New models for large prospective studies: is there a better way?

Association between elevated fibrosis-4 index of liver fibrosis and risk of hemorrhagic stroke

BACKGROUND

Cirrhosis is associated with an increased risk of hemorrhagic stroke. Liver fibrosis, typically a silent condition, is antecedent to cirrhosis. The objective of this study was to test the hypothesis that elevated Fibrosis-4 (FIB-4) index, indicating a high probability of liver fibrosis, is associated with an increased risk of hemorrhagic stroke.

METHODS

We performed a cohort analysis of the prospective United Kingdom Biobank cohort study. Participants 40-69 years old were enrolled between 2007 and 2010 and had available follow-up data until March 1, 2018. We excluded participants with prevalent hemorrhagic stroke or thrombocytopenia. High probability of liver fibrosis was defined as having a value >2.67 of the validated FIB-4 index. The primary outcome was hemorrhagic stroke (intracerebral or subarachnoid hemorrhage), defined based on hospitalization and death registry data. Secondary outcomes were intracerebral and subarachnoid hemorrhage, separately. We used Cox proportional hazards models to evaluate the association of FIB-4 index >2.67 with hemorrhagic stroke while adjusting for potential confounders including hypertension, alcohol use, and antithrombotic use.

RESULTS

Among 452,994 participants (mean age, 57 years; 54% women), approximately 2% had FIB-4 index >2.67, and 1241 developed hemorrhagic stroke. In adjusted models, FIB-4 index >2.67 was associated with an increased risk of hemorrhagic stroke (HR, 2.0; 95% CI, 1.6-2.6). Results were similar for intracerebral hemorrhage (HR, 2.0; 95% CI, 1.5-2.7) and subarachnoid hemorrhage (HR, 2.2; 95% CI, 1.5-3.5) individually.

CONCLUSIONS

Elevated FIB-4 index was associated with an increased risk of hemorrhagic stroke.

Systematic review of 99 extremity bone malignancy survival prediction models

BACKGROUND

Various prediction models have been developed for extremity metastasis and sarcoma. This systematic review aims to evaluate extremity metastasis and sarcoma models using the utility prediction model (UPM) evaluation framework.

METHODS

We followed Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines and systematically searched PubMed, Embase, and Cochrane to identify articles presenting original prediction models with 1-year survival outcome for extremity metastasis and 5-year survival outcome for sarcoma. Identified models were assessed using the UPM score (0-16), categorized as excellent (12-16), good (7-11), fair (3-6), or poor (0-2). A total of 5 extremity metastasis and 94 sarcoma models met inclusion criteria and were analyzed for design, validation, and performance.

RESULTS

We assessed 5 models for extremity metastasis and 94 models for sarcoma. Only 4 out of 99 (4%) models achieved excellence, 1 from extremity metastasis and 3 from sarcoma. The majority were rated good (62%; 61/99), followed by fair (31%, 31/99) and poor (3%, 3/99).

CONCLUSIONS

Most predictive models for extremity metastasis and sarcoma fall short of UPM excellence. Suboptimal study design, limited external validation, and the infrequent availability of web-based calculators are main drawbacks.

LEVEL OF EVIDENCE

This study is classified as Level 2a evidence according to the Oxford 2011 Levels of Evidence. Trial registration This study was registered in PROSEPRO (CRD42022373391, https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=373391 ).

Accelerating Heritability, Genetic Correlation, and Genome-Wide Association Imaging Genetic Analyses in Complex Pedigrees

National and international biobanking efforts led to the collection of large and inclusive imaging genetics datasets that enable examination of the contribution of genetic and environmental factors to human brains in illness and health. High-resolution neuroimaging (~104-6 voxels) and genetic (106-8 single nucleotide polymorphic [SNP] variants) data are available in statistically powerful (N = 103-5) epidemiological and disorder-focused samples. Performing imaging genetics analyses at full resolution afforded in these datasets is a formidable computational task even under the assumption of unrelatedness among the subjects. The computational complexity rises as ~N2-3 (where N is the sample size), when accounting for relatedness among subjects. We describe fast, non-iterative simplifications to accelerate classical variance component (VC) methods including heritability, genetic correlation, and genome-wide association in dense and complex empirical pedigrees. These approaches linearize (from N2-3 to N~1) computational effort while maintaining fidelity (r ~ 0.95) with the VC results and take advantage of parallel computing provided by central and graphics processing units (CPU and GPU). We show that the new approaches lead to a 104- to 106-fold reduction in computational complexity-making voxel-wise heritability, genetic correlation, and genome-wide association studies (GWAS) analysis practical for large and complex samples such as those provided by the Amish and Human Connectome Projects (N = 406 and 1052 subjects, respectively) and UK Biobank (N = 31,681). These developments are shared in open-source, SOLAR-Eclipse software.

The stroke outcome optimization project: Acute ischemic strokes from a comprehensive stroke center

Stroke is a leading cause of disability, and Magnetic Resonance Imaging (MRI) is routinely acquired for acute stroke management. Publicly sharing these datasets can aid in the development of machine learning algorithms, particularly for lesion identification, brain health quantification, and prognosis. These algorithms thrive on large amounts of information, but require diverse datasets to avoid overfitting to specific populations or acquisitions. While there are many large public MRI datasets, few of these include acute stroke. We describe clinical MRI using diffusion-weighted, fluid-attenuated and T1-weighted modalities for 1715 individuals admitted in the upstate of South Carolina, of whom 1461 have acute ischemic stroke. Demographic and impairment data are provided for 1106 of the stroke survivors from this cohort. Our validation demonstrates that machine learning can leverage the imaging data to predict stroke severity as measured by the NIH Stroke Scale/Score (NIHSS). We share not only the raw data, but also the scripts for replicating our findings. These tools can aid in education, and provide a benchmark for validating improved methods.

Investigating sources of non-response bias in a population-based seroprevalence study of vaccine-preventable diseases in the Netherlands

BACKGROUND

PIENTER 3 (P3), conducted in 2016/17, is the most recent of three nationwide serological surveys in the Netherlands. The surveys aim to monitor the effects of the National Immunisation Programme (NIP) by assessing population seroprevalence of included vaccine preventable diseases (VPDs). The response rate to the main sample was 15.7% (n = 4,983), following a decreasing trend in response compared to the previous two PIENTER studies (P1, 55.0%; 1995/1996 [n = 8,356] and P2, 33.0%; 2006/2007 [n = 5,834]). Non-responders to the main P3 survey were followed-up to complete a "non-response" questionnaire, an abridged 9-question version of the main survey covering demographics, health, and vaccination status. We assess P3 representativeness and potential sources of non-response bias, and trends in decreasing participation rates across all PIENTER studies.

METHODS

P3 invitees were classified into survey response types: Full Participants (FP), Questionnaire Only (QO), Non-Response Questionnaire (NRQ) and Absolute Non-Responders (ANR). FP demographic and health indicator data were compared with Dutch national statistics, and then the response types were compared to each other. Random forest algorithms were used to predict response type. Finally, FPs from all three PIENTERs were compared to investigate the profile of survey participants through time.

RESULTS

P3 FPs were in general healthier, younger and higher educated than the Dutch population. Random forest was not able to differentiate between FPs and ANRs, but when predicting FPs from NRQs we found evidence of healthy-responder bias. Participants of the three PIENTERs were found to be similar and are therefore comparable through time, but in line with national trends we found P3 participants were less inclined to vaccinate than previous cohorts.

DISCUSSION

The PIENTER biobank is a powerful tool to monitor population-level protection against VPDs across 30 years in The Netherlands. However, future PIENTER studies should continue to focus on improving recruitment from under-represented groups, potentially by considering alternative and mixed survey modes to improve both overall and subgroup-specific response. Whilst non-responder bias is unlikely to affect seroprevalence estimates of high-coverage vaccines, the primary aim of the PIENTER biobank, other studies with varied vaccination/disease exposures should consider the influence of bias carefully.

Influence of Socioeconomic Gender Inequality on Sex Disparities in Prevention and Outcome of Cardiovascular Disease: Data From a Nationwide Population Cohort in China

Background Knowledge gaps remain in how gender-related socioeconomic inequality affects sex disparities in cardiovascular diseases (CVD) prevention and outcome. Methods and Results Based on a nationwide population cohort, we enrolled 3 737 036 residents aged 35 to 75 years (2014-2021). Age-standardized sex differences and the effect of gender-related socioeconomic inequality (Gender Inequality Index) on sex disparities were explored in 9 CVD prevention indicators. Compared with men, women had seemingly better primary prevention (aspirin usage: relative risk [RR], 1.24 [95% CI, 1.18-1.31] and statin usage: RR, 1.48 [95% CI, 1.39-1.57]); however, women's status became insignificant or even worse when adjusted for metabolic factors. In secondary prevention, the sex disparities in usage of aspirin (RR, 0.65 [95% CI, 0.63-0.68]) and statin (RR, 0.63 [95% CI, 0.61-0.66]) were explicitly larger than disparities in usage of angiotensin-converting enzyme inhibitors/angiotensin receptor blockers (RR, 0.88 [95% CI, 0.84-0.91]) or β blockers (RR, 0.67 [95% CI, 0.63-0.71]). Nevertheless, women had better hypertension awareness (RR, 1.09 [95% CI, 1.09-1.10]), similar hypertension control (RR, 1.01 [95% CI, 1.00-1.02]), and lower CVD mortality (hazard ratio, 0.46 [95% CI, 0.45-0.47]). Heterogeneities of sex disparities existed across all subgroups. Significant correlations existed between regional Gender Inequality Index values and sex disparities in usage of angiotensin-converting enzyme inhibitors/angiotensin receptor blockers (Spearman correlation coefficient, r=-0.57, P=0.0013), hypertension control (r=-0.62, P=0.0007), and CVD mortality (r=0.45, P=0.014), which remained significant after adjusting for economic factors. Conclusions Notable sex disparities remain in CVD prevention and outcomes, with large subgroup heterogeneities. Gendered socioeconomic factors could reinforce such disparities. A sex-specific perspective factoring in socioeconomic disadvantages could facilitate more targeted prevention policy making.

Evaluation of Genetic and Nongenetic Risk Factors for Degenerative Cervical Myelopathy

STUDY DESIGN

Cohort study.

OBJECTIVE

We aimed to evaluate the associations of genetic and nongenetic factors with degenerative cervical myelopathy (DCM).

