from reportlab.lib.pagesizes import A4
from reportlab.lib.styles import getSampleStyleSheet, ParagraphStyle
from reportlab.lib.units import cm
from reportlab.lib import colors
from reportlab.platypus import (
    SimpleDocTemplate, Paragraph, Spacer, Table, TableStyle,
    HRFlowable, PageBreak
)
from reportlab.lib.enums import TA_CENTER, TA_LEFT, TA_JUSTIFY
from reportlab.platypus import ListFlowable, ListItem

OUTPUT = "/home/daytona/workspace/lipid-profile-seminar/Lipid_Profile_Seminar.pdf"

doc = SimpleDocTemplate(
    OUTPUT,
    pagesize=A4,
    rightMargin=2*cm, leftMargin=2*cm,
    topMargin=2.5*cm, bottomMargin=2.5*cm,
    title="Lipid Profile - Seminar Notes",
    author="Orris Medical Library"
)

styles = getSampleStyleSheet()

# Custom styles
title_style = ParagraphStyle(
    'CustomTitle',
    parent=styles['Title'],
    fontSize=26,
    textColor=colors.HexColor('#1a3a5c'),
    spaceAfter=6,
    alignment=TA_CENTER,
    fontName='Helvetica-Bold'
)
subtitle_style = ParagraphStyle(
    'Subtitle',
    parent=styles['Normal'],
    fontSize=12,
    textColor=colors.HexColor('#4a6fa5'),
    spaceAfter=4,
    alignment=TA_CENTER,
    fontName='Helvetica'
)
h1_style = ParagraphStyle(
    'H1',
    parent=styles['Heading1'],
    fontSize=16,
    textColor=colors.white,
    fontName='Helvetica-Bold',
    spaceBefore=18,
    spaceAfter=8,
    backColor=colors.HexColor('#1a3a5c'),
    leftIndent=-12,
    rightIndent=-12,
    borderPad=6
)
h2_style = ParagraphStyle(
    'H2',
    parent=styles['Heading2'],
    fontSize=13,
    textColor=colors.HexColor('#1a3a5c'),
    fontName='Helvetica-Bold',
    spaceBefore=12,
    spaceAfter=4,
    borderPad=2
)
body_style = ParagraphStyle(
    'Body',
    parent=styles['Normal'],
    fontSize=10.5,
    leading=15,
    spaceAfter=8,
    alignment=TA_JUSTIFY,
    fontName='Helvetica'
)
bullet_style = ParagraphStyle(
    'Bullet',
    parent=styles['Normal'],
    fontSize=10.5,
    leading=15,
    leftIndent=18,
    spaceAfter=4,
    fontName='Helvetica'
)
note_style = ParagraphStyle(
    'Note',
    parent=styles['Normal'],
    fontSize=9.5,
    leading=13,
    backColor=colors.HexColor('#eaf2ff'),
    borderColor=colors.HexColor('#4a6fa5'),
    borderWidth=1,
    borderPad=6,
    spaceAfter=8,
    fontName='Helvetica-Oblique'
)
table_header_style = ParagraphStyle(
    'TableHeader',
    parent=styles['Normal'],
    fontSize=10,
    fontName='Helvetica-Bold',
    textColor=colors.white,
    alignment=TA_CENTER
)

story = []

# ---- TITLE PAGE ----
story.append(Spacer(1, 1.5*cm))
story.append(Paragraph("LIPID PROFILE", title_style))
story.append(Paragraph("A Comprehensive Seminar Guide", subtitle_style))
story.append(Spacer(1, 0.3*cm))
story.append(HRFlowable(width="100%", thickness=2, color=colors.HexColor('#1a3a5c')))
story.append(Spacer(1, 0.3*cm))
story.append(Paragraph("Sources: Textbook of Family Medicine 9e | Fuster & Hurst's The Heart 15e | Fischer's Mastery of Surgery 8e", subtitle_style))
story.append(Paragraph("Prepared for Seminar | June 24, 2026", subtitle_style))
story.append(Spacer(1, 1*cm))

# ---- SECTION 1: INTRODUCTION ----
story.append(Paragraph("1. Introduction & Overview", h1_style))
story.append(Paragraph(
    "Lipid levels are routinely measured to evaluate cardiovascular risk. Plasma lipids travel as "
    "lipoproteins - macromolecular complexes of lipid and protein. There are four major classes "
    "of lipoproteins, each with distinct composition, metabolism, and clinical significance.",
    body_style
))
story.append(Paragraph("The Four Major Lipoprotein Classes:", h2_style))

