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HealthMarkers

HealthMarkers

Computing derived clinical indices manually is slow, error-prone, and rarely documented. Researchers working with UK Biobank, NHANES, or any large clinical or phenotypic dataset routinely need HOMA-IR, eGFR, FIB-4, TyG index, ASCVD risk, NLR, Matsuda index, and WHtR, indices that each require a different formula, different input columns, and careful handling of missing values. HealthMarkers takes care of all of that. You pass in your data frame and get the markers back, each implemented from its primary source publication. No formula lookup, no unit checking, no silent NA propagation.

HealthMarkers is an R package for computing, standardising, and summarising derived health indices from routine laboratory and phenotypic data. More than 50 specialist functions cover over 290 validated biomarkers across insulin sensitivity, lipid and cardiovascular risk, renal and hepatic function, inflammation and aging, body composition, psychiatric scales, and alternate biofluids. The unified dispatcher all_health_markers() runs every group at once and returns a single wide tibble; individual domain functions are available when you only need one panel.

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

Install the released version from CRAN:

install.packages("HealthMarkers")

For the development version, install from GitHub:

remotes::install_github("sufyansuleman/HealthMarkers")

Some marker groups need optional packages. Install only those you use:

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",            # frailty and DXA-based insulin sensitivity
  "mice",          # multiple imputation
  "missForest"     # random-forest imputation
))

If an optional package is missing, its corresponding marker group is skipped safely. Use verbose = TRUE to see which groups were computed and which were omitted.

Quick start

A simulated dataset is included so you can explore the package 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: inspect auto-detected columns

hm_col_report(sim_small)

Step 2: compute key marker groups in one call

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

new_cols <- setdiff(names(out), names(sim_small))
head(out[, new_cols])

This example is shown with eval=FALSE so README rendering stays fast. Run it interactively to generate 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

Domain Functions Key outputs
Insulin sensitivity fasting_is(), ogtt_is(), adipo_is(), tracer_dxa_is(), all_insulin_indices() HOMA-IR, QUICKI, Matsuda, Stumvoll, Gutt, SPISE, LIRI, 40+ indices
Glycaemic glycemic_markers() TyG index, METS-IR, LAR, ASI, HOMA-CP, diabetes risk flags
Lipid & atherogenic lipid_markers(), atherogenic_indices(), cvd_marker_aip(), cvd_marker_ldl_particle_number() TC/HDL, AIP, CRI-I/II, Castelli, LDL particle number
Liver liver_markers(), liver_fat_markers() FLI, NFS, FIB-4, APRI, BARD, ALBI, MELD-XI, HSI, LAP
Metabolic syndrome metss(), metabolic_risk_features(), allostatic_load() MetS severity score, component flags, allostatic load index
Cardiovascular risk cvd_risk(), cvd_risk_ascvd(), cvd_risk_qrisk3(), cvd_risk_scorescvd(), cvd_risk_stroke() ASCVD (PCE), QRISK3, SCORE2/SCORE2-OP, 10-yr stroke risk
Renal / CKD kidney_failure_risk(), renal_markers(), ckd_stage(), urine_markers() KFRE 2-yr/5-yr, eGFR (CKD-EPI), CKD stage, UACR, FE-Urea
Pulmonary pulmo_markers(), spirometry_markers(), bode_index() FEV1/FVC z-scores, GLI 2012 % predicted, BODE index
Inflammatory & aging inflammatory_markers(), iAge(), oxidative_markers(), kyn_trp_ratio() NLR, PLR, SII, LMR, iAge clock, 8-OHdG, KTR
Hormonal hormone_markers() T/E2 ratio, TSH/fT4, cortisol/DHEA, LH/FSH, HOMA-B, FAI
Body composition obesity_indices(), adiposity_sds(), adiposity_sds_strat(), alm_bmi_index(), calc_sds() BMI, WHR, ABSI, BRI, BAI, sex/age-stratified SDS, ALM/BMI
Bone & fracture bone_markers(), frax_score() P1NP, osteocalcin, CTX, NTX, FRAX 10-yr fracture probability
Frailty & comorbidity frailty_index(), charlson_index(), sarc_f_score() Rockwood deficit index, Charlson CCI, SARC-F
Vitamins & nutrients vitamin_markers(), vitamin_d_status(), nutrient_markers() Vitamin D status category, B12/folate ratio, ferritin saturation
Alternate biofluids saliva_markers(), sweat_markers(), urine_markers() Cortisol awakening response, sweat chloride, urinary ratios
Neurological nfl_marker(), kyn_trp_ratio(), corrected_calcium() Age-adjusted NfL, kynurenine/tryptophan ratio, corrected calcium
Psychiatric psych_markers(), phq9_score(), gad7_score(), k6_score(), k10_score(), and more PHQ-9, GAD-7, ISI, GHQ-12, K10, K6, WHO-5, ASRS, BIS-11, SPQ

Which function do I need?

