feat: enhance nutrition data processing and visualization with new correlation insights

- Refactored the `calculate_lag_correlation` function to normalize lag payloads and improve correlation calculations for various nutrition metrics.
- Introduced a new function `build_nutrition_correlation_heuristic_items` to generate heuristic insights based on merged nutrition data, enhancing user understanding of dietary impacts on weight and body composition.
- Updated the `get_nutrition_history_viz_bundle` function to include daily calorie balance and protein vs. lean mass data, providing a comprehensive view of nutrition trends.
- Enhanced the frontend to visualize calorie balance and protein vs. lean mass insights, improving the user experience with clear graphical representations of dietary correlations.

backend/data_layer/correlations.py

@@ -40,16 +40,36 @@ def calculate_lag_correlation(profile_id: str, var1: str, var2: str, max_lag_day
             'data_points': N
         }
     """
-    if var1 == 'energy' and var2 == 'weight':
+    v1 = (var1 or "").strip().lower()
-        return _correlate_energy_weight(profile_id, max_lag_days)
+    if v1 in ("energy", "energy_balance"):
-    elif var1 == 'protein' and var2 == 'lbm':
+        v1n = "energy"
-        return _correlate_protein_lbm(profile_id, max_lag_days)
+    elif v1 in ("training_load", "load"):
-    elif var1 == 'training_load' and var2 in ['hrv', 'rhr']:
+        v1n = "training_load"
-        return _correlate_load_vitals(profile_id, var2, max_lag_days)
+    elif v1 == "protein":
+        v1n = "protein"
+    else:
+        v1n = v1
+
+    if v1n == 'energy' and var2 == 'weight':
+        return _normalize_lag_payload(_correlate_energy_weight(profile_id, max_lag_days))
+    elif v1n == 'protein' and var2 == 'lbm':
+        return _normalize_lag_payload(_correlate_protein_lbm(profile_id, max_lag_days))
+    elif v1n == 'training_load' and var2 in ['hrv', 'rhr']:
+        return _normalize_lag_payload(_correlate_load_vitals(profile_id, var2, max_lag_days))
     else:
         return None
 
 
+def _normalize_lag_payload(raw: Optional[Dict]) -> Optional[Dict]:
+    """Charts erwarten u. a. ``best_lag_days``; Layer liefert teils ``best_lag``."""
+    if not raw:
+        return None
+    out = dict(raw)
+    if out.get("best_lag_days") is None and out.get("best_lag") is not None:
+        out["best_lag_days"] = out["best_lag"]
+    return out
+
+
 def _correlate_energy_weight(profile_id: str, max_lag: int) -> Optional[Dict]:
     """
     Correlate energy balance with weight change

backend/data_layer/history_overview_viz.py

@@ -0,0 +1,240 @@
+"""
+Layer 2b: Gesamtansicht «Verlauf» — komponiert nur Bundles aus body-, nutrition-, fitness-, recovery_viz.
+
+Issue #53: keine parallele Business-Logik; ein Router-Endpoint liefert diese Zusammenfassung.
+"""
+
+from __future__ import annotations
+
+from typing import Any, Dict, List, Optional
+
+from data_layer.body_viz import get_body_history_viz_bundle
+from data_layer.correlations import calculate_lag_correlation, calculate_top_drivers
+from data_layer.fitness_viz import get_fitness_dashboard_viz_bundle
+from data_layer.nutrition_viz import get_nutrition_history_viz_bundle
+from data_layer.recovery_viz import get_recovery_dashboard_viz_bundle
+
+
+def _take_kpis(tiles: Any, max_n: int = 4) -> List[Dict[str, Any]]:
+    if not isinstance(tiles, list):
+        return []
+    out: List[Dict[str, Any]] = []
+    for t in tiles[:max_n]:
+        if not isinstance(t, dict):
+            continue
+        out.append(
+            {
+                "key": t.get("key"),
+                "category": t.get("category"),
+                "icon": t.get("icon"),
+                "value": t.get("value"),
+                "sublabel": t.get("sublabel"),
+                "status": t.get("status"),
+                "verdict": t.get("verdict"),
+            }
+        )
+    return out
+
+
+def _short_body_interpretation_tiles(tiles: Any, max_n: int = 3) -> List[Dict[str, Any]]:
+    """Körper-Interpretationskacheln (keine KPI-Kacheln)."""
+    if not isinstance(tiles, list):
+        return []
+    out: List[Dict[str, Any]] = []
+    for t in tiles[:max_n]:
+        if not isinstance(t, dict):
+            continue
+        det = str(t.get("detail") or "")
+        if len(det) > 140:
+            det = det[:137] + "…"
+        out.append(
+            {
+                "title": t.get("title") or t.get("category") or "Hinweis",
+                "detail": det,
+                "status": t.get("status"),
+            }
+        )
+    return out
+
+
+def _take_insights(items: Any, max_n: int = 2) -> List[Dict[str, Any]]:
+    if not isinstance(items, list):
+        return []
+    out: List[Dict[str, Any]] = []
+    for it in items[:max_n]:
+        if not isinstance(it, dict):
+            continue
+        out.append(
+            {
+                "title": it.get("title") or it.get("title_de"),
+                "body": it.get("body") or it.get("detail") or it.get("message"),
+                "tone": it.get("tone") or it.get("status"),
+            }
+        )
+    return out
+
+
+def get_history_overview_viz_bundle(profile_id: str, days: int) -> Dict[str, Any]:
+    """
+    Kompakte Übersicht für den ersten Reiter «Gesamtansicht»: KPI-Kurzformen + Lag-Korrelationen (C1–C4).
+    """
+    eff = max(7, min(int(days), 9999))
+    body = get_body_history_viz_bundle(profile_id, eff)
+    nutr = get_nutrition_history_viz_bundle(profile_id, eff)
+    fit = get_fitness_dashboard_viz_bundle(profile_id, eff)
+    rec = get_recovery_dashboard_viz_bundle(profile_id, eff)
+
+    c1 = calculate_lag_correlation(profile_id, "energy_balance", "weight", 14)
+    c2 = calculate_lag_correlation(profile_id, "protein", "lbm", 14)
+    c3_hrv = calculate_lag_correlation(profile_id, "load", "hrv", 14)
+    c3_rhr = calculate_lag_correlation(profile_id, "load", "rhr", 14)
+    c3 = None
+    if c3_hrv and c3_rhr:
+        c3 = (
+            c3_hrv
+            if abs(float(c3_hrv.get("correlation") or 0)) >= abs(float(c3_rhr.get("correlation") or 0))
+            else c3_rhr
+        )
+        if c3 is c3_hrv:
+            c3 = dict(c3)
+            c3["metric"] = "HRV"
+        else:
+            c3 = dict(c3_rhr)
+            c3["metric"] = "RHR"
+    elif c3_hrv:
+        c3 = dict(c3_hrv)
+        c3["metric"] = "HRV"
+    elif c3_rhr:
+        c3 = dict(c3_rhr)
+        c3["metric"] = "RHR"
+
+    drivers = calculate_top_drivers(profile_id)
+
+    b_sum = body.get("summary") if isinstance(body.get("summary"), dict) else {}
+    last_w = b_sum.get("weight_kg")
+
+    fs = fit.get("summary") if isinstance(fit.get("summary"), dict) else {}
+    if fit.get("has_activity_entries"):
+        ac = int(fs.get("activity_count") or 0)
+        fitness_line = f"{ac} Trainingseinheiten im gewählten Fenster"
+    else:
+        fitness_line = fit.get("message") or "Keine Trainingsdaten"
+
+    drv_list = drivers if isinstance(drivers, list) else []
+
+    return {
+        "days_requested": days,
+        "effective_window_days": eff,
+        "confidence": _overview_confidence(body, nutr, fit, rec),
+        "sections": [
+            {
+                "id": "body",
+                "title": "Körper",
+                "tab_id": "body",
+                "summary_line": (
+                    f"Letztes Gewicht: {last_w} kg"
+                    if last_w is not None
+                    else "Keine Gewichtsdaten im Fenster"
+                ),
+                "interpretation_short": _short_body_interpretation_tiles(body.get("interpretation_tiles"), 3),
+            },
+            {
+                "id": "nutrition",
+                "title": "Ernährung",
+                "tab_id": "nutrition",
+                "summary_line": (
+                    f"Ø {round(float((nutr.get('summary') or {}).get('kcal_avg') or 0))} kcal/Tag"
+                    if nutr.get("has_nutrition_entries")
+                    else (nutr.get("message") or "Keine Ernährungsdaten")
+                ),
+                "kpi_short": _take_kpis(nutr.get("kpi_tiles"), 4),
+                "heuristic_short": (nutr.get("nutrition_correlation_heuristics") or [])[:2],
+            },
+            {
+                "id": "fitness",
+                "title": "Fitness",
+                "tab_id": "activity",
+                "summary_line": fitness_line,
+                "kpi_short": _take_kpis(fit.get("kpi_tiles"), 4),
+                "insights_short": _take_insights(fit.get("progress_insights"), 2),
+            },
+            {
+                "id": "recovery",
+                "title": "Erholung",
+                "tab_id": "activity",
+                "summary_line": "Schlaf & Vitalwerte"
+                if rec.get("has_recovery_data")
+                else (rec.get("message") or "Keine Erholungsdaten"),
+                "kpi_short": _take_kpis(rec.get("kpi_tiles"), 4),
+                "insights_short": _take_insights(rec.get("progress_insights"), 2),
+            },
+        ],
+        "lag_correlations": {
+            "weight_energy": _compact_lag("C1 Energiebilanz ↔ Gewicht", c1),
+            "protein_lbm": _compact_lag("C2 Protein ↔ Magermasse", c2),
+            "load_vitals": _compact_lag(
+                f"C3 Last ↔ {(c3 or {}).get('metric') or 'Vital'}",
+                c3,
+                extra_keys=("metric",),
+            ),
+            "recovery_performance": {
+                "label": "C4 Top-Treiber (Einflussfaktoren)",
+                "drivers": drv_list[:8],
+            },
+        },
+        "meta": {
+            "layer_1": "composed_metrics",
+            "layer_2b": "history_overview_viz",
+            "issue": "53-history-overview",
+            "sources": {
+                "body": "body_viz",
+                "nutrition": "nutrition_viz",
+                "fitness": "fitness_viz",
+                "recovery": "recovery_viz",
+                "lag": "correlations.calculate_lag_correlation",
+                "drivers": "correlations.calculate_top_drivers",
+            },
+        },
+    }
+
+
+def _overview_confidence(b: Dict, n: Dict, f: Dict, r: Dict) -> str:
+    scores = []
+    for x in (b, n, f, r):
+        c = x.get("confidence")
+        if c == "high":
+            scores.append(3)
+        elif c == "medium":
+            scores.append(2)
+        elif c == "low":
+            scores.append(1)
+        else:
+            scores.append(0)
+    s = sum(scores) / max(len(scores), 1)
+    if s >= 2.5:
+        return "high"
+    if s >= 1.5:
+        return "medium"
+    return "low"
+
+
+def _compact_lag(
+    label: str,
+    payload: Optional[Dict[str, Any]],
+    extra_keys: tuple = (),
+) -> Dict[str, Any]:
+    if not payload:
+        return {"label": label, "available": False}
+    out: Dict[str, Any] = {
+        "label": label,
+        "available": payload.get("correlation") is not None,
+        "correlation": payload.get("correlation"),
+        "best_lag_days": payload.get("best_lag_days", payload.get("best_lag")),
+        "confidence": payload.get("confidence"),
+        "interpretation": payload.get("interpretation", ""),
+        "data_points": payload.get("data_points"),
+    }
+    for k in extra_keys:
+        if k in payload:
+            out[k] = payload[k]
+    return out

