HealthMarkers

• HealthMarkers
  • Installation
  • Quick start
  • Package overview
  • Which function do I need?
  • All markers dispatcher all_health_markers():
  • Column mapping
  • Multi-biobank automatic column recognition
  • Selected function families
  • Common problems and solutions
  • Missing data
  • Normalisation
  • Output utilities
  • Citation policy
  • Articles and further reading
  • Development status and validated publications
  • Contributing
  • Citation
  • License
  • AI use disclaimer

HealthMarkers

You have a large data frame of clinical and lab measures. You want HOMA-IR, AIP, eGFR, NLR, and FIB-4, without writing a single formula. That is what HealthMarkers does.

HealthMarkers is an R toolkit for computing, standardising, and summarising clinical and research biomarkers directly from routine laboratory and phenotypic data. Over 50 specialist functions cover more than 290 biomarkers: from insulin sensitivity indices and cardiovascular risk scores to inflammatory aging clocks, frailty indices, psychiatric rating scales, and alternate-biofluids, all accessible through a single unified dispatcher, all_health_markers().

Full documentation, function reference, and articles are available at:
https://sufyansuleman.github.io/HealthMarkers/

Key features:

• Broad coverage. One all_health_markers() call returns glycaemic, lipid, liver, renal, pulmonary, inflammatory, hormonal, bone, psychiatric, nutritional, and frailty markers as a single wide tibble.
• Works with your column names as-is. The built-in synonym dictionary covers 15+ cohorts and biobanks (UK Biobank, NHANES, HUNT, Tromsø, FinnGen, Estonian Biobank, LifeLines, Generation Scotland, Danish NPU codes, OMOP/LOINC codes, and more). In most cases you do not need to rename anything.
• Safe defaults. NA handling, input validation, and column-name inference are built in. A failed marker group is skipped with a warning; your pipeline never crashes.
• Fully traceable. Every function cites its primary source paper. Full bibliography in inst/REFERENCES.bib.

Typical workflow (four steps):

library(HealthMarkers)

# 1. See which columns in your data are recognised automatically
hm_col_report(my_data)

# 2. (If needed) fill in any unmatched keys
col_map <- list(eGFR = "GFR_CKD_EPI", G0 = "fasting_glucose_mmol")

# 3. Compute the markers you need
results <- all_health_markers(my_data,
                               which   = c("glycemic", "lipid", "renal"),
                               col_map = col_map)

# 4. Inspect, summarise, or export the new columns
new_cols <- setdiff(names(results), names(my_data))
health_summary(results[, new_cols])

Installation

# From CRAN
install.packages("HealthMarkers")

# Development version from GitHub
remotes::install_github("sufyansuleman/HealthMarkers")

Several marker groups require optional packages. Install only what you need:

install.packages(c(
  "CVrisk",        # Framingham / basic CVD risk
  "PooledCohort",  # ASCVD Pooled Cohort Equations + stroke risk
  "QRISK3",        # QRISK3
  "RiskScorescvd", # SCORE2 / SCORE2-OP
  "rspiro",        # Spirometry GLI 2012 z-scores
  "di",            # DXA-based insulin sensitivity
  "mice",          # Multiple imputation
  "missForest"     # Random-forest imputation
))

When an optional package is absent, its marker group is skipped safely; verbose = TRUE reports which groups ran and which were skipped.

Quick start

The package ships a simulated dataset (n = 200, 100+ columns) so you can run every example without your own data.

library(HealthMarkers)

sim_path  <- system.file("extdata", "simulated_hm_data.rds", package = "HealthMarkers")
sim       <- readRDS(sim_path)
sim_small <- sim[1:50, ]   # small subset for speed

Step 1: find out what is already recognised in your data:

hm_col_report(sim_small)

Step 2: compute several marker groups at once:

out <- all_health_markers(
  data    = sim_small,
  which   = c("glycemic", "lipid", "renal", "inflammatory"),
  verbose = FALSE
)

# How many new columns were added?
new_cols <- setdiff(names(out), names(sim_small))
length(new_cols)

# Preview the new markers
head(out[, new_cols])

Note: this example is marked eval=FALSE in the README so rendering stays fast. Run it interactively if you want to compute the full demo output.

