Biblioteca Virtual emSaúde . The descriptors used were:'obstructive sleep apnea' AND 'polysomnography' AND 'myocardial infarction' AND'adults NOT 'treatment.' The present work analysed three prospective studies,selected from 142 articles. The studies followed a total sample of 5,067 OSApatients, mostly composed by male participants. All patients underwent nightpolysomnography, and all studies found an association between OSA and fatal andnon-fatal cardiovascular outcomes. Thus, we were able to observe that 644(12.7%) of the 5,067 patients suffered MI or stroke, or required arevascularization procedure, and 25.6% of these cardiovascular events werefatal. MI was responsible for 29.5% of all 644 analysed outcomes. There is anassociation between OSA and MI, in male patients, and apnea and hypopnea index(AHI) are the most reliable markers.Obstructive sleep apnea (OSA) has been associated to cardiovascular risk factors.However, the association between OSA and cardiovascular disease is stillcontroversial. The objective of the present study was to verify the associationbetween OSA and myocardial infarction (MI). This is a systematic review of theliterature performed through electronic data sources MEDLINE/PubMed, PubMedCentral, Web of Science and BVS - The search was performed in the electronicdata sources MEDLINE/PubMed, PubMed Central® (PMC), Web of Science andBVS, through a combination of descriptors, including the terms of MedicalSubject Heading (MeSH) and Health Sciences Descriptors (DeCS). The descriptorschosen to be used together were: "obstructive sleep apnea" AND "polysomnography"AND "myocardial infarction" AND "adults" NOT "treatment". OR "obstructive sleepapnea"[All Fields] OR("obstructive"[All Fields] AND"sleep"[All Fields] AND"apnea"[All Fields])) AND("polysomnography"[MeSH Terms] OR"polysomnography"[All Fields])) AND OR"myocardial infarction"[All Fields])) AND("adult"[MeSH Terms] OR "adult"[AllFields] OR "adults"[AllFields]))The search for descriptors on MEDLINE/PubMed yielded: After that, we did a manual search through selected articles.We included all cohort works found in the databases, with humans over 18 years ofage, published in the last 10 years, in Portuguese, English and Spanish, withOSA diagnosis through polysomnography, with MI as one of the analysed outcomes.We excluded those in which 100% of the patients were under treatment for sleepdisturbances (SD), all groups of treated apneic individuals, pregnant patients,those with other SDs, neurological or psychiatric diseases, and studies in which100% of the population had previous coronary artery disease (CAD). We alsoexcluded works whose population was approached in more than one study and thatalso had similar outcomes. In those cases, we considered the first work.Two independent researchers read the titles and abstracts of each pre-selectedwork, separately identifying articles that met the inclusion and exclusioncriteria. After this stage, each researcher read the complete articles thatrespected the criteria exposed in the abstract and selected only thosecompatible to the systematic review criteria. When there was doubt, a thirdresearchers would be called in, but there was no disagreement between the firsttwo researchers in this study.Two researchers were responsible for the data collection. Characteristicsextracted from the studies were: title, authors, year of publication, sciencejournal where it was published, publication medium, key-words, geographicalorigin, study design, sample size, supervision, financing, methods, researchtime, OSA diagnosis criterion, other results of the research, and conclusions.Moreover, participants' characteristics of each work were registered: number,gender, age, use of medication, comorbidities, number of patients who suffered aMI, and who received an OSA diagnosis, as well as the apnea and hypopnea index(AHI).9 The selectedarticles were evaluated as having fulfilled each item completely or partially,or not at all. Articles considered as having acceptable quality were those thatsatisfactorily contemplated at least 11 aspects. In case of a disagreementbetween the two researchers, a third researcher would have been called in toassess the article, but that was not necessary. This systematic review alsofollowed the recommendations of Preferred Reporting Items for Systematic Reviewsand Meta-Analyses (PRISMA)10and the step-by-step suggested by Cochrane Handbook,11 produced by The Cochrane Collaboration.Two researchers read the articles, and each of them filled out a check list basedon Strengthening the Reporting of Observational Studies in Epidemiology(STROBE).The present systematic review gathered 76 articles through the search strategyoutlined in electronic databases. Four of these articles were repeated in morethan one source, as were two articles of the 66 found in the manual selection.Thus, from the 148 articles found, we counted 142 .9 In the study by Buchner etal.8, 72.7% of theaspects were satisfactorily fulfilled; the one by Marin et al.6 were at 77.3%, and Gottlieb etal. reached the highest percentage (95.4%).After complete reading of the selected works, we observed that all the articlessatisfactorily fulfilled at least 16 aspects of the check-list.6 The Sleep Heart Health Study (SHHS), was to evaluate therelation between OSA, CAD incidence, and heart failure in a sample of thegeneral community of men and women. The study included patients at 40 years ofage or older, recruited among participants of population base studies aboutcardiovascular and pulmonary diseases, including Atherosclerosis Risk inCommunities Study, Cardiovascular Health Study, Framingham Heart Study, StrongHeart Study, Tucson Health and Environment Study, and The New YorkUniversity-Cornell Worksite and Hypertension Study. We excluded individualswhose polysomnography was inconclusive, those under treatment for OSA, with lowquality data, previous CAD or heart failure, without follow-up data, orincomplete data on body mass index (BMI), smoking, blood pressure and use ofmedications that refused to participate in the study.The objective of the work by Gottlieb et al.,6 analyzed a total sample of4,422 patients, including 2,434 who had AHI < 5. We also observed that 5.5%of 3,794 patients were diagnosed with OSA five years after the beginning of thestudy, and of those, 2.1% referred treatment for OSA, but were excluded withoutsignificantly altering the results. In this study, 43.3% of the population wasmale. Among the patients from the sample AHI ≥ 5, the male populationrepresented 55.23% of the sample.SHHS7 was to compare the incidence of fatal and non-fatalcardiovascular events in simple snorers, untreated OSA patients, patients withcontinuous positive airway pressure (CPAP) and healthy men recruited among thegeneral population. In this study, the sample included only men with OSA orsimple snoring from sleep clinics, and a population base sample of healthy menmatched by age and BMI with severe apneic patients, between January 1992 andDecember 1994. The healthy men were recruited from the database of ZaragozaSleep Apnea Study. The objective proposed by Marin et al.8 prospectivelyinvestigated cardiovascular outcomes in treated versus non-treated OSA patients.For this study, recruitment included all patients with suspected sleep-relatedobstructive respiratory disturbances, admitted in a sleep clinic throughnon-selected referral by primary or secondary care physicians between 1993 and1998. Snorers without apnea and patients with central sleep apnea, Cheyne-Stokesbreathing, hypoventilation syndromes or periodic limb movement during sleep wereexcluded from the analysis. The patient sample in this study was predominantlymale - 85.5% of a total of 449, and 83.5% were untreated apneic individuals.Buchner et al.6 evaluated OSA impact on theappearance of CAD and, thus, none of the participants received OSAtreatment.Mean follow-up among the three studies was 8.2 ± 0.99 years. The number ofpatients varied between 449 and 4,422. Only Gottlieb et al.8 Regarding SAH and dyslipidemia, Gottlieb et al.6 reported only the number ofpatients on antihypertensives or lipid-lowering drugs, and Marin etal.7 registered onlythe total number of SAH and dyslipidemia patients. On the other hand, Buchner etal.8 reported theinitial number of SAH patients from those who started follow-up withantihypertensives, and the number of patients, at the end of follow-up, undertreatment for this pathology; the same happened for cases of dyslipidemia.Comorbidities and risk factors common to the three studies were systemic arterialhypertension (SAH), diabetes mellitus (DM), dyslipidemia, and smoking. However,only DM and smoking were related in the same way by all three studies (number ofDM patients and smokers). Treatment with insulin or oral antidiabetic drugs wasmentioned only by Buchner et al.6 Marin etal.,7 and Buchner etal.,8 for the number ofpatients with AHI ≥ 5 and SAH or, in the case of SHHA,6 the use of antihypertensives(39.2% vs 28.5% vs 69.4%)/ dyslipidemia or, in the case of SHHS,6 the use of hypolipidemic drugs(7.5% vs 10.1% vs 57.6%). Gottlieb et al.6 reported the percentage of patients who were onantihypertensives and hypolipidemic drugs, and, therefore, patients withuntreated dyslipidemia or SAH were not registered. Marin et al.7 did not report the number ofpatients under treatment  were diagnosed with mild-moderateOSA.8Buchner et al.,12The present systematic literature review analysed three prospective works thatfollowed a total sample of 5,067 patients, between men and women, of which 53.5% haddifferent degrees of untreated OSA diagnosed by polysomnography. All the studiesfound an association between OSA and fatal and non-fatal cardiovascular outcomes. Itwas observed that 644 (12.7%) of the 5,067 patients suffered MI or stroke orrequired a revascularization procedure, and 25.6% of these events were fatal. Thisis a relevant number considering the main cause of death and disability, in Braziland in the world, is constituted by cardiovascular diseases.13 (AHA), one in every seven deaths in the USA are caused bycardiac diseases - every 34 seconds, an American has a coronary event, and everyminute and 24 seconds, there is a death from MI.13 Thus, the data of the present study are in agreement withthe current literature, since 190 MI cases were counted in the analyzed group(3.75%). However, this final number may be even higher, since we did not considerthe percentage of the group studied by Marin et al.,7 because there was no information on how manypatients had MIs. These authors only reported that the type and frequency of thedifferent outcomes did not differ between the studied groups.7According to the American Heart Association18 Shah et al.4 concluded that OSA increases the risk of MI,revascularization procedures and cardiovascular