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Appendicular Lean Mass to BMI Index

Source: R/alm_bmi_index.R
alm_bmi_index.Rd

Calculates the ratio of appendicular lean mass (ALM) to body mass index (BMI), and flags low muscle mass based on FNIH Sarcopenia Project cut-points.

Usage

alm_bmi_index(
  data,
  col_map = list(alm = "ALM_kg", bmi = "BMI", sex = "Sex"),
  verbose = FALSE,
  na_action = c("keep", "omit", "error", "ignore", "warn"),
  check_extreme = FALSE,
  extreme_action = c("warn", "cap", "error", "ignore", "NA"),
  extreme_rules = NULL
)

Arguments

data

A data.frame or tibble with ALM, BMI, and sex columns.

col_map

Named list with:

  • alm: appendicular lean mass column name (kg)

  • bmi: body mass index column name (kg/m^2)

  • sex: sex column name ("Male"/"Female" or m/f; case-insensitive)

verbose

Logical; if TRUE, emits progress messages.

na_action

One of c("keep","omit","error","ignore","warn").

check_extreme

Logical; if TRUE, scan inputs for plausible ranges.

extreme_action

One of c("warn","cap","error","ignore","NA").

extreme_rules

Optional list with bounds for alm and bmi (defaults: alm = c(5, 40), bmi = c(10, 60)).

Value

A tibble with:

  • alm_bmi_ratio (numeric)

  • low_muscle_mass (logical; TRUE if below sex-specific cut-point; NA if sex unknown or ratio NA)

Details

ALM/BMI reflects muscle mass relative to body size. FNIH cut-points:

  • Men: ALM/BMI < 0.789

  • Women: ALM/BMI < 0.512

ALM should be in kilograms and BMI in kg/m^2.

References

McLean RR, Shardell MD, Alley DE, et al. (2014). “Criteria for clinically relevant weakness and low lean mass: FNIH Sarcopenia Project.” Journal of Gerontology A: Biological Sciences and Medical Sciences, 69(5), 576–583. doi:10.1093/gerona/glu012 . ; Studenski SA, Peters KW, Alley DE, et al. (2014). “The FNIH Sarcopenia Project: Rationale, Study Description, Conference Recommendations, and Final Estimates.” Journal of Gerontology A: Biological Sciences and Medical Sciences, 69(5), 564–570. doi:10.1093/gerona/glu010 .

Examples

df <- data.frame(ALM_kg = c(7.2, 5.8, 6.5), BMI = c(24, 28, 22),
                 Sex = c("male", "female", "male"))
alm_bmi_index(df)
#> # A tibble: 3 × 2
#>   alm_bmi_ratio low_muscle_mass
#>           <dbl> <lgl>          
#> 1         0.3   TRUE           
#> 2         0.207 TRUE           
#> 3         0.295 TRUE