The logistic regression model was used after the adjustment of confounders to detect an autistic child's determinants. The study showed little difference between ASD and TD children with respect to nutrients' intake inadequacy and biochemical-nutritional deficiencies, but did reveal gender-based differences. Autistic girls were at higher risk of inadequate carbohydrate intake, while autistic boys were at higher risk of inadequate vitamin E, vitamin K, and fluoride compared to TD children. More autistic children had been treated in neonatal care units after birth than had TD children. The regression analysis revealed that lower maternal education level , vaginal delivery , family history of autism , and taking dietary supplements during pregnancy  were all determinants of ASD in children. In conclusion, maternal nutrition, postnatal conditions, and nutritional status might be contributors to ASD in children. Pre-school children are at high risk for developing nutritional deficiencies. It is therefore important to maintain optimal nutritional status in pregnant patients, and in children after delivery and during early childhood. Future studies that investigate the role of nutrient deficiencies and nutritional interventions in ASD are necessary. Also required are studies that focus on gender differences in the prevalence of ASD, types and severity of symptoms, and ASD nutrition-related problems.Autism Spectrum Disorder (ASD) is a neurodevelopmental disorder triggered by several factors, including those of genetic and environmental nature. ASD can alter communication, behavior, and children's nutritional status, placing them at high risk for nutritional imbalances. Therefore, this study aims to assess preschool autistic children's nutritional status as compared to that of Typically Developing (TD) children of the same age. The study also revealed some of the ASD risk factors among the Jordanian population. It included 52 ASD and 51 TD children (3–6 years), and considered sociodemographic, obstetric, and nutritional factors of the two groups, stratified by gender. Nutritional status was evaluated through a comprehensive questionnaire, 3-day food record, and anthropometric and biochemical measurements. Differences between groups were identified using the chi-square and independent-sample  Autism spectrum disorder (ASD) is a neurodevelopmental disorder characterized by lack of social and emotional interactions , repetitNutrition assessment is the first step in identifying nutrition-related problems and, thus, implementing a nutritional intervention to restore healthy nutritional status , 17. DatThis case-control study of ASD children compared to their TD counterparts was conducted in Amman (Jordan), between July 2018 and February 2019. The study population included pre-school children aged 3–6 years and their mothers. The caregivers/parents signed the consent of participation with detailed information about the study's purpose, all measurements, and laboratory tests. Children were included in the study if they were previously diagnosed with ASD. Children with severe health issues and illnesses or those with a confirmed intake of supplements  in the last 2 months were excluded from the study. The investigator visited 19 special needs centers in Amman, where most of these centers were at the time of the study. Fourteen centers agreed to take part in the study, however, after conducting thorough interviews with the centers' directors, only 8 centers had ASD children meeting the study inclusion criteria. The investigator met with the caregivers of 69 ASD children from these centers; 14 did not meet the inclusion criteria and 3 refused to participate in the study with a response rate of 94.6%. The number of ASD girls treated in the special needs centers was low. The maximum realizable number of ASD girls participated in this study. The final sample included 52 ASD cases (37 boys and 15 girls). For TD children, 51 children were recruited from two kindergartens in Amman (26 boys and 25 girls). Parents met with the principal investigator to complete questionnaires that gathered information about sociodemographics, maternal history, pregnancy and delivery, and child health and well-being, particularly surrounding their eating habits. Additional questions addressed the child's main ASD characteristics and symptoms. The study protocol was approved by the Institutional Review Board at Jordan University of Science and Technology (JUST) .2 (m2)] . Weight was measured to the nearest 0.1 kg, and height was measured to the nearest 0.1 cm according to a standard procedure . The BMI2 (m2)] . Childre2 (m2)] . Childre2 (m2)] . The z-sThe dietary intake of children was assessed using a 3-days food record (2 weekdays and 1 weekend) which is considered the method of choice for assessing nutrient intake among autistic children , 32. ThrRRID:SCR_008940). For calcium determination, a photometric color test was done using clinical chemistry analyzer . Alkaline Phosphatase (ALP) was evaluated using the kinetic change in color test as described by the International Federation for Clinical Chemistry (Blood samples (5 ml) were collected from each participant in EDTA blood tubes and transferred to the JUST laboratory for subsequent analysis. Red Blood Cell (RBCs) and White Blood Cell (WBCs) counts were determined by the Coulter method . Hemoglohemistry . For anahemistry . The mea2) was performed to test the differences between ASD and TD children. An independent-samples t-test was performed to compare the mean and standard deviation of continuous variables between the two groups. Logistic regression models were used to detect determinants of having an autistic child by taking the TD children as the reference category. For each variable, the number of non-missing values are used. We specify the missing = listwise sub-command to exclude data if there is a missing value for any variable in the list. So, by default, missing values are excluded, and percentages are based on the number of non-missing values. Correlations are computed based on the number of pairs with non-missing data. For regression analysis, if values of any of the variables included in the analysis equation are missing, the entire case is excluded from the analysis. Multivariate-adjusted ORs with 95% CIs were estimated as increased risk for having an autistic child (OR > 1). Findings with a p < 0.05 were considered statistically significant.Data was analyzed using the Statistical Package for Social Sciences “SPSS software” . Continuous variables were described using mean and standard deviation, and categorical variables were described using percentage. Chi-square (χP =0.036). The two groups differ significantly in their fathers' (P = 0.007) and mothers' (P = 0.042) occupation, with more ASD parents either unemployed or working in the governmental sector. ASD parents had a lower education level compared to TD parents. However, the difference between the groups was significant only for the mother's education level (P = 0.030).The final sample included 103 ASD and TD children with a mean age of (4.40 ± 0.80) and (4.18 ± 0.81), respectively (Data is not shown in tables). The general characteristics of the sample are shown in P = 0.007). Some pregnancy-related factors are presented in P = 0.004). TD mothers were more committed to the intake of prenatal supplements  and folic acid  compared to ASD mothers.A comparison of some postnatal characteristics between ASD and TD children are summarized in P = 0.050), and more boys received diet therapy compared to girls . The severity of ASD was more mild in boys (67.6%), and more moderate in girls (46.7%). Also, the ASD girls experienced fever more frequently during their first year of life. For ASD symptoms, girls (86.7%) were more affected by their mood than boys (75.7%), and the presence of ASD-related symptoms such as sleep disturbances, eating problems, and chewing difficulties which were also higher in girls compared to boys. Some Gastrointestinal (GI) symptoms were more prevalent in ASD boys, such as diarrhea, bloating, and vomiting. Also, ASD-related symptoms, including diarrhea, bloating, and hyperactivity, were more likely to be triggered by food in ASD boys compared to ASD girls. The results revealed that ASD girls (60.0%) resist new foods more than do ASD boys (48.6%).There were gender differences among ASD groups, however, these differences did not vary significantly for all the characteristics and symptoms except for speech therapy as a treatment type . All ASDThe mean values for weight-for-age, stature-for-age, BMI-for-age, and z-scores are shown in P = 0.175 for boys and P = 0.184 for girls) as presented in For BMI classifications, insignificant differences indicated between both ASD and TD groups in each gender category (P = 0.036). Other macronutrients intake did not vary significantly between the two groups. Nonetheless, more ASD boys did not meet the requirements for the daily energy and protein intake  compared to TD boys . The inadequate intake of carbohydrates and fats was seen in ASD girls more than ASD boys, who showed a higher percentage of inadequate protein intake.Food records were collected to assess the nutritional status and identify dietary factors associated with autism incidence among our study population. A comparison of the inadequate daily energy and macronutrient intake between ASD and TD children is presented in P = 0.015) and vitamin K  than do TD boys. The results showed higher rates of inadequacy of vitamin B2 and vitamin B6 intake among ASD boys compared to TD boys, while more ASD girls failed to meet the daily needs of pantothenic acid, and biotin compared to TD girls. All children, ASD and TD, fell short of the requirements for vitamin A, vitamin D, vitamin B12, and folate.Micronutrient intake was analyzed to complete the assessment of the children's nutritional status. Gender differences among our study population between both ASD and TD groups were studied for inadequate intake of vitamins  and mineP = 0.037). On the other hand, copper consumption among our study population was nearly better; frequency of inadequate copper intake was low among all groups; it was significantly lower among ASD boys than TD boys .As shown in P = 0.044) and HCT  than did TD boys, with a lower mean of serum HCT . ASD girls had a significantly lower deficiency in MCV compared to TD girls . More ASD boys were deficient in HCT, MCV, MCH, and MCHC compared to ASD girls, and only ASD boys (5.7%) had a deficiency in HGB when compared to all other groups (0.0%). Children from the two groups had normal RBC levels. Although not significant, the mean RBC level was lower in ASD children than TD children, and lower in ASD girls compared to ASD boys. Whereas, there was no deficiency in RDW-CV in any of the groups, the mean RDW-CV was significantly higher in ASD boys when compared to TD boys . In contrast, the majority of ASD and TD children had a deficiency in MCH; insignificantly higher in ASD girls than TD girls (60 vs. 84%). The deficiency in serum ferritin was more common among ASD boys (71.4%) and girls (46.7%) than in TD boys and girls , but the difference was insignificant between ASD and TD children when not separated by gender. Ferritin deficiency was higher in boys from the two groups than in girls. Children were not deficient in serum levels for phosphorus, ALP, or calcium except for ASD boys, 2.9% of whom were deficient in calcium. Most of the children from the two groups were deficient in vitamin D3; though the difference was not significant vitamin D3 deficiency was more common among the TD boys and girls compared to ASD boys and girls.Biochemical analysis was performed to identify the differences between ASD and TD children. Values for each child were compared to the established reference values for each individual parameter to identify the abnormal results and the frequency of deficiency among the study population. Biochemical and