Subclinical hypothyroidism contributes to poor glycemic control in patients with type 2 diabetes mellitus, and ellagic acid attenuates methimazole-induced abnormal glucose metabolism in mice model

Metabolic and Endocrine Correlates of Subclinical Hypothyroidism in Young Adults With First-Episode and Drug-Naive Major Depressive Disorder

BACKGROUND

Major depressive disorder is often associated with subclinical hypothyroidism (SCH), but the clinical and biochemical characteristics in young, first-episode, drug-naive patients remain unclear. This study aims to examine the prevalence and clinical correlates of SCH in this population to enhance screening and management strategies.

METHOD

A cross-sectional study included 917 young Chinese patients (aged 18-35 years) diagnosed with first-episode, drug-naive Major depressive disorder. Comprehensive clinical assessments were conducted, involving demographic data, psychiatric evaluations using the Hamilton Depression Rating Scale, Hamilton Anxiety Rating Scale, and Positive and Negative Syndrome Scale, alongside biochemical measurements such as thyroid stimulating hormone, thyroid peroxidase antibodies, fasting blood glucose, total cholesterol, high-density lipoprotein cholesterol, low-density lipoprotein cholesterol, systolic blood pressure, and diastolic blood pressure. Binary logistic regression identified clinical correlates of SCH.

RESULTS

The prevalence of SCH among the study population was 58%. Logistic regression analysis identified significant predictors of SCH, including higher Hamilton Depression Rating Scale scores (odds ratio [OR] = 1.26), thyroid peroxidase antibodies (OR = 1.003), fasting blood glucose (OR = 2.28), total cholesterol (OR = 1.66), systolic blood pressure (OR = 1.11), and diastolic blood pressure (OR = 1.07). In contrast, lower high-density lipoprotein cholesterol levels (OR = 0.28) were inversely associated with SCH.

CONCLUSION

The high prevalence of SCH in young, first-episode, drug-naive Major depressive disorder patients emphasizes the need for comprehensive metabolic and endocrine evaluations. Regular monitoring of thyroid function, glucose levels, blood pressure, and lipid profiles is crucial for early detection and intervention, potentially improving clinical outcomes in this vulnerable group.

Characteristics of blood lipid and metabolic indicators in subclinical hypothyroidism patients: a retrospective study

Background

The characteristics of blood lipid and metabolic indicators were analyzed in patients with subclinical hypothyroidism (SCH), and the effect of type 2 diabetes mellitus (T2DM) on SCH patients was determined.

Methods

The physical examination data of 2,119 residents in a university community were retrospectively divided into 2 groups (SCH and non-SCH groups). Furthermore, the SCH group was divided into SCH-T2DM and SCH-non-T2DM subgroups, and the data between the groups were analyzed.

Results

The SCH group had significantly higher levels of triglycerides (p = 0.044), total cholesterol (p = 0.001), low-density lipoprotein cholesterol (p = 0.019), very low-density lipoprotein cholesterol (p = 0.044), 2-h plasma glucose (p = 0.023), and globulin (p = 0.000) compared to the non-SCH group. The SCH-T2DM group was older (p < 0.001), had a greater BMI (p = 0.028), a more rapid heart rate (p = 0.025), and a greater waist circumference (p < 0.001) than the SCH-non-T2DM group based on the subgroup analysis. The SCH-T2DM group had significantly higher dyslipidemia and dysglycemia levels than the SCH-non-T2DM group.

Conclusion

Patients with SCH with or without T2DM may have dyslipidemia and dysglycemia and should be evaluated accordingly.

Prospecting Pharmacologically Active Biocompounds from the Amazon Rainforest: In Vitro Approaches, Mechanisms of Action Based on Chemical Structure, and Perspectives on Human Therapeutic Use

The Amazon rainforest is an important reservoir of biodiversity, offering vast potential for the discovery of new bioactive compounds from plants. In vitro studies allow for the investigation of biological processes and interventions in a controlled manner, making them fundamental for pharmacological and biotechnological research. These approaches are faster and less costly than in vivo studies, providing standardized conditions that enhance the reproducibility and precision of data. However, in vitro methods have limitations, including the inability to fully replicate the complexity of a living organism and the absence of a complete physiological context. Translating results to in vivo models is not always straightforward, due to differences in pharmacokinetics and biological interactions. In this context, the aim of this literature review is to assess the advantages and disadvantages of in vitro approaches in the search for new drugs from the Amazon, identifying the challenges and limitations associated with these methods and comparing them with in vivo testing. Thus, bioprospecting in the Amazon involves evaluating plant extracts through bioassays to investigate pharmacological, antimicrobial, and anticancer activities. Phenolic compounds and terpenes are frequently identified as the main bioactive agents, exhibiting antioxidant, anti-inflammatory, and antineoplastic activities. Chemical characterization, molecular modifications, and the development of delivery systems, such as nanoparticles, are highlighted to improve therapeutic efficacy. Therefore, the Amazon rainforest offers great potential for the discovery of new drugs; however, significant challenges, such as the standardization of extraction methods and the need for in vivo studies and clinical trials, must be overcome for these compounds to become viable medications.

