Compute a simplified Inflammatory Age Index (iAge) with QA and verbose summaries

Implements a linear proxy for immunosenescence based on key inflammatory biomarkers, conceptually inspired by the inflammatory aging clock (iAge) literature. This simplified iAge is computed as a weighted sum of C-reactive protein (CRP), interleukin-6 (IL6), and tumor necrosis factor-alpha (TNFa).

Usage

iAge(
  data,
  col_map = NULL,
  weights = c(CRP = 0.33, IL6 = 0.33, TNFa = 0.34),
  verbose = TRUE,
  na_action = c("keep", "omit", "error", "ignore", "warn"),
  na_warn_prop = 0.2
)

Arguments

data

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

col_map

Named list mapping:

CRP -> column name for C-reactive protein (mg/L)
IL6 -> column name for interleukin-6 (pg/mL)
TNFa -> column name for tumor necrosis factor-alpha (pg/mL)

weights

Named numeric vector of weights for each marker (must sum to 1). Defaults to c(CRP = 0.33, IL6 = 0.33, TNFa = 0.34).

verbose

Logical; if TRUE, prints column mapping and computing messages.

na_action

One of c("keep","omit","error","ignore","warn") controlling how missing inputs affect iAge:

"keep": return NA for rows where any required marker is NA (default; consistent with other functions).
"omit": ignore NAs in the weighted sum (treat missing markers as 0).
"error": abort if any required marker contains NA.
"ignore": alias of "omit".
"warn": alias of "omit" but emits missingness warnings (per na_warn_prop).

na_warn_prop

Proportion in \([0,1]\) above which a high-missingness warning is emitted when na_action = "warn". Default 0.2.

Value

A tibble with one column:

iAge (numeric): the computed inflammatory age index.

Details

By default, rows with any missing required marker return NA in the index (consistent with the default behavior of other package functions). Optional diagnostics can warn on high missingness and implausible negative values. Verbose mode prints step-by-step progress and a final summary.

Assumed units (no automatic unit conversion):

CRP: mg/L
IL6: pg/mL
TNFa: pg/mL

Note:

The original iAge model in Sayed et al. (Nature Aging, 2021) is a multi-marker machine learning model. This function provides a simple, linear proxy using three canonical inflammatory biomarkers. It is not identical to the original published iAge but is inspired by its rationale.
This proxy is intended for exploratory feature engineering and cohort-level analyses. It must not be treated as a validated replacement for the published iAge model or used as a standalone clinical decision metric.

References

Sayed N, others (2021). “An inflammatory aging clock (iAge) predicts multimorbidity, immunosenescence, frailty and cardiovascular aging.” Nature Aging, 1, 598–610. doi:10.1038/s43587-021-00082-y . (conceptual background; not method-identical to this implementation) Harris TB, Ferrucci L, Tracy RP, Corti MC, Wacholder S, Ettinger WH, Heimovitz H, Cohen HJ, Wallace R (1999). “Associations of Elevated Interleukin-6 and C-Reactive Protein Levels with Mortality in the Elderly.” The American Journal of Medicine, 106(5), 506–512. doi:10.1016/S0002-9343(99)00066-2 . Bruunsgaard H, Ladelund S, Pedersen AN, Schroll M, Jorgensen T, Pedersen BK (2003). “Predicting death from tumour necrosis factor-alpha and interleukin-6 in 80-year-old people.” Clinical and Experimental Immunology, 132(1), 24–31. doi:10.1046/j.1365-2249.2003.02137.x .

Examples

library(tibble)
df <- tibble(
  CRP  = c(1.2, 3.5, NA),  # mg/L
  IL6  = c(2.0, 4.1, 1.5), # pg/mL
  TNFa = c(1.0, 1.8, 0.9)  # pg/mL
)
# Default behavior (rows with any missing marker return NA)
iAge(
  df,
  col_map = list(CRP = "CRP", IL6 = "IL6", TNFa = "TNFa")
)
#> iAge(): reading input 'df' — 3 rows × 3 variables
#> iAge(): col_map (3 columns — 3 specified)
#>   CRP               ->  'CRP'
#>   IL6               ->  'IL6'
#>   TNFa              ->  'TNFa'
#> iAge(): computing markers:
#>   iAge  [weighted sum: CRP*0.33 + IL6*0.33 + TNFa*0.34]
#> iAge(): results: iAge 2/3
#> # A tibble: 3 × 1
#>    iAge
#>   <dbl>
#> 1  1.40
#> 2  3.12
#> 3 NA   

# Keep NA if any marker missing in a row
iAge(
  df,
  col_map = list(CRP = "CRP", IL6 = "IL6", TNFa = "TNFa"),
  na_action = "keep"
)
#> iAge(): reading input 'df' — 3 rows × 3 variables
#> iAge(): col_map (3 columns — 3 specified)
#>   CRP               ->  'CRP'
#>   IL6               ->  'IL6'
#>   TNFa              ->  'TNFa'
#> iAge(): computing markers:
#>   iAge  [weighted sum: CRP*0.33 + IL6*0.33 + TNFa*0.34]
#> iAge(): results: iAge 2/3
#> # A tibble: 3 × 1
#>    iAge
#>   <dbl>
#> 1  1.40
#> 2  3.12
#> 3 NA   

# Verbose output
iAge(
  df,
  col_map = list(CRP = "CRP", IL6 = "IL6", TNFa = "TNFa"),
  verbose = TRUE
)
#> iAge(): reading input 'df' — 3 rows × 3 variables
#> iAge(): col_map (3 columns — 3 specified)
#>   CRP               ->  'CRP'
#>   IL6               ->  'IL6'
#>   TNFa              ->  'TNFa'
#> iAge(): computing markers:
#>   iAge  [weighted sum: CRP*0.33 + IL6*0.33 + TNFa*0.34]
#> iAge(): results: iAge 2/3
#> # A tibble: 3 × 1
#>    iAge
#>   <dbl>
#> 1  1.40
#> 2  3.12
#> 3 NA