from pptx import Presentation
from pptx.util import Inches, Pt, Emu
from pptx.dml.color import RGBColor
from pptx.enum.text import PP_ALIGN, MSO_ANCHOR
from pptx.util import Inches, Pt
import copy
from lxml import etree

prs = Presentation()
prs.slide_width = Inches(13.333)
prs.slide_height = Inches(7.5)
blank_layout = prs.slide_layouts[6]

# ── Colour palette ──────────────────────────────────────────────
C_NAVY   = RGBColor(0x0D, 0x2B, 0x55)   # deep navy – title bg
C_TEAL   = RGBColor(0x00, 0x7A, 0x8A)   # teal accent
C_GOLD   = RGBColor(0xF0, 0xA5, 0x00)   # gold accent
C_RED    = RGBColor(0xC0, 0x39, 0x2B)   # warning red
C_GREEN  = RGBColor(0x1A, 0x7A, 0x40)   # success green
C_WHITE  = RGBColor(0xFF, 0xFF, 0xFF)
C_LIGHT  = RGBColor(0xF0, 0xF4, 0xF8)   # very light blue-grey bg
C_DARK   = RGBColor(0x1C, 0x1C, 0x2E)   # near-black text
C_GRAY   = RGBColor(0x5A, 0x5A, 0x6A)
C_ORANGE = RGBColor(0xE6, 0x7E, 0x22)
C_PURPLE = RGBColor(0x6C, 0x3A, 0x8A)
C_LTBLUE = RGBColor(0xD6, 0xEA, 0xF8)

# ── Helper: solid fill ──────────────────────────────────────────
def solid_fill(shape, rgb):
    shape.fill.solid()
    shape.fill.fore_color.rgb = rgb

# ── Helper: add rectangle ──────────────────────────────────────
def add_rect(slide, x, y, w, h, rgb):
    shape = slide.shapes.add_shape(1, Inches(x), Inches(y), Inches(w), Inches(h))
    solid_fill(shape, rgb)
    shape.line.fill.background()
    return shape

# ── Helper: add textbox ────────────────────────────────────────
def add_text(slide, text, x, y, w, h, size, bold=False, color=C_DARK,
             align=PP_ALIGN.LEFT, italic=False, wrap=True, font="Calibri"):
    tb = slide.shapes.add_textbox(Inches(x), Inches(y), Inches(w), Inches(h))
    tf = tb.text_frame
    tf.word_wrap = wrap
    tf.margin_left = Pt(0)
    tf.margin_right = Pt(0)
    tf.margin_top = Pt(0)
    tf.margin_bottom = Pt(0)
    p = tf.paragraphs[0]
    p.alignment = align
    run = p.add_run()
    run.text = text
    run.font.name = font
    run.font.size = Pt(size)
    run.font.bold = bold
    run.font.italic = italic
    run.font.color.rgb = color
    return tb

# ── Helper: add bulleted text frame ───────────────────────────
def add_bullets(slide, items, x, y, w, h, size=16, color=C_DARK,
                bullet_char="▸ ", bold_first=False, indent=0, font="Calibri"):
    tb = slide.shapes.add_textbox(Inches(x), Inches(y), Inches(w), Inches(h))
    tf = tb.text_frame
    tf.word_wrap = True
    tf.margin_left = Pt(2)
    tf.margin_right = Pt(2)
    tf.margin_top = Pt(2)
    tf.margin_bottom = Pt(2)
    first = True
    for item in items:
        if first:
            p = tf.paragraphs[0]
            first = False
        else:
            p = tf.add_paragraph()
        p.space_before = Pt(3)
        run = p.add_run()
        run.text = bullet_char + item
        run.font.name = font
        run.font.size = Pt(size)
        run.font.color.rgb = color
        if bold_first and p == tf.paragraphs[0]:
            run.font.bold = True
    return tb

# ── Helper: slide header bar ──────────────────────────────────
def slide_header(slide, title, subtitle=None):
    add_rect(slide, 0, 0, 13.333, 1.1, C_NAVY)
    add_text(slide, title, 0.3, 0.1, 12.5, 0.7, 32, bold=True,
             color=C_WHITE, align=PP_ALIGN.LEFT, font="Calibri")
    if subtitle:
        add_text(slide, subtitle, 0.3, 0.72, 12.5, 0.35, 14,
                 color=C_GOLD, align=PP_ALIGN.LEFT, font="Calibri")
    # bottom accent line
    add_rect(slide, 0, 1.05, 13.333, 0.06, C_TEAL)
    # slide background
    add_rect(slide, 0, 1.1, 13.333, 6.4, C_LIGHT)

# ── Helper: section card ──────────────────────────────────────
def section_card(slide, title, items, x, y, w, h, bg=C_WHITE,
                 title_color=C_NAVY, item_size=14, title_size=16):
    box = slide.shapes.add_shape(1, Inches(x), Inches(y), Inches(w), Inches(h))
    solid_fill(box, bg)
    box.line.color.rgb = C_TEAL
    box.line.width = Pt(1.5)
    # title strip inside card
    title_strip = slide.shapes.add_shape(1, Inches(x), Inches(y), Inches(w), Inches(0.38))
    solid_fill(title_strip, title_color)
    title_strip.line.fill.background()
    add_text(slide, title, x+0.12, y+0.04, w-0.2, 0.32, title_size,
             bold=True, color=C_WHITE, font="Calibri")
    tb = slide.shapes.add_textbox(
        Inches(x+0.1), Inches(y+0.44), Inches(w-0.2), Inches(h-0.52))
    tf = tb.text_frame
    tf.word_wrap = True
    tf.margin_left = Pt(1)
    tf.margin_right = Pt(1)
    tf.margin_top = Pt(1)
    tf.margin_bottom = Pt(1)
    first = True
    for item in items:
        if first:
            p = tf.paragraphs[0]
            first = False
        else:
            p = tf.add_paragraph()
        p.space_before = Pt(2)
        run = p.add_run()
        run.text = "• " + item
        run.font.name = "Calibri"
        run.font.size = Pt(item_size)
        run.font.color.rgb = C_DARK

# ────────────────────────────────────────────────────────────────
#  SLIDE 1 – TITLE SLIDE
# ────────────────────────────────────────────────────────────────
slide = prs.slides.add_slide(blank_layout)
add_rect(slide, 0, 0, 13.333, 7.5, C_NAVY)
# decorative gradient strip
add_rect(slide, 0, 5.5, 13.333, 2.0, C_TEAL)
add_rect(slide, 0, 6.8, 13.333, 0.7, C_GOLD)
# title text
add_text(slide, "OBESITY", 0.6, 0.6, 12.0, 1.4, 64, bold=True,
         color=C_WHITE, align=PP_ALIGN.CENTER, font="Calibri")
add_text(slide, "Definition  •  Pathophysiology  •  Classification  •  Complications  •  Management",
         0.6, 2.0, 12.0, 0.6, 20,
         color=C_GOLD, align=PP_ALIGN.CENTER, font="Calibri")
add_text(slide, "A Comprehensive CME Lecture for MD Physicians",
         0.6, 2.65, 12.0, 0.5, 17, italic=True,
         color=RGBColor(0xB0, 0xC8, 0xE8), align=PP_ALIGN.CENTER)
add_text(slide, "Based on ESI / ICMR / WHO / Harrison's Principles of Internal Medicine",
         0.6, 3.15, 12.0, 0.45, 14,
         color=RGBColor(0x80, 0xA8, 0xD0), align=PP_ALIGN.CENTER)
add_rect(slide, 3.5, 3.85, 6.333, 0.05, C_GOLD)
add_text(slide, "July 2026", 0.6, 6.2, 12.0, 0.45, 15, bold=True,
         color=C_WHITE, align=PP_ALIGN.CENTER)

# ────────────────────────────────────────────────────────────────
#  SLIDE 2 – AGENDA
# ────────────────────────────────────────────────────────────────
slide = prs.slides.add_slide(blank_layout)
slide_header(slide, "Lecture Outline", "Overview of today's CME session")
agenda = [
    ("01", "Definition & Epidemiology",      C_TEAL),
    ("02", "Pathophysiology",                 C_NAVY),
    ("03", "Classification & BMI – Indian Cut-offs", C_TEAL),
    ("04", "Complications & Comorbidities",   C_RED),
    ("05", "Non-Pharmacological Management",  C_GREEN),
    ("06", "Pharmacotherapy – Doses & Efficacy", C_NAVY),
    ("07", "GLP-1 Agonists & New Agents",     C_PURPLE),
    ("08", "Bariatric Surgery",               C_ORANGE),
    ("09", "Treatment Algorithm",             C_TEAL),
    ("10", "Key Take-Aways",                  C_GOLD),
]
cols = 5
col_w = 13.333 / cols
for i, (num, text, col) in enumerate(agenda):
    col_idx = i % cols
    row_idx = i // cols
    cx = col_idx * col_w + 0.05
    cy = 1.3 + row_idx * 2.8
    card = slide.shapes.add_shape(1, Inches(cx), Inches(cy),
                                   Inches(col_w - 0.1), Inches(2.6))
    solid_fill(card, col)
    card.line.fill.background()
    add_text(slide, num, cx + 0.1, cy + 0.15, col_w - 0.3, 0.6, 28,
             bold=True, color=C_WHITE, align=PP_ALIGN.CENTER)
    add_text(slide, text, cx + 0.08, cy + 0.8, col_w - 0.18, 1.6, 13,
             bold=True, color=C_WHITE, align=PP_ALIGN.CENTER, wrap=True)

