labNorm (version 1.0.1)

ln_normalize: Normalize lab values to age and sex

Description

Normalize standard laboratory measurements (e.g. hemoglobin, cholesterol levels) according to age and sex, based on the algorithms described in "Personalized lab test models to quantify disease potentials in healthy individuals" tools:::Rd_expr_doi("10.1038/s41591-021-01468-6").

The "Clalit" reference distributions are based on 2.1B lab measurements taken from 2.8M individuals between 2002-2019, filtered to exclude severe chronic diseases and medication effects. The resulting normalized value is a quantile between 0 and 1, representing the value's position in the reference distribution.

The "UKBB" reference distributions are based on the UK-Biobank, a large-scale population-based cohort study of 500K individuals, which underwent the same filtering process as the "Clalit" reference distributions.

The list of supported labs can be found below or by running LAB_DETAILS$short_name.

Usage

ln_normalize(
  values,
  age,
  sex,
  lab,
  units = NULL,
  reference = "Clalit",
  na.rm = FALSE
)
ln_normalize_multi(labs_df, reference = "Clalit", na.rm = FALSE)

Value

a vector of normalized values. If ln_download_data() was not run, a lower resolution reference distribution will be used, which can have an error of up to 5 quantiles (0.05). Otherwise, the full reference distribution will be used. You can check if the high resolution data was downloaded using ln_data_downloaded().

You can force the function to use the lower resolution distribution by setting options(labNorm.use_low_res = TRUE).

If the quantile information is not available (e.g. "Estradiol" for male patients, various labs which are not available in the UKBB data), then the function will return NA.

Arguments

values

a vector of lab values

age

a vector of ages between 20-89 for "Clalit" reference and 35-80 for "UKBB". Can be a single value if all values are the same age.

sex

a vector of either "male" or "female". Can be a single value if all values are the same sex.

lab

the lab name. See LAB_DETAILS$short_name for a list of available labs.

units

the units of the lab values. See ln_lab_units(lab) for a list of available units for each lab. If NULL then the default units (ln_lab_default_units(lab)) for the lab will be used. If different values have different units then this should be a vector of the same length as values.

reference

the reference distribution to use. Can be either "Clalit" or "UKBB" or "Clalit-demo". Please download the Clalit and UKBB reference distributions using ln_download_data().

na.rm

if TRUE, then NA in age, sex or values will be ignored and 'NA' would be returned. Otherwise, an error will be thrown.

labs_df

a data frame with the columns "value", "age", "sex", "units", and "lab". The "lab" column should be a vector with the lab name per row. See ln_normalize for details on the other columns.

reference distribution

It is highly recommended to use ln_download_data to download the "Clalit" and "UKBB" reference distributions. If you choose not to download the data, the package will use the demo reference distributions included in the package ("Clalit-demo"), which doesn't include all the labs, and has a resolution of 20 quantile bins and therefore may have an error of up to 5 percentiles (0.05), particularly at the edges of the distribution.

labs

The following labs are supported in the "Clalit" reference (some labs are missing from the UKBB reference):

• WBC

• RBC

• Hemoglobin

• Hematocrit

• Platelets

• MCV

• MCH

• MCHC

• RDW

• MPV

• Large unstained cells, Abs

• Albumin

• Total Cholesterol

• Triglycerides

• BMI

• Iron

• Transferrin

• Ferritin

• PDW

• MPXI

• Total Globulin

• PCT

• HDW

• Fibrinogen

• CH

• Chloride

• Large unstained cells, %

• Macrocytic

• Microcytic

• Hyperchromic

• Hypochromic

• Lymphocytes, Abs

• Lymphocytes, %

• Neutrophils, Abs

• Neutrophils, %

• Monocytes, Abs

• Monocytes, %

• Eosinophils, Abs

• Eosinophils, %

• Basophils, Abs

• Basophils, %

• Microcytic:Hypochromic

• Glucose

• Urea

• Creatinine

• Uric Acid

• Calcium

• Phosphorus

• Total Protein

• HDL Cholesterol

• LDL Cholesterol

• Alk. Phosphatase

• AST

• ALT

• GGT

• LDH

• CPK

• Total Bilirubin

• Direct Bilirubin

• Hemoglobin A1c

• Sodium

• Potassium

• Vitamin D (25-OH)

