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Calculate glycemic-, C-peptide-, and additional metabolic markers

Source: R/glycemic_markers.R
glycemic_markers.Rd

Given fasting labs and anthropometry, computes:

  • SPISE (Single-Point Insulin Sensitivity Estimator)

  • METS_IR (Metabolic Score for Insulin Resistance)

  • prediabetes flag (HbA1c >= 42 mmol/mol)

  • diabetes flag (HbA1c >= 48 mmol/mol)

  • HOMA_CP (C-peptide-based HOMA-IR variant; operational formula, see notes)

  • LAR (Leptin/Adiponectin Ratio)

  • ASI (Adiponectin Sensitivity Index; adiponectin/insulin)

  • TyG_index (Triglyceride-Glucose Index)

Usage

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

Arguments

data

A data.frame or tibble containing at least:

  • HDL_c (mmol/L), TG (mmol/L), BMI (kg/m^2) Optional if present: glucose (mmol/L), HbA1c (mmol/mol), C_peptide (pmol/L), G0 (mmol/L), I0 (pmol/L), leptin (ng/mL), adiponectin (ng/mL).

col_map

Optional named list mapping keys to column names in data. Required keys: HDL_c, TG, BMI. Optional keys (if present will be used): glucose, HbA1c, C_peptide, G0, I0, leptin, adiponectin. If NULL (default), the function uses columns named exactly as the keys.

na_action

One of c("ignore","warn","error","keep","omit"). HM-CS aliases: keep == ignore; omit drops rows with any NA/non-finite in used inputs before computing markers. Default "ignore".

na_warn_prop

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

check_extreme

Logical; if TRUE, scan selected input variables for values outside plausible ranges defined in extreme_rules. Default FALSE.

extreme_action

One of c("warn","cap","error","ignore","NA") controlling how to handle extremes when check_extreme = TRUE. If "cap", values are truncated to the allowed range; if "NA", out-of-range values become NA. Default "warn".

extreme_rules

Named list of numeric length-2 ranges for inputs to scan when check_extreme = TRUE. Defaults are broad medical plausibility ranges:

  • HDL_c: c(0.1, 5), TG: c(0.1, 20), BMI: c(10, 80),

  • glucose: c(2, 30), HbA1c: c(20, 200), C_peptide: c(0, 5000),

  • G0: c(2, 30), I0: c(0, 3000), leptin: c(0, 200), adiponectin: c(0, 300). Only variables present in data are checked.

verbose

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

Value

A tibble with columns:

  • SPISE, METS_IR, prediabetes, diabetes, HOMA_CP, LAR, ASI, TyG_index

Details

Assumed units (no automatic conversion of inputs except where noted):

  • HDL_c, TG: mmol/L (TyG internally converts TG to mg/dL via 88.57)

  • BMI: kg/m^2

  • glucose, G0: mmol/L (TyG internally converts glucose to mg/dL via 18)

  • HbA1c: mmol/mol

  • C_peptide, I0: pmol/L (HOMA_CP uses I-like conversion factor 6 as in insulin muU/mL; see notes)

  • leptin, adiponectin: ng/mL

Quality controls and options:

  • Input validation ensures required variables exist and are numeric-coercible.

  • Non-numeric inputs are coerced to numeric with a warning (NAs introduced reported).

  • Missing or non-finite inputs are handled via na_action.

  • Logs and divisions are computed safely (non-positive arguments yield NA).

  • Optional detection/handling of extreme input values via check_extreme and extreme_action.

  • Verbose mode prints step-by-step progress and a completion summary.

Optional marker detection: When col_map = NULL (default), an identity map for all 10 keys is built automatically. The seven optional keys (glucose, HbA1c, C_peptide, G0, I0, leptin, adiponectin) are computed only when the corresponding column exists in data. When verbose = TRUE, any optional markers whose columns are absent are listed in an informational message so the caller knows which derived metrics (e.g., prediabetes, HOMA_CP, LAR) will be NA.

