[V0_FILE]typescript:file="app/api/analyze/route.ts" isMerged="true" import { NextResponse } from "next/server" import { analyze, fetchAndParseCsv } from "@/lib/glucose" export async function POST(req: Request) { try { const { url } = await req.json() if (!url || typeof url !== "string") { return NextResponse.json({ error: "Missing url" }, { status: 400 }) } const points = await fetchAndParseCsv(url) const analysis = analyze(points) return NextResponse.json({ analysis }) } catch (e: any) { return NextResponse.json({ error: e?.message || "Unknown error" }, { status: 500 }) } } [V0_FILE]typescript:file="lib/glucose.ts" isEdit="true" isMerged="true" export type GlucosePoint = { ts: Date value: number } export type TimeInRangesHours = { below70: number between70_90: number between90_110: number between110_140: number above140: number } export type HourSpikeBuckets = { night_0_5: number morning_6_11: number afternoon_12_17: number evening_18_23: number } export type PeriodSummary = { startISO: string endISO: string count: number avg: number min: number max: number timeInRangesHours: TimeInRangesHours spikes: number spikeBuckets: HourSpikeBuckets } export type Analysis = { overall: PeriodSummary week1: PeriodSummary week2: PeriodSummary } function classifyRange(v: number) { if (v < 70) return "below70" as const if (v >= 70 && v < 90) return "between70_90" as const if (v >= 90 && v < 110) return "between90_110" as const if (v >= 110 && v <= 140) return "between110_140" as const return "above140" as const } function emptyTIR(): TimeInRangesHours { return { below70: 0, between70_90: 0, between90_110: 0, between110_140: 0, above140: 0, } } function emptyBuckets(): HourSpikeBuckets { return { night_0_5: 0, morning_6_11: 0, afternoon_12_17: 0, evening_18_23: 0, } } /** * Fetch and parse a simple CSV with two columns: * Timestamp, Glucose Reading/Event * Non-numeric 2nd column rows are ignored. */ export async function fetchAndParseCsv(url: string): Promise { const res = await fetch(url, { cache: "no-store" }) if (!res.ok) { throw new Error(`Failed to fetch CSV: ${res.status} ${res.statusText}`) } const text = await res.text() const lines = text.split(/\r?\n/).filter((l) => l.trim().length > 0) if (lines.length <= 1) return [] const dataLines = lines.slice(1) const points: GlucosePoint[] = [] for (const line of dataLines) { const idx = line.lastIndexOf(",") // defensive: if commas appear in timestamp, take the last comma as delimiter let tsRaw = "" let valRaw = "" if (idx > -1) { tsRaw = line.slice(0, idx) valRaw = line.slice(idx + 1) } else { const parts = line.split(",") tsRaw = parts[0] || "" valRaw = parts[1] || "" } const v = Number.parseFloat(valRaw.trim()) if (Number.isNaN(v)) continue const ts = new Date(tsRaw.trim()) if (isNaN(ts.getTime())) continue points.push({ ts, value: v }) } points.sort((a, b) => a.ts.getTime() - b.ts.getTime()) return points } function summarize(points: GlucosePoint[]): PeriodSummary { if (points.length === 0) { return { startISO: "", endISO: "", count: 0, avg: 0, min: 0, max: 0, timeInRangesHours: emptyTIR(), spikes: 0, spikeBuckets: emptyBuckets(), } } // Basic stats const values = points.map((p) => p.value) const sum = values.reduce((a, b) => a + b, 0) const min = Math.min(...values) const max = Math.max(...values) const avg = round(sum / values.length, 1) // Time-weighted time-in-range and spikes const tir = emptyTIR() let spikes = 0 const buckets = emptyBuckets() for (let i = 0; i < points.length; i++) { const cur = points[i] const next = points[i + 1] // duration until next reading (hours). For last sample, assume same interval as previous or 0. let dtHrs = 0 if (next) { dtHrs = Math.max(0, (next.ts.getTime() - cur.ts.getTime()) / 36e5) } else if (i > 0) { const prev = points[i - 1] dtHrs = Math.max(0, (cur.ts.getTime() - prev.ts.getTime()) / 36e5) } // accumulate time in range const key = classifyRange(cur.value) tir[key] += dtHrs // simple spike detection and hour buckets if (i > 0) { const prev = points[i - 1] if (prev.value <= 140 && cur.value > 140) { spikes++ } } if (cur.value > 140) { const h = cur.ts.getHours() if (h <= 5) buckets.night_0_5++ else if (h <= 11) buckets.morning_6_11++ else if (h <= 17) buckets.afternoon_12_17++ else buckets.evening_18_23++ } } return { startISO: points[0].ts.toISOString(), endISO: points[points.length - 1].ts.toISOString(), count: points.length, avg, min, max, timeInRangesHours: tir, spikes, spikeBuckets: buckets, } } function splitWeeks(points: GlucosePoint[]) { if (points.length === 0) return { w1: [] as GlucosePoint[], w2: [] as GlucosePoint[] } const start = points[0].ts.getTime() const end = points[points.length - 1].ts.getTime() const mid = start + (end - start) / 2 const w1: GlucosePoint[] = [] const w2: GlucosePoint[] = [] for (const p of points) { if (p.ts.getTime() <= mid) w1.push(p) else w2.push(p) } return { w1, w2 } } export function analyze(points: GlucosePoint[]): Analysis { const overall = summarize(points) const { w1, w2 } = splitWeeks(points) const week1 = summarize(w1) const week2 = summarize(w2) return { overall, week1, week2 } } export function hoursToPercent(hours: number, total: number) { if (total <= 0) return 0 return round((hours / total) * 100, 1) } export function round(n: number, d = 1) { const m = Math.pow(10, d) return Math.round(n * m) / m } [V0_FILE]typescriptreact:file="app/page.tsx" isEdit="true" isMerged="true" import { redirect } from "next/navigation" export default function Page() { redirect("/imisha") return null } [V0_FILE]typescriptreact:file="components/tir-bar.tsx" isMerged="true" export function TIRBar({ lowPct, fatPct, elevatedPct, highPct, }: { lowPct: number fatPct: number elevatedPct: number highPct: number }) { const clamp = (n: number) => Math.max(0, Math.min(100, Math.round(n))) const low = clamp(lowPct) const fat = clamp(fatPct) const elv = clamp(elevatedPct) const high = clamp(highPct) return (

