CN111226286A - 实现个性化营养的方法 - Google Patents
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Abstract
本发明一般涉及一种通过对受试者或受试者组的营养需求和营养指导的个体化来实现个性化营养的方法。还提供了一种在受试者或受试者组中维持个人营养素和微量营养素水平的方法。
Description
背景技术
目前,在一般群体中的营养推荐参考系统(膳食参考摄入量,DRI;推荐膳食供给量,RDA)是基于流行病学的现有数据,并且有时来自营养消耗/补充研究。各国每日营养摄入缺乏统一,并且一些微量营养素甚至仍然没有推荐值。这些差异一方面通常是由于缺乏特定营养状况的生物标记物。另一方面,在营养素和微量营养素的整体情况如何随时间变化的方面也存在差异并且该差异与健康状况和慢性病的发展相关。参考系统也根据对全体群体的现有数据进行外推而建立。然而,越来越多的科学证据表明,营养代谢是个体特异性的,这对使用基于群体的参考系统进行个性化营养提出了挑战。这是由于在遗传和代谢水平上固有的个体间和个体内的变异性决定了营养素和微量营养素从胃肠系统吸收到体内生化转化、运输和使用的代谢方式。实际上,当在血液中测量时,大多数营养素和微量营养素的个体内差异超过了个体间对应物的差异(Ricos et al,Scand J Clin Lab Invest1999;59:491-500)。例如,据知色氨酸(一种必需氨基酸)的血液水平的个体间差异比在单个受试者中观察到的日常差异高7倍,这意味着色氨酸的个体指数非常低。色氨酸的吸收、运输、代谢和排泄都依赖于不同基因家族的表达(Palego et al,Journal of Amino Acids2016;16;8952520),遗传异质性是单个受试者中血液浓度变化的主要决定因素。
膳食参考系统认识到广泛定义的人口群体,如婴儿、成人、孕妇和哺乳期的特定的营养需求。然而,目前的参考系统将每种营养素与其他营养素分开考虑。这并不反映实际情况,因为营养素和微量营养素在自然界中以复杂的混合物形式出现,并因此同样地呈现在身体中。众所周知,营养素之间存在竞争,这是由于协同或拮抗的分子间相互作用,例如,当不同的营养素共享相似的运输系统时。现有的营养推荐参考系统的另一个局限性在于,它们实际上是主要基于特定的微量营养素的急性缺乏对个体或群体(例如维生素C缺乏症和坏血病)的影响的了解而建立的,并且因此忽略了这种微量营养素的次优值对个人健康可能造成的长期后果。然而,在生命早期跟踪并确定微量营养素状态(例如,个体特定的微量营养素状态范围的破坏)的早期补偿的可能性,,对于以减少危险因素和预防与营养有关的疾病为目标的个体健康管理将特别重要。
营养素和微量营养素对于细胞正常生长和功能是必需的。少量的微量营养素例如维生素(例如,A、D、E、K、B1、B2、B3、B5、B6、B9、B12、C)和矿物质(例如,碘、铁、锌、镁、钙、硒、锰)是每天且终生都需要的,以控制广泛的生理功能。由于人体根本没有能力产生这些营养素/微量营养素,或缺乏产生足够数量的营养素/微量营养素的能力,这意味着这些营养素是必需的营养素,并且需要定期从饮食中摄取。
然而,了解营养如何控制这些复杂的生理调节过程需要能够捕获整个生命中的整个营养系统的动态的新的科学方法。其中之一是全面测量营养素和微量营养素及其代谢产物和/或相关功能性标记物的生物浓度。这些测量的结果构成了所谓的营养表型,其可以与膳食习惯、生活方式、临床终点和分子生物学测量(基因组学、蛋白质组学和代谢组学)相结合,以确定营养素/微量营养素状态、次优和缺乏水平及其对代谢和生理过程的影响。由于个性化营养的目的是匹配个人特定的营养需求,对营养表型的分析可以指导个性化营养/生活方式的推荐,以满足个人特定的营养需求。营养表型分析还提供了一种量化和监测个性化营养的生物学功效的手段。
附图说明
图1是贝叶斯(Bayesian)网络的区域表示(geographical representation)。M:均值,SD:标准差,B:生物标记物,GT:基因型;G:性别和A:年龄。
图2是根据本发明的方法的实施方案的示意图。对于特定的营养素(微量营养素X),使用自适应贝叶斯方法确定受试者A和B的个体范围。微量营养素X的个体范围在通过目前膳食推荐系统所确定的群体范围内。如果检测到出现水平(例如,血液中X的生物浓度)的偏差,则向个体(或受试者)A和B提供特定的推荐和/或食品(如个性化营养)。个性化营养的功效于是可以通过对血液中X的生物浓度及其相关的功能性标记物的后续分析和通过更新贝叶斯模型来测量。
图3显示了41岁好动的男性受试者体内镁水平的监测结果。镁值显示为实线,横轴显示访问次数,纵轴上显示的镁值的单位为mM。测量零个值时,所述受试者的分层(stratified)起始参考范围为[0.64-0.97]。在测量了一个值为0.86之后,分层参考范围发展为个体参考范围[0.72-0.96]。在测量了第二个值为0.85之后,参考范围进一步个性化为[0.73-0.95],如此等等。
图4显示了两个受试者的个性化同型半胱氨酸范围。左边,第一个受试者的同型半胱氨酸水平较低;在右边,第二个受试者具有高的同型半胱氨酸水平。
