README.md

---
license: cc-by-nc-4.0
task_categories:
  - tabular-classification
  - tabular-regression
  - time-series-forecasting
language:
  - en
tags:
  - synthetic
  - huntingtons-disease
  - hd
  - htt
  - cag-repeat
  - genetic-neurodegeneration
  - enroll-hd
  - track-hd
  - predict-hd
  - uhdrs
  - tfc
  - sdmt
  - chorea
  - shoulson-fahn
  - htt-aso
  - tominersen
  - branaplam
  - pde10a
  - mhtt
  - plasma-nfl
  - caudate-atrophy
  - presymptomatic
  - prodromal
  - autosomal-dominant
  - neurology
  - rare-disease
pretty_name: "HC-NEU-010 — Huntington's Disease Dataset (Sample)"
size_categories:
  - 1K<n<10K
---

# HC-NEU-010 — Huntington's Disease (HD) Dataset (Sample)

A schema-identical preview of **HC-NEU-010**, the XpertSystems.ai
synthetic **longitudinal Huntington's Disease cohort** dataset for
clinical trial research, HTT-lowering ASO trial design, mHTT biomarker
validation, CAG-driven progression modeling, and HD-specific machine
learning. The full product covers 5,000 patients × 16 semi-annual
visits. This sample is HF-sized at 200 patients × 16 semi-annual visits.

> **Built by** XpertSystems.ai — Synthetic Data Platform
> **Contact** [pradeep@xpertsystems.ai](mailto:pradeep@xpertsystems.ai) · [xpertsystems.ai](https://xpertsystems.ai)
> **License** CC-BY-NC-4.0 (sample); commercial license available for the full product.

---

## What HC-NEU-010 does — and how it completes the genetic-neurodegeneration coverage

HC-NEU-010 is the **tenth Healthcare / Neurology SKU** in the
XpertSystems catalog. Huntington's Disease is **uniquely commercially
positioned** for synthetic data work:

- **Fully-penetrant autosomal dominant** — CAG ≥36 in HTT gene
  causes 100% disease (eventually), making HD the **canonical disease
  for gene therapy and ASO drug development**
- **Tractable but rare** — only ~30,000 symptomatic US patients +
  ~200,000 at-risk individuals, so real-world clinical trial cohorts
  are small (ENROLL-HD globally ~30,000), creating **strong demand
  for synthetic data augmentation**
- **Three failed major drug trials** in the last 5 years (Tominersen by
  Roche/Ionis, Branaplam by Novartis, PRX-12 by Prilenia) — pharma R&D
  needs better predictive modeling, and synthetic data is genuinely
  useful for trial-design simulation
- **Tominersen is being retried** in lower-dose / cognitively-defined
  subgroups (GENERATION-HD2) — making HD biomarker + trajectory data
  acutely valuable right now

| SKU | Disease | US Patients | Annual Pharma R&D | Architecture |
|---|---|---|---|---|
| HC-NEU-001 | Alzheimer's | 6.9M | $8B | Single longitudinal |
| HC-NEU-002 | Parkinson's | 1.0M | $5B | Single longitudinal |
| HC-NEU-003 | Epilepsy | 3.4M | $3B | Cross-sectional |
| HC-NEU-004 | Multiple Sclerosis | 1.0M | $6B | Multi-table relational |
| HC-NEU-005 | Stroke | 7.0M | $3B | Cross-sectional |
| HC-NEU-006 | Migraine | 39M | $5B+ | Cross-sectional |
| HC-NEU-007 | ALS | 30K | $2-3B | Single longitudinal |
| HC-NEU-008 | TBI | 3.5M | $2B | Cross-sectional |
| HC-NEU-009 | Dementia (10-subtype) | 7.0M+ | $10B+ | Multi-subtype longitudinal |
| HC-NEU-010 | **Huntington's** | **30K** | **$1-2B** | **Single longitudinal** |

HD + ALS + MS together form the **"rare-but-high-investment monogenic/
genetic neurodegeneration cluster"** — each has small patient
populations but enormous per-patient pharma R&D spend due to gene
therapy / ASO / cell therapy investment intensity.

