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Calculate lipid-panel markers, Visceral Adiposity Index (VAI), Lipid Accumulation Product (LAP), and TyG-BMI index

Source: R/lipid_markers.R
lipid_markers.Rd

Given total cholesterol, HDL, TG (and optionally LDL, ApoB/ApoA1, waist, BMI, glucose), computes:

  • non_HDL_c, remnant_c

  • ratio_TC_HDL, ratio_TG_HDL, ratio_LDL_HDL

  • ApoB_ApoA1

  • VAI_Men, VAI_Women

  • LAP_Men, LAP_Women

  • TyG_BMI

Usage

lipid_markers(
  data,
  col_map = NULL,
  na_action = c("keep", "omit", "error", "ignore", "warn"),
  na_warn_prop = 0.2,
  verbose = TRUE
)

Arguments

data

A data.frame or tibble containing your lipid (and optional anthropometry/glucose) data.

col_map

Named list mapping:

  • TC -> total cholesterol

  • HDL_c -> HDL-C

  • TG -> triglycerides

  • LDL_c -> (optional) LDL-C; if absent, estimated via Friedewald

  • ApoB, ApoA1 -> (optional) apolipoproteins

  • waist -> (optional) waist circumference (cm)

  • BMI -> (optional) body mass index (kg/m^2)

  • glucose -> (optional) fasting glucose (mmol/L); used for TyG_BMI

na_action

One of c("keep","omit","error","ignore","warn").

  • keep/ignore: compute and propagate NA in outputs

  • omit: drop rows with NA in required inputs (TC, HDL_c, TG)

  • error: abort if any required input contains NA

  • warn: like keep, but emit missingness warnings

na_warn_prop

Proportion (0-1) threshold for high-missingness warnings when na_action = "warn". Default 0.2.

verbose

Logical; if TRUE (default), prints step-by-step progress including column mapping, optional input availability, pre-computation notes, physiological range information (informational only, values are not altered), the list of markers being computed with their inputs, and a per-column results summary.

Value

A tibble with computed lipid markers. Required outputs (always present): non_HDL_c, remnant_c, ratio_TC_HDL, ratio_TG_HDL, ratio_LDL_HDL, ApoB_ApoA1. Optional outputs (present when inputs available): VAI_Men, VAI_Women (waist + BMI required); LAP_Men, LAP_Women (waist required); TyG_BMI (BMI + glucose required). If an ID column is detected in data (e.g. id, IID, participant_id), it is prepended as the first output column.

Details

Assumed units (no automatic conversion except where noted):

  • TC, HDL_c, TG, LDL_c: mmol/L

  • glucose: mmol/L (converted to mg/dL internally for TyG_BMI)

  • waist: cm

  • BMI: kg/m^2

Pre-computation (one level deep):

  • If BMI is absent but weight (kg) and height (m or cm) are present, BMI is computed automatically.

  • If glucose is absent but G0 is present (or vice versa), the alias is derived automatically.

  • If LDL_c is absent, it is always estimated via Friedewald (TC - HDL - TG/2.2, mmol/L form). An informational message is emitted when verbose = TRUE.

References

Friedewald WT, Levy RI, Fredrickson DS (1972). “Estimation of the concentration of LDL cholesterol in plasma, without use of preparative ultracentrifuge.” Clinical Chemistry, 18(6), 499–502. doi:10.1093/clinchem/18.6.499 . 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 . Er L, Wu S, Chou H, Hsu L, Teng M, Sun Y, Ko Y (2016). “Triglyceride Glucose-Body Mass Index Is a Simple and Clinically Useful Surrogate Marker for Insulin Resistance in Nondiabetic Individuals.” PLOS ONE, 11(3), e0149731. doi:10.1371/journal.pone.0149731 . Khamseh ME, Malek M, Abbasi R, Taheri E, others (2021). “Triglyceride Glucose Index and Related Parameters (Triglyceride Glucose-Body Mass Index and Triglyceride Glucose-Waist Circumference) Identify Nonalcoholic Fatty Liver and Liver Fibrosis in Individuals with Overweight/Obesity.” Metabolic Syndrome and Related Disorders, 19(3), 167–173. doi:10.1089/met.2020.0109 . (clinical application)

