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README.md

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+---
+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.*