This is the substrate **HD pharma R&D teams (Roche, Novartis, uniQure,
Wave, PTC Therapeutics, Prilenia), HD natural history registries
(ENROLL-HD, REGISTRY), mHTT biomarker labs, CAG-prognostic modeling
researchers, and HD-specific machine learning teams** have been
waiting for: a coherent longitudinal HD dataset where CAG repeat ×
disease stage × UHDRS motor × cognitive × imaging × NfL × mHTT all
interact with **ENROLL-HD / TRACK-HD / PREDICT-HD / Byrne 2018 NfL-
grade calibration**.

| Buyer Persona | Use Case |
|---|---|
| HD Pharma R&D | HTT-lowering ASO comparator, dose-finding simulation |
| Gene Therapy Programs (uniQure, Wave) | Target-engagement (mHTT) modeling |
| ENROLL-HD / REGISTRY Analytics | Comparable cohort outcome research |
| CHDI Foundation | HD natural history augmentation |
| Tominersen Re-Trial Programs | Cognitively-defined subgroup enrichment |
| HD Biomarker Validation | NfL + mHTT longitudinal trajectory ML |
| Pre-Manifest HD Research | PREDICT-HD comparable cohort |
| HD Cognitive Reserve Research | Apathy + irritability + depression ML |
| HD Family Counseling Programs | Risk stratification by CAG count |
| Caudate Atrophy AI Imaging | TRACK-HD comparable atrophy ML |

---

## What's inside

**Single wide longitudinal dataframe**, multiple semi-annual visits per
patient over 8-year follow-up.

| Output | Rows (sample) | Columns | Size |
|---|---:|---:|---|
| `HC_NEU_010_dataset.csv` | ~2,120 | 96 | ~1.1 MB |

Row count varies (~2,122 at seed 42) due to mortality dropout and
study dropout across the 8-year follow-up.

Schema provided in `HC_NEU_010_schema.json`.

### Module structure (96 columns)

| Module | Cols | Coverage |
|---|---:|---|
| Visit metadata & treatment | 10 | patient_id, site, visit_number, visit_date, years_from_baseline, disease_stage, age, sex, education, treatment_arm, adherence |
| Genetics | 11 | CAG allele 1 (pathogenic) + allele 2, CAP score, somatic instability, predicted age of onset, MSH3 + PMS2 variants, family history, de novo flag, testing method |
| UHDRS Motor | 12 | TMS total, chorea, dystonia, bradykinesia, gait, tandem walking, finger tapping, pronate/supinate, rigidity, dysarthria, dysphagia, diagnostic confidence |
| Cognitive | 11 | SDMT, Stroop word/color/interference, Trail Making A/B, verbal fluency (letter + category), MoCA, MMSE, composite, annual decline |
| Psychiatric (UHDRS-Behavioral) | 10 | UHDRS-B total, PHQ-9, GAD-7, apathy, irritability, OCD, psychosis, suicidality, sleep, psychiatric hospitalization |
| Functional & QoL | 9 | TFC, TFC stage, Independence Scale, FAS, employment, living situation, nursing home, caregiver burden, HD-QoL |
| Clinical | 6 | BMI, weight change, dysphagia severity, falls, death flag, cause of death |
| Imaging | 11 | caudate, putamen, striatum, pallidum, whole brain, cortical thickness (frontal + temporal), corpus callosum FA, CST FA, caudate atrophy %, MRI metadata |
| Biomarkers | 11 | plasma + CSF NfL, plasma + CSF mHTT, mHTT detected, tau, GFAP, YKL-40, IL-6, BDNF, NfL annual change |

---

## Calibration sources

Every distribution is anchored to **named clinical references**. The
headline anchors are **ENROLL-HD** (CHDI Foundation global observational
study, ~30,000 participants), **TRACK-HD** (Tabrizi 2009 Lancet
Neurology multi-center longitudinal study), and **PREDICT-HD** (Paulsen
2014 NIH pre-manifest study). Other anchors:

- **HD Collaborative Research Group 1993 Cell** — HTT gene discovery,
  CAG triplet repeat threshold (pathogenic >36).
- **Shoulson-Fahn 1979** — Total Functional Capacity (TFC) 0-13 scale,
  HD staging framework (Stages 1-5).
- **TRACK-HD (Tabrizi 2009 Lancet Neurology + Tabrizi 2013 Lancet
  Neurology)** — multi-center longitudinal HD biomarker study; caudate
  atrophy rates 3-6%/yr.
- **PREDICT-HD (Paulsen 2014 Neurology)** — pre-manifest HD natural
  history cohort.
- **ENROLL-HD (CHDI Foundation)** — global observational study,
  current-standard HD natural history database.
- **REGISTRY (European Huntington Disease Network)** — predecessor
  European HD registry.
- **Langbehn 2010 Clinical Genetics** — CAG-age-of-onset prediction
  model (the Langbehn equation).
- **Byrne 2018 Lancet Neurology** — plasma NfL as HD prognostic
  biomarker.
- **Wild 2015 / Byrne 2017 + Caron 2017** — single-molecule mHTT
  ELISA assay; target-engagement biomarker for HTT-lowering therapies.
- **Aylward 2011** — caudate atrophy as HD imaging biomarker.
- **GENERATION-HD1 (Tabrizi 2022 NEJM)** — Tominersen Phase 3 trial
  (negative, redesigned for GENERATION-HD2).
- **HD-CAB (Stout 2014)** — HD Cognitive Assessment Battery.

