Longitudinal Phenotypes Research Articles

Unlocking the potential of wearable device wear time to enhance postpartum depression screening and detection.

Postpartum depression (PPD) is a mood disorder affecting one in seven women after childbirth that is often under-screened and under-detected. If not diagnosed and treated, PPD is associated with long-term developmental challenges in the child and maternal morbidity. Wearable technologies, such as smartwatches and fitness trackers (e.g., Fitbit), offer continuous and longitudinal digital phenotyping for mood disorder diagnosis and monitoring, with device wear time being an important yet understudied aspect. Using the All of Us Research Program (AoURP) dataset, we assessed the percentage of days women with PPD wore Fitbit devices across pre-pregnancy, pregnancy, postpartum, and PPD periods, as determined by electronic health records. Wear time was compared in women with and without PPD using linear regression models. Results showed a strong trend that women in the PPD cohort wore their Fitbits more those without PPD during the postpartum (PPD: mean=72.9%, SE=13.8%; non-PPD: mean=58.9%, SE=12.2%, P -value=0.09) and PPD time periods (PPD: mean=70.7%, SE=14.5%; non-PPD: mean=55.6%, SE=12.9%, P -value=0.08). We hypothesize this may be attributed to hypervigilance, given the common co-occurrence of anxiety symptoms among women with PPD. Future studies should assess the link between PPD, hypervigilance, and wear time patterns. We envision that device wear patterns with digital biomarkers like sleep and physical activity could enhance early PPD detection using machine learning by alerting clinicians to potential concerns facilitating timely screenings, which may have implications for other mental health disorders.

Longitudinal characterization of impulsivity phenotypes boosts signal for genomic correlates and heritability.

Genomic correlates of impulsivity have been identified in several genome-wide association studies (GWAS) using cross-sectional designs, but no studies have investigated the molecular genetic correlates of impulsivity phenotypes using longitudinally constructed traits. In 3860 unrelated European participants in the Avon Longitudinal Study of Parents and Children (ALSPAC), we constructed longitudinal phenotypes for delay discounting and impulsive personality traits (as measured by the UPPS-P impulsive behavior scales) via assessment at ages 24, 26, and 28. We conducted GWASs of impulsivity using both cross-sectional and longitudinal phenotypes, estimated heritability and their phenotypic and genetic correlations, and evaluated their association with recently-developed polygenic risk scores (PRSs) for the impulsivity indicators themselves and also related psychiatric conditions. Latent growth curve modeling revealed a stable intercept over time for all impulsivity phenotypes. High genetic correlation of cross-sectional measures over time suggested a stable genetic component for delay discounting (rg = 0.53-0.99) and sensation seeking (rg = 0.99). Heritability estimates of the stable longitudinal phenotypes substantively improved as compared to their cross-sectional counterparts, revealing a significant SNP-heritability for delay discounting (0.22; p = 0.03) and sensation seeking (0.35; p = 0.0007). Consistent with previous reports, GWAS and gene-based analyses revealed associations between specific longitudinal impulsivity indicators and CADM2 and NCAM1 genes. The PRSs for the impulsivity indicators and disorders related to self-regulation were also significantly associated with longitudinal impulsivity traits. Finally, we validated the associations between longitudinal impulsivity phenotypes and their PRSs in an independent 13-wave longitudinal study (n = 1019) and the benefit of longitudinal phenotypes in simulation studies. In this first longitudinal genetic study of impulsivity traits, the results revealed stable genomic correlates of delay discounting and sensation seeking over time and further validated the utility of recently-developed PRSs, both in relation to the observed traits and in connecting them to psychiatric disorders. More generally, these findings support using latent intercepts as novel longitudinal phenotypes to boost signal for heritability and genomic correlates of mechanisms contributing to psychiatric disease liability.

Approach to the Pediatric Patient with Glucocorticoid-Induced Osteoporosis.