图5显示了Apo E中具有E2变体的受试者的α-生育酚曲线图。
图6显示了对一名25岁男性运动员的血液L-肉碱水平的监测。
图7显示了对一个受试者中钾水平的监测(实线)以及贝叶斯模型的结果(虚线)。
图8的上图显示了纵轴为铁的曲线图(μM),中图示出纵轴为血红蛋白浓度的曲线图(g/L)和下图为纵轴为转铁蛋白的曲线图(g/L)(全都在同一受试者上),以及相应的个体范围。
图9显示了具有高HDL和高维生素A的第一个受试者的曲线图(图9A和9B);以及具有中-低HDL和中-低维生素A的第二名受试者的曲线图(图9C和9D)。
图10显示了以两种矿物质(镁和钾)、两种脂肪酸(C182n6和C205n3)和两种功能性标记物(HDL和LDL)为例的受试者的曲线图。
图11显示了以两种水溶性维生素(维生素B12和叶酸)、两种脂溶性维生素(α-生育酚和γ-生育酚)和两种氨基酸(蛋氨酸和色氨酸)为例的同一受试者的曲线图。
发明内容
本发明涉及一种通过对受试者或受试者组的营养需求和营养指导的个体化来实现个性化营养的方法,所述方法包括以下步骤:
i)测量来自所述受试者或受试者组的一种或多种代谢和/或营养标记物M的零个、一个或多个值,
ii)对针对步骤i中的所述一种或多种标记物M测量的所述零个、一个或多个值应用自适应贝叶斯模型,得出所述受试者或受试者组中每种标记物M的期望值的个体分布,
iii)由所述个体分布推导出对于每种标记物M的给定特异性水平的一些个体参考Z分数和个体参考范围,
iv)测量所述受试者或受试者组中一种或多种标记物M的一个或多个额外的值,
v)比较所述一个或多个测量值与所述一个或多个个体参考Z分数和个体参考范围,其中所述一个或多个测量值与所述一个或多个个体参考Z分数和范围的偏差代表所述受试者或受试者组的特定营养需求,以及
vi)提出解决所述营养需求的营养推荐。
本发明还涉及一种维持受试者或受试者组中的个人营养素和微量营养素水平的方法,所述方法包括:
a.对测量的代谢和/或营养标记物水平应用统计方法,以得出给定营养素的个体参考范围,
b.将一种或多种额外的代谢和/或营养标记物值的测量结果与其个体参考范围进行比较,以确定所述受试者或受试者组的营养需求,
c.提供解决所述营养需求的推荐;
d.为所述受试者或受试者组提供个性化的营养管理解决方案,以将偏离在所述个体参考范围之外的所测量的代谢和/或营养标记物值纠正回到所述个体参考范围中的值。
本发明还涉及一种使用自适应贝叶斯模型获得人类受试者或受试者组的个体参考范围的方法,其中,特定营养需求是基于所述范围确定的,并且其中,向所述受试者或受试者组提供满足那些营养需求的具有特定成分的食品、饮料、或补品。
本发明还涉及一种设备、系统或装置,所述设备、系统或装置根据本发明的方法向有需要的受试者或受试者组提供营养推荐和/或个性化营养管理解决方案。
具体实施方式
本发明涉及一种通过对受试者或受试者组的营养需求和营养指导的个体化来实现个性化营养的方法,所述方法包括以下步骤:
i)测量来自所述受试者或受试者组的一种或多种代谢和/或营养标记物M的零个、一个或多个值,
ii)对针对步骤i中的所述一种或多种标记物M测量的所述零个、一个或多个值应用自适应贝叶斯模型,得出所述受试者或受试者组中每种标记物M的期望值的个体分布,
iii)由所述个体分布推导出对于每种标记物M的给定特异性水平的一些个体参考Z分数和个体参考范围,
iv)测量所述受试者或受试者组中一种或多种标记物M的一个或多个额外的值,
v)比较所述一个或多个测量值与所述一个或多个个体参考Z分数和个体参考范围,
其中,所述一个或多个测量值与所述一个或多个个体参考Z分数和范围的偏差代表所述受试者或受试者组的特定营养需求,以及
vi)提供解决所述营养需求的营养推荐。
自适应贝叶斯模型使用与受试者相关的可用信息(例如异质性因素,例如年龄、性别、已知的遗传多态性、怀孕和/或膳食信息、锻炼、暴露于太阳)以及任何测量值。当测量值的个数为0(即,零测量)时,自适应贝叶斯模型仅使用所述可用信息(如果有的话)来推导起始参考范围。这个起始参考范围可以看作是群体分层的结果。
受试者可以是人或动物。动物的实例包括脊椎动物,例如哺乳动物,如非人灵长类动物(特别是高等灵长类动物)、狗、啮齿动物(如小鼠、大鼠或豚鼠)、马、骆驼、猪和猫。在一个实施方案中,动物可以是伴侣动物,如狗或猫。
在一个优选的实施方案中,受试者是人。在一个实施方案中,受试者是不超过6岁的幼儿。在一个实施方案中,人类受试者或受试者组是老年人。如果受试者的年纪超过了其原籍国的平均预期寿命的一半,优选地,如果受试者的年纪超过了其原籍国的平均预期寿命的三分之二,更优选地,如果受试者的年纪超过了其原籍国的平均预期寿命的四分之三,最优选地,如果受试者的年纪超过了其原籍国的平均预期寿命的五分之四,则该受试者被认为是“老人”。受试者也可以是孕妇、运动员或医院病人。
在一个实施方案中,营养推荐是基于受试者或受试者组的特定营养需求,这些需求是通过代谢和/或营养标记物的个性化情况来评估的。
在一个实施方案中,受试者或受试者组具有遗传多态性,该遗传多态性导致所述受试者或受试者组中所述一种或多种标记物M的期望值的所述个体分布的变化。
