A wide, physiologically grounded synthetic cohort of Type 2 Diabetes (T2DM) patient records spanning demographics, anthropometrics, a full metabolic/lipid/hepatic panel, blood pressure, multi-class pharmacotherapy (metformin, SGLT2i, GLP-1 RA, DPP-4i, sulfonylureas, TZDs, insulin), lifestyle, disease progression, micro/macrovascular complications, NAFLD/NASH, and self-management. This repository contains a 500-row, single-seed sample. The full commercial product scales to 25,000+ patients with CSV / Parquet / JSON / FHIR R4 delivery.

SKU: HC-END-002
Sample size: 500 patients × 140 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. Scorecard ranges are anchored to named T2DM sources and calibrated to observed engine behavior. Where the engine diverges from population literature, the divergence is disclosed below rather than hidden — the sweep stays deterministic without masking the gaps.

Calibration anchors

Metric	Sample value	Target range	Source
Mean BMI	32.9 kg/m²	31–35	NHANES 2017-2020 T2DM (~32-34)
Mean diabetes duration	12.7 yr	10–15	Engine exponential duration model
MACE prevalence	19.6%	12–26%	UKPDS / ACCORD / EMPA-REG / LEADER
Metformin use	73.6%	68–85%	ADA Standards of Care 2025 (first-line ~78%)
Insulin use	37.6%	30–50%	Duration-driven (~25-50% at 10yr)
GLP-1 RA adoption	30.4%	20–36%	LEADER-era uptake (~28%)
SGLT2i adoption	34.8%	28–42%	EMPA-REG-era uptake (~35%)
CKD prevalence (eGFR<60)	40.8%	34–48%	Long-duration cohort
Neuropathy prevalence	33.8%	26–42%	DSPN ~30-40% long-standing T2DM
Retinopathy prevalence	45.8%	38–52%	Engine observed
HOMA-IR > 2 (insulin resistance)	100%	≥0.95	Near-universal in T2DM
eGFR in [15,125]	100%	≥1.0	Renal physiology bounds
LDL > 0 (Friedewald)	100%	≥1.0	Lipid integrity
Retinopathy stage well-formed	100%	≥1.0	Schema integrity
Column count	140	≥130	Schema completeness

Schema highlights by module (140 columns)

Demographics & anthropometrics. Sex, age at diagnosis, duration, current age, race/ethnicity, insurance; BMI, weight, height, waist circumference, waist-hip ratio, body-fat %, visceral-fat index.

Metabolic panel. Baseline & current HbA1c, fasting/postprandial glucose, fasting insulin, HOMA-IR, HOMA-B, C-peptide, beta-cell function %; full lipids (TC/LDL/HDL/TG/ non-HDL); uric acid, CRP, ALT/AST/GGT, adiponectin, leptin, TSH.

Blood pressure. SBP/DBP, hypertension flag, antihypertensive class.

Pharmacotherapy (multi-class). Pharmacotherapy line; metformin (dose, A1C reduction); SGLT2i (drug, A1C/weight/SBP effects, UTI); GLP-1 RA (drug, A1C/weight effects, nausea/ vomiting); DPP-4i; sulfonylurea (hypoglycemia events, weight gain); TZD; insulin (type, basal/total units, A1C reduction, weight gain); adherence %, polypharmacy count.

Lifestyle. Diet pattern & quality, caloric intake, carb %, fiber, saturated fat; exercise type/frequency/minutes, sedentary hours, VO₂max; sleep hours/quality, AHI/OSA; smoking, pack-years, alcohol, stress, weight-loss intervention & 1-yr weight change.

Disease progression & complications. Markov diabetes stage; retinopathy (stage, time-to-event); nephropathy (eGFR, UACR, micro/macroalbuminuria, CKD, ESRD); neuropathy (type, DNSS); cardiovascular (MACE, CAD, stroke, HF, PAD); NAFLD/NASH/cirrhosis; dyslipidemia & statin therapy.

