RELATIONSHIP BETWEEN DIABETIC PERIPHERAL NEUROPATHY AND COGNITIVE FUNCTIONS

Loading the libraries

library(gtsummary)
library(tidyverse)
library(corrr)
library(ggpubr)
library(flextable)
library(gt)
library(scales)
library(xtable)
library(pillar)
library(kableExtra)
library(paletteer)
library(ggthemes)
library(ggsci)
library(hrbrthemes)
library(easystats)
library(reshape2)

Baseline characteristics

Exploratory statistics 1

## Baseline characteristics
df_clinical %>% 
    select(Age, Sex, Weight, Height, BMI, `Duration of illness`) %>% 
    tbl_summary(                        
        type = list(all_continuous() ~ "continuous2"), 
        
        statistic = list(                 
            all_continuous() ~ c("{min} - {max}",
                                 "{mean} (±{sd})",
                                 "{median} ({p25}, {p75})"), 
            all_categorical() ~ "{n} ({p}%)"
        ),
        digits = list(all_categorical() ~ c(0, 1))
    ) %>% 
    modify_footnote(update = everything() ~ NA)

Characteristic	N = 50
Age
Range	46.0 - 59.0
Mean (±SD)	51.6 (±3.5)
Median (IQR)	52.0 (48.0, 53.8)
Sex
female	26 (52.0%)
male	24 (48.0%)
Weight
Range	61 - 107
Mean (±SD)	88 (±10)
Median (IQR)	89 (83, 94)
Height
Range	154 - 180
Mean (±SD)	167 (±8)
Median (IQR)	169 (162, 174)
BMI
Range	23.90 - 34.70
Mean (±SD)	31.07 (±2.34)
Median (IQR)	31.60 (29.73, 32.68)
Duration of illness
Range	10.00 - 24.00
Mean (±SD)	15.54 (±3.05)
Median (IQR)	15.00 (13.00, 18.00)

Exploratory statistics 2

stats <- c("Mean (SD)" = "{mean} ({sd})", 
           "(IQR)" = "({p25}, {p75})", 
           "Range" = "{min}, {max}")


imap(
  stats,
  ~ df_clinical %>%
    select(Age, Weight, Height, BMI, `Duration of illness`) %>% 
    tbl_summary(missing = "no", statistic = ~.x)  %>%
    modify_header(all_stat_cols() ~ stringr::str_glue("**{.y}**"))
) %>%
  tbl_merge(tab_spanner = FALSE) %>%
  modify_footnote(~NA)

Characteristic	Mean (SD)	(IQR)	Range
Age	51.6 (3.5)	(48.0, 53.8)	46.0, 59.0
Weight	88 (10)	(83, 94)	61, 107
Height	167 (8)	(162, 174)	154, 180
BMI	31.07 (2.34)	(29.73, 32.68)	23.90, 34.70
Duration of illness	15.54 (3.05)	(13.00, 18.00)	10.00, 24.00

Duration of illness

Correlation between Duration of illness and MoCa score

cor.test(df_clinical$`Duration of illness`, df_clinical$moca) %>% report()

## Effect sizes were labelled following Funder's (2019) recommendations.
## 
## The Pearson's product-moment correlation between df_clinical$`Duration of
## illness` and df_clinical$moca is negative, statistically significant, and very
## large (r = -0.77, 95% CI [-0.86, -0.63], t(48) = -8.42, p < .001)

Scatter plot between Baseline characteristics and MoCa score

sn <- 
    as.data.frame(df_clinical) %>%
    select(Age, BMI, `Duration of illness`, moca) %>% 
    melt(id.vars  = "moca") %>% 
    ggscatter(x= "value",
              y = "moca",
              color = "variable",
              palette = "jama",
              add = "reg.line",
              conf.int = TRUE,
              ylab = "MoCa score",
              xlab = " ",
              ggtheme = theme_light(),
              legend = "top",
              title = "Relationship between Age, BMI, Duration of illness and MoCa score"
    )  %>%
    facet(facet.by = "variable",
          scales = "free",
          panel.labs.background =
              list(color = "steelblue", fill = "steelblue", size = 0.5
              )) +
    stat_cor()
g1 <- ggpar(sn, legend.title = " ")
g1

Correlation between baseline charactarestics and MoCa score

cor.test(df_clinical$Age, df_clinical$moca) %>% report()

## Effect sizes were labelled following Funder's (2019) recommendations.
## 
## The Pearson's product-moment correlation between df_clinical$Age and
## df_clinical$moca is negative, statistically not significant, and small (r =
## -0.15, 95% CI [-0.41, 0.14], t(48) = -1.04, p = 0.302)

cor.test(df_clinical$BMI, df_clinical$moca) %>% report()