Step 3: call a single-purpose function when you need one biomerker or one gorup only:

# Atherogenic index of plasma (log TG/HDL): two columns in, one index out
aip <- cvd_marker_aip(sim_small, col_map = list(TG = "TG", HDL_c = "HDL_c"),
                      na_action = "keep")
head(aip[, setdiff(names(aip), names(sim_small))])

Package overview

Which function do I need?

All markers dispatcher all_health_markers():

Use this when you have a large data set with many variables and want to compute many marker groups in one call, returned as a single wide tibble:

results <- all_health_markers(
  data    = my_data,
  which   = c("glycemic", "lipid", "liver", "renal", "mets", "inflammatory"),
  col_map = list(G0 = "fasting_glucose", I0 = "insulin0"),
  verbose = TRUE
)

All available which group keys:

insulin_fasting     insulin_ogtt        insulin_adipose     insulin_tracer_dxa
glycemic            lipid               atherogenic         cvd_aip
cvd_risk            cvd_ldl_particles   cvd_ascvd           cvd_qrisk3
cvd_scorescvd       cvd_stroke          liver               liver_fat
mets                metabolic_risk      allostatic_load     pulmo
spirometry          bode                saliva              sweat
urine               renal               kidney_kfre         ckd_stage
nutrient            vitamin             vitamin_d_status    hormone
inflammatory        iAge                bone                frax
oxidative           allostatic_load     frailty_index       charlson
sarc_f              psych               nfl                 inflammatory_age
calcium_corrected   kyn_trp             adiposity_sds       adiposity_sds_strat
obesity_metrics     alm_bmi

Pass which = "all" to run everything. Groups requiring unavailable optional packages are silently skipped.

Returning only the computed markers (not the full input)

By default all_health_markers() appends the new marker columns to your original data and returns everything together. With large cohort data (e.g. 40,000 rows × 300 columns) this doubles the width of the object. Use return_input = FALSE to get back only the newly computed columns, keeping your pipeline lean:

# Default: original columns + new markers (wide output)
out <- all_health_markers(my_data, which = c("glycemic", "lipid"))

# Markers only: much smaller result
markers_only <- all_health_markers(
  my_data,
  which        = c("glycemic", "lipid", "renal"),
  return_input = FALSE,
  id_col       = "participant_id"   # carry the ID so you can join back later
)

# Join back when you need the full picture
final <- dplyr::left_join(my_data, markers_only, by = "participant_id")

id_col is optional; omit it if you don’t need to join back, or if row order is sufficient.

Column mapping

Step 1: hm_col_report() - see what is detected

Call this before any computation to get a full report of which columns are auto-matched and which need manual mapping:

hm_col_report(my_data)

Output:

── HealthMarkers column report ────────────────────────────────────────────
 Data: 40314 rows × 299 columns   |   Keys in dictionary: 258

 key                  data_column        how matched
 -------------------- ------------------ ------------------
 fasting_glucose      pglu0              exact  ✔
 TG                   trig               exact  ✔
 ALT                  alat               exact  ✔
 eGFR                 ─                  NOT FOUND ✘

 ✔ 187 keys matched   ✘ 71 keys not found

── col_map template for missing keys ──────────────────────────────────────
 col_map <- list(
   eGFR  = "from_your_data",
 )

Step 2: fill in unmatched keys

hm_col_report() returns a named list with every auto-detected key already mapped. Add only the missing keys to that list, then pass the completed col_map to your marker function.

cm        <- hm_col_report(my_data, verbose = FALSE)  # returns named list
cm$eGFR   <- "GFR_ckdepi"   # add your column name for any unmatched key
cm$G0     <- "fasting_glucose_mmol"  # if not already matched

Step 3: pass col_map to any function

all_health_markers(my_data, which = c("renal", "glycemic"), col_map = cm)
fasting_is(my_data, col_map = list(G0 = "fasting_glucose_mmol", I0 = "insulin_uU_mL"))

hm_col_report() options:

hm_col_report(my_data, show_unmatched = TRUE)  # also list every unmatched key
hm_col_report(my_data, fuzzy = TRUE)           # add fuzzy matching as last resort

Common internal keys

Multi-biobank automatic column recognition

The synonym dictionary recognises column names from 15+ major cohorts and biobanks. The same analyte across systems:

Also supported: Danish NPU codes (NPU01994, NPU01567, etc.), Generation Scotland (SBP_mean, DBP_mean, genetic_sex), SCAPIS/TwinGene Swedish terms, and OMOP CDM / All of Us LOINC concept codes for all major analytes.

Selected function families

Use an individual function when you need one specific domain, want fine-grained control over col_map, or are working with specialist data formats (OGTT time-series, DXA output, spirometry). For reference pages and formulas, use ?function_name.

Insulin sensitivity

# Fasting indices (HOMA-IR, QUICKI, Bennett, FIRI, ...)
fasting_is(data, col_map = list(G0 = "glucose", I0 = "insulin"))

# OGTT indices (Matsuda, Stumvoll, Gutt, Avignon, ...)
ogtt_is(data, col_map = list(G0="G0", G30="G30", G60="G60", G120="G120",
                              I0="I0", I30="I30", I60="I60", I120="I120"))

# Adipose-tissue indices (LIRI, SPISE, VAI, LAP, ...)
adipo_is(data, col_map = list(BMI="BMI", WC="WC", TG="TG", HDL_c="HDL_c"))

# DXA / tracer-based indices
tracer_dxa_is(data, col_map = list(fat_mass="FM_kg", lean_mass="LM_kg"))

# All insulin indices at once (fasting + OGTT + adipose + DXA)
all_insulin_indices(data, col_map = list(...), normalize = "none",
                    mode = "both",   # "IS" sensitivity only, "IR" resistance only
                    na_action = "keep")

Cardiovascular risk

# ASCVD Pooled Cohort Equations (requires PooledCohort)
cvd_risk_ascvd(data, year = 10)

# QRISK3 (UK population, requires QRISK3)
cvd_risk_qrisk3(data)

# SCORE2 / SCORE2-OP (European, requires RiskScorescvd)
cvd_risk_scorescvd(data)

# Atherogenic index of plasma
cvd_marker_aip(data, col_map = list(TG = "TG", HDL_c = "HDL_c"))

# LDL particle number from ApoB
cvd_marker_ldl_particle_number(data, col_map = list(ApoB = "ApoB"))

# Run all CVD algorithms at once
cvd_risk(data, model = "ALL")

Renal function

# Kidney Failure Risk Equation (KFRE) 2-year and 5-year
kidney_failure_risk(data, col_map = list(age="age", sex="sex",
                                          eGFR="eGFR", UACR="UACR"))

# eGFR, BUN/creatinine, FE-Urea, renal ratios
renal_markers(data, col_map = list(creatinine="Creat", age="age", sex="sex"))

# KDIGO CKD staging (G1–G5 × A1–A3)
ckd_stage(data, col_map = list(eGFR="eGFR", UACR="UACR"))

# Urine panel: protein/creatinine ratio, microalbumin, osmolality
urine_markers(data, col_map = list(urine_creat="UCr", urine_protein="UPr"))

Pulmonary function

# Spirometry z-scores and % predicted (GLI 2012, requires rspiro)
spirometry_markers(data, col_map = list(fev1="FEV1", fvc="FVC",
                                         age="age", height="ht_cm", sex="sex"))

# Simple pulmonary ratios (no extra packages)
pulmo_markers(data)

# BODE index for COPD prognosis
bode_index(data, col_map = list(fev1_pct="FEV1pct", sixmwd="Walk6m",
                                  mmrc="mMRC", bmi="BMI"))