在一个实施方案中,从所述的步骤(i)中的一个或者多个值推断所述受试者或受试者组中与所述一种或多种标记物M的遗传多态性相关的特定核苷酸序列或特定生理状态(例如疾病、妊娠、哺乳、炎症、吞咽困难、老化)的存在或不存在。在这种情况下,所述遗传多态性或所述特定生理状态表明在步骤v)中的特定营养需求。
例如,Apo A-IV基因中的A等位基因和ApoE基因中的E2变体的存在可以通过测量α-生育酚和/或脂蛋白(例如,LDL和HDL)来推断。在一个实施方案中,APoE基因中的E2变体的存在从α-生育酚的被抑制水平推断,并且其概率不低于90%,最好不低于94%。例如,可以通过提供维生素E补充剂和/或含有特定组合的生育酚和生育三烯酚(α、β、δ、γ形式)的食品方案来解决营养需求。
在一个实施方案中,从健康的受试者或健康的受试者组测量所述的一种或多种代谢和/或营养标记物M的零个、一个或多个值。
在另一个实施方案中,所述的一种或多种代谢和/或营养标记物M的零个、一个或多个值从患有疾病或具有特定生理状况的受试者或受试者组中测量,从而用于疾病或状况的营养管理。
在一个实施方案中,所述代谢和/或营养标记物M选自脂肪酸、氨基酸、有机酸、矿物质、水溶性维生素、脂溶性维生素和其他代谢和/或营养状况的指标。
在一个实施方案中,所述标记物M选自如本文所示的代谢和/或营养标记物列表1、2、3、4、5、6、7和8中的一个或者多个。在一个实施方案中,所述标记物选自列表1。在一个实施方案中,所述标记物选自列表2。在一个实施方案中,所述标记物选自列表3。在一个实施方案中,所述标记物选自列表4。在一个实施方案中,所述标记物选自列表5。在一个实施方案中,所述标记物选自列表6。在一个实施方案中,所述标记物选自列表7。在一个实施方案中,所述标记物选自列表8。
在一个实施方案中,所述标记物M选自如本文所示的标记物列表1a、2a、3a、4a、5a、6a和7a中的一个或者多个。在一个实施方案中,所述标记物选自标记物列表1a。在一个实施方案中,所述标记物选自标记物列表2a。在一个实施方案中,所述标记物选自标记物列表4a。在一个实施方案中,所述标记物选自标记物列表5a。在一个实施方案中,所述标记物选自标记物列表6a。在一个实施方案中,所述标记物选自标记物列表7a。
在一个实施方案中,所述标记物M是状态标记物或功能性标记物。
状态标记物是一种可测量的营养标记物,表示受试者或受试者组的体内的营养素或微量营养素的储存或池水平。例如,维生素D的状态是由25-羟基维生素D的水平来测定的。维生素B6的状态是由磷酸吡哆醛(pyridoxyl phosphate)的水平来测定的。
功能性标记物是一种可测量的营养标记物,表示受试者体内可以导致生理或病理变化的特定分子、生化或生理过程或状况。例如,转铁蛋白饱和度和血红蛋白浓度是与铁状态相关的功能性标记物,并且用于诊断缺铁性(贫血)。
本申请表明,维生素水平与α-生育酚/γ-生育酚比值呈正相关。在群体和个体水平上都可以看到强的相关性。维生素D、维生素B1、核黄素、泛酸、吡哆醛、生物素、叶酸、维生素B12、甲基THF和维生素C中的一种或多种的水平与α-生育酚/γ-生育酚比值呈正相关。
标记物M可以在任何可以从个体身上取样的生物材料中测量,并且是有关个体的营养状况的信息。
一种或多种标记物M的值在全血、血清、血浆、红细胞、白细胞、尿液、唾液、皮肤拭子、头发、房水或汗液中的一种或多种测定。在一个实施方案中,所述的一种或多种标记物M的零个、一个或多个值在全血中测量。
在本发明的另一个方面,本发明还提供一种维持受试者或受试者组中的个人营养素和微量营养素水平的方法,所述方法包括:
a.对测量的代谢和/或营养标记物水平应用统计方法,以得出给定营养素的个体参考范围,
b.将一种或多种额外的代谢和/或营养标记物值的测量结果与其个体参考范围进行比较,以确定所述受试者或受试者组的营养需求,
c.提供解决所述营养需求的推荐,
d.为所述受试者或受试者组提供个性化的营养管理解决方案,以将偏离在所述个体参考范围之外的所测量的代谢和/或营养标记物或其他功能性标记物值纠正回到所述个体参考范围中的值。
在一个实施方案中,统计方法是一种自适应贝叶斯模型。例如,本发明的方法可以利用表1所示的算法。
个体参考范围可以取决于所述受试者或受试者组中的特定核苷酸序列(遗传多态性)或特定生理状态的存在或不存在。例如,它可以取决于在所述受试者或受试者组中的基因(如NOS3、PEMT、DAO、COMT、MAOA、GST/POX、MTHFR等)中存在或不存在特定的核苷酸序列。
在一个实施方案中,胆碱的个体参考范围可以取决于PEMT基因中多态性的存在。在一个实施方案中,同型半胱氨酸的个体参考范围可以取决于5,10-甲基四氢叶酸还原酶(MTHFR)基因中多态性的存在。在一个实施方案中,MTHFR基因中多态性是677C>T。在一个实施方案中,MTHFR基因中多态性是1298C>T。
营养需求或需要可能是HDL(高密度脂蛋白)。本申请表明,C205n3、胆固醇、ApoA1、维生素D2H和/或维生素A中的一种或多种的营养推荐可以解决对HDL的营养需求或需要。在一个实施方案中,所述营养推荐是C205n3。在一个实施方案中,所述营养推荐是胆固醇。在一个实施方案中,所述营养推荐是Apo A1。在一个实施方案中,所述营养推荐是维生素D2H。在一个实施方案中,所述营养推荐是维生素A。
