Obesity and pituitary gland volume - a correlation study using three-dimensional magnetic resonance imaging

deepPGSegNet: MRI-based pituitary gland segmentation using deep learning

Introduction

In clinical research on pituitary disorders, pituitary gland (PG) segmentation plays a pivotal role, which impacts the diagnosis and treatment of conditions such as endocrine dysfunctions and visual impairments. Manual segmentation, which is the traditional method, is tedious and susceptible to inter-observer differences. Thus, this study introduces an automated solution, utilizing deep learning, for PG segmentation from magnetic resonance imaging (MRI).

Methods

A total of 153 university students were enrolled, and their MRI images were used to build a training dataset and ground truth data through manual segmentation of the PGs. A model was trained employing data augmentation and a three-dimensional U-Net architecture with a five-fold cross-validation. A predefined field of view was applied to highlight the PG region to optimize memory usage. The model's performance was tested on an independent dataset. The model's performance was tested on an independent dataset for evaluating accuracy, precision, recall, and an F1 score.

Results and discussion

The model achieved a training accuracy, precision, recall, and an F1 score of 92.7%, 0.87, 0.91, and 0.89, respectively. Moreover, the study explored the relationship between PG morphology and age using the model. The results indicated a significant association between PG volume and midsagittal area with age. These findings suggest that a precise volumetric PG analysis through an automated segmentation can greatly enhance diagnostic accuracy and surveillance of pituitary disorders.

Structure-function relationship of the pituitary gland in anorexia nervosa and intense physical activity

Patients with Anorexia Nervosa (AN) and athletes share intense physical activity and pituitary hormonal disturbances related to absolute (AN) or relative (athletes) undernutrition. Pituitary gland (PG) structure evaluations in those conditions are scarce, and did not differentiate anterior from posterior lobe. We evaluated the structure-function relationship of anterior and posterior PG in AN and athletes, and potential reversibility of this alteration in a group of weight-recovered patients (AN_Rec). Manual delineation of anterior (AP) and posterior (PP) PG was performed on T1-weighted MR images in 17 women with AN, 15 women with AN_Rec, 18 athletes women and 25 female controls. Anthropometric, hormonal, and psychometric parameters were explored and correlated with PG volumes. AP volume (APV) was lower in AN (448 ± 82 mm3), AN_Rec (505 ± 59 mm3), and athletes (540 ± 101 mm3) vs. Controls (615 ± 61 mm3, p < 0.00001, p < 0.00001 and p = 0.02, respectively); and smaller in AN vs. AN_Rec (p = 0.007). PP volume did not show any differences between the groups. APV was positively correlated with weight (R = 0.36, p = 0.011) in AN, and luteinizing hormone (R = 0.35, p = 0.014) in total group. In AN, mean growth hormone (GH) was negatively correlated with global pituitary volume (R = 0.31, p = 0.031) and APV (R = 0.29, p = 0.037). Absolute and relative undernutrition led to a decreased anterior pituitary gland volume, which was reversible with weight gain, correlated with low bodyweight, and blockade of gonadal hypothalamic-pituitary axis. Intriguing inverse correlation between anterior pituitary gland volume and GH plasma level could suggests a low storage capacity of anterior pituitary gland and increased reactivity to low insulin-like growth factor type 1.

Sensitivity of the Neuroendocrine Stress Axis in Metabolic Diseases

Metabolic diseases are prevalent in modern society and have reached pandemic proportions. Metabolic diseases have systemic effects on the body and can lead to changes in the neuroendocrine stress axis, the critical regulator of the body's stress response. These changes may be attributed to rising insulin levels and the release of adipokines and inflammatory cytokines by adipose tissue, which affect hormone production by the neuroendocrine stress axis. Chronic stress due to inflammation may exacerbate these effects. The increased sensitivity of the neuroendocrine stress axis may be responsible for the development of metabolic syndrome, providing a possible explanation for the high prevalence of severe comorbidities such as heart disease and stroke associated with metabolic disease. In this review, we address current knowledge of the neuroendocrine stress axis in response to metabolic disease and discuss its role in developing metabolic syndrome.

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.

Current Insights into the Potential Role of fMRI in Discovering the Mechanisms Underlying Obesity

Obesity is becoming one of the major global health concerns. This chronic disease affects around 650 million people worldwide and is an underlying cause of a number of significant comorbidities. According to the World Health Organization (WHO) report on obesity from 2022, this disorder became the fourth leading cause of deaths in Europe. Thus, understanding the mechanisms underlying obesity is of essential importance to successfully prevent and treat this disease. The aim of this study was to review the current insights into the potential role of fMRI in discovering the mechanisms underlying obesity on the basis of recent scientific literature published up to December 2022 and searches of the PubMed, Google Scholar and Web of Science databases. The literature assessed indicated that a growing body of evidence suggests that obesity leads to changes in both structure and connectivity within the central nervous system. Emerging data from recent functional magnetic resonance imaging (fMRI) studies prove that obese individuals present an increased motivational drive to eat as well as impaired processing in reward- and control-related brain regions. Apart from this, it is clear that fMRI might be a useful tool in detection of obesity-induced changes within the central nervous system.

