9 research outputs found

    The interrelationship and accumulation of cardiometabolic risk factors amongst young adults in the United Arab Emirates: The UAE Healthy Future Study.

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    INTRODUCTION: Similar to other non-communicable diseases (NCDs), people who develop cardiovascular disease (CVD) typically have more than one risk factor. The clustering of cardiovascular risk factors begins in youth, early adulthood, and middle age. The presence of multiple risk factors simultaneously has been shown to increase the risk for atherosclerosis development in young and middle-aged adults and risk of CVD in middle age. OBJECTIVE: This study aimed to address the interrelationship of CVD risk factors and their accumulation in a large sample of young adults in the United Arab Emirates (UAE). METHODS: Baseline data was drawn from the UAE Healthy Future Study (UAEHFS), a volunteer-based multicenter study that recruits Emirati nationals. Data of participants aged 18 to 40 years was used for cross-sectional analysis. Demographic and health information was collected through self-reported questionnaires. Anthropometric data and blood pressure were measured, and blood samples were collected. RESULTS: A total of 5126 participants were included in the analysis. Comorbidity analyses showed that dyslipidemia and obesity co-existed with other cardiometabolic risk factors (CRFs) more than 70% and 50% of the time, respectively. Multivariate logistic regression analysis of the risk factors with age and gender showed that all risk factors were highly associated with each other. The strongest relationship was found with obesity; it was associated with four-fold increase in the odds of having central obesity [adjusted OR 4.70 (95% CI (4.04-5.46)], and almost three-fold increase odds of having abnormal glycemic status [AOR 2.98 (95% (CI 2.49-3.55))], hypertension (AOR 3.03 (95% CI (2.61-3.52))] and dyslipidemia [AOR 2.71 (95% CI (2.32-3.15)]. Forty percent of the population accumulated more than 2 risk factors, and the burden increased with age. CONCLUSION: In this young population, cardiometabolic risk factors are highly prevalent and are associated with each other, therefore creating a heavy burden of risk factors. This forecasts an increase in the burden of CVD in the UAE. The robust longitudinal design of the UAEHFS will enable researchers to understand how risk factors cluster before disease develops. This knowledge will offer a novel approach to design group-specific preventive measures for CVD development

    Smoking cessation for improving mental health

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    Background: There is a common perception that smoking generally helps people to manage stress, and may be a form of 'self‐medication' in people with mental health conditions. However, there are biologically plausible reasons why smoking may worsen mental health through neuroadaptations arising from chronic smoking, leading to frequent nicotine withdrawal symptoms (e.g. anxiety, depression, irritability), in which case smoking cessation may help to improve rather than worsen mental health. Objectives: To examine the association between tobacco smoking cessation and change in mental health. Search Methods: We searched the Cochrane Tobacco Addiction Group's Specialised Register, Cochrane Central Register of Controlled Trials, MEDLINE, Embase, PsycINFO, and the trial registries clinicaltrials.gov and the International Clinical Trials Registry Platform, from 14 April 2012 to 07 January 2020. These were updated searches of a previously‐conducted non‐Cochrane review where searches were conducted from database inception to 13 April 2012. Selection Criteria: We included controlled before‐after studies, including randomised controlled trials (RCTs) analysed by smoking status at follow‐up, and longitudinal cohort studies. In order to be eligible for inclusion studies had to recruit adults who smoked tobacco, and assess whether they quit or continued smoking during the study. They also had to measure a mental health outcome at baseline and at least six weeks later. Data Collection and Analysis: We followed standard Cochrane methods for screening and data extraction. Our primary outcomes were change in depression symptoms, anxiety symptoms or mixed anxiety and depression symptoms between baseline and follow‐up. Secondary outcomes included change in symptoms of stress, psychological quality of life, positive affect, and social impact or social quality of life, as well as new incidence of depression, anxiety, or mixed anxiety and depression disorders. We assessed the risk of bias for the primary outcomes using a modified ROBINS‐I tool. For change in mental health outcomes, we calculated the pooled standardised mean difference (SMD) and 95% confidence interval (95% CI) for the difference in change in mental health from baseline to follow‐up between those who had quit smoking and those who had continued to smoke. For the incidence of psychological disorders, we calculated odds ratios (ORs) and 95% CIs. For all meta‐analyses we used a generic inverse variance random‐effects model and quantified statistical heterogeneity using I2. We conducted subgroup analyses to investigate any differences in associations between sub‐populations, i.e. unselected people with mental illness, people with physical chronic diseases. We assessed the certainty of evidence for our primary outcomes (depression, anxiety, and mixed depression and anxiety) and our secondary social impact outcome using the eight GRADE considerations relevant to non‐randomised studies (risk of bias, inconsistency, imprecision, indirectness, publication bias, magnitude of the effect, the influence of all plausible residual confounding, the presence of a dose‐response gradient). Main Results: We included 102 studies representing over 169,500 participants. Sixty‐two of these were identified in the updated search for this review and 40 were included in the original version of the review. Sixty‐three studies provided data on change in mental health, 10 were included in meta‐analyses of incidence of mental health disorders, and 31 were synthesised narratively. For all primary outcomes, smoking cessation was associated with an improvement in mental health symptoms compared with continuing to smoke: anxiety symptoms (SMD −0.28, 95% CI −0.43 to −0.13; 15 studies, 3141 participants; I2 = 69%; low‐certainty evidence); depression symptoms: (SMD −0.30, 95% CI −0.39 to −0.21; 34 studies, 7156 participants; I2 = 69%' very low‐certainty evidence); mixed anxiety and depression symptoms (SMD −0.31, 95% CI −0.40 to −0.22; 8 studies, 2829 participants; I2 = 0%; moderate certainty evidence). These findings were robust to preplanned sensitivity analyses, and subgroup analysis generally did not produce evidence of differences in the effect size among subpopulations or based on methodological characteristics. All studies were deemed to be at serious risk of bias due to possible time‐varying confounding, and three studies measuring depression symptoms were judged to be at critical risk of bias overall. There was also some evidence of funnel plot asymmetry. For these reasons, we rated our certainty in the estimates for anxiety as low, for depression as very low, and for mixed anxiety and depression as moderate. For the secondary outcomes, smoking cessation was associated with an improvement in symptoms of stress (SMD −0.19, 95% CI −0.34 to −0.04; 4 studies, 1792 participants; I2 = 50%), positive affect (SMD 0.22, 95% CI 0.11 to 0.33; 13 studies, 4880 participants; I2 = 75%), and psychological quality of life (SMD 0.11, 95% CI 0.06 to 0.16; 19 studies, 18,034 participants; I2 = 42%). There was also evidence that smoking cessation was not associated with a reduction in social quality of life, with the confidence interval incorporating the possibility of a small improvement (SMD 0.03, 95% CI 0.00 to 0.06; 9 studies, 14,673 participants; I2 = 0%). The incidence of new mixed anxiety and depression was lower in people who stopped smoking compared with those who continued (OR 0.76, 95% CI 0.66 to 0.86; 3 studies, 8685 participants; I2 = 57%), as was the incidence of anxiety disorder (OR 0.61, 95% CI 0.34 to 1.12; 2 studies, 2293 participants; I2 = 46%). We deemed it inappropriate to present a pooled estimate for the incidence of new cases of clinical depression, as there was high statistical heterogeneity (I2 = 87%). Authors' Conclusions: Taken together, these data provide evidence that mental health does not worsen as a result of quitting smoking, and very low‐ to moderate‐certainty evidence that smoking cessation is associated with small to moderate improvements in mental health. These improvements are seen in both unselected samples and in subpopulations, including people diagnosed with mental health conditions. Additional studies that use more advanced methods to overcome time‐varying confounding would strengthen the evidence in this area.</p

