--- 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 **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).