Estado nutricional e desfechos clínicos em pacientes críticos internados em hospital universitário

Introduction: There is a strong relationship between malnutrition and increased length of hospitalization and morbidity and mortality. Studies have shown that malnourished patients can have up to twenty times more complications than eutrophic ones. In critically ill patients, there is a tendency to catabolism, resulting in the loss of lean body mass, which when it reaches 40% is usually lethal. Methods: A quantitative, descriptive study was conducted on adults from both genders, admitted to the intensive care unit (ICU) of a university hospital, from March to December 2018. The following variables were collected from the evaluation and nutritional records: length of hospitalization in the ICU, date of discharge or death, nutritional risk through specific screening, height, weight and arm circumference (AC). For the screening, the Nutric score was used. For the nutritional evaluation, the body mass index (BMI) and AC indicators and the classifications recommended by the World Health Organization (2004) and Blackburn and Thornton (1979) were used. After collecting the data, they were analyzed in the Statistical Package for Social Sciences (SPSS) 13.0 and for the association of the variables the Chi-square test was used, considering statistical difference when the p value <0.05. Results: The sample consisted of 116 patients, mostly female (53.4%) whose median age was 46 years (interquartile range IQR 31-53). Regarding the frequency of nutritional risk, most patients (61.5%) had a low score. There was an important frequency of malnutrition, according to the AC indicator (73%), although BMI (43.5%) showed eutrophy. Even though most patients had low nutritional risk, those with high nutritional risk (38.5%) had a higher tendency to mortality, however, not statistically confirmed (p> 0.05). There was also a tendency of association between death and malnutrition, although no statistical significance was shown(p> 0.05). Conclusion: Patients at nutritional risk and/or malnutrition appear to be vulnerable to worse clinical outcomes.

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Hubungan Pola Makan Pasien CKD dengan Indeks Massa Tubuh (IMT) Pasien yang Menjalani Hemodialisa di RS Dik Pusdikkes Kodiklat AD

Journal of Nursing Education and Practice ◽

10.53801/jnep.v1i2.29 ◽

2021 ◽

Vol 1 (2) ◽

pp. 52-58

Author(s):

Devi ◽

Bambang S ◽

Sumedi W

Keyword(s):

Body Mass Index ◽

Body Mass ◽

Hemodialysis Patients ◽

Dietary Factors ◽

The Body ◽

P Value ◽

Chi Square ◽

Normal Category ◽

Chi Square Test ◽

The Relationship

Introduction: Dietary factors or eating patterns can cause patients to experience anorexia so that it affects the body mass index measured by CKD patients (Susetyowati, 2014). Therefore, it is important to understand the research when studying the relationship between the diet of CKD patients and the body mass index of patients undergoing hemodialysis at the Dik Pusdikkes Hospital in 2021. Objective: This study aims to determine how big the influence of the relationship between the diet of CKD patients and the body mass index of patients undergoing hemodialysis to improve the quality of life of CKD patients. Method: The type of research used is quantitative research with a correlational design to determine whether there is a relationship between variables using experimental research. The population in this study that became the study population were all hemodialysis patients at the AD Pusdikkes Hospital. The sampling technique used was total sampling. The research instrument used is a questionnaire. The questionnaire is not standardized so that validity and reliability tests are carried out. In this study, the analysis used the chi-square test. Results: the results of the chi-square test showed that the p-value (0.028) <0.05 then Ha was accepted or there was a relationship between the diet of CKD patients and the patient's body mass index. Conclusion: Based on the results of the analysis of the relationship between the evaluation of hemodialysis patients at RS Dik Pusdikkes Kodiklat AD, 9 respondents had a poor diet with a body mass index in the lean category of 6 people (66.7%) and the normal category of 3 people (33.3%). And 11 respondents have a good diet relationship with body mass index in the thin category of 2 people (40.0%) and the normal category of 12 people (60%).