Glucocorticoid (GC) therapy remains the cornerstone of treatment for many conditions of childhood and an important cause of skeletal and endocrine morbidity. Here, we discuss cases that bring to life the most important concepts in the management of pediatric GC-induced osteoporosis (pGIO). Given the wide variety of underlying conditions linked to pGIO, we focus on the fundamental clinical-biological principles that provide a blueprint for management in any clinical context. In so doing, we underscore the importance of longitudinal vertebral fracture phenotyping, how knowledge about the timing and risk of fractures influences monitoring, the role of bone mineral density in pGIO assessments, and the impact of growth-mediated "vertebral body reshaping" after spine fractures on the therapeutic approach. Overall, pGIO management is predicated upon early identification of fractures (including vertebral) in those at risk, and timely intervention when there is limited potential for spontaneous recovery. Even a single, low-trauma long bone or vertebral fracture can signal an osteoporotic event in an at-risk child. The most widely used treatments for pediatric osteoporosis, intravenous bisphosphonates, are currently recommended first-line for the treatment of pGIO. It is recognized, however, that even early identification of bone fragility, combined with timely introduction of the most potent bisphosphonate therapies, may not completely prevent osteoporosis progression in all contexts. Therefore, prevention of first-ever fractures in the highest-risk settings is on the horizon, where there is also a need to move beyond anti-resorptives to the study of anabolic agents.

Microstructural characterization of multiple sclerosis lesion phenotypes using multiparametric longitudinal analysis

Background and objectivesIn multiple sclerosis (MS), slowly expanding lesions were shown to be associated with worse disability and prognosis. Their timely detection from cross-sectional data at early disease stages could be clinically relevant to inform treatment planning. Here, we propose to use multiparametric, quantitative MRI to allow a better cross-sectional characterization of lesions with different longitudinal phenotypes.MethodsWe analysed T1 and T2 relaxometry maps from a longitudinal cohort of MS patients. Lesions were classified as enlarging, shrinking, new or stable based on their longitudinal volumetric change using a newly developed automated technique. Voxelwise deviations were computed as z-scores by comparing individual patient data to T1, T2 and T2/T1 normative values from healthy subjects. We studied the distribution of microstructural properties inside lesions and within perilesional tissue.Results and conclusionsStable lesions exhibited the highest T1 and T2 z-scores in lesion tissue, while the lowest values were observed for new lesions. Shrinking lesions presented the highest T1 z-scores in the first perilesional ring while enlarging lesions showed the highest T2 z-scores in the same region. Finally, a classification model was trained to predict the longitudinal lesion type based on microstructural metrics and feature importance was assessed. Z-scores estimated in lesion and perilesional tissue from T1, T2 and T2/T1 quantitative maps carry discriminative and complementary information to classify longitudinal lesion phenotypes, hence suggesting that multiparametric MRI approaches are essential for a better understanding of the pathophysiological mechanisms underlying disease activity in MS lesions.

Smartphone Voice Calls Provide Early Biomarkers of Parkinsonism in Rapid Eye Movement Sleep Behavior Disorder.

Speech dysfunction represents one of the initial motor manifestations to develop in Parkinson's disease (PD) and is measurable through smartphone. The aim was to develop a fully automated and noise-resistant smartphone-based system that can unobtrusively screen for prodromal parkinsonian speech disorder in subjects with isolated rapid eye movement sleep behavior disorder (iRBD) in a real-world scenario. This cross-sectional study assessed regular, everyday voice call data from individuals with iRBD compared to early PD patients and healthy controls via a developed smartphone application. The participants also performed an active, regular reading of a short passage on their smartphone. Smartphone data were continuously collected for up to 3 months after the standard in-person assessments at the clinic. A total of 3525 calls that led to 5990 minutes of preprocessed speech were extracted from 72 participants, comprising 21 iRBD patients, 26 PD patients, and 25 controls. With a high area under the curve of 0.85 between iRBD patients and controls, the combination of passive and active smartphone data provided a comparable or even more sensitive evaluation than laboratory examination using a high-quality microphone. The most sensitive features to induce prodromal neurodegeneration in iRBD included imprecise vowel articulation during phone calls (P = 0.03) and monopitch in reading (P = 0.05). Eighteen minutes of speech corresponding to approximately nine calls was sufficient to obtain the best sensitivity for the screening. We consider the developed tool widely applicable to deep longitudinal digital phenotyping data with future applications in neuroprotective trials, deep brain stimulation optimization, neuropsychiatry, speech therapy, population screening, and beyond. © 2024 The Author(s). Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.