Calculating toxic pressure for mixtures of endocrine disruptors

Incidence of autoimmune disorders, birth defects, and neurological diseases rose over the past 50 years due to increasing variety and quantity of pollutants. To date, there appear few methods capable to evaluate and predict mixture effects by endocrine disruptors (EDs). For the first time, we have developed calculus to determine mixture effects by all kinds of EDs. Our method uses the golden ratio ϕ and draws from bifurcation and chaos theory. Using also the concept of molecular mimicry, we developed the equation: e f f e c t = 100 % 1 + e 5 · ∑ K i C i - n i ϕ 3 . We successfully tested the equation using a range of cohort studies and biomarkers, and for different pollutants like heavy metals, thyroid hormone mimickants, chromate/chlorate, etc. The equation is simple enough to use with only minor prior knowledge and understanding of basic algebra. The method is universal and calculation is data 'light', requiring only pollutant concentrations [C], potencies K and an integer n for endocrinal involvement. This study offers a comprehensive framework to assess the health effects of pollutant exposure across diverse populations, envisioning far-reaching impact, and presenting practical examples and insights.

The effect of ellagic acid on the metabolic syndrome: A review article

Objective

(s): Metabolic syndrome is a collection of metabolic abnormalities that includes hyperglycemia, dyslipidemia, hypertension, and obesity. Ellagic acid is found in various fruits and vegetables. It has been reported to have several pharmacological properties, such as antibacterial, antifungal, antiviral, anti-inflammatory, hepatoprotective, cardioprotective, chemopreventive, neuroprotective, gastroprotective, and antidiabetic. Our current study aims to shed light on the probable efficiency of ellagic acid in managing metabolic syndrome and its complications.

Materials and methods

To prepare the present review, the databases or search engines utilized included Scopus, PubMed, Science Direct, and Google Scholar, and relevant articles have been gathered with no time limit until March 2023.

Results

Several investigations indicated that ellagic acid could be a potent compound for the treatment of many disorders such as diabetes, hypertension, and hyperlipidemia by various mechanisms, including increasing insulin secretion, insulin receptor substrate protein 1 expression, regulating glucose transporter 4, triglyceride, total cholesterol, low-density lipoprotein (LDL), high-density lipoprotein (HDL), attenuating tumor necrosis factor-alpha (TNF-α), interleukin-6 (IL-6), reactive oxygen species (ROS), malondialdehyde (MDA), and oxidative stress in related tissues. Furthermore, ellagic acid ameliorates mitochondrial function, upregulates uncoupling protein 1 (found in brown and white adipose tissues), and regulates blood levels of nitrate/nitrite and vascular relaxations in response to acetylcholine and sodium nitroprusside.

Conclusion

Ellagic acid can treat or manage metabolic syndrome and associated complications, according to earlier studies. To validate the beneficial effects of ellagic acid on metabolic syndrome, additional preclinical and clinical research is necessary.

Ellagic acid: insight into its protective effects in age-associated disorders

The disparity in the free radical generation and the production of antioxidants to counteract its effect is known as oxidative stress. Oxidative stress causes damage to the macromolecules such as lipids, carbohydrates, proteins, and DNA and RNA. The oxidative damage to the cellular components leads to a process of aging and various age-associated disorders. The literature survey for this review was done using PubMed, Google Scholar, and Science Direct. The papers showing the studies related to aging and age-associated disorders have been selected for reviewing this paper. Ellagic acid has been used as the keyword, and more emphasis has been put on papers from the last 10 years. However, some papers with significant studies prior to 10 years have also been considered. Almost 250 papers have been studied for reviewing this paper, and about 135 papers have been cited. Ellagic acid (EA) is present in high quantities in pomegranate and various types of berries. It is known to possess the antioxidant potential and protects from the harmful effects of free radicals. Various studies have shown its effect to protect cardiovascular, neurodegenerative, cancer, and diabetes. The present review focuses on the protective effect of ellagic acid in age-associated disorders. The effect of EA has been studied in various chronic disorders but the scope of this review is limited to cancer, diabetes, cardiovascular and neurodegenerative disorders. All the disease aspects have not been addressed in this particular review.

Retrospective cohort analysis comparing changes in blood glucose level and body composition according to changes in thyroid-stimulating hormone level

BACKGROUND

In the euthyroid state, the risk of developing diabetes according to changes in thyroid-stimulating hormone (TSH) levels remains controversial. Additionally, the correlation of various body indices affecting blood glucose levels according to changes in TSH levels over a certain period is not well known.

