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Adapting a Prediction Rule for Metabolic Syndrome Risk Assessment Suitable for Developing Countries.

ABSTRACT: Background: Metabolic syndrome (MetS) is a cluster of cardiometabolic disturbances that increases the risk of cardiovascular diseases (CVD) and type 2 diabetes mellitus (DM). The early identification of high-risk individuals is the key for halting these conditions. The world is facing a growing epidemic MetS although the magnitude in Egypt is unknown. Objectives: To describe MetS and its determinants among apparently healthy individuals residing in urban and rural communities in Egypt and to establish a model for MetS prediction. Methods: A cross-sectional study was conducted with 270 adults from rural and urban districts in Alexandria, Egypt. Participants were clinically evaluated and interviewed for sociodemographic and lifestyle factors and dietary habits. MetS was defined according to the harmonized criteria set by the AHA/NHLBI. The risk of ischemic heart diseases (IHDs), DM and fatty liver were assessed using validated risk prediction charts. A multiple risk model for predicting MetS was developed, and its performance was compared. Results: In total, 57.8% of the study population met the criteria for MetS and were at high risk for developing IHD, DM, and fatty liver. Silent CVD risk factors were identified in 20.4% of the participants. In our proposed multivariate logistic regression model, the predictors of MetS were obesity [OR (95% CI) = 16.3 (6.03-44.0)], morbid obesity [OR (95% CI) = 21.7 (5.3-88.0)], not working [OR (95% CI) = 2.05 (1.1-3.8)], and having a family history of chronic diseases [OR (95% CI) = 4.38 (2.23-8.61)]. Consumption of caffeine once per week protected against MetS by 27.8-fold. The derived prediction rule was accurate in predicting MetS, fatty liver, high risk of DM, and, to a lesser extent, a 10-year lifetime risk of IHD. Conclusion: Central obesity and sedentary lifestyles are accountable for the rising rates of MetS in our society. Interventions are needed to minimize the potential predisposition of the Egyptian population to cardiometabolic diseases.

SUBMITTER: Abd El-Wahab EW

PROVIDER: S-EPMC6822183 | biostudies-literature | 2019 Jan-Dec

REPOSITORIES: biostudies-literature

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Adapting a Prediction Rule for Metabolic Syndrome Risk Assessment Suitable for Developing Countries.

Abd El-Wahab Ekram W EW Shatat Hanan Z HZ Charl Fahmy F

Journal of primary care & community health 20190101

<b>Background:</b> Metabolic syndrome (MetS) is a cluster of cardiometabolic disturbances that increases the risk of cardiovascular diseases (CVD) and type 2 diabetes mellitus (DM). The early identification of high-risk individuals is the key for halting these conditions. The world is facing a growing epidemic MetS although the magnitude in Egypt is unknown. <b>Objectives:</b> To describe MetS and its determinants among apparently healthy individuals residing in urban and rural communities in Eg ...[more]

PMID: 31662026

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Project description:BackgroundIndian Diabetic Risk Score (IDRS) and Community Based Assessment Checklist (CBAC) are easy, inexpensive, and non-invasive tools that can be used to screen people for Metabolic Syndrome (Met S). The study aimed to explore the prediction abilities of IDRS and CBAC tools for Met S.MethodsAll the people of age ≥30 years attending the selected rural health centers were screened for Met S. We used the International Diabetes Federation (IDF) criteria to diagnose the Met S. ROC curves were plotted by taking Met S as dependent variables, and IDRS and CBAC scores as independent/prediction variables. Sensitivity (SN), specificity (SP), Positive and Negative Predictive Value (PPV and NPV), Likelihood Ratio for positive and negative tests (LR+ and LR-), Accuracy, and Youden's index were calculated for different IDRS and CBAC scores cut-offs. Data were analyzed using SPSS v.23 and MedCalc v.20.111.ResultsA total of 942 participants underwent the screening process. Out of them, 59 (6.4%, 95% CI: 4.90-8.12) were found to have Met S. Area Under the Curve (AUC) for IDRS in predicting Met S was 0.73 (95%CI: 0.67-0.79), with 76.3% (64.0%-85.3%) sensitivity and 54.6% (51.2%-57.8%) specificity at the cut-off of ≥60. For the CBAC score, AUC was 0.73 (95%CI: 0.66-0.79), with 84.7% (73.5%-91.7%) sensitivity and 48.8% (45.5%-52.1%) specificity at the cut-off of ≥4 (Youden's Index, 2.1). The AUCs of both parameters (IDRS and CBAC scores) were statistically significant. There was no significant difference (p = 0.833) in the AUCs of IDRS and CBAC [Difference between AUC = 0.00571].ConclusionThe current study provides scientific evidence that both IDRS and CBAC have almost 73% prediction ability for Met S. Though CBAC holds relatively greater sensitivity (84.7%) than IDRS (76.3%), the difference in prediction abilities is not statistically significant. The prediction abilities of IDRS and CBAC found in this study are inadequate to qualify as Met S screening tools.

