scholarly journals Risk factors and prognosis of metabolic syndrome in perimenopausal women with hypothyroidism

2021 ◽  
pp. 28-34
Author(s):  
N. V. Pasyechko ◽  
O. O. Chukur
Keyword(s):  
Risk Factors ◽  
Mathematical Model ◽  
Metabolic Syndrome ◽  
Regression Analysis ◽  
Predictive Accuracy ◽  
Thyroid Stimulating Hormone ◽  
Coefficient Of Determination ◽  
Endocrine Disorders ◽  
The Metabolic Syndrome ◽  
Perimenopausal Women

One of the main problems is that the metabolic syndrome (MS) is not clinically manifested until thesignificant carbohydrate and lipid metabolism violations, that initiate a pathological range of symptoms, leading to cardiovascular disease, diabetes mellitus, non-alcoholic steatohepatosis, impairment of reproductive function and other diseases. Conco­mitant endocrine disorders create a background on which dyshormonal and metabolic factors are superimposed. Therefore, an urgent and important task is the correct, and most importantly timely, interpretation of existing risk factors for metabolic disorders in women of perimenopausal age with concomitant hypothyroidism (HT), purposed on their correction to prevent or slowdown the development of complications.Aim — to develop a mathematical model for assessing the prognostic value of risk factors for investigation ofMSdevelopment and progression in perimenopausal women with HT.Materials and methods. 146 perimenopausal women with HT were examined to predict the MS development. Multiple regression analysis was used to construct prognostic model of MS risk.Results. With the help of logistic regression analysis, the following significant multicollinear risk factors for MS have been identified: triglycerides, thyroid-stimulating hormone, vitamin D, and waist circumference. A correlation matrix with the calculation of regression coefficients and coefficient of determination was constructed, and mathematical model was created to determine the coefficient of the MSprogression. The developed mathematical model for predicting of MSdevelopment had a high predictive accuracy, which confirms the compliance of the predicted results with the theoretically expected. The probability that patients will develop MS with a previously predicted risk of MS was 96.1 % (p < 0.05).Conclusions. The developed mathematical model of the predictionof metabolic syndromedevelopment against the background of hypothyroidism is highly informative and allows to determine in advance the contingent of women with high probability of MS development for the timely implementation of appropriate preventive measures.

scholarly journals Predicting the risk of severe menopausal syndrome in perimenopausal women with hypothyroidism

2021 ◽  
Vol 17 (3) ◽  
pp. 219-225
Author(s):  
N.V. Pasechko ◽  
O.O. Chukur ◽  
A.O. Bob ◽  
A.S. Sverstiuk
Keyword(s):  
Mathematical Model ◽  
Regression Analysis ◽  
Risk Factor ◽  
High Risk ◽  
High Risk Group ◽  
Coefficient Of Determination ◽  
Endocrine Disorders ◽  
Average Risk ◽  
Menopausal Syndrome ◽  
Perimenopausal Women

Background. Every year the number of menopausal women increases. At this age, the prevalence of hypothyroidism (HT) reaches its peak. The problem of menopausal syndrome (MS) is relevant for patients with HT, concomitant endocrine disorders create a background for combination with dyshormonal factors. The purpose of the study: to propose an approach to predicting the risk of severe MS in perimenopausal women with HT according to the developed algorithm and mathematical model. Materials and methods. To predict the development of MS, 146 perimenopausal women with autoimmune HT were surveyed. ­Using multiple regression analysis, a prognostic model of the risk of severe MS was created. Results. Logistic regression analysis revealed the following most significant multicollinear risk factors for MS: smoking, alcohol consumption, adverse environmental conditions, physical activity, history of stress and anxiety, thyroid disease. A correlation matrix with calculation of regression coefficients and coefficient of determination was constructed, a mathematical model was created to determine the risk factor for the progression of MS. The predicted value of the risk factor for severe MS with a high degree of probability was determined in 72 (49.32 %) women, with an average probability — in 58 (39.73 %), and with a low probability — in 16 women (10.95 %) with HT. The correspondence of the predicted results with the theoretically expected ones in the high-risk group was recorded in 104.37 %, in the average-risk — in 94.73 %, and in the low-risk — in 89.65 % of cases. Conclusions. The developed algorithm and mathematical model for predicting severe MS on the background of HT are highly informative and allow determining in advance the group of women at high risk of severe MS for the timely implementation of appropriate preventive measures.