# ────────────────────────────────────────────────────────────────
#  SLIDE 3 – DEFINITION & EPIDEMIOLOGY
# ────────────────────────────────────────────────────────────────
slide = prs.slides.add_slide(blank_layout)
slide_header(slide, "Definition & Global Epidemiology",
             "WHO, Harrison's Internal Medicine, ICMR-INDIAB data")

# Definition box
add_rect(slide, 0.25, 1.25, 8.5, 1.5, C_NAVY)
add_text(slide, "WHO Definition:", 0.45, 1.28, 8.1, 0.35, 13, bold=True, color=C_GOLD)
add_text(slide,
    '"Obesity is defined as abnormal or excessive fat accumulation that presents a risk to health.  '
    'A BMI ≥ 30 kg/m² is classified as obese by WHO. For Asian populations (including Indians), '
    'lower cut-offs are recommended: BMI ≥ 23 = overweight; BMI ≥ 25 = obese."',
    0.45, 1.62, 8.1, 1.0, 13, color=C_WHITE, italic=True, wrap=True)

# ABCD concept
add_rect(slide, 0.25, 2.85, 8.5, 0.75, C_TEAL)
add_text(slide, "Adiposity-Based Chronic Disease (ABCD) — Fuster & Hurst 2024:",
         0.45, 2.88, 8.1, 0.28, 12, bold=True, color=C_WHITE)
add_text(slide,
    "A new diagnostic term encoding pathophysiology & treatment goals beyond BMI alone.",
    0.45, 3.15, 8.1, 0.38, 12, color=C_WHITE, italic=True)

# Stats cards right column
stats = [
    ("1 in 8", "adults globally is obese (WHO 2024)", C_ORANGE),
    ("27%",    "Indians have abdominal obesity (ICMR-INDIAB)", C_RED),
    ("135 M+", "Indians meet obesity criteria (BMI ≥25)", C_PURPLE),
]
for i, (val, label, col) in enumerate(stats):
    rx = 9.05
    ry = 1.25 + i * 1.8
    r = slide.shapes.add_shape(1, Inches(rx), Inches(ry), Inches(4.0), Inches(1.6))
    solid_fill(r, col)
    r.line.fill.background()
    add_text(slide, val, rx + 0.1, ry + 0.12, 3.7, 0.75, 36, bold=True,
             color=C_WHITE, align=PP_ALIGN.CENTER)
    add_text(slide, label, rx + 0.1, ry + 0.88, 3.7, 0.6, 12,
             color=C_WHITE, align=PP_ALIGN.CENTER, wrap=True)

# Indian context
section_card(slide, "Indian-Specific Context", [
    "Metabolically Obese Normal Weight (MONW): BMI <25 but ≥2 metabolic risk factors",
    "Waist circ: ≥90 cm men, ≥80 cm women = abdominal obesity (ICMR)",
    "Indians accumulate visceral fat at lower BMI vs. Caucasians",
    "ICMR-INDIAB 2024: MONO + MOO phenotypes predict T2DM, CAD, CKD risk",
], 0.25, 3.72, 8.5, 2.65, bg=C_WHITE, title_color=C_NAVY, item_size=13)

# ────────────────────────────────────────────────────────────────
#  SLIDE 4 – PATHOPHYSIOLOGY (OVERVIEW)
# ────────────────────────────────────────────────────────────────
slide = prs.slides.add_slide(blank_layout)
slide_header(slide, "Pathophysiology of Obesity",
             "Mulholland & Greenfield Surgery; Harrison's IM 22e; Fuster & Hurst 2024")

# Central box
add_rect(slide, 5.4, 2.8, 2.5, 1.0, C_GOLD)
add_text(slide, "CHRONIC\nNUTRIENT\nEXCESS", 5.4, 2.82, 2.5, 0.95, 14,
         bold=True, color=C_DARK, align=PP_ALIGN.CENTER, wrap=True)

cells = [
    ("Adipose Tissue\nDysfunction",
     "• Adipocyte hypertrophy & hyperplasia\n• ↑ Leptin; Leptin resistance\n• ↓ Adiponectin\n• Macrophage infiltration (M1 polarisation)",
     0.2, 1.2, C_TEAL),
    ("Hypothalamus\n& Appetite",
     "• Leptin resistance → lost satiety signal\n• ↑ Ghrelin → hunger\n• Melanocortin pathway dysregulation\n• Reward pathway (dopamine) hijacking",
     4.4, 1.2, C_NAVY),
    ("Gut Microbiome",
     "• Dysbiosis promotes energy harvest\n• ↓ Short-chain fatty acids\n• Increased intestinal permeability\n• ↑ LPS → low-grade endotoxemia",
     8.6, 1.2, C_PURPLE),
    ("Endoplasmic Reticulum\n& Oxidative Stress",
     "• Chronic nutrient excess → ER stress\n• Unfolded protein response (UPR)\n• Mitochondrial dysfunction\n• ↑ ROS, NF-κB activation → inflammation",
     0.2, 4.1, C_ORANGE),
    ("Insulin Resistance\n& Lipotoxicity",
     "• Ectopic fat: liver, muscle, pancreas\n• Ceramides & DAG → IRS-1 inhibition\n• Hepatic glucose overproduction\n• β-cell exhaustion → T2DM",
     4.4, 4.1, C_RED),
    ("Adipokines &\nInflammation",
     "• ↑ TNF-α, IL-6, MCP-1, CRP\n• ↓ Adiponectin (anti-inflammatory)\n• Systemic low-grade inflammation\n• Atherosclerosis acceleration",
     8.6, 4.1, C_TEAL),
]
for (title, body, bx, by, col) in cells:
    card = slide.shapes.add_shape(1, Inches(bx), Inches(by),
                                   Inches(3.9), Inches(2.75))
    solid_fill(card, col)
    card.line.fill.background()
    add_text(slide, title, bx+0.1, by+0.08, 3.7, 0.55, 14,
             bold=True, color=C_WHITE, align=PP_ALIGN.CENTER, wrap=True)
    add_rect(slide, bx+0.05, by+0.6, 3.8, 0.03, RGBColor(0xFF,0xFF,0xAA))
    add_text(slide, body, bx+0.12, by+0.68, 3.7, 1.9, 12,
             color=C_WHITE, wrap=True)

# ────────────────────────────────────────────────────────────────
#  SLIDE 5 – PATHOPHYSIOLOGY HORMONAL AXIS
# ────────────────────────────────────────────────────────────────
slide = prs.slides.add_slide(blank_layout)
slide_header(slide, "Hormonal Regulation of Body Weight",
             "Gut-brain axis, adipokines & energy balance")

hormones = [
    ("LEPTIN", "Adipocyte-derived\nAnorexigenic\n(satiety hormone)",
     "In obesity:\n↑ Levels but resistance develops\nLoss of satiety signalling",
     C_TEAL, 0.2, 1.3),
    ("GHRELIN", "Stomach-derived\nOrexigenic\n(hunger hormone)",
     "In obesity:\n↑ After weight loss → rebound\nDrives weight regain",
     C_RED, 3.4, 1.3),
    ("GLP-1 / PYY", "L-cell derived (gut)\nAnorexigenic\nIncretin effect",
     "In obesity:\n↓ Post-meal secretion\nTherapeutic target for GLP-1 agonists",
     C_GREEN, 6.6, 1.3),
    ("INSULIN", "Pancreas (β-cell)\nAnorexigenic\nCentral & peripheral",
     "In obesity:\nResistance develops (IRS-1 ↓)\nCompensatory hypersecretion",
     C_ORANGE, 9.8, 1.3),
    ("ADIPONECTIN", "Adipocyte-derived\nAnti-inflammatory\nInsulin sensitiser",
     "In obesity:\n↓↓ Low levels\nInversely correlates with BMI",
     C_PURPLE, 0.2, 4.3),
    ("CORTISOL", "HPA axis\nCatabolic, orexigenic\nCentral fat deposition",
     "In obesity:\n↑ HPA axis activation\nVisceral fat accumulation",
     C_NAVY, 3.4, 4.3),
    ("THYROID (T3/T4)", "Thermogenic\nMetabolic rate regulator",
     "In obesity:\nHypothyroidism common\nCheck TSH in all obese patients",
     C_TEAL, 6.6, 4.3),
    ("SEX HORMONES", "Estrogen / Testosterone\nFat distribution",
     "In obesity:\n↓ Testosterone in men (hypogonadism)\n↑ Estrogen (aromatisation in fat)",
     C_GOLD, 9.8, 4.3),
]
for (hname, role, effect, col, hx, hy) in hormones:
    card = slide.shapes.add_shape(1, Inches(hx), Inches(hy),
                                   Inches(3.0), Inches(2.85))
    solid_fill(card, col)
    card.line.fill.background()
    add_text(slide, hname, hx+0.1, hy+0.06, 2.8, 0.42, 15,
             bold=True, color=C_WHITE, align=PP_ALIGN.CENTER)
    add_text(slide, role, hx+0.1, hy+0.5, 2.8, 0.72, 11,
             color=RGBColor(0xFF,0xFF,0xCC), align=PP_ALIGN.CENTER, wrap=True)
    add_rect(slide, hx+0.1, hy+1.22, 2.8, 0.03, RGBColor(0xFF,0xFF,0xAA))
    add_text(slide, effect, hx+0.1, hy+1.28, 2.8, 1.4, 11,
             color=C_WHITE, wrap=True)