nutrition-related, parameter levels are shown in Several genetic and environmental factors are extensively bound with ASD . Our stuIn this study, lower education level among parents correlates with a higher risk of having an autistic child. The results were consistent with a comparative study between ASD and TD children in China . In IndiThe current study supports findings of previous studies that show being the first child in the family, cesarean delivery , and treIn our study, the mothers of ASD children reported a higher occurrence of disease during pregnancy than mothers of TD children. Although our results were not statistically significant, they align with those of a study in which increased presence during pregnancy of medical conditions such as anemia, heart, pulmonary and renal diseases were associated with an increased risk of ASD in children . In lineAnother obstetric factor relating to decreased risk of autism is the intake of folic acid and other maternal supplements. Levine et al.  reportedSome ASD-related symptoms observed in this study such as sleep disturbances, mood swings, and GI abnormality agrees with the literature . The mosIn our study, there were higher WAZ, HAZ, and BAZ among ASD boys compared to TD boys. In Egypt, similar results were found among ASD children compared to TD children . Our stuAlthough most children in the current study did meet their daily energy requirements, more ASD boys and TD girls failed to meet the daily energy needs than did other groups. Previous studies from China and the United States reported that ASD children had lower intakes of energy and all macronutrients than TD children , 64. We 2 and B6. Our results align with previous studies among ASD children in the United States and Spain  in all children, with no difference between ASD and TD children. While there was no difference in the intake of zinc and phosphorus between the two groups in Egypt, magnesium, and selenium intake was lower among the ASD group . ConversThe differences in nutrient intake and nutritional adequacies among ASD and TD children from different countries might be affected by the dietary assessment tool used in the study . These dAs HGB, HCT and MCV are lower among pre-school children, and specifically in ASD children , it was 3 deficiency, serum levels being lower than 30 mg/ml. This is a higher percentage compared to previous studies in the United States and Egypt in which 62 and 57% of children (respectively), had vitamin D deficiency , affecting serum 25(OH)D levels because of the lack of sunlight exposure. However, vitamin DThe raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.The studies involving human participants were reviewed and approved by the Institutional Review Board at Jordan University of Science and Technology, Irbid, Jordan. Written informed consent to participate in this study was provided by the participants' legal guardian/next of kin.HA: study, concept and design, interpretation of data, critical revision of the manuscript for important intellectual content, obtained funding, administrative, technical support, and study supervision. AA: conducted the study, data collection and interpretation, and drafting of the manuscript. KA: data interpretation and critical revision of the manuscript for important intellectual content. MO: study design, administrative and material support and study supervision. IA-S: statistical analysis, interpretation of data, and critical revision of the manuscript for important intellectual content. All authors agree to be accountable for the content of the work.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Fifty-two percent of the study participants were final year veterinary students, respectively, and majority (77.2%) had no previous knowledge of biomedical sciences. Majority age were 22–27 years (24.7 ± 2.8) 79% and multiple career fields post-graduation were preferred. Overall, poor perceptions and knowledge of antimicrobial stewardship were observed with variations among countries and only 36.3% (n = 123) of the students were confident in their ability to choose the ideal antimicrobial agents for a specific patient/group of animals. The majority of the final year students were confident of their knowledge regarding AMR (68%), making of Gram staining (69.2%) and in choosing the most ideal route for administering a specific antimicrobial (74.7%). The final year students had significantly (p < 0.05) higher confidence level for knowledge compared with the pre-final year students. Tetracyclines, penicillins, and sulphonamides represent the three most abused veterinary antimicrobials with similar ranking across countries. South African (69.7 ± 20.5) and Sudanese (68.1 ± 15.4) had significantly (p < 0.0001) higher mean scores compared to the Nigerian students (44.3 ± 6.8) in the student's ability to correctly match some specific antimicrobials against their classes but Nigerian students performed better in ranking antimicrobials. This survey revealed poor to average knowledge of antimicrobial stewardship among veterinary students with significant knowledge gaps across the countries. It is recommended that the relevant regulatory and standardization authorities should make concerted efforts and interventions to regularly review curricula to ensure the delivery of targeted formative and normative training, and improved lectures on antimicrobial usage and stewardship in order to improve the awareness and behaviors of future prescribers. The identified knowledge gaps of veterinary medical students on antimicrobial stewardship must be bridge to safeguard the future.In African countries, antimicrobial resistance (AMR) issue remains pertinent. Despite this, little efforts have been made to assess the future veterinary prescribers on their knowledge, attitudes and practices (KAP) related to antimicrobial usage. This multi-country survey attempts to explore the KAP of future veterinarians on stewardship of antimicrobial and identify knowledge gaps. Eight veterinary schools participated from Nigeria, Sudan and South Africa. Data regarding perceptions and knowledge were analyzed using Chi-square χ Another frightening report of the AMR from researchers of the University of York, established that hundreds of rivers in the world are contaminated with antimicrobials, which would potentially escalate the development of resistance among the environmental microflora with implications for human and animal health  respondent must be registered in an OIE recognized Veterinary Faculty in Africa (2) respondent must be in the pre-final or final year of veterinary medical degree, (3) respondent must have undergone training in veterinary microbiology, pharmacology and medicine as part of the undergraduate curriculum.The study was commissioned by the Vice Chancellor's office and cleared for implementation by the Dean, Faculty of Veterinary Science (FVS). Questionnaire tool was approved by the Department of Production Animal Studies and the Department of Paraclinical Sciences, University of Pretoria. Permission to conduct the questionnaire survey among the student was granted by the Office of the Registrar, University of Pretoria and the Dean's office, FVS, and each student also gave consent to participate or refuse participation willingly and reserved the right to withdraw at any point in the survey. The study ensured that the privacy of each participants was adequately protected. Since no direct human or animal sample was obtained and only an academic questionnaire was involved, and because no individual was handled, specific ethical approval was waived.A semi-quantitative questionnaire was designed at the Faculty of Veterinary Science, University of Pretoria to explore the level of knowledge and perception on AMR amongst pre-final and final year students of Veterinary Science , 27. The2 analysis and p-values. Analysis was considered significant when p ≤ 0.05. Antimicrobial agents were independently ranked according to the degree of abuse based on students' perceptions and ranks were compared between countries using Spearman (Rho) Rank order correlation analysis. All data on perception (26 questions) and knowledge (10 questions) were thereafter analyzed using factor analysis using principal component factoring extraction method. Varimax Rotation Method with Kaiser Normalization (VRM-KN) was used for convergence of perceptions and knowledge on antimicrobials , University of Pretoria, South Africa, one of the key outputs for the research was to produce a specific report for the University of Pretoria, South Africa. Hence a university-level analyses of data were first made for South Africa using part of the pooled data . HoweverA total of 105 (29.7%) respondents studied in Nigeria, 177 (50.1%) in Sudan and 71 (20.1%) in South Africa  giving an = 174; 50.7%), that the misuse of antimicrobials by veterinary practitioners contributes significantly to AMR , that the misuse of antimicrobials by farmers contributes significantly to AMR , that as individual veterinary practitioners, they can significantly contribute to preventing an increase in AMR , and that cultures and antibiotic sensitivity testing should be done as frequently as possible to guide antimicrobial use . Discouragingly, only 45.2% agreed that banning the use of prophylactic antimicrobials in food-producing animals will have a positive effect on decreasing the rise in AMR, 41.7% agreed that banning the use of antimicrobials as growth promoters in food-producing animals will have a positive effect on decreasing the rise in AMR and only 36.3% were confident in their ability to choose the ideal antimicrobial agents for a specific patient/group of animals  higher proportions of those who strongly agreed/agreed to the questions representing relative poorer perceptions among the final year as shown in According to the countries surveyed, veterinary medical students from Sudan generally showed poorer perceptions on antimicrobial stewardship compared to those from veterinary schools in Nigeria and South Africa . Accordin = 223; 68%), making of Gram staining  and of choosing the most ideal route for administering a specific antimicrobial . Whereas, substantial proportion of the overall students had confident knowledge regarding choosing an alternative when the first choice of antimicrobial therapy failed , choosing the desired time-frame for (duration of) therapy  as well as finding reliable sources of information to guide empirical use of antimicrobials . Only 48.1% of the overall students had confident knowledge related to the difference between time-dependent and concentration-dependent antimicrobials  higher confidence level compared to the pre-final year students from all the countries  rank-order correlation coefficient rs = 0.98;  < 0.005 . The spi < 0.005 ; Box 1.  < 0.005 .Box 1Answer: chloramphenicolQ1. Which one of the following antimicrobials should not be used in food-producing animals? Answer: MacrolidesQ2. Which one of the following bacteriostatic agents reaches bactericidal levels in the lungs? Answer: EnrofloxacinQ3. Which one of the following antimicrobial agents are not ideal to use for anaerobic bacteria? Answer: GentamycinQ4. Which one of the following antimicrobials would be most ideal to use in an immune-compromised patient? the magnitude of the total doseQ5. The efficacy of aminoglycosides is mostly dependent on: Answer: Mycoplasma spp.Q6. Penicillin is not effective against: Answer: Answer: early infectionsQ7. Penicillin G would likely be less effective in the following scenarios, EXCEPT: Answer: Ampicillin and amikacin.Q8. Which one of the following combinations is synergistic? Answer: SulphonamidesQ9. A loading dose is generally recommended for the following antibacterial agents: Answer: ErythromycinQ10. Which one of the following antibacterial drugs is not very effective against Gram-negative bacteria? Answer: FluoroquinolonesQ11. Which one of the following antibacterial drugs would be most ideal to use in the presence of pus and exudates? Answer: The route of administrationQ12. The long-acting characteristic of 2-pyrrolidone oxytetracycline is dependent on: Answer: DoxycyclineQ13. Which one of the following drugs does not achieve prolonged antibacterial activity (> than 24 h)? Answer: Nitro-imidazolesQ14. Which one of the following antibacterial agents is not time-dependent? Answer: Resistance against amoxicillin-clavulanate is regarded as being extremely rareQ15. Which one of the following statements is not true for beta-lactamase inhibitors? n = 26; Using the extracted factors analyzed with the principal component factoring extraction method and converged with VRM-KN, perceptions and knowledge on antimicrobials n = 26; , studentPerception influenced by training received, clinical experience and sundry matters ;Perception based on legislation and costs ;Perception based on animal welfarism ;Perception based on idealism ; andPerception based on direct field observation , Cronbach's Alpha values = 0.94, an indication of high reliability ; (2). Knowledge based on instructions and labels ; (3). Knowledge based on previous awareness of AMR ; and (4). Knowledge linked with diagnostic technique , Cronbach's Alpha values = 0.67, an indication of moderate reliability  and their effects on stewardship of antimicrobials in selected African countries. In the survey, we observed a higher proportion of male than the female students but note that country-level differences exist. In addition, the greater proportions of students in the field of veterinary medicine in Africa vary between 22 and 27 years, and will work post-graduation in areas of poultry, small animal practice, mixed practice, pharmaceutical industry, State services and cattle practice, indications that they will most-likely influence antimicrobial prescriptions and usage in food animal and pets. Little or scanty information exist on KAP of veterinary and medical professionals from African countries. It is also a fact that healthcare systems is “less” rigorously regulated in the region  and moreSimilar findings have been obtained in other reports among veterinary clinicians/students, medical and other non-medical students in Nigeria , Europe Overall, and irrespective of academic class or country, general perceptions regarding antibiotics among the veterinary medical students will appear poor . Among tp < 0.05) higher “right” responses compared with the final year students. It can be inferred that for most of the questions on perceptions, that there is significant knowledge and perception gaps in and between the different classes of the students, a reflection of the embedded challenges in communicating the messages on AMR using the current veterinary curricular and courses. A previous study from 5 different veterinary schools in Nigeria  high confidence levels compared to the pre-final year. About 68, 69.2, and 74.7% were confident on their knowledge regarding AMR, making Gram staining, and choosing the most ideal route for administering a specific antimicrobial, respectively . This was possibly because an electronic system was used to dispatch the forms (with some limited paper trail), and several of the institutions does not regularly access internet. In addition, most school have unstable academic sessions during the survey period. While it is agreed that the small sample size (n = 353) may not be representative enough for all African countries, and there may be disparities in facilities available for learning, teaching methods and learning outcomes, because similar levels of competencies are expected from all veterinarians post-graduation, similar platform of evaluation should be utilized as we did in this study. We circulated the questionnaire to all institutions based on the details available on the OIE website for heads of all veterinary institutions in Africa. We also attended the Third Annual OIE Regional Deans Meeting to notify them of the survey and encourage participation. Furthermore, the survey was to target a subset of only the pre-final and final year students, hence it represented our best effort in conducting the survey. It was equally difficult to determine the exact sample size because Africa remain a data-scarce society and we did not know the exact number of veterinary students who should form the sample frame. Future studies should make effort to address these inherent challenges and make adequate budgetary provision to extend this study.We confirmed that perceptions were largely influenced primarily by the training and clinical experienced received during training while current knowledge among veterinary students is a factor of practice and instructions/labels. These understanding should form the basis for future programmes designed for training future veterinarians and medical doctors across Africa. It should be known that this work was subject to the limitation of numbers of schools that responded and participated in the survey (https://data.worldbank.org/indicator/NY.GDP.PCAP.CD?locations=ZG). This may have influenced to some degree the outcome of this study but whether this affected the outcome of this research was not investigated in this study.We are aware that significant socio-economic differences exist between the three countries included in this study. For instance, the GDP per capita (2018 current in US$) for the countries: Nigeria, South Africa and Sudan was US$ 2,028.20; US$ 6,374.00 and US$ 977.30, respectively (It is our conclusion that almost all the spheres and fields tested in this work influenced the knowledge of and perception on antimicrobials, which are key determinants in the use and prescription of antimicrobial by future animal health officers. No knowledge field on antimicrobials should be left out in the training of veterinary students. Finally, differences exist in educational systems and resources used, and students do not believe that they have self-prepare enough to face the challenges of future prescriptions and stewardships of antimicrobials . The ideThe raw data supporting the conclusions of this article will be made available by the authors, without undue reservation, to any qualified researcher.FF, LL-P, PS, and MS: conceptualization. FF, LL-P, PS, LD, and MS: methodology and resources. AS, OA, IO, MA, MF, OF, DD, MSA, OM, MGA, HY, and WE: field investigation and data collection. FF, LL-P, LD, and MS: formal analysis. FF: writing—original draft preparation. FF, SJ, OA, IO, MA, MF, and OF: writing—review and editing. FF, LL-P, PS, and LD: supervision. FF, LL-P, PS, and MS: project administration. FF: funding acquisition. All authors: visualization.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


In this cross-sectional study, we investigated the baseline risk factors of diabetes mellitus (DM) in patients with undiagnosed DM (UDM). We utilized the Korean National Health and Nutrition Examination Survey (KNHANES) 2010–2017 data. Data regarding the participants’ demographic characteristics, health status, health determinants, healthcare accessibility, and laboratory tests were gathered to explore the differences between the DM, UDM, and without-DM groups. Among the 64,759 individuals who participated in the KNHANES 2010–2017, 32,611 individuals aged ≥20 years with fasting plasma glucose levels of <100 or ≥126 mg/dL were selected. The odds ratios (ORs) regarding family history of diabetes and the performance of national health and cancer screening tests were lower in the UDM group than in the DM group : 0.43, 0.66; adjusted OR: 0.74; 95% CI: 0.62, 0.89; adjusted OR: 0.71; 95% CI: 0.60, 0.85). The ORs of hypertension and obesity were higher in the UDM group than in the DM group . Patients with UDM were more likely to be exposed to DM-related risk factors than those with and without DM. Public health interventions to prevent UDM development are necessary. Diabetes mellitus (DM), a group of metabolic disorders characterized by high blood glucose levels, is a major global health issue. Approximately 451 million people worldwide were estimated to have diabetes in 2017, and this value is expected to increase to 693 million by 2045 [Understanding undiagnosed DM (UDM) is critical to mitigating the substantial growth of the DM burden. People with DM are at a high risk of developing several life-threatening and severe complications, such as coronary artery disease, peripheral arterial disease, stroke, neuropathy, nephropathy, and retinopathy ,6. As DMTherefore, this study aimed to investigate the characteristics of people with UDM in terms of demographic characteristics, health status, health determinants, healthcare accessibility, and laboratory testing. The results of this study would aid in the formulation of public health messages targeted at people with UDM for early detection and lifestyle intervention.https://knhanes.cdc.go.kr.The present study included data from the Korean National Health and Nutrition Examination Survey (KNHANES) V, VI, and VII, which were conducted from 2010 to 2017 by the Korean Ministry of Health and Welfare, on the basis of a stratified and multistage probability cluster sampling design. The KNHANES has a cross-sectional design and is based on a non-duplicate complex sample, comprising nationally representative civilians [n = 14,878); (2) people whose fasting plasma glucose (FPG) levels were ≥100 mg/dL and <126 mg/dL (n = 9063); and (3) people with unknown DM status (n = 8207) (The following participants were excluded in the selection of the study population: (1) Korean adults aged <20 years  = 8207) . Those w207 . Thog/dL n = 063; and https://knhanes.cdc.go.kr/knhanes/). Data on demographic characteristics, such as age, sex , educational attainment (high school or less/more than high school), marital status (yes/no), and income level (low/middle/high), were collected. We selected the health-status-related variables that could affect DM status, such as family history of diabetes (yes/no), hypertension status , obesity  levels, and body mass index (BMI) (kg/m2). Obesity variables were classified based on the following BMI categories: underweight, BMI < 18.5 kg/m2; normal weight, 18.5 kg/m2 ≤ BMI < 25 kg/m2; overweight, 25 kg/m2 ≤ BMI < 30 kg/m2; and obese, BMI ≥ 30 kg/m2. The following variables were included in this study as health determinants: current smoking status (yes/no), heavy alcohol consumption (yes/no), walking (yes/no), and muscle training (yes/no). We defined heavy alcohol consumption as at least seven or five cups of alcoholic drinks twice or more per week for men and women, respectively. Access to healthcare was evaluated with variables such as having undergone national health screening within the last 2 years (yes/no), having undergone cancer screening within the last 2 years (yes/no), and having visited an outpatient clinic within the last 2 weeks (yes/no). Finally, laboratory test results, such as those pertaining to total cholesterol (TC) levels (mg/dL), diastolic blood pressure (mmHg), FPG (mg/dL), and triglyceride concentration (TG) (mg/dL), were included in the analysis.We selected 31 variables for which the relevant questionnaire and answers had not changed from 2010 to 2017. The definition of the selected variables was determined according to the KNHANES guidelines .The impact of risk factors on people with UDM was explored in comparison to that on those with/without DM. First, the weighted prevalence of each group was calculated. Variables based on qualitative and quantitative measurements are presented as weighted percentages with standard error (SE) and weighted means with SE, respectively. A chi-square test and analysis of variance (ANOVA), which were calculated with sampling weights, were used to evaluate the association between DM status and covariates. The multinomial logistic regression model was then employed to compare the impacts of the risk factors on the DM and UDM groups, using the without-DM group as the reference. The crude and age- and sex-adjusted models were constructed with sample weights for each variable. The results of the application of age- and sex-adjusted models for each variable are presented in forest plots to evaluate the magnitude of impact of each variable on DM and UDM status. Finally, the logistic regression model with sample weights was used to confirm the difference in the selected variables between the DM and UDM groups. Univariate and multivariable analyses, adjusted for age and sex, were performed for each regression analysis. Additionally, multivariable logistic regression analysis was performed to compare the DM and UDM groups in terms of risk factors. The regression models in those analyses included variables that were significantly associated with DM status. The results are presented in the 2), TC (206.06 ± 1.62 mg/dL), systolic blood pressure (126.82 ± 0.58 mmHg), diastolic blood pressure (80.88 ± 0.40 mmHg), FPG (155.99 ± 1.67 mg/dL), and TG (220.93 ± 7.44 mg/dL) values were the highest in the UDM group , hypertension, BMI, and obesity were higher in the UDM group than in the DM group. Furthermore, the ORs of age, family history of diabetes, health screening within the last 2 years, and cancer screening within the last 2 years were lower in the UDM group than in the DM group. Collectively, compared to the DM group, the patients in the UDM group were more likely to be young and male, with high BMI and blood pressure. Moreover, those in the UDM group were also more likely to believe that they were healthy but were less likely to have undergone cancer or national health screening tests or have a family history of DM.In this study, the status of individuals with DM and UDM was more strongly associated with demographic characteristics compared to that of people without DM. Nevertheless, there was no significant difference between patients with DM and those with UDM. The demographic characteristics of patients with DM and UDM showed consistent results in previous studies ,14,15,16Hypertension is an emerging major risk factor for type 2 DM. Reported works have shown that elevated blood pressure values are significantly associated with chronic inflammation and endothelial dysfunction ,20, whicObesity and BMI are well-established indicators of DM. Our study confirmed that people with UDM were more likely to be obese and have higher BMI values than those classified as with or without DM. People with high BMI values are more likely to have high levels of non-esterified fatty acids, glycerol, hormones, cytokines, pro-inflammatory markers, and other substances, which induce the development of insulin resistance . ConsequThe results of several laboratory tests, such as those pertaining to TC and TG, showed greater abnormalities in the UDM group than in the with- and without-DM groups. The age- and sex-adjusted ORs of TC and TG were significantly higher in the UDM group than in the DM group. Previous studies have shown that the levels of such variables in the UDM group were significantly higher than those in the DM and without-DM groups in Korea ,24,28. HRegarding hypertension, obesity, and screening tests, our results showed higher values and averages in the UDM group than in the other groups. Although some UDM cases may be diagnosed in the early stages, people with UDM predominantly tend to prolong their status for several years, consequently aggravating the risk of developing DM-related complications. Furthermore, the higher risk of complications in the UDM group is evidenced by the results of the analysis that explored other variables, such as health-behavior-related indicators and access to healthcare.Smoking status is a well-established indicator of diabetes development. As shown in previous studies, the odds of having a current smoking habit were significantly higher in the DM and UDM groups than in the group without DM . HoweverThe impact of alcohol consumption on DM varies depending on the amount and frequency of alcohol intake. Baliunas et al., who performed a meta-analysis that reviewed 20 cohort studies, found a U-shaped relationship between the alcohol consumption level and DM in men and women . InteresThe difference in the level of physical activity among the groups was investigated using variables such as walking for 30 min/day and performing muscle training exercises for at least 5 and 2 days per week, respectively. The differences in the levels of physical activity between the DM and UDM groups were not significant; however, the UDM group was significantly less likely to achieve the recommended level of physical activity than the group without DM. Anue et al. found an inverse association between physical activity and DM risk . The invThis study elaborated the potential impact of undergoing a screening test to examine UDM status. Diabetes is an ambulatory care-sensitive condition, which means that early detection and provision of primary care, such as that aimed at glucose control, are critical to reducing the risk of diabetes-related illness and complications . AccordiPatients with UDM should be encouraged to participate in the screening test for detecting DM. Improvement of the participation rate for the diabetes-screening test may significantly reduce the burden of DM. Studies have provided recommendations for screening tests for specific populations at risk of UDM ,25,44,45Our study had several limitations. First, it had a cross-sectional design. Therefore, the results of the analysis did not reveal real casual relationships. However, most of the observed associations were comparable to those of previous prospective cohort, case–control, and cross-sectional studies. Second, our study had conventional limitations associated with the use of a survey. The presence of recall bias could not be ruled out; thus, some participants may have been misclassified due to incorrect responses to our questions. Third, our study could not differentiate the type of DM. However, a previous study showed that the proportion of type 1 DM among all types of diabetes was 6% and that the prevalence decreased during 2011–2013 . Thus, iDespite these limitations, the present study had several strengths. Our study was based on a large population-based sample that represented the Korean population. To our knowledge, this KNHANES study was performed using the largest and latest dataset obtained over the last 5 years. In addition, our study newly found issues regarding access to healthcare services in people with UDM. Therefore, this finding supports our suggestion that a public health intervention targeting people at high risk of UDM in Korea is necessary.In conclusion, our results indicate the urgent need for public health intervention to prevent UDM development in high-risk populations. The American Diabetes Association has established a list of recommendations for the prevention of DM in terms of lifestyle intervention, pharmacologic intervention, prevention of cardiovascular disease, and diabetes self-management education and support . However


Identification of Influential nodes in complex networks is challenging due to the largely scaled data and network sizes, and frequently changing behaviors of the current topologies. Various application scenarios like disease transmission and immunization, software virus infection and disinfection, increased product exposure and rumor suppression, etc., are applicable domains in the corresponding networks where identification of influential nodes is crucial. Though a lot of approaches are proposed to address the challenges, most of the relevant research concentrates only on single and limited aspects of the problem. Therefore, we propose Global Structure Model (GSM) for influential nodes identification that considers self-influence as well as emphasizes on global influence of the node in the network. We applied GSM and utilized Susceptible Infected Recovered model to evaluate its efficiency. Moreover, various standard algorithms such as Betweenness Centrality, Profit Leader, H-Index, Closeness Centrality, Hyperlink Induced Topic Search, Improved K-shell Hybrid, Density Centrality, Extended Cluster Coefficient Ranking Measure, and Gravity Index Centrality are employed as baseline benchmarks to evaluate the performance of GSM. Similarly, we used seven real-world and two synthetic multi-typed complex networks along-with different well-known datasets for experiments. Results analysis indicates that GSM outperformed the baseline algorithms in identification of influential node(s). Such networks have common characteristics, i.e., heterogeneous topologies, that make it impossible to assign the same importance to each node in the network. Therefore, it is essential to identify the influential nodes via quantitative approaches to examine their properties and to practice their proper usage. For example, in infectious diseases network, preventing the spread of rumors and viruses5, in criminal networks, quickly identifying a terrorist organization’s leader6. Similarly, in the traffic system network7, food chain network8, drug network9, and so on. There are a good number of studies have been proposed and deployed in the field of complex network on the identification of influential nodes20 where identification of most important nodes from local and global perspectives is worth mentioning21. Although closeness centrality (CC) and betweenness centrality (BC)22 are path-based indicators that consider the global structure of the network to identify the influence of nodes. However, due to their high computational complexity, they may not be applicable to many networks. It’s common that degree centrality (DC)23 is the simplest method to identify the influential nodes, but it fails to consider the global structure of networks. Kitsak et al.24 proposed the k-shell method for locating influential nodes, but it is too coarse to identify the required influential nodes. Besides these, iterative-based centralities such as eigenvector centrality (EC)25, page rank (PR)26, hypertext induced topic search (HITS)27, and son on are not appropriate for networks with tight connections.The concepts of complex networks are abstracted from real-world system networks28, inverse square law29 and gravity index model30. The profit leader method considers the set of important nodes on the basis of the profit leader concept analysis. In the paper by L.-l. Ma et al.30 proposed gravity formula-based algorithm, which considers both neighbour’s nodes influences and path information. However, in some cases, it is also important to combine the global as well as local structure of nodes in the entire network. Similarly, the inverse square model is based on node interactions and is not suitable for large network. Furthermore, in view of propagation probability, Ma et al.31 proposed hybrid degree centrality (HC), which combines local indicators and degree centrality measures. All in all, these approaches have their own shortcomings and limitations. Still, the identification of influential nodes is a challenge.Recently, some approaches have put efforts forward for the identification of influential nodes such as profit leader30, in this study, we design a new mechanism called GSM that not only considers the self-influence of the node in the network but also concentrates on the global influence of nodes. To analyze the algorithmic performance, we employed GSM on different kinds of real as well as synthetic networks where we used the susceptible-infected-recovered (SIR) and kendall’s From the above discussion, to address these changeling problems, inspired from literatureThe framework of the paper is organized as follows: We present preliminaries and a brief introduction of baseline algorithms, including BC, PL, GIC, HI, CC, ECRM, DNC, IKH and HITS in 32, Closeness Centrality (CC)33, HITS34, H-Index (HI)35, Profit Leader (PL)28, Improved k-shell hybrid method (IKSh)12, Extended Cluster Coefficient Ranking Measure (ECRM)36, Gravity Index Centrality (GIC)30 and Density centrality (DNC)37 are introduced in this section. 32. j and k, and j and k, that pass through node i.Betweenness Centrality (BC): BC calculates influential nodes based on global information33. CC of node i is defined as: Closeness Centrality (CC): CC also calculates influential nodes based on global information. It uses the shortest distance between each pair of nodes to identify the influence of each node34.Hyperlink Induced Topic Search (HITS): This algorithm is based on two factors i-e., Authority Update, and Hub Update. Authority update is computed by considering the number of hub edges associated with the authority website, and Hub Update is computed by considering the number of authority websites linked by the Hub website35.H-Index (HI): This algorithm identifies the influential node’s by taking into account the node’s neighbor and using H-index notation. A high H-index represents that the node has more important than other connected nodes28.Profit Leader (PL): This algorithm is based on profit leader concept analysis and suitable for any network i.e., directed or undirected12.Improved K-shell Hybrid (IKH): This algorithm considers the k-shell, shortest distance between the nodes and parameter 30. GIC(i) is defined as: i.Gravity Index Centrality (GIC): This algorithm basis on universal gravity concept; that considers both neighbor’s nodes influences and path information36.Extended Cluster Coefficient Ranking Measure (ECRM): This algorithm is working on the basis of local clustering coefficients and uses link similarity between adjacent nodes37. DNC(i) is defined as: Density centrality (DNC): It is inspired by the area density formula to identify the influence of nodes in the spreading dynamicsA network can be denoted by G, equated as G = , where V and E represent nodes and edges, respectively. Moreover, Betweenness Centrality (BC)39 , a Global Structure Model (GSM) was proposed, which consists of self and global influences.Several approaches based on the global structure of the network to identify the influence of nodes have been developed and deployed, but better utilization of self as well as global structure influence is still a challenge, needs to be addressed. Inspired from literatureIn this context, we used e  and take k-shell i and node j.The node influence also considers the influence of the other connected nodes to it. Normally, the node influence is increased if its neighborhoods have a high value of k-shell; however, the contact distance between the two nodes cannot be ignored, which is inversely proportional to the influence of the nodes.i and node j,The node The proposed GSM model is divided into four parts; first, construction of corresponding network; second, calculation of the network’s global influence and the k-shell of node and the distance between nodes. In the third step, we consider the self-influence of the network, the self influence of the node itself is computed. Finally, the influence of each node on the entire network is calculated. To further demonstrate GSM method, as shown in Fig. To calculate the self influence and global influence, here we apply Eqs.  and 7);;7); we hmentclass2pt{minimExperimental setup is performed on system with configuration as: CPU: Feiteng 1500A , Operating system: galaxy kirin server os Bios: China-made Kunlun firmware Memory: 3 GB and Hard disk: 2 TB  Susceptible (S), (ii) Infected (I), (iii) Recovered (R). Susceptible (S) refers to a healthy state and can be infected by others. Infected (I) refers infected state and can infect other individuals. Recovered (R) denotes a recovered state, which cannot be infected by other individuals again. For the first time, all the seed nodes are in a susceptible form. At each time step, the seed node can infect its nearest and next-nearest neighbor nodes (in the susceptible state) with a probability nI and nR are the number of infected and recovered nodes, respectively. Where we set We used the SIR model to investigate the spreading dynamic of each node43 to calculate the performance of GSM further. Let suppose, two-node sequences We used kendall’s http://networkrepository.com). The datasets are, e.g., (i) Jazz44: this is a communication network , consist of 198 nodes and 2472 edges. (ii) H-friendship network45: this network reflects user friendship and contains 12534 edges and 1858 nodes. (iii) E-mail network46: this is a communication network of the Roviraa Virgilli University of Spain, where nodes are e-mail user, and edges represent at least one e-mail was sent. (iv) Crime network47: this network consists of 829 nodes and 1476 edges . (v) Dolphin48: it is a social network with 62 nodes and 159 edges. (vi) Web-spam49: this is a famous social netwok provided by Purdue University, which consists of 4767 nodes and 37375 edges. (vii) Astroph-e50: this network consists of 18771 nodes and 198,050 edges. The topological characteristics of the seven real-world multi-typed complex networks are shown in Table We evaluated GSM on seven different real-world networks to validate its efficiency. The seven real networks are publicly available and can be obtained from (52 to generate different synthetic networks for the process of experimentations. (i) Random network: it is an synthetic random network in which we set the number of nodes as V= 1000, probability 0.01, the average degree as There is a bulk of exemplary complex networks exist in the real world and we do not know the details of ground realities about all of them because it’s not even possible to conceal such a large number of information about a matter that is so widely being exploited. Therefore in order to evaluate GSM and the baseline methodologies, we applied the benchmark generator modelTo measure the influence of nodes in different real and synthetic networks and to validate the applicability and effectiveness of the GSM, we used two evaluation metrics i-e., SIR, and Kendall’s models. First, we used a simple graph containing 13 nodes and 17 edges, as shown in Fig. Kendall’s 24. Where it is not easy to differentiate the importance of distinct nodes. For small networks , we set In order to further examine the propagation effect of GSM, we analyzed the spreading impact of the ranked nodes in the SIR model. To better distinguish the influential nodes, the infection probability Moreover, we compared the top ten nodes’ effects selected by our proposed GSM and the corresponding baseline centrality measures for different networks. All top ten nodes are considered as seed nodes and the time t in the range between 1 and 25. Figure O(n). Therefore, the total computational complexity of GSM is There are two main components of the proposed GSM. In the first stage, the time complexity of the node’s global influence is calculated. We used Dijkstra to calculate the shortest distance, and its complexity is We studied the problem of identification of nodes influence in complex networks. Several approaches have been developed and deployed in this area but still, it is a big issue for scientists and researchers. In this regard, we proposed an algorithm called GSM to identify influential nodes, which considers both self as well as global influence of nodes in the networks. We applied the proposed GSM on different real as well as synthetic networks and employed two evaluation metrics (SIR and Kendall


Although extensive research has incorporated dietary assessment of a population through a posteriori application, this study is the first to examine the Malaysian population and use an a posteriori method and principal component analysis (PCA) to assess the dietary patterns of the Malaysian population. The correlation between all dietary patterns derived via PCA and selected nutrient intake were determined in this sample of study; (2) Methods: A total of 3063 respondents (18 to 59 years old) covering Peninsular Malaysia, Sabah, and Sarawak, participated in this study. PCA was applied on the food frequency questionnaire collected from the respondents, and descriptive statistics and PCA were performed using SPSS version 21; (3) Results: Six patterns were identified: “traditional”, “ prudent”, “ modern”, “western”, “Chinese”, and “combination” diets. All together, these six patterns were able to explain 45.9% of the total variability. Few components derived from the factor loadings showed positive association with several nutrient markers. The traditional dietary pattern showed a moderate, positive correlation with total protein and total sugar intake, there was a significant moderate correlation between the prudent dietary pattern and dietary fibre, and there was a moderate positive association between the Chinese dietary pattern and total energy; and (4) Conclusions: The exploration of the PCA approach above may provide justification for assessment of dietary patterns rather than reliance on single nutrients or foods to identify potential connections to overall nutritional wellbeing as well as to explore the diet–disease relationship. However, study of pattern analysis must be conducted among the Malaysian population to produce validity and reproducibility for this dietary approach in light of the numerous methodological issues that arise when performing PCA. In September 2011, the United Nations General Assembly (UNGA) passed a resolution on the Prevention and Control of Non-communicable diseases (NCDs) that clearly highlighted the importance of poor nutrition and early undernutrition as major causes of NCDs in lower and middle-income countries (LMICs). This resolution underlined the need for action to improve diet and physical activity patterns worldwide to curb the rise in obesity and other chronic, diet-related diseases. Overnutrition has dramatically expanded in Malaysia, and although the rate of undernutrition has been reduced, hunger remains a significant problem among isolated groups. As a result, the particular challenges for Malaysia and similar, newly emerging economic powers are twofold: how to ensure that both over- and undernutrition are appropriately addressed, and how to ensure that policies aimed at reducing undernutrition do not conversely spur overnutrition. The UNGA declaration highlighted key actions that member states  needed to take to prevent and control the spread of NCDs. These actions included changes in dietary patterns to increase consumption of fruits and vegetables while reducing intakes of fats, salt, and sugars. For Malaysia to achieve these changes, the first step is to identify the Malaysian population’s patterns of food consumption.In the latter half of the 20th century, researchers started to study dietary patterns rather than the consumption of individual nutrients or foods  because Principal component analysis (PCA) is the most commonly used statistical technique in the a posteriori method. PCA deducts dietary variables through aggregation and produces a unique factor solution. These factor solutions were selected based on certain cut-off points of the eigenvalue, and the remaining factor solution will usually be concluded by naming of the factors. Many studies have explored PCA in determining the dietary patterns of a population ,11. HearTo identify the type of dietary patterns derived by the PCA approach in this study sample.To determine the correlation among all dietary patterns derived by PCA and selected nutrient intake in this study sample.Although there appears to be significant impact when an a posteriori application is utilised when assessing dietary patterns, this method has received little use in Malaysian studies. Therefore, this study will assess the dietary patterns of the Malaysian population using the a posteriori method, and PCA was selected to represent the method. By the end of this study, our objectives are as follows: This study utilised data from the Malaysian Adult Nutrition Survey (MANS), the first Malaysian national nutrition survey, which was conducted from 2002 to 2003; covered Peninsular Malaysia, Sabah, and Sarawak; and was funded by Malaysia Ministry of Health. MANS was