Robert S Sherwin’s research while affiliated with Yale University and other places

Context The neural regulation of appetite and energy homeostasis significantly overlaps with the neurobiology of stress. Frequent exposure to repeated acute stressors may cause increased allostatic load and subsequent dysregulation of the cortico-limbic striatal system leading to inefficient integration of postprandial homeostatic and hedonic signals. It is therefore important to understand the neural mechanisms by which stress generates alterations in appetite that may drive weight gain. Objective To determine glucocorticoid effects on metabolic, neural and behavioral factors that may underlie the association between glucocorticoids, appetite and obesity risk. Methods A randomized double-blind cross-over design of overnight infusion of hydrocortisone or saline followed by a fasting morning perfusion magnetic resonance imaging to assess regional cerebral blood flow (CBF) was completed. Visual Analog Scale (VAS) hunger, cortisol and metabolic hormones were also measured. Results Hydrocortisone relative to saline significantly decreased whole brain voxel based CBF responses in the hypothalamus and related cortico-striatal-limbic regions. Hydrocortisone significantly increased hunger VAS pre-scan, insulin, glucose and leptin, but not other metabolic hormones versus saline CBF groups. Hydrocortisone related increases in hunger were predicted by less reduction of CBF (hydrocortisone minus saline) in the medial OFC, medial brainstem and thalamus, left primary sensory cortex and right superior and medial temporal gyrus. Hunger ratings were also positively associated with plasma insulin on hydrocortisone but not saline day. Conclusions Increased glucocorticoids at levels akin to those experienced during psychological stress, result in increased fasting hunger and decreased regional cerebral blood flow in a distinct brain network of prefrontal, emotional, reward, motivation, sensory and homeostatic regions that underlie control of food intake.

The energy-dissipating properties of brown adipose tissue (BAT) have been proposed as therapeutic targets for obesity and diabetes. Little is known about basal BAT activity. Capitalizing on the dense sympathetic innervation of BAT, we have previously shown that BAT can be detected in humans under resting room temperature (RT) conditions by using (S,S)-¹¹C–O-methylreboxetine (MRB), a selective ligand for the norepinephrine transporter (NET). In this study, we determine whether MRB labeling of human BAT is altered by obesity. Fifteen healthy, nondiabetic Caucasian women (nine lean, age 25.6 ± 1.7, BMI 21.8 ± 1.3 kg/m²; six obese age 30.8 ± 8.8 BMI 37.9 ± 6.6 kg/m²) underwent PET-CT imaging of the neck/supraclavicular region using ¹¹C-MRB under RT conditions. The distribution volume ratio (DVR) for ¹¹C-MRB was estimated via multilinear reference tissue model 2 (MRTM2) referenced to the occipital cortex. Two women (one lean and one with obesity) had no detectable BAT. Of the women with detectable BAT, women with obesity had lower ¹¹C-MRB DVR (0.80 ± 0.12 BAT DVR) compared to lean (1.15 ± 0.19 BAT DVR) (p = 0.004). Our findings are consistent with reports that NET is decreased in obesity and suggest that the sympathetic innervation of BAT is altered in obesity.

