Multi-class urinary antibiotic exposure, dietary sources, and childhood overweight and obesity risk through lipid metabolic disruption: evidence from multicenter cohorts in China.

IF 7.2 2区 环境科学与生态学 Q1 ENVIRONMENTAL SCIENCES
Environmental Pollution Pub Date : 2026-11-01 Epub Date: 2026-08-05 DOI:10.1016/j.envpol.2026.128907
Xinli Song, Jieyu Liu, Li Chen, Yi Zhang, Ruolin Wang, Jianuo Jiang, Lan Zhang, Yanle Xiong, Jingyang Su, Ziqi Dong, Yang Qin, Dengcheng Zhang, Zhenting Lu, Zhiying Song, Jinghong Liang, Jun Ma, Yanhui Dong
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引用次数: 0

Abstract

Given widespread antibiotic use and residues, this study examined associations of multi-class urinary antibiotic exposure with childhood overweight and obesity (OWOB) risk, identified potential dietary sources, and explored lipidomic signatures and potential indirect effects. We analyzed two cohorts from China: the 4C cohort (Inner Mongolia, 2024; n = 692) for diet-exposure analyses and the CBPEF cohort (Xiamen, 2018/2020; n = 594) for repeated-measures exposure-adiposity and lipidomics analyses. Twenty-four urinary antibiotics across six classes were measured by LC-MS/MS. BMI percentile, OWOB, and untargeted serum lipidomics were assessed, with lipidomics performed in a CBPEF subsample of 73 children. Multi-class antibiotic exposure and co-exposure were common in both cohorts, with >94% of children in the 4C cohort and ≥98% in the CBPEF cohort having at least two detected antibiotics. In the 4C cohort, fish/seafood intake was associated with higher detection of sulfonamides, tetracyclines, and quinolones, with the strongest diet-exposure association observed for seafood intake and sulfonamide detection (OR = 2.17, 95% CI: 1.05-4.48), whereas plant-based intake was inversely associated with total detected antibiotic counts (β = -0.492). In the CBPEF cohort, each additional antibiotic detected was associated with a 0.63-point increase in BMI percentile (95% CI: 0.34-0.92), and each additional human antibiotic detected was associated with 14% higher odds of OWOB (OR = 1.14, 95% CI: 1.04-1.24). Cumulative sulfonamide, quinolone, macrolide, tetracycline, and phenicol counts were positively associated with BMI percentile (β range: 1.15-2.45). Lipidomic analyses identified 102 candidate lipids shared by urinary antibiotic exposure metrics and BMI percentile, with PC1-based indirect-effect signals across multiple antibiotic classes and pathway annotations mainly involving steroid/cholesterol-related metabolism. These findings suggest that contemporary Chinese children and adolescents commonly experience multi-class antibiotic exposure, particularly involving quinolones and macrolides, which may partly reflect dietary and environmental sources and contribute to childhood obesity risk, with lipidomic signatures suggesting involvement of steroid/cholesterol-related metabolism.

多类别尿抗生素暴露、饮食来源和儿童超重和肥胖风险通过脂质代谢破坏:来自中国多中心队列的证据
鉴于抗生素的广泛使用和残留,本研究调查了多类尿路抗生素暴露与儿童超重和肥胖(OWOB)风险的关系,确定了潜在的饮食来源,并探索了脂质组学特征和潜在的间接影响。我们分析了来自中国的两个队列:4C队列(内蒙古,2024年,n = 692)用于饮食暴露分析,CBPEF队列(厦门,2018/2020年,n = 594)用于重复测量暴露-肥胖和脂质组学分析。采用LC-MS/MS对6类24种尿用抗生素进行测定。评估BMI百分位数、OWOB和非靶向血清脂质组学,并在73名儿童的CBPEF亚样本中进行脂质组学。多类抗生素暴露和共暴露在两个队列中都很常见,4C队列中有超过94%的儿童检测到至少两种抗生素,CBPEF队列中有超过98%的儿童检测到至少两种抗生素。在4C队列中,鱼类/海鲜摄入量与磺胺类药物、四环素类药物和喹诺酮类药物的检出率较高相关,其中海鲜摄入量与磺胺类药物的检出率相关性最强(OR = 2.17, 95% CI: 1.05-4.48),而植物性摄入量与总抗生素检出率呈负相关(β = -0.492)。在CBPEF队列中,每增加一种抗生素检测与BMI百分位数增加0.63点相关(95% CI: 0.34-0.92),每增加一种人类抗生素检测与OWOB发生率增加14%相关(OR = 1.14, 95% CI: 1.04-1.24)。累积的磺胺、喹诺酮、大环内酯、四环素和酚计数与BMI百分位数呈正相关(β范围:1.15-2.45)。脂质组学分析确定了102种候选脂质,这些脂质通过尿抗生素暴露指标和BMI百分位数共享,基于pc1的间接效应信号跨越多种抗生素类别和主要涉及类固醇/胆固醇相关代谢的途径说明。这些发现表明,当代中国儿童和青少年普遍经历多类抗生素暴露,特别是涉及喹诺酮类和大环内酯类,这可能部分反映了饮食和环境来源,并有助于儿童肥胖风险,脂质组学特征表明涉及类固醇/胆固醇相关代谢。
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来源期刊
Environmental Pollution
Environmental Pollution 环境科学-环境科学
CiteScore
16.00
自引率
6.70%
发文量
2082
审稿时长
2.9 months
期刊介绍: Environmental Pollution is an international peer-reviewed journal that publishes high-quality research papers and review articles covering all aspects of environmental pollution and its impacts on ecosystems and human health. Subject areas include, but are not limited to: • Sources and occurrences of pollutants that are clearly defined and measured in environmental compartments, food and food-related items, and human bodies; • Interlinks between contaminant exposure and biological, ecological, and human health effects, including those of climate change; • Contaminants of emerging concerns (including but not limited to antibiotic resistant microorganisms or genes, microplastics/nanoplastics, electronic wastes, light, and noise) and/or their biological, ecological, or human health effects; • Laboratory and field studies on the remediation/mitigation of environmental pollution via new techniques and with clear links to biological, ecological, or human health effects; • Modeling of pollution processes, patterns, or trends that is of clear environmental and/or human health interest; • New techniques that measure and examine environmental occurrences, transport, behavior, and effects of pollutants within the environment or the laboratory, provided that they can be clearly used to address problems within regional or global environmental compartments.
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