fully coordinated by the Nutrition Section of the Family Health Development Division under the Ministry of Health Malaysia; this division had assembled a technical committee that was responsible for developing the survey design and the survey questionnaire, monitoring the quality of the survey data, analysing the data, and preparing reports. MANS was conducted to determine the nutritional status and food consumption of Malaysian adults, and a total of 3063 respondents, aged 18 to 59 years old, participated in this study. Informed consent was acquired from respondents prior to their involvement in the survey. The data collection ran from October 2002 to July 2003 for Peninsular Malaysia and from January to December 2003 for Sabah and Sarawak. The custodianship of the MANS data was made the responsibility of the Institute of Public Health, Malaysia (IPH) and approval of data usage was obtained from the Director General of IPH. This research and the study protocol were approved by the Medical Research and Ethics Committee, Ministry of Health Malaysia, NMRR ID (NMRR-12-815-13100).MANS analysed both urban and rural areas in western and eastern Malaysia, and only households in the private living quarters (LQs) were included. The study’s designers removed institutional households such as hostels, hotels, hospitals, and prisons, which accounted for just 1% of all households at the time. The sample frame was used for the 2000 Population and Housing Census Enumeration Blocks (EBs), the extent of which the National Household Sampling Frame Department of Statistics determined. The EBs in the sample frame are grouped according to the 2000 Population and Housing Census by urban and rural areas. The first stage of the sample unit is the EB, and the LQ in the same EB is the sample unit in stage two. EBs have been chosen using the “size-probability” form; EBs with a larger LQ size are therefore more likely to be chosen. The LQ in the selected EBs is the second step of the sample unit.The questionnaire consisted of five sections: socio-demography; 24-h diet recall and meal pattern; habitual physical activity and 24-h physical activity recall; anthropometry; frequency of food intake ; and frequency of supplement intake. However, only socio-demography, anthropometry, and frequency of intake were selected because of the contribution of their data in fulfilling the requirement for analysis of this study. Other parts of the questionnaire have been explained elsewhere ,15,16,17TM Diet analysis software was used to determine the respondents’ energy and nutrient intake levels. For the measurement of nutrient intake among respondents, food databases such as the Nutrient Composition of Malaysian Food and the USDA’s standard reference data were used. The calculation for converting FFQ data to daily energy and other nutrients is based on the conversation factor used to estimate food intake based on frequency of intake  [Dietary intake includes findings of the energy, macronutrient intake, and micronutrient intake of the respondents. The Nutritionist Pro Health) : amount The semi-quantitative FFQ included in MANS consists of 126 food items and contains a large number of variables to run the PCA. To run a PCA, the food data must be aggregated into groups  to ease PCA was applied to the 17 food item groups. Prior to performing PCA, the suitability of data for factor analysis was assessed. The Kaiser–Meyer–Olkin test, a measure of sampling adequacy, was applied to examine whether the variables are adequate for correlation . Bartletp-value of <0.05 was considered significant.All analyses were conducted using SPSS version 21 . Preliminary analyses were implemented to avoid violation of the assumptions of normality, linearity, and homoscedasticity. A one-way between-group analysis of variance (ANOVA) was conducted to explore the differences between nutrient intakes by socio-demographic characteristics. Scores for all respondents on each identified pattern were generated in determining association between body mass index (BMI) and other nutritional profiles . The Pearson product-moment correlation coefficient was employed to measure the association between BMI and the estimated factor scores. A two-sided The socio-demographic background of the respondents is presented in t (3009) = 24.5, p < 0.001—with men having a greater energy intake than women had. p < 0.05 were found in protein and fat intake. Hence, a two-way inter-group analysis of variance was performed to explore the impact of gender on protein and fat intake among the Malay, Chinese, and Indian populations. The interaction effect between gender and ethnicity group was not statistically significant for protein intake (gender*ethnicity = 0.501), which indicates that there is no significant difference in the effect of gender on protein intake for the Malay, Chinese, and Indian populations; F  = 0.79, p = 0.50. There was a statistically significant main effect for ethnicity: F  = 21.3, p < 0.001; however, the effect size was small . Next, two-way inter-group analysis of variance was performed on fat intake among the Malay, Chinese, and Indian populations to assess the impact of gender. The interaction effect between gender and ethnicity group was not statistically significant for fat intake (gender*ethnicity = 0.233). However, there was a statistically significant main effect of fat intake for ethnicity—F  = 4.075, p < 0.0—where the effect size was extremely small .The Malaysian Ministry of Health has adopted RNIs as the nomenclature for reference to dietary recommendations. The total mean energy intake of all respondents was 2293 kcal ± 657 SD. RNIs for energy and other nutrients are age and gender dependent, and therefore we categorise men and women into age groups to investigate the mean percentage of RNI achieved for each age group, as shown in With the Kaiser criterion, there are six components (factors) of an eigenvalue greater than 1.0. After six factors, the scree plot also demonstrates a noticeable break. Factor 1 accounts for 9.862% of the variance. The rotated component matrix details the factor loadings onto the six components. The first pattern, Factor 1, with high loadings for flavourings, fish and seafood, confectioneries, eggs, and non-alcoholic beverages was labelled the traditional dietary pattern. This traditional dietary pattern was also associated with lower intake of milk, other types of meat, cereals, and cereal products, and this pattern was labelled traditional because of the rich consumption of flavourings in Malaysian dishes, which include sugar, honey, shrimp sauce, anchovy sauce, shrimp cencalok, thick soy sauce, light soy sauce, ketchup sauce, oyster sauce, and fish sauce. These flavourings are added frequently during food preparation in traditional Malaysian cuisine or are served as side dishes. The most popular dish in Malaysia is nasi lemak (coconut rice), a staple of breakfast and dinner alike in which rice is cooked in a mixture of coconut milk and water. A gravy made with shrimp sauce along with chillies, sugar, and anchovies is then added to the rice. Even though the quantity of this gravy is ostensibly negligible, if it is consumed regularly, the shrimp sauce nevertheless yields an impact on dietary patterns. Other commonly consumed traditional dishes are fried rice and fried noodles; preparation of these foods involves the use of thick soy sauce, light soy sauce, and ketchup sauce. Another popular traditional dish, especially among both the Malay and Chinese populations, is chicken rice, in which the chicken is prepared through marination with soy sauce and honey. Fish and seafood, along with confectioneries, loaded moderately on Factor 1. Fish is the predominant source of protein in Southeast Asia ; ChineseThe second pattern, Factor 2, which heavily features fruits and vegetables, plus smaller yet substantial quantities of legumes, legume products, and milk, was named a prudent dietary pattern, a term that other studies have extensively applied to reflect vegetable and fruit intake ,34,38,39r = 0.482, p < 0.05. The sample correlation coefficient was 0.23, indicating that approximately 5% of the variance in the prudent diet can be explained by total dietary fibre intake. Meanwhile, the modern, western, and combination dietary patterns show a negligible significant relationship among the selected variables. There was a moderate positive correlation between the Chinese dietary pattern and total energy: r = 0.296, p < 0.05. The Chinese dietary pattern, which largely includes other meats  and alcoholic beverages, may explain the increases in total protein and total energy, respectively. Once the dietary patterns were determined, scores for all respondents on each identified pattern were generated. These computed scores were used in determining correlation between BMI and other nutritional profiles . In essence, the majority of the respondents belonged to at least one of the following demographics: residence in an urban area, female, Malay, married, employed, completed upper secondary education but not tertiary education and with a household income of less than MYR 1500, and comparison with the variance in other national sources suggested that the socio-demographic data in this study were representative of the Malaysian population. The average BMI in this study was 23.5, a value within the normal range. Total energy intake was highest among Malays and lowest among Indians. Both men and women had carbohydrate, protein and fat intake that exceeded the daily recommended allowances. The mean total carbohydrate intake was highest among Malay respondents and lowest among Chinese respondents. There was a significant difference in protein and fat intake among the Malay, Chinese, and Indian populations after adjustments for gender; however, intake of both nutrients demonstrated a minor effect. It is vital to understand the variations in nutrient intake among the different ethnic groups because this diversity reflects the cultural, religious, geographical, and genetic idiosyncrasies of each ethnicity .On the basis of these dietary pattern findings, it is clear that Malaysia is undergoing a nutritional transition, in confirmation of national survey data. Dietary patterns have changed rapidly in Malaysia and in most other LMICs worldwide, and this transition has been marked by the Westernisation and modernisation of traditional diets, which greater consumption of meat, fat, salt, and sugar; dishes prepared outside the home; and pre-processed foods have supplanted ,43. The p for trend = 0.048) [Nearly all dietary patterns show a negligible significant correlation with BMI. This result was slightly different from those of other studies, which identified a good correlation between dietary pattern and obesity through factor analysis ,59,60,61= 0.048) . Sichier= 0.048)  in a stu= 0.048) . The aut= 0.048) . The stu= 0.048) . A possi= 0.048) . Newby a= 0.048) . Inconsi= 0.048) . In refe= 0.048) . Investi(i)There was a moderate, positive correlation between the “traditional” dietary pattern and both total protein and total sugar intake;(ii)There was a significant moderate correlation between the “prudent” dietary pattern and dietary fibre;(iii)There was a moderate, positive correlation between the “Chinese” dietary pattern and total energy.This study was designed to explore dietary pattern approaches by PCA approach and its relationship with nutrient intake and BMI. Some components derived from the factor loadings showed a positive correlation with several nutrient markers, namely,It is clear that further study of pattern analysis is warranted among the Malaysian population to produce validity and reproducibility for this dietary approach, especially due to the numerous methodological issues in running PCA. Only after developing a clear set of definitions for the patterns among the Malaysian population can researchers determine correlations between pattern analysis and disease outcomes in the future.


