The metabolic syndrome in normal weight Malay subjects

Al-Mahmood, Abu Kholdun; Ismail, Aziz Al-Safi; Faridah, A. R.; Bebakar, Wan Mohamed Wan; Tai, E Shyong

doi:10.3329/bjms.v15i1.27149

“…Relations between different lipid parameters and insulin resistance have been studied in the past [39][40][41], and it is quite clear that there is an association between dyslipidaemia and insulin resistance. The levels of total cholesterol (TC), LDL cholesterol, and total cholesterol to HDL cholesterol ratio (TC/HDL) were significantly higher in the insulin-resistant group.…”

Section: Discussionmentioning

confidence: 99%

Insulin Resistance in relation to Hypertension and Dyslipidaemia among Men Clinically Diagnosed with Type 2 Diabetes

Alidu

¹

,

Dapare

²

,

Quaye

³

et al. 2023

BioMed Research International

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Pathophysiologically, type 2 diabetes can result from insulin resistance or insulin insufficiency alone. It is unclear whether relative insulin shortage or pronounced insulin resistance is linked to poor cardiometabolic problems like obesity. Therefore, the objective of this study was to evaluate the relationship between insulin resistance (IR), hypertension, and dyslipidaemia, in men with type 2 diabetes mellitus. One hundred and twenty-one (121) type 2 diabetic men participated in this cross-sectional study, which was conducted between September 2018 and September 2019. Sociodemographic information was collected using a self-designed questionnaire. Anthropometric data were also taken and blood samples collected for estimation of insulin, glucose, and lipid concentrations. HOMA-IR was calculated from the fasting insulin and glucose values, and a HOMA − IR ≥ 2 was considered to indicate insulin resistance. Of the 121 participants, 39.7% were classified as insulin-resistant. Levels of total cholesterol ( 4.82 ± 1.2 mmol/L; p = 0.007 vs. 4.25 ± 1.1 mmol/L), LDL cholesterol ( 3.17 ± 0.9 mmol/L; p = 0.001 vs. 2.52 ± 0.8 mmol/L), and TC/HDL-C ratio ( 3.93 ± 0.9 ; p = 0.042 vs. 3.58 ± 0.9 ) and the prevalence of abnormal LDL-C (14.6%; p = 0.015 vs. 2.7%) and elevated BP (83.3%; p = 0.048 vs. 67.1%) were higher in the insulin-resistant group. LDL cholesterol ( AUC = 0.670 ; p = 0.001 ) better classified subjects as being insulin-resistant compared to other lipid markers. The odds of insulin resistance in dyslipidaemia were not statistically significant after adjusting for obesity. The link between insulin resistance and dyslipidaemia and hypertension in male diabetics may thus be mediated by obesity.

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“…Postmenopausal estrogen's effects on heart disease, cancer, and osteoporosis are still being discussed 12,13 . In middle-aged women Total cholesterol and low-density lipoprotein cholesterol (LDL-C) concentrations are associated with cardiovascular events and total mortality in middle-aged women and men 4,14,15 . As per the statement, the percentage of Canadians who had unhealthy levels of LDL (bad) cholesterol showed a considerable rise with increasing age.…”

Section: Methodsmentioning

confidence: 99%

Computational statistical modelling for parameters optimization of LDL-cholesterol levels in patients with type 2 diabetes

