134 research outputs found
Anthropometric predictors of dyslipidemia among adults in Saudi Arabia
Background: dyslipidemia and obesity are key independent modifiable risk factors for many non communicable chronic diseases. Patterns of association between these factors may help prevention and control. This study aims to assess the association between lipids profile and obesity among adults in Kingdom of Saudi Arabia and identify anthropometric predictors of dyslipidemia.
Methods: data were collected and analyzed from a cross-sectional study using WHO STEPwise approach that included 4 990 Saudi adults aged 15- 64 years selected by stratified, multistage, cluster random sampling technique. Lipid profiles (cholesterol categories and triglycerides) were determined spectrophotometrically by colorimetric biochemical methods. Obesity was determined by calculation of body mass index (BMI=Kg/m2), waist and hip circumferences and ratio and waist to height ratio.
Results: the overall prevalence of obesity ranged from 33.8 to 44.4 % and the overall dyslipidemia prevalence ranged from about 25 to 44% depending on type of dyslipidemia and anthropometrics used. Prevalence of dyslipidemia and mean concentration of lipids profile were generally significantly higher in obese than non obese. The indicator waist/height ratio was the significant predictor for all types of dyslipidemia and all levels of serum lipids.
Conclusions: the prevalence dyslipidemia and obesity are high and they are positively associated. Waist/height ratio was the most important predictor of dyslipidemia among adults
A NTHROPO mETRIC PRE dICTORS Of dy SLIPI dEmIA Anthropometric predictors of dyslipidemia among adults in Saudi Arabia
Background: dyslipidemia and obesity are key independent modifiable risk factors for many non communicable chronic diseases. Patterns of association between these factors may help prevention and control. This study aims to assess the association between lipids profile and obesity among adults in kingdom of Saudi arabia and identify anthropometric predictors of dyslipidemia. MeThodS: data were collected and analyzed from a cross-sectional study using Who STePwise approach that included 4 990 Saudi adults aged 15-64 years selected by stratified, multistage, cluster random sampling technique. Lipid profiles (cholesterol categories and triglycerides) were determined spectrophotometrically by colorimetric biochemical methods. obesity was determined by calculation of body mass index (BMI=kg/m 2 ), waist and hip circumferences and ratio and waist to height ratio. reSuLTS: the overall prevalence of obesity ranged from 33.8 to 44.4 % and the overall dyslipidemia prevalence ranged from about 25 to 44% depending on type of dyslipidemia and anthropometrics used. Prevalence of dyslipidemia and mean concentration of lipids profile were generally significantly higher in obese than non obese. The indicator waist/height ratio was the significant predictor for all types of dyslipidemia and all levels of serum lipids. concLuSIonS: the prevalence dyslipidemia and obesity are high and they are positively associated. Waist/height ratio was the most important predictor of dyslipidemia among adults. Key words: Dyslipidemia; Obesity; Adults; Anthropometrics; Saudi Arabia KSAU-HS, King Fahad Medical City, Ministry of Health, POBox 11393, Riyadh, Saudi Arabia. e-mail: [email protected]; [email protected] doi: 10.2427/8733 InTroducTIon Obesity is a complex, multi-factorial, chronic condition that is associated with mortality and significant morbidity and is prevalent worldwide [1][2][3]. Studies in the Kingdom of Saudi Arabia (KSA) and other Gulf countries have highlighted the increasing burden of the reported 13-50% e 8 7 3 3 -1 O RIGINAL ARTICLES Epidemiology Biostatistics and Public Health -2013, Volume 10, Number 1 A NTHROPO m ETRIC PRE dICTORS Of dy SLIPI dEmIA prevalence of overweight and obesity in adults MeThodS This is a cross-sectional, community-based study covering the whole population of KSA in 2005. The WHO STEPwise approach to Surveillance (STEPS) of Non-Communicable Diseases (NCD) risk factors was the basis for conducting the survey and for collecting the data The STEPS approach focuses on obtaining core data on the established risk factors that determine the major disease burden. It is sufficiently flexible to allow each country to expand on the core variables and risk factors, and to incorporate optional modules related to local or regional interests. The STEPS instrument covers three different levels of "steps" of risk factor assessment. These steps are: • Questionnaire • Physical measurements • Biochemical measurements Study population All Saudi population aged 15-64 years from all the 20 health regions of the country comprised the study population. Sampling A multistage stratified cluster random sampling technique was used to recruit the study subjects. Stratification was based on age (five 10-year age groups) and gender (male/female, two groups). All health regions of the country (20 regions) were covered. Based upon the proposed methodology of the WHO STEPwise approach, a sample size of 196 was calculated for each of these ten strata. A list of all Primary