This study investigated the prevalence of low back pain (LBP) and its risk factors among elementary-school students.In this cross-sectional study, 693 elementary students from Hamadan city, western Iran, were selected by multistage stratified cluster sampling. Data were collected through interviews using questionnaires. Posture and psychosocial elements were assessed using the observational Rapid Upper Limb Assessment (RULA) checklist and the standard Strengths and Difficulties Questionnaire, respectively. Penalized logistic regression with the group smoothly-clipped absolute deviation regularization method was used for variable selection and data analysis (α=0.05). The chi-square test was also used.In total, 26.6% of the students (7-12 years old) reported LBP in the last month. Older age , watching TV for more than 3 hours a day , very short seat backrests , excessively curved seat backrests , very short desks , a family history of LBP , carrying a school bag on one shoulder , and RULA scores of 3  or 4  were associated with LBP.A high prevalence of LBP was found among elementary-school students. This study underscores the importance of recognizing vulnerable children and teenagers and developing interventional health promotion programs to prevent LBP based on an appropriate consideration of its contributory factors. According to the 2017 Global Burden of Disease study, low back pain (LBP) was ranked first among health issues in terms of years lived with disability (YLDs) . In deveLBP is defined as pain between the costal margins and inferior gluteal folds, which is usually accompanied by painful motor restrictions and may be related with referral pain to the foot, but is not a result of direct trauma or systemic diseases such as neoplasms, infections, peripheral arterial disease, metabolic conditions, or endocrine malfunction . LBP canAlthough previous studies have investigated a broad range of variables, including biological parameters , psychosocial factors , lifestyle-related variables , and posture, as potential causative factors of LBP in children and teenagers -16, incoTherefore, this study, as a part of broader research project on musculoskeletal problems, from which results about neck and shoulder pain have previously been reported , was conThis cross-sectional study was conducted in Hamadan from early January 2018 to late March 2018. Hamadan is a city in the west of Iran with a population of 573,449 based on the Health Center statistics from 2018.The sample included 693 elementary-school students (7-12 years old) and their parents. Multistage stratified cluster sampling was conducted. Hamadan has 2 educational areas (strata), according to the divisions of the Hamadan Department of Education. In the first phase of sampling, a list was provided by the Department of Education and then 13 schools (clusters) were selected randomly from the above areas based on socioeconomic conditions. In the second phase, all 6 grades of each selected elementary-school (1 class from each grade) were considered, and finally students were selected randomly from each class.The questionnaires developed by Dianat et al.  and HataThe Rapid Upper Limb Assessment (RULA) checklist, which is an observational method for measuring the risk of upper limb musculoskeletal disorders, was used to observe students’ postures. On this checklist, the observations were recorded as numerical scores. These scores were converted into final scores using the RULA scoring matrix, according to which the priority level of corrective steps was determined  . The queThe standard Strengths and Difficulties Questionnaire (SDQ) was used to measure psychosocial variables. This questionnaire uses a 5-factor structure to measure behavioral-emotional problems and prosocial behaviors in 4-17-year-old children. It also involves 4 subscales related to difficulties  along with a strengths measure . This 25-item questionnaire was filled out by students’ parents. Each question was scored on a 3-point Likert scale as 0 (not true), 1 (somewhat true), and 2 (certainly true). Each subscale had 5 questions and the theoretical range of the strengths and difficulties scores was 0-10 and 0-40, respectively. The total difficulty score was obtained from the 4 subscales related to difficulties. According to the obtained scores, each subscale is divided into 3 categories  . The relA pre-shaded manikin picture to show areas of pain was used to measure the prevalence of LBP, with an accompanying question reading “Have you experienced pain or discomfort in your back area for a day or more during the last month? (responses: yes/no)” ,24. Pain2) was calculated and classified according to the World Health Organization (WHO) growth reference charts (2007) into 3 categories: healthy weight , underweight (< 5th percentile), and overweight (≥ 85th percentile) [Finally, the weights of students and their school bags were measured using a digital electronic scale  with a sensitivity of 100 g. Each participant’s standing height was measured using a portable stature meter . Each participant’s body mass index  .LBP was considered as the outcome of interest (a binary response variable). The independent/exposure variables that were included in the analytical models were the students’ demographic and PA/leisure activity characteristics ; classroom furniture/layout design ; amount of homework, position doing homework at home, history of accident or injury related to LBP, family history of LBP, school bag–related variables ; psychosocial factors , and RULA score.Penalized logistic regression was utilized to select important correlates of LBP. We used the group smoothly-clipped absolute deviation (SCAD) penalty in the logistic regression model to select correlates and to measure the associations between LBP and demographic characteristics and PA/leisure activity, school-related factors, psychosocial factors, and RULA scores. Briefly, this model is a shrinkage regression model that imposes an L1 penalty on the regression coefficients. The chi-square test also was used to analyze the data in this study.Stepwise logistic regression is the usual approach to select variables associated with a binary response; nevertheless, it suffers from some disadvantages, including instability of the selected variables and vulnerability to overfitting. Recently, penalized regression models, in which a penalty term is attached to the likelihood function and estimation and variable selection is done simultaneously, have shown promising results. Among the various penalties, the use of SCAD has been well established as a way to obtain reliable results .The SCAD penalty is defined as follows:In the above equation λ is the tuning parameter and its optimum value should be obtained through cross-validation. In this study, we used a 10-fold cross-validation strategy. The value of λ with the smallest Bayesian information criterion was chosen as the optimum value. This method was repeated 1,000 times and the estimated coefficients were averaged over all repetitions. To estimate the standard errors of the coefficients, a bootstrap strategy was used with 1,000 replications. Therefore, 1,000 samples (with replacement) were selected from the original data and then the standard errors of the coefficients were computed to calculate 2-sided p-values. A significance level of 0.05 was adopted for all statistical analyses.The adequacy of the final model was checked using the Hosmer & Lemeshow test, in which a p-value greater than 0.05 indicates no evidence of poor fit .The area under the receiver operating characteristic curve (AUC) was also used to check the predictive accuracy of the model. To do this, the data were divided randomly into 2 subsets (training and testing sets). The model was fitted to the training data and the AUC was obtained for the testing set. AUC values lie between 0.5 and 1, where 0.5 denotes a bad classifier and 1 denotes an excellent classifier.It should be noted that penalized logistic regression simultaneously considers all independent variables, including potential confounding variables, when selecting variables. Therefore, this model can control for the effect of potential confounding variables. In the current study, demographic characteristics, physical leisure activities, psychosocial factors, and family history of LBP among school students, which could serve as potential confounding variables, were entered into the model as independent variables.This study was approved by the Ethics Committee of Hamadan University of Medical Sciences . Informed consent was obtained from the students and their parents.In total, 26.6% of the students reported LBP during the last month. The intensity of LBP differed significantly (p=0.001) between males  and females . However, there were no significant differences (p=0.106) by sex with regard to absence from school due to LBP.2 . Moreover, the predictive accuracy of the model using the selected variables shown in Model adequacy was confirmed by the Hosmer & Lemeshow test , which contributes to LBP . It was In contrast, individual factors related to PA and leisure time  and BMI were not associated with LBP, which accords with other studies conducted by Diepenmaat et al.  in the NWatching TV for more than 3 hours a day had a significant relationship with LBP. Studies have shown that watching TV increases the risk of LBP ,40. The In this study, having a family history of LBP was a predictor of students’ LBP. The presence of such a complaint in children is relevant to the history of similar complaints in their households . The preRULA scores of 3 and 4 were significantly related with LBP. Studies have reported a significant relationship between bending and rotation of the low back and musculoskeletal pains (including LBP) in school-age children and adolescents. We must consider, however, that those mentioned studies used a different instrument from present study (the portable ergonomic observation method) ,45. StudIn the present study, psychosocial factors were not found to be related to LBP. Previous studies have reported significant relationships of psychological and psychosocial factors with musculoskeletal complaints in children and teenagers ,16,47. TAmong the variables related to school bags investigated in this study, only carrying a school bag on 1 shoulder was related to LBP. Studies have shown that heavy school bags and improperly carrying them for long periods of time may adversely affect children’s musculoskeletal system, not only resulting in fatigue and back pain, but also affecting the normal growth of the spine ,49. TherThe current study indicated a high prevalence of LBP among elementary-school students and identified some important risk factors, including demographic factors, leisure activities, classroom furniture, manner of carrying a school bag, family history, and improper sitting postures.LBP may have negative consequences for both the physical performance and the social life of children and adolescents. LBP in childhood and adolescence has also been shown to be associated with LBP in adulthood. Therefore, recognizing these risk factors at an early age can help prevent LBP in adulthood, especially if more effective interventions are developed.This study simultaneously considered all the factors analyzed in previous studies on LBP, which is a distinctive feature of this study that may be one of its strengths. In addition, few studies have addressed the prevalence and risk factors of LBP in younger students. Another strength of this study is the combined use of individual interviews and posture observations to investigate LBP prevalence and its risk factors among elementary students.Nonetheless, the study findings should be interpreted in light of some limitations. For instance, the cross-sectional design of the study makes it impossible to infer a causal relationship between the 2 events. Another limitation is related to the validity and accuracy of the data obtained from students’ and parents’ self-reports, which may be accompanied by under-reporting or over-reporting, although this approach is widely used for diagnosing LBPs and problems among children and teenagers ,24. It iIn conclusion, risk factors such as older age, watching TV for more than 3 hours a day, having a family history of LBP, very short seat backrests, excessively curved seat backrests, very short desks, carrying one’s school bag on 1 shoulder and RULA scores of 3 and 4 were associated with LBP, reflecting the multifactorial nature of this condition. Therefore, multifaceted solutions and ways of making effective healthy changes should be considered for maintaining low back health. Recognizing vulnerable children and teenagers, developing interventions such as health education and promotion programs, and involving the educational system, teachers, parents, and the students themselves in interventions targeting health improvement are required. To develop interventional programs, it is indispensable to investigate ways of improving schools and children’s ergonomic conditions, as well as considering ergonomic essentials at home and school for teenagers who are maturing and whose musculoskeletal system is forming.