# ────────────────────────────────────────────────────────────────
#  SLIDE 6 – CLASSIFICATION (INDIAN CONTEXT)
# ────────────────────────────────────────────────────────────────
slide = prs.slides.add_slide(blank_layout)
slide_header(slide, "Classification of Obesity – Indian Context",
             "ESI Guidelines 2022 | ICMR | WHO Asian cut-offs")

# Table header
add_rect(slide, 0.25, 1.2, 12.8, 0.5, C_NAVY)
headers = ["Category", "WHO BMI (kg/m²)", "Indian BMI (kg/m²)", "Waist (Men)", "Waist (Women)", "Risk Level"]
col_widths = [2.1, 2.1, 2.2, 1.8, 1.8, 2.8]
x_pos = 0.3
for i, (hdr, cw) in enumerate(zip(headers, col_widths)):
    add_text(slide, hdr, x_pos, 1.22, cw, 0.45, 13,
             bold=True, color=C_WHITE, align=PP_ALIGN.CENTER)
    x_pos += cw

rows = [
    ("Underweight",   "< 18.5",   "< 18.5",   "—",        "—",        "↑ Nutritional risk",   C_LTBLUE,   C_DARK),
    ("Normal Weight", "18.5–24.9","18.5–22.9", "< 90 cm",  "< 80 cm",  "Low",                  C_WHITE,    C_DARK),
    ("Overweight",    "25–29.9",  "23–24.9",   "90–94 cm", "80–84 cm", "Moderate",              RGBColor(0xFF,0xF0,0xD0), C_DARK),
    ("Obesity Gr. 1", "30–34.9",  "25–29.9",   "95–99 cm", "85–89 cm", "High – Treat",          RGBColor(0xFF,0xE0,0xC0), C_ORANGE),
    ("Obesity Gr. 2", "35–39.9",  "30–34.9",   "≥100 cm",  "≥90 cm",   "Very High – Pharmacotherapy", RGBColor(0xFF,0xCC,0xAA), C_RED),
    ("Obesity Gr. 3", "≥ 40",     "≥ 35",      "≥100 cm",  "≥90 cm",   "Extreme – Consider Surgery",  RGBColor(0xFF,0xAA,0xAA), C_RED),
]
for ri, (cat, who_bmi, ind_bmi, wm, wf, risk, bg, fc) in enumerate(rows):
    ry = 1.74 + ri * 0.74
    add_rect(slide, 0.25, ry, 12.8, 0.72, bg)
    vals = [cat, who_bmi, ind_bmi, wm, wf, risk]
    xp = 0.3
    for vi, (val, cw) in enumerate(zip(vals, col_widths)):
        bold = (vi == 0)
        fc2 = fc if (vi == 0 or vi == 5) else C_DARK
        add_text(slide, val, xp, ry+0.06, cw, 0.6, 13,
                 bold=bold, color=fc2, align=PP_ALIGN.CENTER, wrap=True)
        xp += cw
    # row divider
    add_rect(slide, 0.25, ry+0.7, 12.8, 0.02, C_GRAY)

# Note box
add_rect(slide, 0.25, 6.22, 12.8, 0.85, C_NAVY)
add_text(slide,
    "★  Indian (Asian) cut-offs are ~2.5 kg/m² lower than WHO due to higher body fat % at same BMI.\n"
    "   ESI 2022: Pharmacotherapy indicated at BMI >27 kg/m², OR >25 kg/m² with ≥1 comorbidity.\n"
    "   New 2-Stage AIIMS/ICMR Model (2024): Stage 1 = excess adiposity (no organ dysfunction) | Stage 2 = excess adiposity + organ/functional impairment",
    0.4, 6.26, 12.5, 0.75, 11, color=C_GOLD, wrap=True)

# ────────────────────────────────────────────────────────────────
#  SLIDE 7 – WAIST-BASED CLASSIFICATION & ADIPOSITY PHENOTYPES
# ────────────────────────────────────────────────────────────────
slide = prs.slides.add_slide(blank_layout)
slide_header(slide, "Beyond BMI – Adiposity Phenotypes & Abdominal Obesity",
             "ICMR-INDIAB 2024 | ESI Guidelines | AIIMS New Delhi")

phenotypes = [
    ("MHNO", "Metabolically Healthy\nNon-Obese",
     "BMI < 25 kg/m²\nNo metabolic abnormalities\nIdeal metabolic state",
     C_GREEN, 0.2, 1.3),
    ("MHO", "Metabolically Healthy\nObese",
     "BMI ≥ 25 kg/m²\nNo / minimal metabolic risk\n'Fat but fit' – watch carefully",
     RGBColor(0x28, 0x9E, 0x58), 3.4, 1.3),
    ("MONO", "Metabolically Obese\nNormal Weight",
     "BMI < 25 kg/m²\n≥2 metabolic risk factors\nHigh risk of T2DM, CAD, CKD",
     C_ORANGE, 6.6, 1.3),
    ("MOO", "Metabolically Obese\nObese",
     "BMI ≥ 25 kg/m²\n≥2 metabolic risk factors\nHighest morbidity & mortality",
     C_RED, 9.8, 1.3),
]
for (abbr, full, desc, col, px, py) in phenotypes:
    c = slide.shapes.add_shape(1, Inches(px), Inches(py),
                                Inches(3.0), Inches(2.2))
    solid_fill(c, col)
    c.line.fill.background()
    add_text(slide, abbr, px+0.05, py+0.08, 2.9, 0.5, 24,
             bold=True, color=C_WHITE, align=PP_ALIGN.CENTER)
    add_text(slide, full, px+0.05, py+0.58, 2.9, 0.5, 12,
             bold=True, color=RGBColor(0xFF,0xFF,0xCC), align=PP_ALIGN.CENTER, wrap=True)
    add_text(slide, desc, px+0.1, py+1.12, 2.8, 0.95, 11,
             color=C_WHITE, wrap=True)

# abdominal obesity box
section_card(slide, "Abdominal Obesity Criteria (Indian / ICMR)",
    ["Waist circumference: MEN ≥ 90 cm | WOMEN ≥ 80 cm",
     "Waist-to-hip ratio: Men > 0.90 | Women > 0.80",
     "Waist-to-height ratio: > 0.50 (universal cut-off)",
     "Body fat %: Men > 25% | Women > 30% (by DXA)",
     "Clinical implication: Abdominal obesity is the primary driver of cardiometabolic risk in Indians",
     ],
    0.2, 3.65, 12.9, 3.55, bg=C_WHITE, title_color=C_TEAL, item_size=14)

# ────────────────────────────────────────────────────────────────
#  SLIDE 8 – COMPLICATIONS (COMPREHENSIVE)
# ────────────────────────────────────────────────────────────────
slide = prs.slides.add_slide(blank_layout)
slide_header(slide, "Complications & Comorbidities of Obesity",
             "Harrison's IM 22e | Mulholland Surgery | Fuster & Hurst Cardiology")

comps = [
    ("🫀 CARDIOVASCULAR", C_RED,
     ["Hypertension (↑ CO + RAAS activation)",
      "Coronary artery disease & ACS",
      "Heart failure (both HFpEF & HFrEF)",
      "Atrial fibrillation",
      "Dyslipidaemia (↑ TG, ↓ HDL, small dense LDL)",
      "Stroke (ischaemic & haemorrhagic)"],
     0.2, 1.2),
    ("🩺 METABOLIC", C_ORANGE,
     ["Type 2 diabetes mellitus",
      "Metabolic syndrome",
      "Non-alcoholic fatty liver (NAFLD/NASH)",
      "Hyperuricaemia & gout",
      "Polycystic ovarian syndrome (PCOS)",
      "Hypogonadism (men)"],
     4.45, 1.2),
    ("🫁 RESPIRATORY", C_NAVY,
     ["Obstructive sleep apnoea (OSA)",
      "Obesity hypoventilation syndrome",
      "Pickwickian syndrome",
      "Asthma (worsening control)",
      "Pulmonary hypertension",
      "Restrictive lung disease"],
     8.7, 1.2),
    ("🦴 MUSCULOSKELETAL", C_TEAL,
     ["Osteoarthritis (knee, hip, ankle)",
      "Low back pain",
      "Plantar fasciitis",
      "Carpal tunnel syndrome",
      "Gout & pseudogout",
      "Reduced mobility & falls"],
     0.2, 4.35),
    ("🧠 NEUROPSYCH", C_PURPLE,
     ["Depression & anxiety",
      "Pseudo-tumour cerebri (IIH)",
      "Dementia risk ↑",
      "Peripheral neuropathy",
      "Poor self-esteem, social stigma",
      "Sleep disturbance, fatigue"],
     4.45, 4.35),
    ("🩻 ONCOLOGY &\nOTHERS", C_DARK,
     ["Cancers: breast, endometrial, colon,\n  oesophageal, renal, gallbladder",
      "Cholelithiasis & cholecystitis",
      "GERD & hiatal hernia",
      "Chronic kidney disease",
      "Venous thromboembolism (DVT/PE)",
      "Skin: acanthosis nigricans, intertrigo"],
     8.7, 4.35),
]
for (title, col, items, cx, cy) in comps:
    card = slide.shapes.add_shape(1, Inches(cx), Inches(cy),
                                   Inches(4.1), Inches(2.95))
    solid_fill(card, col)
    card.line.fill.background()
    add_text(slide, title, cx+0.1, cy+0.06, 3.9, 0.45, 13,
             bold=True, color=C_WHITE, align=PP_ALIGN.CENTER, wrap=True)
    add_rect(slide, cx+0.05, cy+0.5, 4.0, 0.03, RGBColor(0xFF,0xFF,0xAA))
    body = "\n".join("• " + i for i in items)
    add_text(slide, body, cx+0.12, cy+0.56, 3.9, 2.25, 11,
             color=C_WHITE, wrap=True)