backend/data_layer/nutrition_body_merge.py

@@ -0,0 +1,64 @@
+"""
+Layer 1 Hilfslogik: Ernährung + Gewicht + Caliper (forward-filled Magermasse).
+
+Genutzt von Layer 2b (nutrition_viz) und vom Router GET /api/nutrition/correlations.
+"""
+
+from __future__ import annotations
+
+from typing import Any, Dict, List, Optional
+
+from db import get_db, get_cursor, r2d
+from caliper_composition import compute_lean_fat_kg, nearest_weight_kg_from_map
+
+
+def build_merged_daily_nutrition_body_rows(profile_id: str) -> List[Dict[str, Any]]:
+    """
+    Pro Kalendertag: Makros aus nutrition_log, Gewicht, forward-filled Caliper (lean_mass, bf%).
+    Gleiche Semantik wie bisher ``GET /api/nutrition/correlations``.
+    """
+    with get_db() as conn:
+        cur = get_cursor(conn)
+        cur.execute("SELECT * FROM nutrition_log WHERE profile_id=%s ORDER BY date", (profile_id,))
+        nutr = {r["date"]: r2d(r) for r in cur.fetchall()}
+        cur.execute("SELECT date, weight FROM weight_log WHERE profile_id=%s ORDER BY date", (profile_id,))
+        wlog = {r["date"]: r["weight"] for r in cur.fetchall()}
+        cur.execute(
+            "SELECT date, lean_mass, body_fat_pct FROM caliper_log WHERE profile_id=%s ORDER BY date",
+            (profile_id,),
+        )
+        cals = sorted([r2d(r) for r in cur.fetchall()], key=lambda x: x["date"])
+
+    all_dates = sorted(set(list(nutr.keys()) + list(wlog.keys())))
+    mi = 0
+    last_cal: Dict[str, Any] = {}
+    cal_by_date: Dict[Any, Dict[str, Any]] = {}
+    for d in all_dates:
+        while mi < len(cals) and cals[mi]["date"] <= d:
+            last_cal = cals[mi]
+            mi += 1
+        if last_cal:
+            cal_by_date[d] = last_cal
+
+    result: List[Dict[str, Any]] = []
+    for d in all_dates:
+        if d not in nutr and d not in wlog:
+            continue
+        row: Dict[str, Any] = {"date": d}
+        if d in nutr:
+            for k in ("kcal", "protein_g", "fat_g", "carbs_g"):
+                v = nutr[d].get(k)
+                row[k] = float(v) if v is not None else None
+        if d in wlog:
+            row["weight"] = float(wlog[d])
+        if d in cal_by_date:
+            lm = cal_by_date[d].get("lean_mass")
+            bf = cal_by_date[d].get("body_fat_pct")
+            if bf is not None and lm is None:
+                wkg = nearest_weight_kg_from_map(wlog, d)
+                if wkg is not None:
+                    lm, _fat = compute_lean_fat_kg(wkg, float(bf))
+            row["lean_mass"] = float(lm) if lm is not None else None
+            row["body_fat_pct"] = float(bf) if bf is not None else None
+        result.append(row)
+    return result