Objective: To verify the effect of a multicomponent intervention with overweight/obese adolescents on physical fitness, body composition, and insulin biomarkers.Methods: A quasi-experimental study with 37 adolescents, aged 10 to 17 years, of both sexes, overweight and obese, allocated in two groups . The IGs were submitted to a multicomponent intervention for 6 months (three weekly sessions) consisting of physical exercises  and nutritional and psychological guidance. Participants were assessed before and after intervention on body composition , physical fitness [cardiorespiratory fitness (CRF) and abdominal strength], and biomarkers of insulin . The prevalence of responders in both groups was obtained according to the theoretical model applied in previous studies similar to this one to determine the cutoff points for response to intervention. Poisson regression was used to verify the difference in the prevalence ratio (PR) of the interviewees between the groups.Results: The responders' prevalence between groups CG and IG showed significant differences for body fat , WHR , and CRF .Conclusions: A 6-month multicomponent intervention program improved certain body composition parameters and the CRF of overweight and obese adolescents but did not improve insulin biomarkers.Clinical Trial Registration: Clinical Trials under Protocol ID: 54985316.0.0000.5343. Nowadays, excess weight contributes to the increase in factors that lead to morbidity and mortality, such as sleep disorders, eating disorders, physical inactivity, and sedentary behaviors  were invited among the overweight/obese adolescents included in the cross-sectional survey “Schoolchildren's health—phase IV” with overweight and obesity. To increase the sample size in the IG, we invited individuals from the community with the same characteristics, through disclosures and communication strategies ; interested ones should contact the project coordinators. Visits and meetings were held in schools in the municipality, with a pedagogical team, parents, and guardians, to present the intervention program. After that, schools indicated which students would take part in the study.To join the control group (CG), schoolchildren with similar characteristics were invited, such as being overweight or obese and belonging to schools in the municipality. Participants were selected by convenience criteria, according to the available data from the research. There was no intervention in the lifestyle of this group.* Power program  in the afternoon, after the school schedule. The intervention was carried out over 6 months and included the participation of a multicomponent team with professionals of Physical Education, Pharmacy, Physiotherapy, Nutrition, Psychology, Nursing, and Medicine, among others. Physical exercise sessions and psychological and nutritional guidelines were applied.The following physical exercise plans were developed and applied: Monday sessions consisted of a warm-up, stretching, walking, and sports activities . On Wednesdays, sessions consisted of water activities such as water aerobics, recreational activities, and swimming for beginners. On Fridays, aerobic workouts, a circuit of functional and resistance workouts, as well as breathing exercises were proposed. During the exercise, heart rate (HR) was controlled using HR monitors (Polar Cardiac Monitor—FT1), and training intensity was defined to induce HR between 50 and 70% of maximum HR, considering the Karvonen equation (maximum HR = 220 – age). More details and information on physical activity and sports sessions can be accessed in the The nutritional sessions were held once a week, more precisely on Wednesdays, consisting of food re-education with activities that aimed to know foods; reduce fats, sugar, and sodium consumption; choose the healthiest foods; and identify the daily portions; among others. Conversations, games, videos, teaching materials, and practical classes were held with healthy recipes for adolescents.Psychological intervention sessions were held once a week, on Mondays, with collective guidance and cognitive training, using techniques focused on the thoughts and feelings aroused by obesity. Relaxation methods were also developed to restructure these thoughts , to later classify the results through the percentile curves of WHO (p ≥ 85 and p < 97 and with obesity p ≥ 97. A measuring tape with a resolution of 1 mm not extensible was used to measure waist circumference (WC) and hip circumference (HC). Then, waist-to-hip ratio (WHR) was calculated following the equation: waist (cm)/hip (cm). A skinfold caliper  was used to assess triceps and subscapular skinfolds and calculate the body fatness following the equation proposed by Slaughter et al.  was calculated through the equation BMI = weight/heights of WHO , which cr et al. .The assessments of aerobic and muscular fitness were performed according to the protocols and cutoff points defined in the Project Sport Brazil manual  and 5 ml of whole blood for EDTA vacutainer tube (for blood count measurement). Participants were instructed to remain fasting for 12 h. Insulin was measured through the serum . The homeostasis model assessment of insulin resistance (HOMA-IR) was calculated using the formula HOMA = fasting glucose (mmol/L) × insulin (μU/L)/22.5  were performed to rule out possible differences from the total sample. For this, the two groups were matched for sex, age, and anthropometric variables . The t-test for independent samples and the chi-square test was used to compare groups' characteristics. The effect size between pre- and post-intervention was also calculated using the means and standard deviation of the groups through Cohen's d, with results classified as >0.2, small effect; >0.5, moderate effect; >0.8, large effect; and >1.2, very large effect . For this study, it was considered the follow cutoff point to determine responders and non-responders in each one outcome: BMI (change in cutoff points R > −3.29%) and WC (change in cutoff points R > −3.97%) were based on Ranucci et al. (R > −3.57%) was based on Nourse et al. (R > −10.42%) was based on Nardo Junior et al. (R > 12.2%), it was based on Silva et al. (R > 10%). For glucose (change in cutoff points R > −5.88%), insulin (change in cutoff points R > −34.88%) and HOMA-IR (change in cutoff points R > −9.95%) were based on Garanty-Bogacka et al. (R > −16%) was based on McFarlin et al.  and in the sexual maturation stage (p = 0.05) for adolescents at baseline.Participants' characteristics are shown in p = 0.004), WHR (p = 0.003), CRF (p = 0.029), and resistin (p = 0.008). When analyzing the effect sizes of the intervention using Cohen's d, it is observed that the changes in the IG had a very large effect size on WHR (d = 1.33), a large effect size on body fat (d = 1.01) and WC (d = −0.86), and a moderate effect size on CRF (d = 0.65). On the other hand, resistin showed a large effect size (d = 1.09) and HC presented a moderate effect size (d = −0.50) for the changes observed in the CG.p < 0.001), WHR , and CRF ; the other variables did not show differences in the prevalence of responders , with nutritional recommendations and psychological support on body composition, aerobic, and muscular physical fitness and insulin biomarkers in overweight and obese adolescents. There was a significant effect on the prevalence of responders for body fat (70.6%), WHR (76.5%), and CRF (52.5%) of the adolescents participating in the intervention concerning CG.The search for evidence on the effects of physical exercise on overweight and obesity in children and adolescents is broad, given that the reduction in body measures is related to the cardiometabolic risks  of its variance. Considering that the correlations between resistin and BMI were of low significance for the obese group and absent in the lean study group, it is suggested that the parameters of pubertal maturation are the strongest variation of the resistin and that only states of morbid obesity are associated with high levels of resistin . Gerber et al.  concludeIn this sense, Nascimento et al.  did not The effects of excess weight, more specifically obesity, indicate a higher prevalence of cardiometabolic risk factors. However, studies showed a good prognosis for overweight or slightly obese individuals, calling this the obesity paradox. This paradox may be related to unmeasured confounding factors, such as unintentional weight loss in individuals who do not participate in exercise programs or obesity treatment  and nutritional and psychological guidance was effective for the body composition parameters (body fat and WHR) and for the CRF, therefore emphasizing the importance of programs with multicomponent approaches  with efforts aimed at the profile of individuals and with the collaboration of a community body that aims at an integrated service of a multidisciplinary nature.The present study has some limitations, such as the limited sample size to obtain a medium effect, making it impossible to detect small effects, and not having assessed the levels of physical activity of participants in the control and intervention groups to determine whether the effects presented were directly caused by the exercise program. It was also not possible to control eating behavior, as the program only offered guidance on eating habits. We did not perform correlation analysis to find out how the variables are related. However, the study has several strengths, since most studies to date assess the relationship between adiposity and biochemical and physical fitness markers in an observational way. In addition, the study not only assessed the effect of the intervention program but also considered the response to adolescents' inter-individual variability. It is also relevant because it is one of the few studies that evaluated resistin as a biomarker of insulin in the pediatric population.The 6-month multicomponent intervention program with varied exercises  and nutritional and psychological guidance improved certain parameters of body composition and the CRF of overweight and obese adolescents but did not improve insulin biomarkers. The present results suggest that other indicators, such as age and maturation stage, may play a more important role regarding the effects of the intervention on insulin biomarkers in overweight and obese adolescents, highlighting mainly that resistin is still an inflammatory marker with an inconsistent mechanism in this relationship. Therefore, this intervention model can be considered and adapted for schools and community establishments as a way to improve the indicators of body composition and CRF in overweight and obese adolescents.The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.The studies involving human participants were reviewed and approved by all procedures were approved by the Scientific Council of the Research Unit that leads the project under number CAAE: 54985316.0.0000.5343 and opinion number: 1.498.338 and are registered at Clinical Trials under Protocol ID: 54985316.0.0000.5343. Written informed consent to participate in this study was provided by the participants' legal guardian/next of kin.LBorb, CP, AG, JB-S, and CB were responsible for the conception and design, data acquisition, data analysis and interpretation, writing of the initial article, and critical review of important intellectual content. LBorf and JD were responsible for the conception and design, data acquisition, and review of all the minutes of the article. All authors read and approved the final article.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.