    Metabolic Syndrome in Fasting and Non-Fasting Participants: The UAE Healthy Future Study.

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    INTRODUCTION: Metabolic syndrome (MetS) is a multiplex of risk factors that predispose people to the development of diabetes and cardiovascular disease (CVD), two of the major non-communicable diseases that contribute to mortality in the United Arab Emirates (UAE). MetS guidelines require the testing of fasting samples, but there are evidence-based suggestions that non-fasting samples are also reliable for CVD-related screening measures. In this study, we aimed to estimate MetS and its components in a sample of young Emiratis using HbA1c as another glycemic marker. We also aimed to estimate the associations of some known CVD risk factors with MetS in our population. METHODS: The study was based on a cross-sectional analysis of baseline data of 5161 participants from the UAE Healthy Future Study (UAEHFS). MetS was identified using the NCEP ATP III criteria, with the addition of HbA1c as another glycemic indicator. Fasting blood glucose (FBG) and HbA1c were used either individually or combined to identify the glycemic component of MetS, based on the fasting status. Multivariate regression analysis was used to test for associations of selected social and behavioral factors with MetS. RESULTS: Our sample included 3196 men and 1965 women below the age of 40 years. Only about 21% of the sample were fasting at the time of recruitment. The age-adjusted prevalence of MetS was estimated as 22.7% in males and 12.5% in females. MetS prevalence was not statistically different after substituting FBG by HbA1c in the fasting groups (p > 0.05). Age, increased body mass index (BMI), and family history of any metabolic abnormality and/or heart disease were consistently strongly associated with MetS. CONCLUSION: MetS is highly prevalent in our sample of young Emirati adults. Our data showed that HbA1c may be an acceptable tool to test for the glycemic component of MetS in non-fasting samples. We found that the most relevant risk factors for predicting the prevalence of MetS were age, BMI, and family history

    Metabolic Syndrome in Fasting and Non-Fasting Participants: The UAE Healthy Future Study

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    Introduction: Metabolic syndrome (MetS) is a multiplex of risk factors that predispose people to the development of diabetes and cardiovascular disease (CVD), two of the major non-communicable diseases that contribute to mortality in the United Arab Emirates (UAE). MetS guidelines require the testing of fasting samples, but there are evidence-based suggestions that non-fasting samples are also reliable for CVD-related screening measures. In this study, we aimed to estimate MetS and its components in a sample of young Emiratis using HbA1c as another glycemic marker. We also aimed to estimate the associations of some known CVD risk factors with MetS in our population. Methods: The study was based on a cross-sectional analysis of baseline data of 5161 participants from the UAE Healthy Future Study (UAEHFS). MetS was identified using the NCEP ATP III criteria, with the addition of HbA1c as another glycemic indicator. Fasting blood glucose (FBG) and HbA1c were used either individually or combined to identify the glycemic component of MetS, based on the fasting status. Multivariate regression analysis was used to test for associations of selected social and behavioral factors with MetS. Results: Our sample included 3196 men and 1965 women below the age of 40 years. Only about 21% of the sample were fasting at the time of recruitment. The age-adjusted prevalence of MetS was estimated as 22.7% in males and 12.5% in females. MetS prevalence was not statistically different after substituting FBG by HbA1c in the fasting groups (p &gt; 0.05). Age, increased body mass index (BMI), and family history of any metabolic abnormality and/or heart disease were consistently strongly associated with MetS. Conclusion: MetS is highly prevalent in our sample of young Emirati adults. Our data showed that HbA1c may be an acceptable tool to test for the glycemic component of MetS in non-fasting samples. We found that the most relevant risk factors for predicting the prevalence of MetS were age, BMI, and family history
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