## Effect sizes were labelled following Funder's (2019) recommendations.
## 
## The Pearson's product-moment correlation between df_clinical$BMI and
## df_clinical$moca is positive, statistically not significant, and small (r =
## 0.20, 95% CI [-0.09, 0.45], t(48) = 1.39, p = 0.169)

cor.test(df_clinical$`Duration of illness`, df_clinical$moca) %>% report()

## Effect sizes were labelled following Funder's (2019) recommendations.
## 
## The Pearson's product-moment correlation between df_clinical$`Duration of
## illness` and df_clinical$moca is negative, statistically significant, and very
## large (r = -0.77, 95% CI [-0.86, -0.63], t(48) = -8.42, p < .001)

Stratified table by Motor or sensory and nerve

df %>% 
    select(Amplitude, latency, CV, type, nerve) %>%
    tbl_strata(
        strata = type,
        .tbl_fun =
            ~ .x %>%
            tbl_summary(
                by = nerve,
                type = list(all_continuous() ~ "continuous2"), 
                label = list(                
                    CV ~ "Conduction Velocity",
                    latency ~ "Latency"
                ),
                statistic = list(                 
                    all_continuous() ~ c("{min} - {max}",
                                         "{mean} (±{sd})",
                                         "{median} ({p25}, {p75})"
                                         ), 
                    all_categorical() ~ "{n} ({p}%)"
                ),
                digits = list(all_categorical() ~ c(0, 1)),
                missing_text = "No Response"
            ) %>% 
            modify_footnote(update = everything() ~ NA) %>% 
            modify_header(all_stat_cols() ~ "**{level}**")
    )

Characteristic	Motor		sensory
Characteristic	Rt common peroneal	Rt ulnar	Rt sural	Rt ulnar
Amplitude
Range	1.10 - 1.90	3.50 - 10.00	1.50 - 5.60	6.10 - 9.70
Mean (±SD)	1.63 (±0.21)	6.06 (±1.40)	3.96 (±1.02)	8.40 (±0.72)
Median (IQR)	1.70 (1.50, 1.80)	5.60 (5.30, 6.80)	4.00 (3.40, 4.70)	8.45 (7.93, 8.95)
No Response			8	0
Latency
Range	5.50 - 7.30	3.10 - 4.40	4.30 - 5.30	3.60 - 4.70
Mean (±SD)	6.41 (±0.40)	3.65 (±0.29)	4.59 (±0.25)	3.92 (±0.27)
Median (IQR)	6.40 (6.23, 6.60)	3.70 (3.40, 3.80)	4.50 (4.40, 4.70)	3.85 (3.70, 4.10)
No Response			8	0
Conduction Velocity
Range	33.0 - 49.0	38.0 - 54.0	31.0 - 40.0	36.0 - 52.0
Mean (±SD)	38.6 (±4.1)	46.4 (±4.1)	36.9 (±2.0)	46.2 (±3.2)
Median (IQR)	37.5 (36.0, 40.0)	46.0 (44.0, 49.8)	37.0 (36.0, 38.0)	46.0 (44.0, 48.0)
No Response			8	0

Characteristics of sensory nerves measurments

## characteristics of sensory
df_sensory %>% 
    select(Amplitude, latency, CV, nerve) %>% 
    tbl_summary(
        by = nerve,
        label = list(
            latency ~ "Peak latency",
            CV ~ "Conduction velocity"
            ),
        type = list(all_continuous() ~ "continuous2"),
        statistic = list(                 
            all_continuous() ~ c("{min} - {max}",
                                 "{mean} (±{sd})",
                                 "{median} ({p25}, {p75})"
                                 ) 
            ),
        missing_text = "No response"
    )

Characteristic	Rt sural, N = 50	Rt ulnar, N = 50
Amplitude
Range	1.50 - 5.60	6.10 - 9.70
Mean (±SD)	3.96 (±1.02)	8.40 (±0.72)
Median (IQR)	4.00 (3.40, 4.70)	8.45 (7.93, 8.95)
No response	8	0
Peak latency
Range	4.30 - 5.30	3.60 - 4.70
Mean (±SD)	4.59 (±0.25)	3.92 (±0.27)
Median (IQR)	4.50 (4.40, 4.70)	3.85 (3.70, 4.10)
No response	8	0
Conduction velocity
Range	31.0 - 40.0	36.0 - 52.0
Mean (±SD)	36.9 (±2.0)	46.2 (±3.2)
Median (IQR)	37.0 (36.0, 38.0)	46.0 (44.0, 48.0)
No response	8	0