Prevalence of the metabolic syndrome and its risk factors in Inner Mongolia, China

2009 ◽  
Vol 64 (3) ◽  
pp. 397-404 ◽  
Author(s):  
T. Xu ◽  
Y. Zhang ◽  
L. Yu ◽  
W. Tong
Keyword(s):  
Risk Factors ◽  
Metabolic Syndrome ◽  
Inner Mongolia ◽  
The Metabolic Syndrome

scholarly journals Fish Consumption: A Review of Its Effects on Metabolic and Hormonal Health

2021 ◽  
Vol 14 ◽  
pp. 117863882110223
Author(s):  
Carlos O Mendivil
Keyword(s):  
Metabolic Syndrome ◽  
Fish Consumption ◽  
Dietary Habits ◽  
Positive Impact ◽  
Polycystic Ovary ◽  
Scientific Evidence ◽  
Endocrine Disorders ◽  
Omega 3 ◽  
The Metabolic Syndrome ◽  
The Impact

Dietary habits are a major determinant of the risk of chronic disease, particularly metabolic and endocrine disorders. Fish as a food group are a unique source of nutrients with metabolic and hormonal importance including omega-3 fatty acids, iodine, selenium, vitamin D, taurine and carnitine. Fish are also a source of high quality protein and have in general low caloric density. The impact of these nutrients on cardiovascular risk has been extensively reviewed, but the impact of fish on the broader field of endocrine and metabolic health is sometimes not sufficiently appreciated. This article aimed to summarize the impact the effect of regular fish consumption on conditions like the metabolic syndrome, obesity, diabetes, hypothyroidism, polycystic ovary syndrome and the menopausal transition, which are in and of themselves significant causes of morbidity and mortality worldwide. The review revealed that scientific evidence from food science, translational research, epidemiologic studies and interventional trials shows that regular fish consumption has a positive impact on thyroid homeostasis, facilitates maintenance of a healthy body weight, reduces the magnitude of age-associated increases in blood pressure, improves glucose homeostasis helping prevent diabetes and the metabolic syndrome, and has a positive impact on muscle mass preservation among the elderly. These effects are mediated by multiple mechanisms, only some of which have been identified. For most of these effects it holds true that the potential benefits are more substantial when baseline fish consumption is low.

scholarly journals Association of intraocular pressure with the metabolic syndrome and novel cardiometabolic risk factors

Eye ◽  
2009 ◽  
Vol 24 (6) ◽  
pp. 1037-1043 ◽  
Author(s):  
Y C Chang ◽  
J-W Lin ◽  
L C Wang ◽  
H M Chen ◽  
J J Hwang ◽  
...  
Keyword(s):  
Risk Factors ◽  
Metabolic Syndrome ◽  
Intraocular Pressure ◽  
Cardiometabolic Risk ◽  
Cardiometabolic Risk Factors ◽  
The Metabolic Syndrome

scholarly journals Anthropometric cut-points for discriminating diabetes and the metabolic syndrome among Arabs and Asians: the Kuwait Diabetes Epidemiology Program

2021 ◽  
pp. 1-11
Author(s):  
Victor M. Oguoma ◽  
Neil T. Coffee ◽  
Saad Alsharrah ◽  
Mohamed Abu-Farha ◽  
Faisal H. Al-Refaei ◽  
...  
Keyword(s):  
Risk Factors ◽  
Metabolic Syndrome ◽  
Ethnic Groups ◽  
South Asian ◽  
International Diabetes Federation ◽  
Cvd Risk Factors ◽  
Cvd Risk ◽  
Data Set ◽  
Cut Points ◽  
The Metabolic Syndrome

