A National Network of Safe Havens: Scottish Perspective.

For over a decade, Scotland has implemented and operationalized a system of Safe Havens, which provides secure analytics platforms for researchers to access linked, deidentified electronic health records (EHRs) while managing the risk of unauthorized reidentification. In this paper, a perspective is provided on the state-of-the-art Scottish Safe Haven network, including its evolution, to define the key activities required to scale the Scottish Safe Haven network's capability to facilitate research and health care improvement initiatives. A set of processes related to EHR data and their delivery in Scotland have been discussed. An interview with each Safe Haven was conducted to understand their services in detail, as well as their commonalities. The results show how Safe Havens in Scotland have protected privacy while facilitating the reuse of the EHR data. This study provides a common definition of a Safe Haven and promotes a consistent understanding among the Scottish Safe Haven network and the clinical and academic research community. We conclude by identifying areas where efficiencies across the network can be made to meet the needs of population-level studies at scale.

Epidemiology and Heritability of Major Depressive Disorder, Stratified by Age of Onset, Sex, and Illness Course in Generation Scotland: Scottish Family Health Study (GS:SFHS).

The heritability of Major Depressive Disorder (MDD) has been estimated at 37% based largely on twin studies that rely on contested assumptions. More recently, the heritability of MDD has been estimated on large populations from registries such as the Swedish, Finnish, and Chinese cohorts. Family-based designs utilise a number of different relationships and provide an alternative means of estimating heritability. Generation Scotland: Scottish Family Health Study (GS:SFHS) is a large (n = 20,198), family-based population study designed to identify the genetic determinants of common diseases, including Major Depressive Disorder. Two thousand seven hundred and six individuals were SCID diagnosed with MDD, 13.5% of the cohort, from which we inferred a population prevalence of 12.2% (95% credible interval: 11.4% to 13.1%). Increased risk of MDD was associated with being female, unemployed due to a disability, current smokers, former drinkers, and living in areas of greater social deprivation. The heritability of MDD in GS:SFHS was between 28% and 44%, estimated from a pedigree model. The genetic correlation of MDD between sexes, age of onset, and illness course were examined and showed strong genetic correlations. The genetic correlation between males and females with MDD was 0.75 (0.43 to 0.99); between earlier (≤ age 40) and later (> age 40) onset was 0.85 (0.66 to 0.98); and between single and recurrent episodic illness course was 0.87 (0.72 to 0.98). We found that the heritability of recurrent MDD illness course was significantly greater than the heritability of single MDD illness course. The study confirms a moderate genetic contribution to depression, with a small contribution of the common family environment (variance proportion = 0.07, CI: 0.01 to 0.15), and supports the relationship of MDD with previously identified risk factors. This study did not find robust support for genetic differences in MDD due to sex, age of onset, or illness course. However, we found an intriguing difference in heritability between recurrent and single MDD illness course. These findings establish GS:SFHS as a valuable cohort for the genetic investigation of MDD.

Sex-Differences in the Metabolic Health of Offspring of Parents with Diabetes: A Record-Linkage Study.

Maternal diabetes in pregnancy affects offspring health. The impact of parental diabetes on offspring health is unclear. We investigated the impact of parental diabetes on the metabolic-health of adult-offspring who did not themselves have diabetes. Data from the Generation Scotland: Scottish Family Health Study, a population-based family cohort, were record-linked to subjects' own diabetes medical records. From F0-parents, we identified F1-offspring of: mothers with diabetes (OMD, n = 409), fathers with diabetes (OFD, n = 468), no parent with diabetes (ONoPD, n = 2489). Metabolic syndrome, body, biochemical measurements and blood-pressures were compared between F1-offspring groups by sex. A higher proportion of female OMD had metabolic syndrome than female OFD or ONoPD (P<0.0001). In female offspring, predictors of metabolic syndrome were: having a mother with diabetes (OR = 1.78, CI 1.03-3.07, [reference ONoPD]), body mass index (BMI, OR = 1.21, CI 1.13-1.30) and age (OR = 1.03, CI 1.01-1.06). In male offspring, predictors of metabolic syndrome were: BMI (OR = 1.18, CI 1.09-1.29) and percent body-fat (OR = 1.12, CI 1.05-1.19). In both sexes, OMD had higher blood-pressures than OFD (P<0.0001). In females, OMD had higher glucose (P<0.0001) and percent body-fat (P<0.0001) compared with OFD or ONoPD. OMD and OFD both had increased waist-measurements (P<0.0001), BMI (P<0.0001) and percent body-fat (P<0.0001) compared with ONoPD. Female OMD and OFD had lower HDL-cholesterol levels (P<0.0001) than female ONoPD. Parental diabetes is associated with higher offspring-BMI and body-fat. In female offspring, maternal diabetes increased the odds of metabolic syndrome, even after adjusting for BMI. Further investigations are required to determine the mechanisms involved.

