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Senescence is a key event in the impairment of adipose tissue (AT) function with obesity and aging but the underlying molecular and cellular players remain to be fully defined, particularly with respect to the human AT progenitors. We have found distinct profiles of senescent progenitors based on AT location between stroma from visceral versus subcutaneous AT. In addition to flow cytometry, we characterized the location differences with transcriptomic and proteomic approaches, uncovering the genes and developmental pathways that are underlying replicative senescence. We identified key components to include INBHA as well as SFRP4 and GREM1, antagonists for the WNT and BMP pathways, in the senescence‐associated secretory phenotype and NOTCH3 in the senescence‐associated intrinsic phenotype. Notch activation in AT progenitors inhibits adipogenesis and promotes myofibrogenesis independently of TGFβ. In addition, we demonstrate that NOTCH3 is enriched in the premyofibroblast progenitor subset, which preferentially accumulates in the visceral AT of patients with an early obesity trajectory. Herein, we reveal that NOTCH3 plays a role in the balance of progenitor fate determination preferring myofibrogenesis at the expense of adipogenesis. Progenitor NOTCH3 may constitute a tool to monitor replicative senescence and to limit AT dysfunction in obesity and aging. [ABSTRACT FROM AUTHOR]

Background: With the first positive phase III trials for anti‐amyloid approaches in MCI participants, the drug evaluation during the asymptomatic Alzheimer's disease (AD) stage is now more relevant than ever. Currently, cognitively unimpaired participants are included in preventive trials based on risk factors such as amyloid positivity. The lifetime risks for an individual without cognitive impairment does not exceed 30% for a 65‐year‐old with amyloidosis alone (Brookmeyer et al., 2018). Based on this risk factor, most participants included will never develop AD and therefore will not respond to anti‐AD therapies. There is a need to develop new biomarkers with high specificity predicting which individuals in the cognitively unimpaired population will exhibit AD symptoms and which will not to conduct effective preventive clinical trials. Method: Targeted mass spectrometry assays were developed for 81 blood biomarkers (45 proteins and 36 metabolites) pre‐identified in AAV‐AD rats (Audrain et al. 2018). 287 participants were collected in plasma at baseline and followed clinically from 1 to 18 years. Participants' diagnosis (AD, or Healthy Controls) was established at the last clinical visit based on reference diagnosis standard. 73.9% of participants had available amyloid status, 42.9% were determined as amyloid positive. Predictive machine learning models for asymptomatic AD participants (n = 48, followed for an average of 6.1 years) among individuals without cognitive decline (HC, n = 239, followed for an average of 4.6 years) was developed. The training dataset (70%) aimed to select the biomarkers subset, train the algorithm, and define the cut‐off. External test dataset (30%) aimed to validate in blind the locked predictive model. Result: With 20 blood biomarkers and 2 covariates (age and gender), the model predicted asymptomatic AD (n = 16) from HC participants (n = 72) with 81.9% specificity and 56.3% sensitivity during external validation (AUROC = 69.1%, p = 0.02). When current amyloid tests (CSF or PET) and this predictive ML model were applied in series, and participants positive for both were predicted as asymptomatic AD, 90.9% specificity and 54.5% sensitivity were achieved. Conclusion: Multiomics blood peripheral biomarkers predict asymptomatic AD patients among cognitively unimpaired individuals with high specificity. This could constitute an additional tool for the inclusion of participants in preventive clinical trials. [ABSTRACT FROM AUTHOR]

Background: The core criteria for mild cognitive impairment (MCI) or dementia due to Alzheimer's disease (AD) is based in part on cognitive criteria and biological evidence of at least moderately frequent amyloid plaques (Albert et al., 2011; McKhann et al., 2011). They predict AD among cognitively impaired patients with high sensitivity but low specificity (Kokkinou et al. 2021; Martinez et al. 2017; Ritchie et al. 2014): up to 25% of amyloid‐positive patients would be misdiagnosed as AD while suffering from another brain disorder. Therefore, there is a need to develop new biomarkers with high specificity to facilitate earlier diagnosis, rapid therapeutic intervention and monitoring of clinical trials. Method: Targeted mass spectrometry assays were developed for 81 blood biomarkers (45 proteins and 36 metabolites) pre‐identified in AAV‐AD rats (Audrain et al. 2018). 345 cognitively impaired participants (193 MCI and 152 demented) were collected in plasma at baseline and followed clinically from 1 to 13 years. Participants' diagnosis (AD, or non‐AD) was established at the last clinical visit based on cognitive diagnosis standard for each brain disorder subtype. 82.9% of participants had available amyloid status at baseline, 61.9% were determined as amyloid positive. Each clinical group contained amyloid positive and negative participants. Predictive machine learning models for AD participants (n = 123 Prodromal AD, n = 126 AD dementia) among individuals with Non‐AD brain disorder (Non‐AD, n = 96) was developed. The training dataset (70%) aimed to select the biomarkers subset, train the algorithm, and define the cut‐off. External test dataset (30%) aimed to validate in blind the locked predictive model. Result: With 19 blood biomarkers and age, the model predicted AD participants (n = 41 Prodromal AD, n = 43 AD dementia) from non‐AD participants (n = 25) with 92.0% specificity and 52.4% sensitivity during external validation (AUROC = 71.8%, p = 0.001). When amyloid status (CSF or PET) and this predictive ML model were applied in series, and participants positive for both were predicted as AD, 100% specificity and 39.7% sensitivity were achieved. Conclusion: Multiomics blood peripheral biomarkers predicted AD patients among cognitively impaired population with low FPR. In combination with amyloid screening, these biomarkers could identify a nearly pure AD population. [ABSTRACT FROM AUTHOR]