

close to 1, between glycemic metrics obtained with both version of the new

application/software (WI and WOI) and the manufacturer program, which confirmed the validity

of the new application/software. However, this coefficient was higher between the WOI version

and the manufacturer program suggesting that there is a greater proximity between the WOI

version and the manufacturer program than between the WI version and the manufacturer

program.

There were developed two versions of this new application/software: one considering linear

interpolation of the missing data (WI version) and other without any interpolation of the missing

data (WOI version). Linear interpolation is a method of curve fitting using linear polynomials to

construct new data points within the range of a discrete set of known data points. The

appropriateness of this mathematical resource is justified because gaps in FGM data are

common and, irrespective of their origin, they affect the measurements used to calculate GV. 37

By filling the gaps, linear interpolation is used to mitigate their impact in order to provide a

picture that better represents the reality. Its usefulness in this type of system is open to debate

because the size of the gaps is unpredictable, and the adjustments may mitigate them

erroneously. In other words, the gap may be so large that the interpolation formula masks a

fluctuation if the data points before and after the gap are both average.37

According to the paper by Fonda et al.37, relating to gaps in CGM data, the management of gaps

in FGM systems must, also, be managed very carefully because unlike CGM systems, which take

readings every 5 minutes, data in FGM systems are read at 15 minute intervals, meaning that

less lines are plotted and less datapoints are defined, and the number of readings is also lower.

9

It therefore stands to reason that an extremely high sensor use is required in order to minimize

the loss of fluctuations. Furthermore, if the pattern of glucose fluctuation is non-linear, it may

be wrong to interpolate.

Indeed, there was observed a statistically significant difference in both new

application/software versions GMI and the manufacturer program GMI because the formulas

used to calculated GMI are different: WI/WOI versions calculates GMI (%) as 3.31 + 0.02392 x

mean glucose in mg/dL29 while the manufacturer program calculates GMI (%) as (mean glucose

in mg/dL + 46.7) / 28.731. However, it should be noted that there is a slightly smaller difference

between the WI GMI and the manufacturer GMI than between the WOI GMI and the

manufacturer GMI, suggesting that linear interpolation mitigates the difference in this glycemic

metric.

There were not observed significant differences between mean glucose calculated by both

versions of the new application/software and manufacturer program which reinforces that the

use of different formulae is probably the main player in GMI differences observed. On the other

hand, it should be pointed out that the Pearson correlation coefficients between the GMIs are

high, and not erroneously so, as when plotted graphically, the variables are parallel lines.

However, the differences between them require careful analysis, due to the nature of this

variable.

The interest in comparing the HbA1c to GMI dates back to the times when the latter was

considered to be estimated HbA1c. However, due to the difference in values and the likelihood

of potentially confusing discrepancies, it was renamed, and is now referred to as the Glucose

Management Indicator. This empowering and positive calculation offers an additional means of

measuring time in hyperglycemia, in order to manage the disease and personalize treatment

plans, rather than as an estimate of the HbA1c.29 A difference between the GMI and the HbA1c

can be expected for several reasons. HbA1c reflects glucose levels primarily over the last 2–3

months and, in this case, the GMI values are for 28 days; and individual biological variation in

erythrocyte survival or glycation rates may contribute to the discrepancy between them.46,47

However, the available literature indicates that the difference between HbA1c and GMI remains

relatively stable for each individual over time. 29,46

In this work, it was shown an excellent correlation between all GMI and HbA1c, but there was a

striking difference between the GMI values and the HbA1c, with standardized differences of

over 10%. This difference was higher between manufacturer program and HbA1c and lower

between WOI version and HbA1c, suggesting that the formula used by the manufacturer

program to calculate GMI is less accurate and that interpolation is a potential confounding

factor.

10

The formula used by the manufacturer program to calculate GMI was developed by Nathan et

al.31, and is based on the premise that HbA1c levels could be expressed as estimated AG. This

formula was elaborated combining self-monitoring of capillary glucose data and CGM data in

order to measure average glucose and the corresponding HbA1c for subsequent mathematical

calculation. The GMI formula, on the other hand was developed by Bergenstal et al.29 combining

data from the HypoDE study48 and data from Beck et al.49, in order to create a formula that aims

to present GMI using mean glucose. Given the origins of the formulas and their aims and

calculation methods, it is unsurprising that the formula used by the manufacturer program is

less precise, as it is outdated in terms of the collection, interpretation and analysis of this

glycemic metric.

Therefore, these results suggest that the Bergenstal’s29 formula is more suitable to calculate

GMI than Nathan’s31 and, again, claims against linear interpolation.

It is worth noting that the difference between the GMI and HbA1c was less than 0.5% in 77.1%

of the cases using WOI version and in 75.0% of the cases using WI version, which suggests that

both versions perform better than expected when considering the 72.0% outlined in the paper

by Bergenstal et al.29.

There was observed a clinically significant difference between GMI and HbA1c (more than 0.5%)

in 22.9% of the cases by using WOI version versus 25.0% of the cases using WI version.

As Beck et al.21 underline, with advances in CGM and FGM systems, as well as software and

technological resources for the analysis of GV, the status of the HbA1c as the gold standard for

glycemic monitoring is being questioned and even overturned. The HbA1c reflects the mean

pattern of glycemia and does not provide any information on its variation, fluctuations, or time

spent in hyperglycemia or hypoglycemia but it always has a place in the monitoring of CGM and

FGM systems, as these results corroborates.