death, regardless of risk factors,such as SAH, in patients over 50 years of age. However, this cohort did not excludepatients who received treatment for OSA during 2.9 years of follow-up.4 With base on evidence that treatmentwith CPAP decreases the risk of fatal and non-fatal cardiovascular events,19 Shah etal.4 said that the studydesign did not allow them to work with adhesion to treatment and/or treatmenteffects, and such finding would polarize the results to zero. Nevertheless, in thisstudy, 86 patients (6.1%) had some of the outcomes - 74 had OSA and, of those, 21had MI, as well as 33 cases registered as cardiovascular death.4Some studies demonstrated an association between MI and OSA. 2 ina study whose objective was to determine if OSA independently increases the risk ofcoronary events, concluded that AHI was associated to composite cardiovascularoutcome in a univariate analysis, but not in a multivariate analysis. The argumentused to explain this finding was that, possibly, studies with large community basesmay not include important predictors related to OSA, or may selectively relatesubgroup analyses conclusions. Kendzerska et al.2 considered the history referred by the patients, such assmoking, MI, myocardial revascularization surgery, stroke, SAH and/or pulmonarydisease. With the justification that recovery by CPAP was not associated to the riskof an event, the patients who needed treatment were not excluded, and in theanalysis of the untreated patients, in relation to the complete sample, allpredictors remained significantly associated to the outcome, except for daytimedrowsiness.2In contrast, Kendzerska et al.,6 excluded, at thebeginning of the study, patients with previous CAD or heart failure, while the othertwo studies8 included and registered these cases. Severalfactors, including the strict relation between obesity and OSA, make it difficult tounderstand the effect of each pathology and the synergy between them.14 Moreover, multiple comorbiditiesare present in OSA patients with metabolic syndrome, DM, and cardiovascular diseaseitself - a situation that creates the challenge of explaining if secondaryabnormalities are caused by OSA or other pre-existing conditions.14Regarding the inclusion of cardiovascular risk factors, the only comorbiditiesequally studied by the authors of the works, in this systematic review, were DM andsmoking. Age, BMI, and AHI were also mentioned by all the authors. Only Gottlieb etal.2 and Shah etal.4 also includedpotential factors of confusion in their works. What can be observed, in fact, isthat there are several cardiovascular risk factors that are seldom seen together inonly one study, including family history - a target topic for the study by Gami etal.15 These authorsperformed a cross-section study with over 500 apneic individuals, diagnosed bypolysomnography, and found a strong and independent association between OSA andfamily history of premature death from cardiovascular disease.15 This association shows importantimplications for the understanding of cardiovascular risk in these patients andraises this hypothesis so that future cohort works can be performed.15Kendzerska et al.6 reported that the association shownby them is considerably weaker that that of previous studies. The authorsdemonstrated the curves for the rate of survival free of coronary disease and heartfailure, highlighting a drop in these rates throughout the years, according to theseverity of the OSA. This weak association can be attributed to three main aspects:studies that work with cerebrovascular diseases, together with cardiovasculardiseases, have higher rates of outcomes; studies that overestimate untreatedpatients such as those who refuse treatment, and thus neglect other health issues;and the study by Gottlieb et al.6that selected a sample from a community that did not seek sleep medicine servicesand, therefore, did not present signs or symptoms of OSA, with no clinical profileof these participants having been registered. It is possible that OSA, in suchindividuals, may bring a cardiovascular risk that is inferior to that of individualswho go to a sleep clinic for suspected OSA.6In the current literature, the association between OSA and MI is shown by theproportion of events that occur throughout the years. Gottlieb et al.20 have admitted low power to detect differences between thegroups with or without complaints. Regarding cardiovascular events incidence rates,Marin et al.7 registered but didnot distinguish gender and separated values by OSA severity degree and fatal ornon-fatal outcome. On the other hand, Buchner et al.8 did not record this information.Indeed, there is evidence that, in OSA patients without daytime drowsiness, treatmentwith CPAP does not offer a significant reduction in the incidence of SAH orcardiovascular events, though Barbe et al.7 whereas Buchner et al.8 estimated a survival free of diseasein patients with mild to moderate OSA without pre-existing cardiovascular diseaseafter 10 years at 90.7% in groups of treated patients, and at 68.5% in non-treatedpatients groups.Free survival rate was also not checked in the study by Marin et al.,14 Regarding this variable, it was observed that, inthe study by Marin et al.,7 meanvalue of AHI in patients with mild to moderate OSA was 18.2 ev/h and in those withsevere apnea it was 43.3 ev/h. In the sample of untreated patients from the study byBuchner's et al.8, mean value ofAHI for all OSA patients groups was 15.3% ev/h, compatible with the frequency ofmild, moderate, and severe apneic individuals: 56.7%, 28.2%, and 7.1%, respectively.In the group from Gottlieb et al.,6AHI median was 6.2 ev/h (men) and 2.7 ev/h (women), apparently including the valuesof patients with AHI < 5. This inclusion of 829 healthy men and 1605 healthywomen may have interfered in AHI values of SHHA.6 The authors also observed that the association of AHI withheart failure and CAD occurred in patients with AHI ≥ 30.AHI translates the frequency of apneas and hypopneas per hour of sleep and works as ameasurement of OSA severity often related to advanced age, male gender, obesity,daytime drowsiness, and the presence of comorbidities.6 In the study by Marinet al,7 the mean age of severeapneic patients was 49.9 years, the lowest mean registered among the groups; Buchneret al.8 reported a mean age of 57.8years among all untreated apneic patients. It is important to keep those age valuesin mind, because He et al.21suggested that OSA can have more severe cardiovascular consequences in individualsunder 50 years of age. Studies have also demonstrated that younger individuals, withOSA, can be more prone to SAH,22atrial fibrillation,23 and have ahigher risk of death for any other cause.24 With this evidence, it is necessary to understand if anaggressive therapeutic and diagnostic strategy would benefit younger and middle-agedindividuals with OSA.14 For that,other characteristics must be considered in future studies, such as ethnicity,gender and other demographic data.14The patient sample, in this systematic review, was mostly composed by adults over 40years of age, and this was also the inclusion criteria used by Gottlieb etal.6 to show that cardiovascular riskassociated to OSA decreases with age.25 The SHHS6cohort, whose mean age was 62 years, may have underestimated the true cardiovascularrisk in these patients. The authors of SHHS6 argued that cardiovascular risk can decrease with age due tobiological differences in OSA's pathophysiology between patients of different ages.The authors reported that the "healthy survivor" effect is a probable cause for abias towards a null result, since apneic individuals, more susceptible to OSA'scardiovascular effects, are also more prone to cardiovascular diseases and have ahigher risk of death than those with OSA who are resistant to cardiovascularconsequences.6The relative consideration to the age factor allowed Gottlieb et al.7 included only men intheir study. By only observing the population of patients with AHI ≥ 5, wecan see that the percentage increases to 65.57%. After statistical analysis andmatching for age, ethnicity, smoking, and BMI, there was a strong association of AHIwith heart failure in men, but not in women, according to Gottlieb et al.6 In the same way, event ratesincreased with the severity of OSA in men, but that was not demonstrated inwomen.6In this work, 51.23% of the 5,067 analysed patients were male - Marin etal.8 had only 16.5% ofwomen in the non-treated group, and stated they could not extrapolate their resultsto this population. Considering the abovementioned facts, the present work will alsorestrict itself to the analysis of OSA in the male population - in the generalpopulation, the ratio between men and women with OSA is estimated at 2:1 to3:1.8Buchner et al.28 They are part of the metabolic syndrome, whichrepresents an important risk factor for CAD,28 and it isimportant to know the therapeutic status of the population. Gottlieb etal.6 worked only withpatients under treatment, an aspect that may have interfered in the weak associationfound between OSA and MI. Buchner et al.8 also registered the number of patients under treatment, and itwas also the study with the highest number of comorbidities, an aspect that may haveinterfered in the higher number of non-fatal cardiovascular events,revascularization procedures, and infarctions observed in the three articles. Marinet al.,7 in turn, did not reportthe therapeutic status of the population, and it was also the only study thatregistered a large number of fatal cardiovascular events.Regarding factors of confusion, such as SAH, DM, dyslipidemia, we can observe thattreatment of these pathologies has a relevant impact on outcomes such asMI.31 Catecholamine elevation togetherwith sleep deprivation32 areassociated to insulin resistance. There are also data that suggest an associationbetween OSA and glucose intolerance regardless of BMI.34 Chen etal.35 concluded, in ameta-analysis, that treatment with CPAP, even though it does not alter glycatedhemoglobin levels, it significantly improves insulin resistance, positivelyimpacting on DM symptoms. In the present systematic review, 12.7% of the 2,711patients with AHI ≥ 5 also had DM, whereas Gottlieb et al.,6 Marin et al.,7 and Buchner et al.8 registered 13.7% vs 9% vs 15.2%,respectively.Studies show the prevalence of DM type II in the population of apneicindividuals.6 was 31.1 kg/m2, in apneicmen with AHI ≥ 30 ev/h. Marin et al.7 registered a mean of 30.3 kg/m2 in the untreatedsevere OSA group, and 27.5 kg/m2 for mild to moderate OSA. Buchner etal.8 reported a mean of29.3 kg/m2 for the entire sample, 55% of which had mild apnea. Resta etal.36 and Newman etal.37 observed a higherfrequency of OSA among obese individuals, and, similarly, Silva et al.38 concluded that obesity is adetermining