# ────────────────────────────────────────────────────────────────
#  SLIDE 9 – NON-PHARMACOLOGICAL TREATMENT
# ────────────────────────────────────────────────────────────────
slide = prs.slides.add_slide(blank_layout)
slide_header(slide, "Non-Pharmacological Management",
             "Lifestyle Intervention – The Foundation of All Obesity Treatment")

cards_np = [
    ("DIETARY THERAPY", C_GREEN, [
        "Caloric deficit: 500–750 kcal/day below TDEE",
        "Target: 0.5–1 kg weight loss per week",
        "Low-calorie diet: 1200–1500 kcal/day (women) | 1500–1800 (men)",
        "Very-low-calorie diet (VLCD): 800 kcal/day – supervised only",
        "Indian diet: ↓ refined carbs by 10–15%; ↑ protein to 1 g/kg/day",
        "Mediterranean / DASH patterns recommended",
        "Avoid sugar-sweetened beverages & ultra-processed foods",
    ], 0.2, 1.2),
    ("PHYSICAL ACTIVITY", C_TEAL, [
        "Min. 150 min/week moderate aerobic (WHO; AHA; ESI)",
        "Ideal: 200–300 min/week for weight maintenance",
        "Resistance training: ≥2 sessions/week (preserve lean mass)",
        "NEAT (non-exercise activity thermogenesis) – walk, stairs",
        "Start low, go slow: 30 min/day of brisk walking",
        "Combined aerobic + resistance superior to either alone",
        "Yoga & swimming – beneficial for MSK co-morbidities",
    ], 4.45, 1.2),
    ("BEHAVIOURAL &\nPSYCHOLOGICAL", C_PURPLE, [
        "Cognitive Behavioural Therapy (CBT) – gold standard",
        "Motivational interviewing",
        "Self-monitoring: food diary, step count, weight log",
        "Address binge-eating disorder (BED) before pharmacotherapy",
        "Mindful eating techniques",
        "Sleep optimisation (< 7 h sleep → weight gain)",
        "Stress management (cortisol → visceral fat)",
    ], 8.7, 1.2),
    ("EXPECTED OUTCOMES\nLIFESTYLE ALONE", C_NAVY, [
        "5–10% body weight loss in 6 months with intensive intervention",
        "5% weight loss reduces: HbA1c 0.5%, BP 3–8 mmHg, TG 15–30%",
        "10% loss reduces: OSA severity, joint pain, fatty liver",
        "Maintenance requires long-term behavioural support",
        "Weight regain is common without pharmacotherapy",
        "Multidisciplinary team essential: dietician + physician + psychologist",
    ], 0.2, 4.38),
    ("MEDICAL NUTRITION\nTHERAPY (MNT)", C_ORANGE, [
        "Protein: ≥ 1 g/kg ideal body weight/day (preserve lean mass)",
        "Fat: < 30% total calories; ↓ saturated fat < 10%",
        "Fibre: ≥ 25–30 g/day (increases satiety)",
        "Low glycaemic index foods preferred",
        "Meal timing: avoid late-night eating",
        "Hydration: 2–3 L water/day; avoid caloric beverages",
    ], 4.45, 4.38),
    ("DIGITAL HEALTH &\nINDIAN CONTEXT", C_TEAL, [
        "Calorie-tracking apps (MyFitnessPal, HealthifyMe)",
        "Step trackers / smartwatches",
        "Telehealth for rural access",
        "Group-based counselling (cost-effective in India)",
        "Barriers: cost, lack of safe exercise spaces, cultural food norms",
        "Traditional Indian foods can be adapted: millets, legumes, greens",
    ], 8.7, 4.38),
]
for (title, col, items, cx, cy) in cards_np:
    card = slide.shapes.add_shape(1, Inches(cx), Inches(cy),
                                   Inches(4.1), Inches(2.95))
    solid_fill(card, col)
    card.line.fill.background()
    add_text(slide, title, cx+0.1, cy+0.06, 3.9, 0.45, 13,
             bold=True, color=C_WHITE, align=PP_ALIGN.CENTER, wrap=True)
    add_rect(slide, cx+0.05, cy+0.5, 4.0, 0.03, RGBColor(0xFF,0xFF,0xAA))
    body = "\n".join("• " + i for i in items)
    add_text(slide, body, cx+0.12, cy+0.56, 3.9, 2.25, 11,
             color=C_WHITE, wrap=True)

# ────────────────────────────────────────────────────────────────
#  SLIDE 10 – PHARMACOTHERAPY OVERVIEW & INDICATIONS
# ────────────────────────────────────────────────────────────────
slide = prs.slides.add_slide(blank_layout)
slide_header(slide, "Pharmacotherapy of Obesity – Overview & Indications",
             "ESI 2022 | FDA | CDSCO | Harrison's 22e | Katzung 16e")

# Indications
add_rect(slide, 0.25, 1.2, 6.2, 2.4, C_NAVY)
add_text(slide, "INDICATIONS (ESI / ICMR Guidelines – India):", 0.4, 1.22, 5.9, 0.4, 14,
         bold=True, color=C_GOLD)
ind_text = (
    "• BMI > 27 kg/m² (Indian cut-off) OR\n"
    "• BMI > 25 kg/m² + ≥1 comorbidity (T2DM, HTN, dyslipidaemia, OSA)\n"
    "• Used alongside lifestyle modification – never monotherapy\n"
    "• Reassess at 12–16 weeks: ≥5% weight loss = continue; <5% = switch\n"
    "• Western cut-off: BMI ≥30, or ≥27 + comorbidity"
)
add_text(slide, ind_text, 0.4, 1.65, 5.9, 1.85, 13, color=C_WHITE, wrap=True)

# Status in India box
add_rect(slide, 6.6, 1.2, 6.5, 2.4, C_TEAL)
add_text(slide, "APPROVAL STATUS IN INDIA (CDSCO 2024):", 6.75, 1.22, 6.2, 0.4, 14,
         bold=True, color=C_GOLD)
india_text = (
    "✓  Orlistat 120 mg TID – Only licensed anti-obesity drug in India (CDSCO)\n"
    "✓  Metformin – Diabesity; weight-neutral to mildly reducing\n"
    "✓  Liraglutide 3 mg/day – Approved for obesity in India (Saxenda)\n"
    "✓  Semaglutide 2.4 mg/week – Approved (Wegovy, Ozempic off-label)\n"
    "⚠  Phentermine/Topiramate, Naltrexone/Bupropion – NOT approved in India\n"
    "⚠  Tirzepatide – GIP/GLP-1 dual agonist – Phase III data; pending India approval"
)
add_text(slide, india_text, 6.75, 1.65, 6.2, 1.85, 12, color=C_WHITE, wrap=True)

# Drug classes
classes = [
    ("Lipase\nInhibitor", "Orlistat", "Only ↓ fat\nabsorption",     C_GREEN,  0.2,  3.78),
    ("GLP-1 RA",          "Liraglutide\nSemaglutide", "Appetite &\nincretin",  C_TEAL,  2.85, 3.78),
    ("GIP+GLP-1",         "Tirzepatide", "Dual agonist\nbest efficacy",   C_PURPLE,5.5,  3.78),
    ("SGLT2\nInhibitor",  "Empagliflozin\nDapagliflozin", "Glucosuria\n~2–4 kg",  C_NAVY, 8.15, 3.78),
    ("Sympatho-\nMimetic", "Phentermine\n(off-label)", "Appetite\nsuppressant",   C_ORANGE,10.8, 3.78),
]
for (drug_class, drug, mech, col, dx, dy) in classes:
    dc = slide.shapes.add_shape(1, Inches(dx), Inches(dy),
                                 Inches(2.45), Inches(1.5))
    solid_fill(dc, col)
    dc.line.fill.background()
    add_text(slide, drug_class, dx+0.05, dy+0.05, 2.35, 0.35, 13,
             bold=True, color=C_WHITE, align=PP_ALIGN.CENTER, wrap=True)
    add_text(slide, drug, dx+0.05, dy+0.4, 2.35, 0.45, 12,
             color=RGBColor(0xFF,0xFF,0xCC), align=PP_ALIGN.CENTER, wrap=True)
    add_text(slide, mech, dx+0.05, dy+0.88, 2.35, 0.55, 11,
             color=C_WHITE, align=PP_ALIGN.CENTER, wrap=True)