In conclusion, the developed application/software is a step in the right direction, as it is practical,

intuitive updated to include all the most recent glycemic metrics and validated in type 1 diabetic

population. It is an excellent tool to clinical practice everywhere and to promote research into

GV, attending that it does not require neither internet connection nor previous installation.

Although, these results suggest that interpolation may introduce an element of confounding,

erroneously altering the metrics, either due to the non-linear distribution of glycemic variations,

due to fluctuations going unnoticed or being measured inaccurately, or due to the large size of

the gaps, the usefulness of interpolation in FGM systems requires further study, since this

study’s sample size is not adequate to address this question.

11

REFERENCES 1. Diabetes Control and Complications Trial Research Group, Nathan DM, Genuth S, et al.

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2. Loewen SL, Haas LB. Complications of diabetes: acute and chronic. Nurse Pract Forum. 1991;2(3):181-187.

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Table I - General characteristics of the sample studied, expressed as mean, variance and standard deviation for the variables proposed and obtained by the new application/software and the metrics available in the manufacturer program

New application/software

Manufacturer program

With interpolation

Without interpolation

Mean±SD Variance Mean±SD Variance Mean±SD Variance

TIR (%) 51.50±18.05 325.66 52.26±17.37 301.66 52.25±17.44 304.19

TBR (%) 5.26±5.34 28.46 5.73±5.26 27.67 5.75±5.25 27.55

TAR (%) 43.24±19.48 379.53 42.36±18.08 326.80 42.00±18.79 353.15

TB54 (%) 1.93±3.04 9.26 2.25±3.23 10.46 --- ---

TA250 (%) 17.44±14.40 207.42 16.65±13.26 175.83 --- ---

Mean glucose (mg/dL)

178.75±36.83 1356.40 176.02±34.85 1214.69 176.52±34.54 1192.72

GMI (%) 7.58±0.881 0.78 7.52±0.833 0.70 7.75±1.22 1.49

Sensor use (%)

86.09±14.27 203.54 86.09±14.27 203.54 85.75±15.41 237.55

%CV (%) 39.15±7.68 58.48 40.08±8.07 65.09 --- ---

TIR, Time in range - Percentage of readings and time 70–180 mg/dL; TBR, Time below range - Percentage of readings and time < 70 mg/dL; TAR, Time above range - Percentage of readings and time > 180 mg/Dl; TB54, Time below 54 mg/dL - Percentage of readings and time < 54 mg/dL; TA250, Time above 250 mg/dL - Percentage of readings and time > 250 mg/dL; GMI, Glucose Management Indicator; %CV, Percentage of coefficient of variation.

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Table II - The comparison between the new application/software with and without interpolation and the manufacturer program

New software with interpolation and Manufacturer program

New software without interpolation and Manufacturer program

TIR (%) Pearson

correlation 0.983* 0.999*

T test p** 0.130 0.846

Standardized differences*** 0.2380% 0.00413%

TBR (%) Pearson


T test p** 0.027 0.641


TAR (%) Pearson


T test p** 0.050 0.317


Mean glucose (mg/dL)

Pearson correlation 0.951* 0.993*

T test p** 0.183 0.389


GMI (%)


T test p**

0.014 0.001


Sensor use (%)


T test p**

0.505 0.505


*All p-values <0,001, 2-tailed ** All p-values <0.05 were considered statistically significant, 2-tailed **Value <10% is regarded as sufficient

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TIR, Time in range - Percentage of readings and time 70–180 mg/dL; TBR, Time below range - Percentage of readings and time < 70 mg/dL; TAR, Time above range - Percentage of readings and time > 180 mg/dL; GMI, Glucose Management Indicator.

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Table III - The relationship between the Glucose Management Indicator from the new application/software with and without interpolation, the Glucose Management Indicator from the manufacturer program and the glycated hemoglobin

GMI new software with interpolation

GMI new software without

interpolation

GMI Manufacturer

program

HbA1c Pearson correlation

0.813* 0.867* 0.869*

T test p** 0.022 0.069 0.001

Standardized differences***

25.57%

17.35%

30.33%

*All p-values <0,001, 2-tailed ** All p-values <0.05 were considered statistically significant, 2-tailed **Value <10% is regarded as sufficient GMI, Glucose Management Indicator; HbA1c, Glycated hemoglobin.

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Table IV – Analysis of the difference between the Glucose Management Indicator from the new application/software with and without interpolation, the Glucose Management Indicator from the manufacturer program and the glycated hemoglobin

Percentage Frequency

Difference between manufacturer program GMI and HbA1c

<0.5%

>0.5%

62.5%

37.5%

30

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Difference between New Software with interpolation GMI and HbA1c

<0.5%

>0.5%

75.0%

25.0%

36

12

Difference between New Software without interpolation GMI and HbA1C

<0.5%

>0.5%

77.1%

22.9%

37

11

GMI, Glucose Management Indicator; HbA1c, Glycated hemoglobin.

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X and Y, being numbers; TIR, Time in range - Percentage of readings and time 70–180 mg/dL; TBR, Time below range - Percentage of readings and time < 70 mg/dL; TAR, Time above range - Percentage of readings and time > 180 mg/dL; AGP, Ambulatory glucose profile.

Figure 1 – Example of a report created by the manufacturer program

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GMI, Glucose Management Indicator; SD, Standard deviation; % CV, Percentage of coefficient of variation. Figure 2 - The application/software menu presented in the programmed Excel sheet

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