Project description:BackgroundThe musculoskeletal system participates in the pathology of metabolic disorders. Several studies have focused on body composition changes; however, the adipose tissue between muscle bundles with different metabolic statuses has rarely been studied. This study sought to explore the association between body compositions and metabolic disorders in Asians, and identify whether these body compositions can be used to detect metabolic disorders with different waist circumferences (WCs) by computed tomography (CT).MethodsA total of 116 subjects were included in the study and categorized into the following four groups according to WC and metabolic syndrome (MS): (I) the healthy control group; (II) the normal WC with metabolic disorder group; (III) the normal WC with MS group; and (IV) the larger WC with MS group. The International Diabetes Federation (IDF) criteria based on WC, laboratory tests, body mass index (BMI), and medical history was used to diagnose MS. Body composition parameters, such as muscle attenuation, the cross-sectional area of subcutaneous adipose tissue (SAT), muscle, extramyocellular lipid (EMCL), visceral adipose tissue (VAT), and the ratios between different compositions [e.g., the SMR (SAT/muscle), EMR (EMCL/muscle), and VMR (VAT/muscle)] were calculated for the thigh and abdomen. The areas under the curve (AUCs) of the receiver operating characteristic (ROC) curves adjusted for multiple comparisons were used to discriminate among metabolic disorders.ResultsThe groups with metabolic disorders had more SAT (P=0.001) and EMCL (P=0.040) in the thigh, and more VAT (P=0.001) and a higher SMR (P<0.001) in the abdomen. EMCL and muscle attenuation in the thigh (AUCs =0.790 and 0.791), and the VMR and SMR in the abdomen were better able to diagnose metabolic disorders (AUCs =0.752 and 0.746) than other body composition parameters. While SAT and EMCL in the thigh (AUCs =0.768 and 0.760), and VAT and the VMR in the abdomen (AUCs =0.788 and 0.775) were better able to diagnose MS than other parameters.ConclusionsBody composition parameters for the thigh and abdomen could assist in detecting patients with an increased risk of MS.

Project description:BackgroundStunting affects one-third of children under 5 y old in developing countries, and 14% of childhood deaths are attributable to it. A large number of risk factors for stunting have been identified in epidemiological studies. However, the relative contribution of these risk factors to stunting has not been examined across countries. We estimated the number of stunting cases among children aged 24-35 mo (i.e., at the end of the 1,000 days' period of vulnerability) that are attributable to 18 risk factors in 137 developing countries.Methods and findingsWe classified risk factors into five clusters: maternal nutrition and infection, teenage motherhood and short birth intervals, fetal growth restriction (FGR) and preterm birth, child nutrition and infection, and environmental factors. We combined published estimates and individual-level data from population-based surveys to derive risk factor prevalence in each country in 2010 and identified the most recent meta-analysis or conducted de novo reviews to derive effect sizes. We estimated the prevalence of stunting and the number of stunting cases that were attributable to each risk factor and cluster of risk factors by country and region. The leading risk worldwide was FGR, defined as being term and small for gestational age, and 10.8 million cases (95% CI 9.1 million-12.6 million) of stunting (out of 44.1 million) were attributable to it, followed by unimproved sanitation, with 7.2 million (95% CI 6.3 million-8.2 million), and diarrhea with 5.8 million (95% CI 2.4 million-9.2 million). FGR and preterm birth was the leading risk factor cluster in all regions. Environmental risks had the second largest estimated impact on stunting globally and in the South Asia, sub-Saharan Africa, and East Asia and Pacific regions, whereas child nutrition and infection was the second leading cluster of risk factors in other regions. Although extensive, our analysis is limited to risk factors for which effect sizes and country-level exposure data were available. The global nature of the study required approximations (e.g., using exposures estimated among women of reproductive age as a proxy for maternal exposures, or estimating the impact of risk factors on stunting through a mediator rather than directly on stunting). Finally, as is standard in global risk factor analyses, we used the effect size of risk factors on stunting from meta-analyses of epidemiological studies and assumed that proportional effects were fairly similar across countries.ConclusionsFGR and unimproved sanitation are the leading risk factors for stunting in developing countries. Reducing the burden of stunting requires a paradigm shift from interventions focusing solely on children and infants to those that reach mothers and families and improve their living environment and nutrition.

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Adapting a Prediction Rule for Metabolic Syndrome Risk Assessment Suitable for Developing Countries.

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Adapting a Prediction Rule for Metabolic Syndrome Risk Assessment Suitable for Developing Countries.

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