Selected Organ and Endocrine Complications According to BMI and the Metabolic Category of Obesity: A Single Endocrine Center Study

Association between heme oxygenase-1 and hyperlipidemia in pre-diabetic patients: a cross-sectional study

Background

Although the importance and benefit of heme oxygenase-1 (HO-1) in diabetes rodent models has been known, the contribution of HO-1 in the pre-diabetic patients with hyperlipidemia risk still remains unclear. This cross-sectional study aims to evaluate whether HO-1 is associated with hyperlipidemia in pre-diabetes.

Methods

Serum level of HO-1 was detected using commercially available ELISA kit among 1,425 participants aged 49.3-63.9 with pre-diabetes in a multicenter Risk Evaluation of cAncers in Chinese diabeTic Individuals: A lONgitudinal (REACTION) prospective observational study. Levels of total cholesterol (TC) and triglyceride (TG) were measured and used to defined hyperlipidemia. The association between HO-1 and hyperlipidemia was explored in different subgroups.

Result

The level of HO-1 in pre-diabetic patients with hyperlipidemia (181.72 ± 309.57 pg/ml) was obviously lower than that in pre-diabetic patients without hyperlipidemia (322.95 ± 456.37 pg/ml). High level of HO-1 [(210.18,1,746.18) pg/ml] was negatively associated with hyperlipidemia (OR, 0.60; 95% CI, 0.37-0.97; p = 0.0367) after we adjusted potential confounding factors. In subgroup analysis, high level of HO-1 was negatively associated with hyperlipidemia in overweight pre-diabetic patients (OR, 0.50; 95% CI, 0.3-0.9; p = 0.034), especially in overweight women (OR, 0.42; 95% CI, 0.21-0.84; p = 0.014).

Conclusions

In conclusion, elevated HO-1 level was negatively associated with risk of hyperlipidemia in overweight pre-diabetic patients, especially in female ones. Our findings provide information on the exploratory study of the mechanism of HO-1 in hyperlipidemia, while also suggesting that its mechanism may be influenced by body weight and gender.

Integrating machine learning and nontargeted plasma lipidomics to explore lipid characteristics of premetabolic syndrome and metabolic syndrome

Objective

To identify plasma lipid characteristics associated with premetabolic syndrome (pre-MetS) and metabolic syndrome (MetS) and provide biomarkers through machine learning methods.

Methods

Plasma lipidomics profiling was conducted using samples from healthy individuals, pre-MetS patients, and MetS patients. Orthogonal partial least squares-discriminant analysis (OPLS-DA) models were employed to identify dysregulated lipids in the comparative groups. Biomarkers were selected using support vector machine recursive feature elimination (SVM-RFE), random forest (rf), and least absolute shrinkage and selection operator (LASSO) regression, and the performance of two biomarker panels was compared across five machine learning models.

Results

In the OPLS-DA models, 50 and 89 lipid metabolites were associated with pre-MetS and MetS patients, respectively. Further machine learning identified two sets of plasma metabolites composed of PS(38:3), DG(16:0/18:1), and TG(16:0/14:1/22:6), TG(16:0/18:2/20:4), and TG(14:0/18:2/18:3), which were used as biomarkers for the pre-MetS and MetS discrimination models in this study.

Conclusion

In the initial lipidomics analysis of pre-MetS and MetS, we identified relevant lipid features primarily linked to insulin resistance in key biochemical pathways. Biomarker panels composed of lipidomics components can reflect metabolic changes across different stages of MetS, offering valuable insights for the differential diagnosis of pre-MetS and MetS.

ANMCO (Italian Association of Hospital Cardiologists) scientific statement: obesity in adults-an approach for cardiologists

Obesity is a complex, chronic disease requiring a multidisciplinary approach to its management. In clinical practice, body mass index and waist-related measurements can be used for obesity screening. The estimated prevalence of obesity among adults worldwide is 12%. With the expected further increase in overall obesity prevalence, clinicians will increasingly be managing patients with obesity. Energy balance is regulated by a complex neurohumoral system that involves the central nervous system and circulating mediators, among which leptin is the most studied. The functioning of these systems is influenced by both genetic and environmental factors. Obesity generally occurs when a genetically predisposed individual lives in an obesogenic environment for a long period. Cardiologists are deeply involved in evaluating patients with obesity. Cardiovascular risk profile is one of the most important items to be quantified to understand the health risk due to obesity and the clinical benefit that a single patient can obtain with weight loss. At the individual level, appropriate patient involvement, the detection of potential obesity causes, and a multidisciplinary approach are tools that can improve clinical outcomes. In the near future, we will probably have new pharmacological tools at our disposal that will facilitate achieving and maintaining weight loss. However, pharmacological treatment alone cannot cure such a complex disease. The aim of this paper is to summarize some key points of this field, such as obesity definition and measurement tools, its epidemiology, the main mechanisms underlying energy homeostasis, health consequences of obesity with a focus on cardiovascular diseases and the obesity paradox.Level of evidence V: report of expert committees.

