CGS-SIMMA landslide frequency distribution.
\r\n\tAn update on clinical manifestations, their assessment, monitoring, and imagiology, including peripheral arthritis, enthesopathy, and extra-articular findings, and, the differential diagnosis with other diseases which evolves with axial and peripheral calcifications will be provided.
\r\n\r\n\t
\r\n\tAn important component of this book must be dedicated to the more recent treatments namely with biologic therapies but focusing also on new small molecule inhibitors and experimental therapies.
This research aimed to design a model for the detection of landslides at the regional scale using Earth Observation data and remote sensing techniques. The following techniques were used to achieve this goal.
Landslide inventory (LI), for example the CGS-SIMMA1 as the base for the evaluation of the multivariate data generated by new technologies. It provides information about the vector representation such as point objects, date, municipality, mass distribution, type, sub-type, material and origin contributing to landslide detection [1].
At regional scale, landslide detection and landslide distribution analysis allows to analyse the distribution and classification of landslides [2]. These analysis use univariate and multivariate statistical methods, obtaining weights by the correlation between landslides occurrences and conditioning factors. Weight of Evidence (WofE) method is a bivariate approach where the landslides inventory is used to calculate weights of conditioning factors in order to delineate potential areas of landslides. Logistic regression (LR) is one of the statistical methods more used to evaluate the relationship between landslides and related factors [3].
Remote sensing techniques have been used for landslides detection [4]. One of them is morphometry based on Digital Elevation Models (DEMs), for a quantitative analysis of the land surface topography. In this way, the geomorphometry analysis derives land surface parameters such as slope, aspect, curvature or basic local descriptors, regional parameters as catchment area, parameters connected with hydrology like topographic wetness index and so on [5]. Terrain parameters can be related to landslides to build detection models [6]. Change detection technique [7] based on the sudden disappearance of vegetation by NDVI difference computation can serve to landslide detection. Also, SAR-based techniques in rural areas can help to landslide detection [8].
Interferometric spaceborne radar to measure linear deformation rates has been implemented on several studies to landslides detection [9]. PS-InSAR (persistent scatterers) allows measurements with millimetre accuracy of individual features. PS-InSAR is differential measurements with respect to a reference point that is assumed to be stable [10]. PolSAR imagery allows to characterise objects on the ground based on that different structures and geometries show different backscatter values at different SAR polarisations. Quad polarimetric SAR data content information to landslide detection in forested areas under the assumption that the dominant mechanism is surface scattering with high homogeneity [11].
This way Earth Observation (EO) data encompasses sensors like SAR, optical images and GPS on board of platforms either satellite-based, aircraft-based or ground-based provide high spatial, temporal, and spectral resolution to geohazard studies [12]. The above combined with machine learning (ML) techniques like Random Forest, allow the mapping, monitoring and modelling of landslides occurrences.
In this section we describe the study area, the landslide inventory, taken the from the Colombian Geologic Service (CGS), and the Earth Observation data.
The study area is located at the southwest of Colombia and covers the inter and central Andes Mountains in a rectangle within the following WGS84 system coordinates: 02
National and regional location of the study area.
The CGS-SIMMA geo-service allowed to build the landslide inventory database for training the detection model. Landslide database contains an inventory of geomorphologic and catalogue type. The former contained more precise information in its thematic attributes. The overall distribution of SIMMA landslide inventory was 77.4% of slide type, 16.5% of fall type, and 6.1% of flow and creep type for a total of 230 registered events (Table 1). The spatial information contained only allowed mapping in a shapefile of point type.
LS Type | Detritus | Rock | Earth | Other | Total | % |
---|---|---|---|---|---|---|
Slides | 77 | 14 | 78 | 9 | 178 | 77.4 |
Falls | 15 | 8 | 14 | 1 | 38 | 16.5 |
Creep | 0 | 2 | 4 | 1 | 7 | 3.0 |
Flows | 0 | 0 | 3 | 3 | 6 | 2.6 |
Lateral Spreads | 0 | 0 | 1 | 0 | 1 | 0.4 |
Total | 92 | 24 | 100 | 14 | 230 | 100 |
% | 40.0 | 10.4 | 43.4 | 6.1 | 100 |
CGS-SIMMA landslide frequency distribution.
Table 2 summarise the EO data used in this research. EO data corresponds to DEMs, optical remote sensing and radar remote sensing. DEMs provided from SRTM DEM 30 m resolution and Palsar RTC elevation data at 12.5 m. Radar data had as source the spaceborne-based ESA-Copernicus Sentinel-1 satellites and the aerial-based UAVSAR platform. Optical data was provided by time series analysis of the multi-year Landsat 5, Landsat 7, and Landsat 8 NDVI surface reflectance images.
Data | Platform or mission | Band | Resolution (m) | Cycle (days) | Wide swath (km) |
---|---|---|---|---|---|
DEM | SRTM/NASA1 | C/X | 30 | — | 225 |
DEM | AP-RTC/JAXA2 | L | 12.5 | 46 | 70 |
Dual-pol (VV/VH) | Sentinel-1/ESA3 | C | 15 | 24 | 250 |
Quad-pol | UAVSAR/NASA-JPL4 | L | 7 | 365 days | 16 |
Optical | Landsat 8 SR/NASA5 | 30 | 16 days | 185 |
Earth observation data used in this research.
Retrieved from http://srtm.csi.cgiar.org/ and https://earthexplorer.usgs.gov.
Retrieved from https://www.asf.alaska.edu/doi/105067/z97hfcnkr6va/.
Copernicus Sentinel data 2014. Retrieved from ASF DAAC 29 April 2017, processed by ESA.
UAVSARdatacourtesyNASA/JPL-Caltech, retrieved from https://uavsar.jpl.nasa.gov/cgi-bin/data.pl.
Retrieved from https://search.earthdata.nasa.gov.
The main remote sensing techniques over Earth Observation data to extract features and conditioning factors related to landslides are listed in Table 3.
Approach | Variable or method | Software | Software type |
---|---|---|---|
Morphometry | Land surface parameters | Demanal/SAGA/R/ArcSDM1 | Open-source |
Spaceborne-based InSAR | InSAR measurements (coherence and displacement) | SNAP toolbox/SARProZ2 | Open-source and Commercial |
Multi-InSAR | Deformation velocities of persistent scatterers | SARProZ3 | Commercial |
Aerial-based PolSAR | Surface, volume, and double-bounce scattering mechanism | PolSARpro_v5.04 | Open-source |
Optical remote sensing | NDVI vegetation indices | Google Earth Engine5 | Open-source |
Binary model | WofE analysis and Logistic regression | ArcSDM6 | Open-source |
Multidimensional data fusion | Random Forest supervised method | QGIS/ArcCatalog/R software7 | Open-source and commercial |
Methods and approaches of remote sensing techniques used in this research.
DEMANAL package of BLUH software [13].
Retrieved from https://step.esa.int/main/download/snap-download/.
SarProZ software retrieved from www.sarproz.com.
ESA’s PolSARpro-v5.0 software.
Retrieved from https://earthexplorer.google.org.
Arc-SDM tool [14].
R software [15].
