---
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.

- **SKU:** HC-END-005
- **Sample size:** 500 patients × 151 columns
- **License (sample):** CC-BY-NC-4.0 — commercial license available for the full product
- **Contact:** pradeep@xpertsystems.ai · https://xpertsystems.ai

---

## 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

```python
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())
```

```python
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

```bibtex
@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.*