Concomitant treatment of Human Immunodeficiency Virus (HIV) infection and tuberculosis (TB) presents a series of challenges for treatment compliance for both providers and patients. We carried out this study to identify risk factors for default from TB treatment in people living with HIV.P-values.We conducted a cohort study to monitor HIV/TB co-infected subjects in Pernambuco, Brazil, on a monthly basis, until completion or default of treatment for TB. Logistic regression was used to calculate crude and adjusted odds ratios, 95% confidence intervals and 3. Age over 29 years, complete or incomplete secondary or university education and the use of highly active antiretroviral therapy (HAART) were identified as protective factors for the outcome.From a cohort of 2310 HIV subjects, 390 individuals (16.9%) who had started treatment after a diagnosis of TB were selected, and data on 273 individuals who completed or defaulted on treatment for TB were analyzed. The default rate was 21.7% and the following risk factors were identified: male gender, smoking and CD4 T-cell count less than 200 cells/mm3. Default was less likely to occur in patients under HAART, reinforcing the strategy of early initiation of HAART in individuals with TB.The results point to the need for more specific actions, aiming to reduce the default from TB treatment in males, younger adults with low education, smokers and people with CD4 T-cell counts < 200 cells/mm Mycobacterium tuberculosis [Tuberculosis (TB) is the leading cause of morbidity and death among people living with Human Immunodeficiency Virus (HIV), particularly in developing countries . Brazil rculosis . Approxirculosis . The Starculosis . In 2008rculosis .Since 1996, Brazil has guaranteed access to antiretroviral treatment (ART) to all HIV subjects. Concomitant treatment of HIV and TB presents a series of challenges, including a long treatment duration, high frequency of drug administration, potential complex interactions of the drugs, toxicity of the two therapies, and the occurrence of immune reconstitution syndrome ,8.Default from TB treatment is associated with a longer period of TB transmission, treatment failure and mortality. Identification of risk factors for default and subsequent intervention strategies are required, and are particularly important in HIV subjects because of the increase in morbidity and mortality related to progression of the HIV infection itself. Several studies, using different approaches and methodologies, ,10 have This study aimed to estimate the frequency of default from TB treatment in a cohort co-infected with HIV and to identify the risk factors for its occurrence.We conducted a prospective cohort study in HIV subjects older than 18 years who had started treatment after a diagnosis of TB in two referral hospitals for HIV/AIDS in the state of Pernambuco, Brazil, from June 2007 to December 2009. These two referral centers are responsible for the care of about 70% of all HIV subjects in the State, and they follow the guidelines of the Brazilian Ministry of Health for the treatment of TB and HIV. In these centers, both treatments are delivered by the same physician. The study excluded patients whose TB diagnosis was changed during follow-up .Default from TB treatment was defined as failure of the patient to attend the clinic for more than 30 consecutive days after the date that the patient was due to return. Treatment failure was identified by a positive smear 4 months after the start of treatment. TB treatment outcomes were defined by the Brazilian Ministry of Health [We enrolled HIV patients in the study at the time they were notified as having tuberculosis the Surveillance System for Infectious Diseases (SINAN/MS), managed by the Brazilian Ministry of Health (MS). Registering the case in the SINAN/MS is a prerequisite for initiation of TB treatment.We included those patients who were likely to have the opportunity to complete TB treatment before the end of the investigation, i.e., if they started treatment at least 6-8 months before the end of the study. We followed patients monthly until completion of, or default from, TB treatment. A trained health professional interviewed all participants, using a standardized questionnaire, after they gave their informed consent. Additional information was collected from medical records. For patients who defaulted from TB treatment, information was extracted from their medical records, from data of the epidemiological units and from the Mortality Information System to avoid misclassification (with death coded as default).We considered patients to have a diagnosis of active TB if they had begun TB treatment as a result of clinical suspicion or laboratory confirmation. We considered individuals to be HIV-infected if they had been tested positive for HIV using ELISA, immunofluorescence, Western blotting or rapid test. For the purposes of the analysis, independent variables were grouped into five sections: biological variables; socioeconomic variables; variables relating to habits and lifestyle; variables relating to HIV/AIDS and TB-related variables.In relation to alcohol consumption, we classified patients as abstainers (never drank or drank less than eight units a year), light drinkers , heavy drinkers (drank in excess of five doses a day at least 3 to 4 days a week), and alcohol dependent .In relation to smoking, we classified patients as nonsmokers (never smoked in their lives), former smokers (had stopped smoking at least 6 months prior to study entry), and smokers (smokers at the time of inclusion in the study or had stopped smoking less than 6 months before enrollment).The criteria used for defining cases of AIDS were those of the Brazilian Ministry of Health .TB treatment was carried out using the following self-administered regimens: Regimen I ; Regimen IR (ethambutol introduced in the first 2 months of Regimen I); and other regimens ) . HAART wThe cumulative incidences of TB treatment default, successful treatment, and death were calculated. In the analysis to identify risk factors associated with treatment default, we excluded patients who died during the study period or had not completed TB treatment in time to outcome.P-value (Chi-square test or maximum likelihood ratio). The level of significance was set at P < 0.05.Univariate logistic regression was used to analyze the association of each study variable with default and successful TB treatment. The magnitude of the association was measured by the odds ratio (OR) and its statistical significance was tested using the OR confidence interval (CI) and the P < 0.20 in the univariate analysis were successively included in a multivariate logistic regression model, and those showing an association with a P-value ≤ 0.05 remained in the model; 2) final multivariate model: the variables selected in the previous step were introduced in the final multivariate model  and those with a P-value ≤ 0.05 were remained in the final model.We carried out the multivariate analysis in two steps: 1) multivariate analysis of each group to determine the variables closely associated with the outcome: variables associated with the outcome with We checked collinearity for those variables which, in theory, were expected to be associated with each other. The variables which remained in the final multivariate model were checked for interaction.The data entry and double entry validation were performed in parallel with data collection, and the database was managed by the SQL Server 2000 (Microsoft), using GeneXus software (version 7.5). STATA version 8.2 for data analysis.This study is part of the CSV 182/06 - Clinical-Epidemiological Study of TB/HIV Co-infection Project in Recife, approved by the research ethics committee of The Universidade Federal de Pernambuco (registration number at SISNEP FR-067 159/CAAE- 0004.1.172.106-05/REGISTRATION CEP/CCS/UFPE 254/05).From a cohort of 2310 HIV subjects, 566 patients (24.5%) who had begun treatment after a diagnosis of TB were identified. Of these, 170 individuals (30.0%) were not enrolled in the study because they were not contactable by the researchers. Six patients were excluded due to changes in their TB diagnosis . A total of 51 patients (9.0%) started TB treatment during the study period but did not complete treatment in time to assess their outcome. Of the 339 patients studied, 188 (55.5%) were successfully treated, 85 (25.1%) defaulted from treatment and 66 (19.5%) died. There was no documented case of treatment failure, and six patients were lost to follow-up.P = 0.2205), gender (P = 0.39) and TB treatment outcomes (P = 0.991).A total of 273 patients who were successfully treated or defaulted from treatment for TB were included in the analysis, as shown in Figure The age of the study population ranged from 18 to 67 years, with mean of 36.7 years, and 69.7% were male. Approximately one-fifth (21.7%) of subjects defaulted from TB treatment. The univariate analysis of the risk factors for defaulting from TB treatment is shown in table Variables that were significantly associated with treatment default in the multivariate analysis in each group of variables were included in the final multivariate model. The variables that remained in the final model were: sex, age, smoking, education level, CD4 T-cell count and ART Table . No inte3. Age older than 29 years, complete or incomplete secondary or university education, and the use of ART were identified as protective factors against TB treatment default.We observed that 21.7% of HIV subjects receiving TB treatment defaulted from treatment during our study. The risk factors identified for TB treatment default in this cohort were male sex, smoking, and a CD4 count of less than 200 cells/mm2.The percentage of TB treatment default found in the present study was higher than the 14% reported in HIV subjects in Brazil as a whole  but simiIt is not clear whether the use of ART has any impact on defaulting from TB treatment ,23. The We also found that being male was a risk factor for TB treatment default. Similar findings have been reported in populations that included individuals with and without HIV -28. HoweIn this study, smoking was identified as an independent risk factor for TB treatment default. Kittikraisak et al.  have speAlthough we found that alcohol consumption was associated with treatment default in the univariate analysis, this variable did not remain in the final multivariate model, probably due to the sample size. Illicit drug use was not significantly associated with TB treatment default, possibly because of the sample size or because of questions on illicit drug use are sensitive questions and may not be answered correctly.3 was associated with a higher frequency of default from TB treatment. Patients with a lower CD4 count are more severely ill and therefore it is probably harder for them to attend clinics and tolerate medications. However, no association between default from TB treatment and other opportunistic diseases was found.No studies associating a low CD4 T-cell count with TB treatment default were found in the literature. In this study, a CD4 T-cell count of less than 200 cells/mmIn this study, we identified that complete or incomplete secondary or university education was associated with a lower risk of defaulting from TB treatment. It is believed that a low level of education hinders perception of the seriousness of the disease and causes difficulties in understanding medical guidelines, leading to a lack of compliance with use of prescribed medications, as shown in a study carried out by Natal et al. .One limitation of this study was the fact that not all individuals who began treatment for TB in the two health centers participated in the study. However, the inclusion or exclusion of potentially eligible was mainly to the difficulties experienced in recruiting patients. It is believed that this did not interfere with the association found, since a comparison of individuals included and not included in the study showed no differences regarding sex, age, and percentage of success and default from treatment for TB. Another limitation is that, as HIV patients are usually paucibacillary and there were cases of extra-pulmonary TB in the study population, only a few cases had a positive smear and culture at the time of the diagnosis of TB. Therefore, there was no documented case of treatment failure.In the present study, we observed a high rate of default from TB treatment, even though both treatments, for HIV infection and TB, were being conducted in the same department responsible for monitoring the patient, a scenario which should have contributed to reducing the default rate in the study population.The results indicate that there is still need for action to be taken to reduce rates of default from TB treatment in specific groups, in particular males, younger individuals and those with a low level of education. Moderate and heavy drinkers are also a group to be targeted.3 with TB treatment default need to be better understood for more appropriate intervention planning to reduce treatment default in these subjects.The associations of smoking and CD4 T-cell count less than 200 cells/mmAn important finding of this study is that the use of HAART was protective against TB treatment default. This reinforces the strategy of early initiation of HAART in HIV individuals with TB.The authors declare that they have no competing interests.MM, MFPMA, IC, LVM, URM, DBMF, HRL, LCR, RAAX, made substantial contributions to the conception and design of the study. IC, LVM, DBMF, HRL supervised the study. RAAX, URM provided statistical support. MM, MFPMA, IC, LVM, URM, DBMF, HRL, LCR, RAAX contributed to the writing of the manuscript. MM, RAAX, MFPMA, LCR critically revised the manuscript. All authors read and approved the final manuscript.The pre-publication history for this paper can be accessed here:http://www.biomedcentral.com/1471-2334/11/351/prepub


