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Dev R, Behlouli H, Parry M, Raparelli V, Norris CM, Pilote L. Impact of Sex and Gender on Metabolic Syndrome in Adults: A Retrospective Cohort Study From the Canadian Primary Care Sentinel Surveillance Network. Can J Diabetes 2024; 48:36-43.e2. [PMID: 37660834 DOI: 10.1016/j.jcjd.2023.08.008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/13/2023] [Revised: 07/25/2023] [Accepted: 08/25/2023] [Indexed: 09/05/2023]
Abstract
OBJECTIVE Metabolic syndrome (MetS), a cluster of 5 interconnected factors, is the main contributor to cardiovascular disease. Although sex- and gender-related elements have been linked to MetS and its components, this association has not been explored among Canadians with or without MetS. In this study, we aimed to identify sex and gender differences in characteristics of MetS in the Canadian population. METHODS This retrospective cohort study used data from the Canadian Primary Care Sentinel Surveillance Network (CPCSSN) database. The CPCSSN contains de-identified electronic health records of >1.5 million Canadians (2010-2019). Individuals 35 to 75 years of age who had a primary care encounter formed the study sample (N=37,813). Multiple logistic regression models were used to estimate adjusted odds ratios for sex and gender differences among Canadians with and without MetS, which was the primary outcome variable. RESULTS The estimated prevalence of MetS was 41.9%. The risk of developing MetS was significantly lower among females compared with males (odds ratio 0.73, 95% confidence interval 0.70 to 0.76). However, the risk was higher in females who used antidepressants (odds ratio 1.53, 95% confidence interval 1.42 to 1.65). An equal distribution of deprivation indexes was observed between males and females with MetS, with risk slightly higher for those with material deprivation. Females were found to be the most socially deprived. CONCLUSIONS This study provides important sex- and gender-specific differences in MetS among Canadians. Targeting sex- and gender-specific risk factors could assist in reversing the trend of adverse cardiovascular outcomes associated with MetS.
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Affiliation(s)
- Rubee Dev
- Faculty of Applied Science, School of Nursing, University of British Columbia, Vancouver, British Columbia, Canada
| | - Hassan Behlouli
- Department of Epidemiology, Biostatistics and Occupational Health, School of Population and Global Health, McGill University, Montréal, Québec, Canada
| | - Monica Parry
- Lawrence S. Bloomberg Faculty of Nursing, University of Toronto, Toronto, Ontario, Canada
| | - Valeria Raparelli
- Department of Translational Medicine, University of Ferrara, Ferrara, Italy; University Center for Studies on Gender Medicine, University of Ferrara, Ferrara, Italy; Faculty of Nursing, University of Alberta, Edmonton, Alberta, Canada
| | - Colleen M Norris
- Faculty of Nursing, University of Alberta, Edmonton, Alberta, Canada
| | - Louise Pilote
- Centre for Outcomes Research and Evaluation, Research Institute of McGill University Health Centre, Montréal, Québec, Canada; Divisions of Clinical Epidemiology and General Internal Medicine, McGill University, Montréal, Québec, Canada.
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Cerin E, Chan YK, Symmons M, Soloveva M, Martino E, Shaw JE, Knibbs LD, Jalaludin B, Barnett A. Associations of the neighbourhood built and natural environment with cardiometabolic health indicators: A cross-sectional analysis of environmental moderators and behavioural mediators. Environ Res 2024; 240:117524. [PMID: 37898226 DOI: 10.1016/j.envres.2023.117524] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/10/2023] [Revised: 10/12/2023] [Accepted: 10/25/2023] [Indexed: 10/30/2023]
Abstract
BACKGROUND Most studies examining the effects of neighbourhood urban design on cardiometabolic health focused solely on the built or natural environment. Also, they did not consider the roles of neighbourhood socio-economic status (SES) and ambient air pollution in the observed associations, and the extent to which these associations were mediated by physical activity and sedentary behaviours. METHODS We used data from the AusDiab3 study (N = 4141), a national cohort study of Australian adults to address the above-mentioned knowledge gaps. Spatial data were used to compute indices of neighbourhood walkability (population density, intersection density, non-commercial land use mix, commercial land use), natural environment (parkland and blue spaces) and air pollution (annual average concentrations of nitrogen dioxide (NO2) and fine particulate matter <2.5 μm in diameter (PM2.5)). Census indices were used to define neighbourhood SES. Clinical assessments collected data on adiposity, blood pressure, blood glucose and blood lipids. Generalised additive mixed models were used to estimate associations. RESULTS Neighbourhood walkability showed indirect beneficial associations with most indicators of cardiometabolic health via resistance training, walking and sitting for different purposes; indirect detrimental associations with the same indicators via vigorous gardening; and direct detrimental associations with blood pressure. The neighbourhood natural environment had beneficial indirect associations with most cardiometabolic health indicators via resistance training and leisure-time sitting, and beneficial direct associations with adiposity and blood lipids. Neighbourhood SES and air pollution moderated only a few associations of the neighbourhood environment with physical activity, blood lipids and blood pressure. CONCLUSIONS Within a low-density and low-pollution context, denser, walkable neighbourhoods with good access to nature may benefit residents' cardiometabolic health by facilitating the adoption of an active lifestyle. Possible disadvantages of living in denser neighbourhoods for older populations are having limited opportunities for gardening, higher levels of noise and less healthy dietary patterns associated with eating out.