backend/data_layer/nutrition_interpretation.py

@@ -217,3 +217,107 @@ def build_macro_donut_from_averages(navg: Dict[str, Any]) -> Optional[List[Dict[
         {"name": "KH", "value": round(ckcal / tot * 100), "color": "#c17d45", "grams": round(c, 1)},
         {"name": "Fett", "value": round(fkcal / tot * 100), "color": "#6e8eb8", "grams": round(f, 1)},
     ]
+
+
+def build_nutrition_correlation_heuristic_items(
+    merged_rows: List[Dict[str, Any]],
+    tdee_kcal: float,
+    protein_target_low_g: float,
+) -> List[Dict[str, Any]]:
+    """
+    Heuristische Kurz-Aussagen (vormals Reiter «Korrelation») — gleiche Logik wie History.jsx,
+    TDEE aber aus Data-Layer (nutrition_metrics / estimate_tdee), nicht ×1,4 im Frontend.
+    """
+    filtered = [
+        r
+        for r in merged_rows
+        if r.get("kcal") is not None and r.get("weight") is not None
+    ]
+    if len(filtered) < 5:
+        return []
+
+    td = float(tdee_kcal)
+    latest_w = float(filtered[-1].get("weight") or 0) or 80.0
+    pt_low = round(float(protein_target_low_g or 0)) or max(1, round(latest_w * 1.6))
+
+    items: List[Dict[str, Any]] = []
+
+    if len(filtered) >= 14:
+        high_k = [d for d in filtered if float(d.get("kcal") or 0) > td + 200]
+        low_k = [d for d in filtered if float(d.get("kcal") or 0) < td - 200]
+        if len(high_k) >= 3 and len(low_k) >= 3:
+            avg_wh = sum(float(d["weight"]) for d in high_k) / len(high_k)
+            avg_wl = sum(float(d["weight"]) for d in low_k) / len(low_k)
+            avg_wh_r = round(avg_wh * 10) / 10
+            avg_wl_r = round(avg_wl * 10) / 10
+            items.append(
+                {
+                    "icon": "📊",
+                    "status": "good" if avg_wl < avg_wh else "warn",
+                    "title": (
+                        f"Kalorienreduktion wirkt: Ø {avg_wl_r} kg bei Defizit vs. {avg_wh_r} kg bei Überschuss"
+                        if avg_wl < avg_wh
+                        else "Kein klarer Kalorieneffekt auf Gewicht erkennbar"
+                    ),
+                    "detail": (
+                        f"Tage mit Überschuss (>{int(td + 200)} kcal): Ø {avg_wh_r} kg · "
+                        f"Tage mit Defizit (<{int(td - 200)} kcal): Ø {avg_wl_r} kg"
+                    ),
+                }
+            )
+
+    prot_vs_lean = [
+        d
+        for d in filtered
+        if d.get("protein_g") is not None and d.get("lean_mass") is not None
+    ]
+    if len(prot_vs_lean) >= 3:
+        high_p = [d for d in prot_vs_lean if float(d.get("protein_g") or 0) >= pt_low]
+        low_p = [d for d in prot_vs_lean if float(d.get("protein_g") or 0) < pt_low]
+        if len(high_p) >= 2 and len(low_p) >= 2:
+            avg_lh = sum(float(d["lean_mass"]) for d in high_p) / len(high_p)
+            avg_ll = sum(float(d["lean_mass"]) for d in low_p) / len(low_p)
+            avg_lh_r = round(avg_lh * 10) / 10
+            avg_ll_r = round(avg_ll * 10) / 10
+            items.append(
+                {
+                    "icon": "🥩",
+                    "status": "good" if avg_lh >= avg_ll else "warn",
+                    "title": (
+                        f"Hohe Proteinzufuhr (≥{pt_low} g): Ø {avg_lh_r} kg Mager · Niedrig: Ø {avg_ll_r} kg"
+                    ),
+                    "detail": (
+                        f"{len(high_p)} Messpunkte mit hoher vs. {len(low_p)} mit niedriger Proteinzufuhr verglichen."
+                    ),
+                }
+            )
+
+    balances = [float(d["kcal"]) - td for d in filtered if d.get("kcal") is not None]
+    avg_balance = int(round(sum(balances) / len(balances))) if balances else 0
+    ab_s = f"{avg_balance:+d}" if avg_balance > 0 else str(avg_balance)
+    if avg_balance < -100:
+        ic, st = "✅", "good"
+    elif avg_balance > 200:
+        ic, st = "⬆️", "warn" if avg_balance > 300 else "good"
+    else:
+        ic, st = "➡️", "good"
+
+    if avg_balance < -500:
+        bal_detail = "Starkes Defizit – Muskelerhalt durch ausreichend Protein sicherstellen."
+    elif avg_balance < -100:
+        bal_detail = "Moderates Defizit – ideal für Fettabbau bei Muskelerhalt."
+    elif avg_balance > 300:
+        bal_detail = "Kalorienüberschuss – günstig für Muskelaufbau, Fettzunahme möglich."
+    else:
+        bal_detail = "Nahezu ausgeglichen – Gewicht sollte stabil bleiben."
+
+    items.append(
+        {
+            "icon": ic,
+            "status": st,
+            "title": f"Ø Kalorienbilanz: {ab_s} kcal/Tag",
+            "detail": f"Geschätzter TDEE: {int(round(td))} kcal (Data-Layer, konsistent mit Verlauf). {bal_detail}",
+        }
+    )
+
+    return items

backend/data_layer/nutrition_viz.py

@@ -10,9 +10,11 @@ from datetime import date, datetime, timedelta
 from typing import Any, Dict, List, Optional
 
 from db import get_db, get_cursor, r2d
+from data_layer.nutrition_body_merge import build_merged_daily_nutrition_body_rows
 from data_layer.nutrition_interpretation import (
     build_energy_availability_kpi_tile,
     build_macro_donut_from_averages,
+    build_nutrition_correlation_heuristic_items,
     build_nutrition_history_kpi_tiles,
 )
 from data_layer.nutrition_metrics import (
@@ -112,6 +114,58 @@ def _fetch_daily_macro_totals(profile_id: str, cutoff: Optional[str]) -> List[Di
         return [r2d(r) for r in cur.fetchall()]
 
 
+def _filter_merged_rows_by_cutoff(
+    merged: List[Dict[str, Any]], cutoff: Optional[str]
+) -> List[Dict[str, Any]]:
+    if not cutoff:
+        return list(merged)
+    return [r for r in merged if str(r.get("date"))[:10] >= cutoff]
+
+
+def _calorie_balance_daily_series(
+    merged_filtered: List[Dict[str, Any]], tdee: float
+) -> List[Dict[str, Any]]:
+    """Tagesbilanz (Aufnahme − TDEE) + 7-Tage-Mittel der Bilanz — gleiche TDEE-Quelle wie kcal_vs_weight."""
+    rows: List[Dict[str, Any]] = []
+    for r in merged_filtered:
+        if r.get("kcal") is None:
+            continue
+        ds = _iso(r.get("date"))
+        if not ds:
+            continue
+        bal = round(float(r["kcal"]) - float(tdee))
+        rows.append({"date": ds, "balance_kcal": bal})
+    rolled = _rolling_avg([dict(x) for x in rows], "balance_kcal", 7)
+    out: List[Dict[str, Any]] = []
+    for x in rolled:
+        out.append(
+            {
+                "date": x["date"],
+                "balance_kcal": x.get("balance_kcal"),
+                "balance_kcal_avg": x.get("balance_kcal_avg"),
+            }
+        )
+    return out
+
+
+def _protein_lean_mass_points(merged_filtered: List[Dict[str, Any]]) -> List[Dict[str, Any]]:
+    out: List[Dict[str, Any]] = []
+    for r in merged_filtered:
+        if r.get("protein_g") is None or r.get("lean_mass") is None:
+            continue
+        ds = _iso(r.get("date"))
+        if not ds:
+            continue
+        out.append(
+            {
+                "date": ds,
+                "protein_g": round(safe_float(r.get("protein_g")) or 0, 1),
+                "lean_mass_kg": round(safe_float(r.get("lean_mass")) or 0, 2),
+            }
+        )
+    return out
+
+
 def _kcal_weight_points_for_window(
     profile_id: str, cutoff: Optional[str]
 ) -> List[Dict[str, Any]]:
@@ -187,6 +241,9 @@ def get_nutrition_history_viz_bundle(profile_id: str, days: int) -> Dict[str, An
             "energy_balance_meta": {},
             "interpretation_tiles": [],
             "energy_availability_warning": None,
+            "calorie_balance_daily": [],
+            "protein_vs_lean_mass": {"points": [], "protein_target_low_g": None},
+            "nutrition_correlation_heuristics": [],
             "meta": {"layer_1": "nutrition_metrics", "layer_2b": "nutrition_viz"},
         }
 
