PREDICTIVE TOOL FOR METABOLIC DEVELOPMENT

该子项目是利用 由NIH/NCRR资助的中心赠款提供的资源。子项目和 研究者（PI）可能从另一个NIH来源获得主要资金， 因此可以在其他CRISP条目中表示。列出的机构是 中心，不一定是研究者的机构。 目的：为代谢综合征风险增加提供早期预警诊断，这将有助于干预和预防性治疗，并提供对疾病机制基础的见解。 代谢综合征改变影响代谢和炎症的血清因子，包括脂蛋白、脂肪酸和脂肪因子。我们正在使用恒河猴模型来确定这些因素在患病前状态的水平是否可以预测未来的疾病发展。本研究检验了脂蛋白、脂肪酸和脂肪因子促炎特征共同构成代谢综合征发展的早期预测因子的假设。 具体目标1：确定诊断时和疾病发作前2年健康对照组和受损动物血清中的脂蛋白粒度分布特征。 具体目标2：量化疾病发作前后健康和受损动物血清中的脂肪酸浓度和组成。 具体目标3：确定疾病发作前后健康和受损动物血清中脂肪因子和促炎因子的水平。 基本原理：代谢和炎症通过作为两个过程的介质的转录因子的核受体家族联系在一起。 这些关键的调节分子又受到蛋白质和脂质血清因子的影响，其中许多是脂肪组织来源的。肥胖增加影响血清脂肪因子和脂因子水平，可能导致代谢综合征风险增加。这项研究的目的是提供一个关键的早期诊断，将显着改善的结果，肥胖的儿科患者有代谢综合征的发展风险。此外，本研究中产生的数据可以为治疗策略的开发提供新的线索。 在患病前状态下识别早期反应因子将促进我们对代谢功能障碍的潜在生物学的理解，这是这种日益流行的疾病的基础。 动物：本研究的队列选择为来自WNPRC的“饮食限制和衰老”计划项目的雄性恒河猴子集。 由于分析仅涉及血浆，因此我们能够依赖库存样品。 选择标准包括a）具有胰岛素抵抗临床表现的代谢综合征动物，在诊断时和疾病发作前2年可获得血清B）受损动物与年龄和体重相似的健康动物匹配，以及c）控制饮食和生活条件以限制可能干扰分析的影响。 胰岛素敏感性和生物特征数据由WNPRC提供。 结果如下：为了产生预测性诊断，我们研究了对代谢变化做出反应并在炎症中发挥作用的因素。 已知受内脏肥胖增加影响的血清因子包括脂蛋白大小和分布、脂肪酸浓度和组成、脂肪因子和脂肪源性促炎细胞因子。 脂蛋白谱：在诊断时和疾病发作前2年测定了n=8只代谢综合征动物沿着年龄和体重匹配的健康对照动物的HDH、LDL、VLDL粒度和分布。 对盲法数据的初步分析清楚地识别出了患有代谢综合征的动物，并指出了作为早期反应者的潜在候选者。 血清脂肪酸浓度和组成：此时已测定了三分之一队列的5种脂质组内的组成脂肪酸。 从血浆中提取脂质，并分离为i）胆固醇酯，ii）磷脂，iii）二酰基甘油，iv）甘油三酯和v）游离脂肪酸。 气相色谱分析检测到28种不同的脂肪酸，这些脂肪酸在脂质组中以谨慎的比例和种群存在。 初步分析可以区分受损动物和健康对照动物，尽管在数据收集（报告时完成50%）和分析中识别预测性诊断的候选者还为时过早。 Adiokine和细胞因子谱：已收集CRP的数据，我们已验证了用于检测脂肪因子的生物测定法。 我们预计在几周内完成这一数据集。 数据分析：完整的数据集将单独和组合分析，以确定可能与代谢综合征即将发作相关的响应因素。 这些分析将与我们在生物统计学和医学信息学系的同事合作进行。 本研究使用WNPRC动物服务和研究服务。

This subproject is one of many research subprojects utilizing the resources provided by a Center grant funded by NIH/NCRR. The subproject and investigator (PI) may have received primary funding from another NIH source, and thus could be represented in other CRISP entries. The institution listed is for the Center, which is not necessarily the institution for the investigator. Objective: To generate early warning diagnostics for increased metabolic syndrome risk that will facilitate intervention and preventative treatment, and provide insights into the mechanistic basis of the disease. Metabolic syndrome alters serum factors that influence metabolism and inflammation, including lipoproteins, fatty acids and adipokines. We are using the rhesus macaque model to determine whether levels of these factors in the pre-diseased state can be predictive of future disease development. This study tests the hypothesis that lipoprotein, fatty acid, and adipokine pro-inflammatory profiles, in combination, constitute an early predictor of metabolic syndrome development. Specific Aim1: To determine the lipoprotein particle size distribution profile in serum from healthy controls and impaired animals at the time of diagnosis and 2 years before disease onset. Specific Aim 2: To quantify fatty acid concentration and composition in serum from healthy and impaired animals before and after disease onset. Specific Aim 3: To determine levels of adipokines and pro-inflammatory factors in serum from healthy and impaired animals before and after disease onset. Rationale: Metabolism and inflammation are linked through nuclear receptor family of transcription factors that act as mediators of both processes. These key regulatory molecules are in turn influenced by protein and lipid serum factors, many of which are adipose-tissue derived. Increased adiposity influences serum levels of adipokines and