Health Care Centers (PHCCs) in each region was prepared; 10% of these PHCCs were randomly chosen and allocated a regional sample proportionate to the size of their catchment population in sampled PHCCs. To identify the households, a map of the health center coverage area was used to choose the houses. Each house was assigned a number, and a simple random draw was made. data collection Data were collected using the WHO STEPwise approach data collectors Data were collected by 54 male and 54 female collectors who worked in teams. Each field team was made up of four persons: a male e 8 7 3 3 -2 O RIGINAL ARTICLES Epidemiology Biostatistics and Public Health -2013, Volume 10, Number 1 A NTHROPO mETRIC PRE dICTORS Of dy SLIPI dEmIA data collector, a female data collector, a driver, and a female assistant. Data collection teams were supervised by a hierarchy of a local supervisor, regional coordinators, and a national coordinator. Training of data collectors All individuals involved in data collection attended a comprehensive training workshop that included interview techniques, data collection tools, practical applications, and field guidelines. analytical techniques Blood (5 ml) was collected in the morning, after the participants had abstained from eating overnight. Sodium heparin was used as an anticoagulant, and the samples were centrifuged at 3 000 × g for 15 min at 20°C to separate plasma. Aliquots were prepared for storage (−20°C or −80°C) until further analysis. Total cholesterol (TC), triglycerides, and glucose were measured with commercially available enzymatic colorimetric kits from QCA (Amposta, Spain). Seriscann Normal (ref 994148) (QCA, Amposta, Spain) was used for quality control measures. Serum high-density lipoprotein cholesterol (HDL-C) levels were analyzed by the enzymatic method after precipitating serum reagents with phosphotungstic acid and magnesium. LDLcholesterol (LDL-C) was calculated according to the Friedewald formula (LDL cholesterol = total cholesterol -HDL -(TG/5)) Height, weight, and waist and hip circumferences were measured using standard instruments, according to the STEPwise approach anthropometric measurements Body weight and height were measured without shoes, using an electronic measuring scale. Body mass index (BMI) was calculated as weight in kg divided by height in m 2 . Waist circumference (WC) was measured, in cm, midway between the lower costal margin and iliac crest during the end-expiratory phase. Hip circumference (HC) was measured, in cm, at the level of the greater trochanters. The waist-to-hip ratio (WHR) was defined as the waist circumference divided by the hip circumference, while the waist/height (WHtR) ratio was defined as the waist circumference divided by the height in cm. The cutoff levels for obesity are as follows, according to WHO and USA standards Statistical analysis The statistical analysis was performed using SPSS for Windows, version 17.0. The data were given as mean ± standard deviation for continuous variable and as counts and percentage for categorical variables. Association between categorical variables was assessed using a chisquare test, and ANOVA was used to compare means of more than two categorical variables. Logistic regression was used to investigate the associations of the binary dependent variable "dyslipidemia" with the independent "obesity" anthropometric measurement variable. A NTHROPO m ETRIC PRE dICTORS Of dy SLIPI dEmIA linear regression analysis was performed to identify significant predictors for serum lipids levels. Level of significance was set at <0.05 throughout the study. The data were processed using SPSS version 17. Totals counts may vary, due to missing data from certain variables. ethical clearance and confidentiality The protocol and the survey instrument were approved by the Ministry of Health, Center of Biomedical Ethics, and the appropriate authorities in KSA. Informed consent of all subjects was obtained. Confidentiality of data was assured, and that data will be used only for the stated purpose of the survey. The survey was conducted in 2005. reSuLTS Of the 4 990 subjects included in the survey, about 5% (232 subjects) were excluded from final analysis, due to major deficiencies in their data. There were no significant differences between them and the rest of the 4 758 subjects regarding sociodemographic characteristics or obesity status. Waist for height ratio and Waist circumference were significant predictors of almost all types of serum lipid concentration, as shown by the linear regression analysis in dIScuSSIon Obesity is associated with endothelial dysfunction and greater arterial stiffness from as early as the first decade of life. This effect on vascular function is probably mediated in part by low-grade inflammation, insulin resistance, and production by adipose tissue of cytokine-like molecules, collectively termed adipokines and high leptin concentratio
The prevalence of physical activity and its socioeconomic correlates in Kingdom of Saudi Arabia: A cross-sectional population-based national survey