Body composition and anthropometric SDS

# Common obesity and adiposity indices (BMI, WHR, ABSI, BRI, BAI, ...)
obesity_indices(data)

# SDS z-score from any reference mean and SD
calc_sds(x = data$BMI, mean_ref = 22.5, sd_ref = 3.8)

# Sex-stratified SDS for multiple adiposity variables
adiposity_sds_strat(data, col_map = list(sex = "sex"),
                    var_cols = c("BMI", "WC", "WHR"),
                    ref_male   = list(BMI = c(mean = 25, sd = 4)),
                    ref_female = list(BMI = c(mean = 24, sd = 3.8)))

# Appendicular lean mass / BMI index (sarcopenia screening)
alm_bmi_index(data, col_map = list(alm = "ALM_kg", bmi = "BMI", sex = "Sex"))

Psychiatric scores

# Score any combination of standardised scales from item columns.
# Supported: PHQ-9, GAD-7, K6, K10, GHQ-12, WHO-5, ISI, MDQ,
#            ASRS, BIS-11, SPQ, cognitive composite
psych_markers(
  data,
  col_map = list(
    phq9 = list(items = list(phq9_01 = "Q1", phq9_02 = "Q2")),
    gad7 = list(items = list(gad7_01 = "G1", gad7_02 = "G2"))
  ),
  which = c("phq9", "gad7", "k10")
)

# If columns are already named phq9_01 ... phq9_09 etc., no col_map needed:
phq9_score(data)
gad7_score(data)
k10_score(data)

Inflammatory and aging markers

# Blood count-derived ratios (NLR, PLR, SII, LMR, ...)
inflammatory_markers(data, col_map = list(neut="NEUT", lymph="LYMPH",
                                           mono="MONO", plt="PLT"))

# iAge inflammatory aging clock
iAge(data, col_map = list(IL6="IL6", CXCL9="CXCL9"))

Alternate biofluids

saliva_markers(data, col_map = list(cortisol_wake="C_wake", cortisol_30="C_30min"))
sweat_markers(data,  col_map = list(sweat_chloride="Cl_mmol"))
urine_markers(data,  col_map = list(urine_creat="UCr", urine_na="UNa"))

Common problems and solutions

Missing data

All marker functions accept a na_action argument:

# "keep"  : compute what is possible, return NA where inputs are missing (default)
# "omit"  : drop rows with any missing required input before computing
# "error" : abort if any required input is missing
renal_markers(data, na_action = "keep")

na_action aliases: "ignore" is a backward-compatible alias for "keep" (same behaviour; retained so older code continues to work). "warn" is also an alias for "keep" that additionally emits a missingness warning. If you are reading a function’s help page and see "ignore" listed first in the choices, it behaves identically to "keep".

For pre-computation imputation, use the built-in helpers:

# Multiple imputation via mice
imputed <- impute_missing(data, method = "mice", m = 1, maxit = 5)

# Random-forest imputation (good for mixed data types)
imputed <- impute_missing(data, method = "missForest")

# Simple mean/median fill
imputed <- impute_missing(data, method = "mean")

# Then pass imputed data to any marker function
all_health_markers(imputed, which = c("glycemic", "lipid"))

Normalisation

The normalize argument is implemented in the insulin sensitivity functions (fasting_is(), ogtt_is(), adipo_is(), all_insulin_indices()). For all other domain functions, use hm_normalize() to normalise outputs after computation.

hm_normalize(): post-computation normalisation

out <- all_health_markers(data, which = c("glycemic", "lipid", "renal"))

# Identify new marker columns
new_cols <- setdiff(names(out), names(data))

# Normalise only the new marker columns (z-score)
out_z <- hm_normalize(out, cols = new_cols, method = "z")

# Rank-based inverse-normal transform on all numeric columns,
# keeping age and BMI on their original scale
out_int <- hm_normalize(out, method = "inverse", skip_cols = c("age", "BMI"))

# Min-max scaling to [0, 1]
out_range <- hm_normalize(out, cols = new_cols, method = "range")

# Robust median/MAD scaling
out_rob <- hm_normalize(out, cols = new_cols, method = "robust")

Available methods:

normalize_vec(): single-vector normalisation

normalize_vec(x, method = "inverse")
normalize_vec(x, method = "inverse", invnorm_denominator = "blom")
normalize_vec(x, method = "z")
normalize_vec(x, method = "robust")
normalize_vec(x, method = "range", feature_range = c(-1, 1))

See ?hm_normalize and the package website articles page https://sufyansuleman.github.io/HealthMarkers/articles/ for full details.