Introduction. The accuracy and clinical applicability of an improved model-based system for tracking hemodynamic changes is assessed in an animal study on septic shock. Methods. This study used cardiovascular measurements recorded during a porcine trial studying the efficacy of large-pore hemofiltration for treating septic shock. Four Pietrain pigs were instrumented and induced with septic shock. A subset of the measured data, representing clinically available measurements, was used to identify subject-specific cardiovascular models. These models were then validated against the remaining measurements. Results. The system accurately matched independent measures of left and right ventricle end diastolic volumes and maximum left and right ventricular pressures to percentage errors less than 20% (except for the 95th percentile error in maximum right ventricular pressure) and all R2 > 0.76. An average decrease of 42% in systemic resistance, a main cardiovascular consequence of septic shock, was observed 120 minutes after the infusion of the endotoxin, consistent with experimentally measured trends. Moreover, modelled temporal trends in right ventricular end systolic elastance and afterload tracked changes in corresponding experimentally derived metrics. Conclusions. These results demonstrate that this model-based method can monitor disease-dependent changes in preload, afterload, and contractility in porcine study of septic shock. Cardiovascular management of the critically ill is often difficult due to the array of complex circulatory interactions that occur within hemodynamically compromised patients. The limited measurement set typically available in the intensive care unit (ICU) is sometimes insufficient to give a full picture of cardiovascular status. Hence, diagnosis and therapy are often based on the experience, skill, and intuition of the attending medical staff. However, difficulties in interpreting cardiac and circulatory measurements can result in misdiagnosis and suboptimal treatment, which may lead to inefficient use of resources, increased length of stay, and in some cases death –5.in silico system can accurately identify important disease and treatment dependent changes in the cardiovascular system (CVS).To improve hemodynamic monitoring, model-based methods can be used to aggregate common ICU measurements into an easily understandable, physiological form. Other model-based methods have been used to estimate surrogate markers of cardiovascular performance from clinically available data. However, these approaches have only been used to identify a small number of hemodynamic parameters  or only A previously reported model-based method , 20 indiRsys) [Rpul) [In the ICU, some of the most prevalent and deadly cardiovascular disorders are severe sepsis and septic shock , 2 whichRsys) –26, an iThis study used cardiovascular measurements recorded during a porcine trial studying the efficacy of large-pore hemofiltration (LPHF) for treating septic shock . AlthougEscherichia coli serotype 0127:B8; Sigma Chemical, St. Louis, MO, U.S.A) over 30 minutes (from T0 to T30) to initiate sepsis. From T60 onwards the pigs underwent a zero balance continuous venovenous hemofiltration at a rate of 45 mL/kg/h with a 0.7 m2 large-pore (78 Å) membrane with a cut off of 80 kDa  and a BaxterBM 25-BM 14 hemofiltration device . Each animal acted as its own control in this study with baseline measurements taken prior to the endotoxin infusions (T0), representing the undiseased state of the pig. All procedures and protocols used in the porcine experiments were reviewed and approved by the Ethics Committee of the Medical Faculty at the University of Liege (Belgium).Four pure Pietrain pigs weighing 20–30 kg were premedicated, anesthetized, and ventilated as described in , 27. TheThe pulmonary trunk was accessed via medial sternotomy, and a micromanometer-tipped catheter  was inserted into the main pulmonary artery through a stab wound in the RV outflow tract and positioned approximately 2 cm downstream of the pulmonary valve. Aortic pressure was measured using a micromanometer-tipped catheter inserted into the descending thoracic aorta through the right femoral artery. A 7F, 12 electrode conductance micromanometer-tipped catheter , was positioned in each of the left and right ventricles, so that all electrodes were in their respective cavities. Further details on the trials can be found in .Pao, Ppa) and the left and right ventricular pressure and volume waveforms . Continuous waveforms of Pao, Ppa, Plv, Vlv, Prv, and Vrv  were recorded from the four catheters at 30 minutes intervals  between initiation of anaesthesia (T0) and 4 hours subsequent (T240). At each 30 minute interval 6–12 heartbeats of these waveforms were recorded. For each set of waveforms one good heartbeat worth of data was selected. From this selected heartbeat discrete convergence set points for the parameter identification process were extracted. In this study, 34 sets of measurements were extracted from the raw data providing 34 sets of convergence set points from the four pigs. Two data sets were unusable as steady-state measurements could not be obtained.These catheters provided measurements of the aortic and pulmonary pressure waveforms  and flows between the chambers (Q) are defined using parameters of resistance to flow (R), blood inertia (L), and chamber elastance or stiffness (E). Cardiac muscle activation of the ventricles is represented using time varying elastances (driL and driR) which act as the driver functions for the CVS model. The ventricular chambers in the model are actively elastic meaning pressure changes nonlinearly with respect to volume, to model the effects of contraction and relaxation in these chambers. The noncardiac chambers are passively elastic. Thus, pressure is linearly related to volume in these chambers. Flow in and out of the ventricles is controlled via pressure-gated heart valves, and the parallel interaction between the ventricles is modelled through pericardial and septal dynamics. The combination of all these model features and parameters provides a means for accurately depicting ventricular, arterial, and venous blood pressures and volumes, and the blood flow rates through the heart valves, body, and lungs. The full model definition can be found in Hann et al. [The six chamber CVS model represents the left and right ventricles, aorta, pulmonary artery, vena cava, and pulmonary vein, as illustrated schematically in n et al. .Rsys in the CVS model.Please note that this model does not implicitly simulate any autonomic control mechanism. However, such mechanisms can be indirectly simulated through alteration of the model parameters. For example, sympathetic control of the blood pressure could be simulated by increasing the parameter The parameter identification method used for this study was described by Revie et al. , 22. TheLmt = Lav = Ltc = Lpv = 0), as they were found to have negligible effect on the modelled flow. To simplify parameter identification, the six-chamber CVS model was divided into two submodels of the systemic and pulmonary circulations, as shown in The parameter identification process was performed in sequential stages to increase convergence stability. In this process the inertances in the CVS model were ignored (P) that were proportionally related to their corresponding model output were iteratively identified using the ratio of the true measurement to corresponding model output:Rsys, Eao, and Rav, respectively P that weely  were estimated from the derived subject-specific left and right ventricular driver function, (time varying elastances). These driver functions were calculated using the method described by Hann et al. [In this study, global end diastolic volume (GEDV) was assumed to be equal to the sum of the left and right ventricular end diastolic volumes (LVEDV + RVEDV). Stroke volume (SV) was calculated as the average of the amplitudes of the left and right ventricular volume waveforms. The mitral and tricuspid valve closure times . Using one processor the identification method took on average 6 minutes and 24 seconds to identify a subject-specific model of the CVS. Preliminary tests using C programming language, which is better suited for real time applications, have suggested that the identification process time can be reduced by a factor of 20–100, to approximately 4–19 seconds per identified model, which is an acceptable run time in a clinical environment.t-test was used to check temporal variance over T0–T60 to analyse the effect of the endotoxin intervention. P < 0.05 was considered a statistically significant result. The relationship between the modelled and measured maximum left and right ventricular pressures and volumes was analysed statistically using correlation and linear regression analysis, including calculation of absolute percentage errors, and bias and precision analysis . A percd Altman –51.t-tests. Temporal variance was analysed over T0–T60 and T30–T60 because sometimes the symptoms of septic shock do not manifest immediately after the endotoxin infusion at T30. Statistically significant changes (P < 0.05) were seen in the measured systolic and diastolic aortic and pulmonary artery pressures and right ventricular end diastolic volume over the first hour, showing the expected influence of the endotoxin infusion on the circulation. The temporal variance in the measurements of the cohort was analysed between T0–T30, as well as T0–T60 and T30–T60 using paired sample For all the subject-specific models, the model outputs matched the measurements used in the identification process  to percentage errors of less than 0.5%. This result is expected and indicates the convergence of the identification process.Plv,max, Prv,max) to their corresponding measured value. These measurements were not used directly in the identification process, and thus, represent an independent validation of the method. Bias, precision, and correlation metrics were analysed for validation (R2 ≥ 0.76). Modelled left ventricular outputs  had a small negative bias , whereas right ventricular outputs  tended to slightly overestimate  the true measurement. The precision (2 standard deviations) of the LVEDV and RVEDV predictions was 10.2 mL and 10.2 mL and 12.8 mmHg and 12.9 mmHg for Plv,max and Prv,max. All percentage errors were within and acceptable range (<20%), except for the 95th percentile error in the modelled maximum right ventricular pressure. Validation was achieved by comparing model outputs of left and right ventricular end diastolic volumes  and maximum left and right ventricular pressures . These metrics were determined experimentally in [Ees,rvf, and a windkessel model, for Ea. The afterload on the right ventricle in the model is the sum of the resistance of the pulmonary valve and pulmonary vasculature divided by the period of one heartbeat . From Ees,rvf and Ea. Weaker relationships are seen between the modelled and experimentally RVAC metrics . However, the model did capture all the averaged temporal trends for the cohort with R2 = 0.62, 94, and 0.71 for Ees,rvf, Ea, and Ees,rvf /Ea. Note that experimental data was not available for Pig 1 at T0, T30, and T90, and Pig 2 at T150, due to difficulties performing the vena cava occlusion manoeuvre.For further validation, the temporal trends of the identified right ventricular end systolic elastance, pulmonary afterload, and right ventricular arterial coupling (RVAC), from the model, were compared to corresponding metrics . The average modelled LVEDV and RVEDV are shown in Rsys) represents the main component of the afterload on the left ventricle. The average trends for Rsys, along with its pulmonary counterpart, pulmonary vascular resistance (Rpul), can be seen in Rsys after T60, symptomatic of sepsis. Moreover, a steady increase in Rpul after T60 was observed by the subject-specific CVS models, indicating an increase in right ventricle afterload due to the effects of the endotoxin. Again, both these findings are indicative of septic shock [Another main hemodynamic consequence of septic shock is sudden decrease in left ventricle afterload due to a systemic inflammation –26. In tic shock –26.Ees,lvf, Ees,rvf), as shown in Ees,lvf and Ees,rvf stayed relatively constant over the trial. This unexpected outcome may be the consequence of the hemofiltration therapy, which has been shown to improve cardiac function during endotoxic shock in several experimental studies [In the subject-specific CVS models contractility is represented by parameters of left and right ventricular end systolic elastance . Whereas, there are large drops in Rsys with no apparent improvement with treatment in Pigs 3 and 4, indicating the difference response to septic shock between the pigs.Although the averaged results, seen previously, show that the model is capable of identifying the hemodynamic trends of the induced disease, it is also important to know how each subject responds to the disorder and corresponding treatment. Hence, it is essential to analyse the individual time varying trends for each pig, not just the averaged trends of the disease. Plv,max and Prv,max) were identified to an error of less than 20% (except the 95th percentile error in the maximum right ventricular pressure), which is acceptable for this type of physiological monitoring. Furthermore, the time varying trends in these four measurements were identified to a high degree of accuracy, with calculated R2 > 0.90, except for the maximum right ventricular pressure where R2 = 0.76, which is still a very good result. These results suggest that subject-specific models of the CVS could be used to accurately monitor disease-dependent changes in ventricular preload in an intensive care environment. For validation the model was compared to independent measurements that were not used in the identification process, as well as pulmonary hemodynamic indices calculated using an independent method . Almost Ees,rvf and Ea from [Ees,rvf. The primary reason for this bias is because of the assumption in the model identification process that the dead space volume of the right ventricle  is constant and equal to 23 mL, as reported in [Vd,rvf most likely changes as the contractile state of the pigs changes during the trials, but due to the lack of available measurements it cannot be identified. Hence, in the model the changes in contractile state are solely represented by Ees,rvf. Poorer correlations were also noticed for RVAC. The reason for these poor relationships is most likely due to accumulation of errors in calculating this compound metric . These include the addition of error due to measurement noise in the experimental metrics and the addition of modelling errors in the identified parameters. However, a strong temporal relationship for RVAC could be seen of R2 = 0.71 when the effects of measurement noise and modelling error were averaged over the cohort , preload , and contractility . Clinically, the contractile state and preload on the ventricles can be extremely difficult to continuously measure using traditional techniques. Generally, LVEDV and RVEDV can only be estimated intermittently using echocardiography, and there exists no widely accepted clinical measure of contractility. The model-based approach, used in this paper, provides a way to continuously estimate preload and contractility. These model-based metrics could be used to help guide therapy such as using fluids to increase LVEDV and the administering inotropes to control Ees,lvf and Ees,rvf. The accurate and continuous monitoring of preload is especially important in septic shock, as it has been shown that early goal-directed fluid resuscitation can increase patient outcomes [The results show that personalised CVS models can be used to accurately match and predict important hemodynamic markers of afterload  respond positively to the endotoxin insult, due either to autonomous compensatory reflexes or because of the effects of this therapy.From the subject-specific CVS models, identified from this minimal set of measurements, the contrasting reaction of the pigs in response to the induced disease could be seen. Initially, The parameter identification method used in this study is a significant improvement on previous work , 20 espeThe number of parameters that can be identified in the CVS model is limited by the paucity of available measurements in the ICU, and thus, the amount of measurements that were used to identify pig-specific CVS models in this research. In this study only 14 out of a possible 37 parameters of the CVS model were identified. However, these 14 parameters control all the important dynamics in the model, including the contractility of the ventricles, vascular stiffness, and resistance to flow through parts of the CVS. In a clinical setting, these parameters can be controlled using common therapies including the administration of inotropes, vasoactive drugs, and/or fluid resuscitation. The other 23 parameters primarily have lesser effects, such as the inertia of blood through the heart valves, or are relatively constant over the population and are thus modelled as such, enabling accurate relative trend identification. The good validation and diagnostic results obtained in this study support the choice of model parameters to be identified.A further limitation is the use of systemic and vascular resistance as the primary metric for ventricular afterload, although this approach is widely used –61. A moThis study presented a method for identifying pig-specific CVS models of endotoxic shock. These models were able to identify expected disease-dependent changes in ventricular preload, afterload, and contractility in the pigs, using typically available ICU measurements. These indices of cardiac function can be difficult, impossible, or impractical to monitor clinically but are extremely useful to know when guiding cardiovascular therapy. Hence, this model-based approach could potentially be used to help monitor these determinants of cardiac function and assist clinicians with diagnosis and treatment-based decisions.


R2/Q2 plot, receiver operating characteristic curve, variable reduction, and Pearson correlation were performed. RF outperforms the other three classifiers in the given clinical data sets, highlighting its comparative advantages as a suitable classification and biomarker selection tool for clinical metabolomic data analysis.Metabolomic data analysis becomes increasingly challenging when dealing with clinical samples with diverse demographic and genetic backgrounds and various pathological conditions or treatments. Although many classification tools, such as projection to latent structures (PLS), support vector machine (SVM), linear discriminant analysis (LDA), and random forest (RF), have been successfully used in metabolomics, their performance including strengths and limitations in clinical data analysis has not been clear to researchers due to the lack of systematic evaluation of these tools. In this paper we comparatively evaluated the four classifiers, PLS, SVM, LDA, and RF, in the analysis of clinical metabolomic data derived from gas chromatography mass spectrometry platform of healthy subjects and patients diagnosed with colorectal cancer, where cross-validation,  Metabolomics  or metabt-test and analysis of variance (ANOVA) test [To date, the most widely used classification methods in metabolomic data processing include principal component analysis (PCA), projection to latent structures (PLS) analysis, support vector machine (SVM), Linear discriminant analysis (LDA), and univariate statistical analysis such as Student's VA) test , 11. We VA) test , gastricVA) test , knee osVA) test , oral caVA) test , and schVA) test . NeverthA machine learning method, random forest (RF), is reported as an excellent classifier with the following advantages: simple theory, fast speed, stable and insensitive to noise, little or no overfitting, and automatic compensation mechanism on biased sample numbers of groups . RF has R2/Q2 plot was adopted for overfitting measurement. Variable number dependence of the 4 classifiers was explored by eliminating variables step by step. Besides these classification performances, the variable ranking and putative biomarker selection power of RF was examined as well by Pearson correlation.In this research, RF was used in the analysis of a GC-MS derived clinical metabolomic dataset. Its classification and biomarker selection performances were compared with PLS, LDA, and SVM comprehensively. The score plot based on cross validation was used for classification accuracy evaluation. The cross-validation and ROC (receiver operating characteristic) curve were carried out to test their prediction ability and stability. The Colorectal cancer (CRC) is one of the common types of cancer and the leading causes of cancer death in the world . UrinaryThe acquired MS data were pretreated and processed according to our previously published protocols , 7. A toRandom forest (RF), developed by Breiman , is a coRF includes two methods for measuring the importance of a variable or how much it contributes to predictive accuracy. The default method is the Gini score (the method of this study). For any variable, the measure is the increase in prediction error if the values of that variable are permuted across the out-of-bag observations. This measure is computed for every tree, then averaged over the entire ensemble, and divided by the standard deviation over the entire ensemble. Therefore, the larger the Gini score is (ranges from 1 to 100), the more important a variable is.Please refer to the appendices for the introduction of other classifiers .R2/Q2 plot, ROC, and reduction of variable number.The classification performance of RF as well as PLS, LDA, and SVM can be evaluated and compared using several approaches: cross-validation, k-fold and x% hold out were employed to estimate the prediction ability with low bias and low variance. (1) In the k-fold cross-validation, the training set was first divided into k subsets (the folds) of approximately equal size. Sequentially each subset was tested using the classifier trained on the remaining k−1 subsets, where k was set to be 7 and 10 in this study. (2) Holdout cross-validation is similar to k-fold cross-validation except for the repeatedly (100 times) random selection of the two mutually exclusive training and testing (holdout) subsets in accordance with a given ratio. This method was used with an understanding that the more instances left for the holdout set, the higher the bias of the estimate would be. On the other hand, fewer holdout set instances mean that the confidence interval for the accuracy would be wider. Besides accuracy and prediction ability, the repeated holdout cross-validation was used to test the stability of a classifier. The holdout ratios were set as 10%, 15%, and 33%, respectively, on all the classifiers.Two types of cross-validations: Considern represents total number of samples, yi is the predicted class (0 or 1) of the ith sample when all the samples are used for model building, yi).In the equations, R2 and Q2 values on the permuted data set are lower than those on the actual data set. (2) The regression line of Q2  has a negative intercept value on the vertical axis.The criteria for classifier validity are as follows. (1) All the x-axis and sensitivity is plotted on the vertical axis. All the predictive behavior of a classifier can be represented by the points in the ROC curve independent of class distributions or error cost [ROC analysis is a classic method from signal detection theory and is now commonly used in clinical research . ROC of ror cost . The areGenerally, too many irrelevant variables are liable to result in overfitting decisions, whereas differences between groups cannot be extracted and depicted completely if crucial variables are not concerned . VariablTo avoid bias, it is advisable to rank and eliminate variables one by one. Initially, the whole dataset is taken when a classifier is computed. Then, a list of variables in descending order relative to classification importance is established and the variable in the end is eliminated for subsequent analysis. This process is repeated until only one variable is left for classifier building. The last few variables are of great potential to be biomarkers for separating the groups.Prediction ability and stability, overfitting, diagnosis potential, and variable number dependence are important aspects for a classifier. Variable ranking and biomarker selection is of the