个性化营养管理解决方案可以是食品、饮料和/或补品,最好通过赋能者(enablers)分发,包括本文所述的营养推荐。所述个性化营养管理解决方案应提供足够的营养成分(定性和定量二者),以满足个性化的营养需求并管理风险因素。
在一个实施方案中,风险因素是低密度脂蛋白(LDL)(LDL和氧化LDL二者)的血液水平异常。在一个实施方案中,风险因素是糖化血红蛋白(HbA1c)的血液水平异常。在一个实施方案中,风险因素是血压异常。
本发明的方法可以视为贝叶斯网络,其中包含两个强制性节点层,它们对与给定代谢和/或营养标记物相关的差异进行建模。可以增加一个或更多个任选层。这些差异可以是任何来源的,如生物学和分析。对这些差异以及与这些差异相关的超参数建模的概率分布函数与每个节点相关。在一个实施方案中,贝叶斯网络由3个节点组成:表示单个受试者的代谢或营养标记物的值的节点B、以及分别表示这些值的个体均值M和个体标准差(SD)的两个节点。这样的网络假定代谢或营养标记物的值在感兴趣的群体中是正态分布的。
在一个实施方案中,假定受试者内差异是普遍的,并且与节点SD相关的概率分布函数退化为一个唯一值。在另一个实施方案中,与SD相关的非退化分布假定所有受试者可以呈现不同的受试者内差异。与M相关的概率分布也可以假定为正态分布,并且在这种情况下,它的标准偏差对受试者间差异进行建模。
任何类型的分布都可以通过在给定受试者组中的给定的营养生物标记物的差异中存在的知识来假定。在一个实施方案中,分布是参数化的,并且第二层对该分布的参数进行建模。在一个实施方案中,受试者内差异和受试者间差异以CV给出。优选地,额外的节点CV被添加到网络中,并连接到节点M和SD。在可选的实施方案中,假定受试者内差异和受试者间差异的任一或两者的对数正态性。
本发明的方法具有足够的通用性,使得能够对与给定的营养生物标记物相关的分析和生物差异的退纠缠(disentanglement)进行建模。例如,当已知受试者内的生物差异将以正态分布很好地表示,而分析的不确定度以表示总误差的CV或由重测实验估计而给出时,第一层对生物差异进行建模,第二层对分析不确定度进行建模,因为与营养生物标记物值的测量结果相关的分析不确定度位于生物差异之上。换句话说,这些方法不仅可以对与均值有关的分析不确定度(误差建模中经常出现的极限假设)进行建模,而且还可以对在与代谢和/或营养标记物的真实值由于自然的生物差异而与均值不同时的分析不确定度的影响进行建模。
在一个实施方案中,在顶层添加其他任选节点,以对异质性因素对代谢或营养标记物的值的影响进行建模。实例包括性别、年龄、种族和体重。该方法还集成了与营养生物标记物相关的生物通路方面存在的知识,例如,当已知遗传多态性会影响特定营养素的吸收、运输、代谢或排泄时。换句话说,贝叶斯网络可以对存在于给定基因型和相应表型之间的联系进行建模。与代谢或营养标记物相关联的任何生物通路相关的任何遗传多态性可以被包括在这个网络中,只要它们对代谢或营养标记物的值的影响能够定性和定量地进行建模。
在一个实施方案中,基于异质性因素对给定代谢或营养标记物的影响的先验知识以及与该代谢或营养标记物相关的不同类型差异的先验知识,该类型用于预测单个个体的所述代谢和/或营养标记物的期望值。然后,个体的任何信息都可以作为证据添加到网络中,使用标准贝叶斯推理技术更新所有的概率分布函数。例如,如果已知个体为男性,则可以通过将分布函数从一般群体变为男性群体来进行分层。同样,代谢或营养标记物的任何测量值都可以作为证据添加到贝叶斯网络中。
本发明的另一个方面在于对单个个体的营养或健康(例如,风险因素)标记物进行纵向跟踪、以及充分利用该个体的现有信息的参考范围的推断。当对健康受试者的群体给出概率分布时,假定代谢或营养标记物的给定特异性,可以获得参考范围。例如,传统的参考范围假定95%的正常健康受试者的值落入这个区间内。在一个实施方案中,可以选择更高的特异性水平,如99%或99.9%。就概率分布是在测量代谢和/或营养标记物值之前给出的而言,概率分布是预测性的。当获得新的观察结果时,例如作为营养表型测试的一部分,可以检查测量值是否落入指定的区间内。当给出一组饮食健康的受试者的概率分布时,落在区间外的任何值都与正常的稳态营养状况的结果不一致。这些信息可以用来改进营养推荐。