Examples

df <- data.frame(TC = c(5.2, 6.1), HDL_c = c(1.3, 1.1), TG = c(1.8, 2.3),
                 LDL_c = c(3.2, 4.1), waist = c(88, 95), BMI = c(26, 29))
# Full verbose output (default)
lipid_markers(df)
#> lipid_markers(): reading input 'df' — 2 rows × 6 variables
#> lipid_markers(): col_map (6 columns — 6 inferred from data)
#>   TC                ->  'TC'    (inferred)
#>   HDL_c             ->  'HDL_c'    (inferred)
#>   TG                ->  'TG'    (inferred)
#>   LDL_c             ->  'LDL_c'    (inferred)
#>   waist             ->  'waist'    (inferred)
#>   BMI               ->  'BMI'    (inferred)
#> lipid_markers(): pre-computation: glucose cannot be derived -- provide: G0
#> lipid_markers(): optional inputs
#>   present:  waist, BMI
#>   missing:  ApoB, ApoA1, glucose
#>   indices -> NA:
#>   ApoB_ApoA1 -> NA  [missing: ApoB, ApoA1]
#>   TyG_BMI -> NA  [missing: glucose]
#>   derivable:
#>   glucose: provide G0 to enable
#> lipid_markers(): range note (informational, values not altered):
#>   TG: 2 value(s) outside plausible range
#> lipid_markers(): computing markers:
#>   non_HDL_c     [TC, HDL_c]
#>   remnant_c     [TC, HDL_c, LDL_c]
#>   ratio_TC_HDL  [TC, HDL_c]
#>   ratio_TG_HDL  [TG, HDL_c]
#>   ratio_LDL_HDL [LDL_c, HDL_c]
#>   ApoB_ApoA1    NA [ApoB/ApoA1 missing]
#>   VAI_Men/Women [waist, BMI, TG, HDL_c]
#>   LAP_Men/Women [waist, TG]
#>   TyG_BMI       NA [BMI/glucose missing]
#> lipid_markers(): results: non_HDL_c 2/2, remnant_c 2/2, ratio_TC_HDL 2/2, ratio_TG_HDL 2/2, ratio_LDL_HDL 2/2, ApoB_ApoA1 0/2, VAI_Men 2/2, VAI_Women 2/2, LAP_Men 2/2, LAP_Women 2/2
#> # A tibble: 2 × 10
#>   non_HDL_c remnant_c ratio_TC_HDL ratio_TG_HDL ratio_LDL_HDL ApoB_ApoA1 VAI_Men
#>       <dbl>     <dbl>        <dbl>        <dbl>         <dbl>      <dbl>   <dbl>
#> 1       3.9       0.7         4            1.38          2.46         NA    1.75
#> 2       5         0.9         5.55         2.09          3.73         NA    2.68
#> # ℹ 3 more variables: VAI_Women <dbl>, LAP_Men <dbl>, LAP_Women <dbl>
# Suppress messaging for batch use
lipid_markers(df, verbose = FALSE)
#> # A tibble: 2 × 10
#>   non_HDL_c remnant_c ratio_TC_HDL ratio_TG_HDL ratio_LDL_HDL ApoB_ApoA1 VAI_Men
#>       <dbl>     <dbl>        <dbl>        <dbl>         <dbl>      <dbl>   <dbl>
#> 1       3.9       0.7         4            1.38          2.46         NA    1.75
#> 2       5         0.9         5.55         2.09          3.73         NA    2.68
#> # ℹ 3 more variables: VAI_Women <dbl>, LAP_Men <dbl>, LAP_Women <dbl>
# Pre-compute BMI from weight and height
df2 <- data.frame(TC = 5.2, HDL_c = 1.3, TG = 1.8, weight = 70, height = 175)
lipid_markers(df2, verbose = FALSE)
#> # A tibble: 1 × 6
#>   non_HDL_c remnant_c ratio_TC_HDL ratio_TG_HDL ratio_LDL_HDL ApoB_ApoA1
#>       <dbl>     <dbl>        <dbl>        <dbl>         <dbl>      <dbl>
#> 1       3.9     0.818            4         1.38          2.37         NA