# Adjunct drugs
add_rect(slide, 0.2, 5.42, 12.9, 1.82, C_LIGHT)
add_rect(slide, 0.2, 5.42, 12.9, 0.38, C_DARK)
add_text(slide, "Adjunct Medications for Weight-Related Comorbidities (choose weight-friendly agents):",
         0.35, 5.44, 12.6, 0.32, 13, bold=True, color=C_GOLD)
adj_text = (
    "T2DM: Metformin (↓ 1–3 kg) | SGLT2-i: Empa/Dapa (↓ 2–4 kg) | Semaglutide 1 mg or 2.4 mg | Avoid glimepiride/insulin unless needed\n"
    "HTN: ACEI/ARB preferred (RAAS active in obesity) | CCB for peripheral oedema caution\n"
    "Dyslipidaemia: High-intensity statin | Fenofibrate for ↑TG | Avoid weight-gaining agents like thiazolidinediones, sulfonylureas, tricyclics, valproate, steroids"
)
add_text(slide, adj_text, 0.35, 5.84, 12.6, 1.35, 12, color=C_DARK, wrap=True)

# ────────────────────────────────────────────────────────────────
#  SLIDE 11 – PHARMACOTHERAPY DETAILED TABLE
# ────────────────────────────────────────────────────────────────
slide = prs.slides.add_slide(blank_layout)
slide_header(slide, "Anti-Obesity Drug Reference Table",
             "Exact dosages, mechanisms, efficacy & side-effect profile")

# Table
col_headers = ["Drug", "Dose", "Mechanism", "Avg. Weight Loss", "Key Side Effects", "Contraindications"]
col_w2 = [1.8, 2.1, 2.1, 1.7, 2.5, 2.8]

add_rect(slide, 0.2, 1.2, 13.0, 0.52, C_NAVY)
xp2 = 0.25
for hdr, cw in zip(col_headers, col_w2):
    add_text(slide, hdr, xp2, 1.22, cw, 0.48, 12, bold=True,
             color=C_WHITE, align=PP_ALIGN.CENTER)
    xp2 += cw

drug_rows = [
    ("Orlistat\n(Xenical)", "120 mg TID\n(before meals)\n60 mg OTC",
     "GI lipase inhibitor\n↓ fat absorption 30%",
     "2–3 kg over placebo\nat 12 months",
     "Steatorrhea, faecal urgency, flatus, fat-soluble vit deficiency (A,D,E,K)",
     "Chronic malabsorption, cholestasis",
     C_GREEN),
    ("Liraglutide\n(Saxenda)", "0.6→3 mg SC/day\n(escalate weekly,\ntarget 3 mg/day)",
     "GLP-1 receptor agonist\n↑ satiety, ↓ gastric emptying, ↓ appetite",
     "~5–6% at 1 year\n(5.8 kg vs 1.9 kg placebo – SCALE trial)",
     "Nausea, vomiting, diarrhoea, injection-site reactions, ↑ HR",
     "MEN2, medullary thyroid carcinoma, pancreatitis history, IBD",
     C_TEAL),
    ("Semaglutide\n(Wegovy / Ozempic)", "0.25 mg/wk → 2.4 mg/wk SC\n(escalate over 16 wks)",
     "GLP-1 receptor agonist\n(more potent than liraglutide)\nCentral appetite suppression",
     "~15–16% at 68 weeks\n(STEP-1 trial: 15.3% vs 2.4%)\nMeta-analysis 2024: 12.4% mean",
     "Nausea (44%), vomiting, diarrhoea, constipation, headache",
     "MEN2, medullary thyroid Ca, pancreatitis, gastroparesis",
     C_TEAL),
    ("Tirzepatide\n(Mounjaro/Zepbound)", "2.5 mg/wk → 15 mg/wk SC\n(escalate monthly)",
     "Dual GIP + GLP-1 agonist\nMost potent current agent",
     "~20–22% at 72 weeks\n(SURMOUNT-1: 20.9% at 15 mg)\n2024 meta-analysis confirmed",
     "Nausea (33%), vomiting, diarrhoea, constipation, ↑ amylase",
     "MEN2, medullary thyroid Ca; Not yet approved for obesity in India",
     C_PURPLE),
    ("Phentermine/\nTopiramate-ER\n(Qsymia)", "3.75/23 mg → 15/92 mg oral daily",
     "Sympathomimetic amine + GABA agonist\nAppetite suppression + ↑ satiety",
     "~7–8% at 1 year\n(CONQUER trial: 8.1–10.2%)",
     "Tachycardia, dry mouth, constipation, cognitive impairment, teratogenicity",
     "Hyperthyroidism, MAOIs, glaucoma, pregnancy; NOT approved India",
     C_ORANGE),
    ("Naltrexone/\nBupropion SR\n(Contrave)", "8/90 mg BD → 16/180 mg BD\n(titrate over 4 weeks)",
     "Opioid antagonist + dopamine/NE\nreuptake inhibitor; ↓ appetite & craving",
     "~3–5% at 1 year\n(COR-1 trial: 4.8% vs 1.8%)",
     "Nausea (31%), headache, constipation, ↑ BP/HR, seizure risk",
     "Seizure disorder, uncontrolled HTN, opioid use, MAOIs; NOT approved India",
     C_ORANGE),
    ("Orlistat 60 mg\n(OTC – Alli)", "60 mg TID\n(before meals)",
     "Same as Orlistat 120 mg\nLower dose",
     "~3% vs placebo\nat 6 months",
     "Same GI side effects but milder; Requires low-fat diet",
     "Same as Orlistat 120 mg",
     C_GREEN),
]

for ri, (dname, dose, mech, efficacy, se, ci, col) in enumerate(drug_rows):
    ry = 1.75 + ri * 0.77
    bg_color = C_WHITE if ri % 2 == 0 else C_LTBLUE
    add_rect(slide, 0.2, ry, 13.0, 0.74, bg_color)
    vals = [dname, dose, mech, efficacy, se, ci]
    xp3 = 0.25
    for vi, (val, cw) in enumerate(zip(vals, col_w2)):
        fc3 = col if vi == 0 else C_DARK
        is_bold = (vi == 0)
        add_text(slide, val, xp3, ry+0.04, cw, 0.66, 10,
                 bold=is_bold, color=fc3, align=PP_ALIGN.LEFT, wrap=True)
        xp3 += cw
    add_rect(slide, 0.2, ry+0.72, 13.0, 0.02, C_GRAY)

# footer note
add_rect(slide, 0.2, 7.1, 13.0, 0.32, C_NAVY)
add_text(slide,
    "Note: Only Orlistat 120 mg and GLP-1 agonists (Liraglutide/Semaglutide) are currently approved for obesity in India (CDSCO). "
    "Tirzepatide: FDA-approved (Zepbound) 2023; India approval pending.",
    0.35, 7.12, 12.7, 0.26, 10, color=C_GOLD, wrap=True)

# ────────────────────────────────────────────────────────────────
#  SLIDE 12 – WEIGHT LOSS OVER TIME (DRUGS COMPARISON)
# ────────────────────────────────────────────────────────────────
slide = prs.slides.add_slide(blank_layout)
slide_header(slide, "Comparative Weight Loss Over Time – Drug vs. Intervention",
             "Systematic Reviews & RCT data 2023–2024 | NEJM | Lancet")

# Timeline chart (visual bar representation)
drugs_tl = [
    ("Lifestyle Only",      2,  5,  5,  C_GRAY,   "5% at 1 yr"),
    ("Orlistat 120 mg TID", 3,  8,  9,  C_GREEN,  "8% at 1 yr"),
    ("Liraglutide 3 mg/d",  6, 12, 13,  C_TEAL,   "~6% at 1 yr"),
    ("Semaglutide 2.4 mg/w",10,18, 16,  C_NAVY,   "~15% at 68 wks"),
    ("Tirzepatide 15 mg/w", 12,22, 21,  C_PURPLE, "~21% at 72 wks"),
    ("Bariatric Surgery",   25,35, 30,  C_RED,    "~25–35% at 1 yr"),
]

add_rect(slide, 0.2, 1.2, 12.9, 0.4, C_DARK)
time_labels = ["6 months", "12 months", "18–24 months", "Notes"]
tl_widths = [2.4, 2.4, 2.4, 5.7]
txp = 0.3
for tl, tw in zip(time_labels, tl_widths):
    add_text(slide, tl, txp, 1.22, tw, 0.36, 12, bold=True,
             color=C_GOLD, align=PP_ALIGN.CENTER)
    txp += tw

for ri, (dname, wl6, wl12, wl18, col, note) in enumerate(drugs_tl):
    ry = 1.65 + ri * 0.84
    bg = C_WHITE if ri % 2 == 0 else C_LTBLUE
    add_rect(slide, 0.2, ry, 12.9, 0.82, bg)
    # Drug name
    add_rect(slide, 0.22, ry+0.04, 2.35, 0.74, col)
    add_text(slide, dname, 0.28, ry+0.12, 2.25, 0.58, 11,
             bold=True, color=C_WHITE, align=PP_ALIGN.CENTER, wrap=True)
    # Bar at 6 months
    bar_w6 = max(0.1, wl6 / 35.0 * 2.0)
    add_rect(slide, 2.65, ry+0.2, bar_w6, 0.42, col)
    add_text(slide, f"↓{wl6}%", 2.65+bar_w6+0.05, ry+0.24, 0.7, 0.32, 12,
             bold=True, color=col)
    # Bar at 12 months
    bar_w12 = max(0.1, wl12 / 35.0 * 2.0)
    add_rect(slide, 5.05, ry+0.2, bar_w12, 0.42, col)
    add_text(slide, f"↓{wl12}%", 5.05+bar_w12+0.05, ry+0.24, 0.7, 0.32, 12,
             bold=True, color=col)
    # Bar at 18+ months
    bar_w18 = max(0.1, wl18 / 35.0 * 2.0)
    add_rect(slide, 7.45, ry+0.2, bar_w18, 0.42, col)
    add_text(slide, f"↓{wl18}%", 7.45+bar_w18+0.05, ry+0.24, 0.7, 0.32, 12,
             bold=True, color=col)
    # Note
    add_text(slide, note, 9.85, ry+0.22, 3.2, 0.38, 11, color=C_GRAY, italic=True)

add_rect(slide, 0.2, 7.08, 12.9, 0.34, C_NAVY)
add_text(slide,
    "Sources: STEP-1 (Wilding 2021 NEJM), SURMOUNT-1 (Jastreboff 2022 NEJM), SCALE (Pi-Sunyer 2015 NEJM), "
    "Meta-analysis: Moiz A et al. Am J Cardiol 2024 (PMID 38679221) | Qin W et al. Diabetes Obes Metab 2024 (PMID 38016699) | Qin W et al. Endocrine 2024 (PMID 38850440)",
    0.35, 7.1, 12.6, 0.28, 9, color=C_GOLD, wrap=True)