@@ -239,6 +296,19 @@ def get_nutrition_history_viz_bundle(profile_id: str, days: int) -> Dict[str, An
     kw_points = _kcal_weight_points_for_window(profile_id, cutoff)
     pt_low = round(float(targets.get("protein_target_low") or 0))
 
+    merged_all = build_merged_daily_nutrition_body_rows(profile_id)
+    merged_win = _filter_merged_rows_by_cutoff(merged_all, cutoff)
+    tdee_eff = float(tdee) if tdee is not None else float(safe_float(energy_meta.get("estimated_tdee")) or 0)
+    calorie_balance_daily: List[Dict[str, Any]] = (
+        _calorie_balance_daily_series(merged_win, tdee_eff) if tdee_eff > 0 else []
+    )
+    pl_points = _protein_lean_mass_points(merged_win)
+    nutrition_correlation_heuristics = (
+        build_nutrition_correlation_heuristic_items(merged_win, tdee_eff, float(pt_low))
+        if tdee_eff > 0
+        else []
+    )
+
     weeks_for_weekly = max(4, min(52, (chart_days_for_pipeline + 6) // 7))
     weekly_chart = get_weekly_macro_distribution_chart_data(profile_id, weeks_for_weekly)
 
@@ -286,6 +356,12 @@ def get_nutrition_history_viz_bundle(profile_id: str, days: int) -> Dict[str, An
             "confidence": energy_meta.get("confidence"),
             "data_points": energy_meta.get("data_points"),
         },
+        "calorie_balance_daily": calorie_balance_daily,
+        "protein_vs_lean_mass": {
+            "points": pl_points,
+            "protein_target_low_g": pt_low if pt_low > 0 else None,
+        },
+        "nutrition_correlation_heuristics": nutrition_correlation_heuristics,
         "meta": {
             "layer_1": "nutrition_metrics",
             "layer_2b": "nutrition_viz",

backend/routers/charts.py

@@ -35,6 +35,7 @@ from data_layer.body_viz import get_body_history_viz_bundle
 from data_layer.nutrition_viz import get_nutrition_history_viz_bundle
 from data_layer.fitness_viz import get_fitness_dashboard_viz_bundle
 from data_layer.recovery_viz import get_recovery_dashboard_viz_bundle
+from data_layer.history_overview_viz import get_history_overview_viz_bundle
 from data_layer.recovery_chart_payloads import (
     build_recovery_score_chart_payload,
     build_hrv_rhr_baseline_chart_payload,
@@ -336,6 +337,24 @@ def get_recovery_dashboard_viz(
     return serialize_dates(bundle)
 
 
+@router.get("/history-overview-viz")
+def get_history_overview_viz(
+    days: int = Query(
+        default=30,
+        ge=7,
+        le=9999,
+        description="Analysefenster in Tagen (komponiert Körper/Ernährung/Fitness/Erholung)",
+    ),
+    session: dict = Depends(require_auth),
+) -> Dict:
+    """
+    Layer 2b: Gesamtansicht «Verlauf» — KPI-Kurzformen aus den vier History-Bundles + Lag-Korrelationen C1–C4 (Metadaten).
+    """
+    profile_id = session["profile_id"]
+    bundle = get_history_overview_viz_bundle(profile_id, days)
+    return serialize_dates(bundle)
+
+
 @router.get("/circumferences")
 def get_circumferences_chart(
     max_age_days: int = Query(default=90, ge=7, le=365),

backend/routers/nutrition.py

@@ -16,7 +16,7 @@ from db import get_db, get_cursor, r2d
 from auth import require_auth, check_feature_access, increment_feature_usage
 from routers.profiles import get_pid
 from feature_logger import log_feature_usage
-from caliper_composition import compute_lean_fat_kg, nearest_weight_kg_from_map
+from data_layer.nutrition_body_merge import build_merged_daily_nutrition_body_rows
 
 router = APIRouter(prefix="/api/nutrition", tags=["nutrition"])
 logger = logging.getLogger(__name__)
@@ -179,38 +179,9 @@ def get_nutrition_by_date(date: str, x_profile_id: Optional[str]=Header(default=
 
 @router.get("/correlations")
 def nutrition_correlations(x_profile_id: Optional[str]=Header(default=None), session: dict=Depends(require_auth)):
-    """Get nutrition data correlated with weight and body fat."""
+    """Get nutrition data correlated with weight and body fat (Layer 1 Merge, siehe nutrition_body_merge)."""
     pid = get_pid(x_profile_id)
-    with get_db() as conn:
+    return build_merged_daily_nutrition_body_rows(pid)
-        cur = get_cursor(conn)
-        cur.execute("SELECT * FROM nutrition_log WHERE profile_id=%s ORDER BY date",(pid,))
-        nutr={r['date']:r2d(r) for r in cur.fetchall()}
-        cur.execute("SELECT date,weight FROM weight_log WHERE profile_id=%s ORDER BY date",(pid,))
-        wlog={r['date']:r['weight'] for r in cur.fetchall()}
-        cur.execute("SELECT date,lean_mass,body_fat_pct FROM caliper_log WHERE profile_id=%s ORDER BY date",(pid,))
-        cals=sorted([r2d(r) for r in cur.fetchall()],key=lambda x:x['date'])
-    all_dates=sorted(set(list(nutr)+list(wlog)))
-    mi,last_cal,cal_by_date=0,{},{}
-    for d in all_dates:
-        while mi<len(cals) and cals[mi]['date']<=d: last_cal=cals[mi]; mi+=1
-        if last_cal: cal_by_date[d]=last_cal
-    result=[]
-    for d in all_dates:
-        if d not in nutr and d not in wlog: continue
-        row={'date':d}
-        if d in nutr: row.update({k:float(nutr[d][k]) if nutr[d][k] is not None else None for k in ['kcal','protein_g','fat_g','carbs_g']})
-        if d in wlog: row['weight']=float(wlog[d])
-        if d in cal_by_date:
-            lm = cal_by_date[d].get('lean_mass')
-            bf = cal_by_date[d].get('body_fat_pct')
-            if bf is not None and lm is None:
-                wkg = nearest_weight_kg_from_map(wlog, d)
-                if wkg is not None:
-                    lm, _fat = compute_lean_fat_kg(wkg, float(bf))
-            row['lean_mass'] = float(lm) if lm is not None else None
-            row['body_fat_pct'] = float(bf) if bf is not None else None
-        result.append(row)
-    return result
 
