BayesKAT: bayesian optimal kernel-based test for genetic association studies reveals joint genetic effects in complex diseases

Genome-wide Association Studies (GWAS) methods have identified individual single-nucleotide polymorphisms (SNPs) significantly associated with specific phenotypes. Nonetheless, many complex diseases are polygenic and are controlled by multiple genetic variants that are usually non-linearly dependent. These genetic variants are marginally less effective and remain undetected in GWAS analysis. Kernel-based tests (KBT), which evaluate the joint effect of a group of genetic variants, are therefore critical for complex disease analysis. However, choosing different kernel functions in KBT can significantly influence the type I error control and power, and selecting the optimal kernel remains a statistically challenging task. A few existing methods suffer from inflated type 1 errors, limited scalability, inferior power or issues of ambiguous conclusions. Here, we present a new Bayesian framework, BayesKAT (https://github.com/wangjr03/BayesKAT), which overcomes these kernel specification issues by selecting the optimal composite kernel adaptively from the data while testing genetic associations simultaneously. Furthermore, BayesKAT implements a scalable computational strategy to boost its applicability, especially for high-dimensional cases where other methods become less effective. Based on a series of performance comparisons using both simulated and real large-scale genetics data, BayesKAT outperforms the available methods in detecting complex group-level associations and controlling type I errors simultaneously. Applied on a variety of groups of functionally related genetic variants based on biological pathways, co-expression gene modules and protein complexes, BayesKAT deciphers the complex genetic basis and provides mechanistic insights into human diseases.

Dynamic multilayer functional connectivity detects preclinical and clinical Alzheimer's disease

Increasing evidence suggests that patients with Alzheimer's disease present alterations in functional connectivity but previous results have not always been consistent. One of the reasons that may account for this inconsistency is the lack of consideration of temporal dynamics. To address this limitation, here we studied the dynamic modular organization on resting-state functional magnetic resonance imaging across different stages of Alzheimer's disease using a novel multilayer brain network approach. Participants from preclinical and clinical Alzheimer's disease stages were included. Temporal multilayer networks were used to assess time-varying modular organization. Logistic regression models were employed for disease stage discrimination, and partial least squares analyses examined associations between dynamic measures with cognition and pathology. Temporal multilayer functional measures distinguished all groups, particularly preclinical stages, overcoming the discriminatory power of risk factors such as age, sex, and APOE ϵ4 carriership. Dynamic multilayer functional measures exhibited strong associations with cognition as well as amyloid and tau pathology. Dynamic multilayer functional connectivity shows promise as a functional imaging biomarker for both early- and late-stage Alzheimer's disease diagnosis.

A radiomics-based brain network in T1 images: construction, attributes, and applications

T1 image is a widely collected imaging sequence in various neuroimaging datasets, but it is rarely used to construct an individual-level brain network. In this study, a novel individualized radiomics-based structural similarity network was proposed from T1 images. In detail, it used voxel-based morphometry to obtain the preprocessed gray matter images, and radiomic features were then extracted on each region of interest in Brainnetome atlas, and an individualized radiomics-based structural similarity network was finally built using the correlational values of radiomic features between any pair of regions of interest. After that, the network characteristics of individualized radiomics-based structural similarity network were assessed, including graph theory attributes, test-retest reliability, and individual identification ability (fingerprinting). At last, two representative applications for individualized radiomics-based structural similarity network, namely mild cognitive impairment subtype discrimination and fluid intelligence prediction, were exemplified and compared with some other networks on large open-source datasets. The results revealed that the individualized radiomics-based structural similarity network displays remarkable network characteristics and exhibits advantageous performances in mild cognitive impairment subtype discrimination and fluid intelligence prediction. In summary, the individualized radiomics-based structural similarity network provides a distinctive, reliable, and informative individualized structural brain network, which can be combined with other networks such as resting-state functional connectivity for various phenotypic and clinical applications.

Prediction of misfolded proteins spreading in Alzheimer's disease using machine learning and spreading models

The pervasive impact of Alzheimer's disease on aging society represents one of the main challenges at this time. Current investigations highlight 2 specific misfolded proteins in its development: Amyloid-$\beta$ and tau. Previous studies focused on spreading for misfolded proteins exploited simulations, which required several parameters to be empirically estimated. Here, we provide an alternative view based on 2 machine learning approaches which we compare with known simulation models. The first approach applies an autoregressive model constrained by structural connectivity, while the second is based on graph convolutional networks. The aim is to predict concentrations of Amyloid-$\beta$ 2 yr after a provided baseline. We also evaluate its real-world effectiveness and suitability by providing a web service for physicians and researchers. In experiments, the autoregressive model generally outperformed state-of-the-art models resulting in lower prediction errors. While it is important to note that a comprehensive prognostic plan cannot solely rely on amyloid beta concentrations, their prediction, achieved by the discussed approaches, can be valuable for planning therapies and other cures, especially when dealing with asymptomatic patients for whom novel therapies could prove effective.

Identification of preclinical dementia according to ATN classification for stratified trial recruitment: A machine learning approach

INTRODUCTION

The Amyloid/Tau/Neurodegeneration (ATN) framework was proposed to identify the preclinical biological state of Alzheimer's disease (AD). We investigated whether ATN phenotype can be predicted using routinely collected research cohort data.

METHODS

927 EPAD LCS cohort participants free of dementia or Mild Cognitive Impairment were separated into 5 ATN categories. We used machine learning (ML) methods to identify a set of significant features separating each neurodegeneration-related group from controls (A-T-(N)-). Random Forest and linear-kernel SVM with stratified 5-fold cross validations were used to optimize model whose performance was then tested in the ADNI database.

RESULTS

Our optimal results outperformed ATN cross-validated logistic regression models by between 2.2% and 8.3%. The optimal feature sets were not consistent across the 4 models with the AD pathologic change vs controls set differing the most from the rest. Because of that we have identified a subset of 10 features that yield results very close or identical to the optimal.

DISCUSSION

Our study demonstrates the gains offered by ML in generating ATN risk prediction over logistic regression models among pre-dementia individuals.

Using artificial intelligence to learn optimal regimen plan for Alzheimer's disease

BACKGROUND

Alzheimer's disease (AD) is a progressive neurological disorder with no specific curative medications. Sophisticated clinical skills are crucial to optimize treatment regimens given the multiple coexisting comorbidities in the patient population.

OBJECTIVE

Here, we propose a study to leverage reinforcement learning (RL) to learn the clinicians' decisions for AD patients based on the longitude data from electronic health records.

METHODS

In this study, we selected 1736 patients from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database. We focused on the two most frequent concomitant diseases-depression, and hypertension, thus creating 5 data cohorts (ie, Whole Data, AD, AD-Hypertension, AD-Depression, and AD-Depression-Hypertension). We modeled the treatment learning into an RL problem by defining states, actions, and rewards. We built a regression model and decision tree to generate multiple states, used six combinations of medications (ie, cholinesterase inhibitors, memantine, memantine-cholinesterase inhibitors, hypertension drugs, supplements, or no drugs) as actions, and Mini-Mental State Exam (MMSE) scores as rewards.

RESULTS

Given the proper dataset, the RL model can generate an optimal policy (regimen plan) that outperforms the clinician's treatment regimen. Optimal policies (ie, policy iteration and Q-learning) had lower rewards than the clinician's policy (mean -3.03 and -2.93 vs. -2.93, respectively) for smaller datasets but had higher rewards for larger datasets (mean -4.68 and -2.82 vs. -4.57, respectively).

CONCLUSIONS

Our results highlight the potential of using RL to generate the optimal treatment based on the patients' longitude records. Our work can lead the path towards developing RL-based decision support systems that could help manage AD with comorbidities.

Longitudinal changes in hippocampal texture from healthy aging to Alzheimer's disease

Early detection of Alzheimer's disease is essential to develop preventive treatment strategies. Detectible change in brain volume emerges relatively late in the pathogenic progression of disease, but microstructural changes caused by early neuropathology may cause subtle changes in the MR signal, quantifiable using texture analysis. Texture analysis quantifies spatial patterns in an image, such as smoothness, randomness and heterogeneity. We investigated whether the MRI texture of the hippocampus, an early site of Alzheimer's disease pathology, is sensitive to changes in brain microstructure before the onset of cognitive impairment. We also explored the longitudinal trajectories of hippocampal texture across the Alzheimer's continuum in relation to hippocampal volume and other biomarkers. Finally, we assessed the ability of texture to predict future cognitive decline, over and above hippocampal volume. Data were acquired from the Alzheimer's Disease Neuroimaging Initiative. Texture was calculated for bilateral hippocampi on 3T T1-weighted MRI scans. Two hundred and ninety-three texture features were reduced to five principal components that described 88% of total variance within cognitively unimpaired participants. We assessed cross-sectional differences in these texture components and hippocampal volume between four diagnostic groups: cognitively unimpaired amyloid-β- (n = 406); cognitively unimpaired amyloid-β+ (n = 213); mild cognitive impairment amyloid-β+ (n = 347); and Alzheimer's disease dementia amyloid-β+ (n = 202). To assess longitudinal texture change across the Alzheimer's continuum, we used a multivariate mixed-effects spline model to calculate a 'disease time' for all timepoints based on amyloid PET and cognitive scores. This was used as a scale on which to compare the trajectories of biomarkers, including volume and texture of the hippocampus. The trajectories were modelled in a subset of the data: cognitively unimpaired amyloid-β- (n = 345); cognitively unimpaired amyloid-β+ (n = 173); mild cognitive impairment amyloid-β+ (n = 301); and Alzheimer's disease dementia amyloid-β+ (n = 161). We identified a difference in texture component 4 at the earliest stage of Alzheimer's disease, between cognitively unimpaired amyloid-β- and cognitively unimpaired amyloid-β+ older adults (Cohen's d = 0.23, Padj = 0.014). Differences in additional texture components and hippocampal volume emerged later in the disease continuum alongside the onset of cognitive impairment (d = 0.30-1.22, Padj < 0.002). Longitudinal modelling of the texture trajectories revealed that, while most elements of texture developed over the course of the disease, noise reduced sensitivity for tracking individual textural change over time. Critically, however, texture provided additional information than was provided by volume alone to more accurately predict future cognitive change (d = 0.32-0.63, Padj < 0.0001). Our results support the use of texture as a measure of brain health, sensitive to Alzheimer's disease pathology, at a time when therapeutic intervention may be most effective.

Hepatitis delta infection among persons living with HIV in Europe

BACKGROUND AND AIMS

A high prevalence of hepatitis delta virus (HDV) infection, the most severe form of viral hepatitis, has been reported among persons living with HIV (PLWH) in Europe. We analysed data from a large HIV cohort collaboration to characterize HDV epidemiological trends across Europe, as well as its impact on clinical outcomes.

METHODS

All PLWH with a positive hepatitis B surface antigen (HBsAg) in the Swiss HIV Cohort Study and EuroSIDA between 1988 and 2019 were tested for anti-HDV antibodies and, if positive, for HDV RNA. Demographic and clinical characteristics at initiation of antiretroviral therapy were compared between HDV-positive and HDV-negative individuals using descriptive statistics. The associations between HDV infection and overall mortality, liver-related mortality as well as hepatocellular carcinoma (HCC) were assessed using cumulative incidence plots and cause-specific multivariable Cox regression.

RESULTS

Of 2793 HBsAg-positive participants, 1556 (56%) had stored serum available and were included. The prevalence of HDV coinfection was 15.2% (237/1556, 95% confidence interval [CI]: 13.5%-17.1%) and 66% (132/200) of HDV-positive individuals had active HDV replication. Among persons who inject drugs (PWID), the prevalence of HDV coinfection was 50.5% (182/360, 95% CI: 45.3%-55.7%), with similar estimates across Europe, compared to 4.7% (52/1109, 95% CI: 3.5%-5.9%) among other participants. During a median follow-up of 10.8 years (interquartile range 5.6-17.8), 82 (34.6%) HDV-positive and 265 (20.1%) HDV-negative individuals died. 41.5% (34/82) of deaths were liver-related in HDV-positive individuals compared to 17.7% (47/265) in HDV-negative individuals. HDV infection was associated with overall mortality (adjusted hazard ratio 1.6; 95% CI 1.2-2.1), liver-related death (2.9, 1.6-5.0) and HCC (6.3, 2.5-16.0).

CONCLUSION

We found a very high prevalence of hepatitis delta among PWID across Europe. Among PLWH who do not inject drugs, the prevalence was similar to that reported from populations without HIV. HDV coinfection was associated with liver-related mortality and HCC incidence.

Altered large-scale dynamic connectivity patterns in Alzheimer's disease and mild cognitive impairment patients: A machine learning study

Alzheimer's disease (AD) is a common neurodegeneration disease associated with substantial disruptions in the brain network. However, most studies investigated static resting-state functional connections, while the alteration of dynamic functional connectivity in AD remains largely unknown. This study used group independent component analysis and the sliding-window method to estimate the subject-specific dynamic connectivity states in 1704 individuals from three data sets. Informative inherent states were identified by the multivariate pattern classification method, and classifiers were built to distinguish ADs from normal controls (NCs) and to classify mild cognitive impairment (MCI) patients with informative inherent states similar to ADs or not. In addition, MCI subgroups with heterogeneous functional states were examined in the context of different cognition decline trajectories. Five informative states were identified by feature selection, mainly involving functional connectivity belonging to the default mode network and working memory network. The classifiers discriminating AD and NC achieved the mean area under the receiver operating characteristic curve of 0.87 with leave-one-site-out cross-validation. Alterations in connectivity strength, fluctuation, and inter-synchronization were found in AD and MCIs. Moreover, individuals with MCI were clustered into two subgroups, which had different degrees of atrophy and different trajectories of cognition decline progression. The present study uncovered the alteration of dynamic functional connectivity in AD and highlighted that the dynamic states could be powerful features to discriminate patients from NCs. Furthermore, it demonstrated that these states help to identify MCIs with faster cognition decline and might contribute to the early prevention of AD.

Polygenic risk score prediction of multiple sclerosis in individuals of South Asian ancestry

Polygenic risk scores aggregate an individual's burden of risk alleles to estimate the overall genetic risk for a specific trait or disease. Polygenic risk scores derived from genome-wide association studies of European populations perform poorly for other ancestral groups. Given the potential for future clinical utility, underperformance of polygenic risk scores in South Asian populations has the potential to reinforce health inequalities. To determine whether European-derived polygenic risk scores underperform at multiple sclerosis prediction in a South Asian-ancestry population compared with a European-ancestry cohort, we used data from two longitudinal genetic cohort studies: Genes & Health (2015-present), a study of ∼50 000 British-Bangladeshi and British-Pakistani individuals, and UK Biobank (2006-present), which is comprised of ∼500 000 predominantly White British individuals. We compared individuals with and without multiple sclerosis in both studies (Genes & Health: N Cases = 42, N Control = 40 490; UK Biobank: N Cases = 2091, N Control = 374 866). Polygenic risk scores were calculated using clumping and thresholding with risk allele effect sizes obtained from the largest multiple sclerosis genome-wide association study to date. Scores were calculated with and without the major histocompatibility complex region, the most influential locus in determining multiple sclerosis risk. Polygenic risk score prediction was evaluated using Nagelkerke's pseudo-R 2 metric adjusted for case ascertainment, age, sex and the first four genetic principal components. We found that, as expected, European-derived polygenic risk scores perform poorly in the Genes & Health cohort, explaining 1.1% (including the major histocompatibility complex) and 1.5% (excluding the major histocompatibility complex) of disease risk. In contrast, multiple sclerosis polygenic risk scores explained 4.8% (including the major histocompatibility complex) and 2.8% (excluding the major histocompatibility complex) of disease risk in European-ancestry UK Biobank participants. These findings suggest that polygenic risk score prediction of multiple sclerosis based on European genome-wide association study results is less accurate in a South Asian population. Genetic studies of ancestrally diverse populations are required to ensure that polygenic risk scores can be useful across ancestries.

Health state utilities for beta-thalassemia: a time trade-off study

BACKGROUND

Beta-thalassemia (BT) is an inherited blood disorder characterized by reduced levels of functional hemoglobin resulting in phenotypes ranging from clinically asymptomatic to severely anemic. Patients with BT may require lifelong regular blood transfusions supported by appropriate iron chelation therapy (ICT). This study aimed to determine how the UK general population values BT health states associated with differing transfusion burden and ICT.

METHODS

Composite time trade-off (cTTO) methodology was employed to elicit health state utilities in BT. Relevant BT literature related to symptom and quality-of-life impact, including physical, functional, and emotional well-being, and safety profiles of BT treatments were considered when drafting health state descriptions. Eleven health state descriptions were developed and validated by hematologists and patient advocates for clinical accuracy and completeness. 200 individuals from the UK general population participated in the cTTO interviews.

RESULTS

The mean age of participants was 41.50 years (SD 16.01, range 18-81); 88 (46.8%) were female. Utility values ranged from 0.78 (SD 0.34) for non-transfusion dependent BT with oral ICT to 0.37 (SD 0.50) for high transfusion burden with subcutaneous ICT in transfusion-dependent BT.

CONCLUSIONS

This study provides health utilities for a range of BT health states from the UK general population perspective. Importantly, lower transfusion burden and lower burden of anemia were associated with higher utilities. To a lesser extent, differential modes of ICT were found to impact utility valuations in patients with BT. The utilities obtained in this study can be employed as inputs in cost-effectiveness analyses of BT therapies.

Associations of erythrocyte omega-3 fatty acids with cognition, brain imaging and biomarkers in the Alzheimer's disease neuroimaging initiative: cross-sectional and longitudinal retrospective analyses

BACKGROUND

The association between omega-3 (ω-3) PUFAs and cognition, brain imaging and biomarkers is still not fully established.

OBJECTIVES

The aim was to analyze the cross-sectional and retrospective longitudinal associations between erythrocyte ω-3 index and cognition, brain imaging, and biomarkers among older adults.

METHODS

A total of 832 Alzheimer's Disease Neuroimaging Initiative 3 (ADNI-3) participants, with a mean (SD) age of 74.0 (7.9) y, 50.8% female, 55.9% cognitively normal, 32.7% with mild cognitive impairment, and 11.4% with Alzheimer disease (AD) were included. A low ω-3 index (%EPA + %DHA) was defined as the lowest quartile (≤3.70%). Cognitive tests [composite score, AD Assessment Scale Cognitive (ADAS-Cog), Wechsler Memory Scale (WMS), Trail Making Test, Category Fluency, Mini-Mental State Examination, Montreal Cognitive Assessment] and brain variables [hippocampal volume, white matter hyperintensities (WMHs), positron emission tomography (PET) amyloid-β (Aβ) and tau] were considered as outcomes in regression models.

RESULTS

Low ω-3 index was not associated with cognition, hippocampal, and WMH volume or brain Aβ and tau after adjustment for demographics, ApoEε4, cardiovascular disease, BMI, and total intracranial volume in the cross-sectional analysis. In the retrospective analysis, low ω-3 index was associated with greater Aβ accumulation (adjusted β = 0.02; 95% CI: 0.01, 0.03; P = 0.003). The composite cognitive score did not differ between groups; however, low ω-3 index was significantly associated with greater WMS-delayed recall cognitive decline (adjusted β = -1.18; 95% CI: -2.16, -0.19; P = 0.019), but unexpectedly lower total ADAS-Cog cognitive decline. Low ω-3 index was cross-sectionally associated with lower WMS performance (adjusted β = -1.81, SE = 0.73, P = 0.014) and higher tau accumulation among ApoE ε4 carriers.

CONCLUSIONS

Longitudinally, low ω-3 index was associated with greater Aβ accumulation and WMS cognitive decline but unexpectedly with lower total ADAS-Cog cognitive decline. Although no associations were cross-sectionally found in the whole population, low ω-3 index was associated with lower WMS cognition and higher tau accumulation among ApoE ε4 carriers. The Alzheimer's Disease Neuroimaging Initiative (ADNI) is registered at clinicaltrials.gov as NCT00106899.

High incidence of proliferative and membranous nephritis in SLE patients with low proteinuria in the Accelerating Medicines Partnership

OBJECTIVE

Delayed detection of LN associates with worse outcomes. There are conflicting recommendations regarding a threshold level of proteinuria at which biopsy will likely yield actionable management. This study addressed the association of urine protein:creatinine ratios (UPCR) with clinical characteristics and investigated the incidence of proliferative and membranous histology in patients with a UPCR between 0.5 and 1.

METHODS

A total of 275 SLE patients (113 first biopsy, 162 repeat) were enrolled in the multicentre multi-ethnic/racial Accelerating Medicines Partnership across 15 US sites at the time of a clinically indicated renal biopsy. Patients were followed for 1 year.

RESULTS

At biopsy, 54 patients had UPCR <1 and 221 had UPCR ≥1. Independent of UPCR or biopsy number, a majority (92%) of patients had class III, IV, V or mixed histology. Moreover, patients with UPCR <1 and class III, IV, V, or mixed had a median activity index of 4.5 and chronicity index of 3, yet 39% of these patients had an inactive sediment. Neither anti-dsDNA nor low complement distinguished class I or II from III, IV, V or mixed in patients with UPCR <1. Of 29 patients with baseline UPCR <1 and class III, IV, V or mixed, 23 (79%) had a UPCR <0.5 at 1 year.

CONCLUSION

In this prospective study, three-quarters of patients with UPCR <1 had histology showing class III, IV, V or mixed with accompanying activity and chronicity despite an inactive sediment or normal serologies. These data support renal biopsy at thresholds lower than a UPCR of 1.

CSF TNF α levels were associated with conversion from mild cognitive impairment to dementia

We aimed to examine the association of CSF tumor necrosis factor-alpha (TNFα) with conversion from mild cognitive impairment (MCI) to dementia. At baseline, there were a total of 129 participants with MCI in this study. The association of CSF TNFα levels with the incidence of dementia were evaluated using Cox proportional hazards regression analysis adjusted for potential confounders. Individuals were categorized into groups based on the CSF TNFα tertiles. Compared to the low group (the reference group), the intermediate group progressed more rapidly to dementia [HR (95% CI) = 2.2 (1.15–4.1); p = 0.016] after adjusting for other covariates. However, the high group did not progress faster than the low group [HR (95% CI) = 1.5 (0.79–2.8); p = 0.214]. Our study suggested a potential non-relationship between CSF TNFα levels and the risk of development of dementia among MCI older people.

Autosomal dominant and sporadic late onset Alzheimer's disease share a common in vivo pathophysiology

The extent to which the pathophysiology of autosomal dominant Alzheimer's disease corresponds to the pathophysiology of 'sporadic' late onset Alzheimer's disease is unknown, thus limiting the extrapolation of study findings and clinical trial results in autosomal dominant Alzheimer's disease to late onset Alzheimer's disease. We compared brain MRI and amyloid PET data, as well as CSF concentrations of amyloid-β42, amyloid-β40, tau and tau phosphorylated at position 181, in 292 carriers of pathogenic variants for Alzheimer's disease from the Dominantly Inherited Alzheimer Network, with corresponding data from 559 participants from the Alzheimer's Disease Neuroimaging Initiative. Imaging data and CSF samples were reprocessed as appropriate to guarantee uniform pipelines and assays. Data analyses yielded rates of change before and after symptomatic onset of Alzheimer's disease, allowing the alignment of the ∼30-year age difference between the cohorts on a clinically meaningful anchor point, namely the participant age at symptomatic onset. Biomarker profiles were similar for both autosomal dominant Alzheimer's disease and late onset Alzheimer's disease. Both groups demonstrated accelerated rates of decline in cognitive performance and in regional brain volume loss after symptomatic onset. Although amyloid burden accumulation as determined by PET was greater after symptomatic onset in autosomal dominant Alzheimer's disease than in late onset Alzheimer's disease participants, CSF assays of amyloid-β42, amyloid-β40, tau and p-tau181 were largely overlapping in both groups. Rates of change in cognitive performance and hippocampal volume loss after symptomatic onset were more aggressive for autosomal dominant Alzheimer's disease participants. These findings suggest a similar pathophysiology of autosomal dominant Alzheimer's disease and late onset Alzheimer's disease, supporting a shared pathobiological construct.

Causal links among amyloid, tau, and neurodegeneration

Amyloid-β pathology is associated with greater tau pathology and facilitates tau propagation from the medial temporal lobe to the neocortex, where tau is closely associated with local neurodegeneration. The degree of the involvement of amyloid-β versus existing tau pathology in tau propagation and neurodegeneration has not been fully elucidated in human studies. Careful quantification of these effects can inform the development and timing of therapeutic interventions. We conducted causal mediation analyses to investigate the relative contributions of amyloid-β and existing tau to tau propagation and neurodegeneration in two longitudinal studies of individuals without dementia: the Baltimore Longitudinal Study of Aging (N = 103, age range 57-96) and the Alzheimer's Disease Neuroimaging Initiative (N = 122, age range 56-92). As proxies of neurodegeneration, we investigated cerebral blood flow, glucose metabolism, and regional volume. We first confirmed that amyloid-β moderates the association between tau in the entorhinal cortex and in the inferior temporal gyrus, a neocortical region exhibiting early tau pathology (amyloid group × entorhinal tau interaction term β  = 0.488, standard error [SE] = 0.126, P < 0.001 in the Baltimore Longitudinal Study of Aging; β  = 0.619, SE = 0.145, P < 0.001 in the Alzheimer's Disease Neuroimaging Initiative). In causal mediation analyses accounting for this facilitating effect of amyloid, amyloid positivity had a statistically significant direct effect on inferior temporal tau as well as an indirect effect via entorhinal tau (average direct effect =0.47, P < 0.001 and average causal mediation effect =0.44, P = 0.0028 in Baltimore Longitudinal Study of Aging; average direct effect =0.43, P = 0.004 and average causal mediation effect =0.267, P = 0.0088 in Alzheimer's Disease Neuroimaging Initiative). Entorhinal tau mediated up to 48% of the total effect of amyloid on inferior temporal tau. Higher inferior temporal tau was associated with lower colocalized cerebral blood flow, glucose metabolism, and regional volume, whereas amyloid had only an indirect effect on these measures via tau, implying tau as the primary driver of neurodegeneration (amyloid-cerebral blood flow average causal mediation effect =-0.28, P = 0.021 in Baltimore Longitudinal Study of Aging; amyloid-volume average causal mediation effect =-0.24, P < 0.001 in Alzheimer's Disease Neuroimaging Initiative). Our findings suggest targeting amyloid or medial temporal lobe tau might slow down neocortical spread of tau and subsequent neurodegeneration, but a combination therapy may yield better outcomes.

Efficacy and safety of abatacept in active primary Sjögren's syndrome: results of a phase III, randomised, placebo-controlled trial

OBJECTIVES

To evaluate efficacy and safety of abatacept in adults with active primary Sjögren's syndrome (pSS) in a phase III, randomised, double-blind, placebo-controlled trial.