Notes on HOMA_CP:

  • This function retains the package's existing operational formula: HOMA_CP = (G0 (mmol/L) * (C_peptide (pmol/L) / 6)) / 22.5 which mirrors HOMA-IR's structure using a 6 pmol/muU scaling used for insulin. Users should verify unit conventions for their datasets; alternative C-peptide HOMA implementations exist (e.g., HOMA2-CP).

References

Paulmichl K, Hatunic M, Höbaus C, et al. (2016). “Modification and Validation of the Triglyceride-to–HDL Cholesterol Ratio as a Surrogate of Insulin Sensitivity in White Juveniles and Adults without Diabetes Mellitus: The Single Point Insulin Sensitivity Estimator (SPISE).” Clinical Chemistry, 62(9), 1211–1219. doi:10.1373/clinchem.2016.257436 . ; Bello-Chavolla OY, Almeda-Valdes P, García-Sánchez A, et al. (2018). “METS-IR, a novel score to evaluate insulin sensitivity, is predictive of visceral adiposity and incident type 2 diabetes.” European Journal of Endocrinology, 178(5), 533–544. doi:10.1530/EJE-17-0883 . ; Frühbeck G, Catalan V, Rodríguez A, Ramón Sánchez-Recalde Á, Becerril S, Sánchez-González Á, Baena N, Valentí-Azcárate F, Burrell MA, Salvador J (2019). “Adiponectin-leptin Ratio is a Functional Biomarker of Adipose Tissue Inflammation.” Nutrients, 11(2), 454. doi:10.3390/nu11020454 . ; 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 . ; Simental-Mendía LE, Rodríguez-Morán M, Guerrero-Romero F (2008). “The Product of Fasting Glucose and Triglycerides as Surrogate for Identifying Insulin Resistance.” Metabolic Syndrome and Related Disorders, 6(4), 299–304. doi:10.1089/met.2008.0034 . ; Furler SM, Gan SK, Poynten AM, Chisholm DJ, Campbell LV, Kriketos AD (2006). “Relationship of Adiponectin with Insulin Sensitivity in Humans, Independent of Lipid Availability.” Obesity, 14(2), 228–234. doi:10.1038/oby.2006.29 .

Examples

df <- tibble::tibble(
  HDL_c       = c(1.0, 1.3),
  TG          = c(1.3, 2.0),
  BMI         = c(24, 30),
  glucose     = c(5.6, 7.1),
  HbA1c       = c(44, 38),
  C_peptide   = c(300, 500),
  G0          = c(5.5, 6.2),
  I0          = c(60, 120),
  leptin      = c(10, 20),
  adiponectin = c(8, 5)
)
# Quiet defaults
glycemic_markers(df)
#> # A tibble: 2 × 8
#>   SPISE METS_IR prediabetes diabetes HOMA_CP   LAR    ASI TyG_index
#>   <dbl>   <dbl>       <int>    <int>   <dbl> <dbl>  <dbl>     <dbl>
#> 1  8.10     NA            1        0    12.2  1.25 0.133       8.67
#> 2  5.79    318.           0        0    23.0  4    0.0417      9.33
# Warn on missingness and scan for extremes with capping
glycemic_markers(df,
  na_action = "warn", na_warn_prop = 0.1,
  check_extreme = TRUE, extreme_action = "cap",
  verbose = TRUE
)
#> glycemic_markers(): preparing inputs
#> glycemic_markers(): column map: HDL_c -> 'HDL_c', TG -> 'TG', BMI -> 'BMI'
#> glycemic_markers(): results: SPISE 2/2, METS_IR 1/2, prediabetes 2/2, diabetes 2/2, HOMA_CP 2/2, LAR 2/2, ASI 2/2, TyG_index 2/2
#> # A tibble: 2 × 8
#>   SPISE METS_IR prediabetes diabetes HOMA_CP   LAR    ASI TyG_index
#>   <dbl>   <dbl>       <int>    <int>   <dbl> <dbl>  <dbl>     <dbl>
#> 1  8.10     NA            1        0    12.2  1.25 0.133       8.67
#> 2  5.79    318.           0        0    23.0  4    0.0417      9.33