SUMMARY OF BACKGROUND DATA

There is mounting evidence for an inherited predisposition for DCM, but uncertainty remains regarding specific genetic markers involved. Similarly, nongenetic factors are thought to play a role.

MATERIALS AND METHODS

Using diagnosis codes from hospital records linked to the UK Biobank cohort, patients with cervical spondylosis were identified followed by the identification of a subset with DCM. Nongenetic variables evaluated included age, sex, race, Townsend deprivation index, body mass index, occupational demands, osteoporosis, and smoking. Genome-wide association analyses were conducted using logistic regression adjusted for age, sex, population principal components, and follow-up.

RESULTS

A total of 851 DCM cases out of 2787 cervical spondylosis patients were identified. Several nongenetic factors were independently associated with DCM including age [odds ratio (OR)=1.11, 95% CI=1.01-1.21, P =0.024], male sex (OR=1.63, 95% CI=1.37-1.93, P <0.001), and relative socioeconomic deprivation (OR=1.03, 95% CI=1.00-1.06, P =0.030). Asian race was associated with lower DCM risk (OR=0.44, 95% CI=0.22-0.85, P =0.014). We did not identify genome-wide significant (≤5×10 -8 ) single-nucleotide polymorphisms (SNPs) associated with DCM. The strongest genome-wide signals were at SNP rs67256809 in the intergenic region of the genes LINC02582 and FBXO15 on chromosome 18 ( P =1.12×10 -7 ) and rs577081672 in the GTPBP1 gene on chromosome 22 ( P =2.9×10 -7 ). No SNPs reported in prior DCM studies were significant after adjusting for replication attempts.

CONCLUSIONS

Increasing age, male sex, and relative socioeconomic deprivation were identified as independent risk factors for DCM, whereas Asian race was inversely associated. SNPs of potential interest were identified in GTPBP1 and an intergenic region on chromosome 18, but these associations did not reach genome-wide significance. Identification of genetic and nongenetic DCM susceptibility markers may guide understanding of DCM disease processes, inform risk, guide prevention and potentially inform surgical outcomes.

LEVEL OF EVIDENCE

Prognostic level III.

Complexities of cerebral small vessel disease, blood pressure, and dementia relationship: new insights from genetics

Importance

There is increasing recognition that vascular disease, which can be treated, is a key contributor to dementia risk. However, the contribution of specific markers of vascular disease is unclear and, as a consequence, optimal prevention strategies remain unclear.

Objective

To disentangle the causal relation of several key vascular traits to dementia risk: (i) white matter hyperintensity (WMH) burden, a highly prevalent imaging marker of covert cerebral small vessel disease (cSVD); (ii) clinical stroke; and (iii) blood pressure (BP), the leading risk factor for cSVD and stroke, for which efficient therapies exist. To account for potential epidemiological biases inherent to late-onset conditions like dementia.

Design Setting and Participants

This study first explored the association of genetically determined WMH, BP levels and stroke risk with AD using summary-level data from large genome-wide association studies (GWASs) in a two-sample Mendelian randomization (MR) framework. Second, leveraging individual-level data from large longitudinal population-based cohorts and biobanks with prospective dementia surveillance, the association of weighted genetic risk scores (wGRSs) for WMH, BP, and stroke with incident all-cause-dementia was explored using Cox-proportional hazard and multi-state models. The data analysis was performed from July 26, 2020, through July 24, 2022.

Exposures

Genetically determined levels of WMH volume and BP (systolic, diastolic and pulse blood pressures) and genetic liability to stroke.

Main outcomes and measures

The summary-level MR analyses focused on the outcomes from GWAS of clinically diagnosed AD (n-cases=21,982) and GWAS additionally including self-reported parental history of dementia as a proxy for AD diagnosis (ADmeta, n-cases=53,042). For the longitudinal analyses, individual-level data of 157,698 participants with 10,699 incident all-cause-dementia were studied, exploring AD, vascular or mixed dementia in secondary analyses.

Results

In the two-sample MR analyses, WMH showed strong evidence for a causal association with increased risk of ADmeta (OR, 1.16; 95%CI:1.05-1.28; P=.003) and AD (OR, 1.28; 95%CI:1.07-1.53; P=.008), after accounting for genetically determined pulse pressure for the latter. Genetically predicted BP traits showed evidence for a protective association with both clinically defined AD and ADmeta, with evidence for confounding by shared genetic instruments. In longitudinal analyses the wGRSs for WMH, but not BP or stroke, showed suggestive association with incident all-cause-dementia (HR, 1.02; 95%CI:1.00-1.04; P=.06). BP and stroke wGRSs were strongly associated with mortality but there was no evidence for selective survival bias during follow-up. In secondary analyses, polygenic scores with more liberal instrument definition showed association of both WMH and stroke with all-cause-dementia, AD, and vascular or mixed dementia; associations of stroke, but not WMH, with dementia outcomes were markedly attenuated after adjusting for interim stroke.

Conclusion

These findings provide converging evidence that WMH is a leading vascular contributor to dementia risk, which may better capture the brain damage caused by BP (and other etiologies) than BP itself and should be targeted in priority for dementia prevention in the population.

Identification of Novel Genetic Markers for the Risk of Spinal Pathologies: A Genome-Wide Association Study of 2 Biobanks

BACKGROUND

Identifying genetic risk factors for spinal disorders may lead to knowledge regarding underlying molecular mechanisms and the development of new treatments.

METHODS

Cases of lumbar spondylolisthesis, spinal stenosis, degenerative disc disease, and pseudarthrosis after spinal fusion were identified from the UK Biobank. Controls were patients without the diagnosis. Whole-genome regressions were used to test for genetic variants potentially implicated in the occurrence of each phenotype. External validation was performed in FinnGen.

RESULTS

A total of 389,413 participants were identified from the UK Biobank. A locus on chromosome 2 spanning GFPT1, NFU1, AAK1, and LOC124906020 was implicated in lumbar spondylolisthesis. Two loci on chromosomes 2 and 12 spanning genes GFPT1, NFU1, and PDE3A were implicated in spinal stenosis. Three loci on chromosomes 6, 10, and 15 spanning genes CHST3, LOC102723493, and SMAD3 were implicated in degenerative disc disease. Finally, 2 novel loci on chromosomes 5 and 9, with the latter corresponding to the LOC105376270 gene, were implicated in pseudarthrosis. Some of these variants associated with spinal stenosis and degenerative disc disease were also replicated in FinnGen.

CONCLUSIONS

This study revealed nucleotide variations in select genetic loci that were potentially implicated in 4 different spinal pathologies, providing potential insights into the pathological mechanisms.

LEVEL OF EVIDENCE

Prognostic Level III . See Instructions for Authors for a complete description of levels of evidence.

Significance and Interrelationship of the Symptoms Listed in the DSM Criteria for Premenstrual Dysphoric Disorder

Objective

While premenstrual dysphoric disorder (PMDD) as defined in DSM has become an established diagnosis, and a formal indication for drug treatment, the relative impact of the disparate symptoms named in the criteria, and to what extent they indeed constitute parts of one syndrome, remains insufficiently clarified. We have therefore explored the frequency, impact, and inter-relationship of different PMDD symptoms.

Method

Using a web survey, 10,457 Swedish women of fertile age were asked to retrospectively assess if they experience reduced functioning due to symptoms clearly associated with the premenstrual phase. Those responding affirmatively reported presence, severity, and impact of each symptom named in the PMDD criteria.

Result

Nine percent reported impairing premenstrual symptoms. Whereas irritability was reported to cause impairment in 77% of those passing the gate questions, somatic symptoms were common but seldom causing impairment. A vast majority reported presence of at least 5 different symptoms, as required to meet the PMDD criteria, but few reported each of 5 different symptoms to be severe or impairing. An analysis of the association between symptoms revealed clear-cut clustering of somatic and mood symptoms, respectively.

Conclusion

While retrospective account suggested irritability to be the clinically most important premenstrual symptom, some of the complaints named in the PMDD criteria were not or only weakly associated with mood symptoms and also reported to be of limited clinical significance. It is concluded that regarding all symptoms listed in the DSM criteria as clinically relevant manifestations of one and the same syndrome may be questioned.

Association of liver fibrosis with cognitive test performance and brain imaging parameters in the UK Biobank study

INTRODUCTION

We hypothesized that liver fibrosis is associated with worse cognitive performance and corresponding brain imaging changes.

METHODS

We examined the association of liver fibrosis with cognition and brain imaging parameters in the UK Biobank study. Liver fibrosis was assessed using the Fibrosis-4 (FIB-4) score. The primary cognitive outcome was the digit symbol substitution test (DSST); secondary outcomes were additional executive function/processing speed and memory tests. Imaging outcomes were hippocampal, total brain, and white matter hyperintensity (WMH) volumes.

RESULTS

We included 105,313 participants with cognitive test data, and 41,982 with magnetic resonance imaging (MRI). In adjusted models, liver fibrosis was associated with worse performance on the DSST and tests of executive function but not memory. Liver fibrosis was associated with lower hippocampal and total brain volumes, without compelling association with WMH volume.

DISCUSSION

Liver fibrosis is associated with worse performance on select cognitive tests and lower hippocampal and total brain volumes.

HIGHLIGHTS

It is increasingly recognized that chronic liver conditions impact brain health. We performed an analysis of data from the UK Biobank prospective cohort study. Liver fibrosis was associated with worse performance on executive function tests. Liver fibrosis was not associated with memory impairment. Liver fibrosis was associated with lower hippocampal and total brain volumes.

Occupational demands associated with rotator cuff disease surgery in the UK Biobank

OBJECTIVES

Physically-demanding occupations may increase rotator cuff disease (RCD) risk and need for surgery. We linked a job-exposure matrix (JEM) to the UK Biobank cohort study to measure physical occupational exposures and estimate associations with RCD surgery.

METHODS

Jobs and UK Standard Occupational Classification (SOC) codes were recorded during the UK Biobank verbal interview. Lifetime job histories were captured through a web-based survey. UK SOC codes were linked to a JEM based on the US O*NET database. O*NET-based scores [static strength, dynamic strength, general physical activities, handling/moving objects (range=1-7), time spent using hands, whole body vibration, and cramped/awkward positions (range=1-5)] were assigned to jobs. RCD surgeries were identified through linked national hospital inpatient records. Multivariable Cox regression was used to calculate hazard ratios (HR) as estimates of associations with RCD surgery. Among those with lifetime job histories, associations were estimated for duration of time with greatest exposure (top quartile of exposure).

