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Background Cardiovascular disease (CVD) is the leading cause of death in the world. In the United Arab Emirates (UAE), it accounts for 40% of mortality. CVD is caused by multiple cardiometabolic risk factors (CRFs) including obesity, dysglycemia, dyslipidemia, hypertension and central obesity. However, there are limited studies focusing on the CVD risk burden among young Emirati adults. This study investigates the burden of CRFs in a sample of young Emiratis, and estimates the distribution in relation to sociodemographic and behavioral determinants. Methods Data was used from the baseline data of the UAE Healthy Future Study volunteers. The study participants were aged 18 to 40 years. The study analysis was based on self-reported questionnaires, anthropometric and blood pressure measurements, as well as blood analysis. Results A total of 5167 participants were included in the analysis; 62% were males and the mean age of the sample was 25.7 years. The age-adjusted prevalence was 26.5% for obesity, 11.7% for dysglycemia, 62.7% for dyslipidemia, 22.4% for hypertension and 22.5% for central obesity. The CRFs were distributed differently when compared within social and behavioral groups. For example, obesity, dyslipidemia and central obesity in men were found higher among smokers than non-smokers (p Conclusions CRFs are highly prevalent in the young Emirati adults of the UAE Healthy Future Study. The difference in CRF distribution among social and behavioral groups can be taken into account to target group-specific prevention measures.

Peer reviewed: True, 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.

Limited studies have focused on maternal early-life risk factors and the later development of gestational diabetes mellitus (GDM). We aimed to estimate the GDM prevalence and examine the associations of maternal early-life risk factors, namely: maternal birthweight, parental smoking at birth, childhood urbanicity, ever-breastfed, parental education attainment, parental history of diabetes, childhood overall health, childhood body size, and childhood height, with later GDM. This was a retrospective cross-sectional study using the UAE Healthy Future Study (UAEHFS) baseline data (February 2016 to April 2022) on 702 ever-married women aged 18 to 67 years. We fitted a Poisson regression to estimate the risk ratio (RR) for later GDM and its 95% confidence interval (CI). The GDM prevalence was 5.1%. In the fully adjusted model, females with low birthweight were four times more likely (RR 4.04, 95% CI 1.36–12.0) and females with a parental history of diabetes were nearly three times more likely (RR 2.86, 95% CI 1.10–7.43) to report later GDM. In conclusion, maternal birthweight and parental history of diabetes were significantly associated with later GDM. Close glucose monitoring during pregnancy among females with either a low birth weight and/or parental history of diabetes might help to prevent GDM among this high-risk group.

IntroductionThe induction and speed of production of severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) immune biomarkers may vary by type and number of inoculated vaccine doses. This study aimed to explore variations in SARS-CoV-2 anti-spike (anti-S), anti-nucleocapsid (anti-N), and neutralizing immunoglobulin G (IgG) antibodies, and T-cell response by type and number of SARS-CoV-2 vaccine doses received.MethodsIn a naturally exposed and SARS-CoV-2–vaccinated population, we quantified the anti-S, anti-N, and neutralizing IgG antibody concentration and assessed T-cell response. Data on socio-demographics, medical history, and history of SARS-CoV-2 infection and vaccination were collected. Furthermore, nasal swabs were collected to test for SARS-CoV-2 infection. Confounder-adjusted association between having equal or more than a median concentration of the three IgG antibodies and T-cell response by number and type of the inoculated vaccines was quantified.ResultsWe surveyed 952 male participants with a mean age of 35.5 years ± 8.4 standard deviations. Of them, 52.6% were overweight/obese, and 11.7% had at least one chronic comorbidity. Of the participants, 1.4, 0.9, 20.2, 75.2, and 2.2% were never vaccinated, primed with only one dose, primed with two doses, boosted with only one dose, and boosted with two doses, respectively. All were polymerase chain reaction-negative to SARS-CoV-2. BBIBP-CorV (Sinopharm) was the most commonly used vaccine (92.1%), followed by rAd26-S + rAd5-S (Sputnik V Gam-COVID-Vac) (1.5%) and BNT162b2 (Pfizer-BioNTech) (0.3%). Seropositivity to anti-S, anti-N, and neutralizing IgG antibodies was detected in 99.7, 99.9, and 99.3% of the study participants, respectively. The T-cell response was detected in 38.2% of 925 study participants. Every additional vaccine dose was significantly associated with increased odds of having ≥median concentration of anti-S [adjusted odds ratio (aOR), 1.34; 95% confidence interval (CI): 1.02–1.76], anti-N (aOR, 1.35; 95% CI: 1.03–1.75), neutralizing IgG antibodies (aOR, 1.29; 95% CI: 1.00–1.66), and a T-cell response (aOR, 1.48; 95% CI: 1.12–1.95). Compared with boosting with only one dose, boosting with two doses was significantly associated with increased odds of having ≥median concentration of anti-S (aOR, 13.8; 95% CI: 1.78–106.5), neutralizing IgG antibodies (aOR, 13.2; 95% CI: 1.71–101.9), and T-cell response (aOR, 7.22; 95% CI: 1.99–26.5) although not with anti-N (aOR, 0.41; 95% CI: 0.16–1.08). Compared with priming and subsequently boosting with BBIBP-CorV, all participants who were primed with BBIBP-CorV and subsequently boosted with BNT162b2 had ≥median concentration of anti-S and neutralizing IgG antibodies and 14.6-time increased odds of having a T-cell response (aOR, 14.63; 95% CI: 1.78–120.5). Compared with priming with two doses, boosting with the third dose was not associated, whereas boosting with two doses was significantly associated with having ≥median concentration of anti-S (aOR, 14.20; 95% CI: 1.85–109.4), neutralizing IgG (aOR, 13.6; 95% CI: 1.77–104.3), and T-cell response (aOR, 7.62; 95% CI: 2.09–27.8).ConclusionAchieving and maintaining a high blood concentration of protective immune biomarkers that predict vaccine effectiveness is very critical to limit transmission and contain outbreaks. In this study, boosting with only one dose or with only BBIBP-CorV after priming with BBIBP-CorV was insufficient, whereas boosting with two doses, particularly boosting with the mRNA-based vaccine, was shown to be associated with having a high concentration of anti-S, anti-N, and neutralizing IgG antibodies and producing an efficient T-cell response.