lp_data = [
    [Paragraph('<b>Lipoprotein</b>', table_header_style),
     Paragraph('<b>Density</b>', table_header_style),
     Paragraph('<b>Primary Lipid</b>', table_header_style),
     Paragraph('<b>Key Apolipoprotein</b>', table_header_style),
     Paragraph('<b>Clinical Role</b>', table_header_style)],
    ['Chylomicrons', 'Lowest (<0.95)', 'Triglycerides (85-90%)', 'ApoB-48, ApoE, ApoC', 'Dietary fat transport (intestine → blood)'],
    ['VLDL', '0.95-1.006', 'Triglycerides (55-65%)', 'ApoB-100, ApoE, ApoC', 'Endogenous TG transport (liver → periphery)'],
    ['LDL', '1.019-1.063', 'Cholesterol (45-50%)', 'ApoB-100', 'Cholesterol delivery to tissues; atherogenic'],
    ['HDL', '1.063-1.210', 'Protein & Phospholipid', 'ApoA-I, ApoA-II', 'Reverse cholesterol transport; cardioprotective'],
]
lp_table = Table(lp_data, colWidths=[3.2*cm, 2.6*cm, 3.5*cm, 3.5*cm, 4.2*cm])
lp_table.setStyle(TableStyle([
    ('BACKGROUND', (0,0), (-1,0), colors.HexColor('#1a3a5c')),
    ('TEXTCOLOR', (0,0), (-1,0), colors.white),
    ('FONTNAME', (0,0), (-1,0), 'Helvetica-Bold'),
    ('FONTSIZE', (0,0), (-1,-1), 9.5),
    ('ROWBACKGROUNDS', (0,1), (-1,-1), [colors.HexColor('#f0f4fa'), colors.white]),
    ('GRID', (0,0), (-1,-1), 0.5, colors.HexColor('#b0b8c8')),
    ('VALIGN', (0,0), (-1,-1), 'MIDDLE'),
    ('TOPPADDING', (0,0), (-1,-1), 5),
    ('BOTTOMPADDING', (0,0), (-1,-1), 5),
    ('LEFTPADDING', (0,0), (-1,-1), 5),
]))
story.append(lp_table)
story.append(Spacer(1, 0.4*cm))
story.append(Paragraph(
    "Note: Approximately 60-70% of plasma cholesterol is carried as LDL-C. HDL-C accounts for "
    "20-30% of total cholesterol. All atherogenic lipoproteins (LDL, IDL, VLDL, Lp(a)) carry one "
    "molecule of ApoB-100, making ApoB a useful surrogate marker for total atherogenic particle burden.",
    note_style
))

# ---- SECTION 2: STANDARD LIPID PROFILE ----
story.append(Paragraph("2. Standard Lipid Profile", h1_style))
story.append(Paragraph(
    "The standard lipid profile, as recommended by the NCEP ATP III, consists of direct measurement "
    "of total cholesterol, HDL-C, and triglycerides, with a calculated LDL-C - obtained after a "
    "<b>9-hour fast</b>. A standard lipid profile includes: plasma cholesterol, LDL-C, HDL-C, and "
    "plasma triglycerides.",
    body_style
))

story.append(Paragraph("Components of the Standard Lipid Panel:", h2_style))

comp_data = [
    [Paragraph('<b>Component</b>', table_header_style),
     Paragraph('<b>How Measured</b>', table_header_style),
     Paragraph('<b>Normal Range</b>', table_header_style),
     Paragraph('<b>Clinical Significance</b>', table_header_style)],
    ['Total Cholesterol (TC)', 'Direct enzymatic assay', '< 200 mg/dL (desirable)', 'Sum of all cholesterol fractions'],
    ['LDL Cholesterol', 'Calculated (Friedewald) or direct', '< 100 mg/dL (optimal)', 'Primary atherogenic fraction; main treatment target'],
    ['HDL Cholesterol', 'Direct precipitation assay', '> 40 mg/dL (M), > 50 mg/dL (F)', 'Cardioprotective; inverse relationship with CAD risk'],
    ['Triglycerides (TG)', 'Direct enzymatic assay', '< 150 mg/dL (normal)', 'Reflects VLDL & remnant lipoproteins; risk for pancreatitis at >500'],
    ['Non-HDL Cholesterol', 'Calculated: TC - HDL', '< 130 mg/dL (optimal)', 'Includes all atherogenic lipoproteins (LDL + VLDL + IDL + Lp(a))'],
    ['VLDL Cholesterol', 'Estimated as TG/5', '2-30 mg/dL', 'Indirect estimate; invalid when TG > 400 mg/dL'],
]
comp_table = Table(comp_data, colWidths=[3.8*cm, 3.8*cm, 3.8*cm, 5.6*cm])
comp_table.setStyle(TableStyle([
    ('BACKGROUND', (0,0), (-1,0), colors.HexColor('#1a3a5c')),
    ('TEXTCOLOR', (0,0), (-1,0), colors.white),
    ('FONTNAME', (0,0), (-1,0), 'Helvetica-Bold'),
    ('FONTSIZE', (0,0), (-1,-1), 9.5),
    ('ROWBACKGROUNDS', (0,1), (-1,-1), [colors.HexColor('#f0f4fa'), colors.white]),
    ('GRID', (0,0), (-1,-1), 0.5, colors.HexColor('#b0b8c8')),
    ('VALIGN', (0,0), (-1,-1), 'MIDDLE'),
    ('TOPPADDING', (0,0), (-1,-1), 5),
    ('BOTTOMPADDING', (0,0), (-1,-1), 5),
    ('LEFTPADDING', (0,0), (-1,-1), 5),
]))
story.append(comp_table)
story.append(Spacer(1, 0.4*cm))