METHODS

Eligible patients (moderate-to-severe pSS [2016 ACR/European League Against Rheumatism (EULAR) criteria], EULAR Sjögren's Syndrome Disease Activity Index [ESSDAI] ≥5, anti-SS-related antigen A/anti-Ro antibody positive) received weekly subcutaneous abatacept 125 mg or placebo for 169 days followed by an open-label extension to day 365. Primary endpoint was mean change from baseline in ESSDAI at day 169. Key secondary endpoints were mean change from baseline in EULAR Sjögren's Syndrome Patient Reported Index (ESSPRI) and stimulated whole salivary flow (SWSF) at day 169. Other secondary clinical endpoints included glandular functions and patient-reported outcomes. Selected biomarkers and immune cell phenotypes were examined. Safety was monitored.

RESULTS

Of 187 patients randomised, 168 completed double-blind period and 165 continued into open-label period. Mean (SD) baseline ESSDAI and ESSPRI total scores were 9.4 (4.3) and 6.5 (2.0), respectively. Statistical significance was not reached for primary (ESSDAI -3.2 abatacept vs -3.7 placebo, p=0.442) or key secondary endpoints (ESSPRI, p=0.337; SWSF, p=0.584). No clinical benefit of abatacept over placebo at day 169 was seen with other clinical and PRO endpoints. Relative to baseline, abatacept was associated with significant differences vs placebo in some disease-relevant biomarkers (including IgG, IgA, IgM-rheumatoid factor) and pathogenic cell subpopulations (post hoc analyses). No new safety signals were identified.

CONCLUSIONS

Abatacept treatment did not result in significant clinical efficacy compared with placebo in patients with moderate-to-severe pSS, despite evidence of biological activity.

Molecular correlates of response to nivolumab at baseline and on treatment in patients with RCC

BACKGROUND

Nivolumab is an immune checkpoint inhibitor targeting the programmed death-1 receptor that improves survival in a subset of patients with clear cell renal cell carcinoma (ccRCC). In contrast to other tumor types that respond to immunotherapy, factors such as programmed death ligand-1 (PD-L1) status and tumor mutational burden show limited predictive utility in ccRCC. To address this gap, we report here the first molecular characterization of nivolumab response using paired index lesions, before and during treatment of metastatic ccRCC.

METHODS

We analyzed gene expression and T-cell receptor (TCR) clonality using lesion-paired biopsies provided in the CheckMate 009 trial and integrated the results with their PD-L1/CD4/CD8 status, genomic mutation status and serum cytokine assays. Statistical tests included linear mixed models, logistic regression models, Fisher's exact test, and Kruskal-Wallis rank-sum test.

RESULTS

We identified transcripts related to response, both at baseline and on therapy, including several that are amenable to peripheral bioassays or to therapeutic intervention. At both timepoints, response was positively associated with T-cell infiltration but not associated with TCR clonality, and some non-Responders were highly infiltrated. Lower baseline T-cell infiltration correlated with elevated transcription of Wnt/β-catenin signaling components and hypoxia-regulated genes, including the Treg chemoattractant CCL28. On treatment, analysis of the non-responding patients whose tumors were highly T-cell infiltrated suggests association of the RIG-I-MDA5 pathway in their nivolumab resistance. We also analyzed our data using previous transcriptional classifications of ccRCC and found they concordantly identified a molecular subtype that has enhanced nivolumab response but is sunitinib-resistant.

CONCLUSION

Our study describes molecular characteristics of response and resistance to nivolumab in patients with metastatic ccRCC, potentially impacting patient selection and first-line treatment decisions.

TRIAL REGISTRATION NUMBER

NCT01358721.

Outcomes in rheumatoid arthritis patients treated with abatacept: a UK multi-centre observational study

BACKGROUND

Rheumatoid arthritis (RA) is an inflammatory autoimmune disease that causes chronic synovitis, resulting in progressive joint destruction and functional disability and affects approximately 400,000 people in the UK. This real-world study aimed to describe the characteristics, treatment patterns and clinical outcomes of patients who received abatacept in UK clinical practice.

METHODS

This was a multi-centre, retrospective, observational study of patients with RA treated with abatacept at four UK centres between 01 January 2013 and 31 December 2017. Data were collected from medical records of each patient from the index date (date of first bDMARD initiation) until the most recent visit, death or end of study (31 December 2017).

RESULTS

In total, 213 patients were included in the study. Patients received up to eight lines of therapy (LOTs). Treatment with abatacept, or any other bDMARD, was associated with reductions in DAS28-ESR and DAS28-CRP scores at 6 and 12 months. The distribution of EULAR responses (good/moderate/no response) tended to be more favourable for patients when receiving abatacept than when receiving other bDMARDs (22.8%/41.3%/35.9% versus 16.6%/41.4%/42.1% at 6 months, and 27.9%/36.1%/36.1% versus 21.2%/34.5%/44.2% at 12 months). Patients receiving abatacept at LOT1 (n = 68) spent significantly longer on treatment compared with patients receiving other bDMARDs (53.4 vs. 17.4 months; p< 0.01); a similar trend was observed for LOT2. Among patients who discontinued after 6 months, a greater proportion experienced infection requiring antibiotics when receiving other bDMARDs compared to those receiving abatacept.