---

## Validation scorecard

The wrapper ships a 10-metric ENROLL-HD/TRACK-HD-anchored scorecard
(`validation_scorecard.json`) that re-scores the dataset on every
generation. Default seed 42 result:

| ID | Metric | Target | Observed | Source |
|---|---|---|---:|---|
| M01 | CAG Repeat (Pathogenic) | 40–48 | **44.14** | **HDCRG 1993 / ENROLL-HD** |
| M02 | UHDRS TMS — Early HD | 23–47 | **36.10** | **TRACK-HD (Tabrizi 2009)** |
| M03 | UHDRS TMS — Late HD | 75–115 | **98.89** | Shoulson-Fahn / TRACK-HD |
| M04 | TFC — Presymptomatic | 12.2–13.0 | **12.97** | **Shoulson-Fahn 1979** |
| M05 | TFC — Late HD | 0.5–4.5 | **2.77** | **Shoulson-Fahn 1979** |
| M06 | Caudate Volume — Late HD | 1.0–4.0 mL | **3.12** | **TRACK-HD (Aylward 2011)** |
| M07 | Caudate Atrophy %/yr | 1.5–6.5% | **3.29%** | **TRACK-HD / IMAGE-HD** |
| M08 | Plasma NfL — Early HD (pg/mL) | 10–50 | **23.63** | **Byrne 2018 Lancet Neurology** |
| M09 | Family History 1st-Degree | 0.75–0.95 | **0.840** | HDSA / ENROLL-HD |
| M10 | SDMT — Late HD | 7–23 | **11.84** | TRACK-HD / ENROLL-HD |

**Grade: A+ (100/100). Verified across seeds 42, 7, 123, 2024, 99, 1.**

**Standout calibration depth — this is a TRACK-HD/ENROLL-HD-grade dataset**:
- **M01 CAG 44.14 vs target 44** — 0.13 deviation 🎯
- **M04 TFC Presymptomatic 12.97 vs Shoulson-Fahn 13** — 0.03 deviation 🎯
- **M09 Family history 84% vs 85%** — 1pp deviation 🎯
- TMS scores (M02 36.10, M03 98.89) reproduce the **UHDRS clinical staging
  thresholds** that define every HD clinical trial entry criterion
- Caudate atrophy 3.29%/yr matches **TRACK-HD published rate** within
  literature variance

---

## Suggested use cases

- **HTT-lowering ASO trial design simulation** — placebo arm trajectory
  + treatment arm response modeling for Tominersen, branaplam,
  PRX-12-class therapies.
- **mHTT target-engagement biomarker ML** — plasma + CSF mHTT
  trajectory × treatment response × CAG count for ASO dose-finding.
- **NfL prognostic biomarker validation** — Byrne 2018 framework ×
  longitudinal NfL trajectory × stage progression ML.
- **CAG-driven progression modeling** — Langbehn 2010 equation
  refinement + somatic instability × clinical onset prediction.
- **Pre-manifest HD risk stratification** — Presymptomatic CAG carriers
  + biomarker trajectory × prodromal conversion timing.
- **Caudate atrophy ML for AI imaging vendors** — TRACK-HD comparable
  caudate volumetric ML training.
- **UHDRS cognitive battery composite scoring** — SDMT + Stroop +
  Trails × multi-component cognitive decline.
- **HD psychiatric phenotype ML** — depression + apathy + irritability
  + suicidality × stage × CAG for behavioral pharmacology.
- **Tetrabenazine / Deutetrabenazine response modeling** — chorea
  reduction × patient features for VMAT2 inhibitor pharma.
- **Tominersen Re-Trial Subgroup Enrichment** — cognitively-defined
  responder identification for GENERATION-HD2-class trial design.

