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Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>

2026-05-20 23:52:15 +09:00

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title

Nutritional Biochemistry

매 한 줄

"매 nutrient 는 metabolic substrate + cofactor + signal". Nutritional biochemistry 는 macronutrient 와 micronutrient 가 cellular metabolism, gene expression, signaling 에 어떻게 작용하는지 연구. 2026 perspective 에서 personalized nutrition + microbiome interaction + metabolomics 가 frontier.

매 핵심

매 macronutrient pathways

Carbohydrate: glycolysis → pyruvate → acetyl-CoA → TCA → ETC. 4 kcal/g.
Lipid: β-oxidation → acetyl-CoA → TCA. 9 kcal/g. Membrane lipids, eicosanoids.
Protein: amino acid → transamination → urea / gluconeogenesis. 4 kcal/g.

매 micronutrient roles

B-vitamins: coenzymes (NAD, FAD, CoA, THF, PLP).
Fat-soluble (ADEK): signaling (retinoic acid, calcitriol).
Minerals: cofactors (Mg-ATP, Zn-fingers, Fe-heme), electrolytes.

매 응용

Sports nutrition / supplement design.
Disease management (diabetes, NAFLD).
Personalized diet (genotype + microbiome).
Public health policy.

💻 패턴

Basal metabolic rate (Mifflin-St Jeor)

def bmr(weight_kg, height_cm, age_y, sex="M"):
    base = 10*weight_kg + 6.25*height_cm - 5*age_y
    return base + 5 if sex == "M" else base - 161

def tdee(bmr_val, activity="moderate"):
    factors = {"sedentary": 1.2, "light": 1.375,
               "moderate": 1.55, "active": 1.725}
    return bmr_val * factors[activity]

Macro split optimization

def macro_split(tdee, goal="maintain", body_kg=70):
    protein_g = body_kg * (1.6 if goal == "cut" else 1.2)
    p_cal = protein_g * 4
    fat_cal = tdee * 0.25
    carb_cal = tdee - p_cal - fat_cal
    return {"protein_g": protein_g,
            "fat_g": fat_cal / 9,
            "carb_g": carb_cal / 4}

Glycemic load

def glycemic_load(food: dict) -> float:
    return food["gi"] * food["carb_g"] / 100
# Low <10, medium 11-19, high 20+

TCA cycle ATP yield

def atp_per_glucose():
    glycolysis = 2
    nadh_glyc = 2 * 2.5
    pyruvate_to_acetyl = 2 * 2.5
    tca_per_acetyl = (3*2.5) + 1.5 + 1
    tca_total = 2 * tca_per_acetyl
    return glycolysis + nadh_glyc + pyruvate_to_acetyl + tca_total
# ≈ 30 ATP / glucose (modern stoichiometry)

Nitrogen balance

def n_balance(protein_intake_g, urea_n_excreted_g, fecal_skin_g=4):
    n_in = protein_intake_g / 6.25
    n_out = urea_n_excreted_g + fecal_skin_g
    return n_in - n_out

Vitamin D activation cascade

def vit_d_activation():
    return [
        "7-dehydrocholesterol",
        "cholecalciferol (D3, skin UVB)",
        "25-OH-D3 (liver, CYP2R1)",
        "1,25-(OH)2-D3 (kidney, CYP27B1, calcitriol)",
        "VDR-RXR transcription factor",
    ]

매 결정 기준

상황	Approach
Weight loss	Caloric deficit + protein-priority
Performance	Periodized carb + creatine
T2D management	Low GL + Mediterranean
Deficiency screen	Serum 25-OH-D, B12, ferritin, Mg

기본값: TDEE 기반 + protein floor + micronutrient screen.

🔗 Graph

🤖 LLM 활용

언제: meal plan template, nutrient interaction summary, label decoding. 언제 X: clinical diagnosis / Rx — RD / MD 필수.

❌ 안티패턴

Calorie-only thinking: hormone / micronutrient 무시.
Single-nutrient hype: antioxidant / superfood 일반화 — context-free.
Ignoring bioavailability: total intake ≠ absorbed.
Population stat → individual: personal genetics / microbiome 무시.

🧪 검증 / 중복

Verified (Lehninger 8e, Modern Nutrition in Health and Disease 11e, USDA DRI 2024).
신뢰도 A.

🕓 Changelog

날짜	변경
2026-05-08	Phase 1
2026-05-10	Manual cleanup — BMR/macro/TCA/Vit-D 패턴

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