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      How do environmental characteristics jointly contribute to cardiometabolic health? A quantile g-computation mixture analysis

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          Abstract

          Accumulating evidence links cardiometabolic health with social and environmental neighborhood exposures, which may contribute to health inequities. We examined whether environmental characteristics were individually or jointly associated with insulin resistance, hypertension, obesity, type 2 diabetes, and metabolic syndrome in San Diego County, CA. As part of the Community of Mine Study, cardiometabolic outcomes of insulin resistance, hypertension, BMI, diabetes, and metabolic syndrome were collected in 570 participants. Seven census tract level characteristics of participants’ residential environment were assessed and grouped as follows: economic, education, health care access, neighborhood conditions, social environment, transportation, and clean environment. Generalized estimating equation models were performed, to take into account the clustered nature of the data and to estimate β or relative risk (RR) and 95 % confidence intervals (CIs) between each of the seven environmental characteristics and cardiometabolic outcomes. Quantile g-computation was used to examine the association between the joint effect of a simultaneous increase in all environmental characteristics and cardiometabolic outcomes. Among 570 participants (mean age 58.8 ± 11 years), environmental economic, educational and health characteristics were individually associated with insulin resistance, diabetes, obesity, and metabolic syndrome. In the mixture analyses, a joint quartile increase in all environmental characteristics (i.e., improvement) was associated with decreasing insulin resistance (β, 95 %CI: −0.09, −0.18–0.01)), risk of diabetes (RR, 95 %CI: 0.59, 0.36–0.98) and obesity (RR, 95 %CI: 0.81, 0.64–1.02). Environmental characteristics synergistically contribute to cardiometabolic health and independent analysis of these determinants may not fully capture the potential health impact of social and environmental determinants of health.

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          Heart Disease and Stroke Statistics—2020 Update

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            Homeostasis model assessment: insulin resistance and beta-cell function from fasting plasma glucose and insulin concentrations in man.

            The steady-state basal plasma glucose and insulin concentrations are determined by their interaction in a feedback loop. A computer-solved model has been used to predict the homeostatic concentrations which arise from varying degrees beta-cell deficiency and insulin resistance. Comparison of a patient's fasting values with the model's predictions allows a quantitative assessment of the contributions of insulin resistance and deficient beta-cell function to the fasting hyperglycaemia (homeostasis model assessment, HOMA). The accuracy and precision of the estimate have been determined by comparison with independent measures of insulin resistance and beta-cell function using hyperglycaemic and euglycaemic clamps and an intravenous glucose tolerance test. The estimate of insulin resistance obtained by homeostasis model assessment correlated with estimates obtained by use of the euglycaemic clamp (Rs = 0.88, p less than 0.0001), the fasting insulin concentration (Rs = 0.81, p less than 0.0001), and the hyperglycaemic clamp, (Rs = 0.69, p less than 0.01). There was no correlation with any aspect of insulin-receptor binding. The estimate of deficient beta-cell function obtained by homeostasis model assessment correlated with that derived using the hyperglycaemic clamp (Rs = 0.61, p less than 0.01) and with the estimate from the intravenous glucose tolerance test (Rs = 0.64, p less than 0.05). The low precision of the estimates from the model (coefficients of variation: 31% for insulin resistance and 32% for beta-cell deficit) limits its use, but the correlation of the model's estimates with patient data accords with the hypothesis that basal glucose and insulin interactions are largely determined by a simple feed back loop.
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              A comprehensive definition for metabolic syndrome.

              The metabolic syndrome refers to the co-occurrence of several known cardiovascular risk factors, including insulin resistance, obesity, atherogenic dyslipidemia and hypertension. These conditions are interrelated and share underlying mediators, mechanisms and pathways. There has been recent controversy about its definition and its utility. In this article, I review the current definitions for the metabolic syndrome and why the concept is important. It identifies a subgroup of patients with shared pathophysiology who are at high risk of developing cardiovascular disease and type 2 diabetes. By considering the central features of the metabolic syndrome and how they are related, we may better understand the underlying pathophysiology and disease pathogenesis. A comprehensive definition for the metabolic syndrome and its key features would facilitate research into its causes and hopefully lead to new insights into pharmacologic and lifestyle treatment approaches.
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                Author and article information

                Contributors
                Journal
                Prev Med Rep
                Preventive Medicine Reports
                2211-3355
                26 September 2022
                December 2022
                26 September 2022
                : 30
                : 102005
                Affiliations
                [a ]Scripps Institution of Oceanography, UC San Diego, USA
                [b ]Population Sciences, Beckman Research Institute, City of Hope, 1500 E Duarte Rd, Duarte, CA 91010, USA
                [c ]College of Health Solutions, Arizona State University, Phoenix, AZ, USA
                [d ]Department of Medicine, UC San Diego, La Jolla, CA, USA
                [e ]Moores Cancer Center, UC San Diego, La Jolla, CA, USA
                Author notes
                [* ]Corresponding author. nletellier@ 123456ucsd.edu
                Article
                S2211-3355(22)00312-6 102005
                10.1016/j.pmedr.2022.102005
                9562428
                36245803
                a9d6f55b-371a-4c96-886c-09c5ce4d8ca8
                © 2022 The Authors

                This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

                History
                : 31 May 2022
                : 7 September 2022
                : 24 September 2022
                Categories
                Regular Article

                mixture approach,quantile g-computation,cardiovascular health,neighborhood determinants,area-level characteristics,health inequities,social determinants of health

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