RESULTS

Of 277 808 people reporting jobs, 1997 (0.7%) had an inpatient RCD surgery. After adjusting for age, sex, race, education, area deprivation, and body mass index, all O*NET variables considered were associated with RCD surgery (HR per point increase range=1.10-1.45, all P<0.005). A total of 100 929 people reported lifetime job histories, in which greater exposures were significantly associated with RCD surgery after >10 years of work (eg, HR for 11-20 versus 0 years with static strength score ≥4 = 2.06, 95% confidence interval 1.39-3.04).

CONCLUSIONS

Workplace physical demands are an important risk factor for RCD surgery, particularly for workers with more than a decade of exposure.

Identifying type 1 and 2 diabetes in research datasets where classification biomarkers are unavailable: assessing the accuracy of published approaches

OBJECTIVES

We aimed to compare the performance of approaches for classifying insulin-treated diabetes within research datasets without measured classification biomarkers, evaluated against two independent biological definitions of diabetes type.

STUDY DESIGN AND SETTING

We compared accuracy of ten reported approaches for classifying insulin-treated diabetes into type 1 (T1D) and type 2 (T2D) diabetes in two cohorts: UK Biobank (UKBB) n = 26,399 and Diabetes Alliance for Research in England (DARE) n = 1,296. The overall performance for classifying T1D and T2D was assessed using: a T1D genetic risk score and genetic stratification method (UKBB); C-peptide measured at >3 years diabetes duration (DARE).

RESULTS

Approaches' accuracy ranged from 71% to 88% (UKBB) and 68% to 88% (DARE). When classifying all participants, combining early insulin requirement with a T1D probability model (incorporating diagnosis age and body image issue [BMI]), and interview-reported diabetes type (UKBB available in only 15%) consistently achieved high accuracy (UKBB 87% and 87% and DARE 85% and 88%, respectively). For identifying T1D with minimal misclassification, models with high thresholds or young diagnosis age (<20 years) had highest performance. Findings were incorporated into an online tool identifying optimum approaches based on variable availability.

CONCLUSION

Models combining continuous features with early insulin requirement are the most accurate methods for classifying insulin-treated diabetes in research datasets without measured classification biomarkers.

Brain-wide versus genome-wide vulnerability biomarkers for severe mental illnesses

Severe mental illnesses (SMI), including major depressive (MDD), bipolar (BD), and schizophrenia spectrum (SSD) disorders have multifactorial risk factors and capturing their complex etiopathophysiology in an individual remains challenging. Regional vulnerability index (RVI) was used to measure individual's brain-wide similarity to the expected SMI patterns derived from meta-analytical studies. It is analogous to polygenic risk scores (PRS) that measure individual's similarity to genome-wide patterns in SMI. We hypothesized that RVI is an intermediary phenotype between genome and symptoms and is sensitive to both genetic and environmental risks for SMI. UK Biobank sample of N = 17,053/19,265 M/F (age = 64.8 ± 7.4 years) and an independent sample of SSD patients and controls (N = 115/111 M/F, age = 35.2 ± 13.4) were used to test this hypothesis. UKBB participants with MDD had significantly higher RVI-MDD (Cohen's d = 0.20, p = 1 × 10-23 ) and PRS-MDD (d = 0.17, p = 1 × 10-15 ) than nonpsychiatric controls. UKBB participants with BD and SSD showed significant elevation in the respective RVIs (d = 0.65 and 0.60; p = 3 × 10-5 and .009, respectively) and PRS (d = 0.57 and 1.34; p = .002 and .002, respectively). Elevated RVI-SSD were replicated in an independent sample (d = 0.53, p = 5 × 10-5 ). RVI-MDD and RVI-SSD but not RVI-BD were associated with childhood adversity (p < .01). In nonpsychiatric controls, elevation in RVI and PRS were associated with lower cognitive performance (p < 10-5 ) in six out of seven domains and showed specificity with disorder-associated deficits. In summary, the RVI is a novel brain index for SMI and shows similar or better specificity for SMI than PRS, and together they may complement each other in the efforts to characterize the genomic to brain level risks for SMI.

Occupational characteristics associated with SARS-CoV-2 infection in the UK Biobank during August-November 2020: a cohort study

BACKGROUND

Occupational exposures may play a key role in severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection risk. We used a job-exposure matrix linked to the UK Biobank to measure occupational characteristics and estimate associations with a positive SARS-CoV-2 test.

METHODS

People reporting job titles at their baseline interview in England who were < 65 years of age in 2020 were included. Healthcare workers were excluded because of differential access to testing. Jobs were linked to the US Occupational Information Network (O*NET) job exposure matrix. O*NET-based scores were examined for occupational physical proximity, exposure to diseases/infection, working outdoors exposed to weather, and working outdoors under cover (score range = 1-5). Jobs were classified as remote work using two algorithms. SARS-CoV-2 test results were evaluated between August 5th-November 10th, 2020, when the UK was released from lockdown. Cox regression was used to calculate adjusted hazard ratios (aHRs), accounting for age, sex, race, education, neighborhood deprivation, assessment center, household size, and income.

RESULTS

We included 115,451 people with job titles, of whom 1746 tested positive for SARS-CoV-2. A one-point increase in physical proximity score was associated with 1.14 times higher risk of SARS-CoV-2 (95%CI = 1.05-1.24). A one-point increase in the exposure to diseases/infections score was associated with 1.09 times higher risk of SARS-CoV-2 (95%CI = 1.02-1.16). People reporting jobs that could not be done remotely had higher risk of SARS-CoV-2 regardless of the classification algorithm used (aHRs = 1.17 and 1.20). Outdoors work showed an association with SARS-CoV-2 (exposed to weather aHR = 1.06, 95%CI = 1.01-1.11; under cover aHR = 1.08, 95%CI = 1.00-1.17), but these associations were not significant after accounting for whether work could be done remotely.

CONCLUSION

People in occupations that were not amenable to remote work, required closer physical proximity, and required more general exposure to diseases/infection had higher risk of a positive SARS-CoV-2 test. These findings provide additional evidence that coronavirus disease 2019 (COVID-19) is an occupational disease, even outside of the healthcare setting, and indicate that strategies for mitigating transmission in in-person work settings will remain important.

Association between liver fibrosis and incident dementia in the UK Biobank study

BACKGROUND AND PURPOSE

There is growing recognition that chronic liver conditions influence brain health. The impact of liver fibrosis on dementia risk was unclear. We evaluated the association between liver fibrosis and incident dementia in a cohort study.

METHODS

We performed a cohort analysis using data from the UK Biobank study, which prospectively enrolled adults starting in 2007, and continues to follow them. People with a Fibrosis-4 (FIB-4) liver fibrosis score >2.67 were categorized as at high risk of advanced fibrosis. The primary outcome was incident dementia, ascertained using a validated approach. We excluded participants with prevalent dementia at baseline. We used Cox proportional hazards models to evaluate the association between liver fibrosis and dementia while adjusting for potential confounders.

RESULTS

Among 455,226 participants included in this analysis, the mean age was 56.5 years and 54% were women. Approximately 2.17% (95% confidence interval [CI] 2.13%-2.22%) had liver fibrosis. The rate of dementia per 1000 person-years was 1.76 (95% CI 1.50-2.07) in participants with liver fibrosis and 0.52 (95% CI 0.50-0.54) in those without. After adjusting for demographics, socioeconomic deprivation, educational attainment, metabolic syndrome, hypertension, diabetes, dyslipidemia, and tobacco and alcohol use, liver fibrosis was associated with an increased risk of dementia (hazard ratio 1.52, 95% CI 1.22-1.90). Results were robust to sensitivity analyses. Effect modification by sex, metabolic syndrome, and apolipoprotein E4 carrier status was not observed.

CONCLUSION

Liver fibrosis in middle age was associated with an increased risk of incident dementia, independent of shared risk factors. Liver fibrosis may be an underrecognized risk factor for dementia.

Physical work exposure matrix for use in the UK Biobank

BACKGROUND

UK Biobank (UKB) is a large prospective cohort capturing numerous health outcomes, but limited occupational information (job title, self-reported manual work and occupational walking/standing).

AIMS

To create and evaluate validity of a linkage between UKB and a job exposure matrix for physical work exposures based on the US Occupational Information Network (O*NET) database.

METHODS

Job titles and UK Standard Occupational Classification (SOC) codes were collected during UKB baseline assessment visits. Using existing crosswalks, UK SOC codes were mapped to US SOC codes allowing linkage to O*NET variables capturing numerous dimensions of physical work. Job titles with the highest O*NET scores were assessed to evaluate face validity. Spearman's correlation coefficients were calculated to compare O*NET scores to self-reported UKB measures.

RESULTS

Among 324 114 participants reporting job titles, 323 936 were linked to O*NET. Expected relationships between scores and self-reported measures were observed. For static strength (0-7 scale), the median O*NET score was 1.0 (e.g. audiologists), with a highest score of 4.88 for stone masons and a positive correlation with self-reported heavy manual work (Spearman's coefficient = 0.50). For time spent standing (1-5 scale), the median O*NET score was 2.72 with a highest score of 5 for cooks and a positive correlation with self-reported occupational walking/standing (Spearman's coefficient = 0.56).

CONCLUSIONS

While most jobs were not physically demanding, a wide range of physical work values were assigned to a diverse set of jobs. This novel linkage of a job exposure matrix to UKB provides a potentially valuable tool for understanding relationships between occupational exposures and disease.

