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 = NULL,
normalize = "none",
verbose = TRUE,
na_action = c("keep", "omit", "error"),
na_warn_prop = 0.2
)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(default), prints column mapping, the list of indices being computed, and a per-column results summary.- 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.
Value
A tibble with the OGTT-based index columns listed above. If an ID
column is detected in data (e.g. id, IID, participant_id), it is
prepended as the first output column.
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).
Modified_stumvoll and Stumvoll_Demographics use raw pmol/L and mmol/L as published in Stumvoll et al. (2000). BigttSi likewise uses raw units.
Matsuda_AUC is a non-standard AUC-based variant; the original Matsuda index (Matsuda_ISI) uses time-point means, not AUCs.
Cederholm_index uses log(I0 + I120) as implemented (sum, not mean); Gutt_index uses log((I0 + I120)/2) (mean). This mirrors published implementations; the difference is a constant log(2) offset.
Ifc_inv and HIRI_inv are derived composite proxies not attributed to a single formula publication; treat as research tools.
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 . (genetic epidemiology study reviewing IS indices)
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(): reading input 'df' — 1 rows × 10 variables
#> ogtt_is(): col_map (10 columns — 10 specified)
#> G0 -> 'G0'
#> I0 -> 'I0'
#> G30 -> 'G30'
#> I30 -> 'I30'
#> G120 -> 'G120'
#> I120 -> 'I120'
#> weight -> 'weight'
#> bmi -> 'bmi'
#> age -> 'age'
#> sex -> 'sex'
#> ogtt_is(): computing markers:
#> Isi_120 [G120, I120]
#> Cederholm_index [G0, G120, I0, I120, weight]
#> Gutt_index [G0, G120, I0, I120, weight]
#> Avignon_Si0 [G0, I0, weight]
#> Avignon_Si120 [G120, I120, weight]
#> Avignon_Sim [Si0, Si120]
#> Modified_stumvoll [I0, I120, G120]
#> Stumvoll_Demog. [bmi, I120, age]
#> Matsuda_AUC [G0, I0, G_AUC, I_AUC]
#> Matsuda_ISI [G0, I0, G_mean, I_mean]
#> BigttSi [I0, I30, I120, G0, G30, G120, sex, bmi]
#> Ifc_inv [I0, I120]
#> HIRI_inv [G0, G30, I0, I30]
#> Belfiore_isi_gly [I_AUC, G_AUC]
#> 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>