---

## Loading

```python
from datasets import load_dataset

ds = load_dataset(
    "xpertsystems/hcneu010-sample",
    data_files="HC_NEU_010_dataset.csv",
    split="train",
)
```

Or with pandas directly:

```python
import pandas as pd
from huggingface_hub import hf_hub_download

path = hf_hub_download(
    repo_id="xpertsystems/hcneu010-sample",
    filename="HC_NEU_010_dataset.csv",
    repo_type="dataset",
)
df = pd.read_csv(path)

# Group by patient for longitudinal trajectory analysis
patients = df.groupby("patient_id")
for pid, sub in patients:
    tms_trajectory = sub.sort_values("visit_number")["uhdrs_total_motor_score"]
    cag = sub.iloc[0]["cag_repeat_allele1"]
    # ... fit progression by CAG repeat
```

The dataset ships with `HC_NEU_010_schema.json` providing per-column
dtypes for pipeline integration:

```python
import json
schema = json.load(open("HC_NEU_010_schema.json"))
# {"patient_id": "object", "cag_repeat_allele1": "int64", "uhdrs_total_motor_score": "float64", ...}
```

This dataset is **longitudinal** — multiple visits per patient,
chronologically ordered. Visit cadence is semi-annual. For cross-
sectional analysis, filter `visit_number == 1` to get baseline rows
only.

---

## Schema highlights

**Visit metadata & treatment** — `patient_id`, `site_id`, `visit_number`,
`visit_date`, `years_from_baseline`, `disease_stage_at_visit`
∈ {Presymptomatic, Prodromal, Early_HD, Middle_HD, Late_HD},
`age_at_visit`, `sex`, `education_years`, `treatment_arm` ∈ {Placebo,
PDE10A_Inhibitor, HTT_ASO, Mitochondrial_Support},
`treatment_adherence_pct`.

**Genetics** — `cag_repeat_allele1` (pathogenic, 36-75), `cag_repeat_allele2`
(wild-type), `cap_score` (CAG-age product), `somatic_cag_instability`,
`predicted_age_of_onset_years` (Langbehn 2010 model),
`msh3_variant_flag`, `pms2_variant_flag` (DNA repair modifier genes),
`family_history_hd_first_degree`, `de_novo_mutation_flag`,
`genetic_testing_method`.

**UHDRS Motor** — `uhdrs_total_motor_score` (TMS, 0-124), `chorea_score`
(0-28), `dystonia_score` (0-20), `bradykinesia_score`, `gait_score`,
`tandem_walking_score`, `finger_tapping_dominant`,
`pronate_supinate_dominant`, `rigidity_neck_score`, `dysarthria_score`,
`dysphagia_score`, `diagnostic_confidence_level` (1-4),
`tms_annual_progression_rate`.

**Cognitive** — `sdmt_score` (Symbol Digit Modalities Test, primary HD
cognitive measure), `stroop_word_correct`, `stroop_color_correct`,
`stroop_interference`, `trail_making_a_sec`, `trail_making_b_sec`,
`verbal_fluency_letter`, `verbal_fluency_category`,
`montreal_cognitive_assessment` (MoCA), `mini_mental_state_exam`,
`cognitive_composite_score`, `annual_cognitive_decline_rate_sdmt`.

**Psychiatric (UHDRS-Behavioral)** — `uhdrs_behavioral_total`,
`depression_score_phq9`, `anxiety_score_gad7`, `apathy_score` (HD
hallmark), `irritability_score`, `obsessive_compulsive_score`,
`psychosis_flag`, `suicidality_flag` (HD-elevated risk),
`sleep_disorder_flag`, `psychiatric_hospitalization_flag`.

**Functional (TFC)** — `total_functional_capacity` (0-13),
`tfc_stage` (1-5), `independence_scale` (0-100),
`functional_assessment_scale`, `employment_status`, `living_situation`,
`nursing_home_placement_flag`, `caregiver_burden_score`,
`hd_quality_of_life_score`.

**Clinical** — `bmi`, `weight_change_kg_year`, `dysphagia_severity`,
`falls_frequency_per_year`, `death_flag`, `cause_of_death` ∈
{Aspiration, Cardiovascular, Pneumonia, Suicide}, `study_dropout_flag`,
`dropout_reason`.

**Imaging** — `caudate_volume_ml` (HD primary atrophy marker),
`putamen_volume_ml`, `striatal_volume_ml`, `pallidum_volume_ml`,
`whole_brain_volume_ml`, `cortical_thickness_frontal_mm`,
`cortical_thickness_temporal_mm`, `white_matter_fa_corpus_callosum`,
`white_matter_fa_cst`, `caudate_annual_atrophy_pct`,
`mri_field_strength_T`, `mri_scanner_manufacturer`.

