diff --git a/backend/placeholder_resolver.py b/backend/placeholder_resolver.py
index b81e9e7..59bd972 100644
--- a/backend/placeholder_resolver.py
+++ b/backend/placeholder_resolver.py
@@ -1350,54 +1350,58 @@ def get_placeholder_catalog(profile_id: str) -> Dict[str, List[Dict[str, str]]]:
     """
     Get grouped placeholder catalog with descriptions and example values.
 
+    Uses the Placeholder Registry as single source of truth.
+    Falls back to hardcoded legacy placeholders for non-registry items.
+
     Args:
         profile_id: User profile ID
 
     Returns:
         Dict mapping category to list of {key, description, example}
     """
-    # Placeholder definitions with descriptions
-    placeholders = {
+    from placeholder_registry import get_registry
+
+    catalog = {}
+
+    # Get all registered placeholders from Registry
+    registry = get_registry()
+    all_registered = registry.get_all()
+
+    # Group registry placeholders by category
+    for key, metadata in all_registered.items():
+        category = metadata.category
+        if category not in catalog:
+            catalog[category] = []
+
+        # Try to resolve value
+        try:
+            if metadata._resolver_func:
+                example = metadata._resolver_func(profile_id)
+            else:
+                # Fallback to PLACEHOLDER_MAP
+                placeholder = f'{{{{{key}}}}}'
+                resolver = PLACEHOLDER_MAP.get(placeholder)
+                if resolver:
+                    example = resolver(profile_id)
+                else:
+                    example = '[Nicht implementiert]'
+        except Exception as e:
+            example = '[Nicht verfügbar]'
+
+        catalog[category].append({
+            'key': key,
+            'description': metadata.description,
+            'example': str(example)
+        })
+
+    # Legacy placeholders (not in registry yet)
+    legacy_placeholders = {
         'Profil': [
             ('name', 'Name des Nutzers'),
             ('age', 'Alter in Jahren'),
             ('height', 'Körpergröße in cm'),
             ('geschlecht', 'Geschlecht'),
         ],
-        'Körper': [
-            ('weight_aktuell', 'Aktuelles Gewicht in kg'),
-            ('weight_trend', 'Gewichtstrend (7d/30d)'),
-            ('kf_aktuell', 'Aktueller Körperfettanteil in %'),
-            ('bmi', 'Body Mass Index'),
-            ('weight_7d_median', 'Gewicht 7d Median (kg)'),
-            ('weight_28d_slope', 'Gewichtstrend 28d (kg/Tag)'),
-            ('fm_28d_change', 'Fettmasse Änderung 28d (kg)'),
-            ('lbm_28d_change', 'Magermasse Änderung 28d (kg)'),
-            ('waist_28d_delta', 'Taillenumfang Änderung 28d (cm)'),
-            ('waist_hip_ratio', 'Taille/Hüfte-Verhältnis'),
-            ('recomposition_quadrant', 'Rekomposition-Status'),
-        ],
-        'Ernährung': [
-            ('kcal_avg', 'Durchschn. Kalorien (30d)'),
-            ('protein_avg', 'Durchschn. Protein in g (30d)'),
-            ('carb_avg', 'Durchschn. Kohlenhydrate in g (30d)'),
-            ('fat_avg', 'Durchschn. Fett in g (30d)'),
-            ('energy_balance_7d', 'Energiebilanz 7d (kcal/Tag)'),
-            ('protein_g_per_kg', 'Protein g/kg Körpergewicht'),
-            ('protein_adequacy_28d', 'Protein Adequacy Score (0-100)'),
-            ('macro_consistency_score', 'Makro-Konsistenz Score (0-100)'),
-        ],
-        'Training': [
-            ('activity_summary', 'Aktivitäts-Zusammenfassung (7d)'),
-            ('trainingstyp_verteilung', 'Verteilung nach Trainingstypen'),
-            ('training_minutes_week', 'Trainingsminuten pro Woche'),
-            ('training_frequency_7d', 'Trainingshäufigkeit 7d'),
-            ('quality_sessions_pct', 'Qualitätssessions (%)'),
-            ('ability_balance_strength', 'Ability Balance - Kraft (0-100)'),
-            ('ability_balance_endurance', 'Ability Balance - Ausdauer (0-100)'),
-            ('proxy_internal_load_7d', 'Proxy Load 7d'),
-            ('rest_day_compliance', 'Ruhetags-Compliance (%)'),
-        ],
         'Schlaf & Erholung': [
             ('sleep_avg_duration', 'Durchschn. Schlafdauer (7d)'),
             ('sleep_avg_quality', 'Durchschn. Schlafqualität (7d)'),
@@ -1440,13 +1444,17 @@ def get_placeholder_catalog(profile_id: str) -> Dict[str, List[Dict[str, str]]]:
         ],
     }
 
-    catalog = {}
+    # Add legacy placeholders (skip if already in registry)
+    for category, items in legacy_placeholders.items():
+        if category not in catalog:
+            catalog[category] = []
 
-    for category, items in placeholders.items():
-        catalog[category] = []
         for key, description in items:
+            # Skip if already added from registry
+            if any(p['key'] == key for p in catalog[category]):
+                continue
+
             placeholder = f'{{{{{key}}}}}'
-            # Get example value if resolver exists
             resolver = PLACEHOLDER_MAP.get(placeholder)
             if resolver:
                 try: