New Simplified Diagnostic Decision Trees for the Detention of Metabolic Syndrome in the Elderly

Background: A new simplified method for the detention of metabolic syndrome (MetS) is proposed using two variables (anthropometric and minimally invasive). Methods: A study of MetS prevalence was made on a sample of 361 older people. The anthropometric variables analyzed were: blood pressure, body mass index, waist circumference (WC), waist–height ratio, body fat percentage, and waist–hip ratio. A crude and adjusted binary logistic regression was performed, and receiver operating characteristic curves were obtained for determining the predictive capacity of those variables. For the new detection method, decision trees were employed using automatic detection by interaction through Chi-square. Results: The prevalence of the MetS was of 43.7%. The final decision trees uses WC and basal glucose (BG), whose cutoff values were: for men, WC ≥ 102.5 cm and BG > 98 mg/dL (sensitivity = 67.1%, specificity = 90.3%, positive predictive value = 85%, validity index = 79.9%); and for women, WC ≥ 92.5 cm and BG ≥ 97 mg/dL (sensitivity = 65.9%, specificity = 92.7%, positive predictive value = 87.1%, validity index = 81.3%). In older women the best predictive value of MetS was a WC of 92.5 cm. Conclusions: It is possible to make a simplified diagnosis of MetS in older people using the WC and basal capillary glucose, with a high diagnostic accuracy and whose use could be recommended in the resource-poor health areas. A new cutting point in older women for the WC should be valued

Exploring the Utility of MUAC in Classifying Adult Metabolic Syndrome Using NHANES 2015-16

INTRODUCTION: Metabolic syndrome (MetS) is a constellation of cardiometabolic risk factors that, when presented in tandem, increases the risk of heart disease and insulin resistance. Finding a simple and validated screening method is critical to proactively intervene and attenuate the development of cardiometabolic diseases and improving healthcare outcomes. PURPOSE: This study defined and validated a risk criterion for MetS using MUAC as an alternative criterion for MetS classification risk. METHODS: The sample was derived from National Health & Nutrition Examination Survey 2015-2016 data of adults over 18 years (N = 9,971). MetS was defined using the NCEP ATP III 2005 MetS diagnosis criteria. A recursive partitioning methodology (RPM), using Classification & Regression Tree Algorithm, was employed to create binary MUAC criterion by sex, using 75% of the total sample. Validation of the criteria was performed with the remaining 25% of the total sample. RESULTS: Seventeen percent of the total sample presented with the MetS. The RPM resulted in sex specific MetS criteria with the MUAC criterion being \u3e32cm (p = 0.024) and \u3e29cm (p = 0.024) for males and females, respectively. Those presenting with the risk criteria were 9.84, for males, and 9.23, for females, times more likely to present with MetS than without the MUAC criterion. The overall classification accuracy for both the training and validation models were 83% with no statistical difference between models (p = 0.983). CONCLUSION: MUAC shows promise as an effective screening method for MetS in guiding further diagnostic tests to prevent associated cardiometabolic diseases

Data mining for the identification of metabolic syndrome status

Metabolic syndrome (MS) is a condition associated with metabolic abnormalities that are characterized by central obesity (e.g. waist circumference or body mass index), hypertension (e.g. systolic or diastolic blood pressure), hyperglycemia (e.g. fasting plasma glucose) and dyslipidemia (e.g. triglyceride and high-density lipoprotein cholesterol). It is also associated with the development of diabetes mellitus (DM) type 2 and cardiovascular disease (CVD). Therefore, the rapid identification of MS is required to prevent the occurrence of such diseases. Herein, we review the utilization of data mining approaches for MS identification. Furthermore, the concept of quantitative population-health relationship (QPHR) is also presented, which can be defined as the elucidation/ understanding of the relationship that exists between health parameters and health status. The QPHR modeling uses data mining techniques such as artificial neural network (ANN), support vector machine (SVM), principal component analysis (PCA), decision tree (DT), random forest (RF) and association analysis (AA) for modeling and construction of predictive models for MS characterization. The DT method has been found to outperform other data mining techniques in the identification of MS status. Moreover, the AA technique has proved useful in the discovery of in-depth as well as frequently occurring health parameters that can be used for revealing the rules of MS development. This review presents the potential benefits on the applications of data mining as a rapid identification tool for classifying MS

Educational intervention improves fruit and vegetable intake in young adults with metabolic syndrome components

The FRUVEDomics study investigates the effect of a diet intervention focused on increasing fruit and vegetable intake on the gut microbiome and cardiovascular health of young adults with/at risk for metabolic syndrome(MetS). It was hypothesized that the recommended diet would result in metabolic and gut microbiome changes. The 9-week dietary intervention adhered to the US Department of Agriculture Dietary Guidelines for Americansand focused on increasing fruit and vegetable intake to equal half of the diet. Seventeen eligible young adults with/or at high risk of MetS consented and completed preintervention and postintervention measurements, including anthropometric, body composition, cardiovascular, complete blood lipid panel, and collection of stool sample for microbial analysis. Participants attended weekly consultations to assess food logs, food receipts, and adherence to the diet. Following intention-to-treat guidelines, all 17 individuals were included in the dietary, clinical, and anthropometric analysis. Fruit and vegetable intake increased from 1.6 to 3.4 cups of fruits and vegetables (P \u3c .001) daily. Total fiber (P = .02) and insoluble fiber (P \u3c .0001) also increased. Clinical laboratory changes included an increase in sodium (P = .0006) and low-density lipoprotein cholesterol (P = .04). In the fecal microbiome, Erysipelotrichaceae (phylum Firmicutes) decreased (log2 fold change: −1.78, P = .01) and Caulobacteraceae (phylum Proteobacteria) increased (log2 fold change = 1.07, P = .01). Implementing a free-living 9-week diet, with intensive education and accountability, gave young adults at high risk for/or diagnosed with MetS the knowledge, skills, and feedback to improve diet. To yield greater impact, a longer diet intervention may be needed in this population

