Nickel Sensitivity Is Associated with GH-IGF1 Axis Impairment and Pituitary Abnormalities on MRI in Overweight and Obese Subjects

Pituitary T1 signal intensity at magnetic resonance imaging is reduced in patients with obesity: results from the CHIASM study

BACKGROUND

Despite obesity being well known to be associated with several pituitary hormone imbalances, pituitary appearance in magnetic resonance imaging (MRI) in patients with obesity is understudied.

OBJECTIVE

To evaluate the pituitary volume and signal intensity at MRI in patients with obesity.

METHODS

This is a prospective study performed in an endocrine Italian referral center (ClinicalTrial.gov Identifier: NCT03458533). Sixty-nine patients with obesity (BMI > 30 kg/m2) and twenty-five subjects without obesity were enrolled. Thirty-three patients with obesity were re-evaluated after 3 years of diet and lifestyle changes, of whom 17 (51.5%) achieved a > 5% loss of their initial body weight, whereas the remaining 16 (48.5%) had maintained or gained weight. Evaluations included metabolic and hormone assessments, DEXA scan, and pituitary MRI. Pituitary signal intensity was quantified by measuring the pixel density using ImageJ software.

RESULTS

At baseline, no difference in pituitary volume was observed between the obese and non-obese cohorts. At the 3-year follow-up, pituitary volume was significantly reduced (p = 0.011) only in participants with stable-increased body weight. Furthermore, a significant difference was noted in the mean pituitary intensity of T1-weighted plain and contrast-enhanced sequences between the obese and non-obese cohorts at baseline (p = 0.006; p = 0.002), and a significant decrease in signal intensity was observed in the subgroup of participants who had not lost weight (p = 0.012; p = 0.017). Insulin-like growth factor-1 levels, following correction for BMI, were correlated with pituitary volume (p = 0.001) and intensity (p = 0.049), whereas morning cortisol levels were correlated with pituitary intensity (p = 0.007). The T1-weighted pituitary intensity was negatively correlated with truncal fat (p = 0.006) and fibrinogen (p = 0.018).

CONCLUSIONS

The CHIASM study describes a quantitative reduction in pituitary intensity in T1-weighted sequences in patients with obesity. These alterations could be explained by changes in the pituitary stromal tissue, correlated with low-grade inflammation.

Obesity-Associated Hepatic Steatosis, Somatotropic Axis Impairment, and Ferritin Levels Are Strong Predictors of COVID-19 Severity

The full spectrum of SARS-CoV-2-infected patients has not yet been defined. This study aimed to evaluate which parameters derived from CT, inflammatory, and hormonal markers could explain the clinical variability of COVID-19. We performed a retrospective study including SARS-CoV-2-infected patients hospitalized from March 2020 to May 2021 at the Umberto I Polyclinic of Rome. Patients were divided into four groups according to the degree of respiratory failure. Routine laboratory examinations, BMI, liver steatosis indices, liver CT attenuation, ferritin, and IGF-1 serum levels were assessed and correlated with severity. Analysis of variance between groups showed that patients with worse prognoses had higher BMI and ferritin levels, but lower liver density, albumin, GH, and IGF-1. ROC analysis confirmed the prognostic accuracy of IGF-1 in discriminating between patients who experienced death/severe respiratory failure and those who did not (AUC 0.688, CI: 0.587 to 0.789, p < 0.001). A multivariate analysis considering the degrees of severity of the disease as the dependent variable and ferritin, liver density, and the standard deviation score of IGF-1 as regressors showed that all three parameters were significant predictors. Ferritin, IGF-1, and liver steatosis account for the increased risk of poor prognosis in COVID-19 patients with obesity.

Female infertility in the era of obesity: The clash of two pandemics or inevitable consequence?

Obesity is an epidemic that has led to a rise in the incidence of many comorbidities: among others, reduced fertility is often under-evaluated in clinical practice. The mechanisms underlying the link between reduced fertility and obesity are numerous, with insulin resistance, hyperglycaemia and the frequent coexistence of polycystic ovary syndrome being the most acknowledged. However, several other factors concur, such as gut microbiome alterations, low-grade chronic inflammation and oxidative stress. Not only do women with obesity take longer to conceive, but in vitro fertilization (IVF) is also less likely to succeed. We herein provide an updated state-of-the-art regarding the molecular bases of what we could define as dysmetabolic infertility, focusing on the clinical aspects, as well as possible treatment.

Association between urinary nickel with obesity status in adults: A cross-sectional study

Objectives

The prevalence of obesity is on the rise and is connected to numerous factors. However, the relationship between obesity and nickel has never been investigated. Our study aimed to explore the association between urinary nickel and obesity Status in adults.

Methods

From the 2017-2018 National Health and Nutrition Examination Surveys (NHANES), 1,705 participants ≥18 years of age were enrolled. To explore further the relationship among urinary nickel, body mass index (BMI), and waist circumference(WC), Weighted multivariate linear regression analyses and further subgroup analyzes were conducted.