Association of longitudinal hemodynamic phenotypes of coronary artery disease with myocardial perfusion and myocardial blood flow

Association of longitudinal hemodynamic phenotypes of coronary artery disease with myocardial perfusion and myocardial blood flow

Longitudinal fragility phenotyping contributes to the prediction of lifespan and age-associated morbidity in C57BL/6 and Diversity Outbred mice

Aging studies in mammalian models often depend on natural lifespan data as a primary outcome. Tools for lifespan prediction could accelerate these studies and reduce the need for veterinary intervention. Here, we leveraged large-scale longitudinal frailty and lifespan data on two genetically distinct mouse cohorts to evaluate noninvasive strategies to predict life expectancy in mice. We applied a modified frailty assessment, the Fragility Index, derived from existing frailty indices with additional deficits selected by veterinarians. We developed an ensemble machine learning classifier to predict imminent mortality (95% proportion of life lived [95PLL]). Our algorithm represented improvement over previous predictive criteria but fell short of the level of reliability that would be needed to make advanced prediction of lifespan and thus accelerate lifespan studies. Highly sensitive and specific frailty-based predictive endpoint criteria for aged mice remain elusive. While frailty-based prediction falls short as a surrogate for lifespan, it did demonstrate significant predictive power and as such must contain information that could be used to inform the conclusion of aging experiments. We propose a frailty-based measure of healthspan as an alternative target for aging research and demonstrate that lifespan and healthspan criteria reveal distinct aspects of aging in mice.

Longitudinal quantitative assessment of TMEV-IDD-induced MS phenotypes in two inbred mouse strains using automated video tracking technology

Multiple sclerosis (MS) is a chronic disabling disease of the central nervous system affecting over 2.5 million people worldwide. Theiler's murine encephalomyelitis virus-induced demyelinating disease (TMEV-IDD) is a murine model that reproduces the progressive form of MS and serves as a reference model for studying virus-induced demyelination.Certain mouse strains such as SJL are highly susceptible to this virus and serve as a prototype strain for studying TMEV infection. Other strains such as SWR are also susceptible, but their disease course following TMEV infection differs from SJL's.The quantification of motor and behavioral deficits following the induction of TMEV-IDD could help identify the differences between the two strains. Motor deficits have commonly been measured with the rotarod apparatus, but a multicomponent assessment tool has so far been lacking.For that purpose, we present a novel way of quantifying locomotor deficits, gait alterations and behavioral changes in this well-established mouse model of multiple sclerosis by employing automated video analysis technology (The PhenoTyper, Noldus Information Technology). We followed 12 SJL and 12 SWR female mice and their mock-infected counterparts over a period of 9 months following TMEV-IDD induction.We demonstrated that SJL and SWR mice both suffer significant gait alterations and reduced exploration following TMEV infection. However, SJL mice also display an earlier and more severe decline in spontaneous locomotion, especially in velocity, as well as in overall activity. Maintenance behaviors such as eating and grooming are not affected in either of the two strains. The system also showed differences in mock-infected mice from both strains, highlighting an age-related decline in spontaneous locomotion in the SJL strain, as opposed to hyperactivity in the SWR strain.Our study confirms that this automated video tracking system can reliably track the progression of TMEV-IDD for 9 months. We have also shown how this system can be utilized for longitudinal phenotyping in mice by describing useful parameters that quantify locomotion, gait and behavior.

Learning semi-supervised enrichment of longitudinal imaging-genetic data for improved prediction of cognitive decline

BackgroundAlzheimer’s Disease (AD) is a progressive memory disorder that causes irreversible cognitive decline. Given that there is currently no cure, it is critical to detect AD in its early stage during the disease progression. Recently, many statistical learning methods have been presented to identify cognitive decline with temporal data, but few of these methods integrate heterogeneous phenotype and genetic information together to improve the accuracy of prediction. In addition, many of these models are often unable to handle incomplete temporal data; this often manifests itself in the removal of records to ensure consistency in the number of records across participants.ResultsTo address these issues, in this work we propose a novel approach to integrate the genetic data and the longitudinal phenotype data to learn a fixed-length “enriched” biomarker representation derived from the temporal heterogeneous neuroimaging records. Armed with this enriched representation, as a fixed-length vector per participant, conventional machine learning models can be used to predict clinical outcomes associated with AD.ConclusionThe proposed method shows improved prediction performance when applied to data derived from Alzheimer’s Disease Neruoimaging Initiative cohort. In addition, our approach can be easily interpreted to allow for the identification and validation of biomarkers associated with cognitive decline.

