Population-based Biobanks Research Articles

Integrating Population-Based Biobanks: Catalyst for Advances in Precision Health

Precision health extends beyond the scope of precision medicine and involves a broader range of activities, including the prediction, prevention, treatment, and management of diseases. Tailored to specific populations, precision health offers personalized treatment and preventive measures considering genetics, lifestyle behaviors, social determinants of health, and environmental factors. Precision medicine focuses on the personalized treatment of diseases, whereas precision health aims to promote health and prevent diseases using tools such as big data and advanced analytics to predict health risks and prevent diseases at the population level. Biobanks play a crucial role in achieving precision health because they provide well-characterized biological samples and related data for disease prediction, diagnosis, and treatment. Challenges in integrating different biobanks include data format consistency, privacy concerns, and legal constraints. Standardized methodologies and digitalization can mitigate these challenges. The integration of biobanks can facilitate comprehensive analyses across multiple datasets to achieve various research goals. This study proposes strategies to address these challenges, including the development of a dynamic consent mechanism for population-based biobanks using digitalization and blockchain technology. This study recommends the following: 1) integrating population-based biobanks, 2) introducing dynamic consent tools for human biobanks, and 3) using large human biobanks with dynamic consent for research on diverse diseases. These recommendations can increase the utility of biobanks in realizing precision health. A case study implemented at Taoyuan Tiansheng Hospital demonstrated the effectiveness of these recommendations for achieving precision health and enhancing the value of biobanks. Through a comprehensive examination of precision health and biobanks, this study provides valuable insights for researchers, healthcare professionals, and policymakers in the precision healthcare sector.

Early-onset atrial fibrillation is a risk marker for cardiomyopathy: genetic insights from the UK Biobank

Abstract Introduction Atrial fibrillation (AF) is a common cardiac arrhythmia associated with an increased risk of stroke, heart failure and premature death. Genetic associations between AF and rare variants have been identified in several cardiomyopathy-associated genes, and previous studies have shown a high prevalence of deleterious variants among individuals with early onset of AF. Consequently, genetic testing has been suggested in patients with AF onset before 45 years of age. Purpose This study aimed to explore the prevalence and distribution of deleterious genetic variants in cardiomyopathy genes according to age at AF diagnosis. Methods The UK Biobank is a large population-based biobank, comprised of clinical and genetic information on more than 500,000 individuals, enrolled between 2006 and 2010. We analyzed whole-exome sequencing data among unrelated individuals of European ancestry. The cohort was stratified by age at AF onset (AF onset <45 years, AF onset between 45-54 years, AF onset between 55-64 years, AF onset >=65 years, and individuals with no AF diagnosis). We evaluated the presence of rare (minor allele frequency <0.1%) genetic variants in core cardiomyopathy genes, according to guidelines by the American College of Medical Genomics and focused on variants predicted to lead to loss of function. Individuals with a diagnosis of cardiomyopathy prior to AF were excluded. Results Among 374,365 unrelated individuals of European ancestry, 29,119 were diagnosed with AF. Of these, 685 had onset of AF<45 years (2.35%), 2465 had onset between 45-54 years (8.46%), 7778 had onset between 55-64 years (26.71%), and 18191 had onset at or above 65 years of age (62.48%). A total of 884 were carriers of rare loss-of-function variants in cardiomyopathy genes. Among carriers, the majority of variants were in key genes for dilated cardiomyopathy (TTN), hypertrophic cardiomyopathy (MYBPC3), and arrhythmogenic right ventricular cardiomyopathy (PKP2). We observed an inverse dose-response-like relationship between prevalence of the rare genetic variants and age at AF diagnosis. The highest prevalence of carriers was observed among individuals with onset before 45 years (4.96%). In comparison, the proportion of carriers was 3.45% in individuals with AF onset between 45-54 years, 2.96% in individuals with AF onset between 55-64 years, 2.75% in individuals with onset at or after 65 years, and 2.05% among individuals without AF diagnosis. Conclusions Using whole exome sequencing, we identified a high proportion of carriers of deleterious variants in actionable genes of cardiomyopathies among individuals with early onset of AF. We observed an inverse dose-response-like relationship between age of AF diagnosis and prevalence of deleterious genetic variants. Our findings support that very early onset of AF, before 45 years of age, is a potential risk marker for cardiomyopathy and support that genetic screening may be relevant in this patient group.