factor in OSA. Newman et al.37 estimated that obese individuals can have approximately twotimes more chances of developing OSA. An elevated BMI is also associated to anincrease in mortality from several chronic pathologies, especially cardiovasculardiseases.39 Framinghamdemonstrated that an elevated weight increases the risk of CAD, regardless of otherrisk factors.40 These observationshelp us understand the importance of the high BMI value registered by the articles,in this systematic review, as well as the direct relation between BMI and OSAseverity. It is also important to know the personal history of cardiovasculardisease of the individuals in the analyzed sample in this systematic review.Gottlieb et al.,6 Marin etal.,7 and Buchner etal.8 registered 0% vs 6.3%vs 70.5%, respectively.Regarding BMI, the highest median in the study by Gottlieb et al.8 gathered conditions that favoredcardiovascular outcomes: SAH, DM, dyslipidemia, as well as the high percentage ofpatients with a history of cardiovascular disease not being simple independentconfusion factors of OSA. These factors can contribute to the adverse effects of OSAin cardiovascular outcomes; therefore, the higher proportion of cardiovascular riskfactors and diseases could explain the number of cases and treatment benefits inpatients with mild to moderate OSA, as reported by the authors.From the exposed factors, we can observe that Buchner et al.41 reported a prevalence of up to 30% of apneic individualsamong patients with CAD.The understanding of OSA effects may suggest explanations for the association of thispathology with MI. The prevalence of SDs in CAD patients is up to two times higherthan in individuals without CAD. Bhama et al.14 These factors strengthen the argument that OSA hasa strong potential to trigger cardiac ischemia.14 In the long run, the mechanisms of cardiac and vasculardiseases, including endothelial dysfunction and systemic inflammation, can damagethe structures of coronary arteries.14 Sorajja et al.,42 when studying patients with no history of CAD, observed thepresence of an important calcification in the coronary arteries of OSA patients,through the calcification score = 9 (Agatston units) and zero score in patientswithout OSA (p < 0.001).There are pathophysiological mechanisms that suggest the contribution of OSA in theorigin and progression of CAD: severe intermittent hypoxemia, acidosis, increase ofblood pressure and sympathetic vasoconstriction, together with simultaneous changesin transmural, intrathoracic and cardiac pressures.18 The present systematic review also had as limitations theheterogeneity of the selected studies in relation to aspects such as objective,clinical and polysomnographic profile of the patients, as well as the difference inthe presentation of patient groups and their classification regarding AHI.There are reports that the reduced number of cardiovascular events separated amongstthemselves, and the variation of definitions used by research limit the conclusionsof studies that address this theme.14 Moreover, both pathologies also have multifactorial originswith an overlap of common risk factors such as gender, age, obesity, andsmoking.43This context demonstrated the challenges of investigating the causal relation betweenOSA and CAD, considering both conditions are chronic and have long latency periodsbefore the appearance of complaints.Defining the causal relationship between CAD and OSA means clarifying the necessarycare apneic individuals must have regarding the prevention of MI. Making sleep apneaa marker for cardiac disease implies early tracking of these patients, as well as anincentive to treat this disease that is associated to numerous cardiovascularconsequences. Endothelial, neuro-hormonal and metabolic alterations cannot beoverlooked, even if it seems complex to dissociate the onset of CAD and OSA, becauseit is only thus that we can understand if OSA can interfere in the development oraggravation of CAD. OSA can be treated and, therefore, if this is confirmed, it canbe a controllable determinant of CAD.This systematic review has shown that there is an association between OSA and MI. Wewere able to observe that this association was higher among men, and that AHI wasconsidered one of the markers for this relationship.


Urine samples were collected pre-exercise and at 2, 4, and 24 h for targeted analysis by liquid chromatography-mass spectrometry. Time exerted the strongest differentiating effect, followed by exercise mode and health status. The greatest changes were observed in the first post-exercise samples, with a gradual return to baseline at 24 h. RE caused the greatest responses overall, followed by HIIE, while CME had minimal effect. The metabolic fingerprints of the two groups were separated at 2 h, after HIIE and RE; and at 4 h, after HIIE, with evidence of blunted response to exercise in MetS. Our findings show diverse responses of the urinary metabolic fingerprint to different exercise modes in men with and without metabolic syndrome.Exercise is important in the prevention and treatment of the metabolic syndrome (MetS), a cluster of risk factors that raises morbidity. Metabolomics can facilitate the optimization of exercise prescription. This study aimed to investigate whether the response of the human urinary metabolic fingerprint to exercise depends on the presence of MetS or exercise mode. Twenty-three sedentary men (MetS,  Ν-oxide [Metabolic syndrome (MetS) is a cluster of inter-related cardiometabolic risk factors including visceral obesity, hyperglycemia, dyslipidemia, and hypertension . MetS siΝ-oxide , have beRegular physical activity and exercise hold a primary role in the prevention and treatment of MetS, helping to lower the associated risks ,18,19. DThe aim of the study was to investigate whether the response of the human urinary metabolic fingerprint to exercise depends on the occurrence of MetS or on the type of exercise performed. In particular, we aimed to compare the effects of three fundamentally different exercise modes, namely, high-intensity interval exercise (HIIE), continuous moderate-intensity exercise (CME), and resistance exercise (RE), on the urinary metabolic fingerprints of sedentary, middle-aged men with or without MetS through different time points. The choice of urine as biological matrix was based on it being under no homeostatic mechanisms, easily accessible, stable, and less complex than other biofluids; therefore, an ideal source of biomarkers .p = 0.055). Groups also differed significantly in visceral fat rating, total cholesterol, LDL cholesterol, 2max, and HOMA2-IR. All of these differences pointed to a healthier profile of the Healthy group. Regarding body composition measurements, the observed difference in visceral fat rating is expected as MetS is characterized by central adiposity. Ten metabolites, namely, creatine, lactose, lysine, methionine, nicotinamide, pyruvate, thymine, trimethylamine, tryptamine, and tryptophan, were significantly higher in MetS compared to Healthy at baseline.Characteristics of the participants are summarized in p < 0.001).There was no significant difference between the HIIE and CME trials in terms of total distance covered in the 36 min of exercise for either group  vs. 3.53 (3.20–3.78) km, respectively). There was also no significant difference between groups regarding exercise intensity in each of the two trials . As an external validation of the UPLC-MS/MS metabolomic analysis, we also measured the lactate concentration spectrophotometrically. The spectrophotometrically measured lactate concentration exhibited significant correlation with the lactate levels as determined by UPLC-MS/MS , 2 h, 4 h, and 24 h . Urine sN-oxide was the only metabolite to exhibit a significant time x group interaction, with groups being different at 2 and 24 h, MetS showing differences between 4 and 24 h, and Healthy showing differences between 2 h and all other time points. (iii) The amino acids, alanine and homocysteine; the purines, guanine, hypoxanthine, and inosine; the nucleoside, uridine; the carboxylic acids, lactate and pyruvate; the amines, trimethylamine and tryptamine; and riboflavin, presented significant exercise mode x time interactions. These interactions were mainly due to differences at the first post-exercise time point, where RE caused the highest increases. Hypoxanthine, inosine, and uridine also showed differences at the second and third post-exercise time points , with only hypoxanthine being different between HIIE and CME. The results of the three-way ANOVA on all 64 identified metabolites are presented in the form of a heat map in Regarding main effects, glutamine, riboflavin, lysine, and betaine were found to differ significantly among groups. Twelve metabolites differed significantly among exercise modes. Ten of them differed significantly between CME and RE; seven differed between HIIE and CME; and five differed between HIIE and RE. Forty-six metabolites changed significantly over time. The effect of time was predominantly located in the comparison of the first post-exercise sample (2 h) with all other samples. Additionally, hypoxanthine, inosine, and xanthine were significantly different between 0 and 4 h; and alanine, guanine, and hypoxanthine were different between 4 and 24 h. The numbers of significant main effects and interactions referring to all 64 metabolites are summarized as a Venn diagram in When group, exercise mode, and time were each set as the Y variable for pair wise PLS-DA, 16 out of the 39 possible comparisons produced valid models: (i) The two groups were separated at 2 h in HIIE and RE, as well as at 4 h in HIIE. (ii) Separation by exercise mode was achieved at 2 h between HIIE and CME; and between CME and RE. Separation was also achieved at 4 h between HIIE and RE; and between CME and RE. (iii) Separation by time was achieved in HIIE between 0 and 2 h; 0 and 4 h; 2 and 4 h; and 2 and 24 h. No valid models were constructed for CME. In RE, all time points were significantly separated except in the case of 0 vs. 24 h. Score plots for the pair wise comparisons of the different time points in RE are shown in Additionally, we compared exercise modes in each group individually. Separation was achieved between CME and RE at 2 h in both MetS and Healthy, between HIIE and CME at 2 h in Healthy, and between CME and RE at 4 h in Healthy . DiscrimBy applying a UPLC-MS/MS analytical method, we studied changes in the human urinary metabolic fingerprint under the influence of three independent factors: presence or not of MetS, exercise mode, and time relative to exercise. After performing both univariate (ANOVA) and multivariate (PLS-DA) statistical analysis, we found that time exerted the strongest differentiating effect on the metabolic fingerprint, followed