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Adipose insulin sensitivity indices (QUICKI, VAI, LAP, TyG, TG/HDL, Belfiore)

Source: R/adipo_is.R
adipo_is.Rd

Computes adipose-related insulin sensitivity/resistance indices from fasting inputs. Expected input units (converted internally):

  • Glucose G0 mmol/L -> mg/dL (* 18)

  • Insulin I0 pmol/L -> muU/mL (/ 6)

  • TG mmol/L -> mg/dL (* 88.57)

  • HDL mmol/L -> mg/dL (* 38.67)

Reported indices (higher magnitude of negative "_inv" values implies worse adipose IR):

  • Revised_QUICKI = 1 / (log10(I0 (muU/mL)) + log10(G0 (mg/dL)) + log10(FFA (mmol/L)))

  • VAI (sex-specific; inverted as VAI_*_inv so larger negative = worse)

  • TG_HDL_C_inv = -(TG/HDL) in mg/dL

  • TyG_inv = -ln(TG (mg/dL) * G0 (mg/dL) / 2)

  • LAP (sex-specific; inverted)

  • McAuley_index = exp(2.63 - 0.28 ln(I0 (muU/mL)) - 0.31 ln(TG (mmol/L)))

  • Adipo_inv = -(FFA * I0 (muU/mL))

  • Belfiore_inv_FFA = - 2 / (I0 (muU/mL) * FFA + 1)

Usage

adipo_is(
  data,
  col_map,
  normalize = "none",
  na_action = c("keep", "omit", "error"),
  check_extreme = FALSE,
  extreme_action = c("cap", "NA", "error"),
  extreme_rules = NULL,
  verbose = FALSE,
  ...
)

Arguments

data

Data frame or tibble with required columns mapped by col_map

col_map

Named list mapping keys to columns: G0, I0, TG, HDL_c, FFA, waist, bmi

normalize

One of c("none","z","inverse","range","robust"); default "none"

na_action

One of c("keep","omit","error"); default "keep"

check_extreme

Logical; if TRUE, applies range checks before computing

extreme_action

One of c("cap","NA","error"); default "cap" when check_extreme = TRUE

extreme_rules

Optional named list of c(min,max) per key (in original units)

verbose

Logical; when TRUE, emits progress via hm_inform()

...

Reserved

Value

A tibble with columns: Revised_QUICKI, VAI_Men_inv, VAI_Women_inv, TG_HDL_C_inv, TyG_inv, LAP_Men_inv, LAP_Women_inv, McAuley_index, Adipo_inv, Belfiore_inv_FFA

References

Katz A, Nambi SS, Mather K, Baron AD, Follmann DA, Sullivan G, Quon MJ (2000). “Quantitative Insulin Sensitivity Check Index: A Simple, Accurate Method for Assessing Insulin Sensitivity in Humans.” Journal of Clinical Endocrinology & Metabolism, 85(7), 2402–2410. doi:10.1210/jcem.85.7.6661 . ; Amato MC, Giordano C, Galia M, Criscimanna A, Vitabile S, Midiri M, Galluzzo A (2010). “Visceral Adiposity Index: A Reliable Indicator of Visceral Fat Function Associated with Cardiometabolic Risk.” Diabetes Care, 33(4), 920–922. doi:10.2337/dc09-1825 . ; Kahn HS (2005). “The Lipid Accumulation Product Performs Better than Body Mass Index as an Indicator of Cardiovascular Risk in Women.” BMC Cardiovascular Disorders, 5(1), 26. doi:10.1186/1471-2261-5-26 . ; Guerrero-Romero F, Simental-Mendía LE, González-Ortiz M, Martínez-Abundis E, Ramos-Zavala MG, Hernández-González SO, Jacques-Camarena O, Rodríguez-Morán M (2010). “The Product of Triglycerides and Glucose, a Simple Measure of Insulin Sensitivity. Comparison with the Euglycemic-Hyperinsulinemic Clamp.” Journal of Clinical Endocrinology & Metabolism, 95(7), 3347–3351. doi:10.1210/jc.2010-0288 . ; Dobiášová M, Frohlich JJ (2001). “The Plasma Parameter Log(TG/HDL-C) as an Atherogenic Index: Correlation with Lipoprotein Particle Size and Esterification Rate in ApoB-Lipoprotein-Depleted Plasma.” Clinical Biochemistry, 34(7), 583–588. doi:10.1016/S0009-9120(01)00263-6 . ; Belfiore F, Iannello S, Volpicelli G (1998). “Insulin Sensitivity Indices Calculated from Basal and OGTT-Related Insulin and Glucose Levels.” Molecular Genetics and Metabolism, 63(2), 134–141. doi:10.1006/mgme.1997.2658 . ; Raynaud E, Pérez-Martin A, Brun J, Benhaddad AA, Mercier J (1999). “Fasting Plasma Insulin and Insulin Resistance Indices.” Diabetes & Metabolism, 25(6), 524–532. No DOI identified in Crossref/PubMed as of 2026-03-16; see URL, https://pubmed.ncbi.nlm.nih.gov/?term=Fasting+Plasma+Insulin+and+Insulin+Resistance+Indices.

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")
head(out)
#> # A tibble: 2 × 10
#>   Revised_QUICKI VAI_Men_inv VAI_Women_inv TG_HDL_C_inv TyG_inv LAP_Men_inv
#>            <dbl>       <dbl>         <dbl>        <dbl>   <dbl>       <dbl>
#> 1          0.389       -1.18         -1.80        -2.11   -8.51       -24  
#> 2          0.324       -2.38         -3.62        -4.12   -9.08       -66.6
#> # ℹ 4 more variables: LAP_Women_inv <dbl>, McAuley_index <dbl>,
#> #   Adipo_inv <dbl>, Belfiore_inv_FFA <dbl>