Separating Clinical and Subclinical Depression by Big Data Informed Structural Vulnerability Index and Its impact on Cognition: ENIGMA Dot Product

Big Data neuroimaging collaborations including Enhancing Neuro Imaging Genetics through Meta-Analysis (ENIGMA) integrated worldwide data to identify regional brain deficits in major depressive disorder (MDD). We evaluated the sensitivity of translating ENIGMA-defined MDD deficit patterns to the individual level. We treated ENIGMA MDD deficit patterns as a vector to gauge the similarity between individual and MDD patterns by calculating ENIGMA dot product (EDP). We analyzed the sensitivity and specificity of EDP in separating subjects with (1) subclinical depressive symptoms without a diagnosis of MDD, (2) single episode MDD, (3) recurrent MDD, and (4) controls free of neuropsychiatric disorders. We compared EDP to the Quantile Regression Index (QRI; a linear alternative to the brain age metric) and the global gray matter thickness and subcortical volumes and fractional anisotropy (FA) of water diffusion. We performed this analysis in a large epidemiological sample of UK Biobank (UKBB) participants (N=17,053/19,265 M/F). Group-average increases in depressive symptoms from controls to recurrent MDD was mirrored by EDP (r2=0.85), followed by FA (r2=0.81) and QRI (r2=0.56). Subjects with MDD showed worse performance on cognitive tests than controls with deficits observed for 3 out of 9 cognitive tests administered by the UKBB. We calculated correlations of EDP and other brain indices with measures of cognitive performance in controls. The correlation pattern between EDP and cognition in controls was similar (r2=0.75) to the pattern of cognitive differences in MDD. This suggests that the elevation in EDP, even in controls, is associated with cognitive performance - specifically in the MDD-affected domains. That specificity was missing for QRI, FA or other brain imaging indices. In summary, translating anatomically informed meta-analytic indices of similarity using a linear vector approach led to better sensitivity to depressive symptoms and cognitive patterns than whole-brain imaging measurements or an index of accelerated aging.

Comparing empirical kinship derived heritability for imaging genetics traits in the UK biobank and human connectome project

Imaging genetics analyses use neuroimaging traits as intermediate phenotypes to infer the degree of genetic contribution to brain structure and function in health and/or illness. Coefficients of relatedness (CR) summarize the degree of genetic similarity among subjects and are used to estimate the heritability - the proportion of phenotypic variance explained by genetic factors. The CR can be inferred directly from genome-wide genotype data to explain the degree of shared variation in common genetic polymorphisms (SNP-heritability) among related or unrelated subjects. We developed a central processing and graphics processing unit (CPU and GPU) accelerated Fast and Powerful Heritability Inference (FPHI) approach that linearizes likelihood calculations to overcome the ∼N2-3 computational effort dependency on sample size of classical likelihood approaches. We calculated for 60 regional and 1.3 × 105 voxel-wise traits in N = 1,206 twin and sibling participants from the Human Connectome Project (HCP) (550 M/656 F, age = 28.8 ± 3.7 years) and N = 37,432 (17,531 M/19,901 F; age = 63.7 ± 7.5 years) participants from the UK Biobank (UKBB). The FPHI estimates were in excellent agreement with heritability values calculated using Genome-wide Complex Trait Analysis software (r = 0.96 and 0.98 in HCP and UKBB sample) while significantly reducing computational (102-4 times). The regional and voxel-wise traits heritability estimates for the HCP and UKBB were likewise in excellent agreement (r = 0.63-0.76, p < 10-10). In summary, the hardware-accelerated FPHI made it practical to calculate heritability values for voxel-wise neuroimaging traits, even in very large samples such as the UKBB. The patterns of additive genetic variance in neuroimaging traits measured in a large sample of related and unrelated individuals showed excellent agreement regardless of the estimation method. The code and instruction to execute these analyses are available at www.solar-eclipse-genetics.org.

Large-scale collaboration in ENIGMA-EEG: A perspective on the meta-analytic approach to link neurological and psychiatric liability genes to electrophysiological brain activity

BACKGROUND AND PURPOSE

The ENIGMA-EEG working group was established to enable large-scale international collaborations among cohorts that investigate the genetics of brain function measured with electroencephalography (EEG). In this perspective, we will discuss why analyzing the genetics of functional brain activity may be crucial for understanding how neurological and psychiatric liability genes affect the brain.

METHODS

We summarize how we have performed our currently largest genome-wide association study of oscillatory brain activity in EEG recordings by meta-analyzing the results across five participating cohorts, resulting in the first genome-wide significant hits for oscillatory brain function located in/near genes that were previously associated with psychiatric disorders. We describe how we have tackled methodological issues surrounding genetic meta-analysis of EEG features. We discuss the importance of harmonizing EEG signal processing, cleaning, and feature extraction. Finally, we explain our selection of EEG features currently being investigated, including the temporal dynamics of oscillations and the connectivity network based on synchronization of oscillations.

RESULTS

We present data that show how to perform systematic quality control and evaluate how choices in reference electrode and montage affect individual differences in EEG parameters.

CONCLUSION

The long list of potential challenges to our large-scale meta-analytic approach requires extensive effort and organization between participating cohorts; however, our perspective shows that these challenges are surmountable. Our perspective argues that elucidating the genetic of EEG oscillatory activity is a worthwhile effort in order to elucidate the pathway from gene to disease liability.

Identification of a Novel Genetic Marker for Risk of Degenerative Rotator Cuff Disease Surgery in the UK Biobank

BACKGROUND

While evidence indicates that familial predisposition influences the risk of developing degenerative rotator cuff disease (RCD), knowledge of specific genetic markers is limited. We conducted a genome-wide association study of RCD surgery using the UK Biobank, a prospective cohort of 500,000 people (40 to 69 years of age at enrollment) with genotype data.

METHODS

Cases with surgery for degenerative RCD were identified using linked hospital records. The cases were defined as an International Classification of Diseases, Tenth Revision (ICD-10) code of M75.1 determined by a trauma/orthopaedic specialist and surgery consistent with RCD treatment. Cases were excluded if a diagnosis of traumatic injury had been made during the same hospital visit. For each case, up to 5 controls matched by age, sex, and follow-up time were chosen from the UK Biobank. Analyses were limited to European-ancestry individuals who were not third-degree or closer relations. We used logistic regression to test for genetic association of 674,405 typed and >10 million imputed markers, after adjusting for age, sex, population principal components, and follow-up.

RESULTS

We identified 2,917 RCD surgery cases and 14,158 matched controls. We observed 1 genome-wide significant signal (p < 5 × 10-8) for a novel locus tagged by rs2237352 in the CREB5 gene on chromosome 7 (odds ratio [OR] = 1.17, 95% confidence interval [CI] = 1.11 to 1.24). The single-nucleotide polymorphism (SNP) rs2237352 was imputed with a high degree of confidence (info score = 0.9847) and is common, with a minor allele frequency of 47%. After expanding the control sample to include additional unmatched non-cases, rs2237352 and another SNP in the CREB5 gene, rs12700903, were genome-wide significant. We did not detect genome-wide significant signals at loci associated with RCD in previous studies.

CONCLUSIONS

We identified a novel association between a variant in the CREB5 gene and RCD surgery. Validation of this finding in studies with imaging data to confirm diagnoses will be an important next step.

CLINICAL RELEVANCE

Identification of genetic RCD susceptibility markers can guide understanding of biological processes in rotator cuff degeneration and help inform disease risk in the clinical setting.

LEVEL OF EVIDENCE

Prognostic Level III. See Instructions for Authors for a complete description of levels of evidence.

Psychological trauma and the genetic overlap between posttraumatic stress disorder and major depressive disorder

BACKGROUND

Posttraumatic stress disorder (PTSD) and major depressive disorder (MDD) are commonly reported co-occurring mental health consequences of psychological trauma exposure. The disorders have high genetic overlap. Trauma is a complex phenotype but research suggests that trauma sensitivity has a heritable basis. We investigated whether sensitivity to trauma in those with MDD reflects a similar genetic component in those with PTSD.

METHODS

Genetic correlations between PTSD and MDD in individuals reporting trauma and MDD in individuals not reporting trauma were estimated, as well as with recurrent MDD and single-episode MDD, using genome-wide association study (GWAS) summary statistics. Genetic correlations were replicated using PTSD data from the Psychiatric Genomics Consortium and the Million Veteran Program. Polygenic risk scores were generated in UK Biobank participants who met the criteria for lifetime MDD (N = 29 471). We investigated whether genetic loading for PTSD was associated with reporting trauma in these individuals.

RESULTS

Genetic loading for PTSD was significantly associated with reporting trauma in individuals with MDD [OR 1.04 (95% CI 1.01-1.07), Empirical-p = 0.02]. PTSD was significantly more genetically correlated with recurrent MDD than with MDD in individuals not reporting trauma (rg differences = ~0.2, p < 0.008). Participants who had experienced recurrent MDD reported significantly higher rates of trauma than participants who had experienced single-episode MDD (χ2 > 166, p < 0.001).

CONCLUSIONS

Our findings point towards the existence of genetic variants associated with trauma sensitivity that might be shared between PTSD and MDD, although replication with better powered GWAS is needed. Our findings corroborate previous research highlighting trauma exposure as a key risk factor for recurrent MDD.

Comparison of regional brain deficit patterns in common psychiatric and neurological disorders as revealed by big data

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RVI for MDD and AD was derived based on large meta-analytical findings.

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RVI-MDD and AD were significantly elevated in UKBB subjects with respective illnesses.

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There was no elevation of RVI-MDD in subjects with AD or RVI-AD in subjects with MDD.

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RVI captures neuroanatomic deviation patterns.

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RVI is a useful biomarker for assessing similarity to neuropsychiatric illnesses.

Neurological and psychiatric illnesses are associated with regional brain deficit patterns that bear unique signatures and capture illness-specific characteristics. The Regional Vulnerability Index (RVI) was developed to quantify brain similarity by comparing individual white matter microstructure, cortical gray matter thickness and subcortical gray matter structural volume measures with neuroanatomical deficit patterns derived from large-scale meta-analytic studies. We tested the specificity of the RVI approach for major depressive disorder (MDD) and Alzheimer’s disease (AD) in a large epidemiological sample of UK Biobank (UKBB) participants (N = 19,393; 9138 M/10,255F; age = 64.8 ± 7.4 years). Compared to controls free of neuropsychiatric disorders, participants with MDD (N = 2,248; 805 M/1443F; age = 63.4 ± 7.4) had significantly higher RVI-MDD values (t = 5.6, p = 1·10⁻⁸), but showed no detectable difference in RVI-AD (t = 2.0, p = 0.10). Subjects with dementia (N = 7; 4 M/3F; age = 68.6 ± 8.6 years) showed significant elevation in RVI-AD (t = 4.2, p = 3·10⁻⁵) but not RVI-MDD (t = 2.1, p = 0.10) compared to controls. Even within affective illnesses, participants with bipolar disorder (N = 54) and anxiety disorder (N = 773) showed no significant elevation in whole-brain RVI-MDD. Participants with Parkinson’s disease (N = 37) showed elevation in RVI-AD (t = 2.4, p = 0.01) while subjects with stroke (N = 247) showed no such elevation (t = 1.1, p = 0.3). In summary, we demonstrated elevation in RVI-MDD and RVI-AD measures in the respective illnesses with strong replicability that is relatively specific to the respective diagnoses. These neuroanatomic deviation patterns offer a useful biomarker for population-wide assessments of similarity to neuropsychiatric illnesses.

