PDF(Pubmed)
Abstract：
Defining the metabolic syndrome (MetS) in children remains challenging. Furthermore, a dichotomous MetS diagnosis can limit the power to study associations. We sought to characterize the serum metabolite signature of the MetS in early childhood using high-throughput metabolomic technologies that allow comprehensive profiling of metabolic status from a biospecimen.
In the Family Atherosclerosis Monitoring In earLY life (FAMILY) prospective birth cohort study, we selected 228 cases of MetS and 228 matched controls among children age 5 years. In addition, a continuous MetS risk score was calculated for all 456 participants. Comprehensive metabolite profiling was performed on fasting serum samples using multisegment injection-capillary electrophoresis-mass spectrometry. Multivariable regression models were applied to test metabolite associations with MetS adjusting for covariates of screen time, diet quality, physical activity, night sleep, socioeconomic status, age, and sex.
Compared to controls, thirteen serum metabolites were identified in MetS cases when using multivariable regression models, and using the quantitative MetS score, an additional eight metabolites were identified. These included metabolites associated with gluconeogenesis (glucose (odds ratio (OR) 1.55 [95% CI 1.25-1.93]) and glutamine/glutamate ratio (OR 0.82 [95% CI 0.67-1.00])) and the alanine-glucose cycle (alanine (OR 1.41 [95% CI 1.16-1.73])), amino acids metabolism (tyrosine (OR 1.33 [95% CI 1.10-1.63]), threonine (OR 1.24 [95% CI 1.02-1.51]), monomethylarginine (OR 1.33 [95% CI 1.09-1.64]) and lysine (OR 1.23 [95% CI 1.01-1.50])), tryptophan metabolism (tryptophan (OR 0.78 [95% CI 0.64-0.95])), and fatty acids metabolism (carnitine (OR 1.24 [95% CI 1.02-1.51])). The quantitative MetS risk score was more powerful than the dichotomous outcome in consistently detecting this metabolite signature.
A distinct metabolite signature of pediatric MetS is detectable in children as young as 5 years old and may improve risk assessment at early stages of development.
In the Family Atherosclerosis Monitoring In earLY life (FAMILY) prospective birth cohort study, we selected 228 cases of MetS and 228 matched controls among children age 5 years. In addition, a continuous MetS risk score was calculated for all 456 participants. Comprehensive metabolite profiling was performed on fasting serum samples using multisegment injection-capillary electrophoresis-mass spectrometry. Multivariable regression models were applied to test metabolite associations with MetS adjusting for covariates of screen time, diet quality, physical activity, night sleep, socioeconomic status, age, and sex.
Compared to controls, thirteen serum metabolites were identified in MetS cases when using multivariable regression models, and using the quantitative MetS score, an additional eight metabolites were identified. These included metabolites associated with gluconeogenesis (glucose (odds ratio (OR) 1.55 [95% CI 1.25-1.93]) and glutamine/glutamate ratio (OR 0.82 [95% CI 0.67-1.00])) and the alanine-glucose cycle (alanine (OR 1.41 [95% CI 1.16-1.73])), amino acids metabolism (tyrosine (OR 1.33 [95% CI 1.10-1.63]), threonine (OR 1.24 [95% CI 1.02-1.51]), monomethylarginine (OR 1.33 [95% CI 1.09-1.64]) and lysine (OR 1.23 [95% CI 1.01-1.50])), tryptophan metabolism (tryptophan (OR 0.78 [95% CI 0.64-0.95])), and fatty acids metabolism (carnitine (OR 1.24 [95% CI 1.02-1.51])). The quantitative MetS risk score was more powerful than the dichotomous outcome in consistently detecting this metabolite signature.
A distinct metabolite signature of pediatric MetS is detectable in children as young as 5 years old and may improve risk assessment at early stages of development.
摘要：
定义儿童代谢综合征（MetS）仍然具有挑战性。此外，二分法的MetS诊断可能会限制研究关联的能力.我们试图使用高通量代谢组学技术表征儿童早期MetS的血清代谢物特征，该技术允许从生物样本中全面分析代谢状态。
在家庭动脉粥样硬化早期监测（家庭）前瞻性出生队列研究，我们在5岁儿童中选择了228例MetS和228例匹配的对照。此外,我们为所有456名参与者计算了连续MetS风险评分.使用多段注射-毛细管电泳-质谱法对空腹血清样品进行全面的代谢物分析。多变量回归模型被用来测试代谢产物的关联与MetS调整筛选时间的协变量，饮食质量，身体活动,晚上睡觉，社会经济地位,年龄,和性爱。
与对照相比，当使用多变量回归模型时，在MetS病例中鉴定出13种血清代谢物，使用定量的MetS评分，另外鉴定出8种代谢物.这些包括与糖异生相关的代谢物（葡萄糖（比值比（OR）1.55[95％CI1.25-1.93]）和谷氨酰胺/谷氨酸比（OR0.82[95％CI0.67-1.00]）和丙氨酸-葡萄糖循环（丙氨酸（OR1.41[95％CI1.16-1.73]）），氨基酸代谢（酪氨酸（OR1.33[95%CI1.10-1.63]），苏氨酸（OR1.24[95%CI1.02-1.51]），单甲基精氨酸（OR1.33[95%CI1.09-1.64]）和赖氨酸（OR1.23[95%CI1.01-1.50]），色氨酸代谢（色氨酸（OR0.78[95%CI0.64-0.95]）），和脂肪酸代谢（肉碱（OR1.24[95%CI1.02-1.51]））。定量MetS风险评分在持续检测该代谢物特征方面比二分法结果更强大。
在5岁以下的儿童中可以检测到小儿MetS的独特代谢物特征，并且可以改善发育早期的风险评估。
在家庭动脉粥样硬化早期监测（家庭）前瞻性出生队列研究，我们在5岁儿童中选择了228例MetS和228例匹配的对照。此外,我们为所有456名参与者计算了连续MetS风险评分.使用多段注射-毛细管电泳-质谱法对空腹血清样品进行全面的代谢物分析。多变量回归模型被用来测试代谢产物的关联与MetS调整筛选时间的协变量，饮食质量，身体活动,晚上睡觉，社会经济地位,年龄,和性爱。
与对照相比，当使用多变量回归模型时，在MetS病例中鉴定出13种血清代谢物，使用定量的MetS评分，另外鉴定出8种代谢物.这些包括与糖异生相关的代谢物（葡萄糖（比值比（OR）1.55[95％CI1.25-1.93]）和谷氨酰胺/谷氨酸比（OR0.82[95％CI0.67-1.00]）和丙氨酸-葡萄糖循环（丙氨酸（OR1.41[95％CI1.16-1.73]）），氨基酸代谢（酪氨酸（OR1.33[95%CI1.10-1.63]），苏氨酸（OR1.24[95%CI1.02-1.51]），单甲基精氨酸（OR1.33[95%CI1.09-1.64]）和赖氨酸（OR1.23[95%CI1.01-1.50]），色氨酸代谢（色氨酸（OR0.78[95%CI0.64-0.95]）），和脂肪酸代谢（肉碱（OR1.24[95%CI1.02-1.51]））。定量MetS风险评分在持续检测该代谢物特征方面比二分法结果更强大。
在5岁以下的儿童中可以检测到小儿MetS的独特代谢物特征，并且可以改善发育早期的风险评估。