在一个实施方案中,在贝叶斯网络中,测量值可以作为硬性证据输入。有了这个新证据,先验分布就可以通过使用贝叶斯推理转移到后验分布,从而产生可以用于下一次测试的新的参考区间。在这个过程中,一些受试者间差异被移除,后验分布对个体的特异性更强,而对群体的特异性更弱。对于第一次测试,初始参考范围为群体参考范围。当测量零个值时,贝叶斯网络中包含的异质因子会自然地导致代谢和/或营养标记物的分层(起始)参考范围。一旦对同一个体测量的一个或多个值相继相加作为硬性证据,群体参考范围就会适应性地移至个体参考范围。与使用传统的参考范围相比,该方法在灵敏度和特异性方面都有提高。具体来说,代谢和/或营养标记物的个体参考范围的推导,可以极大地改进对代谢和/或营养标记物的生物信号的检测,其中所述代谢和/或营养标记物表现出个体内生物差异显著低于个体间生物差异。绝大多数的代谢和/或营养标记物表现出个体内生物差异低于个体内生物差异。这使得根据本文所述方法推导出的个体参考范围的使用比传统参考范围的使用对于检测大多数代谢和/或营养标记物的生物信号有了重大改进。值得注意的是,个体内差异低于个体间差异不是代谢和/或营养标记物应用该方法的必要性质。该方法提高了呈现不可忽视的受试者间差异的所有代谢和/或营养标记物的信号,然而,可以通过该方法实现的改进与个性化指数相关,该个性化指数以所研究的代谢和/或营养标记物的个体内差异和个体间差异的比率给出。
另一方面涉及从单个个体的代谢和/或营养标记物的测量中推断遗传多态性。例如,编码基因的存在或不存在会对营养素和微量营养素的吸收、输运、代谢和排泄产生重要影响。后述的营养素和/或微量营养素的测量浓度作为硬性证据输入到用于与因果关系相反的方法和推导技术上,以返回存在或不存在编码基因的后验概率分布。该方法使得能够从与基因相关的代谢和/或营养标记物的测量中了解给定个体的遗传特征。遗传信息是推断的而不是测量的。然后,了解个体遗传特征使得能够推导个体参考范围,其中与遗传特征相关的个体间差异被去除。对个体基因组成的了解还使得能够提供更好的个性化营养推荐,例如,对于缺失负责吸收营养的基因的那些人,每天要摄取更多的营养。
本发明还提供一种使用自适应贝叶斯模型获得人类受试者或受试者组的个体参考范围的方法,其中,特定营养需求是基于所述范围确定的,并且其中,向受试者或受试者组提供满足那些营养需求的具有特定成分的食品、饮料、或补品。
本发明的方法依赖于贝叶斯统计,并且适用于当使用贝叶斯推断技术对于给定受试者来说有可用的新信息时。该方法可以应用于对任何类型的营养生物标记物的数据进行建模,并进而为受试者或受试者组做出决策。
本发明还预期一种设备、系统或装置,所述设备、系统或装置根据本发明的方法向受试者或受试者组提供营养推荐和/或个性化营养管理解决方案。例如,本发明的方法可以在具有微处理器的任何设备(例如计算机、智能手机、平板电脑、可穿戴设备、或互联网服务器)上运行。即使在最复杂的情况下,结果也可以在不到一秒钟的时间内返回。
如图2所示,营养推荐和/或食品由操作员(例如,全科医生、个人营养师/教练、或软件应用程序)对个体参考范围(包括个体范围的偏差)的解释来确定。食品(具有足够的营养成分以匹配个体营养需求)和/或生活方式/营养推荐通过赋能者(例如,营养分配机、电子商务、超级市场、全科医生、个人营养师/教练、或软件应用)来分配。
在整个说明书中参考以下代谢和/或营养标记物列表1至8及其组合。可以从列表中的1个或多个中选择单个标记物或标记物组。在列表1a、2a、4a、5a、6a和7a中示出了本发明特别提及的标记物。
营养标记物列表1:脂肪酸
丁酸C4:0;己酸C6:0;辛酸C8:0;癸酸C10:0;十一烷酸C11:0;十二烷酸C12:0;十三烷酸C13:0;十四烷酸C14:0;十五烷酸C15:0;十六烷酸C16:0;十七烷酸C17:0;十八烷酸C18:0;二十烷酸C20:0;二十一烷酸C21:0;二十二烷酸C22:0;二十四烷酸C24:0;十四碳烯酸C14:1n-5;顺式-10-十五烯酸C15:1n-5;棕榈烯酸C16:1n-7;顺式-10-H十七碳烯酸C17:1n-7;反式反油酸C18:1n-9;顺式十八烯酸C18:1n-9;顺式-11-二十烯酸C20:1n-9;芥酸C22:1n-9;二十四烯酸C24:1n-9;反式亚油酸C18:2n-6;顺式亚油酸C18:2n-6;γ-亚麻酸C18:3n-6;α-亚麻酸C18:3n-3;顺式-11,14-二十碳二烯酸C20:2n-6;顺式-8,11,14-廿碳三烯酸C20:3n-6;顺式-11,14,17-廿碳三烯酸20:3n-3;花生四烯酸C20:4n-6;顺式-13,16-二十二碳二烯酸22:2n-6;顺式-5,8,11,14,17-二十碳五烯酸(EPA)C20:5n-3;顺式-4,7,10,13,16,19-二十二碳六烯酸(DHA)C22:6n-3
营养标记物列表1a:亚油酸C18:2n-6(C182n6):顺式-5,8,11,14,17-二十碳五烯酸(EPA)C20:5n-3(C205n3)
营养标记物列表2:氨基酸和相关分子
丙氨酸;β-丙氨酸;肌氨酸;精氨酸;单甲基精氨酸;不对称-二甲基精氨酸(Assym.-dimethylarginine);对称-二甲基精氨酸;天冬酰胺;天冬氨酸;瓜氨酸;谷氨酸;谷氨酸盐;甘氨酸;组氨酸;1-甲基组氨酸;3-甲基组氨酸;异亮氨酸;亮氨酸;赖氨酸;蛋氨酸;鸟氨酸;苯基丙氨酸;脯氨酸;丝氨酸;氨基乙磺酸;苏氨酸;色氨酸;酪氨酸;缬氨酸;羟基脯氨酸;乙醇胺;α-氨基丁酸;β-氨基异丁酸;γ-氨基丁酸;半胱氨酸;同型半胱氨酸;L-肉碱
营养标记物列表2a:蛋氨酸、色氨酸、同型半胱氨酸、L-肉毒碱
营养标记物列表3:有机酸
2-酮丁酸(2-ketobuyric acid);3-甲基-2-氧代丁酸;3-甲基-2-氧代戊酸;4-甲基-2-氧代戊酸
营养标记物列表4:矿物质
