bioRxiv Science⌕ Search
Home/Archive/bioRxiv

bioRxiv · 10.1101/2022.09.15.508088

Brain glucose metabolism and ageing: A 5-year longitudinal study in a large PET cohort

Pak, K.·Malen, T.·Santavirta, S.·Shin, S.·Nam, H. Y.·De Maeyer, S.·Nummenmaa, L.
View original source ↗Export metadata ↓

Abstract

BackgroundAgeing and clinical factors impact brain glucose metabolism. However, there is a substantial variation of the reported effects on brain glucose metabolism across studies due to the limited statistical power and cross-sectional study designs. MethodsWe retrospectively analyzed data from 441 healthy males (mean 42.8, range 38-50 years) who underwent health check-up program twice at baseline and 5-year follow-up. Health check-up program included 1) brain 18F-Fluorodeoxyglucose (FDG) positron emission tomography (PET), 2) anthropometric and body composition measurements, 3) blood samples, and 4) questionnaires for stress and depression. After spatial normalization of brain FDG PET scans, standardized uptake value ratio (SUVR) was measured from 12 region-of-interests. We used hierarchical clustering analysis to reduce their dimensionality before the Bayesian hierarchical modelling. Five clusters were established for predicting regional SUVR; 1) metabolic cluster (body mass index, waist-to-hip ratio, fat percentage, muscle percentage, homeostatic model assessment index-insulin resistance), 2) blood pressure (systolic, diastolic), 3) glucose (fasting plasma glucose level, HbA1c), 4): psychological cluster (stress, depression), and 5) heart rate. The effects of clinical variable clusters on regional SUVR were investigated using Bayesian hierarchical modelling with brms that applies the Markov-Chain Monte Carlo sampling tools. ResultsAll the clinical variables except depression changed during the 5-year follow-up. SUVR decreased in caudate, cingulate, frontal lobe and parietal lobe and increased in cerebellum, hippocampus, occipital lobe, pallidum, putamen, temporal lobe and thalamus. SUVRs of thalamus, pallidum, hippocampus, putamen and parietal lobe were negatively associated with metabolic cluster and the effects of glucose on SUVRs varied across regions. SUVRs of thalamus, hippocampus, cingulate, cerebellum increased and those with occipital lobe decreased with heart rate. The effects of blood pressure and psychological cluster markedly overlapped with zero across regions. ConclusionRegionally selective decline in brain glucose utilization begins already in the middle age, while individual differences in brain glucose metabolism remain stable. In addition to ageing, brain glucose utilization is also associated with metabolic cluster, blood glucose levels and heart rate. These effects are also consistent over the studied period of 5 years in the middle adulthood.

Explore related subjects

neurosciencePreprint

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Pak, K., Malen, T., Santavirta, S., Shin, S., Nam, H. Y., De Maeyer, S., Nummenmaa, L.. 2022-09-17. Brain glucose metabolism and ageing: A 5-year longitudinal study in a large PET cohort. https://doi.org/10.1101/2022.09.15.508088

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related preprints

bioRxiv2026

The Unreasonable Effectiveness of Cell Types in Describing Neuronal Physiological Features

Single-cell RNA sequencing (scRNA-seq) captures detailed gene expression profiles at scale, while patch-clamp recordings measure intrinsic neuronal electrophysiological properties. Modeling the relations between these two modalities remains a challenge. Here, we compare how well electrophysiological features can be predicted by traditional transcriptomic cell type classification, representations derived from a foundational model (scGPT) pretrained on large-scale scRNA-seq datasets, ion channel-coding genes, and highly variable genes. Using paired transcriptomic and electrophysiological patch-sequencing data from 495 human neurons from neurosurgical tissue, we find that cluster-level cell type representations consistently outperform highly variable gene selection, ion channel gene selection, and context-enriched scGPT embeddings. Notably, performance varies across model architectures and initializations, and the best results are obtained by combining the outputs of separate cell type and scGPT-based models. Together, these findings suggest that traditional discrete cellular classification is highly effective in predicting physiological features. For maximum performance it can be complemented by pretrained transformer models.

neuroscience↗
bioRxiv2026

A nonlinear inhibition pathway underlying cortical responses to tuned holographic optogenetic perturbations

Optogenetics enables causal manipulation of cortical activity. Perturbation responses can be counterintuitive due to network interactions, making theory essential for predicting them. Existing approaches often rely on linear approximations, which fail for many biologically relevant perturbations. Here we develop a nonlinear theory of responses to holographic perturbations in cell-type-specific recurrent networks with structured connectivity. We fit a nonlinear model to mouse V1 data, which shows cotuned-ensemble suppression: perturbing spatially clustered neurons with similar preferred orientations yields markedly stronger short-range suppression than perturbing untuned ensembles. We show that cotuned-ensemble suppression arises from a feature-tuned, nonlinear inhibition pathway implicating somatostatin-positive (SST) interneurons. The theory predicts that cotuned ensembles suppress parvalbumin-positive (PV) neurons but facilitate SST neurons, and links the degree of cotuned-ensemble suppression or facilitation to the variance of the SST response. This framework identifies mechanisms by which nonlinear inhibition sculpts cortical dynamics and establishes a predictive basis for targeted optogenetic interventions.

neuroscience↗
bioRxiv2026

Proteomic signatures of APOE ε4 across human tissues and cell types in Alzheimers disease

The apolipoprotein E {varepsilon}4 (APOE {varepsilon}4) allele is the strongest genetic risk factor for late-onset Alzheimers disease (AD). However, the underlying molecular mechanisms remain unclear. This study included 1691 participants from the Religious Orders Study and Rush Memory and Aging Project (ROSMAP), 1226 participants from the Accelerating Medicines Partnership - Alzheimers Disease (AMP-AD) Diverse Cohorts Study, and 735 participants from the Alzheimers Disease Neuroimaging Initiative (ADNI). To characterise APOE {varepsilon}4 molecular effects, we analysed proteomic data from plasma, cerebrospinal fluid (CSF), and induced pluripotent stem cell (iPSC)-derived astrocytes and neurons, as well as transcriptomic and proteomic data from multiple brain regions. The association of APOE {varepsilon}4 with AD neuropathology was also examined. APOE {varepsilon}4 carriers shared a plasma proteomic signature enriched for immune processes, irrespective of AD diagnosis. A machine learning classifier trained on this signature discriminated APOE {varepsilon}4 carriers from non-carriers in an independent cohort using CSF proteomics. APOE {varepsilon}4 carriage was associated with higher Braak stages and Consortium to Establish a Registry for Alzheimers Disease (CERAD) score. However, only limited APOE {varepsilon}4-associated transcriptomic and proteomic changes were observed in bulk brain tissue, with poor cross-layer concordance. Proteomic analyses of iPSC-derived astrocytes and neurons further revealed cell-type-specific APOE {varepsilon}4-associated changes. APOE {varepsilon}4 is associated with a consistent proteomic signature across plasma and CSF. Its molecular effects in the brain differ across cell types, brain regions and molecular layers. These findings support the need for cell-type-resolved multi-omic studies to elucidate how APOE {varepsilon}4 confers AD risk.

neuroscience↗