ArticleFrontiers in neuroscience2025
Relations between neurometabolism and clinical biomarkers in patients with metabolic disease.
Article in Frontiers in neuroscience, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The global prevalence of metabolic diseases, including hypertension, type 2 diabetes mellitus (T2DM), gout, and obesity, has significantly increased over the past two decades. The brain plays a central role in regulating both human behavior and metabolism. Understanding the potential connections among these metabolic diseases and the involvement of the brain in their progression presents an intriguing and critical area of research. In this study, we analyzed PET-CT images and clinical biomarkers from 112 cases of hypertension, 56 cases of T2DM, 11 cases of obesity, and 14 cases of gout. Standardized uptake value ratios (SUVRs) were extracted from various brain regions using the Spatial-Normalization-of-Brain-PET-Images (SNBPI) software. The SUVRs were calculated using the standard methodology, where the mean standardized uptake value (SUV) of each region of interest (ROI) was divided by the mean SUV of the reference region, that is the whole cerebellum. The SNBPI tool was employed for intensity normalization. Partial correlation analysis was conducted to examine the relationships between SUVRs in different brain regions and clinical biomarkers, adjusting for sex, age, and BMI. Brain network metabolic connectivity was assessed using Permutation_IHEP software and visualized with BrainNet Viewer. Our results indicate that SUVRs in most brain regions were decreased in patients with hypertension or T2DM but increased in patients with obesity or gout. Specifically, SUVRs in brain regions associated with blood pressure were correlated with blood uric acid, creatinine, potassium, and apolipoprotein B. SUVRs in brain regions related to blood glucose were associated with blood triglycerides and cholinesterase. SUVRs in BMI-related brain regions correlated with blood urea nitrogen, aspartate aminotransferase, and alkaline phosphatase. SUVRs in brain regions associated with gout were correlated with fasting blood glucose, glutamic oxalacetic transaminase, total bilirubin, and alkaline phosphatase. Furthermore, brain network metabolic connectivity was reduced in patients with hypertension, T2DM, or obesity but increased in patients with gout. Our findings suggest that uric acid may negatively relate with blood pressure and glucose levels, while blood glucose and blood lipid levels may be positively correlated with each other. Gout appears distinct from other metabolic diseases and may offer a protective effect on brain function. The right superior parietal gyrus may be implicated in impaired renal function during the progression of hypertension. The left precentral gyrus and bilateral middle frontal gyri may relate to dyslipidemia and the potential development of atherosclerotic cardiovascular disease in patients with T2DM. In conclusion, our study highlights potential relationships among metabolic diseases and suggests the possible regulatory roles of specific brain regions in the progression of these conditions. These insights could pave the way for novel therapeutic strategies targeting brain metabolism in the management of metabolic diseases.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.