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Purpose: glial activation is one of the earliest mechanisms to be altered in Alzheimer's disease (AD). Glial fibrillary acidic protein (GFAP) relates to reactive astrogliosis and can be measured in both cerebrospinal fluid (CSF) and blood. Plasma GFAP has been suggested to become altered earlier in AD than its CSF counterpart. Although astrocytes consume approximately half of the glucose-derived energy in the brain, the relationship between reactive astrogliosis and cerebral glucose metabolism is poorly understood. Here, we aimed to investigate the association between fluorodeoxyglucose ([18F]FDG) uptake and reactive astrogliosis, by means of GFAP quantified in both plasma and CSF for the same participants. Methods: we included 314 cognitively unimpaired participants from the ALFA + cohort, 112 of whom were amyloid-β (Aβ) positive. Associations between GFAP markers and [18F]FDG uptake were studied. We also investigated whether these associations were modified by Aβ and tau status (AT stages). Results: plasma GFAP was positively associated with glucose consumption in the whole brain, while CSF GFAP associations with [18F]FDG uptake were only observed in specific smaller areas like temporal pole and superior temporal lobe. These associations persisted when accounting for biomarkers of Aβ pathology but became negative in Aβ-positive and tau-positive participants (A + T +) in similar areas of AD-related hypometabolism. Conclusions: higher astrocytic reactivity, probably in response to early AD pathological changes, is related to higher glucose consumption. With the onset of tau pathology, the observed uncoupling between astrocytic biomarkers and glucose consumption might be indicative of a failure to sustain the higher energetic demands required by reactive astrocytes. The project leading to these results has received funding from “la Caixa” Foundation (ID 100010434), under agreement LCF/PR/GN17/50300004 and the Alzheimer’s Association and an international anonymous charity foundation through the TriBEKa Imaging Platform project (TriBEKa-17–519007). Additional support has been received from the Universities and Research Secretariat, Ministry of Business and Knowledge of the Catalan Government under the grant no. 2017-SGR-892. JDG is supported by the Spanish Ministry of Science and Innovation (RYC-2013–13054). MSC receives funding from Instituto de Salud Carlos III (PI19/00155) and from the Spanish Ministry of Science, Innovation and Universities (Juan de la Cierva programme grant IJC2018-037478-I). CM receives funding within the context of EURO-FINGERS, an EU Joint Programme—Neurodegenerative Disease Research (JPND) project. The EURO-FINGERS project is supported through the following funding organizations under the aegis of JPND—www.jpnd.eu: Finland, Academy of Finland; Germany, Federal Ministry of Education and Research; Spain, National Institute of Health Carlos III; Luxembourg, National Research Fund; Hungary, National Research, Development and Innovation Office; and the Netherlands, Netherlands Organisation for Health Research and Development (ZonMW-Memorabel #733051102). HZ is a Wallenberg Scholar supported by grants from the Swedish Research Council (#2018–02532); the European Research Council (#681712); the Swedish State Support for Clinical Research (#ALFGBG-720931); the Alzheimer Drug Discovery Foundation (ADDF), USA (#201809–2016862); the AD Strategic Fund and the Alzheimer’s Association (#ADSF-21–831376-C, #ADSF-21–831381-C, and #ADSF-21–831377-C); the Olav Thon Foundation, the Erling-Persson Family Foundation, Stiftelsen för Gamla Tjänarinnor, Hjärnfonden, Sweden (#FO2019-0228); the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement no 860197 (MIRIADE), and the UK Dementia Research Institute at UCL. KB is supported by the Swedish Research Council (#2017–00915); the Alzheimer Drug Discovery Foundation (ADDF), USA (#RDAPB-201809–2016615); the Swedish Alzheimer Foundation (#AF-742881); Hjärnfonden, Sweden (#FO2017-0243); the Swedish state under the agreement between the Swedish government and the County Councils, the ALF-agreement (#ALFGBG-715986); the European Union Joint Program for Neurodegenerative Disorders (JPND2019-466–236); and the National Institute of Health (NIH), USA (grant #1R01AG068398-01).

Accurate assessment of disease extent is required to select the best primary treatment for advanced epithelial ovarian cancer patients. Estimation of tumour burden is challenging and it is usually performed by means of a surgical procedure. Imaging techniques and tumour markers can help to estimate tumour burden non-invasively. 2-[18F]FDG PET/CT allows the evaluation of the whole-body disease. This study aimed to correlate HE4 and CA125 serum concentrations with tumour burden evaluated by volumetric 2-[18F]FDG PET/CT parameters in advanced high-grade epithelial ovarian cancer. We included 66 patients who underwent 2-[18F]FDG PET/CT and serum tumour markers determination before primary treatment. Volumes of interest were delimited in every pathological uptake. Whole-body metabolic tumour volume (wb_MTV) and total lesion glycolysis (wb_TLG) were calculated summing up every VOI’s MTV value. SUVmax thresholds were set at 40% (MTV40 and TLG40) and 50% (MTV50 and TLG50). In addition, four VOI subgroups were defined: peritoneal carcinomatosis, retroperitoneal nodes, supradiaphragmatic nodes, and distant metastases. MTV and TLG were calculated for each group by adding up the corresponding MTV values. TLG was calculated likewise. wb_MTV and wb_TLG were found to be significantly correlated with serum CA125 and HE4 concentrations. The strongest correlation was observed between HE4 and wb_MTV40 (r = 0.62, p

Linear Mixed Effects (LME) modelling under both frequentist and Bayesian frameworks have been suggested to study longitudinal trajectories. We studied the performance of both approaches on different dataset configurations using hippocampal volumes from longitudinal MRI data across groups - healthy controls (HC), mild cognitive impairment (MCI) and Alzheimer’s Disease (AD) patients -, including subjects that converted from MCI to AD. We started from a big database of 1250 subjects from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) and we created different reduced datasets simulating real-life situations using a random-removal permutation-based approach. The number of subjects needed to differentiate groups and to detect conversion to AD was 147 and 115 respectively. The Bayesian approach allowed estimating the LME model even with very sparse databases, with high number of missing points, which was not possible with the frequentist approach. Our results indicate that the frequentist approach is computationally simpler, but it fails in modelling data with high number of missing values.