Adipose insulin sensitivity indices

Scope

Fasting adipose insulin sensitivity/resistance indices in one call: Revised_QUICKI, McAuley_index, Adipo_inv, Belfiore_inv_FFA, TyG_inv, TG_HDL_C_inv, and sex-specific VAI/LAP (inverted so more negative = worse). Inputs are fasting; common glucose/insulin/TG/HDL unit conversions are handled internally.

When to use this

You need multiple adipose IR markers for metabolic phenotyping or clustering.
You have fasting glucose, insulin, lipids, and adiposity (waist/BMI); sex is optional but improves VAI/LAP labeling.
You want built-in plausibility screening and NA policy controls.

What you need

Required columns: G0 (mmol/L), I0 (pmol/L), TG (mmol/L), HDL_c (mmol/L), FFA (mmol/L), waist (cm), bmi (kg/m^2).
Fasting values only; post-prandial inputs will misstate IR.
Units before conversion: glucose mmol/L, insulin pmol/L, TG mmol/L, HDL mmol/L, FFA mmol/L, waist cm, BMI kg/m^2. If different, convert first.
Optional but helpful: sex (1/2 or labels) to understand which VAI/LAP variant applies.

Load packages and data

Use a small subset of the simulated data. Swap sim_small for your own data.

library(HealthMarkers)
library(dplyr)

sim_path <- system.file("extdata", "simulated_hm_data.rds", package = "HealthMarkers")
sim <- readRDS(sim_path)
sim_small <- dplyr::slice_head(sim, n = 30)

Column map

Map required inputs; left-hand keys are fixed.

col_map <- list(
  G0 = "G0",
  I0 = "I0",
  TG = "TG",
  HDL_c = "HDL_c",
  FFA = "FFA",
  waist = "waist",
  bmi = "BMI"
)

Walkthrough (compute and read results)

adipo_is() returns a tibble of only the computed index columns — not the original data. Bind back to your source rows with dplyr::bind_cols() if needed.

ais <- adipo_is(
  data = sim_small,
  col_map = col_map,
  na_action = "keep",
  check_extreme = FALSE,
  normalize = "none"
)

# Group 1 — non-sex-specific indices
ais %>%
  slice_head(n = 5) %>%
  select(Revised_QUICKI, McAuley_index, Adipo_inv, Belfiore_inv_FFA, TyG_inv)
#> # A tibble: 5 × 5
#>   Revised_QUICKI McAuley_index Adipo_inv Belfiore_inv_FFA TyG_inv
#>            <dbl>         <dbl>     <dbl>            <dbl>   <dbl>
#> 1          0.342          6.50     -8.96           -0.201   -8.39
#> 2          0.354          4.97     -7.18           -0.245   -9.13
#> 3          0.352          6.12     -7.81           -0.227   -8.42
#> 4          0.360          6.62     -5.90           -0.290   -9.07
#> 5          0.340          5.52     -9.22           -0.196   -8.95

# Group 2 — sex-specific and ratio indices
ais %>%
  slice_head(n = 5) %>%
  select(TG_HDL_C_inv, VAI_Men_inv, VAI_Women_inv, LAP_Men_inv, LAP_Women_inv)
#> # A tibble: 5 × 5
#>   TG_HDL_C_inv VAI_Men_inv VAI_Women_inv LAP_Men_inv LAP_Women_inv
#>          <dbl>       <dbl>         <dbl>       <dbl>         <dbl>
#> 1        -1.59      -0.689         -1.05       -11.9         -19.3
#> 2        -3.42      -2.08          -3.17       -71.3         -87.0
#> 3        -2.18      -1.28          -1.95       -40.1         -48.2
#> 4        -4.67      -2.67          -4.04       -92.4        -106. 
#> 5        -3.25      -2.08          -3.16       -79.2         -92.1

Values ending in _inv are inverted so more negative = worse adipose IR. Both sex variants (VAI_Men/Women, LAP_Men/Women) are always returned; pick the appropriate column per subject based on biological sex:

# Bind sex back from source, then derive a single VAI and LAP per subject.
# Adjust the if_else condition to match your sex coding (e.g. 1/2, "M"/"F").
ais_with_sex <- dplyr::bind_cols(dplyr::select(sim_small, sex), ais)

ais_with_sex %>%
  dplyr::mutate(
    VAI_inv = dplyr::if_else(sex == 1, VAI_Men_inv, VAI_Women_inv),
    LAP_inv = dplyr::if_else(sex == 1, LAP_Men_inv, LAP_Women_inv)
  ) %>%
  dplyr::select(sex, Revised_QUICKI, TyG_inv, VAI_inv, LAP_inv) %>%
  slice_head(n = 5)

Missing data and extremes

Screen a small slice, cap extremes, and compare row handling.

demo <- sim_small[1:6, c("G0", "I0", "TG", "HDL_c", "FFA", "waist", "BMI")]
demo$G0[2] <- NA        # missing glucose
demo$TG[3] <- 30        # extreme TG (mmol/L)

ais_keep <- adipo_is(
  data = demo,
  col_map = col_map,
  na_action = "keep",
  check_extreme = TRUE,
  extreme_action = "cap",
  normalize = "none",
  verbose = FALSE
)

ais_omit <- adipo_is(
  data = demo,
  col_map = col_map,
  na_action = "omit",
  check_extreme = TRUE,
  extreme_action = "cap",
  normalize = "none",
  verbose = FALSE
)

list(
  keep_rows = nrow(ais_keep),
  omit_rows = nrow(ais_omit),
  capped_preview = ais_keep %>% select(ends_with("_inv")) %>% slice_head(n = 3)
)
#> $keep_rows
#> [1] 6
#> 
#> $omit_rows
#> [1] 5
#> 
#> $capped_preview
#> # A tibble: 3 × 7
#>   VAI_Men_inv VAI_Women_inv TG_HDL_C_inv TyG_inv LAP_Men_inv LAP_Women_inv
#>         <dbl>         <dbl>        <dbl>   <dbl>       <dbl>         <dbl>
#> 1      -0.689         -1.05        -1.59   -8.39       -11.9         -19.3
#> 2      -2.08          -3.17        -3.42   NA          -71.3         -87.0
#> 3     -22.0          -33.5        -37.4   -11.3       -688.         -828. 
#> # ℹ 1 more variable: Adipo_inv <dbl>

Custom extreme rules

Override the built-in plausibility bounds with extreme_rules (values in original units before any conversion):

custom_rules <- list(
  G0    = c(2, 25),    # mmol/L — tighter glucose cap
  TG    = c(0.1, 15),  # mmol/L — tighter TG cap
  I0    = c(5, 1500),  # pmol/L
  FFA   = c(0.05, 2),  # mmol/L
  waist = c(50, 200)   # cm
)

ais_custom <- adipo_is(
  data           = demo,
  col_map        = col_map,
  na_action      = "keep",
  check_extreme  = TRUE,
  extreme_action = "cap",
  extreme_rules  = custom_rules,
  normalize      = "none"
)

ais_custom %>%
  select(Revised_QUICKI, TyG_inv, TG_HDL_C_inv) %>%
  slice_head(n = 6)
#> # A tibble: 6 × 3
#>   Revised_QUICKI TyG_inv TG_HDL_C_inv
#>            <dbl>   <dbl>        <dbl>
#> 1          0.342   -8.39        -1.59
#> 2         NA       NA           -3.42
#> 3          0.352  -11.0        -28.1 
#> 4          0.360   -9.07        -4.67
#> 5          0.340   -8.95        -3.25
#> 6          0.321   -8.37        -1.55

Normalise outputs

Use normalize to put all indices on a common scale before modelling or dimension reduction:

ais_z <- adipo_is(
  data      = sim_small,
  col_map   = col_map,
  na_action = "keep",
  normalize = "z"      # z-score: mean 0, SD 1
)

ais_z %>%
  slice_head(n = 5) %>%
  select(Revised_QUICKI, McAuley_index, TyG_inv, Adipo_inv)
#> # A tibble: 5 × 4
#>   Revised_QUICKI McAuley_index TyG_inv Adipo_inv
#>            <dbl>         <dbl>   <dbl>     <dbl>
#> 1       -0.342           0.711  1.53     -0.285 
#> 2        0.00367        -1.01  -0.583     0.191 
#> 3       -0.0407          0.284  1.44      0.0219
#> 4        0.186           0.843 -0.412     0.530 
#> 5       -0.389          -0.392 -0.0537   -0.354

Available options: "none" (default — raw scale), "z" (mean 0 / SD 1), "range" (0–1 min–max), "robust" (median / IQR), "inverse" (1/x).