I have… I want… Use
Fasting glucose + insulin HOMA-IR, QUICKI, FIRI, 15+ fasting indices fasting_is()
OGTT glucose + insulin (multiple time points) Matsuda, Stumvoll, Gutt, Avignon, 25+ OGTT indices ogtt_is()
Lipid panel (TC, HDL, LDL, TG) AIP, CRI, Castelli, TC/HDL ratio, LDL particle number lipid_markers() / cvd_marker_aip()
Age, sex, cholesterol, BP, smoking 10-year ASCVD / QRISK3 / SCORE2 cardiovascular risk cvd_risk_ascvd() / cvd_risk_qrisk3() / cvd_risk_scorescvd()
Creatinine, age, sex (± UACR) eGFR, CKD stage, KFRE kidney failure probability renal_markers() / ckd_stage() / kidney_failure_risk()
FEV1, FVC, age, height Spirometry z-scores (GLI 2012) spirometry_markers()
CBC (neutrophils, lymphocytes, platelets) NLR, PLR, SII, LMR inflammatory ratios inflammatory_markers()
BMI, waist, hip ABSI, BRI, BAI, WHR, obesity indices obesity_indices()
Height, weight ± DXA measures Body composition SDS z-scores adiposity_sds()
Questionnaire item columns PHQ-9, GAD-7, K10, ISI, GHQ-12, MDQ scores psych_markers() / phq9_score() / gad7_score()
Many lab variables at once All of the above in one call all_health_markers(which = "all")

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

Key Meaning Example column names
G0 Fasting glucose (mmol/L) pglu0, fasting_glucose, LBXGLU, paastoglukoosi
I0 Fasting insulin (mU/L) insu0, insulin0, ins_fast
G30, G120 30-/120-min OGTT glucose pglu30, pglu120
I30, I120 30-/120-min OGTT insulin insu30, insu120
TG Triglycerides (mmol/L) trig, TAG, triglyserider, LOINC_2571_8
HDL_c HDL cholesterol hdlc, HDL, hdl_kolesteroli, LOINC_2085_9
LDL_c LDL cholesterol ldl, LDL, ldl_kolesteroli, LOINC_13457_7
TC Total cholesterol chol, kokonaiskolesteroli, LOINC_2093_3
ALT Alanine aminotransferase alat, SGPT, LBXSATSI, NPU03429, LOINC_1742_6
creatinine Serum creatinine crea, kreatinin, NPU01994, LOINC_2160_0
UACR Urine albumin/creatinine ratio ualbcrea, ACR
SBP / DBP Systolic/diastolic BP sysbp, systolisk_blodtrykk, LOINC_8480_6
BMI Body mass index bmi, painoindeksi, LOINC_39156_5
waist Waist circumference (cm) waist_cm, WC, midjeomkrets
vitaminD 25-OH vitamin D (nmol/L) vitd25, d_vitamin, NPU10501, LOINC_62292_8
HbA1c Glycated haemoglobin (%) hba1c, hemoglobiini_a1c, NPU27300, LOINC_4548_4

Multi-biobank automatic column recognition

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

Internal key UK Biobank NHANES HUNT/Tromsø FinnGen Estonian BB LifeLines (NL) LOINC
fasting_glucose glucose_0_0 LBXGLU fastende_blodsukker paastoglukoosi p_glukoos nuchtere_glucose LOINC_2345_7
total_cholesterol cholesterol_0_0 LBXSCH total_kolesterol kokonaiskolesteroli kogukolesterool totaal_cholesterol LOINC_2093_3
creatinine creatinine_0_0 LBXSCR kreatinin kreatiniini kreatiniin creatinine LOINC_2160_0
HbA1c glycated_haemoglobin_hba1c_0_0 LBXGH HbA1c hemoglobiini_a1c HbA1c geglycosyleerd_hemoglobine LOINC_4548_4
SBP systolic_blood_pressure_0_0 BPXSY1 systolisk_blodtrykk SBP sbp systolische_bloeddruk LOINC_8480_6
vitaminD vitamin_d_0_0 LBXVD2 d_vitamin D_vitamiini D_vitamiin vitamine_D LOINC_62292_8
ALT alanine_aminotransferase_0_0 LBXSATSI ALAT alaniiniaminotransferaasi ALAT alanineaminotransferase LOINC_1742_6

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

Symptom Likely cause Fix
A marker column is all NA Required input column not found or all-missing Run hm_col_report(data) to see which keys are unmatched; add them to col_map
An entire group is silently skipped Optional package not installed, or no matched columns for that group Set verbose = TRUE to see which groups ran and why others were skipped
hm_col_report() shows 0 matches Column names not in the synonym dictionary Use col_map to manually map your names to internal keys
ASCVD / QRISK3 / SCORE2 result is all NA Optional package (PooledCohort, QRISK3, RiskScorescvd) not installed Install the relevant package; see Installation section
Spirometry z-scores are NA rspiro not installed, or ethnicity column missing/wrong codes Install rspiro; see ?spirometry_markers for ethnicity code table
Output is very wide (300+ columns) which = "all" on a rich dataset Use which = c("glycemic", "lipid", ...) to select only the groups you need, or return_input = FALSE to get markers only
Error: object 'x' not found in examples Using my_data placeholder from README Replace with your actual data object

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:

Method Description
"z" z-score (mean 0, sd 1)
"inverse" Rank-based inverse-normal transform (Rankit)
"range" Min-max to [0, 1] (or custom feature_range)
"robust" Median/MAD scaling

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:

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

Topic Article
Fasting insulin sensitivity (HOMA-IR, QUICKI, 15+ indices) fasting_is
OGTT insulin sensitivity (Matsuda, Stumvoll, Gutt, 25+ indices) adipo_is
Glycaemic markers (TyG, METS-IR, LAR, diabetes flags) glycemic_markers
Lipid and atherogenic indices lipid_markers
Cardiovascular risk scores (ASCVD, QRISK3, SCORE2) cvd_risk
Inflammatory ratios and iAge clock inflammatory_markers
Hormone markers hormone_markers
Missing data and imputation impute_missing
Normalising marker outputs (hm_normalize, normalize_vec) health markers articles
All-in-one dispatcher (all_health_markers) health_markers

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.