• Microalbumin:Creatinine

• Urine Creatinine

• Urine Microalbumin

• Non-HDL

• TSH

• T3, Free

• T4, Free

• Blood Pressure, Systolic

• Blood Pressure, Diastolic

• Urine Specific Gravity

• Urine pH

• PT, INR

• PT, sec

• PT, %

• Vitamin B12

• PSA

• ESR

• aPTT, sec

• CRP

• Amylase

• Folic Acid

• Total:HDL

• Hematocrit:Hemoglobin

• Magnesium

• aPTT, ratio

• Indirect Bilirubin

• RDW-SD

• RDW-CV

• LH

• Estradiol

Examples

# \donttest{
# Normalize Hemoglobin values to age and sex
hemoglobin_data$quantile <- ln_normalize(
    hemoglobin_data$value,
    hemoglobin_data$age,
    hemoglobin_data$sex,
    "Hemoglobin"
)

# plot the quantiles vs values for age 50-60
library(ggplot2)
library(dplyr)
hemoglobin_data %>%
    filter(age >= 50 & age <= 60) %>%
    ggplot(aes(x = value, y = quantile, color = sex)) +
    geom_point() +
    theme_classic()

# Different units
hemoglobin_diff_units <- hemoglobin_data
hemoglobin_diff_units$value <- hemoglobin_diff_units$value * 0.1
hemoglobin_diff_units$quantile <- ln_normalize(
    hemoglobin_data$value,
    hemoglobin_data$age,
    hemoglobin_data$sex,
    "Hemoglobin",
    "mg/mL"
)

# Multiple units
creatinine_diff_units <- creatinine_data
creatinine_diff_units$value <- c(
    creatinine_diff_units$value[1:500] * 0.011312,
    creatinine_diff_units$value[501:1000] * 11.312
)
creatinine_diff_units$quantile <- ln_normalize(
    creatinine_diff_units$value,
    creatinine_diff_units$age,
    creatinine_diff_units$sex,
    "Creatinine",
    c(rep("umol/L", 500), rep("mmol/L", 500))
)

# Use UKBB as reference
hemoglobin_data_ukbb <- hemoglobin_data %>% filter(age >= 35 & age <= 80)
hemoglobin_data_ukbb$quantile_ukbb <- ln_normalize(
    hemoglobin_data_ukbb$value,
    hemoglobin_data_ukbb$age,
    hemoglobin_data_ukbb$sex,
    "Hemoglobin",
    reference = "UKBB"
)

# plot UKBB vs Clalit
hemoglobin_data_ukbb %>%
    filter(age >= 50 & age <= 60) %>%
    ggplot(aes(x = quantile, y = quantile_ukbb, color = sex)) +
    geom_point() +
    geom_abline() +
    theme_classic()
# }

# examples on the demo data
# \dontshow{
hemoglobin_data$quantile <- ln_normalize(
    hemoglobin_data$value,
    hemoglobin_data$age,
    hemoglobin_data$sex,
    "Hemoglobin",
    reference = "Clalit-demo"
)
# }

library(dplyr)
multi_labs_df <- bind_rows(
    hemoglobin_data %>% mutate(lab = "Hemoglobin"),
    creatinine_data %>% mutate(lab = "Creatinine")
)

# \donttest{
multi_labs_df$quantile <- ln_normalize_multi(multi_labs_df)
# }

# on the demo data
# \dontshow{
multi_labs_df$quantile <- ln_normalize_multi(multi_labs_df, reference = "Clalit-demo")
# }

head(multi_labs_df)