Biomedical subjects

E I Mohamed

Publications and source records attributed to E I Mohamed.

At least 19 recordsLinked to original sources

PubMed2003

Mathematical models and their application in body composition research.

Mathematical models are a means of formalizing the knowledge on living systems obtained in clinical physiology and theoretical biophysics. They allow the actual processes in living systems to be described and the mechanisms of these systems to be evaluated. The study of body composition strives to quantitatively evaluate intrinsic body compartments and to obtain important information on the nutritional and energy requirements of healthy persons and those with various pathologies. Although there has been great progress in the development of sophisticated instruments for body composition research, it is not always possible to directly measure certain body compartments (e. g. intracellular water and fat mass) because of either technical difficulties or for ethical reasons. Therefore, mathematical models, which can potentially estimate these compartments indirectly, may be a viable alternative. In this paper, we describe the various advances in the use of mathematical models for body composition research.

Body Composition↗

PubMed2003

The capabilities of artificial neural networks in body composition research.

When estimating in vivo body composition or combining such estimates with other results, multiple variables must be taken into account (e. g. binary attributes such as gender or continuous attributes such as most biosignals). Standard statistical models, such as logistic regression and multivariate analysis, presume well-defined distributions (e. g. normal distribution); they also presume independence among all inputs and only linear relationships, yet rarely are these requirements met in real life. As an alternative to these models, artificial neural networks can be used. In the present work, we describe the pre-processing and multivariate analysis of data using neural network techniques, providing examples from the medical field and making comparisons with classic statistical approaches. We also address the criticisms raised regarding neural network techniques and discuss their potential improvement.

Body Composition↗

PubMed2003

Predicting the intracellular water compartment using artificial neural network analysis.

Artificial neural networks (ANN) are used for a wide variety of data-processing applications such as predicting medical outcomes and classifying clinical data and patients. We investigated the applicability of an ANN for estimating the intracellular water compartment for a population of 104 healthy Italians ranging in age from 19 to 68 years. Anthropometric variables, bioelectric impedance analysis (BIA) variables, and reference values for intracellular water, measured using whole-body (40)K counting (ICW(K40)), were measured for all study participants. The anthropometric variables and the impedance index (height(2)/resistance) were fed to the ANN input layer, which produced as output the estimated values for intracellular water (ICW(ANN)). We also estimated intracellular water using a BIA formula for the same population (ICW(DeLorenzo)) and another for Caucasians (ICW(Gudivaka)). Errors in the estimations generated by ANN and the BIA equations were calculated as the root mean square error (RMSE). The mean (+/-SD) reference value (ICWK40) was 25.01+/-4.50 l, whereas the mean estimated value was 15.20+/-1.79 l (RMSE=11.06 l) when calculated using ICW(DeLorenzo), 18.07+/-1.14 l (RMSE=8.72 l) when using ICW(Gudivaka), and 25.01+/-2.74 l (RMSE=3.22 l) when using ICW(ANN). Based on these results, we deduce that the ANN algorithm is a more accurate predictor for reference ICW(K40) than BIA equations.

Adult↗

PubMed2003

Artificial neural network analysis: a novel application for predicting site-specific bone mineral density.

Dual X-ray absorptiometry (DXA), which is the most commonly used method for the diagnosis and followup of human bone health, is known to produce accurate estimates of bone mineral density (BMD). However, high costs and problems with availability may prevent its use for mass screening. The objective of the present study was to estimate BMD values for healthy persons and those with conditions known to be associated with BMD, using artificial neural networks (ANN). An ANN was used to quantitatively estimate site-specific BMD values in comparison with reference values obtained by DXA (i. e. BMD(spine), BMD(pelvis), and BMD(total)). Anthropometric measurements (i. e. sex, age, weight, height, body mass index, waist-to-hip ratio, and the sum of four skinfold thicknesses) were fed to the ANN as independent input variables. The estimates based on four input variables were generated as output and were generally identical to the reference values for all studied groups. We believe the ANN is a promising approach for estimating and predicting site-specific BMD values using simple anthropometric measurements.

Bone Density↗

PubMed2003

Predicting bone mineral density of postmenopausal healthy and cirrhotic Italian women using age and body mass index.