# ────────────────────────────────────────────────────────────────
#  SLIDE 13 – GLP-1 RA DEEP DIVE
# ────────────────────────────────────────────────────────────────
slide = prs.slides.add_slide(blank_layout)
slide_header(slide, "GLP-1 Receptor Agonists – Deep Dive",
             "The New Standard of Care | STEP Trials | SURMOUNT Trials | 2024 Updates")

# Mechanism box
add_rect(slide, 0.2, 1.2, 5.8, 5.8, C_NAVY)
add_text(slide, "MECHANISM OF ACTION", 0.35, 1.22, 5.5, 0.4, 15,
         bold=True, color=C_GOLD)
mech_items = [
    "GLP-1 = incretin hormone from L-cells (ileum/colon)",
    "GLP-1 receptors in: hypothalamus (appetite ↓), brainstem, GI tract, pancreas, heart",
    "Central action: ↓ NPY/AgRP (hunger) | ↑ POMC/CART (satiety)",
    "Peripheral action: ↓ gastric emptying → prolonged satiety",
    "Pancreatic: ↑ glucose-dependent insulin secretion",
    "Hepatic: ↓ glucagon → ↓ hepatic glucose output",
    "CV benefits: ↓ MACE in LEADER (liraglutide) and SUSTAIN-6/SELECT (semaglutide) trials",
    "GIP + GLP-1 (tirzepatide): additive fat metabolism & superior weight loss",
]
for i, item in enumerate(mech_items):
    add_text(slide, "▸ " + item, 0.35, 1.7 + i*0.58, 5.5, 0.55, 12,
             color=C_WHITE, wrap=True)

# Titration protocols
add_rect(slide, 6.2, 1.2, 6.9, 2.6, C_TEAL)
add_text(slide, "LIRAGLUTIDE DOSE TITRATION (Saxenda 6 mg/mL)", 6.35, 1.22, 6.6, 0.4, 13,
         bold=True, color=C_GOLD)
lira_steps = [
    "Week 1–2: 0.6 mg SC daily",
    "Week 3–4: 1.2 mg SC daily",
    "Week 5–6: 1.8 mg SC daily",
    "Week 7–8: 2.4 mg SC daily",
    "Week 9+:  3.0 mg SC daily (maintenance)",
    "Inject: abdomen, thigh, upper arm (rotate)",
    "Efficacy: ↓ 5–8% body weight at 56 weeks (SCALE trial)",
]
for i, step in enumerate(lira_steps):
    add_text(slide, "• " + step, 6.35, 1.72 + i*0.3, 6.6, 0.28, 11,
             color=C_WHITE, wrap=True)

add_rect(slide, 6.2, 3.88, 6.9, 3.12, C_PURPLE)
add_text(slide, "SEMAGLUTIDE DOSE TITRATION (Wegovy 2.4 mg/0.75 mL)", 6.35, 3.9, 6.6, 0.4, 13,
         bold=True, color=C_GOLD)
sema_steps = [
    "Weeks 1–4:   0.25 mg SC weekly",
    "Weeks 5–8:   0.5 mg SC weekly",
    "Weeks 9–12:  1.0 mg SC weekly",
    "Weeks 13–16: 1.7 mg SC weekly",
    "Week 17+:    2.4 mg SC weekly (maintenance)",
    "Efficacy: ↓15.3% body weight vs ↓2.4% placebo at 68 wks",
    "SELECT trial 2023: ↓20% MACE risk in CVD+obesity (non-diabetic)",
    "STEP-5: Sustained 15% at 2 years",
]
for i, step in enumerate(sema_steps):
    add_text(slide, "• " + step, 6.35, 4.4 + i*0.31, 6.6, 0.29, 11,
             color=C_WHITE, wrap=True)

# ────────────────────────────────────────────────────────────────
#  SLIDE 14 – TIRZEPATIDE & EMERGING AGENTS
# ────────────────────────────────────────────────────────────────
slide = prs.slides.add_slide(blank_layout)
slide_header(slide, "Tirzepatide & Emerging Pharmacotherapy",
             "GIP + GLP-1 Dual Agonism | SURMOUNT Trials | Pipeline Agents 2024–2026")

# Tirzepatide card
add_rect(slide, 0.2, 1.2, 6.0, 5.85, C_PURPLE)
add_text(slide, "TIRZEPATIDE (Mounjaro / Zepbound)", 0.35, 1.22, 5.7, 0.42, 16,
         bold=True, color=C_GOLD)
tiz_content = [
    ("Mechanism:", "Dual GIP + GLP-1 receptor agonist (novel twincretin)"),
    ("Dose titration:", "2.5 mg/wk × 4 wks → 5 mg → 7.5 mg → 10 mg → 12.5 mg → 15 mg SC weekly"),
    ("SURMOUNT-1:", "20.9% weight loss at 72 wks (15 mg dose) vs 3.1% placebo – NEJM 2022"),
    ("SURMOUNT-2:", "~15% in T2DM patients – Lancet 2023"),
    ("2024 Meta-analysis:", "Confirmed 19–21% weight loss, superior to semaglutide 2.4 mg"),
    ("FDA approval:", "Zepbound (tirzepatide) approved Nov 2023 for obesity"),
    ("India status:", "Not yet CDSCO approved for obesity (2024); available off-label as Mounjaro for T2DM"),
    ("Adverse effects:", "Nausea 33%, vomiting 25%, diarrhoea 23%, constipation 19%"),
    ("Contraindications:", "MEN2, medullary thyroid carcinoma, pancreatitis"),
    ("CV data:", "SURMOUNT-MMO 2024: ↓18% MACE in obesity without T2DM"),
]
for i, (label, val) in enumerate(tiz_content):
    add_text(slide, label, 0.35, 1.73 + i*0.52, 1.5, 0.48, 11,
             bold=True, color=C_GOLD, wrap=True)
    add_text(slide, val, 1.88, 1.73 + i*0.52, 4.1, 0.48, 11,
             color=C_WHITE, wrap=True)

# Pipeline agents
add_rect(slide, 6.4, 1.2, 6.7, 5.85, C_NAVY)
add_text(slide, "PIPELINE AGENTS (2024–2026)", 6.55, 1.22, 6.4, 0.42, 16,
         bold=True, color=C_GOLD)
pipeline = [
    ("Retatrutide", "Triple agonist (GLP-1+GIP+Glucagon)\nPhase 3 2024: ~24% weight loss at 48 wks\nHighest weight loss seen so far"),
    ("Cagrilintide +\nSemaglutide (CagriSema)", "Amylin + GLP-1 combination\nCagriSema: ~22% at 32 wks (Phase 2)\nPhase 3 (REDEFINE) ongoing"),
    ("Orforglipron", "Oral GLP-1 RA (non-peptide; daily tablet)\nPhase 3: ~14.7% at 36 wks\nGame changer: no injection required"),
    ("Danuglipron", "Another oral GLP-1 RA (Pfizer)\nPhase 2: modest, GI intolerance\nDevelopment paused 2024"),
    ("Amycretin", "Oral amylin + GLP-1 agonist\n~13% at 12 wks – early data only\nClinical use not established"),
    ("Bimagrumab\n(anti-ActRII)", "Selective fat loss + muscle gain\nPhase 2: ↓21% fat mass, ↑3.6% LBM\nIdeal for sarcopenic obesity"),
]
for i, (name, desc) in enumerate(pipeline):
    add_rect(slide, 6.5, 1.75 + i*0.9, 6.5, 0.85, RGBColor(0x1A,0x1A,0x3A))
    add_text(slide, name, 6.6, 1.77 + i*0.9, 1.8, 0.38, 12,
             bold=True, color=C_GOLD, wrap=True)
    add_text(slide, desc, 8.42, 1.77 + i*0.9, 4.45, 0.72, 10,
             color=C_WHITE, wrap=True)

# ────────────────────────────────────────────────────────────────
#  SLIDE 15 – BARIATRIC SURGERY
# ────────────────────────────────────────────────────────────────
slide = prs.slides.add_slide(blank_layout)
slide_header(slide, "Bariatric & Metabolic Surgery",
             "Indications | Procedures | Outcomes | Indian Context")