ContextCortisol, a glucocorticoid steroid stress hormone, is primarily responsible for stimulating gluconeogenesis in the liver and promoting adipocyte differentiation and maturation. Prolonged excess cortisol leads to visceral adiposity, insulin resistance, hyperglycemia, memory dysfunction, cognitive impairment, and more severe Alzheimer’s disease phenotypes. The intracellular enzyme 11β-hydroxysteroid dehydrogenase type 1 (11β-HSD1) catalyzes the conversion of inactive cortisone to active cortisol; yet the amount of 11β-HSD1 in the brain has not been quantified directly in vivo.Objective We analyzed positron emission tomography (PET) scans with an 11β-HSD1 inhibitor radioligand in twenty-eight individuals (23 M/5F): 10 lean, 13 overweight, and 5 obese individuals. Each individual underwent PET imaging on the high-resolution research tomograph PET scanner after injection of 11C-AS2471907 (n = 17) or 18F-AS2471907 (n = 11). Injected activity and mass doses were 246 ± 130 MBq and 0.036 ± 0.039 μg, respectively, for 11C-AS2471907, and 92 ± 15 MBq and 0.001 ± 0.001 μg for 18F-AS2471907. Correlations of mean whole brain and regional distribution volume (VT) with body mass index (BMI) and age were performed with a linear regression model.ResultsSignificant correlations of whole brain mean VT with BMI and age (VT = 15.23–0.63 × BMI + 0.27 × Age, p = 0.001) were revealed. Age-adjusted mean whole brain VT values were significantly lower in obese individuals. Post hoc region specific analyses revealed significantly reduced mean VT values in the thalamus (lean vs. overweight and lean vs. obese individuals). Caudate, hypothalamus, parietal lobe, and putamen also showed lower VT value in obese vs. lean individuals. A significant age-associated increase of 2.7 mL/cm3 per decade was seen in BMI-corrected mean whole brain VT values.Conclusions In vivo PET imaging demonstrated, for the first time, correlation of higher BMI (obesity) with lower levels of the enzyme 11β-HSD1 in the brain and correlation of increased 11β-HSD1 levels in the brain with advancing age.

Cancer cells are known to adopt aerobic glycolysis in order to fuel tumor growth, but the molecular basis of this metabolic shift remains largely undefined. O-GlcNAcase (OGA) is an enzyme harboring O-linked β-N-acetylglucosamine (O-GlcNAc) hydrolase and cryptic lysine acetyltransferase activities. Here, we report that OGA is upregulated in a wide range of human cancers and drives aerobic glycolysis and tumor growth by inhibiting pyruvate kinase M2 (PKM2). PKM2 is dynamically O-GlcNAcylated in response to changes in glucose availability. Under high glucose conditions, PKM2 is a target of OGA-associated acetyltransferase activity, which facilitates O-GlcNAcylation of PKM2 by O-GlcNAc transferase (OGT). O-GlcNAcylation inhibits PKM2 catalytic activity and thereby promotes aerobic glycolysis and tumor growth. These studies define a causative role for OGA in tumor progression and reveal PKM2 O-GlcNAcylation as a metabolic rheostat that mediates exquisite control of aerobic glycolysis.

Type-1 diabetes mellitus (T1DM) has traditionally been characterized by a complete destruction of beta-cell mass (BCM); however, there is growing evidence of possible residual BCM present in T1DM. Given the absence of in vivo tools to measure BCM, routine clinical measures of beta cell function (e.g., C-peptide release) may not reflect BCM. We previously demonstrated the potential utility of positron emission tomography (PET) imaging with the dopamine D2/D3-receptor agonist 11C-(+)-PHNO to differentiate between healthy control (HC) and T1DM individuals. Methods: Sixteen individuals participated (10M/6F; 9-HC/7-T1DM). Average duration of diabetes was 18±6y (range:14-30y). Individuals underwent PET/CT scanning with a 120-minute dynamic PET scan centered on the pancreas. One- and two-tissue compartment (1TC/2TC) models were used to estimate pancreas and spleen distribution volume (VT). Reference region approaches (spleen as reference) were also investigated. Quantitative PET measures were correlated with clinical outcome measures. Immunohistochemistry was performed to examine colocalization of dopamine receptors with endocrine hormones in HC and T1DM pancreatic tissue. Results: C-peptide release was not detectable in any T1DM individuals while proinsulin was detectable in 3/5 T1DM individuals. Pancreas SUVR-1 (20-30min; spleen as reference region) demonstrated a statistically significant reduction (-36.2%) in radioligand binding (HC:5.6, T1DM:3.6; P = 0.03). Age-at-diagnosis correlated significantly with pancreas SUVR-1 (20-30min) (R2=0.67, P = 0.025). Duration of diabetes was not significantly correlated with pancreas SUVR-1 (20-30 min) (R2=0.36, P = 0.16). Mean acute C-peptide response to arginine at maximal glycemic potentiation did not significantly correlate with SUVR-1 (20-30min) (R2=0.57, P = 0.05), nor did mean baseline proinsulin (R2=0.45, P = 0.10). Immunohistochemistry demonstrated co-localization of dopamine receptor type-3 (DRD3) and dopamine receptor type-2 (DRD2) in healthy controls. No co-localization of the DRD3 or DRD2 was seen with somatostatin, glucagon and polypeptide Y. In a separate T1DM individual, no immunostaining was seen with DRD3, DRD2 or insulin antibodies suggesting the loss of endocrine DRD3 and DRD2 expression accompanies the loss of beta cell functional insulin secretory capacity. Conclusion: 30min scan durations and SUVR-1 provide quantitative outcome measures for 11C-(+)-PHNO, a D3-receptor-preferring agonist PET radioligand, to differentiate BCM in T1DM and HCs.

