R/cpeptide_params.R

In JMLuther/tabletools: Helpful Tools to Make html Tables

#'C-peptide parameters estimates from population data
#'
#'`cpeptide_params` returns a dataframe with C-peptide kinetic parameters from
#'population data. These parameters are commonly used for calculation of Insulin
#'Secretion Rates.  Based on the
#' regression models from \href{https://pubmed.ncbi.nlm.nih.gov/1551497/}{Van
#' Cauter et al}. Participants were considered "Obese" in this study if body
#'weight was >15% above ideal body weight, rather than a commonly used BMI
#'cutoff. The parameters can be used in calculations to estimate insulin
#'secretion rates, shown in the example.
#'
#'@section Usage Notes:
#'
#'  Parameter notation: the kinetic parameter notation (k12, k21, k10) differs
#'  from the original Van Cauter notation, and metabolic modeling papers often
#'  vary in their notation. I have used the standard pharmacokinetics notation
#'  so that k12 indicates rate of movement from compartment 1 to compartment 2
#'  (see \href{https://pubmed.ncbi.nlm.nih.gov/4607867/}{Gibaldi & Perrier}).
#'
#'@section Results:
#'
#'  Parameters included in the output dataframe include the prefix `cp_` which
#'  is intended to aid in variable selection:
#' *  `cp_vd` C-peptide Volume of Distribution (L)
#' *  `cp_halflife_short` C-peptide "short" half life (minutes)
#' *  `cp_halflife_long` C-peptide "short" long life (minutes)
#' *  `cp_fraction` Weighting factor for short/long half-life
#' *  `cp_bsa` Body surface area (\eqn{m^2})
#' *  `cp_k12` C-peptide Rate constant (plasma -> tissue), 1/min
#' *  `cp_k21` C-peptide Rate constant (tissue -> plasma), 1/min
#' *  `cp_k10` C-peptide Rate constant (plasma -> elimination), 1/min
#'
#'
#'@section Basal Insulin Secretion Calculation:
#'
#'  The simplest calculation uses `k10` and `vd` to estimate basal insulin
#' secretion: \eqn{Basal~Insulin~Secretion~(pmol/min) = k_{10} \cdot V_d \cdot
#' C-peptide(nM)*1000} If C-peptide has already been converted to pmol/L, then
#'  do not multiply by 1000. See examples for calculation example.
#'
#'@param age Age in years
#'@param gender Gender
#'@param height Height in meters
#'@param weight Weight in kg
#'@param category Category ("Normal", "Obese", "NIDDM")
#'@param weight_units weight units, if not in kg
#'@param height_units height units, if not in meters
#'
#'@md
#'@return Data frame with variables `cp_vd`, `cp_halflife_short`,
#'  `cp_halflife_long`, `cp_fraction`, `cp_bsa`, `cp_k12`, `cp_k21`, `cp_k10`
#'@export
#'
#' @examples
#' library(tabletools)
#' cpeptide_params(age=28.1, height = 1.744666, weight = 69.4, gender = "F", category = "normal")
#' cpeptide_params(age=28.1, height = 1.744666, weight = 69.4, gender = "M", category = "normal")
#' cpeptide_params(age=35.2, height = 1.678461, weight = 107.9, gender = "F", category = "obese")
#' cpeptide_params(age=35.2, height = 1.678461, weight = 107.9, gender = "M", category = "obese")
#'
#' library(dplyr)
#' dat <- data.frame(id =1:10,
#'   age = rnorm(10, 50, sd=8),
#'   gender = sample(c("M", "F"), 10, replace = T),
#'   height = rnorm(10, 1.7, sd=0.2),
#'   weight = rnorm(10, 100, sd=20),
#'   cpeptide_0 =  abs(rnorm(10, 2.0, 0.91))  ) # ng/dL
#'
#' dat |>
#'   mutate(ibw = calculate_ibw(height, gender, height_units = "m", weight_units = "kg"),
#'          ibw_pct = (weight/ibw-1)*100,
#'          ibw_15 = case_when(ibw_pct>15 ~ "obese", # if >15% above IBW then Obese
#'                             TRUE ~ "normal"),
#'          cpeptide_0_pmol = convert_cpeptide_to_pM(cpeptide_0, cpeptide_units="ng/ml"),
#'          cpeptide_params(age, gender, height, weight, category = ibw_15),
#'          # Basal Insulin Secretion Rate (pmol/min)
#'          ISR_0 = cp_k10*cpeptide_0_pmol*cp_vd)


cpeptide_params <- function(age, gender = NA, height, weight, 
                            category = "normal",
                            weight_units = "kg", height_units = "m"){
  
  # convert units, calculation bsa
  weight_kg = convert_weight_to_kg(weight, weight_units = weight_units)
  height_m = convert_height_to_m(height, height_units = height_units)
  bsa = calculate_bsa(weight=weight_kg, height = height_m)
  # Age in years
  # category = c("normal", "obese", "NIDDM")
  halflife_short = # minutes
    ifelse(category %in% c("normal", "Normal"), 4.95,
           ifelse(category %in% c("obese", "Obese"), 4.55,
                  ifelse(category %in% c("niddm", "NIDDM", "T2DM", "DM", "Diabetic", "Diabetes"), 4.52, NA)))
  fraction =
    ifelse(category %in% c("normal", "Normal"), 0.76,
           ifelse(category %in% c("obese", "Obese"), 0.78,
                  ifelse(category %in% c("niddm", "NIDDM", "T2DM", "DM", "Diabetic", "Diabetes"), 0.78, NA)))
  halflife_long = 0.14*age+29.16 # minutes
  
  Vd = ifelse(gender %in% c("Female", "female", "F", "Fe"), 1.11*bsa+2.04,
              ifelse(gender %in% c("Male", "male", "M", "Ma"), 1.92*bsa+0.64, NA))
  a=log(2)/halflife_short
  b=log(2)/halflife_long
  k21= fraction*b + (1-fraction)*a # k1
  k10= a*b/k21                     # k2
  k12= a+b-k10-k21                 # k3
  
  return (data.frame("cp_vd" = Vd, # L
                     "cp_halflife_short"=halflife_short, # min
                     "cp_halflife_long"=halflife_long,   # min
                     "cp_fraction"=fraction,  # unitless fraction
                     "cp_bsa"=bsa,  # m^2
                     "cp_k12"=k12,  # 1/min
                     "cp_k21"=k21,  # 1/min
                     "cp_k10"=k10)) # 1/min
}