% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/adipo_is.R
\name{adipo_is}
\alias{adipo_is}
\title{Adipose insulin sensitivity indices (QUICKI, VAI, LAP, TyG, TG/HDL, Belfiore)}
\usage{
adipo_is(
  data,
  col_map = NULL,
  normalize = "none",
  na_action = c("keep", "omit", "error"),
  verbose = TRUE,
  ...
)
}
\arguments{
\item{data}{Data frame or tibble with required columns mapped by \code{col_map}}

\item{col_map}{Named list mapping keys to columns: G0, I0, TG, HDL_c, FFA, waist, bmi}

\item{normalize}{One of c("none","z","inverse","range","robust"); default "none"}

\item{na_action}{One of c("keep","omit","error"); default "keep"}

\item{verbose}{Logical; if \code{TRUE} (default), prints column mapping, the list
of indices being computed, and a per-column results summary.}

\item{...}{Reserved}
}
\value{
A tibble with columns:
\code{Revised_QUICKI}, \code{VAI_Men_inv}, \code{VAI_Women_inv}, \code{TG_HDL_C_inv}, \code{TyG_inv},
\code{LAP_Men_inv}, \code{LAP_Women_inv}, \code{McAuley_index}, \code{Adipo_inv}, \code{Belfiore_inv_FFA}.
If an ID column is detected in \code{data} (e.g. \code{id}, \code{IID}, \code{participant_id}), it
is prepended as the first output column.
}
\description{
Computes adipose-related insulin sensitivity/resistance indices from fasting inputs.
Expected input units (converted internally):
\itemize{
\item Glucose G0 mmol/L -> mg/dL (* 18)
\item Insulin I0 pmol/L -> muU/mL (/ 6)
\item TG mmol/L -> mg/dL (* 88.57)
\item HDL mmol/L -> mg/dL (* 38.67)
}

Reported indices (higher magnitude of negative "_inv" values implies worse adipose IR):
\itemize{
\item Revised_QUICKI = 1 / (log10(I0 (muU/mL)) + log10(G0 (mg/dL)) + log10(FFA (mmol/L)))
\item VAI (sex-specific; inverted as VAI_*_inv so larger negative = worse)
\item TG_HDL_C_inv = -(TG/HDL) in mg/dL
\item TyG_inv = -ln(TG (mg/dL) * G0 (mg/dL) / 2)
\item LAP (sex-specific; inverted)
\item McAuley_index = exp(2.63 - 0.28 ln(I0 (muU/mL)) - 0.31 ln(TG (mmol/L)))
\item Adipo_inv = -(FFA * I0 (muU/mL))
\item Belfiore_inv_FFA = - 2 / (I0 (muU/mL) * FFA + 1)
}

\strong{Inversion Note}: Most indices (VAI, LAP, TyG, TG/HDL, Adipo, Belfiore) are algebraically
inverted from their original insulin RESISTANCE definitions so that more negative values
consistently indicate worse adipose insulin sensitivity. Revised_QUICKI and McAuley_index
are retained in their original orientation (already sensitivity indices; higher = better).
See the vignette for detailed interpretation guidance.
}
\examples{
df <- tibble::tibble(
  G0 = c(5.2, 6.1),      # mmol/L
  I0 = c(60, 110),       # pmol/L
  TG = c(1.2, 1.8),      # mmol/L
  HDL_c = c(1.3, 1.0),   # mmol/L
  FFA = c(0.4, 0.6),     # mmol/L
  waist = c(85, 102),    # cm
  bmi = c(24, 31)        # kg/m^2
)
cm <- as.list(names(df)); names(cm) <- names(df)
out <- adipo_is(df, cm, verbose = FALSE, na_action = "keep")
}
\references{
\insertRef{katz2000quicki}{HealthMarkers}
\insertRef{amato2010vai}{HealthMarkers}
\insertRef{kahn2005lap}{HealthMarkers}
\insertRef{guerreroromero2018tyg}{HealthMarkers}
\insertRef{dobiasova2001atherogenic}{HealthMarkers}
\insertRef{belfiore1998insulin}{HealthMarkers}
\insertRef{raynaud1999fasting}{HealthMarkers}
}