Neonatal Vitamin D and Associations with Longitudinal Changes of Eczema up to 25 Years of Age.

Early-life vitamin D is a potentially modifiable risk factor for the development of eczema, but there is a lack of data on longitudinal associations. We measured 25(OH)D3 levels from neonatal dried blood spots in 223 high-allergy-risk children. Latent class analysis was used to define longitudinal eczema phenotype up to 25 years (4 subclasses). Skin prick tests (SPTs) to 6 allergens and eczema outcomes at 6 time points were used to define eczema/sensitization phenotypes. Associations between 25(OH)D3 and prevalent eczema and eczema phenotypes were assessed using logistic regression models. Median 25(OH)D3 level was 32.5 nmol/L (P25-P75 = 23.1 nmol/L). Each 10 nmol/L increase in neonatal 25(OH)D3 was associated with a 26% reduced odds of early-onset persistent eczema (adjusted multinomial odds ratio (aMOR) = 0.74, 95% CI = 0.56-0.98) and 30% increased odds of early-onset-resolving eczema (aMOR = 1.30, 95% CI = 1.05-1.62) when compared to minimal/no eczema up to 12 years. Similar associations were seen for eczema phenotype up to 25 years. We did not see any strong evidence for the association between neonatal 25(OH)D3 and prevalent eczema or eczema/sensitization phenotype. Higher neonatal 25(OH)D3 levels, a reflection of maternal vitamin D levels in pregnancy, may reduce the risk of early-onset persistent eczema.

Identification of Genetic Risk Factors Based on Disease Progression Derived From Longitudinal Brain Imaging Phenotypes.

Neurodegenerative disorders usually happen stage-by-stage rather than overnight. Thus, cross-sectional brain imaging genetic methods could be insufficient to identify genetic risk factors. Repeatedly collecting imaging data over time appears to solve the problem. But most existing imaging genetic methods only use longitudinal imaging phenotypes straightforwardly, ignoring the disease progression trajectory which might be a more stable disease signature. In this paper, we propose a novel sparse multi-task mixed-effects longitudinal imaging genetic method (SMMLING). In our model, disease progression fitting and genetic risk factors identification are conducted jointly. Specifically, SMMLING models the disease progression using longitudinal imaging phenotypes, and then associates fitted disease progression with genetic variations. The baseline status and changing rate, i.e., the intercept and slope, of the progression trajectory thus shoulder the responsibility to discover loci of interest, which would have superior and stable performance. To facilitate the interpretation and stability, we employ l2,1 -norm and the fused group lasso (FGL) penalty to identify loci at both the individual level and group level. SMMLING can be solved by an efficient optimization algorithm which is guaranteed to converge to the global optimum. We evaluate SMMLING on synthetic data and real longitudinal neuroimaging genetic data. Both results show that, compared to existing longitudinal methods, SMMLING can not only decrease the modeling error but also identify more accurate and relevant genetic factors. Most risk loci reported by SMMLING are missed by comparison methods, implicating its superiority in genetic risk factors identification. Consequently, SMMLING could be a promising computational method for longitudinal imaging genetics.

Smartphone keyboard dynamics predict affect in suicidal ideation

While digital phenotyping provides opportunities for unobtrusive, real-time mental health assessments, the integration of its modalities is not trivial due to high dimensionalities and discrepancies in sampling frequencies. We provide an integrated pipeline that solves these issues by transforming all modalities to the same time unit, applying temporal independent component analysis (ICA) to high-dimensional modalities, and fusing the modalities with linear mixed-effects models. We applied our approach to integrate high-quality, daily self-report data with BiAffect keyboard dynamics derived from a clinical suicidality sample of mental health outpatients. Applying the ICA to the self-report data (104 participants, 5712 days of data) revealed components related to well-being, anhedonia, and irritability and social dysfunction. Mixed-effects models (55 participants, 1794 days) showed that less phone movement while typing was associated with more anhedonia (β = −0.12, p = 0.00030). We consider this method to be widely applicable to dense, longitudinal digital phenotyping data.