Novel candidates of pathogenic variants of the BRCA1 and BRCA2 genes from a dataset of 3,552 Japanese whole genomes (3.5KJPNv2)

Identification of the population frequencies of definitely pathogenic germline variants in two major hereditary breast and ovarian cancer syndrome (HBOC) genes, BRCA1/2, is essential to estimate the number of HBOC patients. In addition, the identification of moderately penetrant HBOC gene variants that contribute to increasing the risk of breast and ovarian cancers in a population is critical to establish personalized health care. A prospective cohort subjected to genome analysis can provide both sets of information. Computational scoring and prospective cohort studies may help to identify such likely pathogenic variants in the general population. We annotated the variants in the BRCA1 and BRCA2 genes from a dataset of 3,552 whole-genome sequences obtained from members of a prospective cohorts with genome data in the Tohoku Medical Megabank Project (TMM) with InterVar software. Computational impact scores (CADD_phred and Eigen_raw) and minor allele frequencies (MAFs) of pathogenic (P) and likely pathogenic (LP) variants in ClinVar were used for filtration criteria. Familial predispositions to cancers among the 35,000 TMM genome cohort participants were analyzed to verify the identified pathogenicity. Seven potentially pathogenic variants were newly identified. The sisters of carriers of these moderately deleterious variants and definite P and LP variants among members of the TMM prospective cohort showed a statistically significant preponderance for cancer onset, from the self-reported cancer history. Filtering by computational scoring and MAF is useful to identify potentially pathogenic variants in BRCA genes in the Japanese population. These results should help to follow up the carriers of variants of uncertain significance in the HBOC genes in the longitudinal prospective cohort study.

Occupation and risk of severe COVID-19: prospective cohort study of 120 075 UK Biobank participants

OBJECTIVES

To investigate severe COVID-19 risk by occupational group.

METHODS

Baseline UK Biobank data (2006-10) for England were linked to SARS-CoV-2 test results from Public Health England (16 March to 26 July 2020). Included participants were employed or self-employed at baseline, alive and aged <65 years in 2020. Poisson regression models were adjusted sequentially for baseline demographic, socioeconomic, work-related, health, and lifestyle-related risk factors to assess risk ratios (RRs) for testing positive in hospital or death due to COVID-19 by three occupational classification schemes (including Standard Occupation Classification (SOC) 2000).

RESULTS

Of 120 075 participants, 271 had severe COVID-19. Relative to non-essential workers, healthcare workers (RR 7.43, 95% CI 5.52 to 10.00), social and education workers (RR 1.84, 95% CI 1.21 to 2.82) and other essential workers (RR 1.60, 95% CI 1.05 to 2.45) had a higher risk of severe COVID-19. Using more detailed groupings, medical support staff (RR 8.70, 95% CI 4.87 to 15.55), social care (RR 2.46, 95% CI 1.47 to 4.14) and transport workers (RR 2.20, 95% CI 1.21 to 4.00) had the highest risk within the broader groups. Compared with white non-essential workers, non-white non-essential workers had a higher risk (RR 3.27, 95% CI 1.90 to 5.62) and non-white essential workers had the highest risk (RR 8.34, 95% CI 5.17 to 13.47). Using SOC 2000 major groups, associate professional and technical occupations, personal service occupations and plant and machine operatives had a higher risk, compared with managers and senior officials.

CONCLUSIONS

Essential workers have a higher risk of severe COVID-19. These findings underscore the need for national and organisational policies and practices that protect and support workers with an elevated risk of severe COVID-19.

Framework for a Community Health Observing System for the Gulf of Mexico Region: Preparing for Future Disasters

The Gulf of Mexico (GoM) region is prone to disasters, including recurrent oil spills, hurricanes, floods, industrial accidents, harmful algal blooms, and the current COVID-19 pandemic. The GoM and other regions of the U.S. lack sufficient baseline health information to identify, attribute, mitigate, and facilitate prevention of major health effects of disasters. Developing capacity to assess adverse human health consequences of future disasters requires establishment of a comprehensive, sustained community health observing system, similar to the extensive and well-established environmental observing systems. We propose a system that combines six levels of health data domains, beginning with three existing, national surveys and studies plus three new nested, longitudinal cohort studies. The latter are the unique and most important parts of the system and are focused on the coastal regions of the five GoM States. A statistically representative sample of participants is proposed for the new cohort studies, stratified to ensure proportional inclusion of urban and rural populations and with additional recruitment as necessary to enroll participants from particularly vulnerable or under-represented groups. Secondary data sources such as syndromic surveillance systems, electronic health records, national community surveys, environmental exposure databases, social media, and remote sensing will inform and augment the collection of primary data. Primary data sources will include participant-provided information via questionnaires, clinical measures of mental and physical health, acquisition of biological specimens, and wearable health monitoring devices. A suite of biomarkers may be derived from biological specimens for use in health assessments, including calculation of allostatic load, a measure of cumulative stress. The framework also addresses data management and sharing, participant retention, and system governance. The observing system is designed to continue indefinitely to ensure that essential pre-, during-, and post-disaster health data are collected and maintained. It could also provide a model/vehicle for effective health observation related to infectious disease pandemics such as COVID-19. To our knowledge, there is no comprehensive, disaster-focused health observing system such as the one proposed here currently in existence or planned elsewhere. Significant strengths of the GoM Community Health Observing System (CHOS) are its longitudinal cohorts and ability to adapt rapidly as needs arise and new technologies develop.

Tumour predisposition and cancer syndromes as models to study gene-environment interactions

Cell division and organismal development are exquisitely orchestrated and regulated processes. The dysregulation of the molecular mechanisms underlying these processes may cause cancer, a consequence of cell-intrinsic and/or cell-extrinsic events. Cellular DNA can be damaged by spontaneous hydrolysis, reactive oxygen species, aberrant cellular metabolism or other perturbations that cause DNA damage. Moreover, several environmental factors may damage the DNA, alter cellular metabolism or affect the ability of cells to interact with their microenvironment. While some environmental factors are well established as carcinogens, there remains a large knowledge gap of others owing to the difficulty in identifying them because of the typically long interval between carcinogen exposure and cancer diagnosis. DNA damage increases in cells harbouring mutations that impair their ability to correctly repair the DNA. Tumour predisposition syndromes in which cancers arise at an accelerated rate and in different organs - the equivalent of a sensitized background - provide a unique opportunity to examine how gene-environment interactions influence cancer risk when the initiating genetic defect responsible for malignancy is known. Understanding the molecular processes that are altered by specific germline mutations, environmental exposures and related mechanisms that promote cancer will allow the design of novel and effective preventive and therapeutic strategies.

Approach to High Volume Enrollment in Clinical Research: Experiences from an All of Us Research Program Site

Clinical trials and cohort studies are required to meet target recruitment of study participants within stipulated timelines, especially when the priority is to include populations traditionally unrepresented in biomedical research. By the third quarter of 2019, the University of Arizona‐Banner Health Provider Organization (UA‐Banner HPO) has enrolled > 30,000 core participants into the All of Us Research Program (AoURP), the research cohort of the Precision Medicine Initiative. The majority of enrolled participants meet the criteria for individuals under‐represented in biomedical research. The enrollment goals were calculated based on a target of 20,000 as set by the National Institutes of Health and our health provider organization achieved enrollment numbers between 17% and 86% above the targeted daily enrollment. We evaluated enrollment methods and challenges to enrollments encountered by the UA‐Banner Health Provider Organization into the AoURP. Challenges to enrollment centered around the need for high‐touch engagement methods, time investment necessary for stakeholder inclusion, and the use of purely digital enrollment methods especially in populations under‐represented in biomedical research. These challenges occurred at the level of the individual, provider, institutions, and community, and cumulatively impacted participant enrollment. Successful strategies for engagement and enrollment leveraged provider partners as advocates for the program. For high‐volume enrollment in clinical research, it is important to engage leaders in the healthcare setting, patient providers, and tailor engagement and enrollment to potential participant needs. We emphasize the need for precision engagement and enrollment methods tailored to individual needs.

Risk factors for surgery due to rotator cuff disease in a population-based cohort

AIMS

Few risk factors for rotator cuff disease (RCD) and corresponding treatment have been firmly established. The aim of this study was to evaluate the relationship between numerous risk factors and the incidence of surgery for RCD in a large cohort.

METHODS

A population-based cohort of people aged between 40 and 69 years in the UK (the UK Biobank) was studied. People who underwent surgery for RCD were identified through a link with NHS inpatient records covering a mean of eight years after enrolment. Multivariate Cox proportional hazards regression was used to calculate hazard ratios (HRs) as estimates of associations with surgery for RCD accounting for confounders. The risk factors which were considered included age, sex, race, education, Townsend deprivation index, body mass index (BMI), occupational demands, and exposure to smoking.

RESULTS

Of the 421,894 people who were included, 47% were male. The mean age at the time of enrolment was 56 years (40 to 69). A total of 2,156 people were identified who underwent surgery for RCD. Each decade increase in age was associated with a 55% increase in the incidence of RCD surgery (95% confidence interval (CI) 46% to 64%). Male sex, non-white race, lower deprivation score, and higher BMI were significantly associated with a higher risk of surgery for RCD (all p < 0.050). Greater occupational physical demands were significantly associated with higher rates of RCD surgery (HR = 2.1, 1.8, and 1.4 for 'always', 'usually', and 'sometimes' doing heavy manual labour vs 'never', all p < 0.001). Former smokers had significantly higher rates of RCD surgery than those who had never smoked (HR 1.23 (95% CI 1.12 to 1.35), p < 0.001), while current smokers had similar rates to those who had never smoked (HR 0.94 (95% CI 0.80 to 1.11)). Among those who had never smoked, the risk of surgery was higher among those with more than one household member who smoked (HR 1.78 (95% CI 1.08 to 2.92)). The risk of RCD surgery was not significantly related to other measurements of secondhand smoking.

CONCLUSION

Many factors were independently associated with surgery for RCD, including older age, male sex, higher BMI, lower deprivation score, and higher occupational physical demands. Several of the risk factors which were identified are modifiable, suggesting that the healthcare burden of RCD might be reduced through the pursuit of public health goals, such as reducing obesity and modifying occupational demands. Cite this article: Bone Joint J 2020;102-B(3):352-359.

