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Calculate OGTT-based insulin sensitivity indices

Source: R/ogtt_is.R
ogtt_is.Rd

Given glucose & insulin at 0, 30, 120 min (plus weight, BMI, age, sex), computes:

  • Isi_120

  • Cederholm_index

  • Gutt_index

  • Avignon_Si0

  • Avignon_Si120

  • Avignon_Sim

  • Modified_stumvoll

  • Stumvoll_Demographics

  • Matsuda_AUC

  • Matsuda_ISI

  • BigttSi

  • Ifc_inv

  • HIRI_inv

  • Belfiore_isi_gly

Usage

ogtt_is(
  data,
  col_map,
  normalize = "none",
  verbose = FALSE,
  na_action = c("keep", "omit", "error"),
  na_warn_prop = 0.2,
  check_extreme = FALSE,
  extreme_limit = 1000,
  extreme_action = c("warn", "cap", "error", "ignore", "NA")
)

Arguments

data

A data.frame or tibble containing at least the columns mapped by col_map.

col_map

Named list mapping:

  • G0, G30, G120 -> glucose at 0, 30, 120 min (mmol/L)

  • I0, I30, I120 -> insulin at 0, 30, 120 min (pmol/L)

  • weight -> body weight (kg)

  • bmi -> body-mass index (kg/m^2)

  • age -> age (years)

  • sex -> sex (1 = male, 2 = female)

normalize

One of c("none","z","inverse","range","robust") used by normalize_vec().

verbose

Logical; if TRUE, prints progress messages via hm_inform().

na_action

One of c("keep","omit","error") for missing/non-finite required inputs. Default "keep".

na_warn_prop

Proportion (0-1) for high-missingness diagnostics (debug). Default 0.2.

check_extreme

Logical; if TRUE, scan outputs for |value| > extreme_limit. Default FALSE.

extreme_limit

Positive numeric magnitude threshold for extremes. Default 1e3.

extreme_action

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

Value

A tibble with the OGTT-based index columns listed above.

Details

Units assumed:

  • OGTT glucose in mmol/L (internally converted to mg/dL via *18 for select indices)

  • OGTT insulin in pmol/L (internally converted to muU/mL via /6 for select indices)

  • weight in kg; BMI in kg/m^2; age in years; sex coded 1 = male, 2 = female

Notes

  • Conversions mirror existing implementation to preserve outputs. Some formulas intentionally use unconverted inputs (as in prior code).

  • Logs are safe: log(x) becomes NA when x <= 0 or non-finite.

References

Matsuda M, DeFronzo RA (1999). “Insulin Sensitivity Indices Obtained from Oral Glucose Tolerance Testing: Comparison with the Minimal Model Assessment.” Diabetes Care, 22(9), 1462–1470. doi:10.2337/diacare.22.9.1462 . ; Gutt M, Davis CL, Spitzer SB, et al. (2000). “Validation of the Insulin Sensitivity Index (ISI\(_{0,120}\)) Derived from Oral Glucose Tolerance Testing.” Diabetes Research and Clinical Practice, 47(3), 177–184. doi:10.1016/S0168-8227(99)00116-3 . ; Stumvoll M, Mitrakou A, Pimenta W, et al. (2000). “Use of the Oral Glucose Tolerance Test to Assess Insulin Release and Sensitivity.” Diabetes Care, 23(3), 295–301. doi:10.2337/diacare.23.3.295 . ; Hansen T, Drivsholm T, Urhammer SA, Palacios RR, et al. (2007). “The BIGTT Test.” Diabetes Care, 30(2), 257–262. doi:10.2337/dc06-1240 . ; Avignon A, Charles M, Rabasa-Lhoret R, et al. (1999). “Assessment of Insulin Sensitivity from Oral Glucose Tolerance Test in Normal Subjects and in Insulin-Resistant Patients.” International Journal of Obesity, 23(5), 512–517. doi:10.1038/sj.ijo.0800864 . ; 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 . ; Matthews DR, Hosker JP, Rudenski AS, Naylor BA, Treacher DF, Turner RC (1985). “Homeostasis Model Assessment: Insulin Resistance and Beta-Cell Function from Fasting Plasma Glucose and Insulin Concentrations in Man.” Diabetologia, 28(7), 412–419. doi:10.1007/BF00280883 . ; Suleman S, Madsen AL, Ängquist LH, Schubert M, Linneberg A, Loos RJF, Hansen T, Grarup N (2024). “Genetic Underpinnings of Fasting and Oral Glucose-stimulated Based Insulin Sensitivity Indices.” The Journal of Clinical Endocrinology & Metabolism, 109(11), 2754–2763. doi:10.1210/clinem/dgae275 .

See also

fasting_is(), normalize_vec()

Examples

df <- tibble::tibble(
  G0 = 5.5, I0 = 60,
  G30 = 7.8, I30 = 90,
  G120 = 6.2, I120 = 50,
  weight = 70, bmi = 24, age = 30, sex = 1
)
ogtt_is(
  df,
  col_map = list(
    G0 = "G0", I0 = "I0",
    G30 = "G30", I30 = "I30",
    G120 = "G120", I120 = "I120",
    weight = "weight", bmi = "bmi",
    age = "age", sex = "sex"
  ),
  normalize = "none",
  verbose = TRUE
)
#> ogtt_is(): preparing inputs
#> ogtt_is(): column map: G0 -> 'G0', I0 -> 'I0', G30 -> 'G30', I30 -> 'I30', G120 -> 'G120', I120 -> 'I120', weight -> 'weight', bmi -> 'bmi', age -> 'age', sex -> 'sex'
#> ogtt_is(): results: Isi_120 1/1, Cederholm_index 1/1, Gutt_index 1/1, Avignon_Si0 1/1, Avignon_Si120 1/1, Avignon_Sim 1/1, Modified_stumvoll 1/1, Stumvoll_Demographics 1/1, Matsuda_AUC 1/1, Matsuda_ISI 1/1, BigttSi 1/1, Ifc_inv 1/1, HIRI_inv 1/1, Belfiore_isi_gly 1/1
#> # A tibble: 1 × 14
#>   Isi_120 Cederholm_index Gutt_index Avignon_Si0 Avignon_Si120 Avignon_Sim
#>     <dbl>           <dbl>      <dbl>       <dbl>         <dbl>       <dbl>
#> 1    10.8            1.10       2.67        9.62          10.2        9.93
#> # ℹ 8 more variables: Modified_stumvoll <dbl>, Stumvoll_Demographics <dbl>,
#> #   Matsuda_AUC <dbl>, Matsuda_ISI <dbl>, BigttSi <dbl>, Ifc_inv <dbl>,
#> #   HIRI_inv <dbl>, Belfiore_isi_gly <dbl>