 
 @router.get("/weekly")

frontend/src/pages/History.jsx

@@ -966,6 +966,11 @@ function NutritionSection({ profile, insights, onRequest, loadingSlug, filterAct
   const weeklyMacro = viz.weekly_macro_chart
   const wmLoading = false
   const wmError = null
+  const balDaily = viz.calorie_balance_daily || []
+  const plm = viz.protein_vs_lean_mass || {}
+  const plmPts = plm.points || []
+  const nutHeur = viz.nutrition_correlation_heuristics || []
+  const tdeeRef = viz.tdee_reference_kcal
 
   if (!cdMacro.length || n === 0) {
     return (
@@ -999,6 +1004,95 @@ function NutritionSection({ profile, insights, onRequest, loadingSlug, filterAct
         allTime={period === 9999}
       />
 
+      {balDaily.length > 0 && tdeeRef != null && (
+        <div className="card" style={{ marginBottom: 12 }}>
+          <div style={{ fontSize: 12, fontWeight: 600, color: 'var(--text3)', marginBottom: 6 }}>
+            Kalorienbilanz (Aufnahme − TDEE ~{Math.round(tdeeRef)} kcal)
+          </div>
+          <div style={{ fontSize: 10, color: 'var(--text3)', lineHeight: 1.45, marginBottom: 8 }}>
+            Tagesbilanz und 7-Tage-Mittel — gleiche TDEE-Quelle wie «Kalorien vs. Gewicht» (Data-Layer).
+          </div>
+          <ResponsiveContainer width="100%" height={180}>
+            <LineChart
+              data={balDaily.map((d) => ({ ...d, date: fmtDate(d.date) }))}
+              margin={{ top: 4, right: 8, bottom: 0, left: -16 }}
+            >
+              <CartesianGrid stroke="var(--border)" strokeDasharray="3 3" />
+              <XAxis dataKey="date" tick={{ fontSize: 9, fill: 'var(--text3)' }} tickLine={false} interval={Math.max(0, Math.floor(balDaily.length / 6) - 1)} />
+              <YAxis tick={{ fontSize: 9, fill: 'var(--text3)' }} tickLine={false} />
+              <ReferenceLine y={0} stroke="var(--text3)" strokeWidth={1.5} />
+              <Tooltip
+                contentStyle={{ background: 'var(--surface)', border: '1px solid var(--border)', borderRadius: 8, fontSize: 11 }}
+                formatter={(v, n) => [`${v > 0 ? '+' : ''}${v} kcal`, n === 'balance_kcal_avg' ? 'Ø 7T Bilanz' : 'Tagesbilanz']}
+              />
+              <Line type="monotone" dataKey="balance_kcal" stroke="#EF9F2744" strokeWidth={1} dot={false} name="balance_kcal" />
+              <Line type="monotone" dataKey="balance_kcal_avg" stroke="#EF9F27" strokeWidth={2.5} dot={false} name="balance_kcal_avg" />
+            </LineChart>
+          </ResponsiveContainer>
+        </div>
+      )}
+
+      {plmPts.length >= 3 && (
+        <div className="card" style={{ marginBottom: 12 }}>
+          <div style={{ fontSize: 12, fontWeight: 600, color: 'var(--text3)', marginBottom: 6 }}>
+            Protein vs. Magermasse (Caliper, forward-filled)
+          </div>
+          <div style={{ fontSize: 10, color: 'var(--text3)', lineHeight: 1.45, marginBottom: 8 }}>
+            Zweitachsig; gestrichelte Linie = Protein-Minimum ({plm.protein_target_low_g || ptLow || '—'} g), wenn verfügbar.
+          </div>
+          <ResponsiveContainer width="100%" height={180}>
+            <LineChart data={plmPts.map((d) => ({ ...d, date: fmtDate(d.date), protein: d.protein_g, lean: d.lean_mass_kg }))} margin={{ top: 4, right: 8, bottom: 0, left: -16 }}>
+              <CartesianGrid stroke="var(--border)" strokeDasharray="3 3" />
+              <XAxis dataKey="date" tick={{ fontSize: 9, fill: 'var(--text3)' }} tickLine={false} />
+              <YAxis yAxisId="prot" tick={{ fontSize: 9, fill: 'var(--text3)' }} tickLine={false} domain={['auto', 'auto']} />
+              <YAxis yAxisId="lean" orientation="right" tick={{ fontSize: 9, fill: 'var(--text3)' }} tickLine={false} domain={['auto', 'auto']} />
+              {plm.protein_target_low_g > 0 && (
+                <ReferenceLine
+                  yAxisId="prot"
+                  y={plm.protein_target_low_g}
+                  stroke="#1D9E75"
+                  strokeDasharray="4 4"
+                  strokeWidth={1.5}
+                  label={{ value: `${plm.protein_target_low_g}g`, fontSize: 9, fill: '#1D9E75', position: 'right' }}
+                />
+              )}
+              <Tooltip
+                contentStyle={{ background: 'var(--surface)', border: '1px solid var(--border)', borderRadius: 8, fontSize: 11 }}
+                formatter={(v, n) => [`${v}${n === 'protein' ? 'g' : ' kg'}`, n === 'protein' ? 'Protein' : 'Mager']}
+              />
+              <Line yAxisId="prot" type="monotone" dataKey="protein" stroke="#1D9E75" strokeWidth={2} dot={false} name="protein" />
+              <Line yAxisId="lean" type="monotone" dataKey="lean" stroke="#7F77DD" strokeWidth={2} dot={{ r: 3, fill: '#7F77DD' }} name="lean" />
+            </LineChart>
+          </ResponsiveContainer>
+        </div>
+      )}
+
+      {nutHeur.length > 0 && (
+        <div style={{ marginBottom: 12 }}>
+          <div style={{ fontSize: 12, fontWeight: 600, color: 'var(--text3)', marginBottom: 8 }}>Ernährung — Kurz-Einordnung</div>
+          {nutHeur.map((item, i) => (
+            <div
+              key={i}
+              style={{
+                padding: '10px 12px',
+                borderRadius: 8,
+                marginBottom: 6,
+                background: item.status === 'good' ? 'var(--accent-light)' : 'var(--warn-bg)',
+                border: `1px solid ${item.status === 'good' ? 'var(--accent)' : 'var(--warn)'}33`,
+              }}
+            >
+              <div style={{ display: 'flex', gap: 8, alignItems: 'flex-start' }}>
+                <span style={{ fontSize: 16 }}>{item.icon || '•'}</span>
+                <div>
+                  <div style={{ fontSize: 13, fontWeight: 600 }}>{item.title}</div>
+                  <div style={{ fontSize: 12, color: 'var(--text2)', marginTop: 3, lineHeight: 1.5 }}>{item.detail}</div>
+                </div>
+              </div>
+            </div>
+          ))}
+        </div>
+      )}
+
       <div className="card" style={{ marginBottom: 12 }}>
         <div style={{ fontSize: 12, fontWeight: 600, color: 'var(--text3)', marginBottom: 6 }}>
           Makroverteilung täglich (g) · Fokus Protein
@@ -1148,174 +1242,160 @@ function ActivitySection({ activities, insights, onRequest, loadingSlug, filterA
   )
 }
 