Li;B;Mg;Al;P;S;K;Ca;Ti;V;Cr;Mn;Fe;Co;Ni;Cu;Zn;As;Se;Br;Rb;Sr;Mo;Cd;Sn;I;Cs;Hg;Pb
营养标记物列表4a:K(钾)、Mg(镁)、Fe(铁)
营养标记物列表5:水溶性维生素
维生素B1,硫胺素;维生素B2,核黄素;维生素B5,泛酸;维生素B6,吡哆醇;维生素B6,吡哆醛P.(Pyridoxal P.);维生素B6,吡哆胺;维生素B6,吡哆醛;维生素B6,吡哆酸;维生素B8,生物素;维生素B9,叶酸;维生素B12,钴胺素;维生素C,抗坏血酸
营养标记物列表5a:维生素B12
营养标记物列表6:脂溶性维生素
全反式视黄醛;维生素A;25-OH-维生素D2;25-OH-维生素D3;维生素K1;α生育酚;β生育酚;δ生育酚;γ生育酚;α生育三烯酚;γ生育三烯酚;δ生育三烯酚;叶黄素;玉米黄质;β隐黄质
营养标记物列表6a:维生素A、α生育酚、γ生育酚
代谢和/或营养标记物列表7
钠;钾;氯;镁;钙;磷;铜;铁;铁蛋白;转铁蛋白;UIBC;全反钴胺素(Holotranscobalabin);维生素B12;TSH;游离T3;游离T4;白蛋白;前白蛋白;总蛋白;胆固醇;HDL;LDL;葡萄糖;三酸甘油脂;25-OH-维生素D;尿素;NH3;血浆铜蓝蛋白;ALAT;总胆红素;皮质醇;肌酸激酶;碱性磷酸酶;脂肪酶;hsCRP;尿酸;AST;直接胆红素;CK-MB;肌酸酐;GGT;HbA1C;LDH;叶酸;PTH;酸性磷酸酶;Apo A1;Apo B;胰岛素;甲状腺球蛋白;IgG
代谢和/或营养标记物列表7a:转铁蛋白、铁蛋白、胆固醇、HDL、LDL、Apo A1、叶酸
列表8:其他代谢和/或营养可操作标记物
血压;睡眠质量;心率;内皮功能;肾功能;疲劳;肌无力;认知功能;味觉;触觉;视觉;性功能;运动后恢复;体能表现,例如最大摄氧量;含水量;合成代谢/分解代谢平衡;和氧化应激。
实施例
实施例1
算法
在具体情况下,当已知标记物的个体间差异和个体内差异很好地由正态分布表示时,可以使用简单的算法来应用该方法。表1给出了步骤和算法。另外,需要使用贝叶斯推理技术来运行该方法。
表1中描述的方法用于监测41岁好动的男性受试者中的镁含量。该受试者是随访7个月定期进行测试的33名受试者组中的成员。用标准的临床常规分析仪(DimensionIntegrated Chemistry System Siemens,德国)测量血清中的镁。在33名受试者的群体上,使用变量受试者作为随机效应的方差分析,发现个体内方差和个体间方差分别为0.0017mM2和0.0033mM2。这给出的个体指数为0.0017/0.0033=0.52,远远低于1.0,这证实了有关健康受试者血镁水平的生物学差异的公开数据。
对该受试者进行了7次测试。获得了0.86、0.85、0.88、0.88、0.86、0.83和0.79mM的值。这些值在图3中以实线显示,横轴为访问次数,纵轴给出以mM为单位的镁值。
群体平均值POP_ME为0.8mM。第一次观察的期望值的预测分布为正态分布,均值PRE_ME=0.8mM,方差PRED_VAR=0.0017+0.0032=0.049mM2。假设特异性为98%,则参考区间的最小值(第1个百分位数)等于0.8-2.33*sqrt(0.0049)=0.64mM,参考区间的最大值(第99个百分位数)等于0.8+2.33*sqrt(82)=0.96mM。第一个观测值RES(1)=0.86mM落在[0.64-0.96]mM区间内。
在RES(1)=0.86mM的情况下,可以计算第二次观察的期望值的预测分布:
A=0.0032
B=0.8
X1=1/(1/0.0032+1/0.0017)=0.0011
X2=0.0011*0.8/0.0032+0.0011*0.86/0.0017=0.84
PRED_ME=0.84
PRED_VAR=0.0011+0.0017=0.0028
最小值等于0.84-2.33*sqrt(0.0028)=0.72,最大值为0.84+2.33*sqrt(0.028)=0.96。第二个观测值RES(2)=0.85mM落在[0.72-0.96]mM区间内。
RES(2)=0.85mM,新的迭代给出:
A=0.0011
B=0.84
X1=1/(1/0.0011+1/0.0017)=0.00067
X2=0.00067*0.84/0.0011+0.00067*0.85/0.0017=0.84
PRED_ME=0.84
PRED_VAR=0.00067+0.0017=0.0023
最小值变成0.84-2.33*sqrt(0.0023)=0.73,最大值变成0.84-2.33*sqrt(0.0023)=0.96。对于该受试者,个性化的参考区间为[0.73-0.96]g/L。值0.88、0.88、0.86、0.83和0.79的下一次迭代导致最终的个体参考范围为[0.75-0.95]mM。从群体参考范围到个体参考范围的变化过程在图3中以虚线显示。个体参考范围比基于群体的参考范围要窄得多。在此个体参考区间之外的任何值均与镁的正态差异的假设(98%的特异性)不一致。例如,如果下一次测量值低于0.75mM的值,则该值对于该特定个体是非常低的,尽管它仍处于镁的基于群体的参考范围内。
