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000289367 1001_ $$aAdjei, Nicholas Kofi$$b0
000289367 245__ $$aEthnic differences in metabolic syndrome in high-income countries: A systematic review and meta-analysis.
000289367 260__ $$aDordrecht [u.a.]$$bSpringer Science + Business Media B.V$$c2024
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000289367 520__ $$aThis review aimed to systematically quantify the differences in Metabolic Syndrome (MetS) prevalence across various ethnic groups in high-income countries by sex, and to evaluate the overall prevalence trends from 1996 to 2022. We conducted a systematic literature review using MEDLINE, Web of Science Core Collection, CINAHL, and the Cochrane Library, focusing on studies about MetS prevalence among ethnic groups in high-income countries. We pooled 23 studies that used NCEP-ATP III criteria and included 147,756 healthy participants aged 18 and above. We calculated pooled prevalence estimates and 95% confidence intervals (CI) using both fixed-effect and random-effect intercept logistic regression models. Data were analysed for 3 periods: 1996-2005, 2006-2009, and 2010-2021. The pooled prevalence of MetS in high-income countries, based on the NCEP-ATP III criteria, was 27.4% over the studied period, showing an increase from 24.2% in 1996-2005 to 31.9% in 2010-2021, with men and women having similar rates. When stratified by ethnicity and sex, ethnic minority women experienced the highest prevalence at 31.7%, while ethnic majority women had the lowest at 22.7%. Notably, MetS was more prevalent in ethnic minority women than men. Among ethnic minorities, women had a higher prevalence of MetS than men, and the difference was highest in Asians (about 15 percentage points). Among women, the prevalence of MetS was highest in Asians (41.2%) and lowest in Blacks/Africans (26.7%). Among men, it was highest in indigenous minority groups (34.3%) and lowest among in Blacks/Africans (19.8%). MetS is increasing at an alarming rate in high-income countries, particularly among ethnic minority women. The burden of MetS could be effectively reduced by tailoring interventions according to ethnic variations and risk profiles.
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000289367 650_7 $$2Other$$aBurden
000289367 650_7 $$2Other$$aEthnicity
000289367 650_7 $$2Other$$aHigh-income Countries
000289367 650_7 $$2Other$$aMeta-analysis
000289367 650_7 $$2Other$$aMetabolic syndrome
000289367 650_7 $$2Other$$aPrevalence
000289367 7001_ $$aSamkange-Zeeb, Florence$$b1
000289367 7001_ $$aBoakye, Daniel$$b2
000289367 7001_ $$aSaleem, Maham$$b3
000289367 7001_ $$aChristianson, Lara$$b4
000289367 7001_ $$0P:(DE-He78)547386e1dd3330f9f40321e89ec05354$$aKebede, Mihiretu M$$b5$$udkfz
000289367 7001_ $$aHeise, Thomas L$$b6
000289367 7001_ $$aBrand, Tilman$$b7
000289367 7001_ $$aEsan, Oluwaseun B$$b8
000289367 7001_ $$aTaylor-Robinson, David C$$b9
000289367 7001_ $$aAgyemang, Charles$$b10
000289367 7001_ $$aZeeb, Hajo$$b11
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