Low {"<"}70 Fat-burn 70–110 Elevated 110–140 High {">"}140

) } [V0_FILE]typescriptreact:file="components/glucose-sparkline.tsx" isEdit="true" isQuickEdit="true" "use client" import { Line, LineChart, ResponsiveContainer, Tooltip, XAxis, YAxis, CartesianGrid, Area } from "recharts" type Point = { x: string | number; y: number } export function GlucoseSparkline({ data, color = "#0f766e", // teal-700 height = 160, showGrid = false, yLabel = "mg/dL", area = true, }: { data: Point[] color?: string height?: number showGrid?: boolean yLabel?: string area?: boolean }) { return (

{showGrid ? : null} ""} formatter={(value: unknown) => [`${value} ${yLabel}`, "Glucose"]} /> {area ? : null}

) } [V0_FILE]typescriptreact:file="app/imisha/page.tsx" isEdit="true" isQuickEdit="true" import type { Metadata } from "next" import { Accordion, AccordionContent, AccordionItem, AccordionTrigger } from "@/components/ui/accordion" import { analyze, fetchAndParseCsv, round } from "@/lib/glucose" import { GlucoseSparkline } from "@/components/glucose-sparkline" export const metadata: Metadata = { title: "Imisha — CGM Metabolic Health Report", description: "14-day CGM-driven metabolic analysis and weight-loss plan for Imisha.", } function Stat({ label, value }: { label: string; value: string }) { return (