Comorbidities confound metabolomics studies of human disease

The co-occurrence of multiple chronic conditions, termed multimorbidity, presents an expanding global health challenge, demanding effective diagnostics and treatment strategies. Chronic ailments such as obesity, diabetes, and cardiovascular diseases have been linked to metabolites interacting between the host and microbiota. In this study, we investigated the impact of co-existing conditions on risk estimations for 1375 plasma metabolites in 919 individuals from population-based Estonian Biobank cohort using liquid chromatography mass spectrometry (LC–MS) method. We leveraged annually linked national electronic health records (EHRs) data to delineate comorbidities in incident cases and controls for the 14 common chronic conditions. Among the 254 associations observed across 13 chronic conditions, we primarily identified disease-specific risk factors (92%, 217/235), with most predictors (93%, 219/235) found to be related to the gut microbiome upon cross-referencing recent literature data. Accounting for comorbidities led to a reduction of common metabolite predictors across various conditions. In conclusion, our study underscores the potential of utilizing biobank-linked retrospective and prospective EHRs for the disease-specific profiling of diverse multifactorial chronic conditions.

Genetically Enriched Clinical Trials for Precision Development of Noncancer Therapeutics: A Scoping Review.

Genetically driven clinical trial enrichment has been proposed to accelerate and reduce the cost of developing new therapeutics. Usage of this approach has not been comprehensively reviewed. We searched Ovid MEDLINE, Embase, Web of Science, Cochrane Library, ClinicalTrials.gov, and WHO ICTRP for articles published between 2010 and 2023. Excluding absorption, distribution, metabolism, and elimination pharmacogenetic studies and anti-infectives, we found 95 completed, 4 terminated, and 22 ongoing prospective genetically enriched trials on 110 drugs for 48 nononcology, nonrare syndromic indications. Trial sizes ranged from 4 to 6,147 participants (median 72) and covered numerous disease areas, particularly neurology (30), metabolism (22), and psychiatry (17). Fifty-six completed studies (60%) met their primary end point. Overall, this scoping review demonstrates that genetically enriched trials are feasible and scalable across disease areas and provide critical information for further development, or attrition, of investigational drugs. Large, appropriately designed disease-, hospital-, or population-based biobanks will undoubtedly facilitate this type of precision drug development approach.

Evaluation of coverage, generalisability and validity of the U-CAN lymphoma biobank in Sweden: A comparison with nationwide registers.

Validation of biobanks and large cancer cohorts is essential in ensuring high-quality research results. We examined the coverage, generalisability and validity of the lymphoma collection of the Uppsala-Umeå Comprehensive Cancer Consortium (U-CAN) biobank in Sweden, one of the largest cancer biobanks in Europe. Up until 2022, 889 lymphoma patients in U-CAN Uppsala had available samples, and 329 in U-CAN Umeå. Patients diagnosed in the U-CAN Uppsala area 2011-2021 (n = 843) were linked to the nationwide Swedish Lymphoma Register, and a subset diagnosed before 2019 (n = 727) to population-based registers. The coverage was 39% of all lymphoma patients between 2011 and 2019 diagnosed in the U-CAN Uppsala area, with a pandemic decline to 10% during 2020-2021. The patients included had superior overall survival (hazard ratio = 0.70 [95% confidence interval, CI: 0.60-0.82]) than all lymphoma patients in Sweden. They had better performance status, were younger (odds ratio [OR] = 0.21 [95% CI: 0.13-0.34]) and had less comorbidities (OR = 0.66 [95% CI: 0.56-0.78]). However, cause-specific survival and stage distribution were similar. The questionnaire data captured less comorbidities compared to the national registers. Evaluations of biobanks are important, as even population-based biobanks such as U-CAN select younger patients with higher socioeconomical status and better performance status. However, the similar cause-specific survival as in the registries suggests U-CANs usefulness for prognostic biomarker studies.