same importance in metabolomics study.For RF, variables are ranked by Gini score, a measurement of average accuracy of all trees containing a particular variable . For PLSt-test  was taken as an unbiased reference. The consistency comparison was conducted on two levels: ranks of all variables and ranks of identified metabolites. As each classifier possesses its own algorithm for variable importance ranking with its own strength and weakness, the Pearson correlation coefficient of every two ranks was used to evaluate their consistency and the rank of All the metabolites were identified and verified by public libraries such as HMDB, KEGG, and/or reference standards available in our laboratory. All the classifiers andevaluation methods were carried out using Matlab toolbox .x-axis is the sample index and y-axis is the corresponding “score” of every classifier. RF achieved the best separation with no miss-classified samples (the accuracy is 100%). The performance of SVM, LDA, and PLS was good, with descending accuracy of 90.9%, 87.1%, and 82.6%, respectively. Similarly, RF as well as PLS, LDA, and SVM were applied on the dataset for the two comparative cases Figures . In FiguThe accuracy of classification is crucial for a classifier, while other classification behaviors such as prediction ability, stability, degree of overfitting and diagnostic ability are of equal significance as well.y-axis denotes the error rate . The purple box of RF is always the lowest and shortest in both cases. As to the other three classifiers, their performances are similar showing no significant difference. These results were validated further by more cross-validation results listed in The holdout cross-validation results  of RF (purple), PLS (blue), LDA (red), and SVM (green) on the two cases are presented as box plots . The y-ay-variable and the permuted y-variable (x-axis) versus the R2/Q2 values (y-axis) of RF (purple), PLS (blue), LDA (red), and SVM (green) on the two cases, respectively. A dot denotes R2 and a circle represents Q2. The straight line and dash dot line are the regression lines  of R2 and Q2, respectively. Obviously, the R2/Q2 values of RF on the permuted data sets are all under zero and are well lower than those on the actual data set. Consequently, RF does not over fit the data and so will give out reliable result on new samples. As to SVM, its R2 values on the permuted data sets are slightly lower than or close to that on actual data set, although most of its Q2 values on the permuted data sets are under zero and lower than those on the actual data set. Hence, SVM is overfitted and false positive result is prone to appear. This probably caused by its dependence on kernel functions and support vectors.Figures The ROC curve coupled with its area under the curve (AUC) is a common method used to estimate the diagnosis potential of a classifier in clinical applications. A larger AUC indicates higher prediction ability. The ROC curves and AUC values of all the classifiers in the two cases are plotted in y-axis) against the number of variables (x-axis) involved in the two cases, respectively. It can be seen that with the decrease of variable number used for classifier building, all the curves keep stable initially, and then rise gradually. Further reduction of variables degrades the classifier performance heavily because of the shortage of useful information. The point (or short section) where the curve begins to rise correlates to the optimum number of variables for classifier building. Additionally, RF usually needs fewer variables to achieve the same error rate as the other three classifiers. In case (B), for example, when the error rate is restricted to be less than 0.18 (the red line), RF needs minimum 10 variables whereas PLS, LDA, and SVM require about 150, 45, and 125 ones at least.Figures t-Test) based on all variables (A-B) and identified metabolites (C-D) in the two cases are listed in t-Test have good consistency with each other (high Pearson correlation coefficients) regardless of whether all variables  selection. The Pearson correlation matrixes of ranks from every two classifiers (including ariables  or identariables  are invot-Test (case A) and 0.756 for RF and PLS (case B) indicating the consistency and mutual complementarity of classifiers. All the important metabolites selected by both t-Test (P < 0.05) and PLS (VIP > 1) could be filtered by RF (Gini > 50) as well. Consistent with previous metabolomics study, dysregulated metabolic pathways, such as glycolysis, TCA cycle, urea cycle, pyrimidine metabolism, polyamine metabolism as well as gut microbial-host cometabolism were observed [Interestingly, in observed . AdditioR2/Q2 plot, ROC curve, variable elimination, and Pearson correlation. RF demonstrated the best overall performance including accuracy, prediction ability, overfitting, diagnosis potential, stability, and putative biomarker selection, highlighting its potential as an excellent classification and biomarker selection tool for clinical metabolomic data analysis. PLS outperforms the others in variable ranking and putative biomarker selection whereas its classification ability is not satisfactory enough. LDA demonstrates reasonably good performance in classification but its biomarker selection ability is open to question. SVM may be slightly overfitting regardless of its good classification accuracy and diagnosis potential. In this study, RF was applied successfully in metabolomic data analysis for clinical phenotype discrimination and biomarker selection. Its various performances were evaluated and compared with the other three classifiers PLS, SVM, and LDA by two types of cross-validations, t-Test, and PLS, for example, may provide more comprehensive information for a “global” understanding of the metabolomics or other “omics” data.The combinational usage of multiple methods, RF,


A combination of the congenital abnormalities, Müllerian duct aplasia, renal aplasia, and cervicothoracic somite dysplasia, is defined as the MURCS association. Various genetic defects have been described in the MURCS association so far, yet the unambiguous molecular basis of these disorders has not been established. We report the case of an 18-year-old woman who presented with primary amenorrhea, right kidney, Arnold-Chiari malformation, and Klippel-Feil syndrome. In addition, the patient showed the following unusual features: right ovarian and Skenes gland agenesis, cubitus valgus with hyperextension and decreased range of motion at elbows, and facial changes. Moreover, the performed DNA analysis showed interstitial duplication in chromosome 5 (5q35.1). In the duplicated region, there are genes whose function is not well known. It is thought that they have an influence on the early stages of development and their joining in the later period can lead to neoplastic disorders, especially leukemias. MURCS association is the combined occurrence of the following disorders: Müllerian duct aplasia, renal aplasia, and cervicothoracic somite dysplasia. Cervicothoracic somite dysplasia is described in Klippel-Feil syndrome (KFS). Changes in the relations in the base of the neck and head can lead to the development of Arnold-Chiari malformation (ACM). ACM is a congenital malformation of the central nervous system, traditionally defined as downward herniation of the cerebellar tonsils through the foramen magnum . In our case, in addition to the typical features, the following abnormalities are unusual for MURCS association: right ovarian and Skenes gland agenesis, cubitus valgus with hyperextension and decreased range of motion at elbows, and changes in facial appearance –5. MURCSThe 18-year-old patient was admitted to the Gynecological Endocrinology Department because of primary amenorrhea .2, and occipitofrontal head circumference 59 cm (>97th percentile). She was born at term with hypotrophy. The facial features consisted of a frontal bossing, a long and full face, with middle part hypoplasia, and secondary prognathism, depressed and broad nasal root, with long and fleshy nose, long philtrum, thick lips, low-set ear, and large ear lobes. Palate is high and narrow. Neck is broad and short, secondary to cervical vertebral defects, with low hair line. Cervicothoracic vertebral defects  and Sprengel scapular anomaly with probable torticollis in the infantile period, were evident and induced a little face and thoracic asymmetry. Thoracolumbar scoliosis with excessive lumbar lordosis is visible. Trunk is clearly shorter than legs. Valgus elbows with hyperextension and decreased range of motion. Valgus knee, small feet with sandal gap and fifth toe clinodactyly; nails are hypoplastic. Her height was 1.56 m, weight 66 kg, BMI 27 kg/mArnold-Chiari malformation type 1 was diagnosed in the patient in the prenatal period. Klippel-Feil syndrome was additionally diagnosed in the period of childhood.Examination and psychological testing showed average mental development. The patient in addition to defects in the cervical spine also shows abnormalities of the spine in the thoracic and lumbar segments. The patient in childhood was found to have right kidney agenesis and a displaced left kidney on the left side of the bladder with a wide, short ureter. Ureteral plastic surgery was performed at 2 years of age.The physical examination revealed normal external genital and breast development and normal development of axillary and pubic hair. Gynecological examination showed oversized vaginal labia which hid the entrance to the vestibule of the vagina. There was no entrance to the Skenes gland on the right side. There was a complete vaginal and uterus atresia. Lack of the uterus and the right ovary was confirmed by ultrasound and magnetic resonance imaging.In the ultrasound examination, ectopic left kidney located on the left side of the bladder was found. Centrifugally in relation to the left kidney was an ovary with one dominant follicle and several small follicles. Urography confirmed the normal secretory function of the left kidney and lack of a right kidney.Blood levels of ovarian and pituitary hormones were mostly correct. RANBP17, TLX3, NPM1,and FGF18 and another gene MIR3912 , interstitial duplication in chromosome 5 containing 914.2-973.3 kb (genome position HG18: 170405455-171328275). In the critical region by OMIM, there are four genes— MIR3912 . The exaIn the duplicated region, including <1 Mb, there are genes whose function is not sufficiently known. Other anomalies are associated with the duplication of the region 5q. One of them is a Boston type craniosynostosis. In 2005, Hunter et al.  describeThe knowledge about them results mainly from the comparison of conservative regions and observations on animal models. It is thought that they have an influence on the early stages of development and their joining in the later period may lead to neoplastic disorders .Based on the above, on clinical judgment, the MURCS association with unique chromosomal rearrangement—duplication of chromosome 5q35.1—was diagnosed.The MURCS association is remarkably variable and undoubtedly causally heterogeneous. One must verify various chromosomal imbalances, and therefore a chromosome analysis, especially using the array-CGH method, is appropriate in most cases. In addition to the broad variability of MURCS association, diagnosis may also be complicated by the existence of several syndromes with overlapping features.Oppelt et al.  reviewedβ gene [Guerrier et al.  tried toβ gene , 15. ButThe molecular basis of MURCS association remains unknown. However, the molecular analysis of the similar disorder Mayer-Rokitansky-Kuster-Hauser syndrome made by Ledig et al. revealed the candidate genes for this anomaly . In our patient, interstitial duplication in chromosome 5 (5q35.1) was diagnosed. This mutation was not connected with MURCS association previously. In the accessible literature, we found a description of duplication in the region 5q35.1 connected with the occurrence of the developmental disorders holoprosencephaly and preaxial polydactyly . It is important to underline that the technique applied by us does not show which gene has the decisive influence on the phenotype observed in our patient.Moreover, in our patient with the duplication of 5q35.1, gain-of-function genes exist in the critical region , not loss of function as in the above example. The combination of abnormal ovarian development and MURCS association occurs extremely rarely. Al Kaissi et al.  reportedMURCS is a rare congenital disorder, and therefore difficult to diagnose. This case deserves attention because it also presents unusual abnormalities and previously unreported interstitial duplication of chromosome 5q35.1, which may be associated with certain symptoms observed in the described patient.The patient has given consent for the case report to be published.