The objective of the present report was to develop mathematical prediction formulae for the lumbar spine, pelvis and total bone mineral density (BMD) based on the osteoporosis risk factors age and BMI in healthy and cirrhotic postmenopausal women. The study population comprised 20 postmenopausal cirrhotic women (late PM cirrhotic women), 20 postmenopausal healthy women matched for age and BMI (late PM healthy women), and 19 younger postmenopausal healthy women matched for BMI (early PM healthy women). Segmental and total bone mineral content and BMD, total bone-free lean body mass and total fat mass were measured for all women using dual X-ray absorptiometry (DXA). The prediction formulae for late PM cirrhotic women had higher cumulative correlation coefficients ( r=0.71, p=0.05 for spine BMD, r=0.84, p=0.013 for pelvis BMD, and r=0.89, p=0.004 for total BMD) than those for early PM healthy women ( r=0.64, p=0.015 for spine BMD, r=0.69, p=0.002 for pelvis BMD, and r=0.62, p=0.022 for total BMD) and late PM healthy women ( r=0.29, p=NS for spine BMD, r=0.39, p=NS for pelvis BMD, and r=0.54, p=NS for total BMD). The mathematical formulae based on the variables age and BMI were capable of predicting lumbar spine BMD, pelvis BMD, and total BMD by DXA for the three groups of postmenopausal women.

Aged↗

PubMed2003

Body composition and respiratory function.

Although the effects of sex, ageing, height, race, and current and past health on pulmonary function tests have been described, only non-significant associations have been observed between body weight and lung function among healthy persons after having accounted for the effects of age and height. However, few studies have considered the influence of body compartments (e. g. lean and fat masses and their distribution) on lung function and respiratory gas exchange. The present work consists of a review of the literature on the effects of body weight components and body composition measurements on lung function. One of the important findings of this review was that the central (or upper body) pattern of fat distribution is negatively associated with airway function and that increases in body muscular mass result in linear increases for all spirometric variables in healthy persons. Nonetheless, the role that body composition plays in lung function still needs to be clarified.

Body Composition↗

PubMed2003

The effect of physical activities of various intensities on the energy expenditure of type 2 diabetic men.

Interventions that focus on changing lifestyles through the combined use of dietary management, weight reduction, and increased physical activity are essential for managing type 2 diabetes mellitus (T2DM). The objective of the present study was to examine mildly obese T2DM patients [ n=10; mean age (+/-SD), 51.29+/-6.80 years; body mass index (BMI), 30.26+/-6.19 kg/m(2); and glycosylated haemoglobin (HbA(1c)), 8.16+/-1.16%)] and to compare them with normoglycaemic persons ( n=10; age, 53.00+/-9.48 years; BMI, 27.63+/-3.33 kg/m(2), and HbA(1c), 5.85+/-0.56%) in terms of energy expenditure while performing physical activities of various intensities (i. e. light, moderate, and heavy). The resting metabolic rate for T2DM patients was significantly higher than that for healthy controls (2200+/-354 kcal/day vs. 1628+/-176 kcal/day, respectively; p<0.0001). The values of energy expenditure at all three levels of physical activity were comparable between the two groups. Physical activity seems to have various beneficial effects on mildly obese T2DM patients because it increases the plasma glucose consumption, resulting in similar energy expenditure in comparison with normoglycaemic individuals.

Adult↗

PubMed2003

Assessing body composition in gastrointestinal disorders.

The assessment of body composition and energy requirements is important for the nutrition of healthy and pathological states such as gastrointestinal disorders, which are known to be associated with abnormalities in body composition among persons with malnutrition and malabsorption. Careful monitoring of body composition is thus recommended for assessing body water compartments, predicting caloric needs and physical performance, and evaluating the development of muscle mass in persons with malabsorption. In the present review we briefly describe the basic body composition models and various techniques used for their assessment and discuss the utility of measuring caloric requirements in persons with gastrointestinal disorders. Assessing body composition may improve the prognosis of malnutrition caused by gastrointestinal disorders and may be useful in monitoring diet treatment.

Body Composition↗

PubMed2003

Evaluating body composition of Italian prostate cancer patients without metastases.

Prostate cancer (PCa) is becoming an increasingly important public health problem worldwide, and anthropometric studies have revealed that body composition is a potential risk factor for this type of cancer. The objective of the present study was to compare Italians with PCa and no history of androgen deprivation therapy or bone metastases with healthy controls in terms of body composition. We recruited 11 PCa patients [mean age (+/-SD), 68.67+/-4.93 years; body mass index (BMI), 28.42+/-2.96 kg/m(2)] and 11 healthy controls matched for age and BMI. Anthropometric and body composition parameters were measured for all participants using dual X-ray absorptiometry and bioelectric impedance analysis techniques. We found that lean body mass, but not fat mass, was significantly lower for PCa patients in comparison with healthy controls (50.24+/-5.03 vs. 53.63+/-5.61 kg, p<0.05). Thus, PCa patients with no history of androgen deprivation therapy and no bone metastasis suffer muscle mass depletion.

Aged↗

PubMed2003

Total body water estimation using bioelectrical impedance: a meta-analysis of the data available in the literature.