Verbose diagnostics

Set verbose = TRUE to emit three structured messages per call:

Preparing inputs — start-of-function signal.
Column map — confirms which data column each required key resolved to. Example: adipo_is(): column map: G0 -> 'G0', I0 -> 'I0', TG -> 'TG', ...
Results summary — shows how many rows computed successfully (non-NA) per output column. Example: adipo_is(): results: Revised_QUICKI 28/30, VAI_Men_inv 28/30, McAuley_index 27/30, ...

verbose = TRUE emits at the "inform" level; you also need options(healthmarkers.verbose = "inform") (or "debug") active in the session for the messages to print:

old_opt <- options(healthmarkers.verbose = "inform")

invisible(adipo_is(
  data          = sim_small[1:5, ],
  col_map       = col_map,
  na_action     = "keep",
  check_extreme = TRUE,
  normalize     = "none",
  verbose       = TRUE
))
#> adipo_is(): preparing inputs
#> adipo_is(): column map: G0 -> 'G0', I0 -> 'I0', TG -> 'TG', HDL_c -> 'HDL_c', FFA -> 'FFA', waist -> 'waist', bmi -> 'BMI'
#> adipo_is(): results: Revised_QUICKI 5/5, VAI_Men_inv 5/5, VAI_Women_inv 5/5, TG_HDL_C_inv 5/5, TyG_inv 5/5, LAP_Men_inv 5/5, LAP_Women_inv 5/5, McAuley_index 5/5, Adipo_inv 5/5, Belfiore_inv_FFA 5/5

options(old_opt)  # restore

Enable globally for an entire session: options(healthmarkers.verbose = "inform"). Reset with options(healthmarkers.verbose = NULL) or options(healthmarkers.verbose = "none").

Expectations

All seven required mappings must be provided; missing mappings or columns abort.
na_action controls row handling: keep propagates NA outputs; omit drops rows with missing required inputs; error aborts.
check_extreme screens raw inputs before conversion; extreme_action can warn, cap, NA, or error.
Built-in conversions: glucose 18 (mg/dL), insulin /6 (muU/mL), TG 88.57 (mg/dL), HDL *38.67 (mg/dL); FFA/waist/BMI unchanged.

Common pitfalls

Non-fasting samples inflate TG/glucose and distort TyG/VAI/LAP.
Mis-specified units (e.g., mg/dL glucose without converting) will skew every index; convert upstream if your lab units differ.
Missing sex does not block computation but only one of VAI/LAP applies per subject; interpret accordingly.
For modelling, use normalize = "z" (z-score), "range" (0–1), or "robust" (median/IQR); default "none" keeps raw index scales. See the Normalise outputs section above.

Validation notes

Revised_QUICKI: typical adults ~0.3–0.6; lower is worse IR. Computed from log10(I0 [mU/L]) + log10(G0 [mg/dL]) + log10(FFA [mmol/L]).
McAuley_index: typical adults ~3–10; higher is better insulin sensitivity. Uses ln(TG [mmol/L]) and ln(I0 [mU/L]) — TG is kept in mmol/L before the internal mg/dL conversion.
TyG_inv: typically −8 to −12 for adults; more negative = worse IR. Computed from TG and G0 both in mg/dL.
LAP_Men_inv / LAP_Women_inv: computed as −(WC−threshold) × TG [mmol/L] (Kahn 2005). Typical values −10 to −80; more negative = worse.
VAI_Men_inv / VAI_Women_inv: uses TG and HDL in mmol/L with the Amato 2010 reference constants (1.03, 1.31, 0.81, 1.52). Typical range −1 to −5; more negative = worse.
Spot-check TG_HDL_C_inv: should equal −(TG_mg/dL / HDL_mg/dL).
Use check_extreme = TRUE with lab-appropriate extreme_rules before capping or erroring.