# ---- SECTION 3: FRIEDEWALD FORMULA ----
story.append(Paragraph("3. Friedewald Formula for LDL Calculation", h1_style))
story.append(Paragraph(
    "The Friedewald formula is the standard equation used to calculate LDL-C from a fasting lipid panel:",
    body_style
))

formula_style = ParagraphStyle(
    'Formula',
    parent=styles['Normal'],
    fontSize=13,
    fontName='Helvetica-Bold',
    alignment=TA_CENTER,
    textColor=colors.HexColor('#1a3a5c'),
    spaceBefore=10,
    spaceAfter=10,
    backColor=colors.HexColor('#eaf2ff'),
    borderPad=10
)
story.append(Paragraph("LDL-C = Total Cholesterol - HDL-C - (Triglycerides / 5)", formula_style))
story.append(Spacer(1, 0.3*cm))

story.append(Paragraph("<b>Limitations - Friedewald Formula is INVALID when:</b>", h2_style))
invalid_items = [
    "Chylomicrons are present in the sample (non-fasting state with chylomicronemia)",
    "Triglycerides > 400 mg/dL (leads to <b>underestimation</b> of LDL-C)",
    "Dysbetalipoproteinemia (Type III hyperlipidemia) is present",
    "Hypertriglyceridemia is present (non-LDL particles like IDL are included in the calculation)",
]
for item in invalid_items:
    story.append(Paragraph(f"• {item}", bullet_style))

story.append(Spacer(1, 0.3*cm))
story.append(Paragraph(
    "In these cases, <b>direct LDL measurement</b> (more costly but more accurate) should be used. "
    "Nonfasting total cholesterol and HDL measurements give reliable assessment of CHD risk without "
    "the need to measure triglycerides.",
    note_style
))

# ---- SECTION 4: REFERENCE RANGES ----
story.append(Paragraph("4. Reference Ranges & Risk Classification (NCEP ATP III)", h1_style))

atp_data = [
    [Paragraph('<b>Parameter</b>', table_header_style),
     Paragraph('<b>Category</b>', table_header_style),
     Paragraph('<b>Value (mg/dL)</b>', table_header_style),
     Paragraph('<b>Classification</b>', table_header_style)],
    # Total Cholesterol
    ['Total Cholesterol', 'Desirable', '< 200', 'Low risk'],
    ['', 'Borderline High', '200-239', 'Moderate risk'],
    ['', 'High', '≥ 240', 'High risk'],
    # LDL
    ['LDL-C', 'Optimal', '< 100', 'Target for high-risk pts'],
    ['', 'Near Optimal', '100-129', 'Acceptable'],
    ['', 'Borderline High', '130-159', 'Monitor closely'],
    ['', 'High', '160-189', 'Drug therapy often needed'],
    ['', 'Very High', '≥ 190', 'Aggressive treatment'],
    # HDL
    ['HDL-C', 'Low (Risk Factor)', '< 40', 'Independent CV risk factor'],
    ['', 'Normal', '40-59', 'Average protection'],
    ['', 'High (Protective)', '≥ 60', 'Negative risk factor'],
    # TG
    ['Triglycerides', 'Normal', '< 150', 'No increased risk'],
    ['', 'Borderline High', '150-199', 'Mild concern'],
    ['', 'High', '200-499', 'Increased cardiovascular risk'],
    ['', 'Very High', '≥ 500', 'Risk of acute pancreatitis'],
]

atp_table = Table(atp_data, colWidths=[3.8*cm, 3.8*cm, 3.8*cm, 5.6*cm])
atp_table.setStyle(TableStyle([
    ('BACKGROUND', (0,0), (-1,0), colors.HexColor('#1a3a5c')),
    ('TEXTCOLOR', (0,0), (-1,0), colors.white),
    ('FONTNAME', (0,0), (-1,0), 'Helvetica-Bold'),
    ('FONTSIZE', (0,0), (-1,-1), 9.5),
    ('ROWBACKGROUNDS', (0,1), (-1,-1), [colors.HexColor('#f0f4fa'), colors.white]),
    ('GRID', (0,0), (-1,-1), 0.5, colors.HexColor('#b0b8c8')),
    ('VALIGN', (0,0), (-1,-1), 'MIDDLE'),
    ('TOPPADDING', (0,0), (-1,-1), 5),
    ('BOTTOMPADDING', (0,0), (-1,-1), 5),
    ('LEFTPADDING', (0,0), (-1,-1), 5),
    ('SPAN', (0,1), (0,3)),   # TC span
    ('SPAN', (0,4), (0,8)),   # LDL span
    ('SPAN', (0,9), (0,11)),  # HDL span
    ('SPAN', (0,12), (0,15)), # TG span
]))
story.append(atp_table)

