Skip to contents

Compute Bone Health & Body-Composition Markers (HM-CS v3)

Source: R/bone_markers.R
bone_markers.Rd

Given DXA, anthropometry, and optional bone-turnover markers, computes:

  • OSTA: (weight - age) x 0.2

  • ALMI: Appendicular Lean Mass Index = ALM / height^2

  • FMI: Fat Mass Index = FM / height^2

  • BMD_Tscore: (BMD - ref_mean) / ref_sd and (if in col_map + data) passes through: TBS, HSA, PINP, CTX, BSAP, Osteocalcin.

Usage

bone_markers(
  data,
  col_map = NULL,
  na_action = c("keep", "omit", "error"),
  verbose = TRUE
)

Arguments

data

A data.frame or tibble with subject-level DXA/anthropometry data.

col_map

Named list mapping keys to column names. Required keys:

  • age, weight, height, ALM, FM, BMD, BMD_ref_mean, BMD_ref_sd Optional (passed-through if present and found in data): TBS, HSA, PINP, CTX, BSAP, Osteocalcin.

na_action

One of "keep", "omit", or "error" controlling how missing/non-finite input values are treated.

verbose

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

Value

A tibble with columns: OSTA, ALMI, FMI, BMD_Tscore, and optionally TBS, HSA, PINP, CTX, BSAP, Osteocalcin (in that order).

Details

Notes:

  • Units: height in meters; ALM, FM, weight in kilograms; BMD in g/cm^2; ALMI/FMI in kg/m^2.

  • Non-finite values are treated as NA; division by zero is prevented by input checks.

  • BMD_ref_mean and BMD_ref_sd must be supplied by the user from an appropriate reference population (for example, study-specific values or external norms such as NHANES).

References

Koh LK, Ben Sedrine W, Torralba TP, Kung A, others (2001). “A Simple Tool to Identify Asian Women at Increased Risk of Osteoporosis.” Osteoporosis International, 12(8), 699–705. doi:10.1007/s001980170070 . World Health Organization (1994). Assessment of Fracture Risk and Its Application to Screening for Postmenopausal Osteoporosis, volume 843 of Technical Report Series. World Health Organization. No DOI for this WHO report; see URL, https://iris.who.int/handle/10665/39142.

Examples

library(tibble)
df <- tibble(
  age = c(60, 72), weight = c(65, 50), height = c(1.65, 1.58),
  ALM = c(18.2, 14.7), FM = c(22.0, 20.5),
  BMD = c(0.95, 0.80), BMD_ref_mean = c(1.00, 1.00), BMD_ref_sd = c(0.12, 0.12)
)
col_map <- list(
  age = "age", weight = "weight", height = "height",
  ALM = "ALM", FM = "FM", BMD = "BMD",
  BMD_ref_mean = "BMD_ref_mean", BMD_ref_sd = "BMD_ref_sd"
)
bone_markers(df, col_map)
#> bone_markers(): reading input 'df' — 2 rows × 8 variables
#> bone_markers(): col_map (8 columns — 8 specified)
#>   age               ->  'age'
#>   weight            ->  'weight'
#>   height            ->  'height'
#>   ALM               ->  'ALM'
#>   FM                ->  'FM'
#>   BMD               ->  'BMD'
#>   BMD_ref_mean      ->  'BMD_ref_mean'
#>   BMD_ref_sd        ->  'BMD_ref_sd'
#> bone_markers(): computing markers:
#>   OSTA       [(weight - age) * 0.2]
#>   ALMI       [ALM / height^2]
#>   FMI        [FM / height^2]
#>   BMD_Tscore [(BMD - ref_mean) / ref_sd]
#> bone_markers(): results: OSTA 2/2, ALMI 2/2, FMI 2/2, BMD_Tscore 2/2, TBS 0/2, HSA 0/2, PINP 0/2, CTX 0/2, BSAP 0/2, Osteocalcin 0/2
#> # A tibble: 2 × 10
#>    OSTA  ALMI   FMI BMD_Tscore   TBS   HSA  PINP   CTX  BSAP Osteocalcin
#>   <dbl> <dbl> <dbl>      <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>       <dbl>
#> 1   1    6.69  8.08     -0.417    NA    NA    NA    NA    NA          NA
#> 2  -4.4  5.89  8.21     -1.67     NA    NA    NA    NA    NA          NA