Figure 2 shows the functional representation of the EO data indicating the entire flow processing needed to develop the detection model from multi-dimensional data, data training and test of landslides. Also, the scheme shown in the Figure 3 indicates the fusion of all geospatial information by the Random Forest method.
Functional data for model building.
Chain of processing to landslide detection model.
The CGS-SIMMA landslide inventory was split into training and test subset in a proportion of 70:30 in concordance with the study made by Huang and Zhao [16] in order to determine the accuracy of each remote sensing method applied and the detection model generated in this research.
The results of remote sensing techniques implemented in the study area are presented in this section.
The DEMs Palsar RTC elevation data, SRTM DEM 30 m and 90 resolution, and ASTER-GDEM were evaluated in relation to GPS control points and a reference Topo DEM obtained by interpolation of contours at a scale of 1:25 K. The best in terms of vertical root-mean-square-error (RMSE) was SRTM 30 m resolution, and its accuracy at a 95% confidence level (7.8 m) corresponded to a better scale than 1:25 K. ALOSP RTC elevation data was the second best DEM. For this reason we derived from the latter the land surface parameters (LSP) at 12.5 m resolution using algorithms implemented on SAGA software. Table 4 shows the results of a vertical accuracy assessment of the global DEMs used in this research.
Metric | ALOSP | ASTER | SRTM1 (30 m) | SRTM3 (90 m) |
---|---|---|---|---|
RMSE (m) | 39.68 | 43.52 | 19.95 | 22.77 |
SZ (without bias) (m) | 4.427 | 5.331 | 3.937 | 4.595 |
NMAD (m) | 3.806 | 4.663 | 3.093 | 3.883 |
LE95 (without bias) (m) | 8.853 | 10.236 | 7.795 | 9.144 |
SZ= | 4.62 + 80.5*tan(slp) | 5.60 + 89.8*tan(slp) | 3.54 + 109.9*tan(slp) | 4.689 + 100*tan(slp) |
Vertical accuracy of the global DEMs compared with Topo-map DEM reference.
The land surface parameters: slope, aspect, curvature [17], topographic wetness index (TWI) [18], valley depth (Vdepth) [18], convergence index (CONVI) [19], flow path length (FPL), and insolation [20], were converted into independent components by using Principal Component Analysis (PCA) [21]. These were used as independent variables into a landform detection model and a landslide regression model by WofE methods.
Table 5 shows the results of WofE analysis to relate morphometric and land use conditioning factors with landslide inventory. Only the variable with its class with the most studentised contrast (bigger than 2) C/s(C) is shown. Figure 4 shows the unit soils at a scale of 1:100 k, which covers plain, undulated and mountainous terrains.
LSP | The range of study area | Class | C/s(C) |
---|---|---|---|
Slope | 0 | 6.6 | 2.1 |
13.2 | 2.9 | ||
FPL | 0–2598 m | 0–371 m | 2 |
Soil unit | 9 classes | Humid hill lands (LQ) and very wet cold mountain (MK) | 3.5 |
Landform | 12 classes | Backslopes | 3.6 |
WofE studentised contrast
Soil units of the study area.
InSAR measures are phase, coherence, and displacement and they are obtained by the cross-correlation between two or more SAR images to process the line-of-sight displacements. This research used C-band data provided by Sentinel/1 ESA’s Copernicus programme. In this investigation, the effect of a DEM on the InSAR processing was determined [22], concluding that DEM did not have an effect on InSAR coherence but if the InSAR phase is unwrapped with the DEM variable there are significance differences. The above is due to the inaccuracies of external DEM.
Two landslide regression models obtained either with InSAR coherence or InSAR displacement from a DEM variable showed that SRTM DEM 30 m resolution had the highest association with landslides inventory (Table 6). These models had an accuracy of 62% and 68% respectively.
DEM | InSAR coherence | InSAR displacement | ||||
---|---|---|---|---|---|---|
Coeff. | Pr( | Pr( | Coeff | Pr( | Pr( | |
Intercept | −9.345 | −13.15 | ||||
SRTM DEM 30 m | 2.627 | 0.853 | 0.0014** | 0.870 | 0.0003*** | 1.56e-9 *** |
SRTM DEM 90 m | −2.674 | 0.850 | 0.816 | −0.467 | 0.04* | 0.22 |
Palsar RTC | −0.085 | 0.522 | 0.069 | 0.323 | 0.084. | 0.057. |
Topo-map | 0.474 | 0.004** | 0.0031** | −0.017 | 0.924 | 0.924 |
AUC | 0.62 | 0.68 |
Results of InSAR regression analysis by LR method.
Significance of codes: 0 = ***, 0.001 = **, 0.01 = *, 0.05 =., 0.1 = \' \'.
From the point of view of the InSAR coherence measurement, all DEMs are not statistically significant. However, Topo-map showed a weak relation to landslides. Topo-map and SRTM 30 m contributed to a better explanation of the linear regression model. InSAR displacement with a DEM variable indicated that SRTM 30 m had the lowest p-value suggesting a strong association of the elevation with the probability of having a landslide with a positive coefficient. ANOVA verificated that by adding the SRTM 30 m to the regression model significantly reduces the residual deviance.
WofE analysis showed that the maximum studentised contrast for InSAR coherence was in the range of 0.43 to 0.66.
Multi-InSAR processing by PS-InSAR method allowed to estimate the deformation rate in relation to a reference point target which is assumed to be stable. This approach overloads the substantial limitation of InSAR measurements: the spatial and temporal decorrelation and atmospheric distortions due to ionospheric electron density and tropospheric water vapour.
The C-band sensor on-board the Sentinel-1 satellite served as input data to implement PSInSAR processing to estimate the annual linear velocities and the time series of deformations. Table 7 indicates the geometrical characteristics of S1_A of the ESA’ Copernicus used for PS-InSAR in the study area. The perpendicular baseline in all cases was lower than 150 m, which is an adequate value for studies of terrain deformation. The results were a deformation map which consisted in a set of selected points (12 pts./km
Zone | Dates | Stack | Pass/Pol | Bn(m) | Bt(days) | IncAnc ( |
---|---|---|---|---|---|---|
SE | 10–2014/09–2015 and 01–2016/09–2016 | 24 | Asc/VV | 7 to 144 | 24–384 | 34.2 |
NW | 10–2014/09–2015 and 01–2016/09–2016 | 21 | Asc/VV | 7 to 144 | 24–384 | 34.2 |
NE | 10–2014/05–2016 | 21 | Des/VV | 2 to 117 | 24–312 | 34.1 |
Sentinel-1-IW-SLC dataset.
OK prediction of the displacement velocity in mm/year.
Dual Pol-Sentinel-1 analysis allowed to analyse the sigma nought scattering coefficient with VV and VH polarisation. Copolarization backscattering (−8.5 dB) was higher than cross-polarisation (−14.5 dB) (Figure 6).