Sensory(1): Ulnar nerve

Ulnar sensory nerve correlation tests

cor.test(df_sensory_ulnar$Amplitude, df_sensory_ulnar$moca) %>% report()

## Effect sizes were labelled following Funder's (2019) recommendations.
## 
## The Pearson's product-moment correlation between df_sensory_ulnar$Amplitude and
## df_sensory_ulnar$moca is positive, statistically not significant, and medium (r
## = 0.25, 95% CI [-0.03, 0.50], t(48) = 1.80, p = 0.078)

cor.test(df_sensory_ulnar$CV, df_sensory_ulnar$moca) %>% report()

## Effect sizes were labelled following Funder's (2019) recommendations.
## 
## The Pearson's product-moment correlation between df_sensory_ulnar$CV and
## df_sensory_ulnar$moca is positive, statistically not significant, and medium (r
## = 0.20, 95% CI [-0.08, 0.46], t(48) = 1.45, p = 0.154)

cor.test(df_sensory_ulnar$latency, df_sensory_ulnar$moca) %>% report()

## Effect sizes were labelled following Funder's (2019) recommendations.
## 
## The Pearson's product-moment correlation between df_sensory_ulnar$latency and
## df_sensory_ulnar$moca is negative, statistically significant, and medium (r =
## -0.29, 95% CI [-0.53, -0.01], t(48) = -2.11, p = 0.041)

Correlation coefficients between MoCa score and different variables

df_sensory_ulnar %>% 
    select(moca,Amplitude, CV, latency) %>% 
    correlate(method = "pearson") %>% 
    focus(moca) %>% 
    mutate(p.value = round(cor_to_p(moca, n = 50)$p, 4))

## # A tibble: 3 × 3
##   term        moca p.value
##   <chr>      <dbl>   <dbl>
## 1 Amplitude  0.252  0.0776
## 2 CV         0.204  0.154 
## 3 latency   -0.291  0.0405

Correlation graphs for sensory ulnar nerve

su <- 
    as.data.frame(df_sensory_ulnar) %>%
    select( Amplitude, CV, latency, moca) %>% 
    melt(id.vars  = "moca") %>% 
    ggscatter(x= "value",
              y = "moca",
              color = "variable",
              palette = "jama",
              add = "reg.line",
              conf.int = TRUE,
              ylab = "MoCa score",
              xlab = " ",
              ggtheme = theme_light(),
              legend = "top",
              title = "Rt. Ulnar Nerve (Sensory)"
    )  %>%
    facet(facet.by = "variable",
          scales = "free",
          panel.labs.background =
              list(color = "steelblue", fill = "steelblue", size = 0.5
              )) +
    stat_cor()
g2 <- ggpar(su, legend.title = " ")
g2

Sensory(2): Sural nerve

Sural sensory nerve correlation tests

cor.test(df_sensory_sural$Amplitude, df_sensory_sural$moca) %>% report()

## Effect sizes were labelled following Funder's (2019) recommendations.
## 
## The Pearson's product-moment correlation between df_sensory_sural$Amplitude and
## df_sensory_sural$moca is positive, statistically significant, and very large (r
## = 0.87, 95% CI [0.77, 0.93], t(40) = 11.29, p < .001)

cor.test(df_sensory_sural$CV, df_sensory_sural$moca) %>% report()

## Effect sizes were labelled following Funder's (2019) recommendations.
## 
## The Pearson's product-moment correlation between df_sensory_sural$CV and
## df_sensory_sural$moca is positive, statistically significant, and very large (r
## = 0.46, 95% CI [0.18, 0.67], t(40) = 3.23, p = 0.002)

cor.test(df_sensory_sural$latency, df_sensory_sural$moca) %>% report()

## Effect sizes were labelled following Funder's (2019) recommendations.
## 
## The Pearson's product-moment correlation between df_sensory_sural$latency and
## df_sensory_sural$moca is negative, statistically significant, and very large (r
## = -0.44, 95% CI [-0.66, -0.16], t(40) = -3.12, p = 0.003)

Correlation coefficients between MoCa score and different variables

df_sensory_sural %>% 
    select(moca,Amplitude, CV, latency) %>% 
    correlate(method = "pearson") %>% 
    focus(moca) %>% 
    mutate(p.value = round(cor_to_p(moca, n = 50)$p, 4))

## # A tibble: 3 × 3
##   term        moca p.value
##   <chr>      <dbl>   <dbl>
## 1 Amplitude  0.872  0     
## 2 CV         0.455  0.0009
## 3 latency   -0.443  0.0013