Electronic health record biobank cohort recapitulates an association between the MUC5B promoter polymorphism and ARDS in critically ill adults.

Large population-based DNA biobanks linked to electronic health records (EHRs) may provide advantages over traditional study designs for identifying genetic drivers of ARDS. Can ARDS be identified in an EHR biobank, and can this approach validate a previously reported genetic risk factor for ARDS? We analyzed two genotyped cohorts from one academic medical center: a prospective biomarker study of critically ill adults (VALID cohort), and hospitalized participants in a de-identified EHR biobank (BioVU). ARDS status was assessed by clinician-investigator review in VALID and an EHR-derived algorithm in BioVU (EHR-ARDS). We tested the association between the MUC5B promoter polymorphism (rs35705950) with development of ARDS/EHR-ARDS in each cohort. In VALID, 2,795 patients were included, age was 55 [43, 66] (median [IQR]) years, and 718 (25.7%) developed ARDS. In BioVU, 9,025 hospitalized participants were included, age was 60 [48, 70] , and 1,056 (11.7%) developed EHR-ARDS. We observed a significant interaction between age and rs35705950 on ARDS risk in VALID: in older patients rs35705950 was associated with increased ARDS risk (OR: 1.44; 95%CI 1.08-1.92; p=0.012) whereas among younger patients this effect was attenuated (OR: 0.84; 95%CI: 0.62-1.14; p=0.26). In BioVU, rs35705950 was associated with increased risk for EHR-ARDS among all participants (OR: 1.20; 95%CI: 1.00-1.43, p=0.043) and this relationship did not vary by age. The polymorphism was also associated with more severe oxygenation impairment among BioVU participants who required mechanical ventilation. The MUC5B promoter polymorphism was associated with ARDS in two cohorts of at-risk hospitalized adults. Although age-related effect modification was observed only in the prospective biomarker cohort, the EHR cohort identified a consistent association between MUC5B and ARDS risk regardless of age and a novel association with oxygenation impairment. Our study highlights the potential for EHR biobanks to enable precision-medicine ARDS studies.

Willingness to participate in a personalized health cohort – insights from the swiss health study pilot phase

BackgroundThis paper explores the feasibility of establishing a large-scale population-based cohort and biobank in Switzerland by assessing potential participants’ needs, expectations, and concerns about such an infrastructure providing information on health, lifestyle, and exposure trajectories, the development of disease, and risk factors over time.MethodsWe utilized a scenario-based questionnaire in the Swiss Health Study pilot phase (2020–2021), involving 1349 adults aged 20–69 from the cantons Vaud and Bern. We conducted descriptive statistics supported by R and qualitative content analysis of n = 374 open responses related to attitudes towards research.ResultsWe highlight the benefits and challenges of the scenario-based approach, discuss the sample represented in the pilot phase, and present implications for building a full cohort. We also report on participants’ attitudes towards and previous experience with health research. We analyze references regarding informed consent and feedback, attitudes towards the Swiss Health Study, and recommendations on improving its scope, design, and instruments. Results indicate a high interest (90%) in participating in a national health study, with 85% of a random population sample willing to join a long-term cohort. Only 43% were familiar with biobanks, and 44% preferred general consent. Trust was high for Swiss-based public research but lower for researchers from other countries or private sector. Over 95% expressed willingness to complete online questionnaires, undergo physical examination, and donate biosamples. Almost all participants wanted to know the outcomes of the medical tests (99.5%) and the exposure to environmental stressors (95%) from their study center visit. Preferred tools for monitoring sleep, physical activity, and diet were known smartphone apps with automatic data management.ConclusionOverall, the study reveals a positive attitude towards personalized health research, with a strong willingness to share data and samples. Key insights focus the meaning of informed consent for participation, the relevance of sampling and representativeness, as well as the significance and challenges of personalized feedback, especially regarding environmental health concerns. Findings emphasize participants’ supportive yet reflexive stances, underscoring the importance of aligning research values with individual values in personalized health research. These insights contribute valuable considerations for refining the scope, design, and instruments of future cohort studies.