BS-coefficient (σ
Quad Pol-UAVSAR decomposition allowed to define the mechanism of scattering on landslides inventory using entropy/alpha within the Cloude Pottier method. The scattering mechanism dominant in the study area were volume scattering (vegetation) and surface scattering (Table 8). The results indicate that 50% of the landslide have a scattering mechanism of volume and 25.4% of surface type. The WofE method validated that the H-
H- | Landslide frequency | Relative frequency |
---|---|---|
4-Medium entropy and multiple scattering | 12 | 9.5 |
5-Medium entropy and volume scattering | 63 | 50 |
6-Medium entropy and surface scattering | 32 | 25.4 |
7-Low entropy and multiple scattering | 15 | 11.9 |
8-Low entropy and volume scattering | 4 | 3.2 |
Total: | 126 | 100 |
H-
Time series analysis of the multi-year Landsat NDVI was used as input data for the change detection analysis. In the period 2012 to 2017, the multi-year Landsat NDVI cloud-free yearly composites through Google Earth Engine did not show the statistically significant trends in vegetation. But WofE analysis found that the NDVI range with the highest association to the landslide inventory was between 0.40 and 0.72. Figure 7 shows that only the years 2012, 2014 and 2016 covers, without clouds, the landslide inventory distribution with median values of NDVI.
7-year LANDSAT NDVI composites (2010 to 2017).
All of the variables generated (25) in this research by remote sensing techniques were overlapped and cut into a common sub-zone and then combined into a multidimensional image. Here are found the classification variables. Then the effect of classification variables (derived from remote sensing techniques) over a target variable (landslide inventory) was measured by the algorithm of supervised pixel-based classification called Random Forest. Test data in a proportion of 30% of the entire data set allowed to obtain an independent validation. Table 9 show the Random Forest classification with an overall accuracy of 70.8%. The user’s accuracy refers to the correct classification of the type of movement in relation to the referenced one, and the producer’s accuracy refers to the commission or inclusion error. Due to the high frequency of rotational and translational slide, the method was successful, which did not happen with other less frequent types.
Random Forest classification | Topic | Sw |
---|---|---|
Overall accuracy | 70.8% | |
Training model | Rotational slide | 66.7% |
Translational slide | 96.2% | |
User’s accuracy | Rotational slide | 66.7% |
Translational slide | 71.4 $ | |
Producer’s accuracy | Rotational slide | 80% |
Translational slide | 90.9% |
Random Forest classification.
Figure 8 shows the results of the Random Forest classification for the landslides types: debris fall, flow, planar translational, rotational and translational. Rotational and translational slides had a producer’s accuracy of 80% and 91% respectively resulting in omissions errors of 20% and 9% for each one. User’s accuracy for the same type of landslides was of 67% and 71% indicating commission errors of 33% and 29%. The overall accuracy was 70.8%.
Detection model of landslides by the Random Forest method.
Figure 9 shows the Mean Decrease Accuracy implemented in Random Forest. The variables which contributed more to the study were PolSAR, the displacement InSAR, the NDVI and the morphometric variable slope.
Importance of the variables in decreasing order.
This study confirmed that the slope angle is a key classification factor in landslide detection in a similar way reported by Donnarumffia et al. [23]. So as land use is the most influencing factor to the occurrence of landslides [24].
An analysis of the effect of DEM on InSAR processing to estimate terrain deformations showed that DEM only had a significant impact on InSAR displacement but not on InSAR coherence, such as also is highlighted in Bayer et al. [25].
Dual polarimetric SAR analysis found that VV-polarisation radar backscatter produces stronger scattering than cross-polarisation (VH) on landslide inventory in the same way as is reported by Ningthoujam et al. [26].
C/band Sentinel-1 data allowed to measure very slow ground surface displacements with mm precision by PSInSAR method. However, as is indicated in Colesanti et al. [27], it is necessary to combine data from different sources, i.e. GNSS data, to avoid misinterpretations.
Time series analysis of Landsat NDVI composites with Google Earth Engine [28], allowed to compare measurements of inter-annual NDVI. However, this research only analysed the period 2012–2016. Thus, the lacking of long-term time series of optical satellites data did not detect trends in vegetation cover changes related to landslides. For this reason, inter-annual NDVI in the period 2012–2016 only was taken as conditioning factor to develop of detection model.
Random Forest (RF) algorithm was applied to classify landslides. Conditioning factors provided by remote sensing techniques were stored as grid cells at 30 m of spatial resolution. RF model for landslide classification needed data to train the model and validate its results. The total training dataset was split with a proportion of 70% of samples used to train models and 30% for validation.
Using the test dataset, we found that the overall classification accuracy of the model was 70.8%. This meant that over 70.8% of the test dataset was correctly identified as either a landslide event or non-landslide event in the same sense as is reported in Taalab et al. [29]. The rank of variables importance, based on the relative contribution to the classification accuracy of the model, in order of importance, were: PolSAR, InSAR displacement, NDVI, backslope landform and InSAR coherence.
By using Remote Sensing techniques at the visible and microwave frequencies of EM waves this research did relate EO measurements with ground physical parameters such as scattering mechanisms, topography, land cover type and surface deformation patterns. All of the above in relationship with landslides inventory of the study area.
This research did implement unsupervised and supervised classification methods. The first to understand the pattern of LSI clustering and the second to classify the LSI with multidimensional variables derived from EO data and RS techniques.
All of the EO data collected and generated by RS techniques during this research was stored in appropriate containers of data.
This research used errors’ theory, ANOVA, TUKEY and cross-validation techniques to determine the internal and external precision of the method generated for landslides detection.
Obesity is a key global public health alarm with about 500 million people worldwide affected [1, 2]. In epidemiological studies, anthropometric parameters, body mass index (BMI), waist circumference (WC), neck circumference (NC) and waist-height ratio (WHtR) are often used as measures of obesity [2, 3, 4, 5]. Obesity as directly measured by anthropometric technique has been demonstrated to be associated with hypertension and type 2 diabetes mellitus, common risk factors for CVD [6, 7].
The diagnosis of diabetes is often based on a fasting plasma glucose, random plasma glucose, a 2-hour plasma glucose value in a 75 g oral glucose tolerance test or a glycated hemoglobin (A1C) measurement, but generally on fasting plasma glucose, partly because of its better sensitivity to diagnose diabetes [5, 8]. Hypertension uses information about systolic blood pressure (SBP) and/or diastolic blood pressure (DBP) to derive an estimate for diagnosis [9].
In view of the burden of obesity, hypertension and diabetes and their impact on children and the scarcity of information on the relationship of blood glucose, blood pressure with anthropometric parameters in rural South African communities, more especially in Ellisras, the present study aimed to determine the relationship of blood pressure and blood glucose with anthropometric parameters among Ellisras rural children aged 6 to 11 years and to determine which of the anthropometric parameters is associated with greater odds of high blood pressure and high blood glucose levels in this population.
Ellisras, also known as Lephalale, is a relatively deep rural area located within the North- western area of Limpopo province, South Africa. The population consist of approximately 50000 people residing in 42 settlements and are adjacent to the Botswana border. Majority of residence in this population work at Iscor coal mine and Matimba electricity power station, whereas the remaining work class is involved in subsistence farming, while the minority is in civil services and education [10].
Research design and sampling method for the Ellisras Longitudinal study (ELS) have been reported elsewhere [10].