Bariatric surgery is the only weight-loss treatment available that results in both sustained weight loss and improvements of obesity-related comorbidities. Individuals who meet the eligibility criteria for bariatric surgery are generally older, come from racial or ethnic minorities, are economically disadvantaged, and have low levels of education. However, the population who actually receives bariatric surgery does not reflect the individuals who need it the most. The objective is to conduct a systematic review of the literature exploring the inequities to the access of bariatric surgery.2 or greater; or (2) BMI of 35 kg/m2 or greater with significant weight-related comorbidities. Eligible interventions will include malabsorptive, restrictive, and mixed bariatric procedures.EMBASE and Medline databases will be searched for observational studies that compared at least one of the PROGRESS-PLUS sociodemographic characteristics of patients eligible for bariatric surgery to those who actually received the procedure. Articles published in the year 1980 to present with no language restrictions will be included. For inclusion, studies must only include adults (≥18 years old) who meet National Institutes of Health (NIH) eligibility criteria for bariatric surgery defined as having either (1) a body mass index (BMI) of 40 kg/mThere appears to be inequities in access to bariatric surgery. In order to resolve the health inequity in the treatment of obesity, a synthesis of the literature is needed to explore and identify barriers to accessing bariatric surgery. It is anticipated that the results from this systematic review will have important implications for advancing solutions to minimize inequities in the utilization of bariatric surgery.http://www.crd.york.ac.uk/PROSPERO/display_record.asp?ID=CRD42013004920. Since2. The pre decades. The red decades,7, such  decades. The cur decades,10.2 or greater, or a BMI of 35 kg/m2 or greater with significant weight-related comorbidities. When bariatric surgery is delivered in the appropriate model of care, these procedures are associated with low morbidity and mortality[Bariatric surgery has been documented as being the only treatment available that results in sustained weight loss, leading to significant improvement in obesity-related comorbidities. Indeed,ortality-17 and hortality-20.Despite the evidence supporting the safety, clinical benefits and cost effectiveness of bariatric surgery, uptake of these procedures in eligible patients remains low. Health Place of residence; Race/ethnicity/culture; Occupation; Gender/sex; Religion; Education; Socioeconomic status; Social capital; Plus - additional factors [In an effort to create equity in the access to bariatric surgery for the treatment of obesity, a clear understanding of the apparent disparities is required. The acronym PROGRESS-Plus describes the sociodemographic factors across which disadvantage may exist: le, age),28. ThisRetrospective and prospective cohort studies, case series, case control, and cross-sectional survey studies will be included, irrespective of the blinding employed . To be included, studies must compare study participants on at least one of the PROGRESS-Plus factors.2 or (2) a BMI ≥35 kg/m2 with at least one significant weight-related comorbidity[Adult patients over the age of 18 meeting the NIH criteria for bariatric surgery will be included, irrespective of geographical location. The NIH criteria for bariatric surgery include having: (1) a BMI ≥40 kg/morbidity.The intervention must be a bariatric surgery that may include the following procedure types: Roux-en-Y gastric bypass, sleeve gastrectomy, adjustable gastric band, vertical banded gastroplasty, jejunoileal bypass, biliopancreatic diversion, duodenal switch, mini-gastric bypass, loop gastric bypass, gastric placation, gastric balloon, or scopinaro procedure. Both open and laparoscopic approaches will be considered.Interventions may be either universally implemented or targeted to a specific risk group. The control group must be assigned to receive no intervention, standard care or assigned to a wait-list group.a priori defined as (1) patients who are eligible for bariatric surgery and receive the procedure, and (2) those who are eligible for bariatric surgery but do not receive the procedure.The outcome of interest is the utilization of bariatric surgery. Two groups have been Place of residence will be categorized by the geographical location of residence classified as urban, suburban and rural. Race/ethnicity/culture will be defined by ethnicity with the following categories: British; Eastern European, Western European, Asian, South Asian, Black, Hispanic, Aboriginal, and Other. Occupation will be categorized as being professional, skilled, unskilled and unemployed. Gender/sex will be categorized as being male or female. Religion will be categorized as identifying with Christianity/Catholicism, Judaism, Islam and Other. Education will be categorized by the highest level of education attained . Socioeconomic status will be documented as median and interquartile range (IQR) household income and will be categorized into the following income categories: <$50,000, between 50,000 and $99,000, or ≥ $100,000. Social capital will be defined by family support as being full support, some support or no support. Plus factors will include age and health insurance. Age will be documented as mean interquartile range and categorized into the following age categories: 18 to 19 years, 20 to 39 years, 40 to 59 years, and 60+ years. Health insurance will be defined by the type of insurance, classified as being universal, private or none.The PROGRESS-PLUS sociodemographic factors will be used to explore the factors associated with bariatric surgery delivery. Studies will be indentified through the bibliographic databases of EMBASE, Medline . The finTwo research assistants will review the title, abstraction or description of all trials identified by the literature search. Those studies that aim to explore bariatric surgery among individuals over the age of 18 will be selected for full-text review to determine if they meet inclusion criteria. Any discrepancy will be resolved by a third person.Two researchers will independently extract data from included studies on a prepared data collection form. The data abstraction form will be pilot tested on a random sample of studies to ensure high inter-rater agreement between reviewers. Extracted data will include: study characteristics; primary outcome results; patient risk factors, including BMI, smoking status, mental health status, quality of life, and physical activity level; and details of the surgical intervention. The presence or absence of the following comorbidities will be documented: hypertension, dyslipidemia, diabetes, coronary artery disease, cerebrovascular disease, depression, hypothyroidism, sleep apnea, gastroesophageal reflux, osteoarthrisits, and cholelithiasis.http://www.ohri.ca/programs/clinical_epidemiology/oxford.asp). The NOS has three categories within which risk of study bias can be determined: selection, comparability and outcome. In assessing risk of bias in cohort studies, the NOS awards a ranking for the selection of the cohort, comparability of the cohort and for the assessment of outcomes. In assessing risk of bias in case–control studies, the NOS awards a ranking for the selection of cases and controls, in the comparability of cases and controls and for the ascertainment of the exposure.Two researchers will independently assess the quality of all studies included for review. Any discrepancy will be resolved by a third reviewer. To quantify the degree of bias in the included studies, the Newcastle-Ottawa Scale (NOS) will be used. The NOS2 test or Fisher’s exact test for categorical variables and the Wilcoxon-Mann–Whitney test for continuous variables. Statistical tests will be carried out as 2-tailed tests at α = 0.05. The DerSimonian and Laird method will be used to test heterogeneity of effect sizes between studies. Heterogeneity will be assumed at P <0.05 and I2 ≥25%. Data will be analyzed with SAS .Health inequities in accessing bariatric surgery will be explored using two of the following methods: first, data permitting, metaregression via a multivariate logistic regression using study-level data will be used to explore the PROGRESS-PLUS factors associated with the utilization of bariatric surgery (yes/no). This analysis will allow for the assessment of the impact of any individual covariates on utilization rates, as well as of a potential effect modifier. In addition, differences in PROGRESS-PLUS factors between the surgery and no surgery group within individual studies will be explored. The proportion of study participants categorized within each PROGRESS-PLUS category will be summarized as a percentage with a corresponding 95% confidence interval (CI) for dichotomous and categorical variables, and as median and interquartile range (IQR) for continuous variables. Second, differences in PROGRESS-PLUS factors between groups will be compared using χIrrespective of the presence or absence of heterogeneity the following subgroup analyses on the main outcome will be performed to explore possible effect modifications:1. Participants characteristics2. Intervention type; and3. Study qualityWhile bariatric surgery has been demonstrated to be an effective treatment for obesity, it appears that access to, and uptake of bariatric surgery does not uniformly match with populations with high rates of obesity and obesity-related diseases-25. FactBMI: body mass index; NIH: National Institutes of Health; MeSH: Medical Subject Headings; NOS: Newcastle-Ottawa Scale.The authors declare that they have no competing interests.TJ collaborated in the design of the study and drafted the protocol. ODR conceived the study, collaborated in the design of the study, and edited the protocol. RZ collaborated in the design of the study and edited the protocol. DG collaborated in the design of the study and edited the protocol. JP collaborated in the design of the study and edited the protocol. JIR collaborated in the design of the study and edited the protocol. JS collaborated in the design of the study and edited the protocol. LP developed the search strategy and edited the protocol. LGC collaborated in the design of the study and edited the protocol. All authors read and approved the final manuscript.Literature Search Strategy.Click here for file