Correlation graphs for sensory sural nerve

sn <- 
    as.data.frame(df_sensory_sural) %>%
    select( Amplitude, CV, latency, moca) %>% 
    melt(id.vars  = "moca") %>% 
    ggscatter(x= "value",
              y = "moca",
              color = "variable",
              palette = "jama",
              add = "reg.line",
              conf.int = TRUE,
              ylab = "MoCa score",
              xlab = " ",
              ggtheme = theme_light(),
              legend = "top",
              title = "Rt. Sural Nerve (Sensory)"
    )  %>%
    facet(facet.by = "variable",
          scales = "free",
          panel.labs.background =
              list(color = "steelblue", fill = "steelblue", size = 0.5
              )) +
    stat_cor()
g3 <- ggpar(sn, legend.title = " ")
g3

Characteristics of motor nerves

df_motor %>% 
    select(Amplitude, latency, CV, nerve) %>% 
    tbl_summary(
        by = nerve,
        label = list(
            latency ~ "Distance latency",
            CV ~ "Conduction velocity"
        ),
        type = list(all_continuous() ~ "continuous2"),
        statistic = list(                 
            all_continuous() ~ c("{min} - {max}",
                                 "{mean} (±{sd})",
                                 "{median} ({p25}, {p75})"
            ) 
        )
    )

Characteristic	Rt common peroneal, N = 50	Rt ulnar, N = 50
Amplitude
Range	1.10 - 1.90	3.50 - 10.00
Mean (±SD)	1.63 (±0.21)	6.06 (±1.40)
Median (IQR)	1.70 (1.50, 1.80)	5.60 (5.30, 6.80)
Distance latency
Range	5.50 - 7.30	3.10 - 4.40
Mean (±SD)	6.41 (±0.40)	3.65 (±0.29)
Median (IQR)	6.40 (6.23, 6.60)	3.70 (3.40, 3.80)
Conduction velocity
Range	33.0 - 49.0	38.0 - 54.0
Mean (±SD)	38.6 (±4.1)	46.4 (±4.1)
Median (IQR)	37.5 (36.0, 40.0)	46.0 (44.0, 49.8)

Motor(1): Common peroneal nerve

Common peroneal nerve correlation tests

cor.test(df_motor_peroneal$Amplitude, df_motor_peroneal$moca) %>% report()

## Effect sizes were labelled following Funder's (2019) recommendations.
## 
## The Pearson's product-moment correlation between df_motor_peroneal$Amplitude
## and df_motor_peroneal$moca is negative, statistically not significant, and
## medium (r = -0.26, 95% CI [-0.50, 0.02], t(48) = -1.85, p = 0.070)

cor.test(df_motor_peroneal$CV, df_motor_peroneal$moca) %>% report()

## Effect sizes were labelled following Funder's (2019) recommendations.
## 
## The Pearson's product-moment correlation between df_motor_peroneal$CV and
## df_motor_peroneal$moca is positive, statistically significant, and very large
## (r = 0.44, 95% CI [0.19, 0.64], t(48) = 3.43, p = 0.001)

cor.test(df_motor_peroneal$latency, df_motor_peroneal$moca) %>% report()

## Effect sizes were labelled following Funder's (2019) recommendations.
## 
## The Pearson's product-moment correlation between df_motor_peroneal$latency and
## df_motor_peroneal$moca is negative, statistically significant, and very large
## (r = -0.67, 95% CI [-0.80, -0.48], t(48) = -6.22, p < .001)

Correlation coefficients between MoCa score and different variables

df_motor_peroneal %>% 
    select(moca,Amplitude, CV, latency) %>% 
    correlate(method = "pearson") %>% 
    focus(moca) %>% 
    mutate(p.value = round(cor_to_p(moca, n = 50)$p, 4))

## # A tibble: 3 × 3
##   term        moca p.value
##   <chr>      <dbl>   <dbl>
## 1 Amplitude -0.258  0.0704
## 2 CV         0.444  0.0012
## 3 latency   -0.668  0

Correlation graphs for motor common peroneal nerve

mp <- 
    as.data.frame(df_motor_peroneal) %>%
    select( Amplitude, CV, latency, moca) %>% 
    melt(id.vars  = "moca") %>% 
    ggscatter(x= "value",
              y = "moca",
              color = "variable",
              palette = "jama",
              add = "reg.line",
              conf.int = TRUE,
              ylab = "MoCa score",
              xlab = " ",
              ggtheme = theme_light(),
              legend = "top",
              title = "Rt. Common Peroneal Nerve (Motor)"
    )  %>%
    facet(facet.by = "variable",
          scales = "free",
          panel.labs.background =
              list(color = "steelblue", fill = "steelblue", size = 0.5
              )) +
    stat_cor()
g4 <- ggpar(mp, legend.title = " ")
g4

