A Study of Correlation of Anthropometric Data with Atherogenic Indices of Students of Rivers State University, Port Harcourt, Nigeria

Elekima, Ibioku; Inokon, Aniekan

doi:10.9734/ajrimps/2019/v6i130092

Cited by 4 publications

(4 citation statements)

References 18 publications

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“…A CRI-2 value ≤3.0 is normal while ≥3.0 is indicative of CVD risk. 7,11 Triglyceride/HDL-C ratio predicts CHD and CVD mortality, A TG/HDL-C ratio of 3.5 or greater to be highly correlated with insulin resistance and atherogenic dyslipidemia in men; this threshold was also associated with metabolic syndrome. They proposed that the TG/HDL-C ratio provides a simple way to identify insulin-resistant, dyslipidemic patients who are likely to be at increased risk for CVD.…”

Section: Assessment Of Lipid Abnormalities and Cardiovascular Risk Inmentioning

confidence: 99%

Assessment of lipid abnormalities and Cardiovascular Risk indices in Type 2 Diabetes Mellitus

Baral

Hamal²,

et al. 2019

Asian J Med Sci

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Background: Incidence of cardiovascular events is increased to two to four times among diabetic patients when compared with non-diabetic. Dyslipidaemia in diabetes is major risk factor for cardiovascular events. Atherogenic indices have been used as major laboratory measures in clinical practice to assess cardiovascular risk. Recent studies, have shown that non-high-density lipoprotein cholesterol and other atherogenic indices is like or better than LDL-C alone in the prediction of CVD incidence and mortality. Aim and Objectives: The aim of this study is to assess the lipid abnormalities with cardiovascular risk using atherogenic coefficient (Ac), Cardiac Risk Ratio (TC/HDLc and LDLc/HDLc), TG/HDLc and Non- HDL in diabetes patients. Materials and Methods: A cross-sectional descriptive study conducted at Bir hospital (tertiary care center) Nepal. Patients diagnosed as diabetes with age 30 years or above were selected in this study during a period of 1 yr. Results: The mean age of the patients enrolled was 55.08±1.11 (34-81) year with male (52 %) to female (48%) ratio is 1.08:1. In this study the mean Non HDLc was 165.24±43.40 mg/dl (65-323) and AC was 4.0±1.09. The finding of AC was slightly higher in female compare to male i.e mean ±S.D 4.07±1.31 and 3.94±1.15. This study show the strong correlation of Non HDLc with total cholesterol (r=.990, p=0.000), LDLc(r=.602,p=0.000),TG (r=.411,p=0.000), LDLc/HDLc (r=.580,p=0.000),TC/HDLc ((r=.866,p=0.000), TG/HDLc (r=.390,p=0.000) and AC (p=0.866). Conclusion: The association between abnormal lipid levels and cardiovascular risk is evident among patients with diabetes mellitus. In this study there is the correlation with FBS, Non HDL-c cholesterol, Cardio risk ratio, TG/HDLc, Atherogenic coefficient. Hence Non-HDL cholesterol and Atherogenic indices proves to be more sensitive and a better predictor of cardiovascular events in diabetes patients.

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Section: Assessment Of Lipid Abnormalities and Cardiovascular Risk Inmentioning

confidence: 99%

Assessment of lipid abnormalities and Cardiovascular Risk indices in Type 2 Diabetes Mellitus

Baral

Hamal²,

et al. 2019

Asian J Med Sci

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“…Subjects excluded from the study include those that did not give their consent, males that did not abstain for 3-5days or did spill in the course of semen collection. Also, males that were obese (BMI of > 30.0kg/m 2 ) as described by Elekima & Ugwu [19]; Elekima & Inokon, [20] were excluded. More so, subjects excluded from the study include those with history of or have diabetes mellitus (FBS>7mmol/L), prostate hyperplasia and prostate disorders, thyroid disorders, cardiovascular diseases, on antihypertensive or lipid lowering drugs.…”

Section: Exclusion Criteriamentioning

confidence: 99%

“…The counts were established in four secondary squares and five tertiary squares (four at corner and one central) of the central secondary square as described by WHO [21]. The total number of spermatozoa per ml was calculated by multiplying the number of complete, morphologically mature sperm cells counted x dilution factor (20) x multiplication factor (50,000). The total count was then established as n x 10 6 sperm cells/ ml.…”