Abstract This study aimed to determine anthropometric cut-points for screening diabetes and the metabolic syndrome (MetS) in Arab and South Asian ethnic groups in Kuwait and to compare the prevalence of the MetS based on the ethnic-specific waist circumference (WC) cut-point and the International Diabetes Federation (IDF) and American Heart Association/National Heart, Lung, and Blood Institute WC criteria. The national population-based survey data set of diabetes and obesity in Kuwait adults aged 18–60 years was analysed. Age-adjusted logistic regression and receiver operating characteristic (ROC) analyses were conducted to evaluate for 3589 individuals the utility of WC, waist:height ratio (WHtR) and BMI to discriminate both diabetes and ≥3 CVD risk factors. Areas under the ROC curve were similar for WC, WHtR and BMI. In Arab men, WC, WHtR and BMI cut-offs for diabetes were 106 cm, 0·55 and 28 kg/m2 and for ≥3 CVD risk factors, 97 cm, 0·55 and 28 kg/m2, respectively. In Arab women, cut-offs for diabetes were 107 cm, 0·65 and 33 kg/m2 and for ≥3 CVD risk factors, 93 cm, 0·60 and 30 kg/m2, respectively. WC cut-offs were higher for South Asian women than men. IDF-based WC cut-offs corresponded to a higher prevalence of the MetS across sex and ethnic groups, compared with Kuwait-specific cut-offs. Any of the assessed anthropometric indices can be used in screening of diabetes and ≥3 CVD risk factors in Kuwaiti Arab and Asian populations. ROC values were similar. The WC threshold for screening the MetS in Kuwaiti Arabs and South Asians is higher for women.

Association between Patients with Metabolic Syndrome and Other Various Factors Defining Metabolic Syndrome: A Cross-Sectional Study

2021 ◽  
Vol 8 (4) ◽  
pp. 12-17
Author(s):  
Basavaraj PG ◽  
Ashok P Yenkanchi ◽  
Chidanand Galagali
Keyword(s):  
Risk Factors ◽  
Blood Pressure ◽  
Metabolic Syndrome ◽  
Cardiovascular Diseases ◽  
Abdominal Obesity ◽  
Positive History ◽  
The Metabolic Syndrome ◽  
Study Results ◽  
History Of ◽  
The Mean

Background: Risk factors can lead to clinical conditions, like metabolic syndrome, that predisposes the development of cardiovascular diseases. Objective: The goal of this population-based, prospective and non-randomised cohort study was to study the association between patients with metabolic syndrome and other various factors defining metabolic syndrome. Methods: All the patients referred to the department of Medicine, Al-Ameen Medical college hospital and District Hospital, Vijayapur, Karnataka, India over a period of twenty-two months extending from December 2013 to September 2015 were considered in this study. Results: In the current study, out of 100 patients, 62.9% patients had metabolic syndrome with positive family history of hypertension, diabetes mellitus. 70.8% patients had metabolic syndrome with positive history of smoking. 64.3% patients had metabolic syndrome with positive history of alcohol .73.9% patients had metabolic syndrome with positive history of IHD. 87% of the patients with metabolic syndrome had SBP> 130 mmHg, and 78.85% patients had DBP>85 mmHg. And metabolic syndrome was observed in 71.8% patients on anti hypertensive drugs. The mean level of total cholesterol, LDL cholesterol, triglyceride is increased whereas the mean level of anti-atherogenic HDL cholesterol is low in subjects with MS. At least one lipid abnormality was present in > 95 % of cases. Around 81% subjects with BMI <25 (out of 38) had metabolic syndrome and 58% subjects with BMI>25(out of 62) had metabolic syndrome. Conclusion: All the components defining the metabolic syndrome correlated positively with the abdominal obesity. Systolic blood pressure values were significantly higher than diastolic blood pressure in subjects with abdominal obesity. Metabolic syndrome has multiple risk factors determined by various aspects like the race, the life style, geographical factors larger study is needed to understand the correlation between various components defining it. A healthy lifestyle, that includes avoiding tobacco exposure and proper weight control, must be encouraged in this high-risk population. Keywords: Cardiovascular diseases; Overweight; Risk factors; Smoking.

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