Utilization of the GOAL questionnaire as a standardized screening tool for obstructive sleep apnea

The purpose of our study was to evaluate the application value of the GOAL questionnaire in screening obstructive sleep apnea (OSA) and to compare it with the other three questionnaires in sleep clinics. A cross-sectional study was conducted in 436 patients who had undergone nocturnal polysomnography in the sleep unit of the First Hospital of Shanxi Medical University between September 2021 and May 2022, and all patients completed the four questionnaires (GOAL questionnaire, STOP-Bang questionnaire, NoSAS score and No-Apnea score) truthfully, and the patients were divided into 3 groups: AHI ≥ 5 events/h group, AHI ≥ 15 events/h group and AHI ≥ 30 events/h group. The predictive effect of the questionnaire on different AHI cut-off values was calculated, and performance of four questionnaires was assessed by the discriminatory ability. This study ultimately included 410 patients, and there were statistically significant differences in gender, age, BMI, neck circumference, clinical symptoms, hypertension, diabetes, AHI, and minimum oxygen saturation between OSA and non-OSA groups (P < 0.05). The AUC for No-Apnea score was 0.79, the AUC for STOP-Bang questionnaire was 0.86, the AUC for NoSAS score was 0.81, and the AUC for GOAL questionnaire was 0.77. These four questionnaires were effective in screening OSA when AHI ≥ 15 events/h. Similar to No-Apnea score, STOP-Bang score and NoSAS score, GOAL questionnaire has a good predictive value for OSA, which is a questionnaire suitable for primary health-care centers and clinics.

Increased Risk of Hypothyroidism in People with Asthma: Evidence from a Real-World Population-Based Study

Background: Non-T2 asthma and hypothyroidism share several inflammatory mechanisms in common. However, large-scale, real-world studies evaluating the association between asthma and hypothyroidism are lacking. The objective of this study was to evaluate the risk for asthma patients of developing hypothyroidism. Methods: In the retrospective cohort study, people with asthma were recruited from the Longitudinal Health Insurance Database in Taiwan. After excluding ineligible patients with a previous history of hypothyroidism, 1:1 propensity matching was conducted to select a non-asthma control group. Based on the multivariate Cox regression model, the adjusted hazard ratio of asthma patients developing hypothyroidism was calculated. Results: In total, 95,321 asthma patients were selected as the asthma group and the same amount of people without asthma were selected as the control group. The incidence levels of new-onset hypothyroidism in asthma and non-asthma groups were 8.13 and 6.83 per 100,000 people per year, respectively. Compared with the non-asthma group, the adjusted hazard ratio of the asthma group developing hypothyroidism was 1.217 (95% confidence interval, 1.091–1.357). Conclusions: We found having asthma to be associated with an increased risk of hypothyroidism. Clinicians should be concerned regarding the endocrinological and inflammatory interaction between the two diseases while caring for people with asthma.

Cushing's syndrome is associated with altered adipokine profile

INTRODUCTION

Adipokines are signaling molecules involved in the integration of metabolism. Changes in their concentrations were observed in obesity, metabolic syndrome, diabetes mellitus and cardiovascular diseases, as well as endocrine disorders. Cushing's syndrome is associated with metabolic dysregulation, but the significance of adipokines in this entity and related complications is largely unknown. The aim of our study was to determine the concentrations of adipokines: fetuin A, fatty acid binding protein 4 (FABP4) and retinol binding protein 4 (RBP4) in Cushing's syndrome and to assess their relation to established cardiovascular and diabetes risk markers.

METHODS

We examined 21 subjects with Cushing's syndrome and 24 healthy controls in a cross-sectional manner. Venous blood samples were analysed for adipokines, cortisol, adrenocorticotrophin, glucose, insulin, glycated haemoglobin (HbA1c), triglycerides, cholesterol fractions, thyrotropin and free thyroid hormones concentrations. Patients' body mass index (BMI) was evaluated, homeostatic model assessment-insulin resistance and Systematic Coronary Risk Evaluation (SCORE) were calculated.

RESULTS

We found that the concentration of fetuin A was lower, while FABP4 and RBP4 concentrations were higher in Cushing's syndrome compared to controls [156.4 ± 60.0 µg/ml vs 260.7 ± 49.6 µg/ml; 79.8 (35.2-156.1) ng/ml vs 27.9 (17.1-36.7) ng/ml and 34 (30-37.7) mg/l vs 25.8 (23.6-27.7) mg/l, respectively]. Fetuin A correlated inversely, while FABP4 and RBP4 positively, with the concentrations of urinary free cortisol and adrenocorticotrophin. Fetuin A was positively related to LDL-cholesterol, and negatively to SCORE and HbA1c. FABP4 was associated positively with BMI, HbA1c and triglycerides, while RBP4 correlated positively with triglycerides and systolic blood pressure.

CONCLUSIONS

Adipokines' concentrations change in hypercortisolism. Further research is needed to ascertain whether adipokines are involved in the development of metabolic complications accompanying Cushing's syndrome or secondarily reflect metabolic dysregulation.