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

Implements a linear proxy for immunosenescence based on key inflammatory biomarkers, following the approach introduced by Sayed et al. for the inflammatory aging clock (iAge). 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,
  weights = c(CRP = 0.33, IL6 = 0.33, TNFa = 0.34),
  verbose = FALSE,
  na_action = c("keep", "omit", "error", "ignore", "warn"),
  na_warn_prop = 0.2,
  check_extreme = FALSE,
  extreme_action = c("warn", "cap", "error", "ignore", "NA"),
  extreme_rules = NULL
)

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 stepwise progress and a completion summary. Default FALSE.

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.

check_extreme

Logical; if TRUE, scan inputs for values outside plausible ranges. Default FALSE.

extreme_action

One of c("warn","cap","error","ignore","NA") controlling what to do when extremes are detected.

"warn": issue a warning, do not modify values (default if check_extreme = TRUE).
"cap": truncate values to the allowed range and warn.
"error": abort on detection.
"ignore": do nothing.
"NA": set out-of-range values to NA.

extreme_rules

Optional named list of numeric ranges c(min, max) for CRP, IL6, TNFa. If NULL, broad defaults are used.

Value

A tibble with one column:

iAge (numeric): the computed inflammatory age index.

Details

By default, missing inputs are omitted in the sum (consistent with prior behavior). Optional diagnostics can warn on high missingness and scan for extreme values, with the ability to cap, warn, or error on extremes. 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.

Default extreme ranges (if extreme_rules = NULL):

CRP: 0 to 300 mg/L
IL6: 0 to 1,000 pg/mL
TNFa: 0 to 1,000 pg/mL

These are deliberately broad. Adjust extreme_rules to fit your cohort.

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 .

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 (omit NAs in row-wise sum)
iAge(
  df,
  col_map = list(CRP = "CRP", IL6 = "IL6", TNFa = "TNFa")
)
#> # 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"
)
#> # A tibble: 3 × 1
#>    iAge
#>   <dbl>
#> 1  1.40
#> 2  3.12
#> 3 NA   

# Scan and cap extreme values
iAge(
  df,
  col_map = list(CRP = "CRP", IL6 = "IL6", TNFa = "TNFa"),
  check_extreme = TRUE, extreme_action = "cap", verbose = TRUE
)
#> iAge(): preparing inputs
#> iAge(): column map: CRP -> 'CRP', IL6 -> 'IL6', TNFa -> 'TNFa'
#> iAge(): results: iAge 2/3
#> # A tibble: 3 × 1
#>    iAge
#>   <dbl>
#> 1  1.40
#> 2  3.12
#> 3 NA