Four subgroups based on tau levels in Alzheimer’s disease observed in two independent cohorts

Abstract

Background As Alzheimer’s disease (AD) pathology presents decades before dementia manifests, unbiased biomarker cut-points may more closely reflect presence of pathology than clinically defined cut-points. Currently, unbiased cerebrospinal fluid (CSF) tau cut-points are lacking. Methods We investigated CSF t-tau and p-tau cut-points across the clinical spectrum using Gaussian mixture modelling, in two independent cohorts (Amsterdam Dementia Cohort and ADNI). Results Individuals with normal cognition (NC) (total n = 1111), mild cognitive impairment (MCI) (total n = 1213) and Alzheimer’s disease dementia (AD) (total n = 1524) were included. In both cohorts, four CSF t- and p-tau distributions and three corresponding cut-points were identified. Increasingly high tau subgroups were characterized by steeper MMSE decline and higher progression risk to AD (cohort/platform-dependent HR, t-tau 1.9–21.3; p-tau 2.2–9.5). Limitations The number of subjects in some subgroups and subanalyses was small, especially in the highest tau subgroup and in tau PET analyses. Conclusions In two independent cohorts, t-tau and p-tau levels showed four subgroups. Increasingly high tau subgroups were associated with faster clinical decline, suggesting our approach may aid in more precise prognoses.

Dynamic Changes in Central and Peripheral Neuro-Injury vs. Neuroprotective Serum Markers in COVID-19 Are Modulated by Different Types of Anti-Viral Treatments but Do Not Affect the Incidence of Late and Early Strokes

“…The sustained elevation of YKL40, NCAM-1, and CCL23 may represent an ongoing pathological process in the survivors of COVID-19 as the levels did not decline below those recorded at admission [ 46 , 51 , 107 ]. These bio-spectrum markers are not specific to one type of dementia but rather represent the beginning or acceleration of underlying neurodegeneration processes in the wake of COVID-19 [ 1 , 13 , 108 , 109 ]. This finding is consistent with the suggestion of ongoing brain injury in post-viral syndromes or COVID-19 long-haulers [ 10 , 13 ].…”

“…This raises concerns about the ability of survivors to recover fully from the disease if the initial insult deprived a host of some neuroprotective mechanisms [ 1 , 9 , 13 , 14 , 16 ]. An alteration in the spectrum of neurobiomarkers (protective vs. injury vs. degeneration) suggests a loss of autoregulated homeostasis and has been proposed to explain the origin of dementia [ 99 , 108 ]. Longitudinal surveillance, with a focus on determining a composite level of several biomarkers, may potentially identify survivors most vulnerable to the progress of clinically-evident dementia [ 33 , 56 , 70 , 86 ].…”

“…YKL40 was linked to cardiovascular disorders, while TDB43 was elevated in several atypical dementias or ischemic damage to the brain in general [ 43 , 44 , 52 , 107 , 141 ]. Finally, we did not use the most sensitive tests to detect the levels of several neurodegeneration markers as we believed that COVID-19 inflammation would result in significant brain injury consistent with apparent increases in the serum levels of several markers [ 108 ]. We did not factor in genetic predisposition to several markers’ changes that significantly contribute to the susceptibility to neurodegeneration [ 98 , 108 , 143 ].…”

“…Finally, we did not use the most sensitive tests to detect the levels of several neurodegeneration markers as we believed that COVID-19 inflammation would result in significant brain injury consistent with apparent increases in the serum levels of several markers [ 108 ]. We did not factor in genetic predisposition to several markers’ changes that significantly contribute to the susceptibility to neurodegeneration [ 98 , 108 , 143 ]. Finally, we were unable to assess several environmental and lifestyle factors, including diet, smoking, use of illicit drugs, pre-existing cognitive decline, and others.…”

dynaPhenoM: Dynamic Phenotype Modeling from Longitudinal Patient Records Using Machine Learning

“…To evaluate the effectiveness and robustness of dynaPhenoM, we performed comprehensive analysis on the case of progression from MCI to AD based with the OneFlorida database as development cohort (including 2,995 patients), and the MarketScan database (including 18,805 patients) and the Mount Sinai database (including 689 patients) as validation cohorts. As seen in existing research (18,(60)(61)(62), AD is highly heterogenous, thus categorizing patients into different clinically coherent subgroups is important for understanding the mechanism of AD and develop stratified medicine. Different from existing works that focus on identifying AD subphenotypes according to specific clinical data (e.g., cognitive assessment score) at AD onset, our study identified progression subphenotypes with a diverse set of clinical events during the progression from MCI to AD.…”