# ---- SECTION 5: PHYSIOLOGIC & ANALYTIC VARIATION ----
story.append(PageBreak())
story.append(Paragraph("5. Sources of Variation in Lipid Measurements", h1_style))
story.append(Paragraph(
    "Multiple factors can alter lipid test results. Clinicians must interpret lipid panels in the "
    "context of these pre-analytical and physiological variables:",
    body_style
))

variation_items = [
    ("<b>Fasting state:</b> Failure to fast elevates triglycerides and leads to underestimation of LDL-C. "
     "Total cholesterol and HDL-C are NOT significantly different in fasting vs postprandial state."),
    ("<b>Diet:</b> Dietary changes appear in lipid measurements in ~1-2 weeks. Patients should maintain a "
     "stable diet for <b>3 weeks</b> before testing."),
    ("<b>Time of day:</b> Morning specimens preferred - triglycerides have diurnal variation (lowest in morning, "
     "highest in afternoon)."),
    ("<b>Acute illness/surgery:</b> Recent illness, surgery, MI, stroke, or cardiac catheterization can lower "
     "lipid measurements for several weeks. For major illness, wait 2-3 months before measurement."),
    ("<b>Myocardial Infarction:</b> Cholesterol decreases 24 hours after MI and remains depressed for up to 12 weeks."),
    ("<b>Posture:</b> Lipid values can be up to 10% higher in the upright vs supine position due to fluid shifts."),
    ("<b>Pregnancy:</b> Lipid levels increase significantly during pregnancy; do not measure during pregnancy."),
]
for item in variation_items:
    story.append(Paragraph(f"• {item}", bullet_style))

# ---- SECTION 6: DRUGS AFFECTING LIPIDS ----
story.append(Paragraph("6. Effects of Drugs on Lipid Values", h1_style))
story.append(Paragraph(
    "Many commonly used medications can significantly alter lipid fractions. This is important both "
    "for interpretation of lipid panels and for understanding iatrogenic dyslipidemia:",
    body_style
))

drug_data = [
    [Paragraph('<b>Drug</b>', table_header_style),
     Paragraph('<b>Total Chol</b>', table_header_style),
     Paragraph('<b>LDL-C</b>', table_header_style),
     Paragraph('<b>HDL-C</b>', table_header_style),
     Paragraph('<b>Triglycerides</b>', table_header_style)],
    ['Thiazide diuretics', '↑', '↑', '—', '↑'],
    ['Beta-blockers', '—', '—', '↓', '↑'],
    ['Alpha-blockers', '↓', '↓', '↑', '↓'],
    ['ACE inhibitors', '—', '—', '—', '—'],
    ['Calcium-channel blockers', '—', '—', '—', '—'],
    ['Unopposed estrogens', '↓', '↓', '↑', '↑'],
    ['Unopposed progestogens', '—', '↑', '↓', '↓'],
    ['Tamoxifen', '↓', '↓', '—', '↑'],
    ['Raloxifene', '↓', '↓', '—', '—'],
    ['Isotretinoin', '↑', '↑', '↓', '↑'],
    ['Protease inhibitors (HIV)', '↑', '—', '—', '↑'],
]
drug_table = Table(drug_data, colWidths=[5*cm, 2.8*cm, 2.8*cm, 2.8*cm, 3.6*cm])
drug_table.setStyle(TableStyle([
    ('BACKGROUND', (0,0), (-1,0), colors.HexColor('#1a3a5c')),
    ('TEXTCOLOR', (0,0), (-1,0), colors.white),
    ('FONTNAME', (0,0), (-1,0), 'Helvetica-Bold'),
    ('FONTSIZE', (0,0), (-1,-1), 10),
    ('ROWBACKGROUNDS', (0,1), (-1,-1), [colors.HexColor('#f0f4fa'), colors.white]),
    ('GRID', (0,0), (-1,-1), 0.5, colors.HexColor('#b0b8c8')),
    ('ALIGN', (1,0), (-1,-1), 'CENTER'),
    ('VALIGN', (0,0), (-1,-1), 'MIDDLE'),
    ('TOPPADDING', (0,0), (-1,-1), 5),
    ('BOTTOMPADDING', (0,0), (-1,-1), 5),
    ('LEFTPADDING', (0,0), (-1,-1), 6),
]))
story.append(drug_table)
story.append(Spacer(1, 0.3*cm))
story.append(Paragraph(
    "Source: Adapted from Mantel-Tecewisse AK et al. Drug-induced lipid changes: a review of the "
    "unintended effects of some commonly used drugs on serum lipid levels. Drug Saf 2001;24(6):443-456.",
    note_style
))