Genomic Analysis Identifies Risk Factors in Restless Legs Syndrome.

Restless legs syndrome (RLS) is a neurological condition that causes uncomfortable sensations in the legs and an irresistible urge to move them, typically during periods of rest. The genetic basis and pathophysiology of RLS are incompletely understood. We sought to identify additional novel genetic risk factors associated with RLS susceptibility. We performed a whole-genome sequencing and genome-wide association meta-analysis of RLS cases (n = 9,851) and controls (n = 38,957) in 3 population-based biobanks (All of Us, Canadian Longitudinal Study on Aging, and CARTaGENE). Genome-wide association analysis identified 9 independent risk loci, of which 8 had been previously reported, and 1 was a novel risk locus (LMX1B, rs35196838, OR 1.14, 95% CI 1.09-1.19, p value = 2.2 × 10-9). Furthermore, a transcriptome-wide association study also identified GLO1 and a previously unreported gene, ELFN1. A genetic correlation analysis revealed significant common variant overlaps between RLS and neuroticism (rg = 0.40, se = 0.08, p value = 5.4 × 10-7), depression (rg = 0.35, se = 0.06, p value = 2.17 × 10-8), and intelligence (rg = -0.20, se = 0.06, p value = 4.0 × 10-4). Our study expands the understanding of the genetic architecture of RLS, and highlights the contributions of common variants to this prevalent neurological disorder. ANN NEUROL 2024;96:994-1005.

A genome-wide association study of hand eczema identifies locus 20q13.33 and reveals genetic overlap with atopic dermatitis.

Twin studies revealed that genetic effects play a role in hand eczema (HE), but the responsible genetic factors are unknown. To identify and characterise genetic loci associated with HE and to provide insight into the genetic overlap between HE and atopic dermatitis (AD). We used questionnaire-derived and genotype data from the European population-based Lifelines cohort and biobank. We performed a discovery genome-wide association study (GWAS) of HE (2879 cases and 16 249 controls) and of AD (1706 cases and 17 190 controls). We replicated our findings in an independent Lifelines sample for HE (1188 cases and 6431 controls) and AD (757 cases and 6747 controls). We conducted several post-GWAS analyses and performed genetic correlation analyses between our HE results and independent AD data. The two-step GWAS of HE, regardless of adjusting for AD, identified one independent locus 20q13.33, likely driven by a number of causal single-nucleotide polymorphisms. For the AD GWAS, we replicated a known stop-gained rs61816761 at locus 1q21.3 (FLG, FLGAS1). We found a strong genetic correlation (p < 0.01) between HE and AD (rg = 0.65), regardless of adjusting for AD (rg = 0.63). Locus 20q13.33 is associated with HE, and there is a large genetic overlap between HE and AD.

Metabolomics for early pancreatic cancer detection in plasma samples from a Swedish prospective population-based biobank.

Pancreatic ductal adenocarcinoma (pancreatic cancer) is often detected at late stages resulting in poor overall survival. To improve survival, more patients need to be diagnosed early when curative surgery is feasible. We aimed to identify circulating metabolites that could be used as early pancreatic cancer biomarkers. We performed metabolomics by liquid and gas chromatography-mass spectrometry in plasma samples from 82 future pancreatic cancer patients and 82 matched healthy controls within the Northern Sweden Health and Disease Study (NSHDS). Logistic regression was used to assess univariate associations between metabolites and pancreatic cancer risk. Least absolute shrinkage and selection operator (LASSO) logistic regression was used to design a metabolite-based risk score. We used receiver operating characteristic (ROC) analyses to assess the discriminative performance of the metabolite-based risk score. Among twelve risk-associated metabolites with a nominal P value <0.05, we defined a risk score of three metabolites [indoleacetate, 3-hydroxydecanoate (10:0-OH), and retention index (RI): 2,745.4] using LASSO. A logistic regression model containing these three metabolites, age, sex, body mass index (BMI), smoking status, sample date, fasting status, and carbohydrate antigen 19-9 (CA 19-9) yielded an internal area under curve (AUC) of 0.784 [95% confidence interval (CI): 0.714-0.854] compared to 0.681 (95% CI: 0.597-0.764) for a model without these metabolites (P value =0.007). Seventeen metabolites were significantly associated with pancreatic cancer survival [false discovery rate (FDR) <0.1]. Indoleacetate, 3-hydroxydecanoate (10:0-OH), and RI: 2,745.4 were identified as the top candidate biomarkers for early detection. However, continued efforts are warranted to determine the usefulness of these metabolites as early pancreatic cancer biomarkers.