Bioelectric impedance analysis (BIA) is commonly used in clinical settings and field studies for estimating total, extracellular, and intracellular water compartments. The objective of the present study was to carry out a meta-analysis of published reports in which total body water (TBW) was estimated using BIA techniques and comparisons were made with reference values. We identified 16 reports conducted among healthy and obese adults and individuals with chronic renal failure. Based on the weighted mean difference, we found that those studies using only multi-frequency BIA did not significantly overestimate the TBW compared with the reference values. Thus, among BIA techniques, multi-frequency BIA seems to be a more accurate method for estimating the TBW compartment for healthy and obese adults and for those with chronic renal failure.

Body Water↗

PubMed2003

Analysis of body water compartments after a short sauna bath using bioelectric impedance analysis.

Studies have suggested that long-term sauna bathing may lower blood pressure in persons with hypertension by causing a direct loss of extracellular water and plasma minerals. The objective of the present study was to evaluate the effect of short-term sauna bathing on body water compartments as estimated by bioelectric impedance analysis (BIA). We recruited 15 men [mean age (+/-SD) of 23.93+/-5.12 years and mean body mass index (BMI) of 23.25+/-2.84 kg/m(2)] and 10 women matched for age and BMI. Total body resistance, reactance, and impedance were measured for all participants using BIA, at baseline, after a short sauna bath, and after a rest period. Total, extracellular, and intracellular water compartments were calculated using BIA formulae. There were no significant differences for any of the body water compartments when comparing the measurements taken before and after the sauna bath and after the rest period. However, it remains to be determined whether or not BIA is sensitive to rapid changes in water volume.

Adult↗

PubMed2003

The application of body cell mass index for studying muscle mass changes in health and disease conditions.

Body mass index (BMI) fails to detect altered nutritional state in the presence of overweight or obesity, since malnutrition can be present and masked by the abnormal amount of fat mass. Measuring body cell mass (BCM) contents for the evaluation of muscle mass and protein tissue states is well accepted. The aim of the present study was to apply body cell mass index (BCMI) to monitor the muscular mass changes of male and female Olympic athletes, renal dialysis patients, and anorexia nervosa patients in comparison with healthy subjects. The BCMI values of male subjects from the healthy group and Olympic athletes groups, but not the renal dialysis group, were significantly higher ( p<0.0001) than those of female subjects from the same groups. In addition, subjects with normal or high BMI values may be malnourished as highlighted by a low BCMI. We believe the BCMI is more sensitive than the BMI for studying the nutritional status of the individual.

Adolescent↗

PubMed2003

A comparative analysis of body water compartments for Egyptian patients with Schistosoma mansoni infection.

In light of reports that schistosomiasis is associated with alterations in nutritional status, and considering that the assessment of body composition and water spaces is important for nutritional and hydration management, we conducted a study to evaluate the reproducibility of estimates of total body water, extracellular water, and intracellular water for Egyptians with Schistosoma mansoni infection using various bioelectric impedance analysis (BIA) formulae in comparison with reference values obtained using dilution techniques. The estimates obtained using the various formulae varied from the reference values, representing either overestimates or underestimates. These differences were attributed to possible limitations inherent to the mathematical formulae, including the fact that they are population specific. These results stress the need to develop universal formulae for estimating body water compartments which would include race and pathology as independent parameters, in addition to BIA and anthropometric variables.

Adult↗

PubMed2003

Body composition analysis for healthy Italian vegetarians.

The elementary nutritional needs of vegetarians are totally, or in great part, supplied by vegetarian food; thus the body composition of vegetarians could differ from that of omnivorous persons. The objective of the present study was to compare healthy Italian vegetarians to healthy omnivorous individuals in terms of body composition, determined using dual X-ray absorptiometry. The study population consisted of 20 vegetarians [mean age (+/-SD), 34.78+/-15.07 years; mean BMI, 22.41+/-2.15 kg/m(2)] and 10 omnivorous persons matched for age and BMI. We found no significant differences between the two groups in terms of fat mass, lean body mass, soft tissue, bone mineral content, or bone mineral density. These findings suggest that the vegetarian diet does not induce negative alterations in body composition.

Absorptiometry, Photon↗

PubMed2003

Predicting bone mineral density of postmenopausal healthy and cirrhotic Italian women using anthropometric variables.

BACKGROUND: Chronic liver diseases, including cirrhosis of the liver, have been shown to cause bone osteometabolic disease giving rise to osteoporosis and osteomalacia. AIMS: To develop mathematical prediction equations for the lumbar-spine, pelvis and total bone mineral density based on the osteoporosis risk factors age and body mass index in cirrhotic and healthy postmenopausal women. PATIENTS: Twenty-seven postmenopausal women with liver cirrhosis (Child-Pugh class A) and well-preserved liver function (Late postmenopausal cirrhotic), 27 women matched for age and body mass index (Late postmenopausal healthy) and 27 younger women matched only for body mass index (Early postmenopausal healthy). METHODS: Segmental and total fat mass, lean body mass and bone mineral density were measured for all participant women using dual X-ray absorptiometry. RESULTS: Segmental and total fat mass and bone mineral density were significantly lower for Late postmenopausal cirrhotic women as compared with Late and Early postmenopausal healthy women. Segmental and total lean body mass were comparable among the three study groups. CONCLUSIONS: The mathematical equations based on the variables age and body mass index were capable of predicting lumbar-spine bone mineral density, pelvis bone mineral density and total bone mineral density for the three groups of postmenopausal women with the lowest standard error of estimation and root mean square residuals of predictions for equations describing the Late postmenopausal healthy group.

