Enhancement of Neural Salty Preference in Obesity

Background/Aims: Obesity and high salt intake are major risk factors for hypertension and cardiometabolic diseases. Obese individuals often consume more dietary salt. We aim to examine the neurophysiologic effects underlying obesity-related high salt intake. Methods: A multi-center, random-order, double-blind taste study, SATIETY-1, was conducted in the communities of four cities in China; and an interventional study was also performed in the local community of Chongqing, using brain positron emission tomography/computed tomography (PET/CT) scanning. Results: We showed that overweight/obese individuals were prone to consume a higher daily salt intake (2.0 g/day higher compared with normal weight individuals after multivariable adjustment, 95% CI, 1.2-2.8 g/day, P < 0.001), furthermore they exhibited reduced salt sensitivity and a higher salt preference. The altered salty taste and salty preference in the overweight/obese individuals was related to increased activity in brain regions that included the orbitofrontal cortex (OFC, r = 0.44, P= 0.01), insula (r = 0.38, P= 0.03), and parahippocampus (r = 0.37, P= 0.04). Conclusion: Increased salt intake among overweight/obese individuals is associated with altered salt sensitivity and preference that related to the abnormal activity of gustatory cortex. This study provides insights for reducing salt intake by modifying neural processing of salty preference in obesity

Comparison of ground truth contours (green contour) with the corresponding predicted contours (red contour).

<p>The figures are from the validation set. The results illustrate that the predicted contours are very close to the ground truth contours, indicating the effectiveness of our method.</p

Comparison of the GLCV and five other methods by applying them to segment six HIFU ultrasound images.

<p>The first column shows the original images and the initial contours. For images A and B, the initial contour is an ellipse, and for images C, D, E and F, the initial contours are defined by 5–7 connecting points. Columns 2 to 7 show, respectively, the segmentation results for GAC [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0127873#pone.0127873.ref019" target="_blank">19</a>], CV [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0127873#pone.0127873.ref020" target="_blank">20</a>], LCV [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0127873#pone.0127873.ref022" target="_blank">22</a>], RSF [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0127873#pone.0127873.ref023" target="_blank">23</a>], MSLCV [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0127873#pone.0127873.ref024" target="_blank">24</a>] and GLCV. The green curves are manual segmentation results by an experienced doctor as the ground truth, and the red curves are the final segmentation contours from these methods.</p

Essential Parameters of the Versatile Particle Framework.

<p>Essential Parameters of the Versatile Particle Framework.</p

DSC Values of Training Set and Validation Set.

<p>The experimental results indicates the acquired essential parameters can well fit the training set and the prediction results is very close to the validation set.</p><p>DSC Values of Training Set and Validation Set.</p

Iterative Parameter Estimation Algorithm.

<p>Iterative Parameter Estimation Algorithm.</p

Comparisons of computed DSC [36], MSSD [37] and Hausdroff distance [37] utilizing GAC [19], CV [20], LCV [22], RSF [23], MSLCV [24] and GLCV methods for segmentation of HIFU ultrasound images.

<p>The red lines are the performance line of GLCV, indicating the GLCV method achieves the best segmentation performance.</p