Primary hyperaldosteronism(PHA),the most common cause of secondary hypertension,can lead to cardiovascular and renal damage events.Early diagnosis and accurate typing of PHA is crucial to the choice of treatment options,and its diagnostic modalities usually include prone test screening,captopril test characterization and adrenal vein blood sampling for localization and typing diagnosis.However,with the development of imaging,molecular biology and morphology technologies,the use of nuclear medicine PET-CT,genetic testing and pathological diagnostic methods can also be used to accurately typify PHA.New technologies such as adrenal artery embolization and adrenal radiofrequency ablation are also increasingly being used in the treatment of PHA,which have the advantages of shorter and less costly surgery,fewer postoperative complications,and quicker recovery.This article provides a review of the developments in the screening,diagnosis and treatment of PHA with a view to informing clinical practice.

Primary hyperaldosteronism(PHA),the most common cause of secondary hypertension,can lead to cardiovascular and renal damage events.Early diagnosis and accurate typing of PHA is crucial to the choice of treatment options,and its diagnostic modalities usually include prone test screening,captopril test characterization and adrenal vein blood sampling for localization and typing diagnosis.However,with the development of imaging,molecular biology and morphology technologies,the use of nuclear medicine PET-CT,genetic testing and pathological diagnostic methods can also be used to accurately typify PHA.New technologies such as adrenal artery embolization and adrenal radiofrequency ablation are also increasingly being used in the treatment of PHA,which have the advantages of shorter and less costly surgery,fewer postoperative complications,and quicker recovery.This article provides a review of the developments in the screening,diagnosis and treatment of PHA with a view to informing clinical practice.

Objective:To explore the independent risk factors for predicting the right adrenal dominant secretory side in patients with primary hyperaldosteronism (PHA), so as to construct and verify the prediction model of the dominant secretory side, and provide evidence for the diagnosis and treatment of PHA patients.Methods:Clinical data of 82 PHA patients undergoing adrenal vein sampling (AVS) admitted to the Second Affiliated Hospital of Guangzhou Medical University from January 2015 to July 2023 were retrospectively analyzed, and divided into the training set (58 cases) and the verification set (24 cases) according to the ratio of 7∶3. Single factor and multiple factor logistic regression analysis were used to screen out the risk factors for predicting the right adrenal dominant secretory side, and then combined with the risk factors to build the prediction model of the dominant secretory side. The predictive efficiency and clinical applicability of the predominance secretory nomogram prediction model were verified by drawing receiver operating characteristic (ROC) curve, calibration curve and decision curve analysis (DCA curve).Results:(1) Univariate and multivariate logistic analysis showed that the concentration of aldosterone (ALD2) in inferior vena cava and the results of adrenal CT imaging suggested that the right adrenal unilateral nodule (RJJ) were independent risk factors for the occurrence of right adrenal dominant secretion in PHA patients undergoing AVS (all P<0.05). (2) The area under ROC curve (AUC) corresponding to ALD2 variable was 0.898(95% CI: 0.735 7-0.986 1), and the sensitivity and specificity were 95.2% and 81.1%, respectively. The corresponding AUC of RJJ variable was 0.750(95% CI: 0.683 1-0.976 3), and the sensitivity and specificity were 90.5% and 86.7%, respectively. The AUC of the two variables in the verification set were 0.926 and 0.778, respectively. (3) Calibration curve and DCA curve showed that the prediction model had good consistency and clinical applicability, and can produce good clinical benefits. Conclusions:ALD2 and adrenal CT results suggest that RJJ is an independent risk factor for the prediction of the right adrenal dominant secretory side, and the prediction model based on these risk factors has good predictive efficacy and clinical applicability.

Objective:To explore the independent risk factors for predicting the right adrenal dominant secretory side in patients with primary hyperaldosteronism (PHA), so as to construct and verify the prediction model of the dominant secretory side, and provide evidence for the diagnosis and treatment of PHA patients.Methods:Clinical data of 82 PHA patients undergoing adrenal vein sampling (AVS) admitted to the Second Affiliated Hospital of Guangzhou Medical University from January 2015 to July 2023 were retrospectively analyzed, and divided into the training set (58 cases) and the verification set (24 cases) according to the ratio of 7∶3. Single factor and multiple factor logistic regression analysis were used to screen out the risk factors for predicting the right adrenal dominant secretory side, and then combined with the risk factors to build the prediction model of the dominant secretory side. The predictive efficiency and clinical applicability of the predominance secretory nomogram prediction model were verified by drawing receiver operating characteristic (ROC) curve, calibration curve and decision curve analysis (DCA curve).Results:(1) Univariate and multivariate logistic analysis showed that the concentration of aldosterone (ALD2) in inferior vena cava and the results of adrenal CT imaging suggested that the right adrenal unilateral nodule (RJJ) were independent risk factors for the occurrence of right adrenal dominant secretion in PHA patients undergoing AVS (all P<0.05). (2) The area under ROC curve (AUC) corresponding to ALD2 variable was 0.898(95% CI: 0.735 7-0.986 1), and the sensitivity and specificity were 95.2% and 81.1%, respectively. The corresponding AUC of RJJ variable was 0.750(95% CI: 0.683 1-0.976 3), and the sensitivity and specificity were 90.5% and 86.7%, respectively. The AUC of the two variables in the verification set were 0.926 and 0.778, respectively. (3) Calibration curve and DCA curve showed that the prediction model had good consistency and clinical applicability, and can produce good clinical benefits. Conclusions:ALD2 and adrenal CT results suggest that RJJ is an independent risk factor for the prediction of the right adrenal dominant secretory side, and the prediction model based on these risk factors has good predictive efficacy and clinical applicability.