Best proxy to determine firm performance using financial ratios: A CHAID approach

The main purpose of this study is to investigate the best predictor of firm performance among different proxies. A sample of 287 Czech firms was taken from automobile, construction, and manufacturing sectors. Panel data of the firms was acquired from the Albertina database for the time period from 2016 to 2020. Three different proxies of firm performance, return of assets (RoA), return of equity (RoE), and return of capital employed (RoCE) were used as dependent variables. Including three proxies of firm's performance, 16 financial ratios were measured based on the previous literature. A machine learning-based decision tree algorithm, Chi-squared Automatic Interaction Detector (CHAID), was deployed to gauge each proxy's efficacy and examine the best proxy of the firm performance. A partitioning rule of 70:30 was maintained, which implied that 70% of the dataset was used for training and the remaining 30% for testing. The results revealed that return on assets (RoA) was detected to be a robust proxy to predict financial performance among the targeted indicators. The results and the methodology will be useful for policy-makers, stakeholders, academics and managers to take strategic business decisions and forecast financial performance.Internal Grant Agency (IGA) in Tomas Bata University in Zlin, Czech Republic [IGA/FAME/2022/012]Tomas Bata University in Zlin, TBU: IGA/FAME/2022/01

Exploring the relationships between taste phenotypes, genotypes, ethnicity, gender and taste perception using Chi-square and regression tree analysis

© 2020 Elsevier Ltd It is well known that perceived taste intensity varies greatly among individuals, and that several factors including taste phenotypes (PROP Taster Status (PTS), Sweet Liking Status (SLS), Thermal Taster Status (TTS)), ethnicity and gender, contribute to variation in taste responsiveness, although such factors are usually investigated in isolation. This study aimed to investigate the association between different taste pheno/genotypes, explore whether these taste phenotypes associated with ethnicity (Caucasian vs Asian) and gender, and determine the relative effects of the different factors on perceived taste intensity. As analysis of this type of data with ANOVA can be difficult due to confounding factors, interactions, and small sample sizes in subcategories, the use of regression tree analysis as an alternative approach was investigated. To that end, two-hundred and twenty-three volunteers were phenotyped for their PTS, SLS and TTS and genotyped for TAS2R38 –rs713598 and gustin –rs2274333. They also rated their perceived intensity of five basic taste and metallic solutions on a gLMS scale. No significant association between the three taste phenotypes were found indicating PTS, SLS and TTS are independent taste phenotypes. However, the results indicated that Asians were not only more likely to be PROP supertasters, but also more likely to be thermal tasters or Low Sweet Likers, compared to Caucasians. Gender was also significantly associated with SLS, where males were more likely to be High Sweet Likers. For perceived taste intensity, traditional ANOVA analysis proved to be challenging. The alternative approach, using regression trees, was shown to be an effective tool to provide a visualised framework to demonstrate the multiple interactions in this dataset. For example, ethnicity was the most influencing factor for perceived sour and metallic taste intensity, where Asians had heightened response compared to Caucasians. The regression tree analysis also highlighted that the PTS effect was dependent on ethnicity for sour taste, and PTS and TTS effect was dependent on ethnicity for metallic taste. This study is the first study to use regression tree analysis to explore variation in taste intensity ratings, and demonstrated it can be an effective tool to handle and interpret complex sensory datasets

Nuevas variables predictoras en la incidencia de Síndrome Metabólico y Diabetes Mellitus tipo 2. Estudio longitudinal en población trabajadora

El Síndrome Metabólico (SMet) se define como un estado pluripatólogico, caracterizado por la presencia conjunta de varios factores de riesgo cardiovascular como son la obesidad abdominal, la hipertensión arterial y el metabolismo alterado de glúcidos y lípidos (bajo colesterol-HDL y elevación de triglicéridos). La principal consecuencia para la salud del SMet es su asociación con la incidencia de enfermedades cardiovasculares (cardiopatía isquémica, accidente cerebrovascular, enfermedad renal, diabetes, etc.). El aumento general de la prevalencia de MetS ha sido debido a la epidemia de obesidad que padece la población mundial. El sobrepeso y la obesidad son factores relacionados con la aparición de diabetes tipo 2, hipertensión arterial, dislipidemia y enfermedad cardiovascular. Más concretamente, la obesidad central, entendida como una acumulación excesiva de grasa abdominal, es un importante predictor de riesgo metabólico y SMet. Objetivos Conocer la validez predictiva de los diferentes indicadores de obesidad abdominal (IMC, ICT, ICC, PG, ABSI, etc.) en la incidencia y prevalencia de SMet y DM tipo 2. Proponer un valor de corte único para el ICT en hombres y mujeres, así como para los diferentes grupos de edad. Proponer y validar un nuevo método de detección precoz de SMet en población sana basado, únicamente, en variables antropométricas

The development of a risk index for depression (RID)

&nbsp;This thesis developed a novel methodology for a flexible and modular Risk Index for Depression (RID) that blended data mining and machine learning techniques with traditional statistical techniques. This RID shows great potential for future clinical use.<br /