Motor(2): Ulnar nerve

Ulnar nerve (motor) correlation tests

cor.test(df_motor_ulnar$Amplitude, df_motor_ulnar$moca) %>% report()

## Effect sizes were labelled following Funder's (2019) recommendations.
## 
## The Pearson's product-moment correlation between df_motor_ulnar$Amplitude and
## df_motor_ulnar$moca is positive, statistically significant, and very large (r =
## 0.77, 95% CI [0.63, 0.86], t(48) = 8.38, p < .001)

cor.test(df_motor_ulnar$CV, df_motor_ulnar$moca) %>% report()

## Effect sizes were labelled following Funder's (2019) recommendations.
## 
## The Pearson's product-moment correlation between df_motor_ulnar$CV and
## df_motor_ulnar$moca is positive, statistically significant, and very large (r =
## 0.54, 95% CI [0.30, 0.71], t(48) = 4.39, p < .001)

cor.test(df_motor_ulnar$latency, df_motor_ulnar$moca) %>% report()

## Effect sizes were labelled following Funder's (2019) recommendations.
## 
## The Pearson's product-moment correlation between df_motor_ulnar$latency and
## df_motor_ulnar$moca is negative, statistically significant, and very large (r =
## -0.70, 95% CI [-0.82, -0.52], t(48) = -6.77, p < .001)

Correlation coefficients between MoCa score and different variables

df_motor_ulnar %>% 
    select(moca,Amplitude, CV, latency) %>% 
    correlate(method = "pearson") %>% 
    focus(moca) %>% 
    mutate(p.value = round(cor_to_p(moca, n = 50)$p, 4))

## # A tibble: 3 × 3
##   term        moca p.value
##   <chr>      <dbl>   <dbl>
## 1 Amplitude  0.771  0     
## 2 CV         0.536  0.0001
## 3 latency   -0.699  0

Correlation graphs for motor ulnar nerve

mu <- 
    as.data.frame(df_motor_ulnar) %>%
    select( Amplitude, CV, latency, moca) %>% 
    melt(id.vars  = "moca") %>% 
    ggscatter(x= "value",
              y = "moca",
              color = "variable",
              palette = "jama",
              add = "reg.line",
              conf.int = TRUE,
              ylab = "MoCa score",
              xlab = " ",
              ggtheme = theme_light(),
              legend = "top",
              title = "Rt. Ulnar Nerve (Motor)"
    )  %>%
    facet(facet.by = "variable",
          scales = "free",
          panel.labs.background =
              list(color = "steelblue", fill = "steelblue", size = 0.5
              )) +
    stat_cor()
g5 <-ggpar(mu, legend.title = " ")
g5

Correlation coefficients between MoCa score and different variables

df_clinical %>% 
    select(moca,Age, Weight, Height, BMI, `Duration of illness`) %>% 
    correlate(method = "pearson") %>% 
    focus(moca) %>% 
    mutate(p.value = round(cor_to_p(moca, n = 50)$p, 4))

## # A tibble: 5 × 3
##   term                  moca p.value
##   <chr>                <dbl>   <dbl>
## 1 Age                 -0.149   0.302
## 2 Weight               0.206   0.150
## 3 Height               0.176   0.220
## 4 BMI                  0.197   0.169
## 5 Duration of illness -0.772   0

RELATIONSHIP BETWEEN DIABETIC PERIPHERAL NEUROPATHY AND COGNITIVE FUNCTIONS

2024-01-26

Loading the libraries

Baseline characteristics

Exploratory statistics 1

Exploratory statistics 2

Duration of illness

Correlation between Duration of illness and MoCa score

Scatter plot between Baseline characteristics and MoCa score

Correlation between baseline charactarestics and MoCa score

Stratified table by Motor or sensory and nerve

Characteristics of sensory nerves measurments

Sensory(1): Ulnar nerve

Ulnar sensory nerve correlation tests

Correlation coefficients between MoCa score and different variables

Correlation graphs for sensory ulnar nerve

Sensory(2): Sural nerve

Sural sensory nerve correlation tests

Correlation coefficients between MoCa score and different variables

Correlation graphs for sensory sural nerve

Characteristics of motor nerves

Motor(1): Common peroneal nerve

Common peroneal nerve correlation tests

Correlation coefficients between MoCa score and different variables

Correlation graphs for motor common peroneal nerve

Motor(2): Ulnar nerve

Ulnar nerve (motor) correlation tests

Correlation coefficients between MoCa score and different variables

Correlation graphs for motor ulnar nerve

Correlation coefficients between MoCa score and different variables