How to Make a Cost Model for the Birth Cohort Biobank in China

Biobanks and cohort studies are a popular topic in China these days and even in the global scientific research field. Cohorts with biological material are necessary to investigate potential biological mechanisms behind a disease and its early detection. Establishing a biobank is expensive and the long-term sustainability of biorepositories is a key issue globally. There is some published information on tools to calculate the biospecimen user fee; however, they may not be suitable for China's biobanks (as most of the biobanks in China are not for profit and are funded by government or research grants, and as such, funding is a major constraint). The limited published data also tend to be highly variable and specific to the type of biobank. The authors of this article aim to present the basis of a cost analysis model for a biobank of human biological samples of a birth cohort in Shanghai, China. The results show that it is very practical for us to consider how to build a cost model for the birth cohort biobank from the direct funds, such as storage equipment, temperature monitoring system, information management system, and so on. We conclude that by comparing the similarities and differences between China's cost model and that of other countries, this paper provides valuable information for biobankers to identify new perspectives on potential collaborators and mutual learning opportunities.

Insights from Adopting a Data Commons Approach for Large-scale Observational Cohort Studies: The California Teachers Study

BACKGROUND

Large-scale cancer epidemiology cohorts (CEC) have successfully collected, analyzed, and shared patient-reported data for years. CECs increasingly need to make their data more findable, accessible, interoperable, and reusable, or FAIR. How CECs should approach this transformation is unclear.

METHODS

The California Teachers Study (CTS) is an observational CEC of 133,477 participants followed since 1995-1996. In 2014, we began updating our data storage, management, analysis, and sharing strategy. With the San Diego Supercomputer Center, we deployed a new infrastructure based on a data warehouse to integrate and manage data and a secure and shared workspace with documentation, software, and analytic tools that facilitate collaboration and accelerate analyses.

RESULTS

Our new CTS infrastructure includes a data warehouse and data marts, which are focused subsets from the data warehouse designed for efficiency. The secure CTS workspace utilizes a remote desktop service that operates within a Health Insurance Portability and Accountability Act (HIPAA)- and Federal Information Security Management Act (FISMA)-compliant platform. Our infrastructure offers broad access to CTS data, includes statistical analysis and data visualization software and tools, flexibly manages other key data activities (e.g., cleaning, updates, and data sharing), and will continue to evolve to advance FAIR principles.

CONCLUSIONS

Our scalable infrastructure provides the security, authorization, data model, metadata, and analytic tools needed to manage, share, and analyze CTS data in ways that are consistent with the NCI's Cancer Research Data Commons Framework.

IMPACT

The CTS's implementation of new infrastructure in an ongoing CEC demonstrates how population sciences can explore and embrace new cloud-based and analytics infrastructure to accelerate cancer research and translation.See all articles in this CEBP Focus section, "Modernizing Population Science."

Immune-mediated genetic pathways resulting in pulmonary function impairment increase lung cancer susceptibility

Impaired lung function is often caused by cigarette smoking, making it challenging to disentangle its role in lung cancer susceptibility. Investigation of the shared genetic basis of these phenotypes in the UK Biobank and International Lung Cancer Consortium (29,266 cases, 56,450 controls) shows that lung cancer is genetically correlated with reduced forced expiratory volume in one second (FEV1: rg = 0.098, p = 2.3 × 10-8) and the ratio of FEV1 to forced vital capacity (FEV1/FVC: rg = 0.137, p = 2.0 × 10-12). Mendelian randomization analyses demonstrate that reduced FEV1 increases squamous cell carcinoma risk (odds ratio (OR) = 1.51, 95% confidence intervals: 1.21-1.88), while reduced FEV1/FVC increases the risk of adenocarcinoma (OR = 1.17, 1.01-1.35) and lung cancer in never smokers (OR = 1.56, 1.05-2.30). These findings support a causal role of pulmonary impairment in lung cancer etiology. Integrative analyses reveal that pulmonary function instruments, including 73 novel variants, influence lung tissue gene expression and implicate immune-related pathways in mediating the observed effects on lung carcinogenesis.

Cohort profile: HealthWise Wales. A research register and population health data platform with linkage to National Health Service data sets in Wales

PURPOSE

Recruitment and follow-up in epidemiological studies are time-consuming and expensive. Combining online data collection with a register of individuals who agree to be contacted about research opportunities provides an efficient, cost-effective platform for population-based research. HealthWise Wales (HWW) aims to facilitate research by recruiting a cohort of individuals who have consented to be informed about research projects, advertising studies to participants, supporting data collection on specific topics and providing access to linked healthcare data for secondary analyses. In this paper, we describe the design of the project, ongoing data collection, methods of data linkage to routine healthcare records, baseline characteristics of participants, the strengths and limitations of the register, and the ways in which the project can support researchers.

PARTICIPANTS

Adults (aged 16 years and above) living or receiving their healthcare in Wales are eligible for inclusion. Participants consent to be contacted for follow-up data collection and for their details to be used to access their routinely collected National Health Service records for research purposes. Data are collected using a web-based application, with new questionnaires added every 6 months. Data collection on sociodemographic and lifestyle factors is repeated at intervals of 2-3 years. Recruitment is ongoing, with 21 779 participants alive and currently registered.

FINDINGS TO DATE

99% of participants have complete information on age and sex, and 64% have completed questionnaires on sociodemographic and lifestyle factors. These data can be linked with national health databases within the Secure Anonymised Information Linkage (SAIL) databank, with 93% of participants matching a record in SAIL. HWW has facilitated the recruitment of 43 826 participants to 15 different studies.

FUTURE PLANS

The medium-term goal for the project is to enrol at least 50 000 adults. Recruitment strategies are being devised to achieve a study sample that closely models the population of Wales. Potential biosampling methods are also currently being explored.

Genome-wide meta-analysis of macronutrient intake of 91,114 European ancestry participants from the cohorts for heart and aging research in genomic epidemiology consortium

Macronutrient intake, the proportion of calories consumed from carbohydrate, fat, and protein, is an important risk factor for metabolic diseases with significant familial aggregation. Previous studies have identified two genetic loci for macronutrient intake, but incomplete coverage of genetic variation and modest sample sizes have hindered the discovery of additional loci. Here, we expanded the genetic landscape of macronutrient intake, identifying 12 suggestively significant loci (P < 1 × 10-6) associated with intake of any macronutrient in 91,114 European ancestry participants. Four loci replicated and reached genome-wide significance in a combined meta-analysis including 123,659 European descent participants, unraveling two novel loci; a common variant in RARB locus for carbohydrate intake and a rare variant in DRAM1 locus for protein intake, and corroborating earlier FGF21 and FTO findings. In additional analysis of 144,770 participants from the UK Biobank, all identified associations from the two-stage analysis were confirmed except for DRAM1. Identified loci might have implications in brain and adipose tissue biology and have clinical impact in obesity-related phenotypes. Our findings provide new insight into biological functions related to macronutrient intake.

Validation of the Oxford WebQ Online 24-Hour Dietary Questionnaire Using Biomarkers

The Oxford WebQ is an online 24-hour dietary questionnaire that is appropriate for repeated administration in large-scale prospective studies, including the UK Biobank study and the Million Women Study. We compared the performance of the Oxford WebQ and a traditional interviewer-administered multiple-pass 24-hour dietary recall against biomarkers for protein, potassium, and total sugar intake and total energy expenditure estimated by accelerometry. We recruited 160 participants in London, United Kingdom, between 2014 and 2016 and measured their biomarker levels at 3 nonconsecutive time points. The measurement error model simultaneously compared all 3 methods. Attenuation factors for protein, potassium, total sugar, and total energy intakes estimated as the mean of 2 applications of the Oxford WebQ were 0.37, 0.42, 0.45, and 0.31, respectively, with performance improving incrementally for the mean of more measures. Correlation between the mean value from 2 Oxford WebQs and estimated true intakes, reflecting attenuation when intake is categorized or ranked, was 0.47, 0.39, 0.40, and 0.38, respectively, also improving with repeated administration. These correlations were similar to those of the more administratively burdensome interviewer-based recall. Using objective biomarkers as the standard, the Oxford WebQ performs well across key nutrients in comparison with more administratively burdensome interviewer-based 24-hour recalls. Attenuation improves when the average value is taken over repeated administrations, reducing measurement error bias in assessment of diet-disease associations.

Overview of Federated Facility to Harmonize, Analyze and Management of Missing Data in Cohorts

Cohorts are instrumental for epidemiologically oriented observational studies. Cohort studies usually observe large groups of individuals for a specific period of time to identify the contributing factors to a specific outcome (for instance an illness) and create associations between risk factors and the outcome under study. In collaborative projects, federated data facilities are meta-database systems that are distributed across multiple locations that permit to analyze, combine, or harmonize data from different sources making them suitable for mega- and meta-analyses. The harmonization of data can increase the statistical power of studies through maximization of sample size, allowing for additional refined statistical analyses, which ultimately lead to answer research questions that could not be addressed while using a single study. Indeed, harmonized data can be analyzed through mega-analysis of raw data or fixed effects meta-analysis. Other types of data might be analyzed by e.g., random-effects meta-analyses or Bayesian evidence synthesis. In this article, we describe some methodological aspects related to the construction of a federated facility to optimize analyses of multiple datasets, the impact of missing data, and some methods for handling missing data in cohort studies.

Comparison of On-Site Versus Remote Mobile Device Support in the Framingham Heart Study Using the Health eHeart Study for Digital Follow-up: Randomized Pilot Study Set Within an Observational Study Design

Background

New electronic cohort (e-Cohort) study designs provide resource-effective methods for collecting participant data. It is unclear if implementing an e-Cohort study without direct, in-person participant contact can achieve successful participation rates.

Objective

The objective of this study was to compare 2 distinct enrollment methods for setting up mobile health (mHealth) devices and to assess the ongoing adherence to device use in an e-Cohort pilot study.

Methods

We coenrolled participants from the Framingham Heart Study (FHS) into the FHS–Health eHeart (HeH) pilot study, a digital cohort with infrastructure for collecting mHealth data. FHS participants who had an email address and smartphone were randomized to our FHS-HeH pilot study into 1 of 2 study arms: remote versus on-site support. We oversampled older adults (age ≥65 years), with a target of enrolling 20% of our sample as older adults. In the remote arm, participants received an email containing a link to enrollment website and, upon enrollment, were sent 4 smartphone-connectable sensor devices. Participants in the on-site arm were invited to visit an in-person FHS facility and were provided in-person support for enrollment and connecting the devices. Device data were tracked for at least 5 months.