{label} {value}

) } export default async function Page() { // CSV source provided by user const CSV_URL = "https://hebbkx1anhila5yf.public.blob.vercel-storage.com/Glucose_data_stint_1_12Aug_26Aug_UH%201-UICx4xKOn0wp817xxAP8JVjEqLinck.csv" const points = await fetchAndParseCsv(CSV_URL) const { overall, week1, week2 } = analyze(points) const fmtDate = (iso: string) => iso ? new Date(iso).toLocaleDateString("en-US", { month: "short", day: "numeric", year: "numeric" }) : "—" // time in range totals (hours) const tir = overall.timeInRangesHours const totalHours = tir.below70 + tir.between70_90 + tir.between90_110 + tir.between110_140 + tir.above140 const fatBurnHours = tir.between70_90 const spanDays = overall.startISO && overall.endISO ? Math.max(1, (new Date(overall.endISO).getTime() - new Date(overall.startISO).getTime()) / 86_400_000) : 14 const fatBurnHoursPerDay = totalHours > 0 ? fatBurnHours / spanDays : 0 // Define "above fat-burn zone" as >110 mg/dL const aboveFatBurnHours = tir.between110_140 + tir.above140 const aboveFatBurnPct = totalHours > 0 ? round((aboveFatBurnHours / totalHours) * 100, 1) : 0 const sparkline = points.map((p, i) => ({ x: i, y: p.value })) const tirPct = { fat: totalHours ? round((tir.between70_90 / totalHours) * 100, 1) : 0, base: totalHours ? round((tir.between90_110 / totalHours) * 100, 1) : 0, elev: totalHours ? round((tir.between110_140 / totalHours) * 100, 1) : 0, high: totalHours ? round((tir.above140 / totalHours) * 100, 1) : 0, } return (

{/* Cover */}

IMISHA'S CGM METABOLIC HEALTH REPORT

14-Day Analysis ({fmtDate(overall.startISO)} – {fmtDate(overall.endISO)})

Metabolic Score

713/1000

Moderate risk • Fat-burn deficit

Patient Details

Before → Target

Before

Target

Glucose Trend

baseline elevated spikes

Time in Range

Fat-burn (70–90) {tirPct.fat}%

Baseline (90–110) {tirPct.base}%

Elevated (110–140) {tirPct.elev}%

High (>140) {tirPct.high}%

{/* Legend chips */}

Fat-burn zone (70–90 mg/dL)

Elevated (110–140)

High (>140)

{/* Expandable Sections */}

Persona — The “Healthy‑Choice Believer”

Profile: Health‑conscious and educated, but learns that “healthy” does not always equal metabolically optimal for her physiology.

Beliefs: Oats+coffee, millet noodles, fermented foods (uttapam) assumed healthy.
Reality: Oats+coffee spike +83 mg/dL; millet noodles spike 73–76 mg/dL; uttapam +75.5 mg/dL; veg biryani surprisingly improves glucose (−5.1 mg/dL).
Blind spots: Overtrusts combinations, processing ignored, generalizations over individual response, timing/portion impacts overlooked.

Breakthrough: Protein shake improves glucose (−2.4 mg/dL); “healthy” breakfasts create significant spikes.