In this study, a total of 492 children (n = 296 boys; n = 196 girls) aged 6 to11 who are part of the ELS, participated in this study. Ethical approval prior to this study was obtained from Ethics Committee of the University of Limpopo. Guardians were provided with, and signed, written informed assent.
Anthropometric measurements were done according to the International Society for the Advancement of Kin-anthropometry (ISAK). Weight was measured on an electronic scale to the nearest 0.1 kg, and a Martin anthropometry was used to measure height to the nearest 0.1 cm. Flexible steel tape was used to measure NC and WC. Waist circumference was measured midway between the lower costal margin and iliac crest immediately after exhalation. Neck circumference was measured directly below the thyroid cartilage perpendicular to the long axis of the neck. All measurements were taken with the participants standing in an anatomical position.
Children with BMI <85th, ≥85th and ≥ 95th percentile were considered normal, overweight and obese, respectively [7]. Children with WC ≥90th percentile were considered to have abdominal obesity, and those with NC 90th ≥ were considered to have obesity [3, 11]. BMI was calculated as weight in kilograms (kg) divided by the square of height in metres (m), whereas waist to height ratio was calculated as waist circumference divided by height.
To measure blood glucose, the participants were first made comfortable, by sitting on the chair for at least five minutes before measurements. From each subject at least three reading blood pressure (systolic and diastolic) measurements were taken, at an interval of five minutes apart. Blood pressures readings were taken using electronic Micronta monitoring kit. The device has a bladder which contains an electronic infrasonic transducer that monitors the blood pressure and pulse rate, thus displaying these on the screen. Hypertension was defined by systolic and diastolic blood pressure ≥ 95th percentile of age and sex adjusted reference level [6].
To measure blood glucose level all subjects were asked to do an 10 hours overnight fasting prior to the test, in the morning their capillary blood sample were obtained by a finger prick and blood was caught up in little cuvettes, which were prepared with below mentioned reagents (glucose oxidase and reagents to measure the generation of hydrogen peroxide such as non-toxic phenol red and horseradish peroxidase was bonded to filter paper). After mixing with the reagents, fasting blood glucose was measured using Hemocue® [8]. Type 2 diabetes mellitus was defined by fasting blood glucose ≥7.1 mmol/L of sex and age adjusted reference level.
All the statistical analyses were performed using the Statistical Package for the Social Sciences (SPSS) version 25. Data comparison was done using student t-test for 2 groups. Descriptive statistics were performed for age, anthropometric parameters, fasting blood glucose and blood pressure (systolic and diastolic). The Linear regression model was used to assess the association between blood pressure (systolic and diastolic), blood glucose and anthropometric parameters after adjusting for potential confounders. The logistic regression was used to determine the risk of developing hypertension and type 2 diabetes mellitus using anthropometric parameters. Statistical significance was set at a probability level of 0.05 (Figures 1 and 2).
Gender specific prevalence of obesity based on WC, BMI, WHtR and NC among Ellisras children aged 6 to 11 years. The prevalence of obesity was higher in boys measured by BMI (16.9%), WC (5.4%) and WHtR (9.6%), as compared to girls BMI (12.2%), WC (4.6%) and WHtR (6.2%) and that measured by NC was higher in girls (14.3%) than in boys (11.5%).
Gender-specific prevalence of hypertension and type 2 diabetes mellitus among Ellisras children age 6 to 11 years. The prevalence of hypertension defined by SBP was higher in boys (5.4%) than in girls (5.1%), hypertension defined by DBP was higher in girls (9.7%) than in boys (8.4%) and that of type 2 diabetes mellitus was higher in boys (16.9%) as compared to girls (12.2%).
Table 1 showed descriptive statistics of anthropometric parameters, blood pressure and fasting blood glucose stratified by gender. The study population comprised predominately boys (60%) with boy to girl ratio of 1.51:1. Boys showed higher mean values of age NC, WC, BMI and WHtR than girls. In contrast, girls had higher mean SBP, DBP and FBG values.
Variables | Boys | Girls |
---|---|---|
Age (years) | 9.637 (1.351) | 9.341 (1.086) |
SBP (mmHg) | 96.621 (11.569) | 97.740 (10.540) |
DBP(mmHg) | 65.426 (8.930) | 66.300 (9.216) |
FBG (mmol/L) | 4.542 (1.900) | 4.707 (1.160) |
BMI (kg/m2) | 14.411 (1.203) | 14.182 (1.368) |
NC (cm) | 25.850 (1.350) | 25.287 (1.275) |
WHtR | 0.411 (0.022) | 0.407 (0.023) |
WC (cm) | 54.720 (3.153) | 53.853 (3.354) |
Descriptive statistics of anthropometric parameters, systolic and diastolic blood pressure and blood glucose of Ellisras children age 6 to 11 years.
M = Mean; SD = standard deviation; NC = Neck Circumference (cm); WC = waist circumference (cm); BMI = body mass index (kg/m2); WHtR = waist to height ration; SBP = systolic blood pressure (mmHg); DBP = diastolic blood pressure (mmHg); FBG = fasting blood glucose (mmol/L).
Table 2 showed positive correlation for systolic blood pressure with NC (0.261** and 0.252**), WC (0.276** and 0.208**) and NC (0.264** and 0.233**) in boys and girls respectively. Positive correlation was found for diastolic blood pressure with NC (0.176**), WC (0.272**) and BMI (0.212**) in boys. Negative correlation was found for fasting blood glucose with NC (−0.147*) and positive correlation for fasting blood glucose with BMI (0.176*)
SBP | DBP | FBG | ||||
---|---|---|---|---|---|---|
Boys | Girls | Boys | Girls | Boys | Girls | |
FBG | −0.029 | 0.075 | 0.044 | 0.131 | ||
NC | 0.261** | 0.252** | 0.176** | 0.102 | −0.147* | −0.027 |
WC | 0.276** | 0.208** | 0.272** | 0.065 | −0.065 | 0.111 |
BMI | 0.264** | 0.233** | 0.212** | 0.096 | −0.004 | 0.176* |
WHtR | −0.046 | 0.058 | 0.064 | 0.025 | 0.031 | 0.035 |
Pearson correlation coefficient (r) of blood pressure (systolic and diastolic) and blood glucose with anthropometric parameters.
**p < 0.001; *p < 0.05 statistical significant; NC = Neck Circumference (cm); WC = waist circumference (cm); BMI = body mass index (kg/m2); WHtR = waist to height ration.
Table 3 shows the linear regression for the association of anthropometric parameters (NC, BMI, WC and WHtR) with DBP, SBP and FBG. There was a significant association of SBP with BMI (β = 0.244, 95%CI = 1.390–2.901), WC (β = 0.240, 95%CI = 0.528–1.120) and NC (β = 0.242, 95%CI = 1.294–2.724).