# ---- SECTION 7: SECONDARY CAUSES OF DYSLIPIDEMIA ----
story.append(Paragraph("7. Secondary Causes of Dyslipidemia", h1_style))

sec_data = [
    [Paragraph('<b>Condition / Cause</b>', table_header_style),
     Paragraph('<b>Lipid Effect</b>', table_header_style)],
    ['Hypothyroidism', 'Elevated LDL-C, elevated TG'],
    ['Diabetes mellitus (Type 2)', 'Elevated TG, low HDL-C, elevated small dense LDL'],
    ['Chronic kidney disease (CKD)', 'Elevated TG-rich lipoproteins (VLDL), low HDL-C; high TG; normal or low TC'],
    ['Nephrotic syndrome', 'Elevated TC, elevated LDL-C, elevated TG, low HDL-C'],
    ['Obesity', 'Elevated TG, low HDL-C, elevated LDL-C'],
    ['Alcohol excess', 'Elevated TG (can cause severe hypertriglyceridemia)'],
    ['Obstructive liver disease', 'Elevated TC (abnormal lipoprotein X)'],
    ['Cushing syndrome', 'Elevated TC, elevated TG'],
    ['Anorexia nervosa', 'Elevated TC, elevated LDL-C'],
    ['Pregnancy', 'Elevated TC, elevated TG, elevated HDL-C'],
]
sec_table = Table(sec_data, colWidths=[7*cm, 10*cm])
sec_table.setStyle(TableStyle([
    ('BACKGROUND', (0,0), (-1,0), colors.HexColor('#1a3a5c')),
    ('TEXTCOLOR', (0,0), (-1,0), colors.white),
    ('FONTNAME', (0,0), (-1,0), 'Helvetica-Bold'),
    ('FONTSIZE', (0,0), (-1,-1), 10),
    ('ROWBACKGROUNDS', (0,1), (-1,-1), [colors.HexColor('#f0f4fa'), colors.white]),
    ('GRID', (0,0), (-1,-1), 0.5, colors.HexColor('#b0b8c8')),
    ('VALIGN', (0,0), (-1,-1), 'MIDDLE'),
    ('TOPPADDING', (0,0), (-1,-1), 5),
    ('BOTTOMPADDING', (0,0), (-1,-1), 5),
    ('LEFTPADDING', (0,0), (-1,-1), 6),
]))
story.append(sec_table)

# ---- SECTION 8: LIPOPROTEIN METABOLISM ----
story.append(PageBreak())
story.append(Paragraph("8. Lipoprotein Metabolism", h1_style))
story.append(Paragraph(
    "Understanding lipoprotein metabolism helps explain how dyslipidemia develops and is targeted by therapy.",
    body_style
))
story.append(Paragraph("Exogenous (Dietary) Pathway:", h2_style))
story.append(Paragraph(
    "After ingestion of a fatty meal, dietary triglycerides and cholesterol are packaged into "
    "<b>chylomicrons</b> by intestinal enterocytes. These are secreted into intestinal lymphatics "
    "(lacteals) and enter the blood via the thoracic duct. In the capillaries of muscle and adipose "
    "tissue, <b>lipoprotein lipase (LPL)</b> hydrolyzes the triglycerides, releasing free fatty acids "
    "for cellular use. The resulting <b>chylomicron remnants</b> (enriched in cholesterol) are taken up "
    "by the liver via ApoE/LDL-receptor-related protein (LRP) receptors.",
    body_style
))
story.append(Paragraph("Endogenous Pathway:", h2_style))
story.append(Paragraph(
    "The liver packages endogenous triglycerides and cholesterol into <b>VLDL</b> (ApoB-100 containing) "
    "and secretes them into the bloodstream. LPL progressively hydrolyzes VLDL triglycerides, "
    "converting VLDL → IDL → LDL. LDL (the remnant of VLDL catabolism) is the primary carrier of "
    "cholesterol to peripheral tissues via the <b>LDL receptor (LDLR)</b>, which recognizes ApoB-100. "
    "When LDL receptors are downregulated (high cellular cholesterol), LDL accumulates in plasma.",
    body_style
))
story.append(Paragraph("Reverse Cholesterol Transport (HDL Pathway):", h2_style))
story.append(Paragraph(
    "HDL particles, synthesized in the liver and intestine, acquire cholesterol from peripheral tissues "
    "via <b>ABCA1</b> and <b>SR-BI</b> transporters. The enzyme <b>LCAT</b> (lecithin-cholesterol "
    "acyltransferase) esterifies the cholesterol on HDL. Cholesterol ester transfer protein (CETP) "
    "transfers cholesterol esters from HDL to VLDL and LDL in exchange for triglycerides. HDL "
    "ultimately delivers cholesterol back to the liver for bile synthesis or excretion - this process "
    "is cardioprotective and is the basis for HDL's role as the 'good cholesterol'.",
    body_style
))