lipokines that may contribute to increased risk for metabolic syndrome. The goal of this study is to provide a critical early diagnostic that would dramatically improve outcomes for obese pediatric patients that are at risk of metabolic syndrome development. In addition, data generated in this study could provide novel leads for the development of therapeutic strategies. Identification of early-responding factors in the pre-diseased state will advance our understanding of the underlying biology of the metabolic dysfunction that is the basis for this increasingly prevalent disease. Animals: The cohort for this study was selected as a subset of male rhesus monkeys from the "Dietary restriction and Aging" Program Project at the WNPRC. As the analysis involves plasma only, we were able to rely on banked samples. The criteria for selection include a) metabolic syndrome animals with clinical manifestation of insulin resistance with serum available at time of diagnosis and 2 years prior to onset of disease b) impaired animals were matched with healthy animals of similar age and body weight and c) dietary and living conditions were controlled to limit influences that could interfere with the analysis. Insulin sensitivity and biometric data were provided by WNPRC. Results: In order to generate a predictive diagnostic we investigated factors that are responsive to changes in metabolism and also play a role in inflammation. Serum factors that are known to be influenced by increased visceral adiposity include lipoprotein size and distribution, fatty acid concentration and composition, and adipokine and adipose-derived proinflammatory cytokines. Lipoprotein profiles: HDH, LDL, VLDL particle size and distribution have been determined for n=8 metabolic syndrome animals at time of diagnosis and at 2 years prior to onset of disease along with age and weight matched healthy control animals. A preliminary analysis of the blind data clearly identifies animals with metabolic syndrome and points to potential candidates as early-responders. Serum fatty acid concentration and composition: The constituent fatty acids within 5 lipid groups have been determined for one third of the cohort at this time. Lipids were extracted from plasma and separated into i) cholesterol esters, ii) phospholipids, iii) diacylglycerol, iv) triglycerides and v) free fatty acids. Gas chromatographic analysis detects 28 distinct fatty acid species that are present in discreet proportions and populations among the lipid groups. Preliminary analysis can distinguish the impaired animals from the healthy controls, although it is too early in the data collection (50% complete at time of report) and analysis to identify candidates for a predictive diagnostic. Adiokine and cytokine profile: Data have been collected for CRP and we have validated the bio-assays for detection of adipokines. We anticipate completion of this data set within a matter of weeks. Data analysis: The complete dataset will be analyzed in separately and in combination to determine responsive factors that can be associated with impending onset of metabolic syndrome. These analyses will be conducted in collaboration with our colleagues at the Department of Biostatistics and Medical Informatics. This research uses WNPRC Animal Services and Research Services.