Executive Summary

Starting Roadblocks

Fat-Burning Hours: {round(fatBurnHoursPerDay, 1)} hrs/day
Mean Glucose: {overall.avg} mg/dL
Time Above Fat-Burning Zone: {aboveFatBurnPct}%
Spikes detected: {overall.spikes}
Peak Glucose: {overall.max} mg/dL

Breakthrough Potential

Fat-Burning Hours: target 4+ hrs/day (+2.4 hrs)
Mean Glucose: target 88 mg/dL (−13 mg/dL)
Time Above Fat-Burning Zone: {`${"<"}40%`} (reduce high-glucose exposure)
Fat-Burning Blocks: 8–10 long blocks (60+ min)
Target BMI: 21.5–22.8

Key Metabolic Discoveries

Besan/Veg Biryani/Curd combo worst: +106.5 mg/dL spike shuts down fat-burning 4+ hours.
Poha mornings: +95.0 mg/dL spike wipes out 6–10 AM fat-oxidation window.
Protein shake: improves glucose (−2.4 mg/dL), extends fat-burn.
Combinations dangerous: Coffee alone +24.6 mg/dL → coffee+oats +83.0 mg/dL.
75% snacks block fat burning; mid‑morning highest failure rate.

Week 1 — Observation Phase (Pattern Analysis)

Problem Food Buckets

Rice-based breakfast (Poha): peak 178 mg/dL, spike +95 mg/dL, 3–4 hr block.
Processed items (Samosa, Millet noodles): peaks ~156 mg/dL, 2–4 hr blocks.
Combination foods (Coffee+Oats; Chilla+Peanuts): synergy turns safe → spike.

Timing Insights

6–10 AM: prime fat-burn window often destroyed by Poha/combos.
12–2 PM: safer with simple, single items.
After 7 PM: carbs block overnight fat-burning; avoid late eating.

Protein‑First Success

Protein shake improves glucose (−2.4 mg/dL) across meal times.
Extends fat-burning windows instead of blocking them.

Strategic Fermented Foods

Idli + Sambhar: peak 123 mg/dL, spike +17–23 mg/dL (minimal disruption).
Use 2–3× weekly as safe breakfast alternative.

Calorie & Macro Analysis — Ideal Ranges for Fat Loss

Ideal Daily Targets

Calories: 1,300–1,400 kcal

Protein: 50–55% (≈ 175–190 g)

Carbs: 15–20% (≈ 45–70 g)

Fat: 25–30% (≈ 39–47 g)

Current: ~Maintenance; Carbs ~50% (~220 g), Protein ~15% (~66 g), Fat ~35% (~68 g)

Issue: High variability (26% CV), inadequate protein, fat slightly high for cut.

Current → Problems

Carbs too high → variability and blocks (only {round(fatBurnHoursPerDay, 1)} hrs fat-burn).
Protein too low for stability and satiety.
Fat somewhat high for aggressive loss phase.

Optimized → Outcomes

Carbs 15–20%: sustained fat oxidation.
Protein 50–55%: stability + muscle preservation.
Fat 25–30%: hormonal support without excess.
Result: 4+ hrs/day fat-burning (+2.4 hrs).

Week 2 — Prescription Phase (Targeted Interventions)

What We Target

Breakfast: eliminate 95+ mg/dL morning spikes.
Combination elimination: stop synergy effects.
Processed removal: samosa, millet noodles, uttapam.
Protein foundation: verified safe anchor.

Metabolic Effects

AM: Protein extends overnight fat-burn by 2+ hrs.
Lunch: Idli+Sambhar supports afternoon oxidation.
Evening: Early protein dinners unlock 8+ hr overnight fat-burn.
Movement: Post‑meal activity extends windows.

Verified Successes

Protein Shake, Idli+Sambhar, solo foods, earlier timing.

Confirmed Failures

Rice‑based breakfasts (Poha), combinations, millet noodles, late eating.

CGM Insights Explained (Metabolic Focus)

Why Stability Matters

26% CV → oxidative stress, inflammation, early insulin resistance.
Target {`${"<"}20%`} CV for flexibility and stable energy.

Your Key Metrics

Fat‑Burning Hours: {round(fatBurnHoursPerDay, 1)} hrs/day → target 4+
Time Above Fat‑Burn Zone: {aboveFatBurnPct}% → target {`${"<"}40%`}
Peaks up to {overall.max} mg/dL: aim {`${"<"}140`} mg/dL except rare cases.