# ---- SECTION 9: CARDIOVASCULAR RISK ----
story.append(Paragraph("9. Lipids and Cardiovascular Risk", h1_style))
story.append(Paragraph(
    "Lipid profiling is fundamentally used as a cardiovascular risk stratification tool.",
    body_style
))

risk_points = [
    ("<b>LDL-C and CHD:</b> There is a direct, causal association between elevated LDL-C and coronary "
     "heart disease (CHD). The risk is continuous with no definite threshold."),
    ("<b>HDL-C and CHD:</b> There is a strong independent inverse relationship between HDL-C and CHD. "
     "For every 1 mg/dL decrease in HDL-C, the risk of CAD increases by <b>2-3%</b>."),
    ("<b>Triglycerides:</b> When plasma TG exceeds 440 mg/dL vs <88 mg/dL, risk is approximately "
     "5-fold for MI, 3-fold for ischemic stroke, and 10-fold for acute pancreatitis."),
    ("<b>Remnant Cholesterol (Remnant-C):</b> Calculated as TC - HDL-C - LDL-C. Represents cholesterol "
     "in TG-rich lipoprotein remnants (VLDL + IDL + chylomicron remnants). Increasingly recognized as "
     "an independent ASCVD risk marker."),
    ("<b>Non-HDL Cholesterol:</b> Captures all atherogenic lipoproteins. US, European, and Canadian "
     "guidelines all recommend measuring non-HDL-C alongside LDL-C."),
    ("<b>ApoB:</b> One ApoB per atherogenic particle - reflects total particle number. Considered "
     "superior to LDL-C in some studies for predicting ASCVD risk."),
    ("<b>Lp(a):</b> Shows modest correlation with stroke and CHD risk but routine screening in "
     "asymptomatic individuals is not currently recommended (AHA 2010 guidelines)."),
]
for point in risk_points:
    story.append(Paragraph(f"• {point}", bullet_style))

# ---- SECTION 10: FASTING vs NONFASTING ----
story.append(Paragraph("10. Fasting vs Nonfasting Lipid Profiles", h1_style))

fn_data = [
    [Paragraph('<b>Parameter</b>', table_header_style),
     Paragraph('<b>Fasting (9h fast)</b>', table_header_style),
     Paragraph('<b>Nonfasting</b>', table_header_style)],
    ['Total Cholesterol', 'Standard', 'Reliable (not significantly different)'],
    ['HDL-C', 'Standard', 'Reliable (not significantly different)'],
    ['Triglycerides', 'Required for accuracy', 'Higher by ~26 mg/dL (0.3 mmol/L)'],
    ['LDL-C (calculated)', 'Standard (Friedewald)', 'Unreliable - TG affects calculation'],
    ['Remnant Cholesterol', 'Baseline measurement', 'Higher by ~8 mg/dL (0.2 mmol/L) postprandially'],
    ['Clinical Use', 'Required for full panel incl. TG & LDL', 'Adequate for TC and HDL risk screening'],
]
fn_table = Table(fn_data, colWidths=[4.5*cm, 5.5*cm, 7*cm])
fn_table.setStyle(TableStyle([
    ('BACKGROUND', (0,0), (-1,0), colors.HexColor('#1a3a5c')),
    ('TEXTCOLOR', (0,0), (-1,0), colors.white),
    ('FONTNAME', (0,0), (-1,0), 'Helvetica-Bold'),
    ('FONTSIZE', (0,0), (-1,-1), 10),
    ('ROWBACKGROUNDS', (0,1), (-1,-1), [colors.HexColor('#f0f4fa'), colors.white]),
    ('GRID', (0,0), (-1,-1), 0.5, colors.HexColor('#b0b8c8')),
    ('VALIGN', (0,0), (-1,-1), 'MIDDLE'),
    ('TOPPADDING', (0,0), (-1,-1), 5),
    ('BOTTOMPADDING', (0,0), (-1,-1), 5),
    ('LEFTPADDING', (0,0), (-1,-1), 6),
]))
story.append(fn_table)
story.append(Spacer(1, 0.3*cm))
story.append(Paragraph(
    "The AHA 2010 guidelines recommend against routine measurement of lipoprotein subfractions, "
    "particle size, and density in asymptomatic adults for cardiovascular risk assessment, as these "
    "do not significantly improve predictive capacity over the standard lipid panel.",
    note_style
))