Unadjusted | Adjusted for age and sex | |||||
---|---|---|---|---|---|---|
Para meters | β | P-value | 95%CI | Β | P-value | 95%CI |
Systolic blood pressure | ||||||
BMI | 0.244 | <0.001 | 1.390–2.901 | 0.161 | <0.001 | 0.942–2.529 |
WC | 0.240 | <0.001 | 0.528–1.120 | 0.186 | <0.001 | 0.296–2.072 |
WHtR | −0.009 | 0.838 | −49.434–40.129 | 0.069 | 0.135 | −10.957–80.784 |
NC | 0.242 | <0.001 | 1.294–2.724 | 0.197 | <0.001 | 0.780–2.485 |
Diastolic blood pressure | ||||||
BMI | 0.156 | <0.001 | 0.487–1.731 | 0.072 | 0.004 | 0.319–1.640 |
WC | 0.177 | <0.001 | 0.249–0.734 | 0.171 | <0.001 | 0.190–0.758 |
WHtR | 0.043 | 0.337 | −18.486–53.846 | 0.091 | 0.053 | −0.463–75.012 |
NC | 0.134 | 0.003 | 0.307–1.488 | 0.120 | 0.026 | 0.098–1.515 |
Fasting blood glucose | ||||||
BMI | 0.046 | 0.312 | −0.056–0.173 | 0.083 | 0.081 | −0.014–0.229 |
WC | −0.018 | 0.692 | −0.054–0.036 | 0.034 | 0.516 | −0.035–0.070 |
WHtR | 0.027 | 0.551 | −4.582–8.586 | 0.011 | 0.824 | −6.137–7.704 |
NC | −0.120 | 0.008 | −0.254–-0.039 | −0.104 | 0.054 | −0.257–0.002 |
Linear regression analysis for the association of anthropometric parameters with, blood glucose and blood pressures.
P value < 0.05 = statistical significant; CI = confidence interval; β = beta; NC = Neck Circumference (cm); WC = waist circumference (cm); BMI = body mass index (kg/m2); WHtR = waist to height ration.
Table 4 showed binary logistic regression analysis for determining the odds of high blood pressure and high blood glucose using anthropometric parameters. After multivariate adjustment by age and gender, BMI (P = 0.046, β = 1.340, 95% CI = 1.005–1.788) showed to have significantly greatest odds for high SBP followed by WC (P = 0.025, β = 1.143, 95% CI = 1.017–1.285).
Unadjusted | Adjusted for age and gender | ||
---|---|---|---|
BMI | 1.340 (1.005–1.788) | 0.046 | 1.258(0.925–1.709) |
NC | 1.208 (0.903–1.617) | 0.203 | 1.080(0.763–1.530) |
WC | 1.143 (1.017–1.285) | 0.025 | 1.115(0.975–1.276) |
WHtR | 1.510 (1.106–1.666) | 0.579 | 1.413(0.752–1.777) |
BMI | 1.150 (0.909–1.456) | 0.244 | 1.210(0.941–1.558) |
NC | 1.076 (0.856–1.354) | 0.529 | 1.189(0.901–1.571) |
WC | 1.069 (0.973–1.174) | 0.164 | 1.120(1.004–1.249) |
WHtR | 1.042 (0.144–1.317) | 0.092 | 0.172(0.093–3.181) |
BMI | 0.987 (0.827–1.177) | 0.884 | 1.009(0.838–1.215) |
NC | 0.831 (0.701–0.986) | 0.034 | 0.813(0.663–0.998) |
WC | 0.975 (0.910–1.045) | 0.479 | 0.983(0.907–1.066) |
WHtR | 0.137(0.124–1.432) | 0.083 | 0.978(0.809–1.183) |
Binary logistic regression analysis of anthropometric parameters with systolic and diastolic blood pressure.
P value < 0.05 = statistical significant; CI = confidence interval; OR = odds ratio; NC = Neck Circumference (cm); WC = waist circumference (cm); BMI = body mass index (kg/m2); WHtR = waist to height ration.
The main purpose of the study was to determine the relationship of blood pressure, blood glucose with anthropometric parameters among Ellisras children aged 6 to 11 years old and several major findings emerged.
The present study showed that NC is significantly associated with blood glucose, which corroborates the previous findings that increased NC is an emerging risk factor for high blood glucose [12]. However, it is difficult to explain in the present study why only NC was associated with fasting blood glucose, although BMI, WC and WHtR are also anthropometric parameters. One possible explanation for these findings may be that different anthropometric parameters have varied impact on blood glucose. The mechanism linking anthropometric parameters with blood glucose levels is not clear but main schools of thought on the matter suggest different mechanisms. Firstly, in obesity (i.e WHR >0.90 or BMI >30 kg/m2), abundance of circulating fatty acids and liver-derived triglyceride (VLDL) provide an excellent fuel for muscle, decreasing their requirement for glucose [13]. People with obesity tend to be sedentary, and thus muscle consumes less glucose [14]. In obesity, increased delivery of fatty acids to the liver (as in visceral obesity) enhances gluconeogenesis and thus leading to production of glucose [15]. In obesity the increased fatty acid cause insulin resistance directly by activating enzymes that decrease the response to insulin, thereby aggravates the pre-existing insulin resistance which results in elevated blood glucose level and eventually type 2 diabetes mellitus [16].
The study also demonstrated that NC, WC and BMI are significantly associated with blood pressure, which confirms the observations of the previous findings around the relationship between mean anthropometric parameters and blood pressure values [4, 7, 17]. However, the precise mechanism by which anthropometric parameters act to increase blood pressure is not fully understood. One possible mechanism is linked to the prognostic importance of visceral adipose tissue assessments by WC rather than general obesity assessments by BMI [18, 19]. Visceral adipose tissue produces angiotensinogen, interleukin-6 and leptin [20]. An imbalance in production of these adipokines, particularly angiotensinogen leads to the activation of the rennin-angiotensin system, causing vasoconstriction and reabsorption of sodium [20]. The constriction of blood vessels increases blood pressure and eventually the development of high blood pressure.
This study has several limitations. Firstly this is a cross sectional study and does not allow establishment of cause-effect relationship, secondly the study model does not provide information regarding the ability of anthropometric parameter to future health outcome, and lastly the study was conducted in rural areas in Ellisras so the findings might not be generalizable to the overall Ellisras population.
The study shows that both blood glucose and blood pressure are associated with some anthropometric parameters. These findings suggest that it is crucial to manage and control traditional risk factors in rural South African communities in Ellisras in order to decelerate the increase in obesity, hypertension and type 2 diabetes mellitus and to reduce the burden of cardiovascular disease. The present study highlights the need of incorporating body mass index (BMI), waist circumference (WC), neck circumference (NC) and waist-height ratio (WHtR) while evaluating the association of easily accessed anthropometric parameters with CVD risk factors.
The ELS administrators T.T. Makata, L. Majadibodu, and U.T. Motlogelwa are greatly acknowledged for coding the ELS data.
The authors declare no conflict of interest.
"Open access contributes to scientific excellence and integrity. It opens up research results to wider analysis. It allows research results to be reused for new discoveries. And it enables the multi-disciplinary research that is needed to solve global 21st century problems. Open access connects science with society. It allows the public to engage with research. To go behind the headlines. And look at the scientific evidence. And it enables policy makers to draw on innovative solutions to societal challenges".
\n\nCarlos Moedas, the European Commissioner for Research Science and Innovation at the STM Annual Frankfurt Conference, October 2016.