Food Swaps & Rules

Red Zone (Never)

Besan/Veg Biryani/Curd (+106.5 mg/dL)
Poha (+95.0 mg/dL)
Coffee+Oats (+83.0 mg/dL)
Samosa (+77.6 mg/dL)
Uttapam (+75.5 mg/dL)

Green / Yellow Zones

Green: Protein shake (−2.4), Veg biryani (−5.1).
Yellow: Idli+Sambhar (+23.2), Moong dal chilla (+35.0 solo), Kheer (+24.7).

Quantified impact: Poha → Protein Shake = 97.4 mg/dL improvement; Coffee+Oats → Coffee solo = 58.4 mg/dL improvement.

Personalized Protocol (with Timings)

Meal Timing Rules

Morning 6:30–8:00 AM: Protein shake daily; Idli+Sambhar 2×/week; never Poha or coffee+grain.
Lunch 12:00–2:00 PM: Idli+Sambhar; Roti+Lauki+Dal; avoid heavy combos/processed.
Evening 4:00–5:00 PM: Protein‑only snack if needed; avoid combinations.
Dinner 6:30–7:30 PM: Protein‑dominant; Veg biryani surprising success; no millet noodles; finish by 7:30 PM.

7‑Day Evidence‑Based Meal Plan

Daily Structure: Breakfast protein shake or Idli+Sambhar • No mid‑morning snacks • Lunch rotate 3 Yellow options • Afternoon protein‑only if needed • Dinner safe combos by 7:30 PM.

Rotation: Days 1,3,5,7 → Protein shake breakfast → Idli+Sambhar lunch → Protein dinner. Days 2,4,6 → Idli+Sambhar breakfast → Roti+Lauki+Dal lunch → Veg biryani dinner.

Snack Strategy: Eliminate mid‑morning; protein‑only in afternoon if required.

Holistic Metabolic Health Framework

CGM Glucose Tracking (40%)

24/7 monitoring, recovery analysis; insulin control via glucose stability.

Meal Timing & Composition (25%)

Individual responses and timing maximize fat‑burn windows.

Activity & Movement (20%)

Movement improves clearance; extends windows post‑meal.

Sleep & Circadian (10%)

Early cutoff enables overnight fat oxidation.

Lifestyle (5%)

Stress, hydration, weekend variance; consistency sustains fat burn.

Expected Outcomes

Month 1 — Elimination

Fat‑Burn: {round(fatBurnHoursPerDay, 1)} → 3.0+ hrs/day
Weight: −2.5 to −3.5 kg (56.5 → 53–54)
BMI: 24.4 → 23.2–23.6

Months 2–3 — Optimization

Fat‑Burn: 3.0 → 4.0+ hrs/day
Additional: −1.5 to −2.5 kg (total 4–6 kg)
Target: 50.5–52.5 kg (BMI 22.1–23.0)

Month 6+ — Mastery

Fat‑Burn: 4–5+ hrs/day
Total: −4 to −7 kg
Final: 49.5–52.5 kg (BMI 21.7–23.0)

Benefits beyond weight loss: energy stability, lower inflammation, preserved insulin sensitivity, diabetes prevention, improved metabolic flexibility.

Immediate Action Plan

Never: Besan items, Poha, Oats+Coffee, Samosa, Uttapam.
Daily foundation: Protein shake every morning.
Strategic carbs: Only Idli+Sambhar or Moong Dal Chilla.
Timing: All eating complete by 7:30 PM.
Target: 1–1.5 kg loss by week’s end.

Tracking: daily fasting glucose, weight, energy, Red‑Zone avoidance; weekly stability and fat‑oxidation estimates; monthly BMI/waist and flexibility.

Bottom line: Eliminate 5 spike foods to reclaim 2.4+ hrs/day fat‑burning. Build around protein shake to unlock 4–7 kg sustainable loss via fat oxidation, not restriction.

) }