Output utilities

# Quick numeric summary (n, n_na, mean, sd, median, p25, p75) for any data frame
health_summary(out)

# Summary of marker outputs (variable, mean, sd, IQR)
marker_summary(out)

# Plot frailty-index deficit accumulation against age
plot_frailty_age(data, cols = c("deficit1", "deficit2", "deficit3"), age = "age")

Citation policy

Every function cites at least one reference in its help page (?function_name). We have made every effort to trace citations back to the original primary source: the paper in which the index, formula, or scoring system was first described.

In practice this is not always straightforward:

• The commonly cited paper may be a validation study or a derived index paper rather than the mathematical original. In these cases we cite the paper most widely used in the clinical or epidemiological literature for that formula.
• For statistical and mathematical methods (e.g., rank-based inverse-normal transformation, median absolute deviation, standardised difference scores), the original work may be decades old and the specific parameterisation in common use was formalised in a more recent methodological paper. We cite whichever source most precisely describes the implementation used.
• For multi-component composite indices (e.g., allostatic load, frailty index, ASCVD Pooled Cohort Equations), multiple papers collectively define the algorithm; we aim to cite the most complete or most cited specification.

The full bibliography is in inst/REFERENCES.bib and is rendered in each function’s help page via Rdpack.

If you identify a citation that could be improved (a closer original source, a more authoritative validation paper, or a correction), please open an issue at https://github.com/sufyansuleman/HealthMarkers/issues.

Articles and further reading

Full articles are available on the package website:

• Website: https://sufyansuleman.github.io/HealthMarkers/
• Function reference (all arguments, details, and citations): https://sufyansuleman.github.io/HealthMarkers/reference/
• All articles / tutorials: https://sufyansuleman.github.io/HealthMarkers/articles/

Below are some key articles. For the full list, visit: All articles

For details on any individual function, use ?function_name (e.g., ?fasting_is, ?cvd_risk_ascvd, ?frailty_index): every help page includes the formula, argument details, and primary citations.

Development status and validated publications

HealthMarkers is under active development. All indices are implemented from their original, revised or verified published manuscripts. If you notice an error in any index, please open an issue so it can be corrected.

The insulin sensitivity and resistance indices have been independently verified and are used in the following peer-reviewed publications:

• Suleman S, Madsen AL, Ängquist LH, Schubert M, Linneberg A, Loos RJF, Hansen T, Grarup N. Genetic Underpinnings of Fasting and Oral Glucose-stimulated Based Insulin Sensitivity Indices. J Clin Endocrinol Metab. 2024;109(11):2754–2763. PMID 38635292

• Suleman S, Ängquist L, Linneberg A, Hansen T, Grarup N. Exploring the genetic intersection between obesity-associated genetic variants and insulin sensitivity indices. Sci Rep. 2025;15:15761. PMID 40328835

Contributing

Issues and pull requests are welcome at https://github.com/sufyansuleman/HealthMarkers/issues.

When contributing a new marker function please:

1. Add a unit test in tests/testthat/ with at least one numeric check.
2. Add a @references entry in the roxygen block and cite the primary paper in inst/REFERENCES.bib.
3. Register the function in the all_health_markers() dispatcher if it fits an existing domain.
4. Add or update the relevant article in vignettes/articles/ (articles are published to the pkgdown site at https://sufyansuleman.github.io/HealthMarkers/articles/ but are not bundled with the CRAN package).

Citation

citation("HealthMarkers")

License

MIT Sufyan Suleman (ORCID 0000-0001-6612-6915)

AI use disclaimer

OpenAI (ChatGPT) and Anthropic Claude were used during the development of this package to assist with code refinement, debugging, and editing of documentation content. All outputs were reviewed, verified, and approved by the author.

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