Context Individuals with type 1 diabetes (T1DM) have alterations in brain activity which have been postulated to contribute to the adverse neurocognitive consequences of T1DM; however, the impact of T1DM and hypoglycemic unawareness on the brain’s resting state activity remains unclear. Objective To determine whether individuals with T1DM and hypoglycemia unawareness (T1DM-Unaware) had changes in the brain resting state functional connectivity compared to healthy controls (HC) and those with T1DM and hypoglycemia awareness (T1DM-Aware). Design Observational study Setting Academic medical center Participants 27 individuals with T1DM and 12 healthy control volunteers participated in the study. Intervention All participants underwent BOLD resting state fMRI brain imaging during a 2-step hyperinsulinemic euglycemic (90 mg/dl)-hypoglycemic (60mg/dl) clamp. Outcome Changes in resting state functional connectivity Results Using two separate methods of functional connectivity analysis, we identified distinct differences in the resting state brain responses to mild hypoglycemia amongst HC, T1DM-Aware and T1DM-Unaware participants, particularly in the angular gyrus, an integral component of the default mode network (DMN). Furthermore, changes in angular gyrus connectivity also correlated with greater symptoms of hypoglycemia (r = 0.461, P = 0.003) as well as higher scores of perceived stress (r = 0.531, P = 0.016). Conclusion These findings provide evidence that individuals with T1DM have changes in the brain’s resting state connectivity patterns, which may be further associated with differences in awareness to hypoglycemia. These changes in connectivity may be associated with alterations in functional outcomes amongst individuals with T1DM.

Context Individuals with type 1 diabetes (T1DM) have alterations in brain activity which have been postulated to contribute to the adverse neurocognitive consequences of T1DM; however, the impact of T1DM and hypoglycemic unawareness on the brain’s resting state activity remains unclear. Objective To determine whether individuals with T1DM and hypoglycemia unawareness (T1DM-Unaware) had changes in the brain resting state functional connectivity compared to healthy controls (HC) and those with T1DM and hypoglycemia awareness (T1DM-Aware). Design Observational study Setting Academic medical center Participants 27 individuals with T1DM and 12 healthy control volunteers participated in the study. Intervention All participants underwent BOLD resting state fMRI brain imaging during a 2-step hyperinsulinemic euglycemic (90 mg/dl)-hypoglycemic (60mg/dl) clamp. Outcome Changes in resting state functional connectivity Results Using two separate methods of functional connectivity analysis, we identified distinct differences in the resting state brain responses to mild hypoglycemia amongst HC, T1DM-Aware and T1DM-Unaware participants, particularly in the angular gyrus, an integral component of the default mode network (DMN). Furthermore, changes in angular gyrus connectivity also correlated with greater symptoms of hypoglycemia (r = 0.461, P = 0.003) as well as higher scores of perceived stress (r = 0.531, P = 0.016). Conclusion These findings provide evidence that individuals with T1DM have changes in the brain’s resting state connectivity patterns, which may be further associated with differences in awareness to hypoglycemia. These changes in connectivity may be associated with alterations in functional outcomes amongst individuals with T1DM.