IntroductionAsthma and polycystic ovarian syndrome (PCOS) are linked in several possible ways. To date, there has been no study evaluating whether pediatric asthma is an independent risk factor for adult PCOS. Our study aimed to examine the association between pediatric asthma (diagnosed at 0-19 years) and adult PCOS (diagnosed at ≥20 years). We further assessed whether the aforementioned association differed in two phenotypes of adult PCOS which were diagnosed at 20-25 years (young adult PCOS), and at >25 years (older adult PCOS). We also evaluated whether the age of asthma diagnosis (0-10 vs 11-19 years) modified the association between pediatric asthma and adult PCOS.Material and methodsThis is a retrospective cross-sectional analysis using the United Arab Emirates Healthy Future Study (UAEHFS) collected from February 2016 to April 2022 involving 1334 Emirati females aged 18-49 years. We fitted a Poisson regression model to estimate the risk ratio (RR) and its 95% confidence interval (95% CI) to assess the association between pediatric asthma and adult PCOS adjusting for age, urbanicity at birth, and parental smoking at birth.ResultsAfter adjusting for confounding factors and comparing to non-asthmatic counterparts, we found that females with pediatric asthma had a statistically significant association with adult PCOS diagnosed at ≥20 years (RR=1.56, 95% CI: 1.02-2.41), with a stronger magnitude of the association found in the older adult PCOS phenotype diagnosed at >25 years (RR=2.06, 95% CI: 1.16-3.65). Further, we also found females reported thinner childhood body size had a two-fold to three-fold increased risk of adult PCOS diagnosed at ≥20 years in main analysis and stratified analyses by age of asthma and PCOS diagnoses (RR=2.06, 95% CI: 1.08-3.93 in main analysis; RR=2.74, 95% CI: 1.22-6.15 among those diagnosed with PCOS > 25 years; and RR=3.50, 95% CI: 1.38-8.43 among those diagnosed with asthma at 11-19 years).ConclusionsPediatric asthma was found to be an independent risk factor for adult PCOS. More targeted surveillance for those at risk of adult PCOS among pediatric asthmatics may prevent or delay PCOS in this at-risk group. Future studies with robust longitudinal designs aimed to elucidate the exact mechanism between pediatric asthma and PCOS are warranted.

Protocol for analysis of data from the UAE Healthy Future Study on factors associated with single, dual or poly combustible tobacco use.

Analysis of EC and HTP use among people who do and do not smoke combustible tobacco

Supplementary Material 1

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.

Background The United Arab Emirates Healthy Future Study (UAEHFS) is one of the first large prospective cohort studies and one of the few studies in the region which examines causes and risk factors for chronic diseases among the nationals of the United Arab Emirates (UAE). The aim of this study is to investigate the eight-item Patient Health Questionnaire (PHQ-8) as a screening instrument for depression among the UAEHFS pilot participants. Methods The UAEHFS pilot data were analyzed to examine the relationship between the PHQ-8 and possible confounding factors, such as self-reported happiness, and self-reported sleep duration (hours) after adjusting for age, body mass index (BMI), and gender. Results Out of 517 participants who met the inclusion criteria, 487 (94.2%) participants filled out the questionnaire and were included in the statistical analysis using 100 multiple imputations. 231 (44.7%) were included in the primary statistical analysis after omitting the missing values. Participants’ median age was 32.0 years (Interquartile Range: 24.0, 39.0). In total, 22 (9.5%) of the participant reported depression. Females have shown significantly higher odds of reporting depression than males with an odds ratio = 3.2 (95% CI:1.17, 8.88), and there were approximately 5-fold higher odds of reporting depression for unhappy than for happy individuals. For one interquartile-range increase in age and BMI, the odds ratio of reporting depression was 0.34 (95% CI: 0.1, 1.0) and 1.8 (95% CI: 0.97, 3.32) respectively. Conclusion Females are more likely to report depression compared to males. Increasing age may decrease the risk of reporting depression. Unhappy individuals have approximately 5-fold higher odds of reporting depression compared to happy individuals. A higher BMI was associated with a higher risk of reporting depression. In a sensitivity analysis, individuals who reported less than 6 h of sleep per 24 h were more likely to report depression than those who reported 7 h of sleep.