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Affiliation(s)
- Ester Cerin
- Mary MacKillop Institute for Health Research, Australian Catholic University, 215 Spring St., Melbourne, VIC, Australia; School of Public Health, The University of Hong Kong, 7 Sassoon Rd., Sandy Bay, Hong Kong, Hong Kong SAR, China; Baker Heart and Diabetes Institute, Melbourne, VIC, Australia.
| | - Yih-Kai Chan
- Mary MacKillop Institute for Health Research, Australian Catholic University, 215 Spring St., Melbourne, VIC, Australia.
| | - Mark Symmons
- Mary MacKillop Institute for Health Research, Australian Catholic University, 215 Spring St., Melbourne, VIC, Australia.
| | - Maria Soloveva
- Mary MacKillop Institute for Health Research, Australian Catholic University, 215 Spring St., Melbourne, VIC, Australia.
| | - Erika Martino
- School of Population and Global Health, University of Melbourne, Melbourne, VIC, Australia.
| | - Jonathan E Shaw
- Baker Heart and Diabetes Institute, Melbourne, VIC, Australia; School of Public Health and Preventive Medicine, Monash University, Melbourne, VIC, Australia; School of Life Sciences, La Trobe University, Melbourne, VIC, Australia.
| | - Luke D Knibbs
- Sydney School of Public Health, The University of Sydney, Camperdown, NSW, Australia; Public Health Unit, Sydney Local Health District, Camperdown, NSW, Australia.
| | - Bin Jalaludin
- School of Population Health, University of New South Wales, Randwick, NSW, Australia.
| | - Anthony Barnett
- Mary MacKillop Institute for Health Research, Australian Catholic University, 215 Spring St., Melbourne, VIC, Australia.
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Pan J, Hu K, Yu X, Li W, Shen Y, Song Z, Guo Y, Yang M, Hu F, Xia Q, Du Z, Wu X. Beneficial associations between outdoor visible greenness at the workplace and metabolic syndrome in Chinese adults. Environ Int 2024; 183:108327. [PMID: 38157607 DOI: 10.1016/j.envint.2023.108327] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/13/2023] [Revised: 10/13/2023] [Accepted: 11/12/2023] [Indexed: 01/03/2024]
Abstract
BACKGROUND Greenness surrounding residential places has been found to significantly reduce the risk of diseases such as hypertension, obesity, and metabolic syndrome (MetS). However, it is unclear whether visible greenness exposure at the workplace has any impact on the risk of MetS. METHODS Visible greenness exposure was assessed using a Green View Index (GVI) based on street view images through a convolutional neural network model. We utilized logistic regression to examine the cross-sectional association between GVI and MetS as well as its components among 51,552 adults aged 18-60 in the city of Hangzhou, China, from January 2018 to December 2021. Stratified analyses were conducted by age and sex groups. Furthermore, a scenario analysis was conducted to investigate the risks of having MetS among adults in different GVI scenarios. RESULTS The mean age of the participants was 40.1, and 38.5% were women. We found a statistically significant association between GVI and having MetS. Compared to the lowest quartile of GVI, participants in the highest quartile of GVI had a 17% (95% CI: 11-23%) lower odds of having MetS. The protective association was stronger in the males, but we did not observe such differences in different age groups. Furthermore, we found inverse associations between GVI and the odds of hypertension, low high-density lipoprotein cholesterol, obesity, and high levels of FPG. CONCLUSIONS Higher exposure to outdoor visible greenness in the workplace environment might have a protective effect against MetS.
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Affiliation(s)
- Jiahao Pan
- Center for Clinical Big Data and Analytics School of Public Health and The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou 310000, Zhejiang, China
| | - Kejia Hu
- Center for Clinical Big Data and Analytics School of Public Health and The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou 310000, Zhejiang, China
| | - Xinyan Yu
- Department of Health Management Center and Department of General Medicine, The Second Affiliated Hospital Zhejiang University School of Medicine, Hangzhou, 310009 Zhejiang, China
| | - Wenyuan Li
- Center for Clinical Big Data and Analytics School of Public Health and The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou 310000, Zhejiang, China
| | - Yujie Shen
- Center for Clinical Big Data and Analytics School of Public Health and The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou 310000, Zhejiang, China
| | - Zhenya Song
- Department of Health Management Center and Department of General Medicine, The Second Affiliated Hospital Zhejiang University School of Medicine, Hangzhou, 310009 Zhejiang, China
| | - Yi Guo
- Department of Health Management Center and Department of General Medicine, The Second Affiliated Hospital Zhejiang University School of Medicine, Hangzhou, 310009 Zhejiang, China
| | - Min Yang
- Center for Clinical Big Data and Analytics School of Public Health and The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou 310000, Zhejiang, China
| | - Fang Hu
- Department of Health Management Center and Department of General Medicine, The Second Affiliated Hospital Zhejiang University School of Medicine, Hangzhou, 310009 Zhejiang, China
| | - Qunke Xia
- School of Earth Sciences, Zhejiang University, Hangzhou 310027, China
| | - Zhenhong Du
- School of Earth Sciences, Zhejiang University, Hangzhou 310027, China; Zhejiang Provincial Key Laboratory of Geographic Information Science, Hangzhou 310028, China.
| | - Xifeng Wu
- Center for Clinical Big Data and Analytics School of Public Health and The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou 310000, Zhejiang, China; The Key Laboratory of Intelligent Preventive Medicine of Zhejiang Province, Hangzhou, Zhejiang 310058, China; Cancer Center, Zhejiang University, Hangzhou, Zhejiang 310058 China.
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