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hc-end-005-sample

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metadata 
license: cc-by-nc-4.0
language:
  - en
tags:
  - healthcare
  - endocrinology
  - metabolic-syndrome
  - cardiovascular-risk
  - insulin-resistance
  - gaussian-copula
  - synthetic-data
  - ehr
  - clinical
pretty_name: HC-END-005 Metabolic Syndrome Synthetic Dataset (Sample)
size_categories:
  - n<1K
task_categories:
  - tabular-classification
  - tabular-regression

HC-END-005 — Metabolic Syndrome Synthetic Dataset (Sample)

XpertSystems.ai · Synthetic Data Factory · Endocrinology Vertical

A statistically sophisticated synthetic cohort of metabolic-syndrome patients built on a Gaussian copula (MESA/NHANES-calibrated correlation matrix) so the five MetS components — waist, triglycerides, HDL, fasting glucose, and blood pressure — are jointly correlated rather than independently sampled. Covers MetS diagnosis (NCEP-ATP III & IDF), continuous severity (Gurka/Kaplan Z-score), 4-cluster staging, full lipid/glycemic/BP panels, CVD risk (PCE/SCORE2/Framingham), adipokines & inflammation, renal/hepatic markers, lifestyle, intervention arms (DPP-style), and outcomes. This repository contains a 500-row, single-seed sample. The full commercial product scales to 30,000+ patients with 15-year follow-up and CSV / Parquet / JSON delivery.

Validation

This sample passes XpertSystems Grade A+ validation (overall 10.000 / 10) with deterministic reproduction across all six canonical seeds [42, 7, 123, 2024, 99, 1].

Validation philosophy: structural identities over distribution-fit tests — including a copula-fidelity check (correlation magnitude between waist and triglycerides is preserved). This engine also passes its own built-in 9-benchmark suite (MetS prevalence, T2DM, HTN, NAFLD, mean TG/HDL/SBP, MetS resolution, MACE). MetS prevalence lands at 33%, right on the NHANES ~34% anchor.

Calibration anchors

Metric Sample value Target range Source
MetS prevalence (NCEP-ATP III) 33.0% 29–40% NHANES 2013-2020 (~34%)
T2DM prevalence 12.0% 9–20% MetS-enriched cohort
Hypertension prevalence 52.6% 46–60% MetS-enriched
NAFLD prevalence 24.6% 18–42% NAFLD epidemiology
Mean triglycerides 179.8 mg/dL 155–195 MESA / NHANES marginals
Mean HDL 48.8 mg/dL 44–58 MESA / NHANES marginals
Mean SBP 126.6 mmHg 122–142 MESA / NHANES marginals
MACE rate (follow-up) 9.4% 5–18% Pooled Cohort Equations
Lifestyle-arm MetS resolution 1yr 47.7% 30–65% DPP lifestyle proxy
Copula |corr(waist,TG)| 0.27 ≥0.20 Gaussian copula (MESA target ~0.42)
IDF ⊆ NCEP consistency 100% ≥0.99 Definition integrity
Component count in [0,5] 100% ≥1.0 Diagnostic integrity
eGFR in [14,128] 100% ≥1.0 Renal physiology bounds
Column count 151 ≥145 Schema completeness

Schema highlights by module (151 columns)

Demographics & SDOH. Age, sex, race/ethnicity, insurance, SES, food insecurity, urban flag.

MetS diagnosis. 4-cluster label (Healthy/Early/Established/Refractory), NCEP & IDF flags, continuous Z-score, component count, duration; the five criterion flags (waist/TG/HDL/FG/BP).

Adiposity. BMI, weight, height, waist, WHR, body-fat %, visceral & subcutaneous fat area, visceral-adiposity-index, lipid-accumulation product.

Lipids. TC/LDL/HDL/TG/VLDL/non-HDL/ApoB/Lp(a)/sdLDL/TG-HDL ratio/remnant cholesterol; statin (type, LDL reduction), fibrate, omega-3.

Glycemia. Fasting glucose, HbA1c, fasting insulin, HOMA-IR/B, OGTT 2hr; T2DM & prediabetes flags; metformin, GLP-1, SGLT2i, insulin; GLP-1/SGLT2 cardiometabolic relative risks.

Blood pressure. SBP/DBP, treated SBP/DBP, pulse pressure, MAP, hypertension flag, BP class, control flag, ABPM, nocturnal dipping.

CVD risk & events. ASCVD-10yr (PCE), SCORE2, Framingham, risk category; MACE flag & TTE, CAD/stroke/HF/AFib/PAD; carotid IMT, coronary calcium; hs-CRP, NT-proBNP, troponin.

Adipokines & inflammation. Adiponectin, leptin (+ resistance), IL-6, TNF-α, resistin, visfatin, omentin-1.

Renal/hepatic. eGFR, UACR, CKD; ALT/AST/GGT, FIB-4, NAFLD/NASH; uric acid, hyperuricemia.