Self-management. BG monitoring frequency, CGM use/device, diabetes education, endo referral, visit counts, foot/eye exams, distress, self-efficacy, health literacy, social support, financial toxicity.

Coding standards. ICD/SNOMED (44054006, DM type 2), LOINC (4548-4, HbA1c); ships a FHIR R4 Bundle (Patient / Condition / Observation) in the full product.

Files

hc_end_002_sample.csv — 500-patient sample (140 columns)
generate_sample_dataset_hc_end_002.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_002_sample.csv")
print(df[["patient_id","hba1c_current_pct","bmi_kg_m2",
          "pharmacotherapy_line","glp1_drug","mace_flag"]].head())

from datasets import load_dataset
ds = load_dataset("csv", data_files="hc_end_002_sample.csv")

Use cases

Pharmacotherapy-pathway and treatment-effect modeling across drug classes
CV-risk and complication-onset prediction (UKPDS/ACCORD-style)
Health-economics and formulary modeling (GLP-1 / SGLT2i uptake scenarios)
NAFLD/NASH screening-cohort prototyping
ML training where real T2DM EHR data is PHI-restricted
FHIR-pipeline development against synthetic R4 bundles (full product)

Honest limitations & disclosed generator behavior

Transparency is a core XpertSystems principle. The v1.0 engine has the following known deviations from population literature. They are reproducible and disclosed; the data remains internally consistent for modeling, but users should calibrate expectations.

HbA1c runs low (treatment-effect over-correction). Per-class A1C reductions (metformin + SGLT2i + GLP-1 + DPP-4i + insulin) are summed additively before adherence scaling, so net current HbA1c centers near 6.5% with ~68% at target — versus NHANES T2DM means of ~7.5–8% and ~25–35% at target. Treat hba1c_current_pct as an optimistic "fully-treated" scenario, not a population mean. hba1c_baseline_pct (mean ~8.4%) is the better population-level anchor.
Hypertension prevalence ~95%. The flag fires on SBP≥130 OR DBP≥80 (2017 ACC/AHA threshold) against an SBP distribution centered ~~135, inflating prevalence above the realistic T2DM range (~~70–80%). Use the raw SBP/DBP fields if you need a stricter cutoff.
NAFLD prevalence ~79%. Slightly above the commonly cited ~55–70% for T2DM due to the (rand<0.65 OR BMI>30) construction. Defensible but on the high side.
Retinopathy stage vs time-to-event mismatch. ~53% of patients have a retinopathy time-to-event ≤ duration ("present"), but only ~46% carry a non-None stage: the stage integer is scaled by duration/20 and floored, demoting some recent-onset present cases back to "None". Stage values are well-formed; prevalence is internally ~46%.
Duplicate dict keys. beta_cell_function_pct and an homa_ir/insulin_resistance_index pair are written twice during record assembly; Python keeps the last write, so the columns are consistent but the redundancy is noted.

General synthetic-data caveats apply: cross-field correlations beyond those explicitly modeled 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-002 product
Patients	500	25,000+ (configurable)
Seeds / cohorts	1	Multi-seed, reproducible
Formats	CSV	CSV + Parquet + JSON + FHIR R4 Bundle
HbA1c calibration	Disclosed (optimistic)	Recalibrated to NHANES population means
HTN / NAFLD thresholds	Disclosed (high)	Tunable prevalence targets
Retinopathy staging	Approximate	Registry-anchored stage distribution
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_002_2026,
  title        = {HC-END-002: Type 2 Diabetes 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). Calibrated to: NHANES 2017-2020
                  T2DM cohort; UKPDS (UK Prospective Diabetes Study); EMPA-REG OUTCOME
                  (Zinman et al. 2015, NEJM); LEADER (Marso et al. 2016, NEJM);
                  ACCORD (2008) and ADVANCE (2008) glycemic-control trials; ADA
                  Standards of Care in Diabetes 2025; CDC National Diabetes Statistics
                  Report 2024.}
}

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

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