Early-life nasal microbiota dynamics relate to longitudinal respiratory phenotypes in urban children

Five distinct respiratory phenotypes based on latent classes of longitudinal patterns of wheezing, allergic sensitization. and pulmonary function measured in urban children from ages from 0 to 7 years have previously been described. Our aim was to determine whether distinct respiratory phenotypes are associated with early-life upper respiratory microbiota development and environmental microbial exposures. Microbiota profiling was performed using 16S ribosomal RNA-based sequencing of nasal samples collected at age 12 months (n= 120) or age 36 months (n= 142) and paired house dust samples collected at 3 months (12-month, n= 73; 36-month, n= 90) from all 4 centers in the Urban Environment and Childhood Asthma (URECA) cohort. In these high-risk urban children, nasal microbiota increased in diversity between ages 12 and 36 months (ß= 2.04; P= .006). Age-related changes in microbiota evenness differed significantly by respiratory phenotypes (interaction P= .0007), increasing most in the transient wheeze group. At age 12 months, respiratory illness (R2= 0.055; P= .0001) and dominant bacterial genus (R2= 0.59; P= .0001) explained variance in nasal microbiota composition, and enrichment of Moraxella and Haemophilus members was associated with both transient and high-wheeze respiratory phenotypes. By age 36months, nasal microbiota was significantly associated with respiratory phenotypes (R2= 0.019; P= .0376), and Moraxella-dominated microbiota was associated specifically with atopy-associated phenotypes. Analysis of paired house dust and nasal samples indicated that 12 month olds with low wheeze and atopy incidence exhibited the largest number of shared bacterial taxa with their environment. Nasal microbiota development over the course of early childhood and composition at age 3 years are associated with longitudinal respiratory phenotypes. These data provide evidence supporting an early-life window of airway microbiota development that is influenced by environmental microbial exposures in infancy and associates with wheeze- and atopy-associated respiratory phenotypes through age 7 years.

Gene-dosage- and sex-dependent differences in the prodromal-Like phase of the F344tgHD rat model for Huntington disease.

In Huntington disease (HD) the prodromal phase has been increasingly investigated and is currently in focus for early interventional treatments. Also, the influence of sex on disease progression and severity in patients is under discussion, as a sex-specific impact has been reported in transgenic rodent models for HD. To this end, we have been studying these aspects in Sprague Dawley rats transgenic for HD. Here, we took up on the congenic F344tgHD rat model, expressing a fragmented Htt construct with 51 CAG repeats on an inbred F344 rat background and characterized potential sexual dimorphism and gene-dosage effects in rats during the pre-symptomatic phase (1-8 months of age). Our study comprises a longitudinal phenotyping of motor function, emotion and sensorimotor gating, as well as screening of metabolic parameters with classical and automated assays in combination with investigation of molecular HD hallmarks (striatal cell number and volume estimation, appearance of HTT aggregates). Differences between sexes became apparent during middle age, particularly in the motor and sensorimotor domains. Female individuals were generally more active, demonstrated different gait characteristics than males and less anxiolytic-like behavior. Alterations in both the time course and affected behavioral domains varied between male and female F344tgHD rats. First subtle behavioral anomalies were detected in transgenic F344tgHD rats prior to striatal MSN cell loss, revealing a prodromal-like phase in this model. Our findings demonstrate that the congenic F344tgHD rat model shows high face-validity, closely resembling the human disease's temporal progression, while having a relatively low number of CAG repeats, a slowly progressing pathology with a prodromal-like phase and a comparatively subtle phenotype. By differentiating the sexes regarding HD-related changes and characterizing the prodromal-like phase in this model, these findings provide a foundation for future treatment studies.