[68 Ga]-DOTATATE PET/MR-based evaluation of physiologic somatostatin receptor 2 expression in the adult pituitary gland as a function of age and sex in a prospective cohort

PURPOSE

The pituitary gland has the fourth highest physiologic avidity of [68 Ga]-DOTATATE. In order to guide our understanding of [68 Ga]-DOTATATE PET in clinical contexts, accurate characterization of the normal pituitary gland is first required. This study aimed to characterize the normal pituitary gland using dedicated brain [68 Ga]-DOTATATE PET/MRI as a function of age and sex.

METHODS

A total of 95 patients with a normal pituitary gland underwent brain [68 Ga]-DOTATATE PET examinations for the purpose of diagnosing CNS SSTR2 positive tumors (mean age: 58.9, 73% female). Maximum SUV of the pituitary gland was obtained in each patient. SUV of superior sagittal sinus was obtained to calculate normalized SUV score (SUVR) of the gland. The anatomic size of the gland was collected as maximum sagittal height (MSH). Correlations with age and sex were analyzed.

RESULTS

The mean SUV and SUVR of the pituitary gland were 17.6 (range: 7-59.5, SD = 7.1) and 13.8 (range: 3.3-52.6, SD = 7.2), respectively. Older females had significantly higher SUV of the pituitary gland compared to younger females. When stratified by age and sex, both older and younger females had significantly higher pituitary SUV than older males. SUVR did not differ significantly by age or sex. MSH of the pituitary gland in younger females was significantly greater than in younger males at all age cutoffs.

CONCLUSION

This study provides an empiric profiling of the physiological [68 Ga]-DOTATATE avidity of the pituitary gland. The findings suggest that SUV may vary by age and sex and can help guide the use of [68 Ga]-DOTATATE PET/MRI in clinical and research settings. Future studies can build on these findings to investigate further the relationship between pituitary biology and demographic factors.

Neuroimaging Investigations of Obesity: a Review of the Treatment of Sex from 2010

PURPOSE OF REVIEW

To summarize the results of adult obesity neuroimaging studies (structural, resting-state, task-based, diffusion tensor imaging) published from 2010, with a focus on the treatment of sex as an important biological variable in the analysis, and identify gaps in sex difference research.

RECENT FINDINGS

Neuroimaging studies have shown obesity-related changes in brain structure, function, and connectivity. However, relevant factors such as sex are often not considered. We conducted a systematic review and keyword co-occurrence analysis. Literature searches identified 6281 articles, of which 199 met inclusion criteria. Among these, only 26 (13%) considered sex as an important variable in the analysis, directly comparing the sexes (n = 10; 5%) or providing single-sex/disaggregated data (n = 16, 8%); the remaining studies controlled for sex (n = 120, 60%) or did not consider sex in the analysis (n = 53, 27%). Synthesizing sex-based results, obesity-related parameters (e.g., body mass index, waist circumference, obese status) may be generally associated with more robust morphological alterations in men and more robust structural connectivity alterations in women. Additionally, women with obesity generally expressed increased reactivity in affect-related regions, while men with obesity generally expressed increased reactivity in motor-related regions; this was especially true under a fed state. The keyword co-occurrence analysis indicated that sex difference research was especially lacking in intervention studies. Thus, although sex differences in the brain associated with obesity are known to exist, a large proportion of the literature informing the research and treatment strategies of today has not specifically examined sex effects, which is needed to optimize treatment.

Identification and characterization of the long non-coding RNA NFIA-AS2 as a novel locus for body mass index in American Indians

BACKGROUND

Genome-wide association studies have shown that body mass index (BMI), an estimate of obesity, is highly polygenic. Individual variants typically have small effect sizes, making it challenging to identify unique loci in under-represented ethnic groups which lack statistical power due to their small sample size. Yet obesity is a major health disparity and is particularly prevalent in southwestern American Indians. Here, we identify and characterize a new locus for BMI that was detected by analyzing moderate associations with BMI obtained in a population-based sample of southwestern American Indians together with the well-powered GIANT dataset.

METHODS

Genotypes for 10.5 million variants were tested for association with BMI in 5870 American Indians and 2600 variants that showed an association P < 10^-3 in the American Indian sample were combined in a meta-analysis with the BMI data reported in GIANT (N = 240,608). The newly identified gene, NFIA-AS2 was functionally characterized, and the impact of its lead associated variant rs1777538 was studied both in-silico and in-vitro.

RESULTS

Rs1777538 (T/C; C allele frequency = 0.16 in American Indians and 0.04 in GIANT, meta-analysis P = 5.0 × 10^-7) exhibited a large effect in American Indians (1 kg/m² decrease in BMI per copy of C allele). NFIA-AS2 was found to be a nuclear localized long non-coding RNA expressed in tissues pertinent to human obesity. Analysis of this variant in human brown preadipocytes showed that NFIA-AS2 transcripts carrying the C allele had increased RNA degradation compared to the T allele transcripts (half-lives = 9 h, 13 h respectively). During brown adipogenesis, NFIA-AS2 featured a stage-specific regulation of nearby gene expression where rs1777538 demonstrated an allelic difference in regulation in the mature adipocytes (the strongest difference was observed for L1TD1, P = 0.007).

CONCLUSION

Our findings support a role for NFIA-AS2 in regulating pathways that impact BMI.