by exercise mode and health status. We also found evidence for diminished metabolic flexibility of MetS in response to exercise. Of the three exercise modes, RE caused the greatest responses, followed by HIIE, while CME had minimal effect. Regarding the effect of time, the greatest changes were observed in the first post-exercise samples, with a gradual return to baseline at 24 h.2max), but this was expected, since aerobic capacity tends to be lower in obesity and diabetes. In fact, lean body aerobic capacity was significantly lower in sedentary men with MetS, compared to controls with matched levels of physical activity [Groups differed significantly in maximal aerobic capacity (activity . A delimO-acetyltransferase (CrAT) activity in response to exercise. CrAT catalyzes the reversible conversion of acetyl CoA and carnitine to acetylcarnitine and CoA in the mitochondrial matrix. Thus, CrAT is considered to serve in buffering the matrix acetyl CoA concentration, a pivotal metabolic parameter that increases during exercise as a result of increased carbohydrate and fatty acid catabolism. In fact, CrAT activity, known to regulate substrate switching and glucose tolerance [Multivariate statistical analysis distinguished groups after HIIE and RE. In particular, 3-methylhistidine, a marker of muscle protein turnover , showed olerance , has beeolerance . On the When looking at the metabolite changes as a whole on the heat map in Regarding the comparison of the three exercise modes employed in the present study, it is apparent that RE had the most profound and sustained effect on the urinary metabolic fingerprint, at least as determined with the particular method that mostly identifies polar compounds. HIIE had a lower impact, leaving CME with the weakest effect. As far as dependency on energy systems is concerned, the three exercise modes chosen cover the full spectrum of energy systems, ranging from predominance of the aerobic system in CME to a considerable contribution of the lactate system in HIIE to predominance of the anaerobic systems (lactate and ATP-phosphocreatine) in RE . TherefoA related exercise metabolomic study on well-trained athletes showed distinct differences in the plasma metabolic fingerprint between HIIE and CME, with exercise intensity-dependent changes in tricarboxylic acid cycle intermediates, lactate, alanine, glutamate, tyrosine, and specific nonesterified fatty acids . In anotN-oxide), which are produced by bacterial degradation of choline and carnitine in the gut. These changes could be related to increased blood circulation in the gut after exercise, resulting in increased absorption of these metabolites into the enterohepatic system, or to other unknown factors. It is known that the impact of exercise on metabolism carries on for several hours after the cessation of exercise and is characterized by increased lipid oxidation and glycogen resynthesis . In the n = 9) and Healthy (n = 14). Assignment to the MetS group was according to the National Heart, Lung, and Blood Institute/American Heart Association criteria, that is, presence of at least three of the following: central obesity (waist circumference ≥ 102 cm), high circulating fasting triacylglycerols , low HDL cholesterol , high fasting glucose , and high blood pressure (systolic ≥ 130 mm Hg and/or diastolic ≥ 85 mm Hg) [Twenty-three sedentary men  were divided into two groups: MetS (5 mm Hg) . Exclusi2max) and maximal heart rate (HRmax) were determined through an incremental exercise test to exhaustion, as described [One to two weeks before the onset of the study, all participants were subjected to preliminary tests that included anthropometric measurements , biochemical screening , and blood pressure measurements. Additionally, they filled out medical history, dietary, and physical activity questionnaires. Biochemical analysis was performed in an automatic analyzer . Insulin was measured by enzyme immunoassay, using a kit from IBL International . Body fat was assessed by bioelectrical impedance analysis using the Bodystat 1500 . Trunk and visceral fat were assessed by bioelectrical impedance analysis using the Tanita Viscan AB140 . Maximal oxygen uptake , which served as the control trial, one bout of high-intensity interval exercise (HIIE), one bout of continuous moderate-intensity exercise (CME), and one bout of resistance exercise (RE). Each participant completed the trials in random order. (A random-number generator software was used to provide random sequences.) Trials for each participant were spaced 5 to 7 days apart. Participants were instructed to keep records of their physical activity for two days before each trial and the day of the trial. They were asked to refrain from exercise during this period and carry out only the activities of daily living. Their physical activity levels for those days were verified with the use of a pedometer . Participants were also instructed to keep records of their diet and avoid alcohol and caffeine consumption during the same period. They were further instructed to replicate the diet of the two days preceding the first trial on the two days preceding the next trials  in order to ensure the same pre-trial energy and nutrient intakes. On the day before and the day of each trial, participants followed a prescribed diet, designed to meet their individual energy needs and provide 50% of energy from carbohydrate, 20% from protein and 30% from fat. Individual energy needs were estimated by multiplying resting energy expenditure . After 4 min of warm-up at 3 km/h, they alternated four times between 4 min at 90% of HRmax and 4 min at 3 km/h. Thus, total exercise time was 36 min, 16 min of which was at high intensity. Heart rate was monitored continuously by a telemetric monitor . Speed was adjusted as necessary to maintain 90% of HRmax during the high-intensity segments. In the CME trial, participants walked or ran continuously at 65% of HRmax for 36 min. Heart rate was monitored continuously in the same way as above. In the RE trial, participants performed 6 types of resistance exercises  designed to target all major muscle groups. Participants completed 3 sets of 8–12 repetitions at 75%–80% of 1RM for each exercise. The duration of each set was approximately 2 min, and the total duration of the trial was 45 min. The three exercise trials were timed so as to end one hour after the baseline urine collection .Urine samples were collected in each trial at baseline (set as 0 h), 2 h, 4 h, and 24 h. During exercise and the ensuing one hour, participants were given only water to drink. They were instructed to mark their water consumption during the first trial and replicate it during the rest of the trials. Following urine sampling at 2 h, they consumed a standard mixed breakfast and then left the lab. They were instructed not to eat or drink anything but water for the next 2 h and to collect their urine at 4 h, as well as their first morning urine (after 12 h of overnight fast) at 24 h, along with any night urine between 16 and 24 h . Particig for 18 min. The supernatant was passed through polytetrafluoroethylene (PTFE) filters for high-performance liquid chromatography (0.22 μm pore diameter), and ~90 μL of the filtrate were placed in appropriate glass inserts within LC-MS vials, which were in turn placed in the refrigerated (4 °C) autosampler of the liquid chromatograph for analysis. Samples were thawed and mixed just prior to processing. Then 300 μL of each sample were diluted with 600 μL of cold acetonitrile (−24 °C), mixed for 20 min and centrifuged at 14,573× v/v)) versus solvent B (acetonitrile-water 30:70 (v/v), 0.01% ammonium formate) was used at a flow rate of 0.5 mL/min, and the sample volume injected was 5 μL. A polarity switching mode (both positive and negative) of electrospray ionization was applied. The capillary voltage was +3.5 KV, while the voltage of the cone and the collision energy were adjusted for each analyte as described [Samples were analyzed through a targeted hydrophilic-interaction ultraperformance liquid chromatography–tandem mass spectrometry (HILIC-UPLC-MS/MS) method, as previously described , on an Aescribed , where cescribed .t test for normally distributed data or Mann–Whitney U test for not normally distributed data. Total distance covered in the HIIE and CME trials was compared within each group with paired-sample t test for normally distributed data or Wilcoxon signed-rank test for not normally distributed data. Lactate levels determined by UPLC-MS/MS were correlated with those determined spectrophotometrically using Spearman’s correlation. UPLC-MS/MS data were processed with the TargetLynx software . For the results of the metabolomic analysis, the peak area of each metabolite in the samples of the exercise trials was normalized to the respective value in the REST trial to correct for batch effect and to isolate the effects of exercise from other confounding factors and effects such as feeding, diurnal variation, and possible glomerular hyperfiltration associated with obesity or pre-diabetes [p values below 0.05 were considered significant. Insulin resistance was assessed using the HOMA Calculator v2.2.3  to calculate the HOMA2-IR score . For eacdiabetes . A 2 . Peak areas were subjected to univariate scaling and analyzed with principal component analysis (PCA) and partial least square discriminant analysis (PLS-DA), followed by validation of permutations to check the validity of the generated models . Observations exceeding 99% of the Hotelling’s T2 tolerance region in the PCA score plots were considered outliers and were excluded. Metabolites were considered important in explaining separation in each model based on the respective loadings plots and a Variable Importance on Projection (VIP) score above one.We monitored the changes of 64 urinary metabolites for 24 h after acute bouts of three exercise modes in men with and without MetS. Our findings show that exercise caused profound metabolic perturbations, which persisted for 3 h after RE and HIIE, but subsided the next day. To our knowledge, the metabolic fingerprints of three fundamentally different exercise modes were compared for the first time, and the differences found reflect the differences in the energy systems that dominate in each exercise. The greatest response was observed after RE, followed by HIIE, while CME’s effect was weak. Finally, we found evidence of a blunted response to exercise in individuals with MetS compared to Healthy. Further investigation may bring us closer to personalized exercise prescription for individuals with cardiometabolic risk factors. Overall, our findings indicate changes in several metabolic pathways induced by different exercise modes and support the value of urinary metabolic fingerprinting in the study of exercise metabolism.