Management of treatment resistance-Treatment-resistant schizophrenia

Despite avery high prevalence and substantial impairments among affected individuals, treatment-resistant schizophrenia (TRS) has not been sufficiently researched in clinical research in the field of psychiatric disorders and the pathophysiology is still poorly understood. Abetter clinical and pathophysiological understanding of this heterogeneous and severely affected population of people with persistent symptoms in different domains is necessary in order not only to be able to intervene early but also to develop novel therapeutic strategies or individualized treatment approaches. This review article presents the state of the art criteria of the pharmacological TRS, neurobiological disease models and predictive factors for TRS as well as the phenomenon of pseudo-treatment resistance and the clinical management of TRS. In the future, not only the use of operationalized criteria and definitions of TRS in longitudinal studies and randomized-controlled trials (RCTs) are paramount, but also the observation of trajectories with the integration of multimodal longitudinal phenotyping and the longitudinal collection of clinical routine data in academic research, which will be possible in the newly created German Center for Mental Health (DZPG).

Identifying Longitudinal Intermediate Phenotypes between Genotypes and Clinical Score via Exclusive Relationship-Induced Association Analysis in Alzheimer’s Disease

Identifying Longitudinal Intermediate Phenotypes between Genotypes and Clinical Score via Exclusive Relationship-Induced Association Analysis in Alzheimer’s Disease

Natural history of rare diseases using natural language processing of narrative unstructured electronic health records: The example of Dravet syndrome.

The increasing implementation of electronic health records allows the use of advanced text-mining methods for establishing new patient phenotypes and stratification, and for revealing outcome correlations. In this study, we aimed to explore the electronic narrative clinical reports of a cohort of patients with Dravet syndrome (DS) longitudinally followed at our center, to identify the capacity of this methodology to retrace natural history of DS during the early years. We used a document-based clinical data warehouse employing natural language processing to recognize the phenotype concepts in the narrative medical reports. We included patients with DS who have a medical report produced before the age of 2 years and a follow-up after the age of 3 years ("DS cohort," 56 individuals). We selected two control populations, a "general control cohort" (275 individuals) and a "neurological control cohort" (281 individuals), with similar characteristics in terms of gender, number of reports, and age at last report. To find concepts specifically associated with DS, we performed a phenome-wide association study using Cox regression, comparing the reports of the three cohorts. We then performed a qualitative analysis of the surviving concepts based on their median age at first appearance. A total of 76 concepts were prevalent in the reports of children with DS. Concepts appearing during the first 2 years were mostly related with the epilepsy features at the onset of DS (convulsive and prolonged seizures triggered by fever, often requiring in-hospital care). Subsequently, concepts related to new types of seizures and to drug resistance appeared. A series of non-seizure-related concepts emerged after the age of 2-3 years, referring to the nonseizure comorbidities classically associated with DS. The extraction of clinical terms by narrative reports of children with DS allows outlining the known natural history of this rare disease in early childhood. This original model of "longitudinal phenotyping" could be applied to other rare and very rare conditions with poor natural history description.

Digital Phenotyping for Mood Disorders: Methodology-Oriented Pilot Feasibility Study.

In the burgeoning area of clinical digital phenotyping research, there is a dearth of literature that details methodology, including the key challenges and dilemmas in developing and implementing a successful architecture for technological infrastructure, patient engagement, longitudinal study participation, and successful reporting and analysis of diverse passive and active digital data streams. This article provides a narrative rationale for our study design in the context of the current evidence base and best practices, with an emphasis on our initial lessons learned from the implementation challenges and successes of this digital phenotyping study. We describe the design and implementation approach for a digital phenotyping pilot feasibility study with attention to synthesizing key literature and the reasoning for pragmatic adaptations in implementing a multisite study encompassing distinct geographic and population settings. This methodology was used to recruit patients as study participants with a clinician-validated diagnostic history of unipolar depression, bipolar I disorder, or bipolar II disorder, or healthy controls in 2 geographically distinct health care systems for a longitudinal digital phenotyping study of mood disorders. We describe the feasibility of a multisite digital phenotyping pilot study for patients with mood disorders in terms of passively and actively collected phenotyping data quality and enrollment of patients. Overall data quality (assessed as the amount of sensor data obtained vs expected) was high compared to that in related studies. Results were reported on the relevant demographic features of study participants, revealing recruitment properties of age (mean subgroup age ranged from 31 years in the healthy control subgroup to 38 years in the bipolar I disorder subgroup), sex (predominance of female participants, with 7/11, 64% females in the bipolar II disorder subgroup), and smartphone operating system (iOS vs Android; iOS ranged from 7/11, 64% in the bipolar II disorder subgroup to 29/32, 91% in the healthy control subgroup). We also described implementation considerations around digital phenotyping research for mood disorders and other psychiatric conditions. Digital phenotyping in affective disorders is feasible on both Android and iOS smartphones, and the resulting data quality using an open-source platform is higher than that in comparable studies. While the digital phenotyping data quality was independent of gender and race, the reported demographic features of study participants revealed important information on possible selection biases that may result from naturalistic research in this domain. We believe that the methodology described will be readily reproducible and generalizable to other study settings and patient populations given our data on deployment at 2 unique sites.

