The Relationship Between Menopause and Metabolic Syndrome: Experimental and Bioinformatics Analysis

Risk prediction model of metabolic syndrome in perimenopausal women based on machine learning

INTRODUCTION

Metabolic syndrome (MetS) is considered to be an important parameter of cardio-metabolic health and contributing to the development of atherosclerosis, type 2 diabetes. The incidence of MetS significantly increases in postmenopausal women, therefore, the perimenopausal period is considered a critical phase for prevention. We aimed to use four machine learning methods to predict whether perimenopausal women will develop MetS within 2 years.

METHODS

Women aged 45-55 years who underwent 2 consecutive years of physical examinations in Ninth Clinical College of Peking University between January 2021 and December 2022 were included. We extracted 26 features from physical examinations, and used backward selection method to select top 10 features with the largest area under the receiver operating characteristic curve (AUC). Extreme gradient boosting (XGBoost), Random forest (RF), Multilayer perceptron (MLP) and Logistic regression (LR) were used to establish the model. Those performance were measured by AUC, accuracy, precision, recall and F1 score. SHapley Additive exPlanation (SHAP) value was used to identify risk factors affecting perimenopausal MetS.

RESULTS

A total of 8700 women had physical examination records, and 2,254 women finally met the inclusion criteria. For predicting MetS events, RF and XGBoost had the highest AUC (0.96, 0.95, respectively). XGBoost has the highest F1 value (F1 = 0.77), followed by RF, LR and MLP. SHAP value suggested that the top 5 variables affecting MetS in this study were Waist circumference, Fasting blood glucose, High-density lipoprotein cholesterol, Triglycerides and Diastolic blood pressure, respectively.

CONCLUSION

We've developed a targeted MetS risk prediction model for perimenopausal women, using health examination data. This model enables early identification of high MetS risk in this group, offering significant benefits for individual health management and wider socio-economic health initiatives.

Menopause and metabolic syndrome: anthropometric, lipid, and dietary profiles

OBJECTIVE

The aim of this study was to characterize the anthropometric, lipid, and dietary profiles of postmenopausal women with metabolic syndrome attending a public health service and compare them with a group of women without metabolic syndrome.

METHODS

A cross-sectional study was conducted with 60 postmenopausal women who were divided into two groups: control group and metabolic syndrome group, attending the Climacteric Outpatient Clinic at Santa Casa de São Paulo Hospital, Brazil, between February 2019 and December 2021. Participants were evaluated using a validated semi-quantitative food frequency questionnaire, body mass index, waist circumference, and serum laboratory tests.

RESULTS

Significant differences were observed between the groups regarding body mass index and all parameters of metabolic syndrome. The nutritional profile revealed an imbalance in the number of food portions consumed, particularly in the intake of carbohydrates in the form of flour and sweets, which was higher in the metabolic syndrome group.

CONCLUSION

The analysis of the three profiles of postmenopausal women revealed significant imbalances, particularly in the metabolic syndrome group, highlighting the importance of regular adjustments and evaluations during this phase of a woman's life.

The Perfect Cup? Coffee-Derived Polyphenols and Their Roles in Mitigating Factors Affecting Type 2 Diabetes Pathogenesis

Type 2 diabetes (T2D) is a growing health concern with an estimated 462 million people having been diagnosed worldwide. T2D is characterized by chronically elevated blood glucose and insulin resistance, which culminate in a diminished function of the β-cell mass in its later stages. This can be perpetuated by and result in inflammation, excess reactive oxygen species production, obesity, and the dysregulation of multiple cellular pathways. Many naturally occurring small molecules have been investigated in terms of their roles in modulating glucose homeostasis and β-cell function. Many of these compounds can be found in commonly used sources of food and drink. Interestingly, a correlation has been observed between coffee consumption and T2D incidence. However, the specific compounds responsible for this correlation and their mechanisms are still somewhat undetermined. This paper reviews recent research findings on the effects of several polyphenols that are either found in coffee or are metabolites of compounds found in coffee (enterodiol, enterolactone, matairesinol, secoisolariciresinol, kaempferol, quercetin, and chlorogenic acid) on glucose homeostasis and health complications associated with glucose dysregulation, with a special emphasis on their potential anti-diabetic effects. The factors that affect polyphenol content in coffee are also addressed.

Fenofibrate alleviates insulin resistance by reducing tissue inflammation in obese ovariectomized mice

BACKGROUND

Fenofibrate is a hypolipidemic peroxisome proliferator-activated receptor α (PPARα) agonist used clinically to reduce hypercholesterolemia and hypertriglyceridemia.

