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Compute tracer/DXA-based insulin sensitivity indices

Source: R/tracer_dxa_is.R
tracer_dxa_is.Rd

Uses stable isotope tracer infusion rates and DXA-measured fat mass to compute peripheral and adipose insulin sensitivity and related metrics.

Usage

tracer_dxa_is(
  data,
  col_map,
  normalize = NULL,
  na_action = c("keep", "omit", "error"),
  na_warn_prop = 0.2,
  check_extreme = FALSE,
  extreme_action = c("warn", "cap", "error", "ignore"),
  extreme_rules = NULL,
  verbose = FALSE
)

Arguments

data

A data.frame or tibble containing raw measurements.

col_map

Named list with entries (depending on mode): Adipose-only required: - I0: fasting insulin (pmol/L) - rate_glycerol, rate_palmitate: tracer rates (mumol/min) - fat_mass, weight, bmi: body composition - HDL_c: HDL cholesterol (mmol/L) Full mode additionally requires: - G0, G30, G120: glucose (mmol/L) - I30, I120: insulin (pmol/L) - TG: triglycerides (mmol/L) - FFA: free fatty acids (mmol/L)

normalize

Ignored (kept for backward compatibility).

na_action

One of c("keep","omit","error") for NA handling on required inputs. Default "keep".

na_warn_prop

Proportion \([0,1]\) to trigger high-missingness warnings on required inputs. Default 0.2.

check_extreme

Logical; if TRUE, scan inputs for extreme values. Default FALSE.

extreme_action

One of c("warn","cap","error","ignore") when extremes detected. Default "warn".

extreme_rules

Optional named list of c(min,max) bounds for inputs (keys as in col_map). If NULL, broad defaults are used.

verbose

Logical; if TRUE, prints progress messages and a completion summary.

Value

  • Adipose-only tibble columns: LIRI_inv, Lipo_inv, ATIRI_inv

  • Full-mode tibble columns: I_AUC, FFA_AUC, tracer_palmitate_SI, tracer_glycerol_SI, LIRI_inv, Lipo_inv, ATIRI_inv

Details

Modes:

  • Adipose-only indices when only adipose-related keys are mapped (no OGTT glucose/insulin time series)

  • Full indices otherwise

Expected units:

  • Glucose: mmol/L (internally converted to mg/dL when needed)

  • Insulin: pmol/L (internally converted to muU/mL via /6)

  • TG: mmol/L (to mg/dL via *88.57); HDL-c: mmol/L (to mg/dL via *38.67)

  • Tracer rates: mumol/min

  • Fat mass, weight: kg; BMI: kg/m^2

References

Groop LC, Bonadonna RC, Simonson DC, et al. (1989). “Different Effects of Insulin and Oral Hypoglycemic Agents on Glucose and Lipid Metabolism in Type II Diabetes.” Journal of Clinical Investigation, 84(2), 578–585. doi:10.1172/JCI114142 . ; Steele R (1959). “Influences of Glucose Loading and of Injected Insulin on Hepatic Glucose Output.” Annals of the New York Academy of Sciences, 82(2), 420–430. doi:10.1111/j.1749-6632.1959.tb44923.x . ; Boston RC, Stefanovski D, Moate PJ, Sumner AE, Watanabe RM, Bergman RN (2003). “MINMOD Millennium: A Computer Program to Calculate Glucose Effectiveness and Insulin Sensitivity from the Frequently Sampled Intravenous Glucose Tolerance Test.” Diabetes Technology & Therapeutics, 5(6), 1003–1015. doi:10.1089/152091503322641060 . ; Roden M, Price TB, Perseghin G, et al. (1996). “Mechanism of Free Fatty Acid-Induced Insulin Resistance in Humans.” Journal of Clinical Investigation, 97(12), 2859–2865. doi:10.1172/JCI118742 . ; Gastaldelli A, Ferrannini E, Miyazaki Y, Matsuda M, DeFronzo RA (2004). “Beta-Cell Dysfunction and Glucose Intolerance: Results from the San Antonio Metabolism Study.” Diabetologia, 47(1), 31–39. doi:10.1007/s00125-003-1263-9 . ; Karpe F, Dickmann JR, Frayn KN (2011). “Fatty Acids, Obesity, and Insulin Resistance: Time for a Reevaluation.” Diabetes, 60(10), 2441–2449. doi:10.2337/db11-0425 . ; Petersen KF, Dufour S, Savage DB, et al. (2007). “The Role of Skeletal Muscle Insulin Resistance in the Pathogenesis of the Metabolic Syndrome.” Proceedings of the National Academy of Sciences, 104(31), 12587–12594. doi:10.1073/pnas.0705408104 . ; Santomauro AT, Boden G, Silva ME, et al. (1999). “Overnight Lowering of Free Fatty Acids with Acipimox Improves Insulin Resistance and Glucose Tolerance in Obese Diabetic and Nondiabetic Subjects.” Diabetes, 48(9), 1836–1841. doi:10.2337/diabetes.48.9.1836 .

Examples

df <- data.frame(
  I0 = c(60, 75), rate_glycerol = c(2.1, 2.8), rate_palmitate = c(1.8, 2.3),
  fat_mass = c(18, 24), weight = c(72, 85), BMI = c(24, 29),
  HDL_c = c(1.3, 1.1)
)
col_map <- list(I0="I0", rate_glycerol="rate_glycerol",
                rate_palmitate="rate_palmitate", fat_mass="fat_mass",
                weight="weight", bmi="BMI", HDL_c="HDL_c")
tracer_dxa_is(df, col_map = col_map)
#> # A tibble: 2 × 3
#>   LIRI_inv Lipo_inv ATIRI_inv
#>      <dbl>    <dbl>     <dbl>
#> 1    -1.01      -21     -18  
#> 2    -1.13      -35     -28.7