Defining disease severity in atopic dermatitis and psoriasis for the application to biomarker research: aninterdisciplinary perspective.

More severe atopic dermatitis and psoriasis are associated with a higher cumulative impact on quality of life, multimorbidity and healthcare costs. Proactive, early intervention in those most at risk of severe disease may reduce this cumulative burden and modify the disease trajectory to limit progression. The lack of reliable biomarkers for this at-risk group represents a barrier to such a paradigm shift in practice. To expedite discovery and validation, the BIOMarkers in Atopic Dermatitis and Psoriasis (BIOMAP) consortium (a large-scale European, interdisciplinary research initiative) has curated clinical and molecular data across diverse study designs and sources including cross-sectional and cohort studies (small-scale studies through to large multicentre registries), clinical trials, electronic health records and large-scale population-based biobanks. We map all dataset disease severity instruments and measures to three key domains (symptoms, inflammatory activity and disease course), and describe important codependencies and relationships across variables and domains. We prioritize definitions for more severe disease with reference to international consensus, reference standards and/or expert opinion. Key factors to consider when analysing datasets across these diverse study types include explicit early consideration of biomarker purpose and clinical context, candidate biomarkers associated with disease severity at a particular point in time and over time and how they are related, taking the stage of biomarker development into account when selecting disease severity measures for analyses, and validating biomarker associations with disease severity outcomes using both physician- and patient-reported measures and across domains. The outputs from this exercise will ensure coherence and focus across the BIOMAP consortium so that mechanistic insights and biomarkers are clinically relevant, patient-centric and more generalizable to current and future research efforts.

Disentangling the link between maternal influences on birth weight and disease risk in 36,211 genotyped mother–child pairs

Epidemiological studies have robustly linked lower birth weight to later-life disease risks. These observations may reflect the adverse impact of intrauterine growth restriction on a child’s health. However, causal evidence supporting such a mechanism in humans is largely lacking. Using Mendelian Randomization and 36,211 genotyped mother-child pairs from the FinnGen study, we assessed the relationship between intrauterine growth and five common health outcomes (coronary heart disease (CHD), hypertension, statin use, type 2 diabetes and cancer). We proxied intrauterine growth with polygenic scores for maternal effects on birth weight and took into account the transmission of genetic variants between a mother and a child in the analyses. We find limited evidence for contribution of normal variation in maternally influenced intrauterine growth on later-life disease. Instead, we find support for genetic pleiotropy in the fetal genome linking birth weight to CHD and hypertension. Our study illustrates the opportunities that data from genotyped parent-child pairs from a population-based biobank provides for addressing causality of maternal influences.

Effects of endocrine disrupting chemicals and their interactions with genetic risk scores on cardiometabolic traits