Objective. A number of measures of childhood adiposity are in use, but all are relatively imprecise and prone to bias. We constructed an adiposity index (AI) using a number of different measures. Methods. Detailed body composition data on 460 of the Gateshead Millennium Study cohort at the age of 6–8 years were analysed. The AI was calculated using factor analysis on age plus thirteen measures of adiposity and/or size. Correlations between these variables, the AI, and more traditional measures of adiposity in children were investigated. Results. Based on the factor loading sizes, the first component, taken to be the AI, consisted mainly of measures of fat-mass . The second comprised variables measuring frame size, while the third consisted mainly of age. The AI had a high correlation with body mass index (BMI) (rho = 0.81). Conclusions. While BMI is practical for assessing adiposity in children, the AI combines a wider range of data related to adiposity than BMI alone and appears both valid and valuable as a research tool for studies of childhood adiposity. Further research is necessary to investigate the utility of AI for research in other samples of children and also in adults. It is well known that the prevalence of childhood obesity has increased rapidly in most parts of the world . While t2) is widely accepted as a convenient measure of a person's fatness, but it does not separate fat from lean mass and thus provides a screening but not a diagnostic test [By definition, obesity is excess body fatness and should ideally be defined on the basis of a measure of body fatness. However, all gold standard methods of measurement are expensive or invasive so that simpler proxy measures of excess fatness are usually required . Body matic test . This istic test . Skinfoltic test , 9. Bioetic test , 11. Howtic test . Dual-enWhen we set out to assess adiposity in children from the Gateshead Millennium Study (GMS), recognising that no one measure is reliable in terms of assessing childhood adiposity, we collected a number of different measures. However, in order to avoid the use of multiple endpoints and significance tests, we aimed from the outset to combine these into a single “adiposity index” (AI), which allows the addition of other aspects of adiposity measurement, such as skinfolds, within the same measure, thus creating an average value that should be more precise and accurate. This paper describes how this index was constructed and how it relates to known correlates of adiposity and to the most widely used measure, BMI.The GMS is a birth cohort study which initially comprised 1029 infants and their families recruited shortly after birth between June 1999 and May 2000 in Gateshead, an urban district in north east England. Full details of recruitment and measures taken since birth are reported elsewhere , 14. Thez-scores were also created for BMI according to the UK90 standards [All equipment for this study was supplied by Chasmors . All measurements were by project staff trained in the same way to ensure standardisation of measurements across project staff. Children were measured barefoot and in light indoor clothing. Duplicate measurements of all variables except bone frame were taken, and means were calculated and used in analysis. Height was measured to 0.1 cm with a Leicester Portable height measure, and weight was measured to 0.1 kg (Tanita TBF-300MA), from which BMI was derived. International Obesity Taskforce (IOTF) references were used to classify children into four categories  , 16. z-standards .z-scores for waist circumference were created according to the UK 1988 standards [Waist circumference was measured to 0.1 cm at the minimum circumference between the lowest rib and the iliac crest without compressing the skin. tandards , 19. Skitandards –23. Bioetandards . Bone frtandards , 25.z-scores for BMI [One twin per twin pair (chosen at random) was included to ensure independence. Factor analysis, with a promax rotation, on the correlation matrix of logged data was undertaken for age plus the thirteen measures of size and/or adiposity: height, width of shoulders, diameters of elbow, wrist, hip, and knee, waist circumference, skinfold measures of the subscapular, triceps, biceps, and suprailiac, and the impedance-based measures of fat and lean masses expressed as scores. An AI was then constructed using the first component, which was dominated by the adiposity measures. The correlation of this index was explored with each individual measure included in the factor analysis, as well as with BMI. Additional comparisons were made with the IOTF categories , 16 and  for BMI  and wais for BMI , 19. As Complete body composition data were available for 460 children, 227 boys and 233 girls. Descriptive data on all of the variables in the factor analysis are given in A three-component model was found to explain 79% of the overall variance between all the adiposity- and size-related measures included in the factor analysis. On the basis of the size of the loadings , the firThe median AI overall was −0.14  and was lower for boys ) than it was for girls ).r = 0.55 with the second component, the “frame size component,” and r = 0.18 with the third component, the “age component,” P < 0.001 for both). Correlations between the measures included in the factor analysis, BMI, and the created AI are given in P < 0.001 level, although of varying degrees of magnitude. The AI was significantly correlated with all other variables, although with age the correlation was small . Correlations with the AI were greatest for the fat mass variables and BMI and lowest for the variables with the greatest weights in the second component . While age was significantly correlated with the frame size measures and BMI (P < 0.001), it was less significantly so with the subscapular (P = 0.001), triceps (P = 0.03), and suprailiac (P = 0.009) skinfolds and lean mass score (P = 0.04) and not significant for biceps skinfold (P = 0.08) or fat mass score (P = 0.19).The created AI  was significantly correlated with both of the other components (P = 0.0001). The median AI was −0.44  in the healthy weight group, 0.88  in the overweight group, and 2.25  in the obese group. The AI was also highly correlated with both the z-scores created for BMI using the UK90 standards  and those for waist circumferences .According to IOTF reference values, three children (0.7%) in this study were underweight, 348 (76%) were of healthy weight, 77 (17%) were overweight, and 32 (7%) were obese. There was a significant association between the IOTF groups and the created adiposity index  and extra staff to serve as chaperones. As skinfolds can be uncomfortable, children may object to them, particularly to duplicate measures being taken. Waist measures, though practical, can be problematic as many children are “ticklish” and therefore move whilst the measure is being taken or hold their breath/expand their stomach and are unable to “breathe normally” as required for the optimum measurement. Height and weight measurements require less training, are time efficient, and require only an accurate scale and stadiometer which are widely available in most clinical settings and easily transported to field settings. Importantly, height and weight measures are also generally highly acceptable to children. Bioelectrical impedance is a practical and easily obtained method. However, interpretation of the measurements made is more problematic as values reported by the machines used rely on standard equations not necessarily valid for children. Further, good bioelectrical impedance measures depend on factors such as whether the child's bladder is empty, if they are normally hydrated, which can be particularly an issue for early morning measurements, and unclean feet may interfere with conductivity.McCarthy et al.  recommenOlds and colleagues conducted a meta-analysis of young people (under 19 years old) looking at secular trends in fatness using skinfold thicknesses. They showed significant increases in skinfold thicknesses, in line with the increases in childhood obesity as measured by other techniques . They reAn AI developed using a combination of thirteen different measures of body size and composition is, reassuringly, best explained by measures that would be thought to measure childhood fat mass. While BMI is a highly practical tool for screening and assessment of adiposity in children of this age group, the AI combines a wider range of data related to adiposity than BMI alone, and using this methodology appears to be both valid and valuable as a research tool for studies of childhood adiposity, although; given the limitations of some of the components, its validity may vary for different childhood age groups. Further research is necessary to investigate the utility of AI for research in other samples of children and also in adults.