METHODS

Patients who underwent health check-ups twice at a 2 year interval at a tertiary university hospital between 2009 and 2018 were included. By dividing baseline TSH levels into quartiles (TSH_Q1, Q2, Q3, and Q4), various variables were compared, and their changes after 2 years (∆TSH_Q1, Q2, Q3, and Q4) were confirmed.

RESULTS

Among 15 557 patients, the incidence of diabetes mellitus after 2 years was 2.4% (377/15 557 patients). There was no statistically significant difference in the incidence of diabetes according to TSH_Q (p = 0.243) or ∆TSH_Q (p = 0.131). However, as TSH levels increased, skeletal muscle mass decreased (p < 0.001), and body fat mass and percent body fat significantly increased (p < 0.001). As ∆TSH increased, ∆fasting blood glucose and ∆body mass index also significantly increased (all p < 0.001). The incidence of diabetes decreased significantly as skeletal muscle mass increased (odds ratio 0.734, p < 0.001).

CONCLUSIONS

Owing to the short study period, it was not possible to prove a statistical relationship between the incidence of diabetes mellitus and TSH levels in the euthyroid state. Significant decreases in skeletal muscle mass and increases in body mass index and body fat mass according to baseline TSH levels were demonstrated. Therefore, a focus on improving physical functions, such as increasing muscle mass and decreasing fat, is required in this case.

Predicting Elevated TSH Levels in the Physical Examination Population With a Machine Learning Model

Objective

The purpose of this study was to predict elevated TSH levels by developing an effective machine learning model based on large-scale physical examination results.

Methods

Subjects who underwent general physical examinations from January 2015 to December 2019 were enrolled in this study. A total of 21 clinical parameters were analyzed, including six demographic parameters (sex, age, etc.) and 15 laboratory parameters (thyroid peroxidase antibody (TPO-Ab), thyroglobulin antibody (TG-Ab), etc.). The risk factors for elevated TSH levels in the univariate and multivariate Logistic analyses were used to construct machine learning models. Four machine learning models were trained to predict the outcome of elevated TSH levels one year/two years after patient enrollment, including decision tree (DT), linear regression (LR), eXtreme Gradient boosting (XGBoost), and support vector machine (SVM). Feature importance was calculated in the machine learning models to show which parameter plays a vital role in predicting elevated TSH levels.

Results

A total of 12,735 individuals were enrolled in this study. Univariate and multivariate Logistic regression analyses showed that elevated TSH levels were significantly correlated with gender, FT3/FT4, total cholesterol (TC), TPO-Ab, Tg-Ab, creatinine (Cr), and triglycerides (TG). Among the four machine learning models, XGBoost performed best in the one-year task of predicting elevated TSH levels (AUC (0.87(+/- 0.03))). The most critical feature in this model was FT3/FT4, followed by TPO-Ab and other clinical parameters. In the two-year task of predicting TSH levels, none of the four models performed well.

Conclusions

In this study, we trained an effective XGBoost model for predicting elevated TSH levels one year after patient enrollment. The measurement of FT3 and FT4 could provide an early warning of elevated TSH levels to prevent relative thyroid diseases.

TSH Combined with TSHR Aggravates Diabetic Peripheral Neuropathy by Promoting Oxidative Stress and Apoptosis in Schwann Cells

Subclinical hypothyroidism (SCH) is associated with diabetic peripheral neuropathy (DPN); however, the mechanism underlying this association remains unknown. This study is aimed at examining neurofunctional and histopathological alterations in a type 2 diabetes (T2DM) mouse model of SCH and investigating the impact of thyroid-stimulating hormone (TSH) in an in vitro DPN cell model established using RSC96 cells under high glucose (HG) and palmitic acid (PA) stimulation. Our results indicated that T2DM, in combination with SCH, aggravated abnormal glucose and lipid metabolism in T2DM and dramatically destroyed the peripheral nervous system by increasing paw withdrawal latency, decreasing motor nerve conduction velocity, and exacerbating ultrastructural deterioration of the damaged sciatic nerve caused by diabetes. Furthermore, the results of our in vitro experiments showed that TSH intensified HG/PA-induced RSC96 cell damage by inducing oxidative stress, mitochondrial dysfunction, and apoptosis. More importantly, TSHR knockout or inhibition of PA-induced TSHR palmitoylation could alleviate the apoptosis induced by TSH. Overall, in this study, the novel mechanisms by which TSH, as an independent risk factor for DPN progression, aggravating Schwann cell apoptosis and demyelination, are elucidated. These findings indicate that TSHR could be a potential target for both the prevention and treatment of DPN and, possibly, other microvascular diseases, and have implication in the clinical management of patients with DPN.