Lack of physical activity and poor eating habits have been associated with numerous negative health outcomes, including diabetes, cardiovascular disease, and obesity. Previous research demonstrated that physical activity decreases throughout young adulthood and adolescence. This was a qualitative investigation into potential barriers and motivators to healthy eating and being physically active among a diverse group of college students in a US institution in the Middle East. A total of six focus groups were conducted (N = 35) to discuss motivators and barriers to healthy eating and being physically active, perceptions, culture, and intervention ideas. Stress and time management emerged as barriers, while social supports were a motivating factor for students. Future research should investigate the unique effect of transitioning into college in the context of the Middle East. Interventions targeting factors that affect the physical activity and eating habits of young adults can help improve longer-term health outcomes.

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.

Background:The Rockefeller University Center for Clinical and Translational Science (RU-CCTS) and Clinical Directors Network (CDN), a Practice-Based Research Network (PBRN), fostered a community–academic research partnership involving Community Health Center (CHCs) clinicians, laboratory scientists, clinical researchers, community, and patient partners. From 2011 to 2018, the partnership designed and completed Community-Associated Methicillin-ResistantStaphylococcus AureusProject (CAMP1), an observational study funded by the National Center for Advancing Translational Sciences (NCATS), and CAMP2, a Comparative Effectiveness Research Study funded by the Patient-Centered Outcomes Research Institute (PCORI). We conducted a social network analysis (SNA) to characterize this Community-Engaged Research (CEnR) partnership.Methods:Projects incorporated principles of Community-Based Participatory Research (CAMP1/2) and PCORI engagement rubrics (CAMP2). Meetings were designed to be highly interactive, facilitate co-learning, share governance, and incentivize ongoing engagement. Meeting attendance formed the raw dataset enriched by stakeholder roles and affiliations. We used SNA software (Gephi) to form networks for four project periods, characterize network attributes (density, degree, centrality, vulnerability), and create sociograms. Polynomial regression models were used to study stakeholder interactions.Results:Forty-seven progress meetings engaged 141 stakeholders, fulfilling 7 roles, and affiliated with 28 organizations (6 types). Network size, density, and interactions across organizations increased over time. Interactions between Community Members or Recruiters/Community Health Workers and almost every other role increased significantly across CAMP2 (P< 0.005); Community Members’ centrality to the network increased over time.Conclusions:In a partnership with a highly interactive meeting model, SNA using operational attendance data afforded a view of stakeholder interactions that realized the engagement goals of the partnership.

Supplemental Digital Content is available in the text., Problem Engaging basic scientists in community-based translational research is challenging but has great potential for improving health. Approach In 2009, The Rockefeller University Center for Clinical and Translational Science partnered with Clinical Directors Network, a practice-based research network (PBRN), to create a community-engaged research navigation (CEnR-Nav) program to foster research pairing basic science and community-driven scientific aims. The program is led by an academic navigator and a PBRN navigator. Through meetings and joint activities, the program facilitates basic science–community partnerships and the development and conduct of joint research protocols. Outcomes From 2009–2014, 39 investigators pursued 44 preliminary projects through the CEnR-Nav program; 25 of those became 23 approved protocols and 2 substudies. They involved clinical scholar trainees, early-career physician–scientists, faculty, students, postdoctoral fellows, and others. Nineteen (of 25; 76%) identified community partners, of which 9 (47%) named them as coinvestigators. Nine (of 25; 36%) included T3–T4 translational aims. Seven (of 25; 28%) secured external funding, 11 (of 25; 44%) disseminated results through presentations or publications, and 5 (71%) of 7 projects publishing results included a community partner as a coauthor. Of projects with long-term navigator participation, 9 (of 19; 47%) incorporated T3–T4 aims and 7 (of 19; 37%) secured external funding. Next Steps The CEnR-Nav program provides a model for successfully engaging basic scientists with communities to advance and accelerate translational science. This model's durability and generalizability have not been determined, but it achieves valuable short-term goals and facilitates scientifically meaningful community–academic partnerships.