Cardiovascular disease is the number one cause of death in the United States and in most European countries. Cardiovascular health, as defined by the American Heart Association, is comprised of seven health metrics . No studies have compared US data with data collected elsewhere, using this index of cardiovascular healthWe performed comparative analyses of cardiovascular health status in participants from 2 study sites in 2 different countries: the Maine-Syracuse Study, conducted in Central New York, USA in 2001–2006 (n = 673), and the Observation of Cardiovascular Risk Factors in Luxembourg, conducted in 2007–2009 (n = 1145).The Cardiovascular Health Score, the sum of the total number of metrics at ideal levels, was higher in the Luxembourg site than in the Central New York site. Ideal cardiovascular health levels for body mass index, smoking, physical activity, and diet were more prevalent in the Luxembourg site than the Central New York site. Ideal levels for blood pressure were more prevalent in Central New York. Differences between the two sites remained with control for age, gender and socioeconomic indicators.Cardiovascular health, as indexed by seven health metrics, was higher in the European study site than in the US study site. The largest differences were for the four lifestyle/behavior metrics, namely body mass index, smoking, physical activity, and diet. Preventative and intervention strategies will continue to be important for both countries in order to improve cardiovascular health. Furtherr health ,6, and cr health ,5,7,8 haIn order to make a cross-national comparison, we used data collected from two prominent studies with similar data on CVD risk factors, health behaviors, and demographic variables: the Maine-Syracuse Longitudinal Study (MSLS) in the United States, and the Observation of Cardiovascular Risk Factors in Luxembourg (ORISCAV-LUX). The MSLS was conducted in Syracuse, New York (NY), USA and its catchment area . ORISCAV-LUX was a nation-wide, population-based study. Several considerations make these specific comparisons important and meaningful. First, existing multinational data collection efforts  do not measure key aspects of cardiovascular health  and we know of no nationally representative data set that permit comparisons on these indices. Second, the MSLS and ORISCAV-LUX collect measures in essentially identical fashions, and both samples are representative of their respective geographic areas. Data collected from Luxembourg are of particular interest due to the multinational nature of its population, with a large number of individuals originating from neighboring European countries living in Luxembourg. As in the US, CVD is the number one cause of mortality in Luxembourg, with CVD related illnesses accounting for 33.8% of all deaths in 2011 .Our overall objective was to compare the pattern of cardiovascular health between two geographically and culturally distinct sites in the US and in Luxembourg. Specifically, the first objective was to compare the sites with respect to the total number of health metrics at ideal levels, indexed by a global Cardiovascular Health Score (CHS), derived from the AHA construct of cardiovascular health. The second objective was to examine the prevalence of poor, intermediate, and ideal health for each health behavior , and health factor  at each site. The third objective was to assess whether any observed differences in the cardiovascular health components between the two study sites remained after controlling for socioeconomic indicators. As European countries have higher rates of walking, cycling, and active transportation than in the United States , and a nThe MSLS is a community-based study of aging, cardiovascular risk factors and cognitive functioning in adults, aged 23–98 years -19. At iORISCAV-LUX, a nationwide, cross-sectional study conducted in 2007–2009, was designed to gather information on the prevalence of cardiovascular risk factors among the adult population of Luxembourg. Exclusions were institutionalized people (n = 12), pregnancy n = 21), serious mental and/or physical handicap (n = 5), prisoners (n = 1), people outside the determined age range (n = 2) and those deceased before recruitment (n = 5) , serious. After eIn both studies only participants aged 30–69 were included in order to compare two age-homogeneous samples. The final sample comprised 1818 individuals (673 from MSLS and 1145 from ORISCAV-LUX). Further details related to the methods of sampling for both studies appear elsewhere ,21,22. AParticipants at both study sites underwent physical and anthropometric measurements, blood tests, and completed self-administered questionnaires to gain information on demographic and socioeconomic characteristics. Standardized protocols for data collection were used. Body weight, height, BMI and BP measures were assessed as described previously for both studies ,19,21-23In the MSLS, physical activity was measured with the Nurses’ Health Study (NHS) Activity Questionnaire . DietaryIn ORISCAV-LUX, physical activity was measured using the short format International Physical Activity Questionnaire (IPAQ) . DetaileFor the diet metric, two food scores were calculated for each study, a Recommended Food Score (RFS) , and a nThe non-RFS  includedPoor, intermediate, and ideal health levels for smoking, BMI, physical activity, total cholesterol, BP, and fasting plasma glucose were calculated using the AHA definitions   to 8 . This global score was then categorized into low , medium (3–5) or high (6–8).According to the type of variable , independent samples t-tests and Chi-square tests were used to compare demographic variables, mean scores of the cardiovascular health metrics and other health variables in the two samples .The proportion of each sample in the poor, intermediate and ideal categories for each cardiovascular health metric were calculated. As prevalence estimates are strongly gender and age-dependent , prevalence comparisons between populations may be misleading if the underlying age and/or gender composition differs in the populations being compared . To overPrevalence of each health category  for each health metric in the two study sites were compared by computing the comparative morbidity ratio and testing it for statistical significance . This waGeneral linear models were used to examine associations between study site and each cardiovascular health component (as continuous variables). Statistical adjustment was made for age, gender, education and income. Interactions of study site with gender and age were not significant. SPSS version 21 and SAS (version 6.1) were used for all analyses. P values of <0.05 were considered statistically significant.Sensitivity analyses were performed excluding the minority populations in each sample (n = 52 in ORISCAV-LUX and n = 65 in MSLS) and excluding the persons residing in states other than NY at the time of wave 6 MSLS (10%). The results were unchanged (data not shown).Table As shown in Table The observed differences in BMI, physical activity and diet (RFS and non-RFS) remained statistically significant with the additional adjustment for age, gender, education, and income  Table . Weekly Fasting plasma glucose was significantly higher in the Central NY sample than in the Luxembourg sample p < 0.001), while mean systolic and diastolic BP were both significantly higher in Luxembourg (both p < 0.001)  was significantly higher at the Luxembourg site 4.2) than at the Central NY site (3.8)  are shown in Table Poor health levels for BMI and diet (RFS and non-RFS) were statistically significantly higher at the Central NY site than at the Luxembourg site . Prevalence of poor levels for BP and total cholesterol were higher in Luxembourg than in Central NY (both p < 0.001).Figure The present study is, to our knowledge, the first to make cross-national comparisons using the AHA-defined components of cardiovascular health. Although we are unable to generalize beyond the two geographic study sites in Central NY, USA and Luxembourg, the study provides insight into how cardiovascular health differs between two sites in the US and Europe. Luxembourg, a centrally located European country with a large proportion of the population coming from Portugal and neighboring countries including Belgium, Germany and France, serves as a good representation of western Europe. Syracuse and its surrounding counties are ethnically diverse, and constitute the economic and educational hub of Central NY state.The overall CHS, generated from the individual health metrics, was higher at the Luxembourg site than at the Central NY site. Ideal levels for BMI, smoking, physical activity, and diet were more prevalent in Luxembourg than in Central NY. However, ideal levels for BP were more prevalent in Central NY. Differences between the two sites with respect to BMI, physical activity, diet and BP cannot be attributed to age, gender, income and education as findings remained after adjustment for these potential confounders.Importantly, the prevalence of overall ideal cardiovascular health  was low at both the Central NY site 0.4%) and the Luxembourg site 1.0%). This is consistent with smaller state-based studies and national data in the USA, with prevalences ranging from 0–0.1% .0%. This,5 to 1.2% and theThe role of physical activity in weight control is well established. While the age-adjusted proportion of participants not engaging in any physical activity was similar in both study sites 10-12%), the mean time spent engaging in physical activity per week was over two times higher in the Luxembourg site than Central NY, equating to a difference of approximately 6.5 hours per week. Adjusting for BMI, waist circumference and waist/hip ratio in secondary analyses did not change the results (data not shown). This is consistent with data showing that Europeans walk and cycle over two and four times, respectively, the number of kilometres per person per year, than residents of the United States -12%, the. As per In contrast to these findings, average age and gender-adjusted BP was 133/84 mmHg in the Luxembourg sample compared with 123/70 mmHg in the Central NY sample. Epidemiological research utilizing national survey data from six European countries, the US and Canada, also found higher BP levels in Europe, and reported very similar levels to those in the present study: 136/83 mmHg in Europe and 127/77 mmHg in North America . Of noteIn addition, poor awareness of relatively ‘silent’, asymptomatic cardiovascular risk factors including hypertension and dyslipidemia has been demonstrated in this Luxembourg population, with 60% unaware of their diagnosed hypertension (diagnosis made from the ORISCAV-LUX survey) . This leThere are several study limitations. ORISCAV-LUX was a community, national population-based study, whereas MSLS was a community-based sample restricted to Central NY. The MSLS sample is not nationally representative, but both Luxembourg and the Syracuse metropolitan statistical area (MSA) have similar population sizes of slightly more than half a million inhabitants. In 2008, Central NY state had an almost identical age-adjusted CVD mortality estimate as the US (244.0 versus 244.8 per 100,000 deaths ,43. In OThere are several study strengths. This is the first study to compare three levels of cardiovascular health in two studies from two different countries. We are not aware of published data using ideal cardiovascular health in countries outside of the USA. To compare prevalence estimates across these two samples with different age compositions, the effects of variation in age structure were removed by using a ‘world’ standard population to standardize age . The preThe purpose of our study was solely to describe the epidemiological patterns of cardiovascular health in these two study sites. The contrasts found are of substantial magnitude, however the findings neither provide causal explanations nor effectiveness data on the health care system in either region.The main finding of note in the present study is in the recognition that the majority of participants at both study sites had overall cardiovascular health scores that fell within the intermediate range, but the overall CHS was higher in Luxembourg. The differences found for the BP, diet, and physical activity metrics are particularly notable. As both Luxembourg and the New York State Department of Health have implemented public health policies to promote and maintain population cardiovascular health ,45, it iHowever the current findings suggest that different strategies for intervention will be important for different countries and underscore the need for cross-national comparisons. The continued improvement of education programs and focus on prevention measures may be helpful in both countries. Infrastructure to support active means of transportation may be an important consideration. Regardless of the approaches taken to achieve CVD reduction, even small health behavior changes at a population level would produce relatively large increases in the proportion of individuals in both ideal and intermediate categories . In a siAHA: American Heart Association; BMI: Body mass index; CHS: Cardiovascular Health Score; CRP: C-reactive protein; CVD: Cardiovascular disease; FFQ: Food frequency questionnaire; IPAQ: International Physical Activity Questionnaire; MSLS: Maine-Syracuse Longitudinal Study; non-RFS: non-Recommended Food Score; NY: New York; ORISCAV-LUX: Observation of Cardiovascular Risk Factors in Luxembourg; RFS: Recommended Food Score.The authors declare that they have no competing interests.GC: conceptualization of the present study, data analyses and interpretation, manuscript drafting. ME: MSLS chief investigator, manuscript drafting and revision. NS: statistical analyses and interpretation, critical review of manuscript. AD: critical review and drafting of manuscript. JB, CD: ORISCAV-LUX investigators, critical review of manuscript. AA: ORISCAV-LUX investigator, study design, data analyses and interpretation, critical review of manuscript. All authors read and approved the final manuscript.The pre-publication history for this paper can be accessed here:http://www.biomedcentral.com/1471-2458/14/253/prepub


