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Objective: Clusterin is closely correlated with insulin resistance and its associated comorbidities. This study aimed to investigate the correlation between serum clusterin levels and non-alcoholic fatty liver disease (NAFLD) and further explore the mediating role of insulin resistance in this relationship.

Methods: This study enrolled 195 inpatients (41 males and 154 females) aged 18-61 years. Twenty-four patients were followed up for 12 months after bariatric surgery. Serum clusterin levels were measured using a sandwich enzyme-linked immunosorbent assay. Fatty liver disease was diagnosed on the basis of liver ultrasonography. The fatty liver index (FLI) was calculated to quantify the degree of hepatic steatosis. The mediating role of homeostasis model assessment-insulin resistance (HOMA-IR) was assessed using mediation analysis.

Results: Participants with NAFLD had significantly higher serum clusterin levels than those without NAFLD (444.61 [325.76-611.52] mg/L versus 294.10 [255.20-373.55] mg/L, P < 0.01). With increasing tertiles of serum clusterin levels, the prevalence of NAFLD displayed an upward trend (P < 0.01). Multivariate linear regression analysis showed that serum clusterin levels were a positive determinant of FLI (standardized β = 0.271, P < 0.001) after adjusting for multiple metabolic risk factors. Serum clusterin levels significantly decreased after bariatric surgery (298.77 [262.56-358.10] mg/L versus 520.55 [354.94-750.21] mg/L, P < 0.01). In the mediation analysis, HOMA-IR played a mediating role in the correlation between serum clusterin levels and FLI; the estimated percentage of the total effect was 17.3%.

Conclusion: Serum clusterin levels were associated with NAFLD. In addition, insulin resistance partially mediated the relationship between serum clusterin levels and FLI.

Hemoglobin A1c (HbA1c) has long been considered a cornerstone of diabetes mellitus (DM) management, as both an indicator of average glycemia and predictor of long-term complications among people with DM. However, HbA1c is subject to non-glycemic influences which confound interpretation and as a measure of average glycemia does not provide information regarding glucose trends or about the occurrence of hypoglycemia and/or hyperglycemia episodes. As such, solitary use of HbA1c, without accompanying glucose data, does not confer actionable information that can be harnessed to guide targeted therapy in many patients with DM. While conventional capillary blood glucose monitoring (BGM) sheds light on momentary glucose levels, in practical use the inherent infrequency of measurement precludes elucidation of glycemic trends or reliable detection of hypoglycemia or hyperglycemia episodes. In contrast, continuous glucose monitoring (CGM) data reveal glucose trends and potentially undetected hypo- and hyperglycemia patterns that can occur in between discrete BGM measurements. Use of CGM has grown significantly over the past decades as an ever-expanding body of literature demonstrates a multitude of clinical benefits for people with DM. Continually-improving CGM accuracy and ease of use have further fueled the widespread adoption of CGM. Furthermore, percent time in range (TIR) correlates well with HbA1c, is accepted as a validated indicator of glycemia, and is associated with risk of several DM complications. We explore the benefits and limitations of CGM use, use of CGM in clinical practice, and the application of CGM to advanced diabetes technologies.

Growth hormone deficiency (GHD) is a clinical syndrome that can manifest either as isolated or associated with additional pituitary hormone deficiencies. Although diminished height velocity and short stature are useful and important clinical markers to consider testing for GHD in children, the signs and symptoms of GHD are not always so apparent in adults. Quality of life and metabolic health are often impacted in patients with GHD; thus, making an accurate diagnosis is important so that appropriate GH replacement therapy can be offered to these patients. Screening and testing for GHD require sound clinical judgment that follows after obtaining a complete medical history of patients with a hypothalamic-pituitary disorder and thorough physical examination with specific features for each period of life, while targeted biochemical testing and imaging are required to confirm the diagnosis. Random measurements of serum GH levels are not recommended to screen for GHD (except in neonates) as endogenous GH secretion is episodic and pulsatile throughout the lifespan. One or more GH stimulation tests may be required, but existing methods of testing might be inaccurate, difficult to perform, and can be imprecise. Furthermore, there are multiple caveats when interpreting test results including individual patient factors, differences in peak GH cut-offs (by age and test), testing time points, and heterogeneity of GH and IGF-I assays. In this article, we provide a global overview of the accuracy and cut-offs for diagnosis of GHD in children and adults, and discuss the caveats in conducting and interpreting these tests.

Objective: To assess the efficacy of a very low-calorie ketogenic diet (VLCKD) method versus a Mediterranean low-calorie diet (LCD) in obese PCOS women of a reproductive age.

Design: Randomized controlled open label trial. The treatment period was 16 weeks; VLCKD for 8 weeks then LCD for 8 weeks, according to the Pronokal® method (experimental group; n=15) versus Mediterranean LCD for 16 weeks (control group; n=15). Ovulation monitoring was carried out at baseline and after 16 weeks, while a clinical exam, bioelectrical impedance analysis (BIA), anthropometry, and biochemical analyses were performed at baseline, at week 8, and at week 16.

Results: BMI decreased significantly in both groups, and to a major extent in the experimental group (-13.7% vs -5.1%, p=0.0003). Significant differences between the experimental and the control groups were also observed in the reduction of waist circumference (-11.4% vs -2.9%), BIA-measured body fat (-24.0% vs -8.1%), and free T (-30.4% vs -12.6%) after 16 weeks (p=0.0008, p=0.0176, and p=0.0009, respectively). HOMA-IR significantly decreased only in the experimental group (p=0.0238), but without significant differences with respect to the control group (-23% vs -13.2%, p>0.05). At baseline, 38.5% participants in the experimental group and 14.3% participants in the control group had ovulation, which increased to 84.6% (p=0.031) and 35.7% (p>0.05) at the end of the study, respectively.

Conclusion: In obese PCOS patients, 16-weeks of VLCKD protocol with the Pronokal® method was more effective than Mediterranean LCD in reducing total and visceral fat, and in ameliorating hyperandrogenism and ovulatory dysfunction.

Objective: Hypercortisolism is a risk factor for obesity. Cortisol increases in response to food intake in lean subjects. In obese subjects, disturbances of the food-induced cortisol peak were reported, but data from sufficiently powered and well-controlled trials are lacking. Understanding the cortisol response to food is essential as amplified, or recurrent cortisol surges could lead to hypercortisolism and contribute to obesity. Therefore, we investigate the cortisol response to food in lean and obese subjects.

Design: This is a non-randomized, open-label study.

Methods: We assessed serum cortisol values after a high-calorie meal in lean and obese male subjects. Cortisol levels were frequently assessed before and for 3 hours after food intake.

Results: 36 subjects (18 lean, 18 obese) were included. There was no difference in overall cortisol levels between both groups during the study (area under the curve (AUC) obese: 55409 ±16994, lean: 60334 ±18001, p=0.4). Total cortisol levels reached peak concentrations 20 minutes after food intake in both groups; the maximum cortisol increase was similar in both groups (cortisol increase obese: 69.6 ±135.5 nmol/l, lean: 134.7 ±99.7 nmol/l; p=0.1). There was no correlation between body mass index (BMI) and baseline cortisol values (R2=0.001, p=0.83), cortisol increase (R2= 0.05, p=0.17), or cortisol AUC (R2= 0.03, p=0.28).

Conclusions: This study demonstrates that high-calorie food intake causes an immediate and substantial cortisol response in lean and obese subjects and is independent of body weight.