Section: Establishment Of Total Sperm Countmentioning

confidence: 99%

Some Heavy Metals Correlated Negatively with Total Antioxidant Capacity, Glutathione Peroxidase, Fructose, and Testosterone in Seminal Plasma of Oligospermic and Azoospermic Males

Umoinyang

Elekima

Onwuli

2021

JAMPS

Self Cite

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Aim: The study aimed to investigate the relationship between some heavy metals and total antioxidant capacities, glutathione levels, fructose, and testosterone in seminal plasma of infertile azoospermic and oligospermic males in Akwa Ibom State of Nigeria. Study Design: A cross-sectional design with a total of 124 males included in the study of which 32 were azoospermic, 38, oligospermic, and 54, normospermic. Place and Duration of Study: Semen samples were collected from the urology or fertility clinic of UUTH, St. Luke’s Hospital, Anua, and Ibom specialist hospital of Akwa Ibom State. However, laboratory assays were performed at the Department of Medical Laboratory Science, Rivers State University, Port Harcourt between May, 2018 and January, 2021. Methodology: Semen specimens were collected after 3-5 days abstinence according to WHO criteria while seminal plasma were obtained from semen by spinning at 4500 rpm for 10 minutes and stored at -700C prior to laboratory analysis. Atomic absorption spectrometer (AAS) was used to determine the levels of heavy metals while ELISA methods were used to determine testosterone and GSH concentrations. TAC and fructose assays were carried out using spectrophotometric methods. Results: Results showed that non-essential heavy metals such as lead, mercury, and arsenic correlated negatively with testosterone, fructose, and antioxidant activities of the seminal plasma in azoospermic subjects. In addition, lead and mercury correlated positively in the azoospermic subjects. Meanwhile, selenium, an essential heavy metal, correlated positively with testosterone and antioxidant activities in oligospermic subjects with 11-19 x106cells/ml. Conclusion: The levels of non-essential heavy metals in azoospermic subjects precipitated poor anti-oxidant and testosterone activities inducing oxidative stress while in oligospermic subjects, selenium and antioxidant parameters and testosterone were in good association indicating improved antioxidant activities and testicular function.

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“…Finding correlations between anthropometric measurements (AMs) and laboratory findings is of great interest in the medical field [1], as it would lead to less invasive means of patient exploration. Examples of this can be found in the literature: from atherogenic markers [2], or diabetes assessment [3], or cardiovascular risk [4]. In some occasions AMs can correlate to other AMs of patients bodies, which is useful for weight and height estimation from other measures, and helps in dose assessment for ICU patients [5].…”

Section: Introductionmentioning

confidence: 99%

A Non-Invasive Approach for Total Cholesterol Level Prediction Using Machine Learning

et al. 2022

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Artificial intelligence techniques have been increasingly applied in healthcare to help in many areas, from assisting clinical diagnoses to preventing diseases. In this paper, a machine learning approach to predict cholesterol levels using non-invasive and easy-to-collect data is presented. Specifically, it uses clinical and anthropometric data gathered by nutritionists during weight loss intervention (dieting) periods. The prediction power analysis of different patient variables is aimed at improving both non-invasive diagnosis quality and screening of associated diseases. Moreover, a clustering analysis has been carried out to identify different groupings of patients that might share some characteristics that have so far remained inconspicuous but might contain a valuable diagnosis or prognosis information for clinical experts.The experiments show a mean absolute percentage error rate (MAPE) of 4.39% in cholesterol estimation via regression, as well as clustering of patients within four profiles in which variable values share commonalities among cluster members.INDEX TERMS digital health assessment, 3D body reconstruction, clinical data regression, patient data clustering, pattern recognition.

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A Study of Correlation of Anthropometric Data with Atherogenic Indices of Students of Rivers State University, Port Harcourt, Nigeria

Cited by 4 publications

References 18 publications

Assessment of lipid abnormalities and Cardiovascular Risk indices in Type 2 Diabetes Mellitus

Assessment of lipid abnormalities and Cardiovascular Risk indices in Type 2 Diabetes Mellitus

Some Heavy Metals Correlated Negatively with Total Antioxidant Capacity, Glutathione Peroxidase, Fructose, and Testosterone in Seminal Plasma of Oligospermic and Azoospermic Males

A Non-Invasive Approach for Total Cholesterol Level Prediction Using Machine Learning

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