Metabolic derangement triggered by a high fat diet has been associated with decreased brain glucose uptake in mice through downregulation of GLUT-1 expression at the blood-brain barrier. Likewise, a prior study by our group using 1H magnetic resonance spectroscopy (MRS) showed diminished change in brain glucose level during acute hyperglycemia in patients with obesity and diabetes. However, whether this reflected differences in absolute brain concentrations remains unknown. We utilized 13C MRS scanning at 4 Tesla to compare the rate of glucose transport (Tmax) and cerebral metabolic rate of glucose (CMRgl) in the occipital lobe during a 2-hour hyperglycemic clamp. Plasma glucose concentration of ∼180mg/dl was achieved using a variable 1-13C-glucose infusion. The study included 8 healthy lean (1M/7F, age 29 ± 5 years, BMI 21 ± 2 kg/m2, HgbA1c 5.4 ± 0.2%) and 6 obese participants (4M/2F, age 30 ± 3 years, BMI 33 ± 3 kg/m2, HgbA1c 5.5 ± 0.2%). Insulin and free fatty acids (FFA) levels were measured during the clamp. Despite nearly identical plasma glucose concentrations at steady state (lean 182.5 ± 9.0 mg/dL, obese 186.5 ± 13.5 mg/dL, P=0.55, averaged over time 60-120 minutes), the absolute brain glucose concentrations were 30% less in obese compared to lean participants (P=0.02). Moreover, using previously reported Michaelis-Menten Kt of 1.1 mM, the calculated Tmax/CMRgl at steady state were 37% less in obese participants (P=0.01), suggesting reduced glucose transport. A trend of higher insulin and FFA levels in obese participants during hyperglycemia was observed. In addition, FFA levels at steady state were negatively correlated with absolute brain glucose concentrations (r= -0.575, P=0.05). We conclude that obesity is associated with diminished absolute concentration of brain glucose during acute hyperglycemia, likely explained by reduced cerebral glucose transport capacity. These findings may have implications for understanding the impact of obesity on central regulation of feeding behavior as well as neurocognitive function. Disclosure F. Gunawan: None. L. Jiang: None. J. Leventhal: None. J.J. Pach: None. E. Sanchez Rangel: None. R. Belfort-DeAguiar: Research Support; Self; Silver Palate Kitchens, Inc. A. Coppoli: None. D.L. Rothman: None. R. Sherwin: Other Relationship; Self; ICON plc., IQVIA, MannKind Corporation. G.F. Mason: None. J.J. Hwang: None. Funding American Diabetes Association (1-17-ICTS-013 to J.J.H.)

While studies have investigated changes in brain connectivity following diet and weight loss, it has not yet been explored whether baseline brain connectivity can be used to predict weight loss during diet or to identify individual subjects for which a particular diet may be effective. The following study uses Connectome-based predictive modeling (CPM), a data-driven analysis approach for producing predictive models of individual brain-behavior relationships from brain connectivity data using cross-validation, to help identify individual participants who will lose weight after an 8-week low calorie diet. 16 Healthy OB subjects (10F/6M, age 44.4±8 years, BMI 32.7±2) and 9 T2DM subjects (5F/4M, age 48±9, BMI 33.9±2) underwent functional MRI (fMRI) during a hyperglycemic-euglycemic clamp. Blood-oxygen-level-dependent (BOLD) brain activity was assessed while subjects viewed food (high-calorie, low-calorie) pictures and non-food images. The study was repeated after 8 weeks of a reduced calorie diet. CPM was applied to BOLD activity during hyperglycemia and euglycemia. Analyses show that models built on individual subject brain connectivity matrices averaged between hyperglycemia and euglycemia before an 8-week low calorie diet are able to predict 50.2% and 53.4% of the variance in BMI and weight loss (kg), respectively (p<0.05). While models built on brain connectivity matrices during euglycemia (similar to the post-prandial state), but not hyperglycemia predicted 42.2% of the variance in BMI change (p=0.017). These results suggest the possibility that individual brain connectivity signatures may be used to identify subjects for whom a low calorie diet might be an effective weight loss strategy, while also revealing the relevant functional connections associated with weight loss success. Disclosure D. Groskreutz: None. W. Lam: None. C. Lacadie: None. A. Elshafie: None. J.J. Hwang: None. D. Seo: None. M. Savoye: None. R. Sinha: None. T. Constable: None. R. Sherwin: Other Relationship; Self; ICON plc., IQVIA, MannKind Corporation. R. Belfort-DeAguiar: Research Support; Self; Silver Palate Kitchens, Inc. Funding National Institutes of Health