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Hubungan Pekerjaan dan Jadwal Kerja dengan Indeks Massa Tubuh Balita di Wilayah Kerja Puskesmas Yosodadi Kota Metro

Malahayati Nursing Journal ◽

10.33024/mnj.v1i1.5571 ◽

2021 ◽

Vol 1 (1) ◽

pp. 103-114

Author(s):

Rilyani Rilyani ◽

Lidya Aryanti ◽

Calvyn Reza Trisnantyas

Keyword(s):

Body Mass Index ◽

Body Mass ◽

Statistical Tests ◽

The Body ◽

P Value ◽

Work Schedule ◽

Chi Square ◽

Cross Sectional ◽

Chi Square Test ◽

The Relationship

ABSTRACT: EMPLOYMENT RELATIONSHIP AND WORKING SCHEDULE WITH BODY MASS INDEX IN YOSODADI PUSKESMAS WORKING AREA METRO CITY Introduction: The coverage of children under five in Lampung Province in 2019 was 79%, and this figure was above the target (76.18%). This figure illustrates that participation from the community to come to posyandu is still quite good in Lampung Province, while for Metro City the percentage of data is weighted at 78.8%. Data obtained through the Metro City Health Office in 2020 at the Yosodadi Health Center obtained as many as 656 toddlers aged 1-5 years who were weighingObjective: To find out the relationship between work and work schedule with the body mass index of toddlers aged 1-5 years.Methods: This type of research is quantitative. The design in this study uses an analytical survey design with a cross-sectional approach. The population is mothers and children aged 3-5 years, which is then calculated using the Slovin formula to obtain 197 children. Sampling technique purposive sampling Analysis of univariate and bivariate data using the chi-square test.Results: working mothers as many as 107 respondents (54.3%)., standard work schedule is 8 hours per day as many as 109 respondents (55.3%), BMI is not ideal, namely < 18.5 and > 22.9 as many as 103 respondents ( 52.3%. The results of statistical tests using the chi-square test obtained p-value = 0.000 (<α0.05), p-value = 0.085 (>α0.05).Conclusion: The results of statistical tests using the chi-square test obtained p-value = 0.000 (<α0.05) which means the relationship between mother's work and body mass index of toddlers aged 3-5. The p-value = 0.085 (>α0.05) means that there is no relationship between the mother's work schedule and the body mass index of children aged 3-5 years. It is recommended that parents who have toddlers with underweight and obese nutritional status should pay more attention to parenting patterns Keywords : Mother's Work Work Schedule, Toddler Body Mass Index INTISARI: HUBUNGAN PEKERJAAN DAN JADWAL KERJA DENGAN INDEKS MASSA TUBUH BALITA DI WILAYAH KERJA PUSKESMAS YOSODADI KOTA METRO Pendahuluan: Cakupan balita ditimbang di Provinsi lampung tahun 2019 sebesar 79 %, dimana angka ini diatas target (76,18%). Angka ini menggambarkan bahwa partisipasi dari masyarakat untuk datang ke posyandu masih cukup baik di Provinsi Lampung, sedangkan untuk Kota Metro persentase data ditimbang sebesar 78,8%. Data yang diperoleh melalui Dinkes Kota Metro tahun 2020 di Puskesmas Yosodadi didapat sebanyak 656 balita usia 1-5 tahun yang melakukan penimbangan berat badanTujuan: Diketahui hubungan pekerjaan dan jadwal kerja dengan indeks massa tubuh balita usia 1-5 tahun.Metode: Jenis penelitian kuantitatif, Rancangan dalam penelitian ini menggunakan desain Survei Analitik dengan pendekatan cross sectional. Populasi adalah ibu dan anak usia 3-5 tahun, yang kemudian dilakukan perhitungan sampel dengan rumus slovin didapat sebanyak 197 anak. Teknik sampling purposive sampling Analisa data univariat dan bivariat menggunakan uji chi square.Hasil : ibu bekerja sebanyak 107 responden (54,3%)., jadwal kerja standar yaitu 8 jam per hari sebanyak 109 responden (55,3%), IMT tidak ideal yaitu < 18,5 dan > 22,9 sebanyak 103 responden (52,3%). Hasil uji statistik menggunakan uji chi square didapat nilai p-value = 0.000 (<α0.05), nilai p-value = 0.085 (>α0.05).Kesimpulan : Hasil uji statistik menggunakan uji chi square didapat nilai p-value = 0.000 (<α0.05) yang artinya ada hubungan pekerjaan ibu dengan indeks massa tubuh balita usia 3-5. Nilai p-value = 0.085 (>α0.05) yang artinya tidak ada hubungan jadwal kerja ibu dengan indeks massa tubuh balita usia 3-5 tahun. Disarankan kepada orang tua yang memiliki balita dengan status gizi kurus dan gemuk, harus lebih memperhatikan lagi mengenai pola asuh anak Kata Kunci : Pekerjaan Ibu Jadwal Kerja, Indeks Masa Tubuh Balita