# ---- SECTION 11: SCREENING RECOMMENDATIONS ----
story.append(PageBreak())
story.append(Paragraph("11. Screening Recommendations", h1_style))
story.append(Paragraph(
    "The NCEP ATP III recommended lipid screening as a tool to promote cardiovascular disease risk "
    "reduction. Key screening guidelines:",
    body_style
))
screen_items = [
    "<b>Adults ≥ 20 years:</b> Fasting lipid panel every 5 years (NCEP ATP III)",
    "<b>Men ≥ 35 years</b> and <b>women ≥ 45 years:</b> Screen for lipid disorders (USPSTF)",
    "<b>Any age</b> with cardiovascular risk factors (diabetes, hypertension, smoking, family history): Screen earlier",
    "<b>Acute illness:</b> Postpone testing - triglycerides increase and LDL decreases in inflammatory states",
    "<b>Repeat testing:</b> If baseline values are borderline, repeat within 1-8 weeks (2 measurements averaged)",
    "<b>Children:</b> Screen if family history of premature CVD or familial hypercholesterolemia (after age 2)",
]
for item in screen_items:
    story.append(Paragraph(f"• {item}", bullet_style))

# ---- SECTION 12: DYSLIPIDEMIA CLASSIFICATION ----
story.append(Paragraph("12. Classification of Dyslipidemias (Fredrickson/WHO Classification)", h1_style))

fred_data = [
    [Paragraph('<b>Type</b>', table_header_style),
     Paragraph('<b>Common Name</b>', table_header_style),
     Paragraph('<b>Elevated Lipoprotein</b>', table_header_style),
     Paragraph('<b>Lipid Elevation</b>', table_header_style),
     Paragraph('<b>CVD Risk</b>', table_header_style)],
    ['Type I', 'Familial hyperchylomicronemia', 'Chylomicrons', 'TG ↑↑↑', 'Low (pancreatitis risk)'],
    ['Type IIa', 'Familial hypercholesterolemia', 'LDL', 'TC ↑↑, LDL ↑↑', 'Very High'],
    ['Type IIb', 'Combined hyperlipidemia', 'LDL + VLDL', 'TC ↑, TG ↑, LDL ↑', 'Very High'],
    ['Type III', 'Dysbetalipoproteinemia', 'IDL (remnants)', 'TC ↑, TG ↑', 'High'],
    ['Type IV', 'Familial hypertriglyceridemia', 'VLDL', 'TG ↑↑', 'Moderate'],
    ['Type V', 'Mixed hyperlipidemia', 'VLDL + Chylomicrons', 'TC ↑, TG ↑↑↑', 'Moderate (pancreatitis risk)'],
]
fred_table = Table(fred_data, colWidths=[2*cm, 4.5*cm, 3.5*cm, 3.8*cm, 3.2*cm])
fred_table.setStyle(TableStyle([
    ('BACKGROUND', (0,0), (-1,0), colors.HexColor('#1a3a5c')),
    ('TEXTCOLOR', (0,0), (-1,0), colors.white),
    ('FONTNAME', (0,0), (-1,0), 'Helvetica-Bold'),
    ('FONTSIZE', (0,0), (-1,-1), 9.5),
    ('ROWBACKGROUNDS', (0,1), (-1,-1), [colors.HexColor('#f0f4fa'), colors.white]),
    ('GRID', (0,0), (-1,-1), 0.5, colors.HexColor('#b0b8c8')),
    ('VALIGN', (0,0), (-1,-1), 'MIDDLE'),
    ('TOPPADDING', (0,0), (-1,-1), 5),
    ('BOTTOMPADDING', (0,0), (-1,-1), 5),
    ('LEFTPADDING', (0,0), (-1,-1), 5),
]))
story.append(fred_table)

# ---- SECTION 13: TREATMENT ----
story.append(Paragraph("13. Treatment of Dyslipidemia", h1_style))
story.append(Paragraph("Therapeutic Lifestyle Changes (TLC) - First Line:", h2_style))
tlc_items = [
    "Reduce dietary saturated fat to <7% of total calories",
    "Reduce dietary cholesterol to <200 mg/day",
    "Increase soluble fiber (10-25 g/day) - reduces LDL by 5-10%",
    "Use plant stanols/sterols (2 g/day) - reduces LDL by 6-15%",
    "Weight reduction - reduces LDL, TG; raises HDL",
    "Increase aerobic physical activity (30+ min most days) - primarily raises HDL",
    "Reduce alcohol to lower triglycerides",
]
for item in tlc_items:
    story.append(Paragraph(f"• {item}", bullet_style))