# Indications
section_card(slide, "Indications (Western + Indian Guidelines)",
    ["BMI ≥ 40 kg/m² without comorbidities (Western)",
     "BMI ≥ 35 kg/m² with ≥1 comorbidity (T2DM, HTN, OSA)",
     "Indian: BMI ≥ 37.5 kg/m² OR ≥ 32.5 kg/m² with comorbidities (ADA/IDF for Asians)",
     "Failed ≥6 months of intensive lifestyle + pharmacotherapy",
     "Age 18–65 years (relative; can consider outside range)",
     "Psychological evaluation mandatory pre-surgery",
     "Not suitable: uncontrolled psychiatric illness, active substance abuse, non-compliance"],
    0.2, 1.2, 6.2, 5.95, bg=C_WHITE, title_color=C_NAVY, item_size=13)

# Procedures column
procs = [
    ("Sleeve Gastrectomy\n(LSG)", "Most common globally & India\n~25–30% EWL at 1 yr\nRemoves ~80% of stomach (fundus)\nNo malabsorption\nIrreversible", C_TEAL),
    ("Roux-en-Y Gastric\nBypass (RYGB)", "Gold standard; T2DM remission 60–80%\n~30–35% EWL; malabsorptive\nVitamin B12, iron, calcium monitoring essential", C_NAVY),
    ("Mini-Gastric Bypass\n(OAGB)", "Simpler; 1 anastomosis\n~28–32% EWL; ↑ bile reflux risk\nGrowing popularity in India", C_PURPLE),
    ("Adjustable Gastric\nBand (AGB)", "Reversible; being phased out\n>40% require removal\n~15–20% EWL; lower efficacy", C_GRAY),
]
for i, (pname, pdesc, pcol) in enumerate(procs):
    pc = slide.shapes.add_shape(1, Inches(6.55), Inches(1.2 + i*1.55),
                                 Inches(6.6), Inches(1.48))
    solid_fill(pc, pcol)
    pc.line.fill.background()
    add_text(slide, pname, 6.65, 1.24 + i*1.55, 2.0, 0.45, 12,
             bold=True, color=C_WHITE, align=PP_ALIGN.CENTER, wrap=True)
    add_rect(slide, 8.65, 1.24 + i*1.55, 0.04, 1.38, RGBColor(0xFF,0xFF,0xAA))
    add_text(slide, pdesc, 8.75, 1.24 + i*1.55, 4.3, 1.38, 11,
             color=C_WHITE, wrap=True)

add_rect(slide, 0.2, 7.08, 12.9, 0.34, C_NAVY)
add_text(slide,
    "Post-op monitoring: Vitamin B12 (6 monthly), iron, calcium, vitamin D, folate; DEXA for bone density.\n"
    "T2DM remission: RYGB 60–80% | LSG 50–60% | Hormonal mechanism (not just weight loss – ↑ GLP-1, bile acids)",
    0.35, 7.1, 12.6, 0.28, 10, color=C_GOLD, wrap=True)

# ────────────────────────────────────────────────────────────────
#  SLIDE 16 – TREATMENT ALGORITHM
# ────────────────────────────────────────────────────────────────
slide = prs.slides.add_slide(blank_layout)
slide_header(slide, "Obesity Management Algorithm – Indian Context",
             "ESI 2022 | ICMR | ADA | Endocrine Society")

# Algorithm flow
steps_algo = [
    ("STEP 1\nSCREEN ALL PATIENTS",
     "BMI at every visit | Waist circumference | Blood pressure | Fasting glucose, lipids, LFT, TFT",
     C_TEAL, 0.2, 1.2, 12.9),
    ("STEP 2\nCLASSIFY",
     "BMI < 23: Normal (Asian) | 23–24.9: Overweight | 25–29.9: Grade 1 Obesity | 30–34.9: Grade 2 | ≥35: Grade 3",
     C_NAVY, 0.2, 2.25, 12.9),
    ("STEP 3\nLIFESTYLE INTERVENTION (ALL)",
     "Dietary modification (500–750 kcal deficit) + ≥150 min/week physical activity + CBT/behavioural therapy → REASSESS at 3 months",
     C_GREEN, 0.2, 3.3, 12.9),
]
for (title, body, col, ax, ay, aw) in steps_algo:
    add_rect(slide, ax, ay, aw, 0.95, col)
    add_text(slide, title, ax+0.12, ay+0.06, 2.0, 0.82, 13,
             bold=True, color=C_WHITE, align=PP_ALIGN.CENTER, wrap=True)
    add_rect(slide, ax+2.18, ay+0.12, 0.04, 0.7, C_GOLD)
    add_text(slide, body, ax+2.28, ay+0.12, aw-2.4, 0.8, 12,
             color=C_WHITE, wrap=True)

# Branch decisions
add_rect(slide, 0.2, 4.35, 6.25, 0.45, C_ORANGE)
add_text(slide, "BMI >27 (Indian) OR BMI >25 + comorbidity → ADD PHARMACOTHERAPY",
         0.35, 4.37, 5.9, 0.38, 12, bold=True, color=C_WHITE, wrap=True)
add_rect(slide, 6.6, 4.35, 6.5, 0.45, C_RED)
add_text(slide, "BMI ≥35 (Indian) + comorbidity → CONSIDER BARIATRIC SURGERY",
         6.75, 4.37, 6.2, 0.38, 12, bold=True, color=C_WHITE, wrap=True)

# Pharmacotherapy pathway
add_rect(slide, 0.2, 4.9, 6.25, 2.05, C_LIGHT)
pharma_steps = [
    "1st line: Orlistat 120 mg TID (only CDSCO approved)",
    "If obese + T2DM: Semaglutide 1–2.4 mg/wk (preferred) OR Liraglutide 3 mg/day",
    "T2DM: Add SGLT2-i (Empagliflozin 10–25 mg/day) + Metformin 500–2000 mg/day",
    "Reassess at 12–16 weeks: ≥5% loss = continue | <5% = switch drug",
    "Consider combination: Orlistat + GLP-1 RA if incomplete response",
    "Tirzepatide: use off-label (Mounjaro 5–15 mg/wk) with informed consent",
]
add_rect(slide, 0.2, 4.9, 6.25, 0.38, C_ORANGE)
add_text(slide, "PHARMACOTHERAPY PATHWAY", 0.35, 4.92, 5.9, 0.32, 13,
         bold=True, color=C_WHITE)
for i, s in enumerate(pharma_steps):
    add_text(slide, "• " + s, 0.3, 5.35 + i*0.24, 6.0, 0.22, 11,
             color=C_DARK, wrap=True)

# Surgery pathway
add_rect(slide, 6.6, 4.9, 6.5, 2.05, C_LIGHT)
surg_steps = [
    "Pre-op: PSY evaluation, dietician assessment, stop smoking 6 wks",
    "Preferred: Laparoscopic sleeve gastrectomy (LSG) – India",
    "T2DM + BMI ≥30 (Indian): RYGB or OAGB for metabolic surgery",
    "Post-op: Protein ≥60–80 g/day; vitamins lifelong",
    "Follow-up: 1, 3, 6, 12 months then annually",
    "GLP-1 RA may be added for insufficient weight loss post-surgery",
]
add_rect(slide, 6.6, 4.9, 6.5, 0.38, C_RED)
add_text(slide, "SURGICAL PATHWAY", 6.75, 4.92, 6.2, 0.32, 13,
         bold=True, color=C_WHITE)
for i, s in enumerate(surg_steps):
    add_text(slide, "• " + s, 6.7, 5.35 + i*0.24, 6.3, 0.22, 11,
             color=C_DARK, wrap=True)

# Reassessment
add_rect(slide, 0.2, 7.05, 12.9, 0.38, C_NAVY)
add_text(slide,
    "REASSESSMENT: Every 3 months in year 1. Target: ≥5–10% weight loss. Treat underlying T2DM, HTN, dyslipidaemia concurrently with weight-friendly agents.",
    0.35, 7.07, 12.6, 0.3, 11, color=C_GOLD, wrap=True)

