In this study, we investigated the neurochemical alterations in mouse hippocampus using in vivo proton magnetic resonance spectroscopy. We also examine the effect of high-fat diet on the levels of abdominal fat, plasma leptin, and corticosterone. The decrease in mIns concentration seen in HF diet mice without corresponding Gln-Glu alternation may reflect changes in glial function. In addition, the observed total choline levels indicate attenuated membrane turnover in HF diet mice. We therefore suggest that diets rich in saturated fats induce a stress-related response through metabolic disturbance and HPA axis dysfunction, which may indicate a relationship between obesity and depression.
Animal
Fourteen male, 4 week-old, C57BL/6 mice (16–18g) were arbitrarily assigned into two groups: low fat (LF; n = 7) and high fat (HF; n = 7). LF and HF groups were fed diets consisting of 10% and 60% fat, respectively, for 10 weeks and weighed once per week.
Magnetic resonance imaging and spectroscopy
MRS scans were conducted following 10 weeks for each group. In vivo 1H MRS spectra were acquired using point resolved spectroscopy (PRESS) pulse sequences applied to the hippocampus with variable power and optimized relaxation delays. Parameters were as follows: TR = 5000 ms, TE = 13.40 ms, spectral width = 5.0 kHz, average = 384, number of data points = 2048, voxel size = 1.2*1.5*2.0 mm. During the MRI and MRS scans, mice were anesthetized using 1.5–2.5 % of isoflurane. In vivo spectra were analyzed using LC modeling software with a simulated basis set. Metabolite concentrations with a Cramer-Rao lower bound (CRLB) below 20% were regarded as acceptable. Figures 2 and 3 show representative T2-weighted images with their respective voxel plans and spectra.
Abdominal fat quantification MRI
Abdominal fat MRI scans were performed 3 days following the brain MRS scan. T1-weighted images were acquired using the following parameters: TR = 900 ms, Average = 4, TE = 9.14 ms. Abdominal fat quantification was performed using Image J.
Enzyme-linked immunosorbent assay (ELISA)
Blood samples were obtained following the brain MRS scans. Plasma leptin and corticosterone levels were measured using an ELISA kit (Enzo Life Science) following the manufacturer’s protocol.
Statistical analyses were performed using the SPSS software. An independent t-test was used to compare differences between the two groups (* p< 0.05, ** p<0.01, *** p<0.001).
Discussions
Inhibitory (GABA) and Excitatory (Glu) mechanisms have been proposed to play an important role in mood depression and cognition. However, in our study we did not observe significant changes in glutamate-glutamine (Gln-Glu) cycling or GABA release2. We did find evidence of elevated hypothalamus-pituitary-adrenal (HPA) axis activity induced by increased plasma corticosterone levels. Altered corticosterone release and HPA axis dysfunction are related to the stress response, which has implications on the pathophysiology of both obesity and depression.
The hippocampus is regarded as a second site for glucocorticoid-mediated (corticosterone in the case of rodents) negative feedback regulation of the HPA axis. Choline containing compounds, derived from formation and degradation products of the cell membrane, as well as myo-inositol (mIns) are regarded as glial cell markers. The decrease in mIns concentration seen in HF diet mice without corresponding Gln-Glu alternation may reflect changes in glial function. In addition, the observed total choline levels indicate attenuated membrane turnover in HF diet mice. We therefore suggest that diets rich in saturated fats induce a stress-related response through metabolic disturbance and HPA axis dysfunction, which may indicate a relationship between obesity and depression.
1. Hryhorczuk C, Sharma S, Fulton SE (2013) Metabolic disturbances connecting obesity and depression. Front Neurosci 7:177. doi: 10.3389/fnins.2013.00177
2. Raider K, Ma D, Harris JL, et al (2016) A high fat diet alters metabolic and bioenergetic function in the brain: A magnetic resonance spectroscopy study. Neurochem Int 1–9. doi: 10.1016/j.neuint.2016.04.008