表1:评估呈现正态分布差异的生物标记物的方法
实施例2
个性化范围
高水平的同型半胱氨酸是包括心血管疾病、血栓形成、神经精神疾病、免疫疾病和肾脏疾病在内的多种疾病的危险因素。已知基于群体的同型半胱氨酸参考范围在很大程度上取决于异质性因素,例如年龄、性别和种族。同型半胱氨酸代谢异常,尤其是用于编码同型半胱氨酸的再甲基化和转硫的酶的基因的遗传变体的异常,例如5,10-甲基四氢叶酸还原酶(MTHFR)基因的变体(包括677C>T和1298C>T功能多态性),与高的同型半胱氨酸水平相关(Brustolin等,Braz J Med Biol Res.2010;43(1):1-7)。由于这些原因,已知同型半胱氨酸的个体指数非常低,个体间差异明显高于个体内差异。
在38位[22-53]岁的健康受试者组中监测了同型半胱氨酸的水平。图4显示了2位受试者的个性化的同型半胱氨酸范围。左侧,第一个受试者的同型半胱氨酸水平低,个体参考范围为[2.4-6.8]uM;右侧,第二位受试者的同型半胱氨酸水平高,个体参考范围为[9.8-14.3]uM。两个参考范围没有重叠,表明同型半胱氨酸水平的个体间差异很大。
实施例3
遗传多态性对营养素水平的影响
α-生育酚是维生素E的最具生物活性的形式。作为一种脂溶性抗氧化剂,α-生育酚可以清除膜和血浆脂蛋白中的自由基。已知血液中α-生育酚的浓度取决于多种遗传多态性,包括编码由肠道分泌的脱辅基蛋白的载脂蛋白(Apo)A-IV基因,编码用于脂蛋白清除的Apo E基因和编码参与通过细胞膜摄取脂质的膜蛋白的清道夫受体B类I型(SR-BI)基因。
例如,已知具有Apo A-IV遗传多态性中的A等位基因的受试者(97%)比对T等位基因纯合的受试者(3%)的血液中的α-生育酚浓度低8umol/L。同样,根据Apo E基因中的变体E2、E3、E4,存在最高达14umol/L的差异,等位基因频率分别为7%、79%和14%(Borel etal.,J.Nutr.137:2653–2659,2007)。
该信息在图1的贝叶斯网络中引入,其中α-生育酚为营养标记物,Apo A-IV和ApoV为遗传多态性。在一组38位健康受试者中测量了α-生育酚、低密度脂蛋白(LDL)和高密度脂蛋白(HDL)的浓度,其中在每个受试者的血液中测得7个值的平均值。使用贝叶斯推理得出所有38个受试者的个体参考范围以及Apo A-IV和Apo E基因中每个基因变体的概率。在这38名受试者中,发现有2名受试者的apo I-IV中的T等位基因是纯合的,而这2名受试者之一的Apo E也是E4变体并且概率高于90%。在这38名受试者中,有34名受试者没有表现出影响α-生育酚代谢的罕见的遗传多态性,并且概率高于90%。其余2名受试者在Apo A-IV基因中具有普遍的A等位基因并且概率高于90%,但也在Apo E中具有罕见的E2变体,这是已知的心血管疾病的危险因素,原因在于使得饮食中脂肪的清除缓慢。从表型标记物的测量中推断出这种遗传信息为开出适合每个个体的遗传构成的饮食提供了关键信息。图5显示了在Apo E中具有变体E2的受试者的α-生育酚曲线图,概率为94%。
实施例4
包含L-肉碱的个性化营养管理解决方案
肉碱是一种参与能量代谢和线粒体保护的物质。由于由SLC22A5基因(导致尿液中肉碱的排泄增加)编码的OCTN2中的遗传多态性,肉碱水平的缺乏可以遗传。鉴于其在脂肪酸代谢中的作用,补充肉碱在好动的人中很常见,尤其是在运动员中。图6显示了对25岁男性运动员血液中L-肉碱水平的监测。第七个值低于个体参考范围,尽管该值仍处于基于群体范围内,但仍可以为该受试者推荐含L-肉碱的食品、饮料或补品。
实施例5
包含钾的个性化营养管理解决方案
在一组健康受试者中监测钾水平。图7显示了一个受试者中的钾水平(实线)以及贝叶斯模型的结果(虚线)。该受试者表现出数个值低于个体参考范围,表明循环钾水平显著降低。对于该受试者,推荐使用含钾的食品、饮料或补品。
实施例6
铁状态的功能性标记物
可以使用铁贮存、循环铁和血液参数的测量来评估没有炎性疾病、寄生虫感染和肥胖症的健康人的铁状态。可以测量血清铁蛋白(铁贮存蛋白)、血清铁、总铁结合能力和转铁蛋白(血液中主要的铁载体)的饱和度来评估铁状态。当铁贮存耗尽时,可溶性转铁蛋白受体(sTfR)也可以用作铁状态的指标。如果出现贫血,血液标记物(包括血红蛋白浓度、平均红细胞血红蛋白浓度、红细胞的平均红细胞体积和网状细胞血红蛋白含量)可以帮助检测异常。
在7个月的时间内监测了38位健康受试者的红细胞生成。测量全血和血清中铁的全血细胞计数。图8的上图显示了铁水平(μM),中图为血红蛋白浓度(g/L)和下图为转铁蛋白浓度(g/L)。该受试者具有较高的铁水平,较高的血红蛋白浓度和较低的转铁蛋白浓度。血红蛋白和转铁蛋白是与铁状态相关的功能性标记物。
实施例7
测量其他标记物的水平