-// ── Correlation Section ───────────────────────────────────────────────────────
+function LagCorrelationMetaCard({ title, block }) {
-function CorrelationSection({ corrData, insights, profile, onRequest, loadingSlug, filterActiveSlugs }) {
+  if (!block) return null
-  const filtered = (corrData||[]).filter(d=>d.kcal&&d.weight)
+  const ok = block.available
-  if (filtered.length < 5) return (
+  return (
-    <EmptySection text="Für Korrelationen werden Gewichts- und Ernährungsdaten benötigt (mind. 5 gemeinsame Tage)."/>
+    <div className="card" style={{ marginBottom: 8, padding: '10px 12px' }}>
+      <div style={{ fontSize: 11, fontWeight: 600, color: 'var(--text3)', marginBottom: 4 }}>{title}</div>
+      {!ok ? (
+        <div style={{ fontSize: 11, color: 'var(--text3)' }}>Nicht genug Daten für diese Auswertung.</div>
+      ) : (
+        <>
+          <div style={{ fontSize: 12, color: 'var(--text1)' }}>
+            r ≈ {block.correlation != null ? Number(block.correlation).toFixed(3) : '—'}
+            {block.best_lag_days != null ? ` · Lag ${block.best_lag_days} Tage` : ''}
+            {block.metric ? ` · ${block.metric}` : ''}
+            {block.confidence ? ` · ${block.confidence}` : ''}
+          </div>
+          {block.interpretation ? (
+            <div style={{ fontSize: 11, color: 'var(--text2)', marginTop: 6, lineHeight: 1.45 }}>{block.interpretation}</div>
+          ) : null}
+        </>
+      )}
+    </div>
   )
+}
 
-  const sex    = profile?.sex||'m'
+// ── Gesamtansicht (Layer 2b: GET /charts/history-overview-viz) ─────────────────
-  const height = profile?.height||178
+function HistoryOverviewSection({ insights, onRequest, loadingSlug, filterActiveSlugs }) {
-  const latestW = filtered[filtered.length-1]?.weight||80
+  const navigate = useNavigate()
-  const age    = profile?.dob ? Math.floor((Date.now()-new Date(profile.dob))/(365.25*24*3600*1000)) : 35
+  const [period, setPeriod] = useState(30)
-  const bmr    = sex==='m' ? 10*latestW+6.25*height-5*age+5 : 10*latestW+6.25*height-5*age-161
+  const [data, setData] = useState(null)
-  const tdee   = Math.round(bmr*1.4)  // light activity baseline
+  const [err, setErr] = useState(null)
+  const [loading, setLoading] = useState(true)
 
-  // Protein vs Lean Mass (only days with both)
+  useEffect(() => {
-  const protVsLean = filtered.filter(d=>d.protein_g&&d.lean_mass)
+    let cancelled = false
-    .map(d=>({date:fmtDate(d.date),protein:d.protein_g,lean:d.lean_mass}))
+    const daysReq = period === 9999 ? 3650 : period
-
+    setLoading(true)
-  // Chart 3: Activity kcal vs Weight change
+    api.getHistoryOverviewViz(daysReq)
-  const actVsW = filtered.filter(d=>d.weight)
+      .then((d) => {
-    .map((d,i,arr)=>{
+        if (!cancelled) {
-      const prev = arr[i-1]
+          setData(d)
-      return {
+          setErr(null)
-        date: fmtDate(d.date),
+        }
-        weight: d.weight,
-        weightDelta: prev ? Math.round((d.weight-prev.weight)*10)/10 : null,
-        kcal: d.kcal||0,
-      }
-    }).filter(d=>d.weightDelta!==null)
-
-  // Chart 4: Calorie balance (intake - estimated TDEE)
-  const balance = filtered.map(d=>({
-    date: fmtDate(d.date),
-    balance: Math.round((d.kcal||0) - tdee),
-  }))
-  const balWithAvg = rollingAvg(balance,'balance')
-  const avgBalance = Math.round(balance.reduce((s,d)=>s+d.balance,0)/balance.length)
-
-  // ── Correlation insights ──
-  const corrInsights = []
-
-  // 1. Kcal → Weight correlation
-  if (filtered.length >= 14) {
-    const highKcal  = filtered.filter(d=>d.kcal>tdee+200)
-    const lowKcal   = filtered.filter(d=>d.kcal<tdee-200)
-    if (highKcal.length>=3 && lowKcal.length>=3) {
-      const avgWHigh = Math.round(highKcal.reduce((s,d)=>s+d.weight,0)/highKcal.length*10)/10
-      const avgWLow  = Math.round(lowKcal.reduce((s,d)=>s+d.weight,0)/lowKcal.length*10)/10
-      corrInsights.push({
-        icon:'📊', status: avgWLow < avgWHigh ? 'good' : 'warn',
-        title: avgWLow < avgWHigh
-          ? `Kalorienreduktion wirkt: Ø ${avgWLow}kg bei Defizit vs. ${avgWHigh}kg bei Überschuss`
-          : `Kein klarer Kalorieneffekt auf Gewicht erkennbar`,
-        detail: `Tage mit Überschuss (>${tdee+200} kcal): Ø ${avgWHigh}kg · Tage mit Defizit (<${tdee-200} kcal): Ø ${avgWLow}kg`,
       })
+      .catch((e) => {
+        if (!cancelled) setErr(e.message || 'Laden fehlgeschlagen')
+      })
+      .finally(() => {
+        if (!cancelled) setLoading(false)
+      })
+    return () => {
+      cancelled = true
     }
+  }, [period])
+
+  if (loading) {
+    return (
+      <div>
+        <SectionHeader title="📊 Gesamtansicht" />
+        <PeriodSelector value={period} onChange={setPeriod} />
+        <div className="spinner" style={{ margin: 24 }} />
+      </div>
+    )
+  }
+  if (err) {
+    return (
+      <div>
+        <SectionHeader title="📊 Gesamtansicht" />
+        <div className="card" style={{ color: 'var(--danger)', marginTop: 8 }}>{err}</div>
+      </div>
+    )
   }
 
-  // 2. Protein → Lean mass
+  const lag = data?.lag_correlations || {}
-  if (protVsLean.length >= 3) {
+  const c4 = lag.recovery_performance
-    const ptLow  = Math.round(latestW*1.6)
+  const sections = data?.sections || []
-    const highProt = protVsLean.filter(d=>d.protein>=ptLow)
-    const lowProt  = protVsLean.filter(d=>d.protein<ptLow)
-    if (highProt.length>=2 && lowProt.length>=2) {
-      const avgLH = Math.round(highProt.reduce((s,d)=>s+d.lean,0)/highProt.length*10)/10
-      const avgLL = Math.round(lowProt.reduce((s,d)=>s+d.lean,0)/lowProt.length*10)/10
-      corrInsights.push({
-        icon:'🥩', status: avgLH >= avgLL ? 'good' : 'warn',
-        title: `Hohe Proteinzufuhr (≥${ptLow}g): Ø ${avgLH}kg Mager · Niedrig: Ø ${avgLL}kg`,
-        detail: `${highProt.length} Messpunkte mit hoher vs. ${lowProt.length} mit niedriger Proteinzufuhr verglichen.`,
-      })
-    }
-  }
-
-  // 3. Avg balance
-  corrInsights.push({
-    icon: avgBalance < -100 ? '✅' : avgBalance > 200 ? '⬆️' : '➡️',
-    status: avgBalance < -100 ? 'good' : avgBalance > 300 ? 'warn' : 'good',
-    title: `Ø Kalorienbilanz: ${avgBalance>0?'+':''}${avgBalance} kcal/Tag`,
-    detail: `Geschätzter TDEE: ${tdee} kcal (Mifflin-St Jeor ×1,4). ${
-      avgBalance<-500?'Starkes Defizit – Muskelerhalt durch ausreichend Protein sicherstellen.':
-      avgBalance<-100?'Moderates Defizit – ideal für Fettabbau bei Muskelerhalt.':
-      avgBalance>300?'Kalorienüberschuss – günstig für Muskelaufbau, Fettzunahme möglich.':
-      'Nahezu ausgeglichen – Gewicht sollte stabil bleiben.'}`,
-  })
 
   return (
     <div>
-      <SectionHeader title="🔗 Korrelationen"/>
+      <SectionHeader title="📊 Gesamtansicht" />
-
+      <PeriodSelector value={period} onChange={setPeriod} />
       <p style={{ fontSize: 11, color: 'var(--text3)', lineHeight: 1.45, marginBottom: 12 }}>
-        Das Diagramm <strong>Kalorien (Ø 7T) vs. Gewicht</strong> liegt unter <strong>Verlauf → Ernährung</strong> (gleiche Datenbasis).
+        Kurzüberblick aus denselben Data-Layer-Bundles wie die Reiter Körper bis Erholung (Issue&nbsp;53). Lag-Korrelationen C1–C4
+        stammen aus <code style={{ fontSize: 10 }}>correlations.py</code> / Chart-Endpunkte.
       </p>
 