Results

Compared with the individuals who declined, individuals who consented to our pilot study (on-site, n=101; remote, n=93) were more likely to be women, highly educated, and younger. In the on-site arm, the connection and initial use of devices was ≥20% higher than the remote arm (mean percent difference was 25% [95% CI 17-35] for activity monitor, 22% [95% CI 12-32] for blood pressure cuff, 20% [95% CI 10-30] for scale, and 43% [95% CI 30-55] for electrocardiogram), with device connection rates in the on-site arm of 99%, 95%, 95%, and 84%. Once connected, continued device use over the 5-month study period was similar between the study arms.

Conclusions

Our pilot study demonstrated that the deployment of mobile devices among middle-aged and older adults in the context of an on-site clinic visit was associated with higher initial rates of device use as compared with offering only remote support. Once connected, the device use was similar in both groups.

Design and Preliminary Findings From a New Electronic Cohort Embedded in the Framingham Heart Study

BACKGROUND

New models of scalable population-based data collection that integrate digital and mobile health (mHealth) data are necessary.

OBJECTIVE

The aim of this study was to describe a cardiovascular digital and mHealth electronic cohort (e-cohort) embedded in a traditional longitudinal cohort study, the Framingham Heart Study (FHS).

METHODS

We invited eligible and consenting FHS Generation 3 and Omni participants to download the electronic Framingham Heart Study (eFHS) app onto their mobile phones and co-deployed a digital blood pressure (BP) cuff. Thereafter, participants were also offered a smartwatch (Apple Watch). Participants are invited to complete surveys through the eFHS app, to perform weekly BP measurements, and to wear the smartwatch daily.

RESULTS

Up to July 2017, we enrolled 790 eFHS participants, representing 76% (790/1044) of potentially eligible FHS participants. eFHS participants were, on average, 53±8 years of age and 57% were women. A total of 85% (675/790) of eFHS participants completed all of the baseline survey and 59% (470/790) completed the 3-month survey. A total of 42% (241/573) and 76% (306/405) of eFHS participants adhered to weekly digital BP and heart rate (HR) uploads, respectively, over 12 weeks.

CONCLUSIONS

We have designed an e-cohort focused on identifying novel cardiovascular disease risk factors using a new smartphone app, a digital BP cuff, and a smartwatch. Despite minimal training and support, preliminary findings over a 3-month follow-up period show that uptake is high and adherence to periodic app-based surveys, weekly digital BP assessments, and smartwatch HR measures is acceptable.

Reference range of liver corrected T1 values in a population at low risk for fatty liver disease-a UK Biobank sub-study, with an appendix of interesting cases

Purpose

Corrected T1 (cT1) value is a novel MRI-based quantitative metric for assessing a composite of liver inflammation and fibrosis. It has been shown to distinguish between non-alcoholic fatty liver disease (NAFL) and non-alcoholic steatohepatitis. However, these studies were conducted in patients at high risk for liver disease. This study establishes the normal reference range of cT1 values for a large UK population, and assesses interactions of age and gender.

Methods

MR data were acquired on a 1.5 T system as part of the UK Biobank Imaging Enhancement study. Measures for Proton Density Fat Fraction and cT1 were calculated from the MRI data using a multiparametric MRI software application. Data that did not meet quality criteria were excluded from further analysis. Inter and intra-reader variability was estimated in a set of data. A cohort at low risk for NAFL was identified by excluding individuals with BMI ≥ 25 kg/m² and PDFF ≥ 5%. Of the 2816 participants with data of suitable quality, 1037 (37%) were classified as at low risk.

Results

The cT1 values in the low-risk population ranged from 573 to 852 ms with a median of 666 ms and interquartile range from 643 to 694 ms. Iron correction of T1 was necessary in 36.5% of this reference population. Age and gender had minimal effect on cT1 values.

Conclusion

The majority of cT1 values are tightly clustered in a population at low risk for NAFL, suggesting it has the potential to serve as a new quantitative imaging biomarker for studies of liver health and disease.

Adiposity-Mortality Relationships in Type 2 Diabetes, Coronary Heart Disease, and Cancer Subgroups in the UK Biobank, and Their Modification by Smoking

OBJECTIVE

The obesity paradox in which overweight/obesity is associated with mortality benefits is believed to be explained by confounding and reverse causality rather than by a genuine clinical benefit of excess body weight. We aimed to gain deeper insights into the paradox through analyzing mortality relationships with several adiposity measures; assessing subgroups with type 2 diabetes, with coronary heart disease (CHD), with cancer, and by smoking status; and adjusting for several confounders.

RESEARCH DESIGN AND METHODS

We studied the general UK Biobank population (N = 502,631) along with three subgroups of people with type 2 diabetes (n = 23,842), CHD (n = 24,268), and cancer (n = 45,790) at baseline. A range of adiposity exposures were considered, including BMI (continuous and categorical), waist circumference, body fat percentage, and waist-to-hip ratio, and the outcome was all-cause mortality. We used Cox regression models adjusted for age, smoking status, deprivation index, education, and disease history.

RESULTS

For BMI, the obesity paradox was observed among people with type 2 diabetes (adjusted hazard ratio for obese vs. normal BMI 0.78 [95% CI 0.65, 0.95]) but not among those with CHD (1.00 [0.86, 1.17]). The obesity paradox was pronounced in current smokers, absent in never smokers, and more pronounced in men than in women. For other adiposity measures, there was less evidence for an obesity paradox, yet smoking status consistently modified the adiposity-mortality relationship.

CONCLUSIONS

The obesity paradox was observed in people with type 2 diabetes and is heavily modified by smoking status. The results of subgroup analyses and statistical adjustments are consistent with reverse causality and confounding.

Cross-sectional and longitudinal analyses of outdoor air pollution exposure and cognitive function in UK Biobank

Observational studies have shown consistently increased likelihood of dementia or mild cognitive impairment diagnoses in people with higher air pollution exposure history, but evidence has been less consistent for associations with cognitive test performance. We estimated the association between baseline neighbourhood-level exposure to airborne pollutants (particulate matter and nitrogen oxides) and (1) cognitive test performance at baseline and (2) cognitive score change between baseline and 2.8-year follow-up, in 86,759 middle- to older-aged adults from the UK Biobank general population cohort. Unadjusted regression analyses indicated small but consistent negative associations between air pollutant exposure and baseline cognitive performance. Following adjustment for a range of key confounders, associations were inconsistent in direction and of very small magnitude. The largest of these indicated that 1 interquartile range higher air pollutant exposure was associated on average with 0.35% slower reaction time (95% CI: 0.13, 0.57), a 2.92% higher error rate on a visuospatial memory test (95% CI: 1.24, 4.62), and numeric memory scores that were 0.58 points lower (95% CI: -0.96, -0.19). Follow-up analyses of cognitive change scores did not show evidence of associations. The findings indicate that in this sample, which is five-fold larger than any previous cross-sectional study, the association between air pollution exposure and cognitive performance was weak. Ongoing follow-up of the UK Biobank cohort will allow investigation of longer-term associations into old age, including longitudinal tracking of cognitive performance and incident dementia outcomes.

Multiple Score Comparison: a network meta-analysis approach to comparison and external validation of prognostic scores

BACKGROUND

Prediction models and prognostic scores have been increasingly popular in both clinical practice and clinical research settings, for example to aid in risk-based decision making or control for confounding. In many medical fields, a large number of prognostic scores are available, but practitioners may find it difficult to choose between them due to lack of external validation as well as lack of comparisons between them.

METHODS

Borrowing methodology from network meta-analysis, we describe an approach to Multiple Score Comparison meta-analysis (MSC) which permits concurrent external validation and comparisons of prognostic scores using individual patient data (IPD) arising from a large-scale international collaboration. We describe the challenges in adapting network meta-analysis to the MSC setting, for instance the need to explicitly include correlations between the scores on a cohort level, and how to deal with many multi-score studies. We propose first using IPD to make cohort-level aggregate discrimination or calibration scores, comparing all to a common comparator. Then, standard network meta-analysis techniques can be applied, taking care to consider correlation structures in cohorts with multiple scores. Transitivity, consistency and heterogeneity are also examined.

RESULTS

We provide a clinical application, comparing prognostic scores for 3-year mortality in patients with chronic obstructive pulmonary disease using data from a large-scale collaborative initiative. We focus on the discriminative properties of the prognostic scores. Our results show clear differences in performance, with ADO and eBODE showing higher discrimination with respect to mortality than other considered scores. The assumptions of transitivity and local and global consistency were not violated. Heterogeneity was small.

CONCLUSIONS

We applied a network meta-analytic methodology to externally validate and concurrently compare the prognostic properties of clinical scores. Our large-scale external validation indicates that the scores with the best discriminative properties to predict 3 year mortality in patients with COPD are ADO and eBODE.

Comparison of Sociodemographic and Health-Related Characteristics of UK Biobank Participants With Those of the General Population

The UK Biobank cohort is a population-based cohort of 500,000 participants recruited in the United Kingdom (UK) between 2006 and 2010. Approximately 9.2 million individuals aged 40–69 years who lived within 25 miles (40 km) of one of 22 assessment centers in England, Wales, and Scotland were invited to enter the cohort, and 5.5% participated in the baseline assessment. The representativeness of the UK Biobank cohort was investigated by comparing demographic characteristics between nonresponders and responders. Sociodemographic, physical, lifestyle, and health-related characteristics of the cohort were compared with nationally representative data sources. UK Biobank participants were more likely to be older, to be female, and to live in less socioeconomically deprived areas than nonparticipants. Compared with the general population, participants were less likely to be obese, to smoke, and to drink alcohol on a daily basis and had fewer self-reported health conditions. At age 70–74 years, rates of all-cause mortality and total cancer incidence were 46.2% and 11.8% lower, respectively, in men and 55.5% and 18.1% lower, respectively, in women than in the general population of the same age. UK Biobank is not representative of the sampling population; there is evidence of a “healthy volunteer” selection bias. Nonetheless, valid assessment of exposure-disease relationships may be widely generalizable and does not require participants to be representative of the population at large.