story.append(Paragraph("Pharmacological Therapy:", h2_style))
rx_data = [
    [Paragraph('<b>Drug Class</b>', table_header_style),
     Paragraph('<b>Mechanism</b>', table_header_style),
     Paragraph('<b>Effect on Lipids</b>', table_header_style),
     Paragraph('<b>Primary Use</b>', table_header_style)],
    ['Statins (HMG-CoA reductase inhibitors)', 'Inhibit hepatic cholesterol synthesis → upregulate LDL receptors', 'LDL ↓ 25-50%, TG ↓, HDL ↑ slightly', 'First-line for elevated LDL-C; CVD prevention'],
    ['Ezetimibe', 'Blocks intestinal cholesterol absorption (NPC1L1)', 'LDL ↓ 15-20%', 'Adjunct to statins or statin-intolerance'],
    ['PCSK9 Inhibitors (evolocumab, alirocumab)', 'Inhibit PCSK9 → more LDL receptors recycled', 'LDL ↓ 50-60%', 'Familial hypercholesterolemia; high CV risk'],
    ['Fibrates (gemfibrozil, fenofibrate)', 'PPAR-α agonists → ↑ LPL activity', 'TG ↓ 30-50%, HDL ↑ 10-15%', 'Hypertriglyceridemia; low HDL'],
    ['Niacin (Nicotinic acid)', 'Inhibits hepatic VLDL secretion', 'TG ↓, LDL ↓, HDL ↑ 15-35%', 'Broad spectrum; low HDL (limited use now)'],
    ['Bile acid sequestrants (cholestyramine)', 'Interrupt enterohepatic circulation of bile acids', 'LDL ↓ 10-30%', 'Adjunct therapy; safe in pregnancy'],
    ['Omega-3 fatty acids', 'Reduce hepatic TG synthesis', 'TG ↓ 20-50% at high doses', 'Severe hypertriglyceridemia (≥500 mg/dL)'],
]
rx_table = Table(rx_data, colWidths=[4.2*cm, 4.5*cm, 3.5*cm, 4.8*cm])
rx_table.setStyle(TableStyle([
    ('BACKGROUND', (0,0), (-1,0), colors.HexColor('#1a3a5c')),
    ('TEXTCOLOR', (0,0), (-1,0), colors.white),
    ('FONTNAME', (0,0), (-1,0), 'Helvetica-Bold'),
    ('FONTSIZE', (0,0), (-1,-1), 9),
    ('ROWBACKGROUNDS', (0,1), (-1,-1), [colors.HexColor('#f0f4fa'), colors.white]),
    ('GRID', (0,0), (-1,-1), 0.5, colors.HexColor('#b0b8c8')),
    ('VALIGN', (0,0), (-1,-1), 'MIDDLE'),
    ('TOPPADDING', (0,0), (-1,-1), 5),
    ('BOTTOMPADDING', (0,0), (-1,-1), 5),
    ('LEFTPADDING', (0,0), (-1,-1), 5),
]))
story.append(rx_table)

# ---- SECTION 14: QUICK SUMMARY ----
story.append(PageBreak())
story.append(Paragraph("14. Quick Review - Key Points for Seminar", h1_style))

summary_points = [
    "Lipid profile = Total Cholesterol + HDL-C + Triglycerides (direct) + LDL-C (calculated)",
    "Friedewald: LDL = TC - HDL - TG/5 (invalid if TG > 400, chylomicrons present, or Type III hyperlipidemia)",
    "LDL-C is the primary atherogenic fraction and main treatment target",
    "HDL-C is cardioprotective: every 1 mg/dL fall in HDL → 2-3% rise in CAD risk",
    "All atherogenic lipoproteins carry ApoB-100 (one molecule per particle)",
    "9-hour fast required for accurate TG and calculated LDL; TC and HDL reliable nonfasting",
    "Triglycerides lowest in morning, highest in afternoon (diurnal variation)",
    "Acute illness/MI falsely lowers lipids - wait 2-3 months after major illness",
    "Non-HDL-C = TC - HDL-C: captures all atherogenic particles; increasingly recommended alongside LDL-C",
    "Remnant-C = TC - HDL-C - LDL-C: represents TG-rich lipoprotein cholesterol",
    "First-line treatment is always Therapeutic Lifestyle Changes (TLC)",
    "Statins are first-line pharmacotherapy for elevated LDL-C and CV risk reduction",
    "Fibrates are preferred for hypertriglyceridemia and low HDL",
    "PCSK9 inhibitors achieve up to 60% LDL reduction for familial hypercholesterolemia",
    "Type III hyperlipidemia (dysbetalipoproteinemia) invalidates the Friedewald formula and is treated with fibrates",
]

for i, point in enumerate(summary_points, 1):
    story.append(Paragraph(f"<b>{i}.</b> {point}", bullet_style))

story.append(Spacer(1, 0.5*cm))
story.append(HRFlowable(width="100%", thickness=1, color=colors.HexColor('#b0b8c8')))
story.append(Spacer(1, 0.3*cm))
story.append(Paragraph(
    "References: Textbook of Family Medicine, 9th Edition (Rakel) | Fuster & Hurst's The Heart, "
    "15th Edition | Fischer's Mastery of Surgery, 8th Edition | Tietz Textbook of Laboratory Medicine, "
    "7th Edition | NCEP ATP III Guidelines | AHA 2010 Cholesterol Guidelines",
    ParagraphStyle('Ref', parent=styles['Normal'], fontSize=8.5, textColor=colors.grey, fontName='Helvetica-Oblique', alignment=TA_CENTER)
))

# Build the PDF
doc.build(story)
print("PDF generated successfully at:", OUTPUT)