Trauma contributes significantly to the burden of disease and mortality throughout the world, but particularly in developing countries. In Tanzania, there is an enormous research gap on trauma; the limited data available reflects realities in cities and areas with moderately- to highly-resourced treatment centers. Our aim was to provide a description of the injury epidemiology across all of Tanzania. Our data will serve as a basis for future larger studies.This is a subgroup analysis of a cross-sectional, prospective study of the clinical epidemiology of patients presenting at all public district and regional hospitals in Tanzania. The study was conducted between May 2012 and December 2012. A team of emergency doctors used a purpose-designed data collection sheet to gather the demographic and clinical information of all patients presenting during the day-site visit to each hospital. Descriptive statistics, including means, standard deviations, medians, and ranges are reported.A total of 5227 patients were seen in 24-h period in 105 (100% response rate) district (or designated district) and regional hospitals in mainland Tanzania. Of these patients, 508 (9.7%) presented with trauma-related complaints. Among patients with trauma-related complaints, 286 (56.3%) were male, and the overall median age of 30 (interquartile range of 22–35) years. Road traffic crash was the most common mechanism of injury, accounting for 227 (44.7%) complaints. Open wounds and bone fractures were the two most frequent diagnoses, with a combined 300 (59%) cases. Most of the patients - 325 (64%) - were discharged, 11 (2.2%) went to operating theatres and 4 (0.8%) of patients died while receiving care at the acute intake areas.Trauma-related complaints constitute a substantial burden among patients seeking care in acute intake areas of hospitals across Tanzania. There is a need to develop, implement and study systems that can support the improvement of trauma care and optimize outcomes of trauma patients. Trauma contributes significantly to the burden of disease and mortality throughout the world, but particularly in developing countries –4. TraumIn Africa, injuries rank among the top ten mortality causes, and claim more lives than tuberculosis, malaria and HIV combined . In addiTanzania is a low income-country with a population of 55 million people, of whom 80% lives in rural area. The life expectancy is approximately 65 years, and HIV is the leading cause of mortality . There iThis is a subgroup analysis of a larger cross-sectional, prospective study, which described the clinical presentation, investigations, procedures and diagnoses of patients presenting to public district and regional hospitals in Tanzania. The larger cross-sectional, prospective study enrolled a consecutive convenience sample of all patients presenting during the randomly selected daylong clinical shift to each hospital. This study was part of Tanzania Emergency Care Capacity Site Survey (TECCSS) project, which aimed at evaluating three main components of emergency care: the disaster preparedness, equipment availability and disease burden in all district and regional hospitals. The TECCSS project was conducted between May 2012 and December 2012.All district and regional hospitals in Tanzania mainland were included in this audit. Mainland Tanzania (excluding the island of Zanzibar) at the time of this study had 25 regional and 80 district (or designated district) hospitals. The regional hospitals are expected to receive referral of patients from district hospitals and hence offer an expanded range of care and more specialty services than district facilities.The data collection was overseen by one of the five authors , who are all emergency physicians. The researchers were randomly divided into different geographical and political areas of Tanzania based on locations of regional and district hospitals. A purpose-designed data collection sheet was used and the data collectors asked treating providers to complete de-identified data sheets on all patients who presented within that period with the assistance of the researcher. Information gathered included basic demographics, clinical presentations, investigations done (example: imaging and laboratory work), procedures done, clinical management including medications, and disposition. Data not gathered as the patient moved through the facility were collected later the same day from patients’ medical records.The study data were transferred from the hand-written data forms into an Excel database  and analyzed with SAS . Procedure, frequency and univariate functions of SAS were performed to check for any outliers and clean the dataset. Descriptive statistics, including means, standard deviations, medians, and ranges were calculated.The study protocol was reviewed and approved by the Institutional Review Board of the Muhimbili University of Health and Allied Sciences (MUHAS) and Ministry of Health issued a permission to survey all of the hospitals.We surveyed all 105 district and regional hospitals across mainland Tanzania, and had a 100% response rate. Of these hospitals, 80 (76.2%) were district (or designated district) hospitals, and 25 (23.8%) regional hospitals. The hospitals have been divided in geo-political zones to cover the whole country: There are 14, 10, 10, 19, 10, 6, and 11, district (or designated district) hospitals in the Lake, Eastern, Western, Northern, Southern, Central and Southern Highland zones respectively. Each of 25 political regions had one hospital, and those zones had 3,6, 3, 4, 3, 2, and 4, regional hospitals respectively. Only 11 (10.5%) hospitals had a dedicated 24-h acute intake area with a dedicated doctor on duty to tend to emergencies..A total of 5227 patients were seen in all hospitals. Of these, 508 (9.7%) patients presented with trauma-related complaints. Most 286 (56.3%) of these patients were male, and the overall median age among all patients was 30 years (interquartile range 22 to 35 years). The Eastern zone 151 (29.7%) and southern Western zone 44 (8.7%) had the highest and lowest number of patients per zones respectively (Table .RTC 227 (44.7%) represented the most common mechanism of injury, while poisoning and drowning represented the least common mechanisms (Table p < 0.001). Burns and falls were the most common mechanisms among patients under 5 years and 5–15 years of age, respectively, while RTCs were most common among those in the groups of patients 16–55 years and above 55 years of age  was the least frequent diagnosis (Table .Most of the patients 325 (64%) were discharged, 11 (2.2%) of patients were channelled straight towards the operating theatres, and 0.8% of patients died while receiving care at the acute intake areas (Table Our study represents one of the most comprehensive assessments of one-day trauma burden presenting to acute intake areas in districts and regional public hospitals of Tanzania. Most of the previous studies , 20, 24 Trauma-related complaints accounted for about 10% of the overall complaints of patients presenting to acute intake areas of the health facilities in Tanzania. Middle aged males were the most affected group in our study population, consistent with previously published literature in Tanzania and Sub-Saharan Africa , 25–27. The trauma distribution patterns within different age groups differ from those observed in developed countries , 31, witBeyond the huge and diverse trauma burden, another major finding of our study was that only 10% of hospitals had a dedicated 24-h acute intake area with a dedicated doctor on duty to provide care for patients presenting with emergencies. This is a substantial gap in access to care for patients with trauma-related emergencies that may cause a delay in appropriate treatment. While our study was not designed to identify the reasons for this lack of dedicated 24-h acute intake areas, the observed gap might be a result of combination of resource-related factors and the infancy of emergency medicine as a dedicated discipline in Tanzania . Prior sOver half of the trauma-related complaints were reported in the hospitals of the Eastern and Western zones of Tanzania. These zones have regions with both the highest populations and fastest-growing economies, including rapid urbanization and motorization . While tThis study provides a description of a one-day trauma burden among patients seeking emergent care in hospitals across Tanzania. As expected, there is a high burden of trauma in both rural and urban areas. While we have provided pilot data to help guide policy and resource allocation, the study has highlighted areas of further research. We propose that the priorities should be given to supporting the implementation of 24-h emergency care units in all districts and regional hospitals so as to support the provision of life saving resuscitation to trauma patients. Furthermore, future research should focus on implementing a standardized trauma documentation system across the country, to further characterize the risk factors, clinical care, referral patterns and outcomes in order to identify areas of improvement and optimize outcomes.This was a one-day study that was collected on different days and by different health care professionals. In addition, our results reflect reported and recorded patient characteristics, which may be incomplete. We did, however, include all district and regional hospitals, and gather data in real time during on site visits to each facility, which we believe increases the accuracy of this cross-sectional characterization.Trauma-related complaints constitute a substantial burden among patients seeking care in acute intake areas of hospitals across Tanzanian. There is a need to develop, implement and study systems that can support the improvement of trauma care and optimize outcomes of such patients.