Impaired interferon response in plasmacytoid dendritic cells from children with persistent wheeze

Impaired interferon response and allergic sensitization may contribute to virus-induced wheeze and asthma development in young children. Plasmacytoid dendritic cells (pDCs) play a key role in antiviral immunity as critical producers of type I interferons. pDCs also express the high-affinity IgE receptor through which type I interferon production may be negatively regulated. Whether antiviral function of pDCs is associated with recurrent episodes of wheeze in young children is not well understood. We sought to evaluate the phenotype and function of circulating pDCs in children with a longitudinally defined wheezing phenotype. We performed multiparameter flow cytometry on PBMCs from 38 children presenting to the emergency department with an acute episode of respiratory wheeze and 19 controls. RNA sequencing on isolated pDCs from the same individuals was also performed. For each subject, their longitudinal exacerbation phenotype was determined using the Western Australia public hospital database. We observed a significant depletion of circulating pDCs inyoung children with a persistent phenotype of wheeze. The same individuals also displayed upregulation of the FcεRI on their pDCs. Based on transcriptomic analysis, pDCs from these individuals didnot mount a robust systemic antiviral response as observed inchildren who displayed a nonrecurrent wheezing phenotype. Our data suggest that circulating pDC phenotype and function are altered in young children with a persistent longitudinal exacerbation phenotype. Expression of high-affinity IgE receptor is increased and their function as major interferon producers is impaired during acute exacerbations of wheeze.

Longitudinal genome wide association studies in Alzheimer’s disease

AbstractBackgroundA large number of subjects are generally required for the genome wide association studies (GWAS) in complex traits such as Alzheimer’s disease (AD)1,2. Here we incorporated longitudinal data in AD‐GWAS to improve the statistical power for the identification of AD‐associated genetic variants with a limited sample‐size. Time‐varying genetic contributions towards AD could also be modeled and captured in this longitudinal GWAS.Method1,877 subjects from the ADNI database3 with genotyping data available were included in this analysis. Subjects’ genotype data were imputed to the Haplotype Reference Consortium using the Michigan Imputation Server4, with 9,573,130 single nucleotide polymorphisms (SNPs) remaining after the quality control step. Subjects’ longitudinal diagnosis at each visit were obtained from ADNI, providing 10,832 phenotypes for 1,877 subjects. Subjects’ diagnoses were further binarized into the normal and diseased groups. We applied the retrospective varying coefficient mixed model association test (RVMMAT) to detect time‐varying genetic effect on this longitudinal binary phenotype5. Briefly, dynamic genetic effect was modeled using smoothing splines and estimated by maximizing a double penalized quasi‐likelihood function via a retrospective approach. Subjects’ sex, age at each diagnosis, and the first 5 principal components of whole genome were included as covariates. A categorical variable for genotyping platforms was included as an additional covariate.ResultRVMMAT showed no evidence of inflation in the quantile‐quantile (Q‐Q) plot, with an inflation factor (lambda) of 0.99 (Fig. 1A). 45 SNPs reached genome‐wide significance after false discovery rate correction for multiple comparisons (Fig. 1(B)). Among these genetic variants, 35 SNPs were clustered at the APOE, APOC1, TOMM40, and NECTIN2 genes on chromosome 19, and 6 SNPs were associated with SLAIN2 gene on chromosome 4.ConclusionWe demonstrate that RVMMAT could boost the statistical power in AD‐GWAS with a limited sample‐size. We expect this method to benefit the identification of genetic variants associated with pathological or clinical biomarker‐based longitudinal phenotypes in AD.