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<p>Down syndrome (DS) is associated with elevated rates of insulin resistance and chronic metabolic disease, yet its detailed metabolic and lipidomic profiles, particularly in pediatric populations, remain poorly defined. To characterize plasma lipid profiles in children and young adults with DS and overweight or obesity, to determine degree of lipid heterogeneity and if the observed dyslipidemia is independent of obesity severity. An extended objective is to search for metabolite features that may differentiate DS from weight‑matched controls. Plasma samples from 12 African‑American participants with DS (age 11–21 years, all overweight or obese) and 513 age‑matched overweight or obese controls were profiled by Nightingale&#xa0;<sup>1</sup>H‑NMR spectroscopy (249 metabolites). Because all participants with DS in our cohort were overweight or obese, we restricted the control group to individuals with comparable weight status to minimize confounding by adiposity. Metabolites were log₂‑transformed and standardized to z-scores. Partial least‑squares discriminant analysis (two components) was followed by k‑means clustering (k = 2). Cluster distributions were compared by χ² test, and metabolite differences between clusters, stratified by obesity class, were assessed using Welch’s t‑tests and Benjamini-Hochberg false‑discovery correction. Nine of twelve DS samples (75%) clustered into a dyslipidemic profile (cluster 1), compared to 209 of 513 controls (41%), demonstrating a significant enrichment (p = 0.033). Among controls, cluster assignment showed no association with obesity class. Across all obesity strata, 92 metabolites consistently differed between clusters. Cluster 1 exhibited a distinct lipidomic pattern marked by triglyceride enrichment across the lipoprotein spectrum [from extra-extra-large very-low-density lipoprotein (XXL-VLDL) to high-density lipoprotein (HDL)], elevated remnant cholesterol, increased intermediate-density lipoprotein (IDL) and HDL particle concentrations, and cholesterol-ester-poor, triglyceride-rich HDL particles. Additionally, this cluster showed elevated levels of saturated and monounsaturated fatty acids, alongside a relative depletion of Ω-6 polyunsaturated fatty acids. Together, these features recapitulate a lipid profile associated with insulin resistance and pro-inflammatory metabolic dysfunction. A lipidomic profile characterized by high triglyceride and low cholesterol ester content is highly prevalent among children with DS and overweight or obesity, and present in approximately 40% of overweight or obese controls, irrespective of obesity severity. This insulin-resistant phenotype, independent of adiposity, likely reflects intrinsic alterations in lipid metabolism. The use of the Nightingale&#xa0;<sup>1</sup>H‑NMR offers a scalable and clinically accessible platform for detecting this metabolic signature, offering promise for early risk stratification and precision management of metabolic dysfunction in DS. </p>

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Pro‑inflammatory insulin‑resistant lipid phenotype in down syndrome identified by 1H‑NMR metabolomics in obesity-matched African‑American children and young adults

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年11月26日

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  • Hui-Qi Qu,
  • John J. Connolly,
  • Garnet Eister,
  • Frank Mentch,
  • Joseph Glessner,
  • Hakon Hakonarson

摘要

Down syndrome (DS) is associated with elevated rates of insulin resistance and chronic metabolic disease, yet its detailed metabolic and lipidomic profiles, particularly in pediatric populations, remain poorly defined. To characterize plasma lipid profiles in children and young adults with DS and overweight or obesity, to determine degree of lipid heterogeneity and if the observed dyslipidemia is independent of obesity severity. An extended objective is to search for metabolite features that may differentiate DS from weight‑matched controls. Plasma samples from 12 African‑American participants with DS (age 11–21 years, all overweight or obese) and 513 age‑matched overweight or obese controls were profiled by Nightingale 1H‑NMR spectroscopy (249 metabolites). Because all participants with DS in our cohort were overweight or obese, we restricted the control group to individuals with comparable weight status to minimize confounding by adiposity. Metabolites were log₂‑transformed and standardized to z-scores. Partial least‑squares discriminant analysis (two components) was followed by k‑means clustering (k = 2). Cluster distributions were compared by χ² test, and metabolite differences between clusters, stratified by obesity class, were assessed using Welch’s t‑tests and Benjamini-Hochberg false‑discovery correction. Nine of twelve DS samples (75%) clustered into a dyslipidemic profile (cluster 1), compared to 209 of 513 controls (41%), demonstrating a significant enrichment (p = 0.033). Among controls, cluster assignment showed no association with obesity class. Across all obesity strata, 92 metabolites consistently differed between clusters. Cluster 1 exhibited a distinct lipidomic pattern marked by triglyceride enrichment across the lipoprotein spectrum [from extra-extra-large very-low-density lipoprotein (XXL-VLDL) to high-density lipoprotein (HDL)], elevated remnant cholesterol, increased intermediate-density lipoprotein (IDL) and HDL particle concentrations, and cholesterol-ester-poor, triglyceride-rich HDL particles. Additionally, this cluster showed elevated levels of saturated and monounsaturated fatty acids, alongside a relative depletion of Ω-6 polyunsaturated fatty acids. Together, these features recapitulate a lipid profile associated with insulin resistance and pro-inflammatory metabolic dysfunction. A lipidomic profile characterized by high triglyceride and low cholesterol ester content is highly prevalent among children with DS and overweight or obesity, and present in approximately 40% of overweight or obese controls, irrespective of obesity severity. This insulin-resistant phenotype, independent of adiposity, likely reflects intrinsic alterations in lipid metabolism. The use of the Nightingale 1H‑NMR offers a scalable and clinically accessible platform for detecting this metabolic signature, offering promise for early risk stratification and precision management of metabolic dysfunction in DS.