The absence of instruments capable of measuring the level of knowledge ofhypertensive patients in cardiac rehabilitation programs about their diseasereflects the lack of specific recommendations for these patients.To develop and validate a questionnaire to evaluate the knowledge ofhypertensive patients in cardiac rehabilitation programs about theirdisease.A total of 184 hypertensive patients  were evaluated. Reproducibility was assessed by calculation of theintraclass correlation coefficient using the test-retest method. Internalconsistency was assessed by the Cronbach’s alpha and the construct validityby the exploratory factorial analysis.The final version of the instrument had 17 questions organized in areasconsidered important for patient education. The instrument proposed showed aclarity index of 8.7 (0.25). The intraclass correlation coefficient was0.804 and the Cronbach’s correlation coefficient was 0.648. Factor analysisrevealed five factors associated with knowledge areas. Regarding thecriterion validity, patients with higher education level and higher familyincome showed greater knowledge about hypertension.The instrument has a satisfactory clarity index and adequate validity, andcan be used to evaluate the knowledge of hypertensive participants incardiac rehabilitation programs. Thus, hypertensive patient’sknowledge about his condition is part of the therapeutic success, who becomesco-responsible for the treatment.15In this context, an efficient SAH management depends on patient’s understanding abouthis condition and treatment.19 others include only questions deemed as relevantby the authors, without undergoing a psychometric validation.23Nevertheless, there are few validated tools able to provide accurate informationabout education of hypertensive patients. While some instruments does not focusCR,9educational needs and of specific conditions involving paradigms of health anddisease, which are likely to change.10 Thus, the aim of this study was to develop and psychometricallyvalidate an instrument to assess the knowledge about the disease of patientsenrolled in CR programs (HIPER-Q).This gap in the knowledge opens the possibility of investigation, since assessmenttools are important instruments in educational programs. These instruments enablethe identification of patients’This study was approved by the research ethics committee of Santa Catarina StateUniversity (UDESC) . The study had across-sectional, observational design.24 The bibliographic search wasperformed in Pubmed database from January 2010 to September 2016.In the first stage of the study, a literature review was performed to identifythe pieces of knowledge about SAH considered relevant to hypertensiveindividuals.The questionnaire was constructed and revised by a commission composed of 17health specialists, with experience in CR. These specialists carried out ananalysis of content and clarity of the instruments, to verify its adequacy tohypertensive patients participating in CR programs.The second stage was a pilot study to evaluate both applicability andreproducibility of the instrument, as well as patients’ understanding of theitems (clarity). A convenience sample of hypertensive patients, who participatedin CR programs, was studied, and the results were used for refinement of theHIPER-Q instrument. Patients of the pilot study did not participate in thepsychometric validation.Núcleo de Cardiologia e Medicina do Esporte, NCME)of the clinic, and the Santa Catarina Institute of Cardiology (ICSC). Data werecollected between November 2015 and May 2016.The third step was the psychometric validation. The refined tool was used in alarger convenience sample, composed of hypertensive patients participating in CRprograms at the Clinic of Cardiology and Cardiopulmonary and MetabolicRehabilitation (Cardiosport), the Center of Cardiology and Sports Medicine to 10 (veryclear) scale.26 whorecommend a minimal sample size of 10 subjects per item and/or a minimum of 100participants. Since the questionnaire was composed of 17 items, a sample of 170hypertensive subjects was considered sufficient.Sample calculation for the psychometric analysis was performed according to Hair& Anderson’s28Test-retest reproducibility of the instrument was validated in the pilot studygroup using the intraclass correlation coefficient (ICC). The items should meetthe minimal recommended standard - ICC > 0.7.26 Values greater than 0.60 aregenerally considered acceptable.29 Second, criterion validity was analyzed by relating theHIPER-Q scores to patients’ educational attainment and family income, using theSpearman correlation. Third, the dimensional structure  was evaluated by exploratory factor analysis. A component method forfactor extraction was performed, considering only those factors withcharacteristic values > 1.0. When necessary, items with low factor loading(< 0.35) were excluded.Psychometric properties of the new tool were assessed by analysis of internalconsistency, criterion validity and factorial structure. First, internalconsistency was analyzed in the psychometric validation group by Cronbach'salpha, reflecting the internal correlation between items and factors.30 andthe Spearman correlation was used for analysis of criterion validity.Once the factors were selected, a correlation matrix was generated, in which theassociations between items and factors were identified by factorial loadingsgreater than 0.30 in only one factor. The promax method was used for matrixinterpretation,Finally, a descriptive analysis of HIPER-Q was performed using mean values andstandard deviations of normally distributed variables, and median andinterquartile ranges for variables with non-normal distribution. Data normalitywas evaluated by the Kolmogorov-Smirnov test. Due to non-normality of data, weused the chi-square test to evaluate the association between the HIPER-Q scoresbased on patients’ sociodemographic and clinical characteristics. Patients’total knowledge was represented by the median of total score.Statistical Package forSocial Sciences (SPSS) version 20 , and thelevel of significance was set at 5% for all tests.Statistical analyses were performed using the For content validation, 17 health professionals with experience in CR wereconsulted: 6 (35.5%) physicians, 6 physiotherapists (35.3%), 2 nurses (11.8%), 2physical educators (11.8%) and 1 dietitian (5.9%). For the pilot test, 30hypertensive patients participating in CR programs were recruited by convenienceto answer the questionnaire; 11 (22%) of them were women, with mean age of 62± 8 years.For psychometric validation, 184 hypertensive patients with mean age of 60.5±10 years and median time of diagnosis of 8 years (interquartile range 18years) completed the HIPER-Q. Of these patients, 101 (54.9%) were retired.Participants’ characteristics are described in 31 for each item, one answer isconsidered the “most correct” one and receives score 3, and another answer isconsidered “partially corrected” and receives score 1. The other two answeroptions - the incorrect option and the “don’t know” option receives no score(zero). According to the classification described in The literature review on health education for hypertensive patients in CRprograms revealed consistent findings between the articles. The first version ofthe HIPER-Q was developed based on literature data. Nineteen items wereconstructed encompassing seven important domains in patient education:self-care, treatment, diagnosis, physical exercise, concept and pathophysiology,signs and symptoms and risk factors. Similar to other educationalinstruments,The construction rules of the item sources and of the theoretical analysis of theitems, content and semantics were considered ‘clear’ by 79% of the specialists,with a median clarity score of 8.5 (0.75). However, most of the items receivedcomments on their semantic contexts. Each item was widely discussed by theauthors, and all changes suggested by the specialists were accepted. Thisversion of the questionnaire was analyzed by the same professionals, and thefinal version was then provided, with 96% of agreement between the items andmedian clarity score of 9.54 (0.30).The average time for completion of the questionnaire by the participants (n = 30)was 15.4 ± 2.2 minutes. The median clarity score was 8.7 (0.25), and noitem had a clarity score lower than 7.0, indicating that the questionnaire waswell understood by the target population.27 The items“Also with respect to systemic arterial hypertension, we can affirm that” and“What is the best diet for patients with systemic arterial hypertension?” had aICC lower than 0.7  and were excluded from thefinal version,27 which wasthen composed of 17 questions. The ICC of each question is presented in Table 3Total ICC of the instrument was 0.804, obtained by the final test-retestscores.The HIPER-Q was administered to participants of CR programs, and the mean scoresof the questionnaire items are shown in With respect to criterion validity, a relationship of HIPER-Q total score wasfound with educational attainment and family income. Weak positive correlationswere found of knowledge level with educational attainment  and family income .2X 2066.56; p < 0.001) indicatedadequacy of data for factor analysis. Five factors were extracted and, together,they accounted for 51.1% of the total variance of the items, whosecharacteristic values were > 1.1. Dimensional structure was evaluated by exploratory factor analysis. TheKaiser-Meyer-Olkin (KMO = 0.669) test and the Bartlett’s sphericity test. In patients’ classification,a high prevalence (44.6%) of “acceptable knowledge” was observed. Patientsshowed greater knowledge about the items: “If a health professional says thatyour blood pressure is altered, you should”, “On the basis of your knowledgeabout systemic arterial hypertension, answer the following:” and “Which of therisk factor groups below has the greatest influence on the development ofsystemic arterial hypertension?”