OBJECTIVES: Prediabetes is associated with an increased risk of cognitive impairment and neurodegenerative diseases. However, the exact mechanism of prediabetes-related brain diseases has not been fully elucidated. The brain structure of patients with prediabetes has been damaged to varying degrees, and these changes may affect the topological characteristics of large-scale brain networks. The structural covariance of connected gray matter has been demonstrated valuable in inferring large-scale structural brain networks. The alterations of gray matter structural covariance networks in prediabetes remain unclear. This study aims to examine the topological features and robustness of gray matter structural covariance networks in prediabetes. METHODS: A total of 48 subjects were enrolled in this study, including 23 patients with prediabetes (the PD group) and 25 age-and sex-matched healthy controls (the Ctr group). All subjects' high-resolution 3D T₁ images of the brain were collected by a 3.0 Tesla MR machine. Mini-mental state examination was used to evaluate the cognitive status of each subject. We calculated the gray matter volume of 116 brain regions with automated anatomical labeling (AAL) template, and constructed gray matter structural covariance networks by thresholding interregional structural correlation matrices as well as graph theoretical analysis. The area under the curve (AUC) in conjunction with permutation testing was employed for testing the differences in network measures, which included small world parameter (Sigma), normalized clustering coefficient (Gamma), normalized path length (Lambda), global efficiency, characteristic path length, local efficiency, mean clustering coefficient, and network robustness parameters. RESULTS: The network in both groups followed small-world characteristics, showing that Sigma was greater than 1, the Lambda was much higher than 1, and Gamma was close to 1. Compared with the Ctr group, the network of the PD group showed increased Sigma, Lambda, and Gamma across a range of network sparsity. The Gamma of the PD group was significantly higher than that in the Ctr group in the network sparsity range of 0.12-0.16, but there was no difference between the 2 groups (all P>0.05). The grey matter network showed an increased characteristic path length and a decreased global efficiency in the PD group, but AUC analysis showed that there was no significant difference between groups (all P>0.05). For the network separation measures, the local efficiency and mean clustering coefficient of the gray matter network in the PD group were significantly increased and AUC analysis also confirmed it (P=0.001 and P=0.004, respectively). In addition, network robustness analysis showed that the grey matter network of the PD group was more vulnerable to random damage (P=0.001). CONCLUSIONS The prediabetic gray matter network shows an increased average clustering coefficient and local efficiency, and is more vulnerable to random damage than the healthy control, suggesting that the topological characteristics of the prediabetes grey matter covariant network have changed (network separation enhanced and network robustness reduced), which may provide new insights into the brain damage relevant to the disease.
Humans ; Gray Matter/diagnostic imaging* ; Prediabetic State ; Magnetic Resonance Imaging ; Cerebral Cortex ; Brain

OBJECTIVE: To investigate the structural abnormalities of brain in patients with antisocial personality disorder (ASPD) but without alcoholism and drug abuse. METHODS: Volunteers from Hunan Reformatory (n=36) and the matched healthy subjects (n=26) were examined by high-spatial resolution magnetic resonance imaging (MRI) and diffusion tensor imaging (DTI). Voxel-based morphometry and fractional anisotropy (FA) maps were generated for each subject to reveal structural abnormalities in patients with ASPD. RESULTS: Compared with the healthy controls, ASPD patients showed significantly higher gray matter volumes in the inferior parietal lobule (P≤0.001, uncorrected), white matter volumes in the precuneus (P≤0.001, uncorrected), FA in the left lingual gyrus, bilateral precuneus, right superior frontal gyrus and right middle temporal gyrus (P≤0.01, uncorrected). CONCLUSION Our results revealed the abnormal neuroanatomical features in ASPD patients, which might be related to the external behavioral traits in ASPD patients.
Anisotropy ; Antisocial Personality Disorder ; diagnosis ; Brain ; anatomy & histology ; Case-Control Studies ; Diffusion Tensor Imaging ; Humans ; Magnetic Resonance Imaging

OBJECTIVE: To investigate the functional magnetic resonance imaging (fMRI) data of deception in antisocial personality disorders (ASPD). METHODS: A total of 32 criminals meeting the criteria for ASPD underwent fMRI at 1.5T while responding truthfully questions or lying. We compared the brain activities between truth-telling and lie-telling, and then computed the correlation coefficient between the contrast brain activities and the inclination to deception. RESULTS: The left anterior cingulate gyrus, the bilateral dorsolateral prefrontal cortex, and left inferior parietal lobule were associated with the executive aspects of deception among people with ASPD. But with the greater inclination to deception, the blood oxygen level dependent (BOLD) activities in those regions decreased. CONCLUSION Evaluations of truthful and untruthful communications pertaining to ASPD subjects may be differentiated in terms of brain BOLD activities, though those activities may decrease in habitual liars, which remains a challenge to the diagnostic accuracy in lie detection.
Adolescent ; Antisocial Personality Disorder ; physiopathology ; psychology ; Brain ; physiology ; Deception ; Humans ; Lie Detection ; Magnetic Resonance Imaging ; Male ; Prefrontal Cortex ; physiology ; Young Adult