PREDICTIVE TOOL FOR METABOLIC DEVELOPMENT

这个子项目是利用资源的许多研究子项目之一。 由NIH/NCRR资助的中心拨款提供。对子项目的主要支持 子项目的首席调查员可能是由其他来源提供的， 包括美国国立卫生研究院的其他来源。为子项目列出的总成本可能 表示该子项目使用的中心基础设施的估计数量， 不是由NCRR赠款提供给次级项目或次级项目工作人员的直接资金。 目的：生成代谢综合征风险增加的早期诊断，以便于干预和预防性治疗，并为疾病的机制基础提供见解。 代谢综合征会改变影响新陈代谢和炎症的血清因素，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子在发病前状态的水平是否可以预测未来的疾病发展。这项研究验证了这样一种假设，即脂蛋白、脂肪酸和脂肪因子的促炎性特征组合在一起，构成了代谢综合征发展的早期预测因子。 目的：测定诊断时和发病前2年健康对照和受损动物血清中脂蛋白颗粒的分布情况。 具体目标2：量化健康和受损动物发病前后血清中的脂肪酸浓度和组成。 具体目的3：测定发病前后健康和受损动物血清中脂肪因子和促炎性因子的水平。 理论基础：新陈代谢和炎症通过核受体转录因子家族联系在一起，这些转录因子充当这两个过程的中介。这些关键的调节分子反过来又受到蛋白质和脂肪血清因素的影响，其中许多因素来自脂肪组织。肥胖增加会影响血清脂肪因子和脂因子水平，这可能会增加患代谢综合征的风险。这项研究的目标是提供关键的早期诊断，极大地改善有代谢综合征发展风险的肥胖儿童患者的预后。此外，这项研究中产生的数据可以为治疗策略的发展提供新的线索。早期反应因子在疾病前状态的识别将促进我们对代谢功能障碍的潜在生物学的理解，代谢功能障碍是这种日益流行的疾病的基础。 动物：这项研究的队列是从WNPRC的“饮食限制和衰老”计划项目中选择的雄性恒河猴的子集。由于分析只涉及血浆，我们能够依赖银行样本。选择的标准包括a)有胰岛素抵抗临床表现的代谢综合征动物，在诊断时和发病前2年有血清可用；b)受损动物与年龄和体重相似的健康动物匹配；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计量数据由WNPRC提供。 结果：为了产生预测性诊断，我们调查了对新陈代谢变化有反应并在炎症中起作用的因素。已知的影响内脏肥胖增加的血清因素包括脂蛋白的大小和分布、脂肪酸的浓度和组成以及脂肪因子和脂肪衍生的促炎细胞因子。 脂蛋白谱：测定了n=8只代谢综合征动物在诊断时和发病前2年的HDH、LDL、VLDL颗粒大小和分布，以及年龄和体重匹配的健康对照组动物。对盲目数据的分析清楚地识别出患有代谢综合征的动物，并指出潜在的候选者是早期应答者。 血清脂肪酸浓度和组成：目前已测定了1/3队列中5个脂类的组成脂肪酸。从血浆中提取脂类，并将其分离为I)胆固醇酯，II)磷脂，III)二酰甘油，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，它们在脂类中以谨慎的比例和种群存在。血清分析可以区分受损动物和健康对照动物，我们已经确定了预测性诊断的候选对象。 Adiokine和细胞因子概况：已经收集了CRP的数据，我们验证了检测脂肪因子的生物测定已经完成。 数据分析：对所有数据集进行单独和组合分析，以确定可能与即将发生的代谢综合征相关的反应因素。这些分析是与我们在生物统计和医学信息学系的同事合作进行的。 这项研究使用了WNPRC动物服务和研究服务。 一份手稿正在准备中。

This subproject is one of many research subprojects utilizing the resources provided by a Center grant funded by NIH/NCRR. Primary support for the subproject and the subproject's principal investigator may have been provided by other sources, including other NIH sources. The Total Cost listed for the subproject likely represents the estimated amount of Center infrastructure utilized by the subproject, not direct funding provided by the NCRR grant to the subproject or subproject staff. 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. 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. Serum analysis can distinguish the impaired animals from the healthy controls and we have identified candidates for a predictive diagnostic. Adiokine and cytokine profile: Data have been collected for CRP and we validated bio-assays for detection of adipokines have been completed. Data analysis: All datasets were analyzed separately and in combination to determine responsive factors that can be associated with impending onset of metabolic syndrome. These analyses were conducted in collaboration with our colleagues at the Department of Biostatistics and Medical Informatics. This research uses WNPRC Animal Services and Research Services. A manuscript is in preparation.