Amir W Ahmad,

Mohamad Ghazali,

Shahzad

et al. 2024

Bangladesh J Med Sci

0

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Introduction LDL is the acronym used to denote low-density lipoproteins. When the level of LDL is high, it leads to the accumulation of cholesterol in the arteries, and as a result, it is commonly known as “bad” cholesterol. Recognizing the significance, a study is conducted on computational and statistical modelling for parameter optimization of LDL-cholesterol levels. In recent years, there has been an increase in the application of precise statistical analysis methodologies. Consequently, scientists are more focused on producing reliable results and are more determined to provide credible findings. Objective This study aims to develop and validate a proposed hybrid method that combines Multilayer Feedforward Neural Network (MLFFNN), and Multiple Linear Model (MLR) and to present the R-syntax applications of the proposed hybrid method with clinical study data. Material and Methods The proposed hybrid model will be built using the generalized mixture model, which is formulated using an MLFFNN framework and Linear Model (LM). The performance of the hybrid model will be assessed utilizing the predicted mean square error neural network (MSE.net) and the predicted mean square error (P.MSE), which will serve as a benchmark for precision and efficacy. Results: The result indicates that hybrid method modelling is superior, with the highest R-squared value and the lowest MSE.net value. The hybrid model technique was found to produce a more precise forecast of the outcome when the data is separated into training and testing datasets. The R-square score in this report demonstrates that the LM model is a good fit (84.97%), and the MSE.net value of 0.00529 indicates that the model is both accurate and predictive. Conclusion: The research concludes that the hybrid model method proposed is preferable. This critical conclusion helps us comprehend the hybrid method’s proportional contribution to this illustration’s result. Bangladesh Journal of Medical Science Vol. 23 No. 02 April’24 Page : 498-506

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“…Won et al found that a decrease in adiponectin levels correlates with an increased level of CRP and is associated with a decrease in the anti-inflammatory effect of protective adipokine. 20 Obviously, with a combination of MS and CHF, these changes can contribute to the progressive remodeling of the myocardium. In obesity, especially in its abdominal form, the hormones of adipose tissue -adipokines -act as important regulators of energy metabolism and, in particular, energy substrates in heart cells.…”

Section: Ethical Clearancementioning

confidence: 99%

Metabolic syndrome and chronic heart failure: a modern aspect of the problem

Fedorova

¹

,

Semenenko

²

,

Tazina

³

et al. 2022

Bangladesh J Med Sci

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Objective: The increase of morbidity results from both an increase of life expectancy of the population, and influence of various risk factors contributing to development and increase of chronic heart failure (CHF). The combination of several atherogenic mechanisms (abdominal obesity (AO), insulin resistance (IR), arterial hypertension (AH), hyperglycemia, dyslipidemia), combined as “metabolic syndrome” (MS), causes a more rapid development of CHF. Materials and methods: The research finding of 74 patients with class II-III of CHF, including 37 patients (50%) with MS, are presented. The age structure of the pathology, severity of clinical course, data of laboratory and instrumental examination in various groups of patients were evaluated. A special program included an echocardiographic test with an assessment of various myocardial parameters. Results and Discussion: Research materials find out a number of characteristics of CHF clinical course (its earlier development and severe course) in patients with MS. Echocardiographic tests reveal an increase of heart chambers sizes, thickness of left and right ventricle, pulmonary hypertension. Myocardium morpho-functional changes are more significant in patients with CHF and MS than in those without MS. An increase in leptin levels, a marker of obesity, fibrosis and inflammation, has been found. Leptin, C-reactive peptide (CRP) and high-sensitive troponin in patients with MS significantly exceeded those in patientswithout MS. Correlations of leptin levels, adiponectin, CRP and left ventricular mass, thickness of epicardial fat (TEF), ejection fraction were established. Conclusion: Materials of the research indicate the important role of inflammatory and dysmetabolic processes in development and progression of CHF in patients with MS. Bangladesh Journal of Medical Science Vol. 21(1) 2022 Page : 105-113

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The metabolic syndrome in normal weight Malay subjects

Cited by 3 publications

References 11 publications

Insulin Resistance in relation to Hypertension and Dyslipidaemia among Men Clinically Diagnosed with Type 2 Diabetes

Insulin Resistance in relation to Hypertension and Dyslipidaemia among Men Clinically Diagnosed with Type 2 Diabetes

Computational statistical modelling for parameters optimization of LDL-cholesterol levels in patients with type 2 diabetes

Metabolic syndrome and chronic heart failure: a modern aspect of the problem

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