Installation

Before using ‘isi_calculator’function from the ’InsuSensCalc’ package, ensure that the necessary dependencies are installed and loaded into your R session for example: dplyr, magrittr and tibble.

Additionally, you need to install the ‘InsuSensCalc’ package itself. If It is available on CRAN, install it using:

install.packages(“InsuSensCalc”)

Or, if it’s not on CRAN, you can install it directly from its repository, for example, GitHub: using devtools::install_github(“sufyansuleman/InsuSensCalc”)

Usage

The ‘isi_calculator’ function is designed to be flexible, accommodating various types of input data related to insulin sensitivity calculations:

• Fasting: Requires fasting glucose and insulin levels.
• OGTT: Requires glucose and insulin levels at multiple time points post-glucose load (e.g., 30 and 120 minutes).
• Adipose Tissue (adipo): Requires measurements related to adipose tissue, such as triglycerides and HDL cholesterol.
• Tracer and DXA (tracer_dxa): Includes specialized indices involving tracer measurements and dual-energy X-ray absorptiometry (DXA).

# Load the package
library(InsuSensCalc)

# Load your data
# example_data <- read.csv("path/to/your/data.csv")

# Sample usage of the function
result_fasting <- isi_calculator(example_data, category = "fasting")
#> Calculating fasting indices...
result_ogtt <- isi_calculator(example_data, category = "ogtt")
#> Calculating ogtt indices...
result_adipo <- isi_calculator(example_data, category = "adipo")
#> Calculating adipo indices...
result_combined <- isi_calculator(example_data, category = c("fasting", "ogtt", "adipo", "tracer_dxa"))
#> Calculating fasting indices...
#> Calculating ogtt indices...
#> Calculating adipo indices...
#> Calculating tracer daxa indices...

# Print a result to view
print(result_fasting)
#> # A tibble: 3 × 27
#>     age   sex weight    G0    I0  G120  I120   G30   I30   bmi waist    TG HDL_c
#>   <dbl> <dbl>  <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1    30     1     70   5     7     7.8    50   6      25    25    90   1.8  1.2 
#> 2    40     2     60   5.5   8     8.1    45   6.5    30    30    85   2    1.1 
#> 3    35     1     65   6     7.5   8      48   6.2    28    27    88   1.9  1.15
#> # ℹ 14 more variables: FFA <dbl>, fat_mass <dbl>, rate_glycerol <dbl>,
#> #   rate_palmitate <dbl>, Fasting_inv <dbl>, Raynaud <dbl>, Homa_IR_inv <dbl>,
#> #   Firi <dbl>, Quicki <dbl>, Belfiore_basal <dbl>, Ig_ratio_basal <dbl>,
#> #   Isi_basal <dbl>, Bennett <dbl>, HOMA_IR_rev_inv <dbl>

Output Interpretation

The output of ‘isi_calculator’ is a dataframe containing the calculated indices for each specified category. These values are provided without normalization or transformation. It is advisable to normalize these values according to your analysis needs, especially when planning to conduct statistical testing or visualization.

Conclusion

The ‘InsuSensCalc’ package and its ‘isi_calculator’ function offer a robust solution for calculating insulin sensitivity indices from a variety of data types. By following the guidelines in this vignette, you can effectively utilize this tool for your research or clinical assessments related to insulin sensitivity and metabolic health.