Table 1.

Function in R to Extract Glycemic Variability Indices From csv Files.

# index(data, d, n)

# Arguments

# data is a database with covariates: id (identification), day (1, 2, 3, …), Glucose (mg/dl)

# d, time (in min) between two consecutive glucose measurements

# n, for CONGAn estimation (at 1, 2, … hours)

index <- function(data, d, n) {

glucosemg <- data$Glucose

day <- data$day

id <- unique(data$id)

glucose <- glucosemg/18

N <- length(glucose)

m <- length(table(day)) # number of monitoring days

Th <- 24*m # total time in hours

# number of observations in 1 hour

K <- dim(data[day = = 1, ])[1]

dat <- cbind(c(1:N), data)

colnames(dat)[1] <- “num” ; orden <- dat$num

sd <- tapply(dat$glucose, dat$day, sd) # SD for each day

IGV <- 120 # Ideal glucose value

MG <- mean(glucosemg)

SD <- sd(glucosemg)

CV <- 100*SD/MG

IQR <- IQR(glucosemg)

M <- mean(abs(10*log10(glucosemg/IGV))^3) +

(max(glucosemg)-min(glucosemg))/20 # M-value

J <- 0.001*(MG + SD)^2 # J-Index (mg/dl)

# J <- 0.324*(MG + SD)^2 # J-Index (mmol/l)

FG <- 1.509*((log(glucosemg))^1.084 - 5.381)

rl <- ifelse(FG < 0, 10*FG^2, 0)

rh <- ifelse(FG > 0, 10*FG^2, 0)

LBGI <- 1/N*sum(rl) # Low Blood Glucose Index

HBGI <- 1/N*sum(rh) # High Blood Glucose Index

LR = HR = NULL

for (i in 1:m) {

LR <- max(rl[day = = i])

HR <- max(rh[day = = i])

}

ADRR <- 1/m*sum(LR + HR) # Average Daily Risk Ratio

HYPO <- 100*mean((glucosemg < 70)) # Hypoglycaemia (%)

HYPER <- 100*mean((glucosemg > 140)) # Hyperglycaemia (%)

LI <- sum((glucose[1:N-1] - glucose[2:N])^2)/d # Lability Index

# Glycemia Risk Assessment Diabetes Equation Score

GRADE <- median(425*(log10(log10(glucose)) + 0.16)^2)

# Continuous Overall Glycemic Action

CONGA <- sd(glucose[(n+1):N] - glucose[1:(N-n)]

# Mean of Daily Differences

MODD <- sum(abs(glucose[(n+1):N] - glucose[(1+K):N])) / (K*m-1)

# Area under the curve

AUC <- (1/2*5*sum(glucosemg[1:N-1] + glucosemg[2:N])) / (m*24*60)

# Mean Amplitude of Glycemic Excursions

infl = maxi = mini = dif = downs = NULL

for (i in 2:(N - 1)) {

if ((glucosemg[i] - glucosemg[i - 1]) * (glucosemg[i + 1] - glucosemg[i]) < = 0) {

infl = c(infl, i)}

}

infl <- c(infl,N)

eps <- 8

n_infl <- length(infl)

for (j in 1:(n_infl)) {

I1 <- (infl[j] - eps):(infl[j] + eps)

I <- subset(I1, I1 < = max(infl) & 0 < I1)

if (max(glucosemg[I]) = = glucosemg[infl[j]] |

min(glucosemg[I]) = = glucosemg[infl[j]]) {

maxi = c(maxi, infl[j])

mini = c(mini, infl[j])

}

}

mm <- c(sort(c(maxi, mini)), infl[n_infl])

def <- ifelse(glucosemg[mm[1:(length(mm)-1)]] = = glucosemg[mm[2:length(mm)]],     NA, mm[1:length(mm)-1])

def <- c(subset(def, def ! = “NA”), infl[n_infl])

for (k in 1:m) {

ii <- day[def] = = k

deff = def[ii]

diff = NULL

for (j in 1:length(deff)) {

diff <- c(diff, glucosemg[deff[j]] - glucosemg[def[j + 1]])

}

downs <- c(downs, (subset(diff, diff > 0 & diff > sd(k)))

}

MAGE <- sum(downs)/length(downs)

values <- round(c(id, MG, SD, CV, IQR, M, AdM, J, LBGI, HBGI, ADRR,

HYPO, HYPER, LI, MAG, GRADE, MAGE, CONGA, MODD, AUC), 6)

return(values)

}