Objective: The aim of this study was to evaluate the effects of using ACE inhibitors on insulin resistance, glucose metabolism, body fat composition, and lipid profile in children over 10 years of age with obesity-associated metabolic syndrome (MS).Methods: A total of 53 children with MS, who had been followed for at least one year were included in the study. The sample was divided into two groups: Group 1-30 obese children  who were not using an ACE inhibitor and Group 2-23 obese children  who were using an ACE inhibitor. Anthropometric and laboratory dataobtained at baseline and at the 3rd, 6th, and 12th months of follow-up were compared in the two groups.Results: Comparison of the data in the two groups at 3rd, 6th, and 12th months revealed no statistically significant differences in terms of weight standard deviation score (SDS), body mass index SDS, weight for height percentile, body fat percentage, and very low-density lipoprotein (VLDL)values. However, there were statistically significant differences in mean glucose and insulin levels, homeostasis model assessment for insulin resistance, LDL and high-density lipoprotein values, and highly significant differences in mean triglyceride values.Conclusions: The positive effects of ACE inhibitor drugs, particularly on hypertriglyceridemia and insulin resistance, might bring them forth as first-line drugs in the treatment of obese and hypertensive children. Randomized, controlled, double-blind, and long-term studies are needed for a definitive conclusion. Conflict of interest:None declared. The worldwide prevalence of childhood obesity has increased greatly over the past 3 decades, and this obesity epidemic is believed to lead to an increasing occurrence of some disorders [e.g. type 2 diabetes mellitus (T2DM)] in children ,2.Some individuals are genetically predisposed to insulin resistance. In these individuals, factors such as irregular lifestyles, physical inactivity, unbalanced, and excessive nutrition trigger the development of insulin resistance, a state which ultimately leads to development of the metabolic syndrome (MS) . ComponeObesity plays the most important role in the pathophysiology of the MS, a condition which is accompanied by hyperinsulinism/insulin resistance, hypertension, and hyperlipidemia. Recent studies with children and adolescents have shown that the atherosclerotic process begins at an early age and that it is associated with obesity and other components of the MS .The prevalence of MS varies depending on the criteria set forth for the syndrome, and also on the weight and age group of the subjects. Cook et al  reportedThere are a number of studies showing a relationship between ACE inhibitors and carbohydrate and lipid metabolism; however, some of the results remain controversial ,9. The pA total of 53 hypertensive or normotensive children with MS who had been followed up at Sisli Etfal Training and Research Hospital Pediatric Endocrinology outpatient clinic for at least one year were included in the study and were evaluated retrospectively. Thirty of these patients (Group 1) were not using and 23 (Group 2) were using ACE inhibitors. Group 1 consisted of 13 female (43.3%) and 17 male (56.7%) children with a mean age of 13.85±1.67 years (distribution: 11.40 - 17.20 years), and Group 2 consisted of 13 female (56.5%) and 10 (43.5%) male children with a mean age of 14.21±1.66 years (distribution: 11.50-17.70 years). The children in Group 2 had been followed up for essential hypertension and were started on treatment with ACE inhibitors  due to the inability to control their hypertension with exercise and diet.Children under the age of 10 years, those with MS who were on medication for T2DM, those with pathological findings in thyroid function tests, with additional chronic diseases, and patients with secondary hypertension associated with any other reason were not included in the study.The criteria set for a diagnosis of MS in children in the current study were :• Body mass index (BMI) (according to age and sex): z-score≥ 2• Hypertension: diastolic and/or systolic blood pressure>95th percentile• Triglyceride level : >95th percentile• High-density lipoprotein (HDL) cholesterol level: <5thpercentile• Fasting glucose level: ≥ 110mg/dLPatients who met at least 3 out of the 5 criteria listed above were accepted to have MS.After obtaining their detailed medical histories, a physical examination including blood pressure measurements, weight and height measurements was performed in all patients. The standard deviation scores (SDS) for body mass index were calculated and percentile values for weight were estimated. Bioimpedance measurements were performed for body fat analysis while the patients were in a fasting state. Fasting glucose and insulin levels, homeostasis model assessment for insulin resistance (HOMA-IR) indices [fasting insulin (mU/L) x fasting glucose (mg/dL) / 405], lipid profiles, and thyroid, liver and kidney functions were also monitored. Adrenocorticotropic hormone and cortisol level measurements were requested only in cases with suspicion for Cushing’s syndrome.Oral glucose tolerance test was performed in children with fasting blood glucose levels of 100-125 mg/dL, along with hemoglobin A1c (HbA1c) tests. Secondary causes of hypertension, such as renal and cardiac pathologies, were tried to be eliminated in children with blood pressures values above 140/90 mmHg (or >95th percentile according to age). Antihypertensive treatment was not immediately started in patients with hypertension caused by undetermined secondary causes. Instead, these patients were advised to diet and exercise regularly while restricting salt intake. Patients who still had high blood pressure after following these recommendations were started on ACE inhibitors.Instead of providing a strict diet regimen for the treatment of obesity, the children and their families were informed about healthy nutrition, including the restriction of candies and fast or junk food, and were encouraged to increase physical activity.Patient follow-ups were planned with intervals of three months. Laboratory and USG tests were repeated depending on the patients’ conditions at follow-up visits.The findings at the initial (0), 3rd, 6th, and 12th month follow-up visits were compared in terms of sex, age, weight SDS, BMI SDS, body fat percentage, weight for height percentile, fasting insulin and glucose levels, HOMA-IR indices, and lipid profiles.Statistical AnalysisThe statistical analyses were performed using the Statistical Package for the Social Sciences  version 13.0. Descriptive statistics were presented as mean ± standard deviation (SD) values. For the analyses of the groups, Student’s t-test (independent sample t-test) was used for homogeneous distribution, and Mann-Whitney U-test was used otherwise. Pearson’s correlation analysis was used for correlation analyses with “r” as the coefficient. Results were evaluated with a 95% confidence interval and a significance level of 0.05 (p < 0.05). For the evaluation of the repeated tests within each group, analysis of variance (ANOVA) was used.There were no significant differences between the groups in terms of initial parameters, which were sex, age, weight SDS, BMI SDS, body fat percentage, weight for height percentile, fasting insulin and glucose levels, HOMA-IR indices, and lipid profiles  and 2.Anthropometric comparison of the groups at 3rd, 6th, and 12th months revealed no statistically significant differences in terms of weight SDS, BMI SDS, and weight for height percentile . HoweverThe aim of the current study was to evaluate the effects of using ACE inhibitors on MS components. To our knowledge, there were no previous publications investigating the effects of the use of ACE inhibitors or ARBs on glucose metabolism in children at the time of the present study. The relationship between ACE inhibitors and glucose metabolism was first noticed in case reports showing that ACE inhibitors could cause hypoglycemic events in diabetic adult patients using insulin ,11. ThesThe case reports regarding the use of ACE inhibitors in diabetic patients were followed by several case-control studies which documented the relationship between ACE inhibitior and hypoglycemia ,9,12. InInsulin resistance and visceral fat accumulation are the main characteristics of the MS. Insulin resistance is detected in nearly 50% of hypertensive patients, and as a result of insulin resistance, hyperinsulinemia contributes to the elevation of blood pressure by promoting sympathetic nervous system and renin-angiotensin activities .Some studies have shown the effectiveness of renin-angiotensin blockade in weight loss. In an in vivo study, telmisartan was shown to be effective in preventing weight gain and increasing insulin sensitivity in rats made obese through diet . TelmisaIn a meta-analysis of 18 studies evaluating thousands of patients in total, it was concluded that ACE inhibitors decelerate progression to diabetes . In the Recently, certain ARBs have been determined to exert their effects on insulin resistance through a mechanism - independent of this effect - other than RAS inhibitior; this mechanism is the partial peroxisome proliferator-activated receptor (PPAR) gamma activation. It was shown that, among ARBs, the PPAR-gamma stimulation was present only in telmisartan at therapeutic concentrations . In two In a placebo-controlled, randomized, prospective study by Nagel et al , hyperteThe effects of antihypertensive agents on MS components are variable. Diuretics and beta blockers are known to have positive effects in the prevention of diabetes progression . In contMore studies are needed in order to recommend the use of ACE inhibitors and ARBs as first-line drugs in the treatment of MS. Clinical studies that will provide further evidence on the metabolic benefits of these drugs are still ongoing in adults, but to our knowledge, there are currently no ongoing studies on the metabolic effects of these drugs in children. To our knowledge, the present study was the first one to present the effects of ACE inhibitors on the components of MS in childhood. However, one limitation of the current study was that the data were collected retrospectively. Another limitation was that no obese children using an antihypertensive drug other than an ACE inhibitor were included in the study. Furthermore, the study did not include any data on the patients’ daily calorie intakes, nutritional habits and exercise status. Another limitation of the present study was the smallness of the sample, which was not adequate for a power statistical analysis, and sorting of the groups by gender and Tanner staging was not attempted. This was the major limitation of the present study. Nevertheless, we were able to show the benefits of using ACE inhibitors on the most important criteria of MS, namely, hypertriglyceridemia and insulin resistance. Randomized, controlled, double-blind, and long-term studies are needed for a definitive conclusion on the use of ACE inhibitors as first-line drugs in the treatment of obese and hypertensive children.