Comparison of Recruitment Strategy Outcomes in the National Children's Study

BACKGROUND AND OBJECTIVES

In 2000, the US Congress authorized the National Institutes of Health to conduct a prospective national longitudinal study of environmental influences on children's health and development from birth through 21 years. Several recruitment methodologies were piloted to determine the optimal strategy for a main National Children's Study.

METHODS

After an initial pilot recruitment that used a household enumeration strategy performed poorly, the National Children's Study Vanguard Study developed and evaluated the feasibility, acceptability, and cost of 4 alternate strategies to recruit a large prospective national probability sample of pregnant women and their newborn children. We compare household-based recruitment, provider-based recruitment, direct outreach, and provider-based sampling (PBS) strategies with respect to overall recruitment success, efficiency, cost, and fulfillment of scientific requirements.

RESULTS

Although all 5 strategies achieved similar enrollment rates (63%-81%) among eligible women, PBS achieved the highest recruitment success as measured by the ratio of observed-to-expected newborn enrollees per year of 0.99, exceeding those of the other strategies (range: 0.35-0.48). Because PBS could reach the enrollment target through sampling of high volume obstetric provider offices and birth hospitals, it achieved the lowest ratio of women screened to women enrolled and was also the least costly strategy. With the exception of direct outreach, all strategies enrolled a cohort of women whose demographics were similar to county natality data.

CONCLUSIONS

PBS demonstrated the optimal combination of recruitment success, efficiency, cost, and population representativeness and serves as a model for the assembly of future prospective probability-based birth cohorts.

The 'cognitive footprint' of psychiatric and neurological conditions: cross-sectional study in the UK Biobank cohort

OBJECTIVE

We aimed to quantify the prevalence of cognitive impairment in adults with a history of mood disorder, schizophrenia, multiple sclerosis or Parkinson's disease, within a large general population cohort.

METHOD

Cross-sectional study using UK Biobank data (n = 502 642). Psychiatric and neurological exposure status was ascertained via self-reported diagnoses, hospital records and questionnaires. Impairment on reasoning, reaction time and memory tests was defined with reference to a single unexposed comparison group. Results were standardised for age and gender. Sensitivity analyses examined the influence of comorbidity, education, information sources and missing data.

RESULTS

Relative to the unexposed group, cognitive impairment was least common in major depression (standardised prevalence ratios across tests = 1.00 [95% CI 0.98, 1.02] to 1.49 [95% CI 1.24, 1.79]) and most common in schizophrenia (1.89 [95% CI 1.47, 2.42] to 3.92 [95% CI 2.34, 6.57]). Prevalence in mania/bipolar was similar to that in multiple sclerosis and Parkinson's disease. Estimated population attributable prevalence of cognitive impairment was higher for major depression (256 per 100 000 [95% CI 130, 381]) than for all other disorders.

CONCLUSION

Although the relative prevalence of cognitive impairment was lowest in major depression, the population attributable prevalence was highest overall for this group.

The American Cancer Society's Cancer Prevention Study 3 (CPS-3): Recruitment, study design, and baseline characteristics

BACKGROUND

Prospective cohort studies contribute importantly to understanding the role of lifestyle, genetic, and other factors in chronic disease etiology.

METHODS

The American Cancer Society (ACS) recruited a new prospective cohort study, Cancer Prevention Study 3 (CPS-3), between 2006 and 2013 from 35 states and Puerto Rico. Enrollment took place primarily at ACS community events and at community enrollment "drives." At enrollment sites, participants completed a brief survey that included an informed consent, identifying information necessary for follow-up, and key exposure information. They also provided a waist measure and a nonfasting blood sample. Most participants also completed a more comprehensive baseline survey at home that included extensive medical, lifestyle, and other information. Participants will be followed for incident cancers through linkage with state cancer registries and for cause-specific mortality through linkage with the National Death Index.

RESULTS

In total, 303,682 participants were enrolled. Of these, 254,650 completed the baseline survey and are considered "fully" enrolled; they will be sent repeat surveys periodically for at least the next 20 years to update exposure information. The remaining participants (n = 49,032) will not be asked to update exposure information but will be followed for outcomes. Twenty-three percent of participants were men, 17.3% reported a race or ethnicity other than "white," and the median age at enrollment was 47 years.

CONCLUSIONS

CPS-3 will be a valuable resource for studies of cancer and other outcomes because of its size; its diversity with respect to age, ethnicity, and geography; and the availability of blood samples and detailed questionnaire information collected over time. Cancer 2017;123:2014-2024. © 2017 American Cancer Society.

Recruitment strategies in a prospective longitudinal family study on parents with obesity and their toddlers

Background

Recruitment of participants with obesity is a real challenge. To reduce time and costs in similar projects, we investigated various recruiting strategies used in a longitudinal family study with respect to their enrolment yield and cost effectiveness. Results may help other research groups to optimize their recruitment strategies.

Methods

We applied different recruitment strategies to acquire families with children aged 6 to 47 months and at least one parent with obesity (risk group) or two parents of normal weight (control group) for a longitudinal non-interventional study. Based on four main strategies-via media, kindergartens, health professionals and focusing on the community-we examined 15 different subcategories of strategies. Based on enrolment yield and relative costs (e.g., material expenses, staff time) we analyzed the effectiveness of each recruitment strategy.

Results

Following different recruitment approaches, 685 families contacted us; 26% (n = 178) of these met the inclusion criteria. Of the four main strategies, the community-focused strategy was the most successful one (accounting for 36.5% of the sample) followed by contacts with kindergartens (accounting for 28.1% of the sample). Of the subcategories, two strategies were outstanding: Posters (community-focused strategies), and recruitment via kindergartens using phone contacts rather than emailing. Only a small number of participants were recruited via announcements in newspapers (lower cost strategy), advertisements on public transport or face-to-face recruitment at various places (higher cost strategies).

Conclusions

Results revealed that only a combination of different active and passive methods and approaches led to a sufficient sample size. In this study, recruitment via posters and contacting kindergartens on the phone produced the highest numbers of participants (high enrolment yield) at moderate costs.

Using nationwide ‘big data’ from linked electronic health records to help improve outcomes in cardiovascular diseases: 33 studies using methods from epidemiology, informatics, economics and social science in the ClinicAl disease research using LInked Bespoke studies and Electronic health Records (CALIBER) programme

Background

Electronic health records (EHRs), when linked across primary and secondary care and curated for research use, have the potential to improve our understanding of care quality and outcomes.

Objective

To evaluate new opportunities arising from linked EHRs for improving quality of care and outcomes for patients at risk of or with coronary disease across the patient journey.

Design

Epidemiological cohort, health informatics, health economics and ethnographic approaches were used.

Setting

230 NHS hospitals and 226 general practices in England and Wales.

Participants

Up to 2 million initially healthy adults, 100,000 people with stable coronary artery disease (SCAD) and up to 300,000 patients with acute coronary syndrome.

Main outcome measures

Quality of care, fatal and non-fatal cardiovascular disease (CVD) events.

Data platform and methods

We created a novel research platform [ClinicAl disease research using LInked Bespoke studies and Electronic health Records (CALIBER)] based on linkage of four major sources of EHR data in primary care and national registries. We carried out 33 complementary studies within the CALIBER framework. We developed a web-based clinical decision support system (CDSS) in hospital chest pain clinics. We established a novel consented prognostic clinical cohort of SCAD patients.

Results

CALIBER was successfully established as a valid research platform based on linked EHR data in nearly 2 million adults with > 600 EHR phenotypes implemented on the web portal (seehttps://caliberresearch.org/portal). Despite national guidance, key opportunities for investigation and treatment were missed across the patient journey, resulting in a worse prognosis for patients in the UK compared with patients in health systems in other countries. Our novel, contemporary, high-resolution studies showed heterogeneous associations for CVD risk factors across CVDs. The CDSS did not alter the decision-making behaviour of clinicians in chest pain clinics. Prognostic models using real-world data validly discriminated risk of death and events, and were used in cost-effectiveness decision models.

Conclusions

Emerging ‘big data’ opportunities arising from the linkage of records at different stages of a patient’s journey are vital to the generation of actionable insights into the diagnosis, risk stratification and cost-effective treatment of people at risk of, or with, CVD.

Future work

The vast majority of NHS data remain inaccessible to research and this hampers efforts to improve efficiency and quality of care and to drive innovation. We propose three priority directions for further research. First, there is an urgent need to ‘unlock’ more detailed data within hospitals for the scale of the UK’s 65 million population. Second, there is a need for scaled approaches to using EHRs to design and carry out trials, and interpret the implementation of trial results. Third, large-scale, disease agnostic genetic and biological collections linked to such EHRs are required in order to deliver precision medicine and to innovate discovery.

Study registration

CALIBER studies are registered as follows: study 2 – NCT01569139, study 4 – NCT02176174 and NCT01164371, study 5 – NCT01163513, studies 6 and 7 – NCT01804439, study 8 – NCT02285322, and studies 26–29 – NCT01162187. Optimising the Management of Angina is registered as Current Controlled Trials ISRCTN54381840.

Funding

The National Institute for Health Research (NIHR) Programme Grants for Applied Research programme (RP-PG-0407-10314) (all 33 studies) and additional funding from the Wellcome Trust (study 1), Medical Research Council Partnership grant (study 3), Servier (study 16), NIHR Research Methods Fellowship funding (study 19) and NIHR Research for Patient Benefit (study 33).

The Socio-Exposome: Advancing Exposure Science and Environmental Justice in a Post-Genomic Era

We propose the socio-exposome as a conceptual framework for integrative environmental health research. Environmental scientists coined the term "exposome" with the goal of inventorying and quantifying environmental exposures as precisely as scientists measure genes and gene expression. To date, the exposome's proponents have not thoroughly engaged social scientific theoretical and methodological expertise, although the exclusion of sociological expertise risks molecularizing complex social phenomena and limiting the possibility of collective action to improve environmental conditions. As a corrective, and to demonstrate how "omic" technologies could be made more relevant to public health, our socio-exposome framework blends insights from sociological and public health research with insights from environmental justice scholarship and activism. We argue that environmental health science requires more comprehensive data on more and different kinds of environmental exposures, but also must consider the socio-political conditions and inequalities that allow hazards to continue unchecked. We propose a multidimensional framework oriented around three axes: individual, local, and global, and suggest some sociomarkers and data sources that could identify exposures at each level. This framework could also guide policy, by creating a predictive framework that helps communities understand the repercussions of corporate and regulatory practices for public health and social justice.