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Toward Personalized Medicine Implementation: Survey of Military Medicine Providers in the Area of Pharmacogenomics

Military Medicine ◽

10.1093/milmed/usz419 ◽

2019 ◽

Vol 185 (3-4) ◽

pp. 336-340 ◽

Cited By ~ 1

Author(s):

Jesse DeLuca ◽

Daniel Selig ◽

Lucas Poon ◽

Jeffrey Livezey ◽

Thomas Oliver ◽

...

Keyword(s):

Personalized Medicine ◽

Statistical Significance ◽

The Body ◽

P Value ◽

Small Subset ◽

Chi Square ◽

Military Health ◽

Direct To Consumer ◽

Knowledge Question ◽

The Right

Abstract Introduction Personalized medicine is the right treatment, to the right patient, at the right dose. Knowledge of genetic predisposition to variable metabolism and distribution of drugs within the body is currently available as pharmacogenomic testing and is one of the pillars of personalized medicine. Pharmacogenomic testing is growing. It has become part of guidelines for dosing on FDA labels and has been used by health care organizations to improve outcomes and reduce adverse events. Additionally, it has been FDA approved for direct-to-consumer purchase and has been cause of concern of patient self-dosing and medication changes. Presumably in the near future, pharmacogenomics will be impressed upon the military health system (MHS) provider from either a top-down, command requested, or from a bottom-up, patient requested, approach. To date, widespread implementation of pharmacogenomic testing does not seem to be established within the MHS. This survey sheds light on the knowledge, exposure, use, comfort, and interest among family medicine providers in the MHS. It compares similar results in other national and international surveys and compares results among a small subset of residents to staff. Materials and Methods The questions were part of a larger survey conducted by the Clinical Investigations Committee of the Uniformed Services Academy of Family Physicians (USAFP) at the USAFP 2019 annual meeting. The study received approval from the Uniformed Services University Institutional Review Board. Submitted questions were written using multiple choice, fill-in, five-point Likert scale, and best answer. Direct results are reported as well as chi-square statistics for categorical data with statistical significance to attain a P-value of < 0.05. Results Among the 532 USAFP-registered conference attendees eligible to complete the survey, 387 attendees responded to the survey, for a response rate of 72.7%. Some results included were a knowledge question in which 37% of respondents answered correctly. Less than half of respondents agreed that they could define pharmacogenomics, and resident respondents were more likely to have received teaching in graduate medical education. Additionally, 12% of providers responded to being exposed to direct-to-consumer results, and 28% of those exposed were influenced to change medications, while 14% were influenced to change medications on multiple occasions. Chi-square comparisons resulted in statistically significant direct relationships to exposure to direct to consumer testing, previous training, and confidence of those that answered the knowledge question correctly. Conclusions This survey establishes a baseline for the possible needs associated with implementation of a pharmacogenomic program, and it argues an actionable level for the use of pharmacogenomics among the patient population within the MHS.