Ubiquitous non-persistent endocrine disrupting chemicals (EDCs) have inconsistent associations with cardiometabolic traits. Additionally, large-scale genome-wide association studies (GWASs) have yielded many genetic risk variants for cardiometabolic traits and diseases. This study aimed to investigate the associations between a wide range of EDC exposures (parabens, bisphenols, and phthalates) and 14 cardiometabolic traits and whether these are moderated by their respective genetic risk scores (GRSs). Data were from 1074 participants aged 18 years or older of the Lifelines Cohort Study, a large population-based biobank. GRSs for 14 cardiometabolic traits were calculated based on genome-wide significant common variants from recent GWASs. The concentrations of 15 EDCs in 24-hour urine were measured by isotope dilution liquid chromatography tandem mass spectrometry technology. The main effects of trait-specific GRSs and each of the EDC exposures and their interaction effects on the 14 cardiometabolic traits were examined in multiple linear regression. The present study confirmed significant main effects for all GRSs on their corresponding cardiometabolic trait. Regarding the main effects of EDC exposures, 26 out of 280 EDC-trait tests were significant with explained variances ranging from 0.43 % (MMP- estimated glomerular filtration rate (eGFR)) to 2.37 % (PrP-waist-hip ratio adjusted body mass index (WHRadjBMI)). We confirmed the association of MiBP and MBzP with WHRadjBMI and body mass index (BMI), and showed that parabens, bisphenol F, and many other phthalate metabolites significantly contributed to the variance of WHRadjBMI, BMI, high-density lipoprotein (HDL), eGFR, fasting glucose (FG), and diastolic blood pressure (DBP). Only one association between BMI and bisphenol F was nominally significantly moderated by the GRS explaining 0.36 % of the variance. However, it did not survive multiple testing correction. We showed that non-persistent EDC exposures exerted effects on BMI, WHRadjBMI, HDL, eGFR, FG, and DBP. However no evidence for a modulating role of GRSs was found.

Population-Based Biobanking.

Population-based biobanking is an essential element of medical research that has grown substantially over the last two decades, and many countries are currently pursuing large national biobanking initiatives. The rise of individual biobanks is paralleled by various networking activities in the field at both the national and international level, such as BBMRI-ERIC in the EU. A significant contribution to population-based biobanking comes from large cohort studies and national repositories, including the United Kingdom Biobank (UKBB), the CONSTANCES project in France, the German National Cohort (NAKO), LifeLines in the Netherlands, FinnGen in Finland, and the All of Us project in the U.S. At the same time, hospital-based biobanking has also gained importance in medical research. We describe some of the scientific questions that can be addressed particularly well by the use of population-based biobanks, including the discovery and calibration of biomarkers and the identification of molecular correlates of health parameters and disease states. Despite the tremendous progress made so far, some major challenges to population-based biobanking still remain, including the need to develop strategies for the long-term sustainability of biobanks, the handling of incidental findings, and the linkage of sample-related and sample-derived data to other relevant resources.

O.4.2-9 Shared genetic factors may partly explain the associations between physical activity and cardiometabolic diseases

Abstract Shared genetic factors may contribute to the associations between higher levels of physical activity (PA) and lower risk for cardiometabolic diseases (CMDs), and may partially explain these associations observed in cohort studies. To explore this, we used novel methodology to calculate PA genotypes (polygenic risk score, PRS) and validated them against measured or reported PA in three independent cohorts. We then investigated the associations between polygenic inheritance of PA and cardiometabolic risk factors and diseases in two large population-based biobank datasets, and examined whether selected associations were independent of self-reported PA. Our study utilized the UK Biobank as a base dataset (N = 400,124) and constructed genomewide PRSs for both self-reported and device-measured PA using single nucleotide polymorphism (SNP)-specific weights and SBayesR methodology. Both PRSs for PA included over one million SNPs. PRSs were constructed in the Finnish Twin cohort (N = 759–11,528), the Northern Finland Birth Cohort 1966 (N = 3,263–4,061), the Trøndelag Health Study cohort (HUNT, N = 47,148), and the FinnGen (N = 218,792). Cardiometabolic risk factors were measured in laboratory conditions, and CMD outcomes were derived from national health registers (ICD codes). We utilized linear, logistic, and cox regression methods for analysis. Our results showed that genotypes predisposing to higher PA were associated with higher levels of PA in independent datasets, but PRSs accounted for only a limited amount of variation (0.13-1.44%). Genotypes supporting higher PA were associated with lower body mass index [B=-0.002 in HUNT and B=-0.025 in FinnGen] and favorable cardiometabolic health in HUNT (waist circumference [B=-0.003] and HDL cholesterol [B = 0.004]). Genotypes supporting higher PA volumes were associated with lower incidence of CMDs in both HUNT and FinnGen. The strongest associations were found in hypertensive diseases and Type 2 Diabetes. In HUNT, the observed associations were not materially changed after accounting for self-reported PA. Higher PRS for PA was also associated with lower risk of mortality in FinnGen. Our findings suggest small pleiotropic effects between PA and CMDs. This means that same genetic variation may explain both physical activity behaviour and risk of diseases. PRSs provide new tools for genetic studies in sport science, but they currently have substantial practical limitations.