AbstractObjectivesTo determine the levels of physical activity in the Saudi population and to assess its socio-demographic correlates.MethodsThe data were part of a cross-sectional representative national survey of 4758 participants conducted in Kingdom of Saudi Arabia. A multistage stratified cluster random sampling design was used. Physical activity was assessed using the Global Physical Activity Questionnaire (GPAQ) version 2.0. Logistic regression analyses were used to identify the determinants and were adjusted in relation to various factors.ResultsOverall, physical inactivity was found to be 66.6% (95% C.I.: 65.3%–68%), 60.1% (95% C.I.: 58.1%–62.1%) for males and 72.9% (95% C.I.: 71.1%–74.7%) for females. Leisure time physical inactivity was found to be 87.9%, 85.6% for males and 90.2% for females. The northern and central regions reported the highest prevalence of no physical activity at work, leisure and transportation. Gender, geographical location and employment status exhibited a statistically significant correlation.ConclusionsThere is a high level of physical inactivity in various regions and population groups in the Kingdom of Saudi Arabia. Population interventions are greatly needed, especially those focusing on physical activity in their leisure time
Prevalence, Awareness, Treatment, and Control of Hypertension among Saudi Adult Population: A National Survey
This cross-sectional study aimed at estimating prevalence, awareness, treatment, control, and predictors of hypertension among Saudi adult population. Multistage stratified sampling was used to select 4758 adult participants. Three blood pressure measurements using an automatic sphygmomanometer, sociodemographics, and antihypertensive modalities were obtained. The overall prevalence of hypertension was 25.5%. Only 44.7% of hypertensives were aware, 71.8% of them received pharmacotherapy, and only 37.0% were controlled. Awareness was significantly associated with gender, age, geographical location, occupation, and comorbidity. Applying drug treatment was significantly more among older patients, but control was significantly higher among younger patients and patients with higher level of physical activity. Significant predictors of hypertension included male gender, urbanization, low education, low physical activity, obesity, diabetes, and hypercholesterolemia. In conclusion prevalence is high, but awareness, treatment, and control levels are low indicating a need to develop a national program for prevention, early detection, and control of hypertension
Network Qos architecture
There is huge growth in internet recently. This growth is started initially at slow rate with limited number of application, However as the time progresses the nehvork has expanded in term of users and applications, This expansion dictated a alteration and modification of the original protocol to enable higher Quality of service. This chapter provides tan over view about QoS requirement, architecture and mobility suppor
AI-enhanced education: bridging educational disparities
This chapter explores the symbiotic relationship between AI and education, highlighting its transformative impact and potential to bridge educational disparities. It provides insights into using AI to create engaging and efficient learning environments, covering various topics such as AI-driven teaching techniques, ethical considerations, and case studies. AI tools enhance learning experiences and help address disparities among students. The integration of AI in education can optimize resources, personalize learning, and improve academic performance. However, challenges such as lack of familiarity and technical difficulties need to be addressed through training and support for teachers. By embracing AI, educators can revolutionize teaching methods and promote equity and inclusivity in education
Correlations of complete blood count, liver enzyme and serum uric Acid in Sudanese pre-eclamptic cases
Background: Pre-eclampsia is a serious disorder of pregnancy with unknown ethological factors that may occur at any stage of second or third trimester of pregnancy. The objectives of the present study were to assess changes in complete blood counts including platelets, liver enzymes and serum uric acid in pre-eclamptic cases compared to second-half normal pregnant and non-pregnant Sudanese women and their correlations to other biomarkers.Methods: This was a cross-sectional, case-control study performed from December 2008 to December 2010; in Omdurman Maternity Hospital, in concomitance with other studies in pre-eclampsia. The sample size included three groups, 72 up pre-eclamptic cases in their recent pregnancies, 96 normal pregnant in their second half of pregnancy and 63 non- pregnant (control) women; a total of 231 subjects. Questionnaire Interviews and clinical examination were done for all participants. Laboratory investigations were done including complete blood picture, liver enzymes and uric acid. Results: The mean Hb concentration of the pre-eclamptic (11.3g/dl±1.7) was statistically significantly lower than that of the non-pregnant (12.1g/dl±0.2) (P=0.01) but not from that of the normal pregnant (11.4g/dl±0.1) (P=0.882) .There was no statistical significant difference in the mean WBC count between the pre-eclamptic (7.4x103/mm3±0.3) and