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THE RELATIONSHIP BETWEEN PHYSICAL ACTIVITIES AND BODY MASS INDEX (BMI) IN ADOLESCENTS AGED 16-18 YEARS OLD IN SMA NEGERI 2 DENPASAR

Majalah Ilmiah Fisioterapi Indonesia ◽

10.24843/mifi.2019.v07.i01.p05 ◽

2019 ◽

Vol 7 (1) ◽

Author(s):

Luh Dwi Erma Krismawati ◽

Ni Luh Nopi Andayani ◽

Nila Wahyuni

Keyword(s):

Body Mass Index ◽

Body Mass ◽

Strong Relationship ◽

Simple Random Sampling ◽

Physical Activities ◽

The Body ◽

Chi Square ◽

Cross Sectional ◽

Analytical Research ◽

The Relationship

Any movement of the body produced by skeletal muscles that requires energy expenditure, and muscle workwhich called physical activities. Increased BMI occurs because of an energy imbalance between food intake with energyreleased to cause the buildup of energy in the form of fat that can lead to obesity or increased BMI.This study purposewas to determine the relationship between physical activities and body mass index (BMI) in adolescents aged 16-18years old in SMA Negeri 2 Denpasar. This research was analytical research with cross sectional approach done inFebruary-March 2018. Sampling was done by Simple Random Sampling. Sample amounted to 70 people. The resultsshowed the relationship between physical activities with body mass index. In the calculation of chi square data analysiscould be seen that significant value (2-tailed) was 0,000 which meant that significant value was p < 0.05. Based on theresults of this study, it can be concluded that there is a strong relationship between physical activities and body massindex (BMI) in adolescents aged 16-18 years old in SMA Negeri 2 Denpasar. Keywords : Physical activity, Body Mass Index (BMI)

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Food Addiction Mediates the Relationship between Perceived Stress and Body Mass Index in Taiwan Young Adults

Nutrients ◽

10.3390/nu12071951 ◽

2020 ◽

Vol 12 (7) ◽

pp. 1951

Author(s):

Yi-Syuan Lin ◽

Yu-Tang Tung ◽

Yu-Chun Yen ◽

Yi-Wen Chien

Keyword(s):

Body Mass Index ◽

Young Adults ◽

Body Mass ◽

Perceived Stress ◽

Food Addiction ◽

The Body ◽

Mediating Effect ◽

World Health ◽

P Value ◽

The Relationship

Perceived stress is the degree of stress experienced by an individual in the face of a stressor. Studies have shown that stress affects emotions, leads to behavioral changes, and is likely to trigger physical illnesses. According to the World Health Organization (WHO), stress is classified as a health epidemic of the 21st century; in the meantime, the percentage of adults being overweight and with obesity has continued to grow after reaching 38.9% in 2016. Hence, it is unclear whether perceived stress has become a factor affecting progressive obesity and whether food addiction (FA) is an intermediate factor. The purposes of this study were to (1) investigate the FA prevalence among young adults in Taiwan, (2) understand correlations among perceived stress, FA, and the body mass index (BMI), and (3) determine the potential mediating effect of FA due to perceived stress on BMI. The study was conducted through an online questionnaire, composed of a basic data form, the Perceived Stress Scale (PSS), and the Yale Food Addiction Scale (YFAS). We received 1994 responses and analyzed 1780 valid samples. Results showed that 231 participants met the FA criteria, accounting for 12.98%. Perceived stress was positively correlated with BMI (95% confidence interval (CI) 0.013~0.088, p-value 7.8 × 10−3), and perceived stress was positively associated to FA (95% CI 1.099~1.154, p-value < 10−4), which was also positively correlated with BMI (95% CI 0.705~2.176, p-value 10−4). FA significantly mediated the relationship between PSS and BMI with an indirect effect size of 25.18% and 25.48% in the group that scored 31~40 on the PSS. The study concluded that among people seeking weight loss, proper stress management and screening for FA in order to apply related therapies may be an effective method for weight management.

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Differences in the Cardiovascular Risk Assessment in Cardiology Outpatients in Mali: Comparison between Framingham Body Mass Index-Based Tool and Low-Information World Health Organization Chart

International Journal of Hypertension ◽

10.1155/2021/8862762 ◽

2021 ◽

Vol 2021 ◽

pp. 1-7

Author(s):

Bâ Hamidou Oumar ◽

Sangaré Ibrahima ◽

Camara Youssouf ◽

Sidibé Noumou ◽

Coulibaly Souleymane ◽

...