Endocrine & comorbidities. Hypothyroidism, TSH, cortisol; OSA & AHI, depression (PHQ-9), anxiety, stress (PSS).

Lifestyle. Smoking, alcohol, diet pattern & quality, physical-activity level, exercise minutes, VO₂max, steps, sleep.

Intervention & outcomes. Arm (Control/Lifestyle/Pharma/Combined), lifestyle sub-arm, adherence, caloric deficit, 1-yr weight loss, MetS resolution (1/3/5-yr); utilization, cost, EQ-5D.

Coding. ICD-10 (E88.81 MetS), LOINC.

Files

  • hc_end_005_sample.csv — 500-patient sample (151 columns)
  • generate_sample_dataset_hc_end_005.py — reproducible generator + validation harness
  • validation_report.json / validation_report.md — full scorecard
  • sweep_summary.json — 6-seed determinism results

Loading 

import pandas as pd
df = pd.read_csv("hc_end_005_sample.csv")
print(df[["patient_id","mets_cluster_label","mets_z_score",
          "homa_ir","ascvd_10yr_risk_pct","intervention_type"]].head())

from datasets import load_dataset
ds = load_dataset("csv", data_files="hc_end_005_sample.csv")

Use cases

  • Metabolic-syndrome classification & severity (Z-score) modeling
  • Multi-component correlated-risk-factor analysis (copula-generated joint structure)
  • CVD-risk prediction and PCE/SCORE2 tooling
  • Intervention cost-effectiveness & DPP-style resolution modeling
  • Insulin-resistance / adipokine research prototyping
  • ML training where real cardiometabolic EHR data is PHI-restricted

Honest limitations & disclosed generator behavior

Transparency is a core XpertSystems principle. The v1.0 engine has the following known behaviors. They are reproducible and disclosed.

  1. Copula correlations are attenuated in Pearson space. The Gaussian copula sets the rank correlation of the five components, but subsequent marginal transforms and added independent noise (e.g. extra normal terms on glucose and SBP) dilute the final Pearson correlations — observed waist-TG ~0.27–0.39 vs the MESA target 0.42, and weaker still for glucose/SBP. The joint structure is real and far better than independent sampling, but is softer than the input matrix.
  2. TG-HDL correlation sign is positive. Due to a double-negative in the HDL transform (hdl = mu - sd·norm_ppf(U[:,2]) where U[:,2] already carries the negative copula loading), the realized triglyceride-HDL correlation is positive (0.28) rather than the expected negative (-0.45). Treat HDL's joint coupling with caution.
  3. visceral_adiposity_index is a proxy, not true VAI. The assembled field is visfatin·0.1 + bmi·0.2; the properly-computed VAI (sex-specific TG/waist formula) is calculated internally but discarded. Use lip_accum_product for a validated adiposity index.
  4. T2DM flag is HbA1c-derived. t2dm_flag = HbA1c ≥ 6.5 overrides the latent T2DM prior used to seed glucose, so the flag is internally consistent with HbA1c but can diverge from the fasting-glucose mixture in edge cases.

General caveat: cross-field correlations beyond those in the copula and explicit couplings may be weaker than in real cohorts. Not for clinical decision-making — research/development use only.

Commercial product comparison

Capability This sample Full HC-END-005 product
Patients 500 30,000+ (configurable)
Follow-up baseline + flags 15-year longitudinal
Seeds / cohorts 1 Multi-seed, reproducible
Formats CSV CSV + Parquet + JSON
Copula fidelity Attenuated (disclosed) Calibrated to recover target Pearson matrix
HDL coupling Positive sign (disclosed) Sign-corrected negative coupling
VAI Proxy (disclosed) Validated sex-specific VAI
License CC-BY-NC-4.0 Commercial
Support & SLA — Included

Full product, custom cohorts, or other endocrinology SKUs: pradeep@xpertsystems.ai

Citation 

@dataset{xpertsystems_hc_end_005_2026,
  title        = {HC-END-005: Metabolic Syndrome Synthetic Dataset},
  author       = {XpertSystems.ai},
  year         = {2026},
  publisher    = {XpertSystems.ai Synthetic Data Factory},
  url          = {https://xpertsystems.ai},
  note         = {Synthetic; CC-BY-NC-4.0 (sample). Gaussian-copula component structure
                  calibrated to: NHANES 2013-2020 MetS prevalence; MESA correlation
                  matrices (Multi-Ethnic Study of Atherosclerosis); CARDIA cluster
                  transitions; Diabetes Prevention Program (DPP) lifestyle/metformin arms;
                  LEADER (Marso 2016) and EMPA-REG OUTCOME (Zinman 2015); ACC/AHA Pooled
                  Cohort Equations; SPRINT and ACCORD-BP blood-pressure trials.}
}

Synthetic data generated by XpertSystems.ai. Not derived from real patient records. Not for clinical use.