A FinnGen pilot clinical recall study for Alzheimer’s disease

Successful development of novel therapies requires that clinical trials are conducted in patient cohorts with the highest benefit-to-risk ratio. Population-based biobanks with comprehensive health and genetic data from large numbers of individuals hold promise to facilitate identification of trial participants, particularly when interventions need to start while symptoms are still mild, such as for Alzheimer’s disease (AD). This study describes a process for clinical recall studies from FinnGen. We demonstrate the feasibility to systematically ascertain customized clinical data from FinnGen participants with ICD10 diagnosis of AD or mild cognitive disorder (MCD) in a single-center cross-sectional study testing blood-based biomarkers and cognitive functioning in-person, computer-based and remote. As a result, 19% (27/140) of a pre-specified FinnGen subcohort were successfully recalled and completed the study. Hospital records largely validated registry entries. For 8/12 MCD patients, other reasons than AD were identified as underlying diagnosis. Cognitive measures correlated across platforms, with highest consistencies for dementia screening (r = 0.818) and semantic fluency (r = 0.764), respectively, for in-person versus telephone-administered tests. Glial fibrillary acidic protein (GFAP) (p < 0.002) and phosphorylated-tau 181 (pTau-181) (p < 0.020) most reliably differentiated AD from MCD participants. We conclude that informative, customized clinical recall studies from FinnGen are feasible.

Persuasive Appeals in Genetic Biobank Recruitment Campaigns: Social and Ethical Implications.

The social and ethical implications of large-scale biobank donor recruitment campaigns have remained understudied. We use two recent campaigns of the population-based genetic biobank in Estonia as an example to demonstrate how campaign spokespersons try to persuade potential donors by appealing to (1) gaining self-knowledge, (2) gaining control over one's health, (3) fear of illness, (4) contributing to healthcare, (5) contributing to science, and (6) contributing to one's country. While these campaigns succeeded in recruiting 15 percent of the country's adult population as donors, we explain how the use of some of these appeals may (a) create unrealistic expectations regarding the benefits donors could receive and (b) conceal the risks regarding health data. The study lays a necessary groundwork for future empirical research on the ethics of biobank recruitment campaigns.

Evaluation of Brain-Body Health in Individuals With Common Neuropsychiatric Disorders