-      {/* Chart: Calorie balance */}
+      {sections.map((sec) => (
-      <div className="card" style={{marginBottom:12}}>
+        <div key={sec.id} className="card" style={{ marginBottom: 12 }}>
-        <div style={{fontSize:12,fontWeight:600,color:'var(--text3)',marginBottom:8}}>
+          <div style={{ display: 'flex', alignItems: 'flex-start', justifyContent: 'space-between', gap: 8, marginBottom: 8 }}>
-          ⚖️ Kalorienbilanz (Aufnahme − TDEE {tdee} kcal)
+            <div style={{ fontSize: 14, fontWeight: 700 }}>{sec.title}</div>
-        </div>
+            <button
-        <ResponsiveContainer width="100%" height={160}>
+              type="button"
-          <LineChart data={balWithAvg} margin={{top:4,right:8,bottom:0,left:-16}}>
+              className="btn btn-secondary"
-            <CartesianGrid stroke="var(--border)" strokeDasharray="3 3"/>
+              style={{ fontSize: 11, padding: '4px 10px', flexShrink: 0 }}
-            <XAxis dataKey="date" tick={{fontSize:9,fill:'var(--text3)'}} tickLine={false}
+              onClick={() => navigate('/history', { state: { tab: sec.tab_id } })}
-              interval={Math.max(0,Math.floor(balWithAvg.length/6)-1)}/>
+            >
-            <YAxis tick={{fontSize:9,fill:'var(--text3)'}} tickLine={false}/>
+              Zum Reiter
-            <ReferenceLine y={0} stroke="var(--text3)" strokeWidth={1.5}/>
+            </button>
-            <Tooltip contentStyle={{background:'var(--surface)',border:'1px solid var(--border)',borderRadius:8,fontSize:11}}
-              formatter={(v,n)=>[`${v>0?'+':''}${v} kcal`,n==='balance_avg'?'Ø 7T Bilanz':'Tagesbilanz']}/>
-            <Line type="monotone" dataKey="balance"     stroke="#EF9F2744" strokeWidth={1} dot={false}/>
-            <Line type="monotone" dataKey="balance_avg" stroke="#EF9F27"   strokeWidth={2.5} dot={false} name="balance_avg"/>
-          </LineChart>
-        </ResponsiveContainer>
-        <div style={{fontSize:10,color:'var(--text3)',textAlign:'center',marginTop:4}}>
-          Über 0 = Überschuss · Unter 0 = Defizit · Ø {avgBalance>0?'+':''}{avgBalance} kcal/Tag
-        </div>
-      </div>
-
-      {/* Chart 3: Protein vs Lean Mass */}
-      {protVsLean.length >= 3 && (
-        <div className="card" style={{marginBottom:12}}>
-          <div style={{fontSize:12,fontWeight:600,color:'var(--text3)',marginBottom:8}}>
-            🥩 Protein vs. Magermasse
           </div>
-          <ResponsiveContainer width="100%" height={160}>
+          <div style={{ fontSize: 12, color: 'var(--text2)', marginBottom: 8 }}>{sec.summary_line}</div>
-            <LineChart data={protVsLean} margin={{top:4,right:8,bottom:0,left:-16}}>
+          {(sec.interpretation_short || []).map((it, i) => (
-              <CartesianGrid stroke="var(--border)" strokeDasharray="3 3"/>
+            <div key={i} style={{ fontSize: 12, marginBottom: 6, paddingLeft: 4, borderLeft: `3px solid ${getStatusColor(it.status || 'good')}` }}>
-              <XAxis dataKey="date" tick={{fontSize:9,fill:'var(--text3)'}} tickLine={false}/>
+              <strong style={{ color: 'var(--text1)' }}>{it.title}</strong>
-              <YAxis yAxisId="prot" tick={{fontSize:9,fill:'var(--text3)'}} tickLine={false} domain={['auto','auto']}/>
+              <div style={{ color: 'var(--text2)', marginTop: 2 }}>{it.detail}</div>
-              <YAxis yAxisId="lean" orientation="right" tick={{fontSize:9,fill:'var(--text3)'}} tickLine={false} domain={['auto','auto']}/>
+            </div>
-              <ReferenceLine yAxisId="prot" y={Math.round(latestW*1.6)} stroke="#1D9E75" strokeDasharray="4 4" strokeWidth={1.5}
+          ))}
-                label={{value:`Ziel ${Math.round(latestW*1.6)}g`,fontSize:9,fill:'#1D9E75',position:'right'}}/>
+          {(sec.kpi_short || []).map((k, i) => (
-              <Tooltip contentStyle={{background:'var(--surface)',border:'1px solid var(--border)',borderRadius:8,fontSize:11}}
+            <div key={i} style={{ fontSize: 12, marginBottom: 4 }}>
-                formatter={(v,n)=>[`${v}${n==='protein'?'g':' kg'}`,n==='protein'?'Protein':'Mager']}/>
+              <span style={{ color: getStatusColor(k.status || 'good') }}>{k.icon} {k.category}</span>
-              <Line yAxisId="prot" type="monotone" dataKey="protein" stroke="#1D9E75" strokeWidth={2} dot={false} name="protein"/>
+              {' · '}
-              <Line yAxisId="lean" type="monotone" dataKey="lean"    stroke="#7F77DD" strokeWidth={2} dot={{r:4,fill:'#7F77DD'}} name="lean"/>
+              <span style={{ fontWeight: 600 }}>{k.value}</span>
-            </LineChart>
+              {k.sublabel ? <span style={{ color: 'var(--text3)', fontSize: 10 }}> — {k.sublabel}</span> : null}
-          </ResponsiveContainer>
+            </div>
-          <div style={{fontSize:10,color:'var(--text3)',textAlign:'center',marginTop:4}}>
+          ))}
-            <span style={{color:'#1D9E75'}}>— Protein g/Tag</span> · <span style={{color:'#7F77DD'}}>● Magermasse kg</span>
+          {(sec.heuristic_short || []).map((h, i) => (
-          </div>
+            <div key={i} style={{ fontSize: 12, marginTop: 6, color: 'var(--text2)' }}>
-        </div>
+              <strong>{h.title}</strong>
-      )}
+              <div style={{ fontSize: 11, marginTop: 2 }}>{h.detail}</div>
-
+            </div>
-      {/* Correlation insights */}
+          ))}
-      {corrInsights.length > 0 && (
+          {(sec.insights_short || []).map((ins, i) => (
-        <div style={{marginBottom:12}}>
+            <div key={i} style={{ fontSize: 12, marginTop: 6, color: 'var(--text2)', lineHeight: 1.45 }}>
-          <div style={{fontSize:12,fontWeight:600,color:'var(--text3)',marginBottom:8}}>KORRELATIONSAUSSAGEN</div>
+              <strong>{ins.title}</strong>
-          {corrInsights.map((item,i) => (
+              <div>{ins.body}</div>
-            <div key={i} style={{padding:'10px 12px',borderRadius:8,marginBottom:6,
-              background:item.status==='good'?'var(--accent-light)':'var(--warn-bg)',
-              border:`1px solid ${item.status==='good'?'var(--accent)':'var(--warn)'}33`}}>
-              <div style={{display:'flex',gap:8,alignItems:'flex-start'}}>
-                <span style={{fontSize:16}}>{item.icon}</span>
-                <div>
-                  <div style={{fontSize:13,fontWeight:600}}>{item.title}</div>
-                  <div style={{fontSize:12,color:'var(--text2)',marginTop:3,lineHeight:1.5}}>{item.detail}</div>
-                </div>
-              </div>
             </div>
           ))}
-          <div style={{fontSize:11,color:'var(--text3)',padding:'6px 10px',background:'var(--surface2)',
-            borderRadius:6,marginTop:6}}>
-            ℹ️ TDEE-Schätzung basiert auf Mifflin-St Jeor ×1,4 (leicht aktiv). Für genauere Werte Aktivitätsdaten erfassen.
-          </div>
         </div>
-      )}
+      ))}
 