",metaTitle:"About Open Access",metaDescription:"Open access contributes to scientific excellence and integrity. It opens up research results to wider analysis. It allows research results to be reused for new discoveries. And it enables the multi-disciplinary research that is needed to solve global 21st century problems. Open access connects science with society. It allows the public to engage with research. To go behind the headlines. And look at the scientific evidence. And it enables policy makers to draw on innovative solutions to societal challenges.\n\nCarlos Moedas, the European Commissioner for Research Science and Innovation at the STM Annual Frankfurt Conference, October 2016.",metaKeywords:null,canonicalURL:"about-open-access",contentRaw:'[{"type":"htmlEditorComponent","content":"The Open Access publishing movement started in the early 2000s when academic leaders from around the world participated in the formation of the Budapest Initiative. They developed recommendations for an Open Access publishing process, “which has worked for the past decade to provide the public with unrestricted, free access to scholarly research—much of which is publicly funded. Making the research publicly available to everyone—free of charge and without most copyright and licensing restrictions—will accelerate scientific research efforts and allow authors to reach a larger number of readers” (reference: http://www.budapestopenaccessinitiative.org)
\\n\\nIntechOpen’s co-founders, both scientists themselves, created the company while undertaking research in robotics at Vienna University. Their goal was to spread research freely “for scientists, by scientists’ to the rest of the world via the Open Access publishing model. The company soon became a signatory of the Budapest Initiative, which currently has more than 1000 supporting organizations worldwide, ranging from universities to funders.
\\n\\nAt IntechOpen today, we are still as committed to working with organizations and people who care about scientific discovery, to putting the academic needs of the scientific community first, and to providing an Open Access environment where scientists can maximize their contribution to scientific advancement. By opening up access to the world’s scientific research articles and book chapters, we aim to facilitate greater opportunity for collaboration, scientific discovery and progress. We subscribe wholeheartedly to the Open Access definition:
\\n\\n“By “open access” to [peer-reviewed research literature], we mean its free availability on the public internet, permitting any users to read, download, copy, distribute, print, search, or link to the full texts of these articles, crawl them for indexing, pass them as data to software, or use them for any other lawful purpose, without financial, legal, or technical barriers other than those inseparable from gaining access to the internet itself. The only constraint on reproduction and distribution, and the only role for copyright in this domain, should be to give authors control over the integrity of their work and the right to be properly acknowledged and cited” (reference: http://www.budapestopenaccessinitiative.org)
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\\n\\nBook chapters published in edited volumes are distributed under the Creative Commons Attribution 3.0 Unported License (CC BY 3.0). IntechOpen upholds a very flexible Copyright Policy. There is no copyright transfer to the publisher and Authors retain exclusive copyright to their work. All Monographs/Compacts are distributed under the Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0). Read more
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\\n\\nOA Publishing Fees
\\n\\nThe Open Access publishing model employed by IntechOpen eliminates subscription charges and pay-per-view fees, enabling readers to access research at no cost. In order to sustain operations and keep our publications freely accessible we levy an Open Access Publishing Fee for manuscripts, which helps us cover the costs of editorial work and the production of books. Read more
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\\n\\nOpen Science is transparent and accessible knowledge that is shared and developed through collaborative networks.
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The Open Access publishing movement started in the early 2000s when academic leaders from around the world participated in the formation of the Budapest Initiative. They developed recommendations for an Open Access publishing process, “which has worked for the past decade to provide the public with unrestricted, free access to scholarly research—much of which is publicly funded. Making the research publicly available to everyone—free of charge and without most copyright and licensing restrictions—will accelerate scientific research efforts and allow authors to reach a larger number of readers” (reference: http://www.budapestopenaccessinitiative.org)
\n\nIntechOpen’s co-founders, both scientists themselves, created the company while undertaking research in robotics at Vienna University. Their goal was to spread research freely “for scientists, by scientists’ to the rest of the world via the Open Access publishing model. The company soon became a signatory of the Budapest Initiative, which currently has more than 1000 supporting organizations worldwide, ranging from universities to funders.
\n\nAt IntechOpen today, we are still as committed to working with organizations and people who care about scientific discovery, to putting the academic needs of the scientific community first, and to providing an Open Access environment where scientists can maximize their contribution to scientific advancement. By opening up access to the world’s scientific research articles and book chapters, we aim to facilitate greater opportunity for collaboration, scientific discovery and progress. We subscribe wholeheartedly to the Open Access definition:
\n\n“By “open access” to [peer-reviewed research literature], we mean its free availability on the public internet, permitting any users to read, download, copy, distribute, print, search, or link to the full texts of these articles, crawl them for indexing, pass them as data to software, or use them for any other lawful purpose, without financial, legal, or technical barriers other than those inseparable from gaining access to the internet itself. The only constraint on reproduction and distribution, and the only role for copyright in this domain, should be to give authors control over the integrity of their work and the right to be properly acknowledged and cited” (reference: http://www.budapestopenaccessinitiative.org)
\n\nOAI-PMH
\n\nAs a firm believer in the wider dissemination of knowledge, IntechOpen supports the Open Access Initiative Protocol for Metadata Harvesting (OAI-PMH Version 2.0). Read more
\n\nLicense
\n\nBook chapters published in edited volumes are distributed under the Creative Commons Attribution 3.0 Unported License (CC BY 3.0). IntechOpen upholds a very flexible Copyright Policy. There is no copyright transfer to the publisher and Authors retain exclusive copyright to their work. All Monographs/Compacts are distributed under the Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0). Read more
\n\nPeer Review Policies
\n\nAll scientific works are Peer Reviewed prior to publishing. Read more
\n\nOA Publishing Fees
\n\nThe Open Access publishing model employed by IntechOpen eliminates subscription charges and pay-per-view fees, enabling readers to access research at no cost. In order to sustain operations and keep our publications freely accessible we levy an Open Access Publishing Fee for manuscripts, which helps us cover the costs of editorial work and the production of books. Read more
\n\nDigital Archiving Policy
\n\nIntechOpen is committed to ensuring the long-term preservation and the availability of all scholarly research we publish. We employ a variety of means to enable us to deliver on our commitments to the scientific community. Apart from preservation by the Croatian National Library (for publications prior to April 18, 2018) and the British Library (for publications after April 18, 2018), our entire catalogue is preserved in the CLOCKSS archive.
\n\nOpen Science is transparent and accessible knowledge that is shared and developed through collaborative networks.