Keyword(s):

Body Mass Index ◽

Cardiovascular Risk ◽

Body Mass ◽

Risk Score ◽

Statistical Significance ◽

Weighted Kappa ◽

The Body ◽

World Health ◽

Categorical Variables ◽

Cardiology Department

Objective. This study aimed to compare 2 laborless tools, namely, the body mass index-based Framingham (bmi-Frm) and low-information WHO- (li-WHO-) based risk scores, and assess their agreement in outpatients in a cardiology department. Methodology. Data stem from a cross-sectional previous study performed from May to September 2016 in the Cardiology Department of University Hospital Gabriel Touré (UH-GT) in Bamako. All patients aged 40 and more were included in the study allowing the assessment of bmi-Frm and li-WHO prediction charts. The cardiovascular risk (CVR) was evaluated using a calculator prepared by D‘Agostino et al. for the bmi-Frm and the li-WHO chart for the Afro-D region of the WHO. The risk score for both ranged from <10 to ≥40. The data were entered in an ACCESS 2010 database, then processed by MS Excel 2010, and finally analysed using IBM SPSS Statistics 20. Continuous variables were presented as means and standard deviations, and categorical variables were presented as frequencies with percentages. P < 0.05 was considered the statistical significance level. After sample description, the risk score was assessed using bmi-Frm and li-WHO prediction tools. Finally, a kappa test was performed to check for the interreliability of both methods. For weighted kappa, coefficients were given all five classes of risk groups in 0, 25 steps from 1 for total concordance to 0 for total discordance. Results. This study involved 793 outpatients, 63.7% being female, 35.1% of them younger than 50 years, 57.9% with no formal education, and 67.7% with no medical insurance. Means for age, body mass index (BMI), and systolic blood pressure (SBP) were, respectively, 53.81 ± 16.729 years, 25.29 ± 06.151 kg/m2, and 139.49 ± 27.110 mm Hg. Using the li-WHO prediction chart gives a much higher proportion of low-risk patients compared to bmi-Frm (83.6 vs. 37.7). Sociodemographic characteristics such as education or income level were not different in risk score neither for the bmi-Frm nor for the li-WHO risk score. The percentage of agreement between both tools was 40.4%, and agreement (kappa of 0.1 and weighted kappa of 0.2) was found to be slight. Conclusion. Using the bmi-Frm and li-WHO tool gives a similar risk estimation in younger female patients. Older patients must be evaluated using high-information tools with cholesterol, e.g., versions of the Framingham risk equation or WHO using cholesterol. These must be confirmed in further studies and compared to data from prospective studies

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Treatment Outcome Of Hyperglycemic Emergency And Predictors In Ethiopia

10.21203/rs.3.rs-25700/v2 ◽

2020 ◽

Author(s):

Pawlos Asfaw Tekeste ◽

Gashaw Binega Mekonnen ◽

Achenef Asmamaw Muche ◽

Bereket Molla Tigabu

Keyword(s):

Treatment Outcome ◽

Hospital Stay ◽

Goodness Of Fit ◽

Statistical Significance ◽

University Hospital ◽

P Value ◽

Continuous Variables ◽

Goodness Of Fit Test ◽

Chi Square ◽

Duration Of Hospital Stay

Abstract Background: Hyperglycemic Emergency (HE) denotes critical cases of decompensated diabetes mellitus (DM). Diabetic ketoacidosis (DKA) and hyperosmolar hyperglycemic state (HHS) are the extreme cases of HE. This study aims to assess the treatment outcome of HE and predictors in Ethiopia.Method: Four-year medical records of DM patients admitted for HE at Hiwot Fana Specialized University Hospital (HFSUH) were reviewed retrospectively. Data were entered into and cleaned by Epi-InfoTM7 software. The statistical analysis was executed using the statistical package for social sciences software (SPSS) version 24. Chi-square test and student’s t-test were done to compare categorical and continuous variables. Logistic regression with the level of α set at 0.05 and AOR of 95% CI was done to determine the predictors. Statistical significance was established at AOR ≠1 within a 95% CI and P-value < 0.05. The model was verified using the Hosmer-Lemeshow goodness of fit test (P = 0.392).Results: This study included 321 eligible patients. The median duration of hospital stay was 7 days. The pooled HE mortality was 16.5%; 21.4% of HHS and 11.1% of DKA died. Infection (AOR = 3.74, 95% CI: 1.85-7.57, P <0.001), comorbidity (AOR = 2.95, 95% CI: 1.34-6.48, P = 0.007), and lower admission Glasgow Coma Scale score ≤ 8 (AOR = 2.58, 95% CI: 1.17-5.71, P = 0.019) were the independent predictors of HE mortality.Conclusion: Mortality and duration of hospital stay stand high among DM patients admitted with HE. Infection, comorbidity, and lower admission GCS ≤ 8 are the independent predictors of HE mortality.

