scholarly journals MODEL OF HUMAN METABOLIC AGE

2021 ◽  
Vol 77 (3) ◽  
pp. 71-75
Author(s):  
Anatoliy Pisaruk ◽  
Valerii Shatilo ◽  
Ivanna Shchehlova ◽  
Svitlana Naskalova ◽  
Ludmila Mechova
Keyword(s):  
Metabolic Syndrome ◽  
Accelerated Aging ◽  
Absolute Error ◽  
Healthy People ◽  
Clinical Tests ◽  
Average Absolute Error ◽  
Biochemical Tests ◽  
Anthropometric Parameters ◽  
Metabolic Age ◽  
Apparently Healthy

With aging, regular changes develop in metabolism, first of all, these are changes in lipid and carbohydrate metabolism. With accelerated aging, metabolic disorders are more expressed, which leads to the development of metabolic syndrome. The purpose of the work was to develop a method for calculating metabolic age using available clinical tests and to assess the rate of metabolic aging in people with metabolic syndrome. Materials and methods. The study involved 283 apparently healthy people aged 20 to 80 years and 82 people with metabolic syndrome. Anthropometric parameters and biochemical tests were measured for all people included in the study. The formula for calculating metabolic age was obtained by the method of stepwise multiple regression.Results. The calculation of the metabolic age in healthy people according to the formula we obtained showed that the average absolute error is 6.01 years. In 20.5% of people with metabolic syndrome, metabolic age exceeds chronological age by more than 10 years. At the same time, in the group of healthy people, the share of such people was only 4.2%. Conclusions. The method we have developed for assessing the rate of metabolic aging has a sufficiently high accuracy and can be used to assess the risk of developing metabolic syndrome and other agerelated pathology.

Impact of lifestyle on metabolic syndrome in apparently healthy people

2014 ◽  
Vol 19 (2) ◽  
pp. 225-232 ◽  
Author(s):  
Silvio Buscemi ◽  
Delia Sprini ◽  
Giuseppe Grosso ◽  
Fabio Galvano ◽  
Antonio Nicolucci ◽  
...  
Keyword(s):  
Metabolic Syndrome ◽  
Healthy People ◽  
Apparently Healthy

Clinical acceptance of a multi-test reference region for biochemical-panel results.

1983 ◽  
Vol 29 (10) ◽  
pp. 1724-1726 ◽  
Author(s):  
T C Durbridge
Keyword(s):  
Signal Strength ◽  
Reference Intervals ◽  
Healthy People ◽  
Test Results ◽  
Reference Region ◽  
Trial Period ◽  
Biochemical Tests ◽  
Practical Advantage ◽  
Hospital Inpatients ◽  
Apparently Healthy

Abstract Results of a panel of six biochemical tests on a patient's specimen were mathematically combined into a "six-test signal strength" (STSS) value. This value indicated the overall extent of change from physiological results, and it was calculated in a way that ensured that a STSS value less than or equal to 1 occurred in 95% of apparently healthy people. STSS was reported with the test results for hospital inpatients during a four-month trial period. Doctors requested a repeat of the panel less often when a low STSS was reported, even if some test results were outside their separate reference intervals. Clinicians expressed differing opinions about its usefulness, some finding that a high STSS value had saved them from overlooking abnormal results, others not finding the value to be any practical advantage. Using a multi-test normal region resolves a statistical dilemma, while compounding the problem of knowing what results really mean.

Metabolic Age, an Index Based on Basal Metabolic Rate, Can Predict Individuals That are High Risk of Developing Metabolic Syndrome

2021 ◽  
Author(s):  
Sarahi Vásquez-Alvarez ◽  
Sergio K. Bustamante-Villagomez ◽  
Gabriela Vazquez-Marroquin ◽  
Leonardo M. Porchia ◽  
Ricardo Pérez-Fuentes ◽  
...  
Keyword(s):  
Metabolic Syndrome ◽  
High Risk ◽  
Metabolic Rate ◽  
Basal Metabolic Rate ◽  
Metabolic Age

scholarly journals Abnormal Electrocardiograms in Apparently Healthy People

Circulation ◽  
1960 ◽  
Vol 21 (2) ◽  
pp. 204-213 ◽  
Author(s):  
F. A. L. MATHEWSON ◽  
G. S. VARNAM
Keyword(s):  
Healthy People ◽  
Apparently Healthy

scholarly journals Metabolic syndrome in patients with prostate cancer

2008 ◽  
Vol 126 (5) ◽  
pp. 274-278 ◽  
Author(s):  
Iúri Amorim de Santana ◽  
Gustavo Souza Moura ◽  
Nivaldo Farias Vieira ◽  
Rosana Cipolotti
Keyword(s):  
Prostate Cancer ◽  
Metabolic Syndrome ◽  
Body Mass ◽  
Specific Antigen ◽  
Density Lipoprotein ◽  
Positive Association ◽  
Serum Glucose ◽  
Anthropometric Parameters ◽  
Cross Sectional ◽  
Waist To Hip Ratio