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\n\nOpen Science refers to doing traditional science with more transparency involved at various stages, for example by openly sharing code and data. It implies a growing set of practices - within different disciplines - aiming at:
\n\nWe aim at improving the quality and availability of scholarly communication by promoting and practicing:
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He also obtained an MSc in Molecular and Genetic Medicine, and a Ph.D. in Clinical Immunology and Human Genetics from the University of Sheffield, UK. He also completed a short-term fellowship in Pediatric Clinical Immunology and Bone Marrow Transplantation at Newcastle General Hospital, England. Dr. Rezaei is a Full Professor of Immunology and Vice Dean of International Affairs and Research, at the School of Medicine, Tehran University of Medical Sciences, and the co-founder and head of the Research Center for Immunodeficiencies. He is also the founding president of the Universal Scientific Education and Research Network (USERN). Dr. Rezaei has directed more than 100 research projects and has designed and participated in several international collaborative projects. He is an editor, editorial assistant, or editorial board member of more than forty international journals. He has edited more than 50 international books, presented more than 500 lectures/posters in congresses/meetings, and published more than 1,100 scientific papers in international journals.",institutionString:"Tehran University of Medical Sciences",institution:{name:"Tehran University of Medical Sciences",country:{name:"Iran"}}},{id:"180733",title:"Dr.",name:"Jean",middleName:null,surname:"Engohang-Ndong",slug:"jean-engohang-ndong",fullName:"Jean Engohang-Ndong",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/180733/images/system/180733.png",biography:"Dr. Jean Engohang-Ndong was born and raised in Gabon. After obtaining his Associate Degree of Science at the University of Science and Technology of Masuku, Gabon, he continued his education in France where he obtained his BS, MS, and Ph.D. in Medical Microbiology. He worked as a post-doctoral fellow at the Public Health Research Institute (PHRI), Newark, NJ for four years before accepting a three-year faculty position at Brigham Young University-Hawaii. Dr. Engohang-Ndong is a tenured faculty member with the academic rank of Full Professor at Kent State University, Ohio, where he teaches a wide range of biological science courses and pursues his research in medical and environmental microbiology. Recently, he expanded his research interest to epidemiology and biostatistics of chronic diseases in Gabon.",institutionString:"Kent State University",institution:{name:"Kent State University",country:{name:"United States of America"}}},{id:"188773",title:"Prof.",name:"Emmanuel",middleName:null,surname:"Drouet",slug:"emmanuel-drouet",fullName:"Emmanuel Drouet",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/188773/images/system/188773.png",biography:"Emmanuel Drouet, PharmD, is a Professor of Virology at the Faculty of Pharmacy, the University Grenoble-Alpes, France. As a head scientist at the Institute of Structural Biology in Grenoble, Dr. Drouet’s research investigates persisting viruses in humans (RNA and DNA viruses) and the balance with our host immune system. He focuses on these viruses’ effects on humans (both their impact on pathology and their symbiotic relationships in humans). He has an excellent track record in the herpesvirus field, and his group is engaged in clinical research in the field of Epstein-Barr virus diseases. He is the editor of the online Encyclopedia of Environment and he coordinates the Universal Health Coverage education program for the BioHealth Computing Schools of the European Institute of Science.",institutionString:null,institution:{name:"Grenoble Alpes University",country:{name:"France"}}},{id:"131400",title:"Prof.",name:"Alfonso J.",middleName:null,surname:"Rodriguez-Morales",slug:"alfonso-j.-rodriguez-morales",fullName:"Alfonso J. Rodriguez-Morales",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/131400/images/system/131400.png",biography:"Dr. Rodriguez-Morales is an expert in tropical and emerging diseases, particularly zoonotic and vector-borne diseases (especially arboviral diseases). He is the president of the Travel Medicine Committee of the Pan-American Infectious Diseases Association (API), as well as the president of the Colombian Association of Infectious Diseases (ACIN). He is a member of the Committee on Tropical Medicine, Zoonoses, and Travel Medicine of ACIN. He is a vice-president of the Latin American Society for Travel Medicine (SLAMVI) and a Member of the Council of the International Society for Infectious Diseases (ISID). Since 2014, he has been recognized as a Senior Researcher, at the Ministry of Science of Colombia. He is a professor at the Faculty of Medicine of the Fundacion Universitaria Autonoma de las Americas, in Pereira, Risaralda, Colombia. He is an External Professor, Master in Research on Tropical Medicine and International Health, Universitat de Barcelona, Spain. He is also a professor at the Master in Clinical Epidemiology and Biostatistics, Universidad Científica del Sur, Lima, Peru. In 2021 he has been awarded the “Raul Isturiz Award” Medal of the API. Also, in 2021, he was awarded with the “Jose Felix Patiño” Asclepius Staff Medal of the Colombian Medical College, due to his scientific contributions to COVID-19 during the pandemic. He is currently the Editor in Chief of the journal Travel Medicine and Infectious Diseases. His Scopus H index is 47 (Google Scholar H index, 68).",institutionString:"Institución Universitaria Visión de las Américas, Colombia",institution:null},{id:"332819",title:"Dr.",name:"Chukwudi Michael",middleName:"Michael",surname:"Egbuche",slug:"chukwudi-michael-egbuche",fullName:"Chukwudi Michael Egbuche",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/332819/images/14624_n.jpg",biography:"I an Dr. Chukwudi Michael Egbuche. I am a Senior Lecturer in the Department of Parasitology and Entomology, Nnamdi Azikiwe University, Awka.",institutionString:null,institution:{name:"Nnamdi Azikiwe University",country:{name:"Nigeria"}}},{id:"284232",title:"Mr.",name:"Nikunj",middleName:"U",surname:"Tandel",slug:"nikunj-tandel",fullName:"Nikunj Tandel",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/284232/images/8275_n.jpg",biography:'Mr. Nikunj Tandel has completed his Master\'s degree in Biotechnology from VIT University, India in the year of 2012. He is having 8 years of research experience especially in the field of malaria epidemiology, immunology, and nanoparticle-based drug delivery system against the infectious diseases, autoimmune disorders and cancer. He has worked for the NIH funded-International Center of Excellence in Malaria Research project "Center for the study of complex malaria in India (CSCMi)" in collaboration with New York University. The preliminary objectives of the study are to understand and develop the evidence-based tools and interventions for the control and prevention of malaria in different sites of the INDIA. Alongside, with the help of next-generation genomics study, the team has studied the antimalarial drug resistance in India. Further, he has extended his research in the development of Humanized mice for the study of liver-stage malaria and identification of molecular marker(s) for the Artemisinin resistance. At present, his research focuses on understanding the role of B cells in the activation of CD8+ T cells in malaria. Received the CSIR-SRF (Senior Research Fellow) award-2018, FIMSA (Federation of Immunological Societies of Asia-Oceania) Travel Bursary award to attend the IUIS-IIS-FIMSA Immunology course-2019',institutionString:"Nirma University",institution:{name:"Nirma University",country:{name:"India"}}},{id:"334383",title:"Ph.D.",name:"Simone",middleName:"Ulrich",surname:"Ulrich Picoli",slug:"simone-ulrich-picoli",fullName:"Simone Ulrich Picoli",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/334383/images/15919_n.jpg",biography:"Graduated in Pharmacy from Universidade Luterana do Brasil (1999), Master in Agricultural and Environmental Microbiology from Federal University of Rio Grande do Sul (2002), Specialization in Clinical Microbiology from Universidade de São Paulo, USP (2007) and PhD in Sciences in Gastroenterology and Hepatology (2012). She is currently an Adjunct Professor at Feevale University in Medicine and Biomedicine courses and a permanent