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481. SARS-CoV-2 Infection in Hospitalized Children: An Elevated Body Mass Index is a Marker of Increased Risk of Acute Respiratory Failure

Open Forum Infectious Diseases ◽

10.1093/ofid/ofab466.680 ◽

2021 ◽

Vol 8 (Supplement_1) ◽

pp. S341-S342

Author(s):

Catherine Foster ◽

Shelley Kumar ◽

Elizabeth Tocco ◽

Galit Holzmann-Pazgal ◽

Judith R Campbell ◽

...

Keyword(s):

Body Mass Index ◽

Intensive Care Unit ◽

Intensive Care ◽

Body Mass ◽

Polymerase Chain Reaction Test ◽

P Value ◽

Hospitalized Children ◽

Chi Square ◽

Increased Risk ◽

Length Of Hospitalization

Abstract Background Several risk factors are known to increase the severity of coronavirus disease 2019 (COVID-19) illness in adults, including age and obesity. Specific comorbidities affecting COVID-19 outcomes in children are less well defined. Methods We performed a retrospective cohort study of overweight and obese (OW) children compared to underweight and normal weight (NW) children with severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) infection. Children between 2 and 18 years of age who were admitted to Texas Children’s Hospital from April through December of 2020 with a positive SARS-CoV-2 polymerase chain reaction test were included. Asymptomatic patients undergoing surveillance testing for SARS-CoV-2 were excluded. Body mass index (BMI) was calculated using the Centers for Disease Control definition. Demographic and clinical information was obtained from the electronic medical record. Statistical analyses were performed using SAS 9.0. Results We identified 145 total children who met inclusion criteria. Fifty-five (38%) children were NW and 90 (62%) children were OW. Demographics and characteristics are shown (Figure 1). Underlying asthma or chronic lung disease was present in 13 (24%) vs 31 (34%) in the NW and OW groups respectively (P=0.17). OW children were more likely to have pneumonia than NW children [relative risk1.6 (CI 1.40-2.45)]. An elevated BMI was also associated with an increased risk of requiring oxygen [relative risk 1.4 (CI 1.03-1.96)]. The median length of hospitalization was 4 days for NW versus 5 days for OW children (P=0.6). Admission to the Intensive Care Unit (ICU) was similar between the groups (P=0.7). There was no significant difference in treatments administered to children in the two groups, although there was a trend towards increased steroid (29 (53%) vs 59 (67%), P=0.13) and remdesivir (12 (22%) vs 30 (33%), P=0.14) use in the OW children. Four children in each group died. Characteristics of Hospitalized Children with SARS-CoV-2 Infection by Weight Category Abbreviations: ECMO, extracorporeal membrane oxygenation; ICU, intensive care unit; MIS-C, multisystem inflammatory syndrome in children; MV, mechanical ventilation; NC, nasal cannula *Denotes statistically significant P-value a. Calculated using chi-square or fisher exact unless otherwise noted. a. Calculated using chi-square or fisher exact unless otherwise noted. A P-value <0.05 was considered significant. b. Calculated using Wilcoxon rank sum test. c. Includes patients with home noninvasive MV (2) or tracheostomy and home MV(4). Conclusion For children admitted with symptomatic COVID-19, being overweight or obese was significantly associated with having pneumonia and with requiring oxygen. A difference in ICU admission, length of hospitalization, and mortality was not observed. Obesity prevention along with vaccination efforts may prevent COVID-19 related morbidity in this group. Disclosures All Authors: No reported disclosures

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Comparison of body mass index and lipid accumulation product as a better indicator of metabolic syndrome

International Journal of Advances in Medicine ◽

10.18203/2349-3933.ijam20172262 ◽

2017 ◽

Vol 4 (3) ◽

pp. 728 ◽

Cited By ~ 2

Author(s):

Yashavanth H. S. ◽

Bharath M. S.