non-pregnant (7.3x103/mm3±0.3) (P=0.797) and between the pre-eclamptic and normal pregnant (7.7x103/mm3±0.2) (P=0.270). There was a considerable statistical significant decrease in the mean platelets count of the pre-eclamptic (236.4/mm3±8.3) compared to the non-pregnant group (322.0/mm3±10.4) (P=0.0001) s well as to the normal pregnant (275.0/mm3±8.9) (P = 0.003). In the pre-eclamptic cases, serum ALT correlated significantly with TWCC (r=0.26, P=0.03) and serum AST (r=0.65, P=0.000). In the pre-eclamptic cases, serum AST correlated significantly with Hb (r=0.26, P=0.03), serum ALT and serum uric acid (r=0.36, P=0.01).Conclusions: There was a considerable statistical significant decrease in mean platelets count of the pre-eclamptic compared to the non-pregnant group and to the normal pregnant may be explained by hemodilution; whereas further decrease was due to pre-eclampsia. ALT and AST are strong prognostic indicators of pre-eclampsia
Enhancing medical services through machine learning and UAV technology: applications and benefits
This chapter focuses on the enhancement of medical services through the integration of unmanned
aerial vehicle (UAV) technology and machine learning algorithms. It explores the broad spectrum of
applications and benefits that arise from combining these two technologies. By employing UAVs for
automated delivery, medical supplies can be efficiently transported to remote or inaccessible regions,
thereby improving access to vital items. Remote patient monitoring, facilitated through UAVs and machine
learning, enables real-time data collection and analysis, enabling the early identification of health
issues. UAVs equipped with medical equipment and machine learning capabilities enhance emergency
medical response by providing immediate assistance during critical situations. Disease surveillance and
outbreak management can benefit from the use of UAVs and machine-learning algorithms to identify
disease hotspots and predict the spread of illnesses
Mobility management enhancement in smart cities using software defined networks
Achieving sustainability in cities relies on effective mobility management (MM) that serves
current and future generations. It involves establishing an inclusive transportation system to
address many issues, like traffic congestion, air pollution, and greenhouse gas emissions. Beyond
environmental concerns, robust mobility management has social and economic advantages,
fostering improved access to vital services like healthcare, education, and employment. Softwaredefined networking (SDN) presents a viable solution for enhancing MM within networks. Unlike
traditional setups, SDN merges MM through a programmable control plane, streamlining network
configurations and enabling features like handover, load balancing, and quality of service (QoS).
The utilization of SDN technology extends to various facets of sustainable city networks,
encompassing areas like network security, performance optimization, big data processing, energy
efficiency, emergency management, carbon emissions reduction, intelligent services, and MM in
vehicular networks. Despite the advantages of SDN-based mobility management, it’s crucial to
acknowledge the challenges and limitations posed by traditional MM methods that SDN aims to
overcome. The paper explores SDN’s potential in sustainable cities, focusing on how it can
transform mobile device management, support various networking technologies, and evaluate the
impact of SDN methods on existing MM systems, considering factors like scalability and
compatibility. The paper asserts that SDN-based MM has substantial potential for promoting
sustainable urban development. By centralizing control, adapting to changing conditions, and
optimizing resource allocation, SDN can contribute to reduced energy consumption, lower carbon
emissions, and more efficient urban mobility. It emphasizes the importance of addressing potential drawbacks to ensure successful implementation in sustainable cities
Monitoring of wildlife using unmanned aerial vehicle (UAV) with machine learning
Wildlife monitoring is critical for ecological study, conservation, and wildlife management, but traditional
approaches have drawbacks. The combination of unmanned aerial vehicles (UAVs) with machine learning
(ML) offers a viable approach to overcoming the limits of traditional wildlife monitoring methods and
improving wildlife management and conservation tactics. The combination of UAVs and ML provides
efficient and effective solutions for wildlife monitoring. UAVs with high-resolution cameras record airborne
footage, while machine learning algorithms automate animal detection, tracking, and behavior
analysis. The chapter discusses challenges, limitations, and future directions in using UAVs and ML for
wildlife monitoring, addressing regulatory, technical, and ethical considerations, and emphasizing the
need for ongoing research and technological advancements. Overall, the integration of UAVs and ML
provides a promising solution to overcome the limitations of traditional wildlife monitoring methods
and enhance wildlife management and conservation strategies
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