There is a dearth of studies on how neighbourhood environmental attributes relate to the metabolic syndrome (MetS) and profiles of MetS components. We examined the associations of interrelated aspects of the neighbourhood environment, including air pollution, with MetS status and profiles of MetS components. We used socio-demographic and MetS-related data from 3681 urban adults who participated in the 3rd wave of the Australian Diabetes, Obesity and Lifestyle Study. Neighbourhood environmental attributes included area socio-economic status (SES), population density, street intersection density, non-commercial land use mix, percentages of commercial land, parkland and blue space. Annual average concentrations of NO2 and PM2.5 were estimated using satellite-based land-use regression models. Latent class analysis (LCA) identified homogenous groups (latent classes) of participants based on MetS components data. Participants were then classified into five metabolic profiles according to their MetS-components latent class and MetS status. Generalised additive mixed models were used to estimate relationships of environmental attributes with MetS status and metabolic profiles. LCA yielded three latent classes, one including only participants without MetS (“Lower probability of MetS components” profile). The other two classes/profiles, consisting of participants with and without MetS, were “Medium-to-high probability of high fasting blood glucose, waist circumference and blood pressure” and “Higher probability of MetS components”. Area SES was the only significant predictor of MetS status: participants from high SES areas were less likely to have MetS. Area SES, percentage of commercial land and NO2 were associated with the odds of membership to healthier metabolic profiles without MetS, while annual average concentration of PM2.5 was associated with unhealthier metabolic profiles with MetS. This study supports the utility of operationalising MetS as a combination of latent classes of MetS components and MetS status in studies of environmental correlates. Higher socio-economic advantage, good access to commercial services and low air pollution levels appear to independently contribute to different facets of metabolic health. Future research needs to consider conducting longitudinal studies using fine-grained environmental measures that more accurately characterise the neighbourhood environment in relation to behaviours or other mechanisms related to MetS and its components.

中文翻译：

---

代谢综合征的邻里环境和概况

关于邻里环境属性如何与代谢综合征 (MetS) 和 MetS 成分的概况相关的研究很少。我们检查了邻里环境的相互关联方面（包括空气污染）与 MetS 状态和 MetS 成分概况之间的关联。我们使用了参与第三波澳大利亚糖尿病、肥胖和生活方式研究的 3681 名城市成年人的社会人口统计和大都会相关数据。邻里环境属性包括地区社会经济地位（SES）、人口密度、街道交叉口密度、非商业用地组合、商业用地、公园用地和蓝色空间的百分比。使用基于卫星的土地利用回归模型估计 NO2 和 PM2.5 的年平均浓度。潜在类别分析 (LCA) 根据 MetS 组件数据确定参与者的同质组（潜在类别）。然后根据他们的 MetS 成分潜伏类别和 MetS 状态将参与者分为五种代谢特征。广义加性混合模型用于估计环境属性与 MetS 状态和代谢概况的关系。LCA 产生了三个潜在类别，一个只包括没有 MetS 的参与者（“MetS 组件的低概率”配置文件）。其他两个类别/资料，包括有和没有 MetS 的参与者，分别是“高空腹血糖、腰围和血压的中高概率”和“高概率的 MetS 成分”。区域 SES 是 MetS 状态的唯一重要预测指标：来自高 SES 地区的参与者不太可能患有 MetS。面积 SES、商业用地百分比和 NO2 与在没有 MetS 的情况下属于更健康的代谢特征的几率相关，而 PM2.5 的年平均浓度与在有 MetS 的情况下不健康的代谢特征相关。本研究支持将 MetS 作为潜在类别的 MetS 成分和 MetS 状态在环境相关研究中的组合的效用。更高的社会经济优势、良好的商业服务和低空气污染水平似乎独立地促进了代谢健康的不同方面。