图9显示了具有高HDL和高维生素A的第一位受试者的曲线图(图9A和9B);以及具有中-低HDL和中-低维生素A的第二名受试者的曲线图(图9C和9D)。在图10和11中进一步说明了本发明方法的应用,其显示了两种脂溶性维生素、两种水溶性维生素、两种氨基酸、两种矿物质、两种脂肪酸和两种功能性标记物的测量结果。图10显示了受试者中镁、钾、C182n6、C205n3、HDL和LDL的水平。图11显示了同一受试者中维生素B12、叶酸、α-生育酚、γ-生育酚、蛋氨酸和色氨酸的水平。
实施例8
HDL/LDL的比、HDL/胆固醇的比、HDL与所有变量的相关性
HDL/LDL的比、HDL/胆固醇的比、HDL与所有变量的相关性在个体水平上进行测量。还测量了这种相关性的R值(即效应值)。
以下变量显示出与HDL/LDL呈显著的正相关(P>0.2):HDL、Apo A1、HDL/LDL和HDL/胆固醇。以下变量显示出与HDL/LDL呈显著的反相关(P<-0.2):C182n6、C183n3、胆固醇、LDL、三酸甘油酯、ApoB和A Toc H。
以下变量显示出与HDL/胆固醇呈显著的正相关:苏氨酸、HDL、Apo A1、HDL/LDL和HDL/胆固醇。以下变量显示出与HDL/胆固醇呈显著的反相关:C182n6、C183n3、C226n3、胆固醇、LDL、三酸甘油酯、尿酸、Apo B、A Toc H和吡哆胺。
以下变量显示出与HDL呈显著的正相关:C205n3、胆固醇、HDL、Apo A1、V D2 H、V AH、HDL/LDL和HDL/胆固醇。以下变量显示出与HDL呈显著的负相关:三酸甘油酯、尿酸和吡哆醇。
可以在用于增强受试者中的HDL的方法中使用以上信息。
Claims (15)
1.一种通过对受试者或受试者组的营养需求和营养指导的个体化来实现个性化营养的方法,所述方法包括以下步骤:
i)测量来自所述受试者或受试者组的一种或多种代谢和/或营养标记物M的零个、一个或多个值,
ii)对针对步骤i中的所述一种或多种标记物M测量的所述零个、一个或多个值应用自适应贝叶斯模型,得出所述受试者或受试者组中每种标记物M的期望值的个体分布,
iii)由所述个体分布推导出对于每种标记物M的给定特异性水平的一些个体参考Z分数和个体参考范围,
iv)测量所述受试者或受试者组中一种或多种标记物M的一个或多个额外的值,
v)比较所述一个或多个测量值与所述一个或多个个体参考Z分数和个体参考范围,
其中,所述一个或多个测量值与所述一个或多个个体参考Z分数和范围的偏差代表所述受试者或受试者组的特定营养需求,以及
vi)提供解决所述营养需求的营养推荐。
2.根据权利要求1所述的方法,其中,从人类受试者或人类受试者组测量所述的一种或多种标记物M的零个、一个或多个值。
3.根据权利要求1和2所述的方法,其中,从所述的步骤i)中的一个或多个值推断与所述受试者或受试者组中的所述一种或多种标记物M的遗传多态性相关的特定核苷酸或特定生理状态的存在或不存在。
4.根据权利要求3所述的方法,其中从测量的α-生育酚水平推断所述受试者或受试者组中标记物载脂蛋白E的E2变体的存在。
5.根据前述权利要求中任一项所述的方法,其中,对于给定的营养素和/或微量营养素和/或其他标记物,所述一个或多个测量值与所述一个或多个个体参考Z分数和个体参考范围的偏差指示了步骤v)中的特定营养需求。
6.根据前述权利要求中任一项所述的方法,其中,所述标记物M选自脂肪酸、氨基酸、有机酸、矿物质、水溶性维生素、脂溶性维生素、和其他代谢和/或营养状况的指示剂。
7.根据前述权利要求中任一项所述的方法,其中,所述标记物M选自代谢和/或营养标记物列表1、2、3、4、5、6、7和8。
8.根据前述权利要求中任一项所述的方法,其中,所述标记物M选自代谢和/或营养标记物列表1a、2a、4a、5a、6a和7a。
9.根据前述权利要求中任一项所述的方法,其中,在血液、血清、血浆、红细胞、白细胞、尿液、唾液、皮肤拭子、头发、房水、或汗液中的一种或多种中测量所述的一种或多种标记物M的零个、一个或多个值。
10.根据权利要求9所述的方法,其中,在血液中测量所述的一种或多种标记物M的零个、一个或多个值。
11.一种保持受试者或受试者组中的个人营养素和微量营养素水平的方法,所述方法包括:
a.对测量的代谢和/或营养标记物水平应用统计方法,以得出给定营养素的个体参考范围,
b.将一种或多种额外的代谢和/或营养标记物值的测量结果与其个体参考范围进行比较,以确定所述受试者或受试者组的营养需求,
c.提供解决所述营养需求的推荐,
d.为所述受试者或受试者组提供个性化的营养管理解决方案,以将偏离在所述个体参考范围之外的所测量的代谢和/或营养标记物值纠正回到所述个体参考范围中的值。
12.根据权利要求11所述的方法,其中所述统计方法是自适应贝叶斯模型。
13.根据权利要求11和12所述的方法,其中,所述个性化营养管理解决方案是食品、饮料和/或补品。
14.一种使用自适应贝叶斯模型获得人类受试者或受试者组的个体参考范围的方法,其中,特定营养需求是基于所述范围确定的,并且其中,向所述受试者或受试者组提供满足那些营养需求的具有特定成分的食品、饮料、或补品。
15.一种设备、系统或装置,所述设备、系统或装置根据权利要求1至14的方法向受试者或受试者组提供营养推荐和/或个性化营养管理解决方案。
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