CONTEXT AND OBJECTIVE: Prostate cancer (PCa) is the second most common cancer among men in Brazil. Recently, several studies have hypothesized a relationship between PCa and metabolic syndrome (MS). The aim here was to identify an association between MS and PCa. DESIGN AND SETTING: Cross-sectional study, Fundação de Beneficência Hospital de Cirurgia (FBHC) and Universidade Federal de Sergipe. METHODS: Laboratory and anthropometric parameters were compared between PCa patients (n = 16) and controls (n = 16). RESULTS: The PCa patients showed significantly greater frequency of MS than did the controls (p = 0.034). Serum glucose was higher and high-density lipoprotein-cholesterol was lower than in the controls, although without significant differences. There were significant differences in blood pressure (p = 0.029) and waist-to-hip ratio (p = 0.004). Pearson linear correlation showed a positive association between waist-to-hip ratio and prostate specific antigen (r = 0.584 and p = 0.028). Comparing subgroups with and without MS among the PCa patients, significant differences (p < 0.05) in weight, height, body mass index, hip circumference and lean body mass were observed, thus showing higher central obesity in those with MS. The serum glucose values were also higher in MS patients (p = 0.006), thus demonstrating that insulin resistance has a role in MS physiopathology. CONCLUSIONS: Our study suggests that MS may exert an influence on the development of PCa. However, it would be necessary to expand the investigation field with larger sample sizes and cohorts studied, to test the hypothesis generated in this study.

scholarly journals Hidden Semi-Markov Models for Predictive Maintenance

2015 ◽  
Vol 2015 ◽  
pp. 1-23 ◽  
Author(s):  
Francesco Cartella ◽  
Jan Lemeire ◽  
Luca Dimiccoli ◽  
Hichem Sahli
Keyword(s):  
Markov Models ◽  
Real Data ◽  
Information Criterion ◽  
Absolute Error ◽  
Predictive Maintenance ◽  
Average Absolute Error ◽  
Current State ◽  
Automatic Model Selection ◽  
State Duration ◽  
Useful Lifetime

Realistic predictive maintenance approaches are essential for condition monitoring and predictive maintenance of industrial machines. In this work, we propose Hidden Semi-Markov Models (HSMMs) with (i) no constraints on the state duration density function and (ii) being applied to continuous or discrete observation. To deal with such a type of HSMM, we also propose modifications to the learning, inference, and prediction algorithms. Finally, automatic model selection has been made possible using the Akaike Information Criterion. This paper describes the theoretical formalization of the model as well as several experiments performed on simulated and real data with the aim of methodology validation. In all performed experiments, the model is able to correctly estimate the current state and to effectively predict the time to a predefined event with a low overall average absolute error. As a consequence, its applicability to real world settings can be beneficial, especially where in real time the Remaining Useful Lifetime (RUL) of the machine is calculated.

scholarly journals ANALYSIS OF CHANGES IN HOMOCYSTEIN USED AS A MARKER OF METABOLIC SYNDROME IN PATIENTS WITH INCREASED BODY MASS INDEX WHO HAVE MULTIPLE TRAUMA

2018 ◽  
Vol 18 (4) ◽  
pp. 30-33
Author(s):  
V. V. Kucheryavchenko
Keyword(s):  
Metabolic Syndrome ◽  
Body Mass Index ◽  
Body Mass ◽  
Apache Ii ◽  
Anthropometric Parameters ◽  
Mean Values ◽  
Initial Values ◽  
The Mean ◽  
The Moment ◽  
Combined Injuries

In recent years, the concept of "metabolic syndrome" has become more spreading, and in parallel with disorders of carbohydrate and lipid metabolism, endothelial dysfunction is no less significant. The aim of our work was to analyze changes in homocysteine (HC) as a marker of metabolic syndrome in patients with an increased body mass index (IBMI) in polytrauma. The study involved 224 patients with polytrauma, who had different initial values of body mass index (BMI) and were treated at the polytrauma department and the intensive care unit for patients with combined injuries for a period from 1 day to 1 year since the moment of injury. All the patients were subjected to identifying the level of serum HC. The patients had the same severity according to the APACHE II scale, 14 ± 5.8, at the admission to the hospital, and were divided into 3 stratified clinical groups depending on the initial values of anthropometric parameters and BMI. The study was conducted on the 1, 3, 7, 14, 30 and 360 days from the date of polytrauma. Assessment of blood serum HC was performed by ELISA. It was found that the overweight patients with BMI ≤ 29.9 demonstrated an increase in the mean values of blood HC on the 7th and 14th days of the treatment, with a further decrease on the 15th day since the date of injury. For the patients with BMI within the range of 30.0 - 39.9, the persistence of the HC index during the first month of the treatment at baseline with an increase on the 360th day was found out. The patients with BMI ˃ 40.0 showed an increase in the level of blood HC through the year since the date of polytrauma. It was revealed that the level of HС directly affects the course of traumatic disease in patients with increased BMI, its severity in terms of uniformity of injuries received and the same range of severity according to the APACHE II scale depends on BMI at the admission to the hospital.

scholarly journals Study of a Privacy Preserving Logistic Regression Algorithm (PPLRA) For Data Privacy in the Context of Big Data

2021 ◽  
Vol 2083 (3) ◽  
pp. 032059
Author(s):  
Qiang Chen ◽  
Meiling Deng
Keyword(s):  
Machine Learning ◽  
Logistic Regression ◽  
Privacy Protection ◽  
Data Privacy ◽  
Absolute Error ◽  
Average Absolute Error ◽  
Regression Algorithms ◽  
Hadoop Platform ◽  
Logistic Regression Algorithm ◽  
Computing Speed

Abstract Regression algorithms are commonly used in machine learning. Based on encryption and privacy protection methods, the current key hot technology regression algorithm and the same encryption technology are studied. This paper proposes a PPLAR based algorithm. The correlation between data items is obtained by logistic regression formula. The algorithm is distributed and parallelized on Hadoop platform to improve the computing speed of the cluster while ensuring the average absolute error of the algorithm.

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