professor of the Academic Master\\'s Degree in Virology. She has experience in the field of Microbiology, with an emphasis on Bacteriology, working mainly on the following topics: bacteriophages, bacterial resistance, clinical microbiology and food microbiology.",institutionString:null,institution:{name:"Universidade Feevale",country:{name:"Brazil"}}},{id:"229220",title:"Dr.",name:"Amjad",middleName:"Islam",surname:"Aqib",slug:"amjad-aqib",fullName:"Amjad Aqib",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/229220/images/system/229220.png",biography:"Dr. Amjad Islam Aqib obtained a DVM and MSc (Hons) from University of Agriculture Faisalabad (UAF), Pakistan, and a PhD from the University of Veterinary and Animal Sciences Lahore, Pakistan. Dr. Aqib joined the Department of Clinical Medicine and Surgery at UAF for one year as an assistant professor where he developed a research laboratory designated for pathogenic bacteria. Since 2018, he has been Assistant Professor/Officer in-charge, Department of Medicine, Manager Research Operations and Development-ORIC, and President One Health Club at Cholistan University of Veterinary and Animal Sciences, Bahawalpur, Pakistan. He has nearly 100 publications to his credit. His research interests include epidemiological patterns and molecular analysis of antimicrobial resistance and modulation and vaccine development against animal pathogens of public health concern.",institutionString:"Cholistan University of Veterinary and Animal Sciences",institution:null},{id:"62900",title:"Prof.",name:"Fethi",middleName:null,surname:"Derbel",slug:"fethi-derbel",fullName:"Fethi Derbel",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/62900/images/system/62900.jpeg",biography:"Professor Fethi Derbel was born in 1960 in Tunisia. He received his medical degree from the Sousse Faculty of Medicine at Sousse, University of Sousse, Tunisia. He completed his surgical residency in General Surgery at the University Hospital Farhat Hached of Sousse and was a member of the Unit of Liver Transplantation in the University of Rennes, France. He then worked in the Department of Surgery at the Sahloul University Hospital in Sousse. Professor Derbel is presently working at the Clinique les Oliviers, Sousse, Tunisia. His hospital activities are mostly concerned with laparoscopic, colorectal, pancreatic, hepatobiliary, and gastric surgery. He is also very interested in hernia surgery and performs ventral hernia repairs and inguinal hernia repairs. He has been a member of the GREPA and Tunisian Hernia Society (THS). During his residency, he managed patients suffering from diabetic foot, and he was very interested in this pathology. For this reason, he decided to coordinate a book project dealing with the diabetic foot. Professor Derbel has published many articles in journals and collaborates intensively with IntechOpen Access Publisher as an editor.",institutionString:"Clinique les Oliviers",institution:null},{id:"300144",title:"Dr.",name:"Meriem",middleName:null,surname:"Braiki",slug:"meriem-braiki",fullName:"Meriem Braiki",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/300144/images/system/300144.jpg",biography:"Dr. Meriem Braiki is a specialist in pediatric surgeon from Tunisia. She was born in 1985. She received her medical degree from the University of Medicine at Sousse, Tunisia. She achieved her surgical residency training periods in Pediatric Surgery departments at University Hospitals in Monastir, Tunis and France.\r\nShe is currently working at the Pediatric surgery department, Sidi Bouzid Hospital, Tunisia. Her hospital activities are mostly concerned with laparoscopic, parietal, urological and digestive surgery. She has published several articles in diffrent journals.",institutionString:"Sidi Bouzid Regional Hospital",institution:null},{id:"229481",title:"Dr.",name:"Erika M.",middleName:"Martins",surname:"de Carvalho",slug:"erika-m.-de-carvalho",fullName:"Erika M. de Carvalho",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/229481/images/6397_n.jpg",biography:null,institutionString:null,institution:{name:"Oswaldo Cruz Foundation",country:{name:"Brazil"}}},{id:"186537",title:"Prof.",name:"Tonay",middleName:null,surname:"Inceboz",slug:"tonay-inceboz",fullName:"Tonay Inceboz",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/186537/images/system/186537.jfif",biography:"I was graduated from Ege University of Medical Faculty (Turkey) in 1988 and completed his Med. PhD degree in Medical Parasitology at the same university. I became an Associate Professor in 2008 and Professor in 2014. I am currently working as a Professor at the Department of Medical Parasitology at Dokuz Eylul University, Izmir, Turkey.\n\nI have given many lectures, presentations in different academic meetings. I have more than 60 articles in peer-reviewed journals, 18 book chapters, 1 book editorship.\n\nMy research interests are Echinococcus granulosus, Echinococcus multilocularis (diagnosis, life cycle, in vitro and in vivo cultivation), and Trichomonas vaginalis (diagnosis, PCR, and in vitro cultivation).",institutionString:"Dokuz Eylül University",institution:{name:"Dokuz Eylül University",country:{name:"Turkey"}}},{id:"71812",title:"Prof.",name:"Hanem Fathy",middleName:"Fathy",surname:"Khater",slug:"hanem-fathy-khater",fullName:"Hanem Fathy Khater",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/71812/images/1167_n.jpg",biography:"Prof. Khater is a Professor of Parasitology at Benha University, Egypt. She studied for her doctoral degree, at the Department of Entomology, College of Agriculture, Food and Natural Resources, University of Missouri, Columbia, USA. She has completed her Ph.D. degrees in Parasitology in Egypt, from where she got the award for “the best scientific Ph.D. dissertation”. She worked at the School of Biological Sciences, Bristol, England, the UK in controlling insects of medical and veterinary importance as a grant from Newton Mosharafa, the British Council. 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She was a recipient of a Commonwealth fellowship supported by British Council tenable at the Centre for Entomology and Parasitology (CAEP), Keele University, United Kingdom between 2004 and 2005. She was awarded an Honorary Visiting Research Fellow at the same university from 2005 to 2007. \nShe has been an external examiner to the Department of Veterinary Microbiology and Parasitology, University of Ibadan, MSc programme between 2010 and 2012. She is a member of the Nigerian Society of Experimental Biology (NISEB), Parasitology and Public Health Society of Nigeria (PPSN), Science Association of Nigeria (SAN), Zoological Society of Nigeria (ZSN), and is Vice Chairperson of the Organisation of Women in Science (OWSG), LASU chapter. She served as Head of Department of Zoology and Environmental Biology, Lagos State University from 2007 to 2010 and 2014 to 2016. 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Behind these definitions are hidden all the aspects of normal and pathological functioning of all processes that the topic ‘Metabolism’ will cover within the Biochemistry Series. 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Thus proteomics, an area of research that detects all protein forms expressed in an organism, including splice isoforms and post-translational modifications, is more suitable than genomics for a comprehensive understanding of the biochemical processes that govern life. The most common proteomics applications are currently in the clinical field for the identification, in a variety of biological matrices, of biomarkers for diagnosis and therapeutic intervention of disorders. From the comparison of proteomic profiles of control and disease or different physiological states, which may emerge, changes in protein expression can provide new insights into the roles played by some proteins in human pathologies. Understanding how proteins function and interact with each other is another goal of proteomics that makes this approach even more intriguing. Specialized technology and expertise are required to assess the proteome of any biological sample. Currently, proteomics relies mainly on mass spectrometry (MS) combined with electrophoretic (1 or 2-DE-MS) and/or chromatographic techniques (LC-MS/MS). MS is an excellent tool that has gained popularity in proteomics because of its ability to gather a complex body of information such as cataloging protein expression, identifying protein modification sites, and defining protein interactions. 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