OBJECTIVE

We investigated the effects of fenofibrate on insulin resistance and tissue inflammation in a high-fat diet (HFD)-fed ovariectomized (OVX) C57BL/6J mice, a mouse model of obese postmenopausal women.

METHODS

Female OVX mice were randomly divided into 3 groups and received a low-fat diet, an HFD, or an HFD supplemented with 0.05% (w/w) fenofibrate for 9 weeks. Parameters of insulin resistance and tissue inflammation were measured using blood analysis, histological analysis, immunohistochemistry, and quantitative real-time polymerase chain reaction.

RESULTS

When fenofibrate was administered to HFD-fed OVX mice for 9 weeks, we observed reductions in body weight gain, adipose tissue mass, and the size of visceral adipocytes without the change of food intake. Fenofibrate improved mild hyperglycemia, severe hyperinsulinemia, and glucose tolerance in these mice. It also reduced pancreatic islet size and insulin-positive β-cell area to levels similar to those in OVX mice fed a low-fat diet. Concomitantly, administration of fenofibrate not only suppressed pancreatic lipid accumulation but also decreased CD68-positive macrophages in both the pancreas and visceral adipose tissue. Treatment with fenofibrate reduced tumor necrosis factor α (TNFα) mRNA levels in adipose tissue and lowered serum TNFα levels.

CONCLUSION

These results suggest that fenofibrate treatment attenuates insulin resistance in part by reducing tissue inflammation and TNFα expression in HFD-fed OVX mice.

The Incidence of Metabolic Syndrome in People Over 40 Years Old with Normal Fasting Plasma Glucose and the Predictive Cutoff Value of Effective Fasting Plasma Glucose for Metabolic Syndrome in Guiyang

Purpose

This study aimed to investigate the incidence of metabolic syndrome (MS) in people over 40 years of age with normal blood glucose levels in Guiyang's urban areas and determine the effective glycemic cutoff value for predicting MS.

Methods

The analysis was based on anthropometric and biochemical indicators of residents aged 40 years or older in urban areas of Guiyang City who participated in the "Epidemiological Study of Tumor Risk in Chinese Patients with Type 2 Diabetes" in 2011. This study included 3509 patients (2567 females and 942 males) with normal fasting plasma glucose (FPG) and no MS. Multivariate logistic regression was used to analyze the correlation between FPG and MS ROC was used to analyze the effective cutoff value of FPG for the incidence of MS.

Results

After 3-year follow-up, 675 patients had MS (567 females and 108 males). MS incidence in the total population was 19.24%, 11.46% in males, and 22.09% in females, and it increased with FPG. After multivariate logistic regression analysis, the risk of MS corresponding to FPG in females and males was OR=4.607,95% CI (3.477-6.105) and OR=2.944, 95% CI (1.785-4.855), respectively. ROC results demonstrated that FPG could predict the onset of MS (AUC: 0.720 in males and 0.666 in females).

Conclusion

Increased FPG correlated with the incidence of metabolic syndrome. Subjects with FPG in the normal range still had a high incidence of MS. The population cutoff value for predicting effective FPG for metabolic syndrome was 5.545 mmol/L in men and 5.605 mmol/L in women. Epidemiological investigations are needed to determine whether a lower FPG cutoff value is required to diagnose MS. FPG not only diagnoses diabetes but also serves as a cost-effective and convenient screening method for developing of MS in the general Chinese population.

Translational Bioinformatics Applied to the Study of Complex Diseases

Translational Bioinformatics (TBI) is defined as the union of translational medicine and bioinformatics. It emerges as a major advance in science and technology by covering everything, from the most basic database discoveries, to the development of algorithms for molecular and cellular analysis, as well as their clinical applications. This technology makes it possible to access the knowledge of scientific evidence and apply it to clinical practice. This manuscript aims to highlight the role of TBI in the study of complex diseases, as well as its application to the understanding and treatment of cancer. An integrative literature review was carried out, obtaining articles through several websites, among them: PUBMED, Science Direct, NCBI-PMC, Scientific Electronic Library Online (SciELO), and Google Academic, published in English, Spanish, and Portuguese, indexed in the referred databases and answering the following guiding question: "How does TBI provide a scientific understanding of complex diseases?" An additional effort is aimed at the dissemination, inclusion, and perpetuation of TBI knowledge from the academic environment to society, helping the study, understanding, and elucidating of complex disease mechanics and their treatment.