. The lowest level of knowledge was seen for theitems: “With respect to self-measurement of blood pressure, it is correct to saythat”, “About the As shown in 31 In thepresent study, a new tool for the assessment of knowledge in hypertensive patientsenrolled in CR programs was developed and psychometrically validated by a rigorousprocess. In general, clarity, internal consistency, reliability, dimensionalstructure and criterion validity were established, indicating the validity andusefulness of the HIPER-Q in the assessment of hypertensive patients’ knowledgeabout the disease.Patient’s education is one of the central components of CR, and is crucial forpromoting the understanding about secondary prevention strategies and adherence totreatment.32 Second, comparisons of thefactorial analysis reported in similar studies33 revealed that the HIPER-Q showed similararrangement of factors and items; in each of the five factors, those items withsimilar knowledge domains were predominant in the instruments. The factors wereclustered by stability, interpretation of the areas and basic principles ofconstruction rules, in order to establish a reliable, consistent construct. In eachdomain, the factors included different amounts of terms that were correlated witheach other, which may be explained by the fact that SAH is characterized as asystemic, multifactorial disease.24The first data to be considered is the clarity index, generated by professionals andpatients, demonstrating that the instrument proposed can be easily understood by thestudy population.19,34-36 andstudies with similar structure.33 This indicates an adequatecorrelation between the items of the questionnaire. Nevertheless, the HIPER-Q wasvalidated in public and private CR programs with different characteristics, whichmay have affected the alpha value (not as high as those of similar studies).Results of internal consistency  were consistent with thosereported in previous studies involving instruments of assessment of hypertensivepatients’ knowledge about their conditions,33Regarding the criterion validity, both educational attainment and family income wererelated to the knowledge about SAH. These findings suggest that socioeconomicalfactors are determinants of knowledge about health, as previouslydemonstrated.21 reflectthe importance of evaluating the knowledge about health and formulating hypothesisthat elucidate the determining factors of the information gaps. Therefore, patienteducation is an important component of CR programs28 and isassociated with a successful self-management of disease and patient’s behaviorchanges.33The current study also evaluated the level of knowledge of the sample patients, whoshowed an overall knowledge classified as “acceptable”. Our findings, supported byother authors,35We did not find in the literature, longitudinal studies demonstrating the effects ofa higher level of knowledge about SAH on outcomes, such as worse prognosis ormortality. Thus, one may expect that the HIPER-Q can be used in this regard infuture studies. In this context, studies on other chronic diseases have shownpromising results, suggesting that disease-related education may be determinant inthe control of risk factors, such as sedentary lifestyle, smoking and continuity oftreatment, which may lead to reductions in comorbidities, health costs and evenmortality.31Most of the studies reviewed have only developed SAH questions deemed as relevant bythe authors,23 without conducting a psychometricvalidation as performed in the present study.36 In addition,other validated studies have not specifically evaluated the knowledge ofhypertensive patients in CR.39 Therefore, ourstudy aimed to develop an instrument to healthcare professionals, capable ofestablishing educational strategies directed to patients’ needs,31 and that would help in the evaluation and planning of theeducational process of hypertensive subjects in CR programs.In this scenario, there is a lack of instruments to measure the knowledge about thedisease in participants of CR.36 Fifth, the currentstudy did not achieve the sample size recommended by the test-retestprocedure.36 Sixth,participants were not asked about their occupations, which may also have influencedthe results, since patients graduated in medicine and/or other health-related areas,for example, may have had greater chance of giving correct answers. Further studiesare needed to evaluate whether the HIPER-Q is sensitive to longitudinal changes byassessing patients’ knowledge before and after their participation in CRprograms.Caution is needed in interpreting these findings. First, the results cannot begeneralized, due to the facts that the sample was selected by convenience, and onlythree CR programs were included, which affects the achievement of the outcomes.Second, the development of the instrument proposed was based on consensus andguidelines, which encompass numerous SAH-related issues not necessarily covered byCR programs. Third, although all patients included were participants of CR programs,the programs were different (of public and private nature), with differentapproaches, which may have influenced the results. Fourth, the instrument was notdeveloped using plain language techniques, or “simple” language, which may havecreated difficulties in the interpretation of the items, and consequently affectedthe results.The present study demonstrated that the HIPER-Q showed sufficient reliability,consistency and validity, corroborating its use in future studies to evaluate theknowledge of SAH patients in CR programs. This instrument is expected to support theassessment of the educational component of CR programs and to identify the knowledgethat is compatible with patients’ need for information.


Apolygus lucorum has caused increasing damage to cotton and fruit trees in China. The salivary enzymes secreted by A. lucorum when sucking on host plants induce a series of biochemical reactions in plants, and the pre-oral digestion benefits the bug feeding. In this study, the food intake of A. lucorum from 1st instar nymphs to adults was measured, and the corresponding salivary activity of pectinase, amylase, cellulase, protease, polyphenol oxidase and peroxidase was determined. Daily food intake varied with developmental stage, peaking in 3rd and 4th instar nymphs. Pectinase, amylase, cellulase and protease were detected in both nymphal and adult saliva of A. lucorum, while neither polyphenol oxidase nor peroxidase was detected. Protease activity varied with food intake peaking at the 3rd-4th instar, and then slightly decreasing at the 5th instar. Levels of pectinase, amylase and cellulase increased significantly with the daily feeding level until the 3rd instar, corresponding with increasing damage to host plants. The activity of both cellulase and protease had a significant linear relationship with the average daily food intake. The increasing activity of enzymes in saliva explain stage-specific impacts of A. lucorum on the host plants, and suggest that optimal management of A. lucorum would be confined to its control threshold prior to the peak of daily feeding in the 3rd instar.In recent years,  Apolygus lucorum (Meyer-Dur) (Heteroptera: Miridae), a significant pest of several key agricultural crops and fruit trees, is the most widely distributed species of mirid in China , where W stands for the weight of green bean pods before consumption, L for the weight of green bean pods after consumption, a = % loss due to dehydration, estimated as (the weight of green bean pods before experiment in control group—the weight of green bean pods after experiment in control group) / the weight of green bean pods before experiment in control group, and b (the weight of green bean pods before experiment in control group—the weight of green bean pods after experiment in control group) / the weight of green bean pods after experiment in control group. Any A. lucorum was collected as follows. Three bugs, starved for 6 h and at the same instar, were put into a transparent plastic tube open at both ends . The upper end was covered with two layers of parafilm with 200 μL of nutrient solution sandwiched between them , and the other end covered with gauze to ensure ventilation. The tubes were held at 25 ± 0.5°C, 65 ± 1% RH, and a 16:8 h L:D photoperiod. After allowing the bugs to feed on the solution for six hours, the remaining liquid between the layers of parafilm  was collected. This liquid was then centrifuged for 25 min in a high-speed (15000 g/min), refrigerated (4°C) centrifuge, and the supernatant, called “enzyme-containing extract”, was collected and stored at -20°C. The whole experiment was replicated five times.Using a nutrient solution placed between two sheets of parafilm, saliva of Protease activity was determined with the folin-phenol method ,which dePectolytic enzyme activity was determined using a spectrophotometer –34, defiAmylase enzyme activity was measured using the 3,5-Dinitrosalicylic acid method , defininCellulase activity was detected using a spectrophotometer –36, defiPolyphenol oxidase and peroxidase activity was detected using a spectrophotometer , defininA. lucorum from 1st instar nymph to adult was assayed using the above method. The nutrient solution was run in parallel for all enzyme assays in the same way.Salivary enzyme secreted into nutrient solution by Data were processed with Excel software and analyzed with SPSS20.0 by one-way analysis of variance (ANOVA) followed by LSD multiple range test when significant differences were found at P = 0.05.A. lucorum bugs differed significantly among instars   . Total fst (2.21 U/mL) and 2nd (2.03 U/mL) instars, peaked in the 5th instar (14.94 U/mL), and decreased in adulthood (8.48 U/mL) (Pectinase activity was lowest in 148 U/mL) .st (70.60 U/mL) and 2nd (78.94 U/mL) instars, then rose in the 3rd instar (337.30 U/mL) and remained at statistically identical levels thereafter, through the adult stage , reaching relatively high levels in the 3rd instar (39.76 U/mL) and remaining high thereafter, through the adult stage , reached its highest in the 4th instar (1.20U/mL) and then decreased in the 5th (0.94U/mL) instar and adulthood (0.74U/mL). However, none of these differences were statistically significant despite large differences in means  of  feeding . Boyd etgritulus ,17.Myzus persicae (Sulzer) saliva and later in Lygus hesperus [Schizaphis graminum saliva, which were assumed to digest oligosaccharides in foods [Lygus saliva was discovered, which could decompose the cell wall of host plants, helpful for in vitro digestion of foods [Various digestive enzymes have been found in the saliva of different insects, aiding in the digestion of food. The first saliva enzyme to be isolated in insects was pectinase, which enables insects to digest pectin, discovered in hesperus –42. Cellhesperus –42. Variin foods . Amylasein foods . A trypsof foods –47.A. lucorum saliva enzyme, and the levels vary with bug feeding levels, illustrating the digestive functions determined in previous research. As was shown, the activity of pectinase increased significantly from the 3rd instar and peaked at the 5th instar, accelerating the breakdown of the pectin found in the plant cell wall, which aids the penetration of A. lucorum’s stylet into host plant tissues. The activity of amylase, cellulase and protease peaked at the 3rd instar and maintained at a relatively high level until adulthood, potentially accelerating the decomposition of cellulose in the cell wall and starch in plants.In our study, pectinase, amylase, cellulase and protease were all found in A. lucorum saliva in this study, possibly because either quite low amounts of these two enzymes make it hard to determine their activity, or because the two enzymes were not there. More research is needed to improve saliva extraction to determine whether or not these two enzymes are to be found in A. lucorum saliva at all.No polyphenol oxidase and peroxidase were detected in st instar Eurygaster integriceps nymphs, which do not feed, contains very low levels of amylase, which gradually increase with age [Mayetiola destructor (Say), on the other hand, secretes many kinds of proteins at an early developmental stage, and nymphs can accurately determine whether or not to damage plants in response to plant resistance elicited by the secretion of protease [A. lucorum saliva all increased with age, and were significantly higher in 3rd instars than in 1st instars. This change in the activity of salivary enzymes at different life stages indicated an adaptation to the requirements of plant feeding and life development.Different piercing-sucking insects had different salivary components to adapt to varied hosts. Feeding on different host plants, their salivary components will change, which is an important factor in insect and plant evolution .The saliwith age . Mayetioprotease . We founA. lucorum in the immature stages increased constantly up to the 3rd and 4th instars, which showed no significant difference between the two. Daily intake was significantly lower at the 5th instar than the 4th, and then increased slightly at adulthood. Mehrabadi et al found that activities of salivary enzymes were increased in response to feeding in comparison to starved insects, and held their production was induced by the feeding pulse [rd instar nymphs, and cellulase and proteinase had a certain relationship with daily food consumption. A significant linear relationship existed between cellulase and protease activity and food consumption. The activity level of these four salivary enzymes is directly related to the damage to host plants from insect feeding. This being so, cellulase and protease would seem to have a greater impact on the damage caused by A. lucorum.Feeding is usually thought to intensify at late nymphal stages and adulthood. This, however, was not what we found. Daily food intake of ng pulse . Our stuA. lucorum are interconnected with its damage to host plants in a complicated way, and the damage of A. lucorum to plants is the comprehensive outcome of its saliva enzyme. The optimal management of A. lucorum should be confined to a control threshold prior to the peak of daily feeding level, which our results suggest peaks, along with salivary enzyme activity, at 3–4 instars. This reinforces the conventional control phase in field management at an early nymph stage, usually before the 3rd instar. These findings will help inform the outbreak mechanism of A. lucorum by elucidating the levels of salivary enzymes related to the damage on host plants within each nymphal instar and adult, suggesting the optimal timing on the bug management.Thus, the components and activity of salivary enzyme in S1 Table(DOCX)Click here for additional data file.


FFMI percentiles increase at a steady state reaching a peak at age 12 in boys and girls. Smoothed body composition percentiles showed a different pattern in boys and girls. The use of TBFP, FMI, and FFMI along with BMI provides valuable information in epidemiological, nutritional, and clinical research.Overweight children and childhood obesity are a public health problem in Mexico. Obesity is traditionally assessed using body mass index (BMI), but an excess of adiposity does not necessarily reflect a high BMI. Thus, body composition indexes are a better alternative. Our objective was to generate body composition percentile curves in children from Mexico City. A total of 2026 boys and 1488 girls aged 6 to 12 years old were studied in Mexico City. Body weight, height, and BMI calculation were measured. Total body fat percentage (TBFP) was derived from the skinfold thicknesses, and fat mass (FMI) and free fat mass indexes (FFMI) were calculated. Finally, age- and gender-specifıc smoothed percentile curves were generated with Cole’s Lambda, Mu, and Sigma (LMS) method. In general, height, weight, waist circumference (WC), and TBFP were higher in boys, but FFM was higher in girls. TBFP appeared to increase significantly between ages 8 and 9 in boys (+2.9%) and between ages 10 and 11 in girls (+1.2%). In contrast, FFM% decreased noticeably between ages 8 and 9 until 12 years old in boys and girls. FMI values peaked in boys at age 12 (P97 = 14.1 kg/m Overweight and childhood obesity is a major public health problem worldwide and with rising rates in Mexico ,2. ExcesIn Mexico, the National Health and Nutrition Survey (ENSANUT) is carried out every six years, where weight, height, and body mass index (BMI) data are reported in all age groups. According to ENSANUT 2016, the prevalence of obesity in the population aged 5 to 11 years was 33.2%, without any significant changes since 2012 ), and free fat mass index  were calculated with the following [2 (kg/m2) where, FM = (TBFP × weight)/100FMI = FM/height2)FFMI = BMI − FMI (kg/m2) = FMI (kg/m2) + FFMI (kg/m2)Note that, BMI (kg/mSkinfold thicknesses were measured three times in two different sites (triceps and subscapular) on the left half of the body with the caliper brand name el C-130 ,15. Totael C-130 . Finallyollowing ,5,17:FMThe analysis was performed separately by age and gender. Anthropometric and body composition data was summarized by mean and standard deviation. Atypical values were identified by the Jacknife method and were excluded if α ≥ 0.03. The smoothed percentile curves of BMI, total body fat percentage (TBFP), fat mass index (FMI), and free fat mass index (FFMI) were calculated  by using Cole’s LMS method LMS Chart Maker Pro™ software program version 2.54  . DescripSmoothed LMS curves for the 3rd, 10th, 25th, 50th, 75th, 85th, 90th, and 97th percentiles of BMI, TBFP and FFM% for boys and girls are presented in 2 in boys and 8.8 kg/m2 in girls at age 11. Finally, FFMI percentiles increased at a steady state, with a maximum in girls of 20.0 kg/m2 and 17.9 kg/m2 in boys at age 12 at the 97th percentile. The purpose of this study was to generate smoothed percentile curves of BMI, TBFP, FFM, FMI, and FFMI for age and gender in Mexican children. The pattern of TBFP and FMI development differed for boys and girls between the ages 9 and 12, where boys presented higher body fat values. On the other hand, FFMI and FFM percentiles were higher in girls among all ages. We were faced with a challenge while comparing our results to other studies, due to differences in sample size, studied ages, body composition analysis, procedures, and study design. In 2007, Del-Rio Navarro  attempteWhile comparing our BMI percentiles with the 2007 World Health Organization’s international (WHO) reference growth charts , we founBanik et al.  observedOn the other hand, Latin American countries have also attempted to generate their national body composition growth charts. Avalos et al.  conducteSimilar to our study, Laurson et al., 2011  created In Laurson’s et al. study , the skiAmong the studies that generated FMI and FFMI growth charts from ages 10 to 18, Kim et al., 2013 [Although there is no consensus on cutoff TBFP in child population, Williams et al., 1992  identifiA limitation to the proposed thresholds is that the pediatric population represents a great challenge due to variability in hormonal development and maturation of adipose tissue. Thus, reinforcing the need to perform further longitudinal studies to determine age- and gender-specific cutoff points for TBFP, FMI, and FFMI. Even though our study was conducted in a single state of Mexico and lacks the possibility to be generalized at a national level, due to the distribution and socioeconomic status of the studied schools, we believe that this sample adequately reflects the characteristics of the Mexican pediatric population. Another limitation is the lack of sexual maturation analysis because a Tanner scale was not performed in the medical evaluation due to local regulations. However, we propose to include this in further studies to better assess the contribution of secondary sexual characteristics and growth development.Despite these limitations, our study has several strengths. To date, this study represents the first effort to create smoothed percentile curves using the LMS method in children ages 6 to 12 in Mexico City, one of the most important states in the country due to its urban capital and population density. Second, by using FFMI and FMI, we were able to better assess growth development and nutritional status from a dynamic perspective throughout childhood . The useThis study provides age- and gender-specific smoothed TBFP, BMI, FMI, and FFMI percentile curves in Mexican children aged 6 to 12. The smoothed percentile curves showed a different pattern in boys and girls. The purpose of this study is to serve as a reference to enrich the methods of evaluation of obesity in Mexican children and its comparison with studies worldwide. We observed that in the majority of age groups, TBFP and BMI values were higher among boys, presenting a pattern of behavior different than expected, which should be studied further. More studies to evaluate nutritional status using smoothed percentiles are needed because of its methodological simplicity and clinical utility to determine excess fat. We finally recommend that FMI and FFMI percentiles should be used for a complete nutritional assessment and examining health conditions in Mexican children.