CONCLUSIONS

RA patients who received bDMARDs, including abatacept, experienced reduced disease activity. When receiving abatacept as first or second line of therapy, patients persisted with treatment significantly longer than those receiving other bDMARDs.

Lenalidomide as maintenance treatment for patients with multiple myeloma after autologous stem cell transplantation: A pharmaco-economic assessment

OBJECTIVE

Autologous stem cell transplantation (ASCT) has improved progression-free survival (PFS) and overall survival in eligible patients with newly diagnosed multiple myeloma (NDMM); however, relapse occurs. Maintenance therapy with lenalidomide (Len-Mt) extends survival and delays relapse and the subsequent initiation of costly second-line regimens. Here, we report the cost-effectiveness of Len-Mt following ASCT from a Dutch healthcare service perspective.

METHODS

A partitioned survival model was developed to assess the lifetime costs and benefits for patients with NDMM. Efficacy was taken from a pooled meta-analysis of clinical trial data. Costs and subsequent therapy data were taken from sources appropriate for the Dutch market.

RESULTS

Lenalidomide produced a quality-adjusted life year gain of 2.46 and a life year gain of 2.79 vs no maintenance treatment. The cost of lenalidomide was partially offset by savings of EUR 77 462 in subsequent treatment costs. The incremental cost-effectiveness ratio of Len-Mt vs no maintenance treatment was EUR 30 143. Key model drivers included subsequent therapies, dosing schedule, and time horizon.

CONCLUSION

Lenalidomide is cost-effective after ASCT vs no maintenance therapy in the Netherlands. By extending PFS, lenalidomide delays the cost burdens associated with relapse and subsequent treatment lines.

The Impact of 6 and 12 Months in Space on Human Brain Structure and Intracranial Fluid Shifts

As plans develop for Mars missions, it is important to understand how long-duration spaceflight impacts brain health. Here we report how 12-month (n = 2 astronauts) versus 6-month (n = 10 astronauts) missions impact brain structure and fluid shifts. We collected MRI scans once before flight and four times after flight. Astronauts served as their own controls; we evaluated pre- to postflight changes and return toward preflight levels across the 4 postflight points. We also provide data to illustrate typical brain changes over 7 years in a reference dataset. Twelve months in space generally resulted in larger changes across multiple brain areas compared with 6-month missions and aging, particularly for fluid shifts. The majority of changes returned to preflight levels by 6 months after flight. Ventricular volume substantially increased for 1 of the 12-month astronauts (left: +25%, right: +23%) and the 6-month astronauts (left: 17 ± 12%, right: 24 ± 6%) and exhibited little recovery at 6 months. Several changes correlated with past flight experience; those with less time between subsequent missions had larger preflight ventricles and smaller ventricular volume increases with flight. This suggests that spaceflight-induced ventricular changes may endure for long periods after flight. These results provide insight into brain changes that occur with long-duration spaceflight and demonstrate the need for closer study of fluid shifts.

Clinical meaningfulness of subtle cognitive decline on longitudinal testing in preclinical AD

INTRODUCTION

Demonstrating the "clinical meaningfulness" of slowing early cognitive decline in clinically normal (CN) older adults with elevated amyloid-β (Aβ+) is critical for Alzheimer's disease secondary prevention trials and for understanding early cognitive progression.

METHODS

Cox regression analyses were used to determine whether 3-year slopes on the preclinical Alzheimer's cognitive composite predicted MCI diagnosis and global Clinical Dementia Rating>0 in 267 Aβ+ CN individuals participating in the Harvard Aging Brain Study, Australian Imaging, Biomarker and Lifestyle Study, and Alzheimer's Disease Neuroimaging Initiative.

RESULTS

Steeper preclinical Alzheimer's cognitive composite decline over 3 years was associated with increased risk for MCI diagnosis and global Clinical Dementia Rating>0 in the following years across all cohorts. Hazard ratios using meta-analytic estimates were 5.47 (95% CI: 3.25-9.18) for MCI diagnosis and 4.49 (95% CI: 2.84-7.09) for Clinical Dementia Rating>0 in those with subtle decline (>-.14 to -.26 preclinical Alzheimer's cognitive composite standard deviations/year) on longitudinal cognitive testing.

DISCUSSION

Early "subtle cognitive decline" among Aβ+ CN on a sensitive cognitive composite demonstrably increases risk for imminent clinical disease progression and functional impairment.