Physical health and chronic medical comorbidities are underestimated, inadequately treated, and often overlooked in psychiatry. A multiorgan, systemwide characterization of brain and body health in neuropsychiatric disorders may enable systematic evaluation of brain-body health status in patients and potentially identify new therapeutic targets. To evaluate the health status of the brain and 7 body systems across common neuropsychiatric disorders. Brain imaging phenotypes, physiological measures, and blood- and urine-based markers were harmonized across multiple population-based neuroimaging biobanks in the US, UK, and Australia, including UK Biobank; Australian Schizophrenia Research Bank; Australian Imaging, Biomarkers, and Lifestyle Flagship Study of Ageing; Alzheimer's Disease Neuroimaging Initiative; Prospective Imaging Study of Ageing; Human Connectome Project-Young Adult; and Human Connectome Project-Aging. Cross-sectional data acquired between March 2006 and December 2020 were used to study organ health. Data were analyzed from October 18, 2021, to July 21, 2022. Adults aged 18 to 95 years with a lifetime diagnosis of 1 or more common neuropsychiatric disorders, including schizophrenia, bipolar disorder, depression, generalized anxiety disorder, and a healthy comparison group were included. Deviations from normative reference ranges for composite health scores indexing the health and function of the brain and 7 body systems. Secondary outcomes included accuracy of classifying diagnoses (disease vs control) and differentiating between diagnoses (disease vs disease), measured using the area under the receiver operating characteristic curve (AUC). There were 85 748 participants with preselected neuropsychiatric disorders (36 324 male) and 87 420 healthy control individuals (40 560 male) included in this study. Body health, especially scores indexing metabolic, hepatic, and immune health, deviated from normative reference ranges for all 4 neuropsychiatric disorders studied. Poor body health was a more pronounced illness manifestation compared to brain changes in schizophrenia (AUC for body = 0.81 [95% CI, 0.79-0.82]; AUC for brain = 0.79 [95% CI, 0.79-0.79]), bipolar disorder (AUC for body = 0.67 [95% CI, 0.67-0.68]; AUC for brain = 0.58 [95% CI, 0.57-0.58]), depression (AUC for body = 0.67 [95% CI, 0.67-0.68]; AUC for brain = 0.58 [95% CI, 0.58-0.58]), and anxiety (AUC for body = 0.63 [95% CI, 0.63-0.63]; AUC for brain = 0.57 [95% CI, 0.57-0.58]). However, brain health enabled more accurate differentiation between distinct neuropsychiatric diagnoses than body health (schizophrenia-other: mean AUC for body = 0.70 [95% CI, 0.70-0.71] and mean AUC for brain = 0.79 [95% CI, 0.79-0.80]; bipolar disorder-other: mean AUC for body = 0.60 [95% CI, 0.59-0.60] and mean AUC for brain = 0.65 [95% CI, 0.65-0.65]; depression-other: mean AUC for body = 0.61 [95% CI, 0.60-0.63] and mean AUC for brain = 0.65 [95% CI, 0.65-0.66]; anxiety-other: mean AUC for body = 0.63 [95% CI, 0.62-0.63] and mean AUC for brain = 0.66 [95% CI, 0.65-0.66). In this cross-sectional study, neuropsychiatric disorders shared a substantial and largely overlapping imprint of poor body health. Routinely monitoring body health and integrated physical and mental health care may help reduce the adverse effect of physical comorbidity in people with mental illness.

Recruiting African American Prostate Cancer Survivors for a Population-based Biobank Study.

Prostate cancer affects African American men disproportionately compared with men of other racial/ethnic groups. To identify biological bases for this health disparity, we sought to create a state-wide biobank of African American prostate cancer survivors in Florida. African American men diagnosed with prostate cancer between 2013 and 2017 and living in Florida at diagnosis were identified through the State of Florida's cancer registry. Individuals were approached via mail and telephone, assessed for eligibility, and asked for informed consent. χ2 and t tests were conducted to identify differences between eligible and reachable individuals (i.e., had valid contact information) versus consented participants. Of the 5,960 eligible and reachable individuals, 3,904 were eligible and contacted at least once, and 578 consented [overall consent rate = 10% (578/5,960); adjusted consent rate = 15% (578/3,904)]. Statistically significant (Ps < 0.05) but small differences in demographic and clinical variables were observed. Consented participants were less likely to be older than 64 (35% vs. 41%) and less likely to have received radiotherapy (36% vs. 41%) and hormone therapy (16% vs. 21%), but more likely to have regional prostate cancer (13% vs. 11%) and have undergone surgery (44% vs. 39%). Consented participants did not differ from reachable individuals on other demographic and clinical factors (Ps > 0.05). Recruiting African American prostate cancer survivors to biobanking research through a cancer registry is feasible. However, the consent rate was low, and existing challenges limit consent and participation. Strategies for overcoming barriers to informed consent and increasing participation in biospecimen research are needed to address cancer disparities.

Genetic Validation of Psoriasis Phenotyping in UK Biobank Supports the Utility of Self-Reported Data and Composite Definitions for Large Genetic and Epidemiological Studies

Genetic Validation of Psoriasis Phenotyping in UK Biobank Supports the Utility of Self-Reported Data and Composite Definitions for Large Genetic and Epidemiological Studies