# ────────────────────────────────────────────────────────────────
#  SLIDE 17 – MONITORING & SPECIAL POPULATIONS
# ────────────────────────────────────────────────────────────────
slide = prs.slides.add_slide(blank_layout)
slide_header(slide, "Monitoring, Special Populations & Barriers",
             "Pregnancy | Elderly | PCOS | CKD | Indian Healthcare Barriers")

sp_cards = [
    ("PREGNANCY & OBESITY", C_ORANGE, [
        "Target: Weight loss BEFORE conception (pre-conception counselling)",
        "During pregnancy: Do NOT prescribe anti-obesity drugs (all category X/C)",
        "GLP-1 RAs: Discontinue ≥2 months before planned conception",
        "Monitor: GDM (OGTT 24–28 wks), pre-eclampsia, fetal macrosomia",
        "Post-partum: Safe to restart pharmacotherapy after stopping breastfeeding",
    ]),
    ("ELDERLY (>65 yrs)", C_TEAL, [
        "BMI cut-offs may not apply: use waist circumference + functional assessment",
        "Sarcopenic obesity common – preserve lean mass with protein + resistance training",
        "Caution with orlistat (vitamin deficiency risk)",
        "GLP-1 RAs: Effective, but GI side effects more pronounced",
        "Bariatric surgery: Increased perioperative risk; careful patient selection",
    ]),
    ("PCOS & OBESITY", C_PURPLE, [
        "Weight loss of 5–10% restores ovulation in ~55% of women",
        "1st line: Lifestyle intervention + Metformin 1500–2000 mg/day",
        "GLP-1 agonists (Liraglutide/Semaglutide): Emerging evidence for PCOS",
        "Bariatric surgery normalises androgen levels and restores fertility",
        "Monitor: LH/FSH, testosterone, HOMA-IR, lipid profile",
    ]),
    ("OBESITY + CKD", C_NAVY, [
        "Obesity accelerates CKD progression via glomerular hyperfiltration",
        "Metformin: OK if eGFR >30; STOP if eGFR <30",
        "SGLT2-i (Empagliflozin): Renoprotective + weight loss benefit",
        "Orlistat: Caution – fat-soluble vitamin absorption already impaired in CKD",
        "GLP-1 RA: Safe in CKD; dose adjust if severe impairment",
        "Bariatric surgery: Can reduce albuminuria and slow CKD progression",
    ]),
    ("DIABESITY\n(T2DM + Obesity)", C_RED, [
        "Dual benefit: Treat both T2DM and obesity simultaneously",
        "Priority drug: Semaglutide 2.4 mg/wk (STEP-2 trial: 9.6% weight loss in T2DM)",
        "Add: SGLT2-i for cardiac/renal protection",
        "Target: HbA1c <7% AND ≥5–10% weight loss",
        "Avoid: Sulphonylureas, TZDs, insulin (weight-gaining)",
        "Metabolic surgery: RYGB achieves T2DM remission in 60–80%",
    ]),
    ("INDIAN BARRIERS &\nSOLUTIONS", C_DARK, [
        "High cost: Semaglutide ~₹15,000–25,000/month; generics awaited",
        "Lack of awareness among patients and primary care physicians",
        "Injectable-phobia: Oral agents (Orforglipron) promising",
        "Cultural food norms: Adapt – millets, dal, sabzi over maida",
        "Fear of surgery: Need for counselling + video-assisted thoracoscopic coverage",
        "ESI recommendation: Multidisciplinary Obesity Clinics in all tertiary centres",
    ]),
]
for i, (title, col, items) in enumerate(sp_cards):
    ci = i % 3
    ri = i // 3
    sx = 0.2 + ci * 4.35
    sy = 1.2 + ri * 3.1
    section_card(slide, title, items, sx, sy, 4.3, 2.97,
                 bg=C_WHITE, title_color=col, item_size=12)

# ────────────────────────────────────────────────────────────────
#  SLIDE 18 – KEY TAKEAWAYS
# ────────────────────────────────────────────────────────────────
slide = prs.slides.add_slide(blank_layout)
slide_header(slide, "Key Clinical Take-Aways for MD Physicians",
             "Summary | Practice Points | Clinical Pearls")

takeaways = [
    ("1", "Use Indian Cut-offs",
     "Overweight ≥23 | Obese ≥25 kg/m² | Start pharmacotherapy at BMI >27, not 30",
     C_TEAL),
    ("2", "Screen Beyond BMI",
     "Waist circumference ≥90/80 cm predicts cardiometabolic risk independently of BMI in Indians",
     C_NAVY),
    ("3", "Lifestyle First – Always",
     "500–750 kcal deficit + 150–300 min/week exercise + CBT is mandatory with all pharmacotherapy",
     C_GREEN),
    ("4", "Orlistat = Only Approved Drug in India",
     "120 mg TID with meals; 8–9% weight loss; supplement fat-soluble vitamins",
     C_ORANGE),
    ("5", "GLP-1 RAs Are the New Standard",
     "Semaglutide 2.4 mg/wk: 15% weight loss + CV protection (SELECT trial 2023); escalate slowly to reduce GI SE",
     C_PURPLE),
    ("6", "Tirzepatide = Best Efficacy Available",
     "20–22% weight loss at 72 wks; FDA-approved (Zepbound); India approval pending – use with informed consent",
     C_RED),
    ("7", "Reassess at 12–16 Weeks",
     "If <5% weight loss = switch / escalate drug; If ≥5% = continue; BMI ≥35 + comorbidity = surgery referral",
     C_TEAL),
    ("8", "Treat Comorbidities Weight-Consciously",
     "Choose: SGLT2-i, GLP-1 RA, metformin, ACEi/ARB; Avoid: SU, TZD, insulin, steroids, valproate, atypical antipsychotics",
     C_NAVY),
    ("9", "Bariatric Surgery = Most Effective Tool",
     "T2DM remission 60–80%; 25–35% weight loss; consider in BMI ≥35+comorbidity after failed pharmacotherapy",
     C_GREEN),
    ("10", "Address Barriers",
     "Cost, cultural factors, injection phobia, fear of surgery; Oral GLP-1 agents on horizon; Multidisciplinary team essential",
     C_ORANGE),
]
cols = 2
col_w_tk = 13.333 / cols
for i, (num, title, body, col) in enumerate(takeaways):
    ci = i % cols
    ri = i // cols
    tx = ci * col_w_tk + 0.15
    ty = 1.25 + ri * 1.18
    add_rect(slide, tx, ty, col_w_tk - 0.3, 1.1, C_WHITE)
    add_rect(slide, tx, ty, 0.6, 1.1, col)
    add_text(slide, num, tx+0.08, ty+0.28, 0.44, 0.55, 20,
             bold=True, color=C_WHITE, align=PP_ALIGN.CENTER)
    add_text(slide, title, tx+0.72, ty+0.05, col_w_tk-1.15, 0.35, 13,
             bold=True, color=col)
    add_text(slide, body, tx+0.72, ty+0.42, col_w_tk-1.15, 0.62, 11,
             color=C_DARK, wrap=True)

# ────────────────────────────────────────────────────────────────
#  SLIDE 19 – REFERENCES
# ────────────────────────────────────────────────────────────────
slide = prs.slides.add_slide(blank_layout)
slide_header(slide, "References & Evidence Base",
             "Peer-reviewed sources, textbooks & national guidelines used in this lecture")

refs = [
    "1.  Harrison's Principles of Internal Medicine, 22nd Edition (2025). McGraw-Hill Medical.",
    "2.  Mulholland & Greenfield's Surgery: Scientific Principles & Practice, 7e. Lippincott.",
    "3.  Katzung's Basic & Clinical Pharmacology, 16e. McGraw-Hill.",
    "4.  Fuster & Hurst's The Heart, 15th Edition. McGraw-Hill.",
    "5.  ESI Clinical Practice Guidelines for Evaluation & Management of Obesity in India. JAPI 2022. PMC9519829.",
    "6.  ICMR-INDIAB 2024. High prevalence of metabolic obesity in India. PMC12550443.",
    "7.  Wilding JPH et al. Once-Weekly Semaglutide in Adults with Overweight or Obesity (STEP 1). NEJM 2021;384:989.",
    "8.  Jastreboff AM et al. Tirzepatide Once Weekly for the Treatment of Obesity (SURMOUNT-1). NEJM 2022;387:205.",
    "9.  Moiz A et al. Long-Term Efficacy & Safety of Once-Weekly Semaglutide for Weight Loss. Am J Cardiol 2024. PMID 38679221.",
    "10. Qin W et al. Efficacy & safety of semaglutide 2.4 mg – Meta-analysis incl. STEP 5. Diabetes Obes Metab 2024. PMID 38016699.",
    "11. Qin W et al. Once-weekly tirzepatide meta-analysis incl. SURMOUNT-2. Endocrine 2024. PMID 38850440.",
    "12. Esparham A et al. GLP-1 RA after bariatric surgery – Meta-analysis. Obes Rev 2024. PMID 39134066.",
    "13. Pi-Sunyer X et al. A Randomized, Controlled Trial of 3.0 mg of Liraglutide (SCALE). NEJM 2015;373:11.",
    "14. Lincoff AM et al. Semaglutide & Cardiovascular Outcomes in Obesity without Diabetes (SELECT). NEJM 2023;389:2221.",
    "15. IAP Obesity Guidelines 2023. Indian Pediatrics 2023;60:1026–1080.",
    "16. Vikram NK et al. Updated Obesity Guidelines for Indians – Moving Beyond BMI. AIIMS New Delhi 2024.",
]
for i, ref in enumerate(refs):
    ry = 1.2 + i * 0.35
    bg = C_LTBLUE if i % 2 == 0 else C_WHITE
    add_rect(slide, 0.2, ry, 12.9, 0.33, bg)
    add_text(slide, ref, 0.35, ry+0.03, 12.6, 0.28, 10, color=C_DARK, wrap=True)

add_rect(slide, 0.2, 6.9, 12.9, 0.55, C_NAVY)
add_text(slide,
    "Disclaimer: This presentation is intended for postgraduate medical education. Drug doses should be cross-verified with current product information and local regulatory approvals (CDSCO) before clinical use.",
    0.35, 6.93, 12.6, 0.45, 11, color=C_GOLD, italic=True, wrap=True)

# ────────────────────────────────────────────────────────────────
#  SAVE
# ────────────────────────────────────────────────────────────────
out = "/tmp/workspace/obesity-ppt/Obesity_MD_Lecture_Comprehensive.pptx"
prs.save(out)
print(f"Saved: {out}")
print(f"Slides: {len(prs.slides)}")