Effects of risk factor numbers on the development of the metabolic syndrome

Lee, Jangwon; Lee, Hyunju

doi:10.12965/jer.2040202.101

Cited by 4 publications

(3 citation statements)

References 13 publications

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“…We identified common potential confounding variables between greenness exposure and MetS risk from the available literature, and incorporated them in a directed acyclic graph (DAG) to select a minimal set of covariates ( 30 ). PA, BMI, and air pollution exposure appeared in the DAG as mediators between greenness and MetS ( Supplementary Figure S3 ), as shown in the previous literature ( 15 , 16 , 31 , 32 ). PA was measured by the days of conducting physical activity per week.…”

Section: Methodssupporting

confidence: 79%

“…One previous study also reported similarities, indicating that only PM 10 , NO 2 , and O 3 had mediating effects ( 15 ). Second, BMI is a leading risk factor for MetS ( 31 ), and high community greenness exposure is reported to be associated with reduced obesity risk ( 46 ). People living with high greenness exposure are more likely to participate in regular physical activity and be associated with lower BMI ( 47 ).…”

Section: Discussionmentioning

confidence: 99%

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Associations and pathways between residential greenness and metabolic syndromes in Fujian Province

Li¹,

Wang

Feng

et al. 2022

Front. Public Health

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BackgroundGreenness exposure is beneficial to human health, but its potential mechanisms through which the risk for metabolic syndrome (MetS) could be reduced have been poorly studied. We aimed to estimate the greenness-MetS association in southeast China and investigate the independent and joint mediation effects of physical activity (PA), body mass index (BMI), and air pollutants on the association.MethodsA cross-sectional study was conducted among the 38,288 adults based on the Fujian Behavior and Disease Surveillance (FBDS), established in 2018. MetS was defined as the presence of three or more of the five components: abdominal obesity, elevated triglyceride, reduced high-density lipoprotein cholesterol (HDL-C), high blood pressure, and elevated fasting glucose. The residential greenness exposure was measured as the 3-year mean values of the normalized difference vegetation index (NDVI) and enhanced vegetation index (EVI) within the 250, 500, and 1,000 meters (m) buffer zones around the residential address of each participant. Logistic regression models were used to estimate the greenness-MetS association. The causal mediation analysis was used to estimate the independent and joint mediation effects of PA, BMI, particulate matter with an aerodynamic diameter of 2.5 μm (PM2.5), particulate matter with an aerodynamic diameter ≤ 10 μm (PM10), nitrogen dioxide (NO2), and sulfur dioxide (SO2).ResultsEach interquartile range (IQR) increase in greenness was associated with a decrease of 13% (OR = 0.87 [95%CI: 0.83, 0.92] for NDVI500m and OR = 0.87 [95%CI: 0.82, 0.91] for EVI500m) in MetS risk after adjusting for covariates. This association was stronger in those aged < 60 years (e.g., OR = 0.86 [95%CI: 0.81, 0.92] for NDVI500m), males (e.g., OR = 0.73 [95%CI: 0.67, 0.80] for NDVI500m), having an educational level of primary school or above (OR = 0.81 [95%CI: 0.74, 0.89] for NDVI500m), married/cohabitation (OR = 0.86 [95%CI: 0.81, 0.91] for NDVI500m), businessman (OR = 0.82 [95%CI: 0.68, 0.99] for NDVI500m), other laborers (OR = 0.77 [95%CI: 0.68, 0.88] for NDVI500m), and non-smokers (OR = 0.77 [95%CI: 0.70, 0.85] for NDVI500m). The joint effect of all six mediators mediated about 48.1% and 44.6% of the total effect of NDVI500m and EVI500m on the MetS risk, respectively. Among them, BMI showed the strongest independent mediation effect (25.0% for NDVI500m), followed by NO2 and PM10.ConclusionExposure to residential greenness was associated with a decreased risk for MetS. PA, BMI, and the four air pollutants jointly interpreted nearly half of the mediation effects on the greenness-MetS association.

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Section: Methodssupporting

confidence: 79%

Section: Discussionmentioning

confidence: 99%

Associations and pathways between residential greenness and metabolic syndromes in Fujian Province

Li¹,

Wang

Feng

et al. 2022

Front. Public Health

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“…Obesity and overweight are two important risk factors for MetS ( 25 ). According to Suliga et al ( 26 ) and Kabała and Wilczyński ( 27 ), the BMI is the most common metric for determining obesity as it is simple to calculate and has well-defined cut-off points.…”

Section: Introductionmentioning

confidence: 99%

Anthropometric indices and cut-off points for screening of metabolic syndrome among South African taxi drivers

et al. 2022

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BackgroundDetecting the early onset of metabolic syndrome (MetS) allows for quick intervention which may slow progression to a variety of health consequences, hence, determining the best measurement to detect MetS is essential.AimThis research aimed at examining the MetS predictive power of anthropometric indices, such as body mass index (BMI), waist circumference (WC), waist-to-height ratio (WHtR), body shape index (ABSI), body roundness index (BRI), percentage body fat (%BF), conicity index (CI), and Clínica Universidad de Navarra-body adiposity estimator (CUN-BAE) to determine the cut-off points to identify male South African taxi drivers with MetS.MethodA cross-sectional study was conducted among 185 male taxi drivers. Their weight, height, WC, blood lipid profile were measured. International Diabetes Federation (IDF) definition was used to define MetS. Receiver Operating Characteristic (ROC) curves were used to compare the predictive ability of Anthropometric indices to detect MetS.ResultsThe mean age of the participants was 39.84 years. Overall, 41.6% (N = 77) of the participants presented with MetS. The mean values for BMI, WC, WHtR, %BF, BRI, CUN-BAE, ABSI and CI were 28.60 ± 6.20 kg/m2, 99.13 ± 17.59 cm, 0.58 ± 0.10, 27.28 ± 8.28%, 5.09 ± 2.33, 27.78 ± 8.34, 0.08 ± 0.01 and 1.70 ± 0.19, respectively. The mean values for these indices were significantly (p < 0.001) higher in participants with MetS. The highest area under the curve (AUC) outcomes for screening MetS were for the %BF and CUN-BAE, followed by the BMI and WHtR, and lastly the BRI. All these anthropometric indices had outstanding discriminatory powers for predicting MetS with AUCs and sensitivity values above 80%. The BMI, WHtR, %BF, BRI, and CUN-BAE, had cut-off points for detection of metS in South African men at 28.25 kg/m2, 0.55, 25.29%, 4.55, and 27.10, respectively. Based on the logistic regression models abnormal BMI, WHtR, %BF, BRI, CUN-BAE, TG, FBG, systolic BP, diastolic BP and WC showed increased risk of MetS.ConclusionWhile the %BF, CUN-BAE, BMI, WC, WHtR, BRI, CI and CUN-BAE could predict MetS among South African male taxi drivers, these indices were less effective in predicting the individual MetS risk factors such as TG, BP, and FBG.

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Effects of different fractions of polysaccharides from Dictyophora indusiata on high-fat diet-induced metabolic syndrome in mice

Yao,

Yang,

et al. 2024

International Journal of Biological Macromolecules

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Effects of risk factor numbers on the development of the metabolic syndrome

Cited by 4 publications

References 13 publications

Associations and pathways between residential greenness and metabolic syndromes in Fujian Province

Associations and pathways between residential greenness and metabolic syndromes in Fujian Province

Anthropometric indices and cut-off points for screening of metabolic syndrome among South African taxi drivers

Effects of different fractions of polysaccharides from Dictyophora indusiata on high-fat diet-induced metabolic syndrome in mice

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