Brooke Scardino, Akshat Agrawal, Diensn G Xing, Jackson L St Pierre, Md Mostafizur Rahman Bhuiyan, Kanon Kamronnaher, Md Shenuarin Bhuiyan, Oren Rom, Steven A Conrad, John A Vanchiere, A Wayne Orr, Christopher G Kevil, Mohammad Alfrad Nobel Bhuiyan
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引用次数: 0
Abstract
Background: Metabolic syndrome (MetS), which affects one-third of the population of the United States, is a risk factor for chronic diseases such as cardiovascular diseases, stroke, and type 2 diabetes mellitus. Heavy metals (HM) and volatile organic compounds (VOC) are environmental factors typically occurring as mixtures. Although exposures to these substances have been studied separately, the impact of combined HM and VOC exposure on humans and their subsequent risk of developing MetS has not been explored. This study investigates whether combined exposure to HMs and VOCs affects the risk of developing MetS.
Methods: The National Health and Nutrition Examination Survey database from 2011 to 2020 was used to determine exposure to HMs and VOCs detected in urine samples from individuals with MetS. Multiple Chi-squared and t-tests were performed to identify variables significantly associated with MetS. Logistic regression analysis was performed on unmatched and age-matched 1:1 case-control data to evaluate whether an association exists among HMs, VOCs, and demographic factors and MetS. A hierarchical cluster analysis was performed to identify combinations of HMs and VOCs linked with an increased risk of MetS.
Results: Logistic regression analysis on unmatched and matched data showed that increasing age and female sex were significantly associated (p < 0.05) with MetS. Among the HMs and VOCs, only N-acetyl-S-(2-cyanoethyl)-l-cysteine and N-acetyl-S-(2-hydroxyethyl)-l-cysteine were found to be significantly associated with MetS. Cluster analysis showed that Cluster 3 was significantly associated with MetS (p < 0.05; OR = 1.49), suggesting that exposure to barium, cadmium, cesium, lead, and VOCs may increase the risk of MetS. After adjusting for covariates, none of the clusters showed a significant association (p > 0.05). In contrast, age (OR = 1.07) and monthly poverty level index ≤ 1.3 (OR = 1.16) were significantly associated with MetS (p < 0.05).
Conclusion: This study revealed that age, lower socioeconomic status, and multiple exposures to combined HM and VOC may have a greater impact with an increased risk of MetS. Cluster analysis highlighted the potential combination of the exposures linked to MetS and the likelihood that demographic factors affect MetS more than exposure to HMs and VOCs. However, further research is needed.
Supplementary information: The online version contains supplementary material available at 10.1007/s13755-025-00375-1.
背景：代谢综合征（MetS）影响着美国三分之一的人口，是心血管疾病、中风和2型糖尿病等慢性疾病的危险因素。重金属（HM）和挥发性有机化合物（VOC）是通常以混合物形式出现的环境因素。虽然对这些物质的暴露已经分别进行了研究，但HM和VOC联合暴露对人类的影响及其随后发生MetS的风险尚未得到探讨。本研究调查了混合暴露于有机污染物和挥发性有机化合物是否会影响患MetS的风险。方法：使用2011年至2020年国家健康与营养检查调查数据库，确定MetS患者尿液样本中检测到的HMs和VOCs暴露情况。进行多重卡方检验和t检验以确定与MetS显著相关的变量。对未匹配和年龄匹配的1:1病例对照数据进行Logistic回归分析，以评估HMs、VOCs、人口因素和MetS之间是否存在关联。进行了分层聚类分析，以确定与MetS风险增加相关的HMs和VOCs组合。结果：对未匹配和匹配的数据进行Logistic回归分析，年龄的增加与女性的性别显著相关（p p p > 0.05）。年龄（OR = 1.07）和月贫困水平指数（OR = 1.16）与MetS有显著相关性(p)。结论：年龄、较低的社会经济地位和多次暴露于HM和VOC联合暴露可能对MetS的风险增加有更大的影响。聚类分析强调了与MetS相关的暴露的潜在组合，以及人口因素比暴露于HMs和VOCs更可能影响MetS。然而，还需要进一步的研究。补充信息：在线版本包含补充资料，提供地址为10.1007/s13755-025-00375-1。
期刊介绍：
Health Information Science and Systems is a multidisciplinary journal that integrates artificial intelligence/computer science/information technology with health science and services, embracing information science research coupled with topics related to the modeling, design, development, integration and management of health information systems, smart health, artificial intelligence in medicine, and computer aided diagnosis, medical expert systems. The scope includes: i.) smart health, artificial Intelligence in medicine, computer aided diagnosis, medical image processing, medical expert systems ii.) medical big data, medical/health/biomedicine information resources such as patient medical records, devices and equipments, software and tools to capture, store, retrieve, process, analyze, optimize the use of information in the health domain, iii.) data management, data mining, and knowledge discovery, all of which play a key role in decision making, management of public health, examination of standards, privacy and security issues, iv.) development of new architectures and applications for health information systems.
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