-      <InsightBox insights={insights} slugs={filterActiveSlugs(['gesamt','ziele'])} onRequest={onRequest} loading={loadingSlug}/>
+      <div style={{ fontSize: 12, fontWeight: 600, color: 'var(--text3)', marginBottom: 8, marginTop: 8 }}>
+        Lag-Korrelationen (C1–C3)
+      </div>
+      <LagCorrelationMetaCard title={lag.weight_energy?.label || 'C1'} block={lag.weight_energy} />
+      <LagCorrelationMetaCard title={lag.protein_lbm?.label || 'C2'} block={lag.protein_lbm} />
+      <LagCorrelationMetaCard title={lag.load_vitals?.label || 'C3'} block={lag.load_vitals} />
+
+      <div style={{ fontSize: 12, fontWeight: 600, color: 'var(--text3)', marginBottom: 8, marginTop: 12 }}>
+        Einflussfaktoren (C4)
+      </div>
+      <div className="card" style={{ marginBottom: 12, padding: '10px 12px' }}>
+        {c4?.drivers?.length ? (
+          c4.drivers.map((d, i) => (
+            <div key={i} style={{ fontSize: 12, marginBottom: 8, lineHeight: 1.45 }}>
+              <strong>{d.factor}</strong>
+              <span style={{ color: 'var(--text3)', marginLeft: 6 }}>({d.status})</span>
+              <div style={{ color: 'var(--text2)', marginTop: 2 }}>{d.reason}</div>
+            </div>
+          ))
+        ) : (
+          <div style={{ fontSize: 11, color: 'var(--text3)' }}>Keine Treiber-Daten.</div>
+        )}
+      </div>
+
+      <InsightBox insights={insights} slugs={filterActiveSlugs(['gesamt', 'ziele'])} onRequest={onRequest} loading={loadingSlug} />
     </div>
   )
 }
@@ -1440,23 +1520,22 @@ function PhotoGrid() {
 
 // ── Main ──────────────────────────────────────────────────────────────────────
 const TABS = [
+  { id:'overview',    label:'📊 Gesamt'     },
   { id:'body',        label:'⚖️ Körper'     },
   { id:'nutrition',   label:'🍽️ Ernährung'  },
   { id:'activity',    label:'🏋️ Fitness'  },
-  { id:'correlation', label:'🔗 Korrelation' },
   { id:'photos',      label:'📷 Fotos'       },
 ]
 
 export default function History() {
   const { activeProfile } = useProfile()  // Issue #31: Get global quality filter
   const location = useLocation?.() || {}
-  const [tab, setTab] = useState((location.state?.tab)||'body')
+  const [tab, setTab] = useState((location.state?.tab) || 'overview')
   const [weights,    setWeights]    = useState([])
   const [calipers,   setCalipers]   = useState([])
   const [circs,      setCircs]      = useState([])
   const [nutrition,  setNutrition]  = useState([])
   const [activities, setActivities] = useState([])
-  const [corrData,   setCorrData]   = useState([])
   const [insights,   setInsights]   = useState([])
   const [prompts,    setPrompts]    = useState([])
   const [profile,    setProfile]    = useState(null)
@@ -1466,11 +1545,11 @@ export default function History() {
   const loadAll = () => Promise.all([
     api.listWeight(365), api.listCaliper(), api.listCirc(),
     api.listNutrition(90), api.listActivity(25_000),
-    api.nutritionCorrelations(), api.latestInsights(), api.getProfile(),
+    api.latestInsights(), api.getProfile(),
     api.listPrompts(),
-  ]).then(([w,ca,ci,n,a,corr,ins,p,pr])=>{
+  ]).then(([w,ca,ci,n,a,ins,p,pr])=>{
     setWeights(w); setCalipers(ca); setCircs(ci)
-    setNutrition(n); setActivities(a); setCorrData(corr)
+    setNutrition(n); setActivities(a)
     setInsights(Array.isArray(ins)?ins:[]); setProfile(p)
     setPrompts(Array.isArray(pr)?pr:[])
     setLoading(false)
@@ -1486,6 +1565,10 @@ export default function History() {
       setTab('activity')
       return
     }
+    if (t === 'correlation') {
+      setTab('nutrition')
+      return
+    }
     if (t && TABS.some(x => x.id === t)) setTab(t)
   }, [location.state?.tab])
 
@@ -1530,10 +1613,10 @@ export default function History() {
           </div>
         </nav>
         <div className="history-content">
+          {tab==='overview'    && <HistoryOverviewSection {...sp}/>}
           {tab==='body'        && <BodySection profile={profile} {...sp}/>}
           {tab==='nutrition'   && <NutritionSection profile={profile} {...sp}/>}
           {tab==='activity'    && <ActivitySection  activities={activities} globalQualityLevel={activeProfile?.quality_filter_level} {...sp}/>}
-          {tab==='correlation' && <CorrelationSection corrData={corrData} profile={profile} {...sp}/>}
           {tab==='photos'      && <PhotoGrid/>}
         </div>
       </div>

frontend/src/utils/api.js

@@ -643,6 +643,11 @@ export const api = {
   getFitnessDashboardViz:             (days=28)  => req(`/charts/fitness-dashboard-viz?days=${days}`),
   /** Layer 2b: Erholung — KPI, Insights, Charts R1–R5 (recovery_metrics) */
   getRecoveryDashboardViz:            (days=28)  => req(`/charts/recovery-dashboard-viz?days=${days}`),
+  getHistoryOverviewViz:              (days=30) => req(`/charts/history-overview-viz?days=${days}`),
+  getWeightEnergyCorrelationChart:   (maxLag=14) => req(`/charts/weight-energy-correlation?max_lag=${maxLag}`),
+  getLbmProteinCorrelationChart:      (maxLag=14) => req(`/charts/lbm-protein-correlation?max_lag=${maxLag}`),
+  getLoadVitalsCorrelationChart:     (maxLag=14) => req(`/charts/load-vitals-correlation?max_lag=${maxLag}`),
+  getRecoveryPerformanceChart:        () => req('/charts/recovery-performance'),
   getEnergyBalanceChart:              (days=28)  => req(`/charts/energy-balance?days=${days}`),
   getProteinAdequacyChart:            (days=28)  => req(`/charts/protein-adequacy?days=${days}`),
   getNutritionConsistencyChart:       (days=28)  => req(`/charts/nutrition-consistency?days=${days}`),