Keyword(s):

Diabetes Mellitus ◽

Metabolic Syndrome ◽

Body Mass Index ◽

Body Mass ◽

Lipid Accumulation ◽

Statistical Significance ◽

The Body ◽

P Value ◽

Strong Positive Correlation ◽

Lipid Accumulation Product

Background: Metabolic syndrome (MS) is defined to be cluster of metabolic interrelated risk factors of such as obesity, elevated blood pressures, glucose metabolism disturbances and dyslipidemia. "Lipid accumulation product" (LAP) – is a simple and novel index based on a combination of an anthropometric dimension and a metabolic dimension.Methods: One hundred patients with metabolic syndrome were included in study. Data was collected through a prepared proforma which included various parameters related to history, thorough clinical examination, and laboratory parameters. Further the patients were assessed for the body mass index (BMI) and lipid accumulation product (LAP). BMI and LAP then correlated with metabolic syndrome.Results: Our present study is conducted among the population meeting the IDF criteria of MS, with a mean age of 54.52±12.65years and female predominance (54%). 72% and 62% of the study group had Diabetes Mellitus and Hypertension respectively. The mean BMI in our study is 29.04±5.11 kg/m2 with 25% of the population are non-obese. The average LAP value in our present study is 111.51±59.71 cm mmol/l and shown increasing trend with increasing age. LAP had a mean value of 85.19 cm mmol/l, 118.52 cm mmol/l and 122.37 cm mmol/l in the study population satisfying 3/5, 4/5 and 5/5 criteria of Metabolic Syndrome respectively. This shows LAP (P<0.001) has better correlation with MS when compared to BMI (P<0.001) in ANOVA test. In our present study, the presence of diabetes mellitus resulted in significant elevated LAP values i.e. 85.10±31.40 cm mmol/l among non-diabetics to 121.78±64.92 cm mmol/l among diabetics, which is of statistical significance (p value 0.005).Conclusions: LAP showed strong positive correlation with metabolic syndrome and it also positively correlated with increasing number of components of metabolic syndrome. LAP found to be better predictor of MS than BMI.

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Hubungan Status Sosial Ekonomi dengan Indeks Massa Tubuh (IMT) di Kecamatan Tamalanrea Kota Makassar

Indonesian Journal of Health ◽

10.33368/inajoh.v0i0.25 ◽

2021 ◽

pp. 91-101

Author(s):

Meilinda Aji Syahputri ◽

Zulfahmidah Zulfahmidah ◽

Windy Nurul Aisyah

Keyword(s):

Body Mass Index ◽

Nutritional Status ◽

Body Mass ◽

Good Health ◽

The Body ◽

Categorical Variables ◽

P Value ◽

Chi Square ◽

Design Data ◽

Chi Square Analysis

Background: Nutrition is a factor that plays an important role in the development of a nation. One simple waythat can be used to determine nutritional status is to measure the Body Mass Index (BMI) or Body Mass Index(BMI). One of the factors that play a role in determining a person's nutritional status is the socioeconomic level.The socio-economic level includes education, income, and work which are indirect causes of nutritionalproblems. Efforts to improve good health are related to economic capacity with income levels and socialconditions of household members. How economics plays a role in the health record of each individual. Thepurpose of this study was to see the status of the socio-economic relationship with the Body Mass Index (BMI).Methods: This study is a descriptive research design. Data collected using a questionnaire. The data of thisstudy were categorical variables from 2 groups so that it used the Chi-Square test. Results: From 84 samples,based on the calculation of IBM SPSS Statistic 23 using the Chi-Square analysis test, it was obtained that the P-value (0.125) ≥ 0.05, then H0 was accepted and Ha was rejected, meaning that there was no influence of BMIinfluence on socio-economic factors. Conclusion: There is no relationship between socioeconomic status andBody Mass Index (BMI).

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