---
id: wiki-2026-0508-alcoholism
title: Alcoholism
category: 10_Wiki/Topics
status: verified
canonical_id: self
aliases: [Alcohol Use Disorder, AUD, Alcohol Dependence]
duplicate_of: none
source_trust_level: A
confidence_score: 0.9
verification_status: applied
tags: [health, neuroscience, addiction, public-health, computational-health]
raw_sources: []
last_reinforced: 2026-05-10
github_commit: pending
tech_stack:
  language: Python
  framework: scikit-learn / PyTorch
---

# Alcoholism

## 매 한 줄
> **"매 chronic relapsing brain disorder — 매 willpower 의 X, 매 reward circuit 의 hijack."**. Alcoholism (DSM-5: Alcohol Use Disorder, AUD) 의 dopamine / GABA / glutamate 의 imbalance — 매 2026 의 GLP-1 (semaglutide) 의 craving suppression evidence + naltrexone / acamprosate 의 mainline + AI-driven relapse prediction.

## 매 핵심

### 매 Neurobiology
- **Dopamine surge** in nucleus accumbens → euphoria.
- **GABA-A potentiation** → anxiolysis, sedation.
- **NMDA glutamate antagonism** → cognitive slowing.
- **Allostasis**: chronic use → reward set-point shift, withdrawal hypersensitivity.
- **HPA axis dysregulation**: stress → relapse trigger.

### 매 Diagnosis (DSM-5 AUD)
- 11 criteria, 2+ in 12 months → AUD.
- Severity: mild (2-3), moderate (4-5), severe (6+).
- AUDIT-C screen: 3-question, score ≥4 (M) / ≥3 (F) → flag.

### 매 Treatment (2026)
- **Pharmacotherapy**: naltrexone, acamprosate, disulfiram. GLP-1 (semaglutide / tirzepatide) emerging — Phase 3 trials show ~40% craving ↓.
- **Psychosocial**: CBT, motivational interviewing, 12-step (AA).
- **Digital**: reSET-O FDA-cleared, Quit Genius.
- **AI**: ML relapse prediction from EMA + wearable HRV.

### 매 응용
1. Public health screening (AUDIT in EHR).
2. Personalized treatment (pharmacogenomics).
3. Relapse prediction (ML on smartphone passive sensing).
4. Policy modeling (alcohol tax, MUP).

## 💻 패턴

### Pattern 1 — AUDIT-C scoring

```python
def audit_c(freq: int, drinks_per_day: int, binge_freq: int) -> int:
    return freq + drinks_per_day + binge_freq  # each 0-4

def flag(score: int, sex: str) -> bool:
    return score >= (4 if sex == "M" else 3)
```

### Pattern 2 — Relapse prediction (logistic regression on EMA)

```python
from sklearn.linear_model import LogisticRegression
features = ["craving_vas", "stress", "sleep_h", "social_isolation", "hrv_rmssd"]
clf = LogisticRegression(class_weight="balanced", max_iter=500)
clf.fit(X[features], y_relapse_7d)
```

### Pattern 3 — Just-in-time intervention (JITAI)

```python
def maybe_intervene(state):
    if state.craving > 7 or state.location_near_bar:
        send_push("Coping skill: 4-7-8 breath. Call sponsor?")
```

### Pattern 4 — HRV-based stress proxy (wearable)

```python
def stress_proxy(rmssd_ms: float, baseline: float) -> float:
    return max(0.0, (baseline - rmssd_ms) / baseline)
```

## 매 결정 기준

| 상황 | Approach |
|---|---|
| Screening (PCP visit) | AUDIT-C |
| Mild AUD, motivated | Brief intervention + naltrexone |
| Severe AUD, withdrawal | Inpatient detox + benzodiazepine taper |
| Comorbid obesity | GLP-1 (semaglutide) — emerging |
| High relapse risk | CBT + naltrexone + digital JITAI |

**기본값**: AUDIT-C → if positive: brief intervention + naltrexone trial + referral.

## 🔗 Graph
- 부모: [[Addiction Neuroscience]]
- Adjacent: [[Dopamine]] · [[Cognitive-Behavioral-Therapy]]

## 🤖 LLM 활용
**언제**: care navigator, coping skill coaching, EMA prompting, literature synthesis for clinicians.
**언제 X**: diagnosis (clinician role), crisis (route to hotline / 988), withdrawal management (medical emergency).

## ❌ 안티패턴
- **"Just willpower" framing**: stigma, evidence-contradicted.
- **Cold-turkey alone in severe AUD**: delirium tremens 의 fatal risk.
- **One-size-fits-all RX**: 매 patient 의 phenotype heterogeneous.
- **Ignoring comorbid depression / PTSD**: untreated → relapse near-certain.

## 🧪 검증 / 중복
- Verified (DSM-5, NIAAA guidelines, NEJM 2024 GLP-1/AUD trial).
- 신뢰도 A.

## 🕓 Changelog
| 날짜 | 변경 |
|---|---|
| 2026-05-08 | Phase 1 |
| 2026-05-10 | Manual cleanup — FULL content (DSM-5, GLP-1, JITAI ML) |