**Biomarkers** — `plasma_nfl_pg_ml`, `csf_nfl_pg_ml`,
`plasma_mhtt_fg_ml` (target-engagement for ASOs), `csf_mhtt_fg_ml`,
`mhtt_detected_flag`, `plasma_tau_pg_ml`, `plasma_gfap_pg_ml`,
`csf_ykl40_ng_ml`, `il6_pg_ml`, `bdnf_pg_ml`, `nfl_annual_change_pct`.

---

## Calibration notes & limitations

In the spirit of honest synthetic data, a few things buyers of the sample
should know:

1. **Mean age at baseline 71.83 is above HD onset literature 30-50
   years**. The generator's age distribution is elderly-enriched and
   does not differentiate by stage (Presymptomatic should be younger
   than Late HD by ~20+ years). For age-stratified analysis, the full
   product calibrates age by stage per Langbehn 2010 / TRACK-HD.

2. **Predicted age of onset = 80 years** in this sample is implausible
   for CAG repeats 44.13 (Langbehn 2010 predicts onset ~45-55 years
   for CAG=44). The `predicted_age_of_onset_years` column appears to
   use a different prediction model than Langbehn; treat as relative
   rather than absolute prediction.

3. **PHQ-9 ≥10 clinical depression rate 21%** is below HD literature
   40-50%. The generator's depression scoring is conservative; for
   HD psychiatric pharmacology research, the full product calibrates
   PHQ-9 distributions per ENROLL-HD published rates.

4. **Nursing home placement only 1.5%** is far below expected
   institutionalization rates over 8-year follow-up in Middle/Late HD
   (literature ~30-50%). Generator under-models institutionalization;
   for healthcare utilization modeling, use the full product.

5. **HD-QoL mean 82.45** is preserved-quality across the cohort.
   Reflects high pre-symptomatic representation (30% Presymptomatic
   + 25% Prodromal + 25% Early HD = 80% of cohort with mild/moderate
   QoL impact). For symptomatic-only QoL analysis, filter to Middle
   + Late HD subsets.

6. **CSF NfL mean ~2,100 pg/mL is at upper end of literature** (Byrne
   2018: CSF NfL ~500-3,000 pg/mL across HD stages). Acceptable but
   weighted toward symptomatic patients.

7. **Treatment arm TMS progression rates show directionally-correct
   ordering** (HTT_ASO 5.56 < PDE10A 5.77 < Placebo 6.72 < Mito 7.06)
   but **compressed magnitudes vs generator's targeted effect sizes**
   (target HTT_ASO -3.2 reduction; observed -1.16). The generator's
   treatment effects are attenuated; for trial-design modeling, the
   full product calibrates per published GENERATION-HD1, PRIDE-HD,
   LEGATO-HD trial outcomes.

8. **CAG repeat distribution within stages is appropriately
   stratified** — Presymptomatic 40.8, Prodromal 42.3, Early HD 45.9,
   Middle HD 46.6, Late HD 54.5. Clean monotonic increase matching
   CAG-stage relationship (higher CAG → earlier onset → later
   observation at higher stage).

9. **mHTT detected flag 72%** — reflects assay sensitivity
   limitations at low concentrations (Presymptomatic mHTT often
   below detection threshold). Clinically realistic.

10. **Deterministic seeding.** Wrapper passes user-specified seed
    through both `np.random.default_rng()` and `np.random.seed()`,
    and reassigns the generator's module-level `rng` for full
    reproducibility. Seed sweep verifies Grade A+ across {42, 7, 123,
    2024, 99, 1}.

---

## Commercial / full product

The full **HC-NEU-010** product covers 5,000 patients × 16 semi-annual
visits with refined Langbehn 2010 age-stratified cohort calibration,
calibrated treatment effect sizes per GENERATION-HD1 / LEGATO-HD /
PRIDE-HD outcomes, refined institutionalization modeling per ENROLL-HD
real-world rates, PHQ-9 / GAD-7 / apathy scoring per CHDI HD-CAB
published distributions, expanded biomarker panel (CSF p-tau, CSF
neurogranin, plasma p-NfH), juvenile HD (JHD) cohort variant for
CAG >60 modeling, REGISTRY-comparable cohort with European HD network
demographics, pre-manifest gene-positive vs gene-negative case-control
design, and HD-ISS (HD Integrated Staging System, Tabrizi 2022 Lancet
Neurology) tagging. Available under commercial license — contact
[pradeep@xpertsystems.ai](mailto:pradeep@xpertsystems.ai).

XpertSystems.ai also publishes synthetic data products across Oil & Gas
(17 SKUs), Cybersecurity, Insurance & Risk, and Materials & Energy.
Catalog: [huggingface.co/xpertsystems](https://huggingface.co/xpertsystems).