Adipose Tissue↗

PubMed2002

The impact of body-weight components on forced spirometry in healthy italians.

Many studies have investigated lung function in relation to age and height among Caucasians, however, most of these studies did not consider the individual components of body weight. The objective of the present study was to study the effect of body weight components [bone-free lean body mass (BF-LBM), bone mineral content (BMC), and fat mass (FM)] measured by dual x-ray absorptiometry (DXA) on the lung-function variables (FVC, FEV1, and PEF) and to derive prediction equations for these variables in healthy adult Italians. Dynamic spirometric tests and body composition analysis by DXA were performed on 58 nonsmoking males, mean age ( +/-SE) 26.72 +/- 1.98 years and BMI 25.51 +/- 0.64 kg/m2, and 60 nonsmoking females matched for age and BMI (29.61 +/- 1.65 years and 26.45 +/- 1.05 kg/m2, respectively). Bivariate linear regression analysis showed the variables age, height, BF-LBM, BMC, and the interaction term BF-LBM*Height, but not weight and FM, to correlate significantly with lung-function variables for males and for females separately. Multiple linear regression analysis showed that sex, age, height, and BF-LBM*Height were significantly associated with FVC, FEV1, and PEF. The prediction equations developed for FVC, FEV1, and PEF on the basis of the independent variables i.e. sex, age (y), height (m), and BF-LBM*Height (kg. m) had a significantly higher cumulative correlation coefficient and a lower SEE compared with those based on age and height only. The present report suggests that the BF-LBM, expressed independently from height, can be considered for predicting lung-function variables.

Adolescent↗

PubMed2002

Body composition and pulmonary function in obese type 2 diabetic women.

Most patients with Type 2 diabetes mellitus (T2DM) are overweight and their fat-distribution pattern shows more truncal and less peripheral subcutaneous fat. Obesity also influences the respiratory system by mechanical effects on the diaphragm and chest wall depending on the distribution and size of excess adipose tissues. To determine if the impairment of pulmonary function in T2DM is associated with truncal fat distribution, we performed dynamic spirometric tests and body composition analysis, by dual X-ray absorptiometry, in 12 non-smoker mildly obese T2DM women and in an equal number of control normoglycaemic females, matched for age, body mass index and smoking habits. The forced vital capacity and forced expiratory volume in one second for T2DM women (2.67 +/- 0.30 and 2.15 +/- 0.20 1, respectively) were significantly (p < 0.05) lower than for control women (3.22 +/- 0.30 and 2.58 +/- 0.31 1, respectively). However, trunk fat mass and trunk lean body mass for T2DM women (16.68 +/- 2.31 and 20.87 +/- 1.91 kg, respectively) were comparable with those for controls (16.46 +/- 2.54 and 19.65 +/- 3.42 kg, respectively). Thus, we deduce that pulmonary function impairment in T2DM obese women is not associated with truncal fat mass deposition or with lean mass depletion.

Absorptiometry, Photon↗

PubMed2002

Predicting Type 2 diabetes using an electronic nose-based artificial neural network analysis.

Diabetes is a major health problem in both industrial and developing countries, and its incidence is rising. Although detection of diabetes is improving, about half of the patients with Type 2 diabetes are undiagnosed and the delay from disease onset to diagnosis may exceed 10 yr. Thus, earlier detection of Type 2 diabetes and treatment of hyperglycaemia and related metabolic abnormalities is of vital importance. The objectives of the present study were to examine urine samples from Type 2 diabetic patients and healthy volunteers using the electronic nose technology and to evaluate possible application of data classification methods such as self-learning artificial neural networks (ANN) and logistic regression (LR) in comparison with principal components analysis (PCA). Urine samples from Type 2 diabetic patients and healthy controls were processed randomly using a simple 8-sensors electronic nose and individual electronic nose patterns were qualitatively classified using the "Approximation and Classification of Medical Data" (ACMD) network based on 2 output neurons, binary LR analysis and PCA. Distinct classes were found for Type 2 diabetic subjects and controls using PCA, which had a 96.0% successful classification percentage mean while qualitative ANN analysis and LR analysis had successful classification percentages of 92.0% and 88.0%, respectively. Therefore, the ACMD network is suitable for classifying medical and clinical data.

Aged↗