This review examines lipids and lipid-binding sites on proteins in relation to cardiovascular disease. Lipid nutrition involves food energy from ingested fatty acids plus fatty acids formed from excess ingested carbohydrate and protein. Non-esterified fatty acids (NEFA) and lipoproteins have many detailed attributes not evident in their names. Recognizing attributes of lipid-protein interactions decreases unexpected outcomes. Details of double bond position and configuration interacting with protein binding sites have unexpected consequences in acyltransferase and cell replication events. Highly unsaturated fatty acids (HUFA) have n-3 and n-6 motifs with documented differences in intensity of destabilizing positive feedback loops amplifying pathophysiology. However, actions of NEFA have been neglected relative to cholesterol, which is co-produced from excess food. Native low-density lipoproteins (LDL) bind to a high-affinity cell surface receptor which poorly recognizes biologically modified LDLs. NEFA increase negative charge of LDL and decrease its processing by “normal” receptors while increasing processing by “scavenger” receptors. A positive feedback loop in the recruitment of monocytes and macrophages amplifies chronic inflammatory pathophysiology. Computer tools combine multiple components in lipid nutrition and predict balance of energy and n-3:n-6 HUFA. The tools help design and execute precise clinical nutrition monitoring that either supports or disproves expectations.

这篇综述检查了与心血管疾病相关的蛋白质上的脂质和脂质结合位点。脂质营养涉及来自摄入的脂肪酸的食物能量以及由过量摄入的碳水化合物和蛋白质形成的脂肪酸。非酯化脂肪酸 (NEFA) 和脂蛋白具有许多名称中不明显的详细属性。识别脂质-蛋白质相互作用的属性可减少意外结果。与蛋白质结合位点相互作用的双键位置和构型的细节在酰基转移酶和细胞复制事件中具有意想不到的后果。高度不饱和脂肪酸 (HUFA) 具有 n-3 和 n-6 基序，在不稳定的正反馈回路放大病理生理学的强度上有记录的差异。然而，相对于胆固醇，NEFA 的作用被忽视了，这是由多余的食物共同产生的。天然低密度脂蛋白 (LDL) 与难以识别生物修饰的 LDL 的高亲和力细胞表面受体结合。NEFA 增加 LDL 的负电荷并减少“正常”受体的处理，同时增加“清道夫”受体的处理。招募单核细胞和巨噬细胞的正反馈循环放大了慢性炎症病理生理学。计算机工具结合了脂质营养中的多种成分，并预测能量平衡和 n-3:n-6 HUFA。这些工具有助于设计和执行精确的临床营养监测，支持或反驳预期。NEFA 增加 LDL 的负电荷并减少“正常”受体的处理，同时增加“清道夫”受体的处理。招募单核细胞和巨噬细胞的正反馈循环放大了慢性炎症病理生理学。计算机工具结合了脂质营养中的多种成分，并预测能量平衡和 n-3:n-6 HUFA。这些工具有助于设计和执行精确的临床营养监测，支持或反驳预期。NEFA 增加 LDL 的负电荷并减少“正常”受体的处理，同时增加“清道夫”受体的处理。招募单核细胞和巨噬细胞的正反馈循环放大了慢性炎症病理生理学。计算机工具结合了脂质营养中的多种成分，并预测能量平衡和 n-3:n-6 HUFA。这些工具有助于设计和执行精确的临床营养监测，支持或反驳预期。