Results

Urinary nickel does not correlate with BMI level but positively correlates with WC. In the subgroup analyzed according to sex, Urinary nickel has a positive correlation with BMI and WC in males but has a negative correlation in females. Secondary stratification analysis according to sex and race, Urinary nickel positively correlates with BMI in White males. It also positively correlates with WC in both White and Black males.

Conclusions

A correlation was found between urinary nickel levels and BMI and WC in adult males. Adult men, especially those already obese, may need to reduce nickel exposure.

Metal Allergy: State-of-the-Art Mechanisms, Biomarkers, Hypersensitivity to Implants

Metal allergy is mainly an environmental disorder which can cause allergic contact dermatitis. Environmental metal exposures include jewelry, everyday metal items, mobile phones, leather, metal-rich food and implants, including stents or anchors. While consumer exposure is liable for the majority of metal hypersensitivity cases, the significance of occupational exposure to metals remains relevant. Although the most common metal allergens are nickel, chromium, and cobalt; however, lately, gold, palladium, titanium, and some others have also attracted attention. This review highlights advances in metal allergy mechanisms, biomarkers for potential patients' stratification as well as biological treatments. The most recent evidence of human exposure to metal for risk assessment is discussed, as well as the relationship between the occurrence of metal hypersensitivity and implanted devices, including non-characteristic symptoms. The latest data on the diagnosis of metal hypersensitivity are also reported.

A Case of Vernal Keratoconjunctivitis With Growth Hormone Deficiency

Hormonal abnormalities are considered to play a role in the development of vernal keratoconjunctivitis (VKC). However, little is known whether about growth hormone (GH) is related to VKC development. The patient was an 11-year-old male with VKC treated with 0.1% betamethasone eye drops and 0.1% cyclosporin eye drops. The papillary growth of both superior and inferior palpebral conjunctiva worsened, and masses started to appear at the lower palpebral margin. He was referred to our hospital and was treated with 0.1% tacrolimus and 0.1% fluorometholone. Six weeks later, the condition improved remarkably. At this timepoint, we noticed the patient's short stature and asked again about his past history. Two years ago, he was diagnosed with GH deficiency (GHD), which had been treated with somatropin in a pediatric clinic. Thus, it is necessary to keep in mind the possibility of GHD when treating VKC patients.

Growth Hormone Secretory Capacity Is Associated with Cardiac Morphology and Function in Overweight and Obese Patients: A Controlled, Cross-Sectional Study

Obesity is associated with increased cardiovascular morbidity. Adult patients with growth hormone deficiency (GHD) show morpho-functional cardiological alterations. A total of 353 overweight/obese patients are enrolled in the period between 2009 and 2019 to assess the relationships between GH secretory capacity and the metabolic phenotype, cardiovascular risk factors, body composition and cardiac echocardiographic parameters. All patients underwent GHRH + arginine test to evaluate GH secretory capacity, DEXA for body composition assessment and transthoracic echocardiography. Blood samples are also collected for the evaluation of metabolic parameters. In total, 144 patients had GH deficiency and 209 patients had normal GH secretion. In comparing the two groups, we found significant differences in body fat distribution with predominantly visceral adipose tissue accumulation in GHD patients. Metabolic syndrome is more prevalent in the GHD group. In particular, fasting glycemia, triglycerides and systolic and diastolic blood pressure are found to be linearly correlated with GH secretory capacity. Epicardial fat thickness, E/A ratio and indexed ventricular mass are worse in the GHD group. In the population studied, metabolic phenotype, body composition, cardiovascular risk factors and cardiac morphology are found to be related to the GH secretory capacity. GH secretion in the obese patient seems to be an important determinant of metabolic health.

Application of a Machine Learning Technology in the Definition of Metabolically Healthy and Unhealthy Status: A Retrospective Study of 2567 Subjects Suffering from Obesity with or without Metabolic Syndrome

The key factors playing a role in the pathogenesis of metabolic alterations observed in many patients with obesity have not been fully characterized. Their identification is crucial, and it would represent a fundamental step towards better management of this urgent public health issue. This aim could be accomplished by exploiting the potential of machine learning (ML) technology. In a single-centre study (n = 2567), we used an ML analysis to cluster patients with metabolically healthy (MHO) or metabolically unhealthy (MUO) obesity, based on several clinical and biochemical variables. The first model provided by ML was able to predict the presence/absence of MHO with an accuracy of 66.67% and 72.15%, respectively, and included the following parameters: HOMA-IR, upper body fat/lower body fat, glycosylated haemoglobin, red blood cells, age, alanine aminotransferase, uric acid, white blood cells, insulin-like growth factor 1 (IGF-1) and gamma-glutamyl transferase. For each of these parameters, ML provided threshold values identifying either MUO or MHO. A second model including IGF-1 zSDS, a surrogate marker of IGF-1 normalized by age and sex, was even more accurate with a 71.84% and 72.3% precision, respectively. Our results demonstrated high IGF-1 levels in MHO patients, thus highlighting a possible role of IGF-1 as a novel metabolic health parameter to effectively predict the development of MUO using ML technology.