Impact of amyloid and cardiometabolic risk factors on prognostic capacity of plasma neurofilament light chain for neurodegeneration

Background: Plasma neurofilament light chain (NfL) is a blood biomarker of neurodegeneration, including Alzheimer’s disease. However, its usefulness may be influenced by common conditions in older adults, including amyloid-β (Aβ) deposition and cardiometabolic risk factors like hypertension, diabetes mellitus (DM), impaired kidney function, and obesity. This longitudinal observational study using the Alzheimer’s Disease Neuroimaging Initiative cohort investigated how these conditions influence the prognostic capacity of plasma NfL. Methods: Non-demented participants (cognitively unimpaired or mild cognitive impairment) underwent repeated assessments including the Alzheimer’s Disease Assessment Scale-Cognitive subscale (ADAS-Cog) scores, hippocampal volumes, and white matter hyperintensity (WMH) volumes at 6- or 12-month intervals. Linear mixed-effect models were employed to examine the interaction between plasma NfL and various variables of interest, such as Aβ (evaluated using Florbetapir positron emission tomography), hypertension, DM, impaired kidney function, or obesity. Results: Over a mean follow-up period of 62.5 months, participants with a mean age of 72.1 years (n = 720, 48.8% female) at baseline were observed. Higher plasma NfL levels at baseline were associated with steeper increases in ADAS-Cog scores and WMH volumes, and steeper decreases in hippocampal volumes over time (all p-values \u3c 0.001). Notably, Aβ at baseline significantly enhanced the association between plasma NfL and longitudinal changes in ADAS-Cog scores (p-value 0.005) and hippocampal volumes (p-value 0.004). Regarding ADAS-Cog score and WMH volume, the impact of Aβ was more prominent in cognitively unimpaired than in mild cognitive impairment. Hypertension significantly heightened the association between plasma NfL and longitudinal changes in ADAS-Cog scores, hippocampal volumes, and WMH volumes (all p-values \u3c 0.001). DM influenced the association between plasma NfL and changes in ADAS-Cog scores (p-value \u3c 0.001) without affecting hippocampal and WMH volumes. Impaired kidney function did not significantly alter the association between plasma NfL and longitudinal changes in any outcome variables. Obesity heightened the association between plasma NfL and changes in hippocampal volumes only (p-value 0.026). Conclusion: This study suggests that the prognostic capacity of plasma NfL may be amplified in individuals with Aβ or hypertension. This finding emphasizes the importance of considering these factors in the NfL-based prognostic model for neurodegeneration in non-demented older adults

Sex differences in interacting genetic and functional connectivity biomarkers in Alzheimer’s disease

As of 2023, it is estimated that 6.7 million individuals in the United States live with Alzheimer’s disease (AD). Prior research indicates that AD disproportionality affects females; females have a greater incidence rate, perform worse on a variety of neuropsychological tasks, and have greater total brain atrophy. Recent research shows that hippocampal functional connectivity differs by sex and may be related to the observed sex differences in AD, and apolipoprotein E (ApoE) ε4 carriers have reduced hippocampal functional connectivity. The purpose of this study was to determine if the ApoE genotype plays a role in the observed sex differences in hippocampal functional connectivity in Alzheimer’s disease. The resting state fMRI and T2 MRI of individuals with AD (n = 30, female = 15) and cognitively normal individuals (n = 30, female = 15) from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) were analyzed using the functional connectivity toolbox (CONN). Our results demonstrated intrahippocampal functional connectivity differed between those without an ε4 allele and those with at least one ε4 allele in each group. Additionally, intrahippocampal functional connectivity differed only by sex when Alzheimer’s participants had at least one ε4 allele. These results improve our current understanding of the role of the interacting relationship between sex, ApoE genotype, and hippocampal function in AD. Understanding these biomarkers may aid in the development of sex-specific interventions for improved AD treatment

Defining failed induction of labor

BACKGROUND: While there are well-accepted standards for the diagnosis of arrested active-phase labor, the definition of a "failed" induction of labor remains less certain. One approach to diagnosing a failed induction is based on the duration of the latent phase. However, a standard for the minimum duration that the latent phase of a labor induction should continue, absent acute maternal or fetal indications for cesarean delivery, remains lacking. OBJECTIVE: The objective of this study was to determine the frequency of adverse maternal and perinatal outcomes as a function of the duration of the latent phase among nulliparous women undergoing labor induction. METHODS: This study is based on data from an obstetric cohort of women delivering at 25 U.S. hospitals from 2008-2011. Nulliparous women who had a term singleton gestation in the cephalic presentation were eligible for this analysis if they underwent a labor induction. Consistent with prior studies, the latent phase was determined to begin once cervical ripening had ended, oxytocin was initiated and rupture of membranes (ROM) had occurred, and was determined to end once 5 cm dilation was achieved. The frequencies of cesarean delivery, as well as of adverse maternal (e.g., cesarean delivery, postpartum hemorrhage, chorioamnionitis) and perinatal outcomes (e.g., a composite frequency of either seizures, sepsis, bone or nerve injury, encephalopathy, or death), were compared as a function of the duration of the latent phase (analyzed with time both as a continuous measure and categorized in 3-hour increments). RESULTS: A total of 10,677 women were available for analysis. In the vast majority (96.4%) of women, the active phase had been reached by 15 hours. The longer the duration of a woman's latent phase, the greater her chance of ultimately undergoing a cesarean delivery (P<0.001, for time both as a continuous and categorical independent variable), although more than forty percent of women whose latent phase lasted for 18 or more hours still had a vaginal delivery. Several maternal morbidities, such as postpartum hemorrhage (P < 0.001) and chorioamnionitis (P < 0.001), increased in frequency as the length of latent phase increased. Conversely, the frequencies of most adverse perinatal outcomes were statistically stable over time. CONCLUSION: The large majority of women undergoing labor induction will have entered the active phase by 15 hours after oxytocin has started and rupture of membranes has occurred. Maternal adverse outcomes become statistically more frequent with greater time in the latent phase, although the absolute increase in frequency is relatively small. These data suggest that cesarean delivery should not be undertaken during the latent phase prior to at least 15 hours after oxytocin and rupture of membranes have occurred. The decision to continue labor beyond this point should be individualized, and may take into account factors such as other evidence of labor progress

The genetic architecture of the human cerebral cortex

The cerebral cortex underlies our complex cognitive capabilities, yet little is known about the specific genetic loci that influence human cortical structure. To identify genetic variants that affect cortical structure, we conducted a genome-wide association meta-analysis of brain magnetic resonance imaging data from 51,665 individuals. We analyzed the surface area and average thickness of the whole cortex and 34 regions with known functional specializations. We identified 199 significant loci and found significant enrichment for loci influencing total surface area within regulatory elements that are active during prenatal cortical development, supporting the radial unit hypothesis. Loci that affect regional surface area cluster near genes in Wnt signaling pathways, which influence progenitor expansion and areal identity. Variation in cortical structure is genetically correlated with cognitive function, Parkinson's disease, insomnia, depression, neuroticism, and attention deficit hyperactivity disorder

Brain multiplexes reveal morphological connectional biomarkers fingerprinting late brain dementia states

Accurate diagnosis of mild cognitive impairment (MCI) before conversion to Alzheimer’s disease (AD) is invaluable for patient treatment. Many works showed that MCI and AD affect functional and structural connections between brain regions as well as the shape of cortical regions. However, ‘shape connections’ between brain regions are rarely investigated -e.g., how morphological attributes such as cortical thickness and sulcal depth of a specific brain region change in relation to morphological attributes in other regions. To fill this gap, we unprecedentedly design morphological brain multiplexes for late MCI/AD classification. Specifically, we use structural T1-w MRI to define morphological brain networks, each quantifying similarity in morphology between different cortical regions for a specific cortical attribute. Then, we define a brain multiplex where each intra-layer represents the morphological connectivity network of a specific cortical attribute, and each inter-layer encodes the similarity between two consecutive intra-layers. A significant performance gain is achieved when using the multiplex architecture in comparison to other conventional network analysis architectures. We also leverage this architecture to discover morphological connectional biomarkers fingerprinting the difference between late MCI and AD stages, which included the right entorhinal cortex and right caudal middle frontal gyrus

Multimodal and Multiscale Deep Neural Networks for the Early Diagnosis of Alzheimer’s Disease using structural MR and FDG-PET images

Alzheimer’s Disease (AD) is a progressive neurodegenerative disease where biomarkers for disease based on pathophysiology may be able to provide objective measures for disease diagnosis and staging. Neuroimaging scans acquired from MRI and metabolism images obtained by FDG-PET provide in-vivo measurements of structure and function (glucose metabolism) in a living brain. It is hypothesized that combining multiple different image modalities providing complementary information could help improve early diagnosis of AD. In this paper, we propose a novel deep-learning-based framework to discriminate individuals with AD utilizing a multimodal and multiscale deep neural network. Our method delivers 82.4% accuracy in identifying the individuals with mild cognitive impairment (MCI) who will convert to AD at 3 years prior to conversion (86.4% combined accuracy for conversion within 1–3 years), a 94.23% sensitivity in classifying individuals with clinical diagnosis of probable AD, and a 86.3% specificity in classifying non-demented controls improving upon results in published literature