Cohort Profile: Generation Scotland: Scottish Family Health Study (GS:SFHS). The study, its participants and their potential for genetic research on health and illness.

GS:SFHS is a family-based genetic epidemiology study with DNA and socio-demographic and clinical data from about 24 000 volunteers across Scotland aged 18-98 years, from February 2006 to March 2011. Biological samples and anonymized data form a resource for research on the genetics of health, disease and quantitative traits of current and projected public health importance. Specific and important features of GS:SFHS include the family-based recruitment, with the intent of obtaining family groups; the breadth and depth of phenotype information, including detailed data on cognitive function, personality traits and mental health; consent and mechanisms for linkage of all data to comprehensive routine health-care records; and 'broad' consent from participants to use their data and samples for a wide range of medical research, including commercial research, and for re-contact for the potential collection of other data or samples, or for participation in related studies and the design and review of the protocol in parallel with in-depth sociological research on (potential) participants and users of the research outcomes. These features were designed to maximize the power of the resource to identify, replicate or control for genetic factors associated with a wide spectrum of illnesses and risk factors, both now and in the future.

Quality, quantity and harmony: the DataSHaPER approach to integrating data across bioclinical studies.

BACKGROUND: Vast sample sizes are often essential in the quest to disentangle the complex interplay of the genetic, lifestyle, environmental and social factors that determine the aetiology and progression of chronic diseases. The pooling of information between studies is therefore of central importance to contemporary bioscience. However, there are many technical, ethico-legal and scientific challenges to be overcome if an effective, valid, pooled analysis is to be achieved. Perhaps most critically, any data that are to be analysed in this way must be adequately 'harmonized'. This implies that the collection and recording of information and data must be done in a manner that is sufficiently similar in the different studies to allow valid synthesis to take place. METHODS: This conceptual article describes the origins, purpose and scientific foundations of the DataSHaPER (DataSchema and Harmonization Platform for Epidemiological Research; http://www.datashaper.org), which has been created by a multidisciplinary consortium of experts that was pulled together and coordinated by three international organizations: P³G (Public Population Project in Genomics), PHOEBE (Promoting Harmonization of Epidemiological Biobanks in Europe) and CPT (Canadian Partnership for Tomorrow Project). RESULTS: The DataSHaPER provides a flexible, structured approach to the harmonization and pooling of information between studies. Its two primary components, the 'DataSchema' and 'Harmonization Platforms', together support the preparation of effective data-collection protocols and provide a central reference to facilitate harmonization. The DataSHaPER supports both 'prospective' and 'retrospective' harmonization. CONCLUSION: It is hoped that this article will encourage readers to investigate the project further: the more the research groups and studies are actively involved, the more effective the DataSHaPER programme will ultimately be.

Cholesterol fractions and apolipoproteins as risk factors for heart disease mortality in older men.

BACKGROUND: The relevance of blood lipid levels as risk factors for ischemic heart disease (IHD) in older people is uncertain; hence, cholesterol-lowering therapy is not routinely prescribed in older populations. METHODS: We assessed IHD mortality associations with plasma levels of total cholesterol, low-density lipoprotein cholesterol, high-density lipoprotein cholesterol, apolipoprotein B, and apolipoprotein A(1) measured in older men. Ischemic heart disease was assessed in a 7-year follow-up of a cohort of 5344 men (mean age, 76.9 years), including 74.3% without cardiovascular disease (CVD) or statin use and 25.6% with CVD or statin use. Hazard ratios (HRs) for 447 deaths from IHD were estimated for a 2-SD difference in usual plasma lipid levels. RESULTS: Ischemic heart disease mortality was not significantly associated with total cholesterol levels in all men (HR, 1.05), but a significant positive association in men without CVD and a slight nonsignificant inverse association in men with CVD were observed (HR, 1.47 vs 0.84). The patterns were similar for low-density lipoprotein cholesterol levels (HR, 1.50 vs 0.98) and for apolipoprotein B levels (HR, 1.68 vs 0.93). Ischemic heart disease risks were inversely associated with high-density lipoprotein cholesterol levels and with apolipoprotein A(1) levels in men with and without CVD. Ischemic heart disease risks were strongly associated with total/high-density [corrected] lipoprotein cholesterol levels (HR, 1.57) and apolipoprotein B/apolipoprotien [corrected] A(1) levels (HR, 1.54), and remained strongly related at all ages. CONCLUSIONS: Blood lipid levels other than total cholesterol levels were associated with IHD in older men. Differences in lipid levels that are achievable by statin use were associated with about a one-third lower risk of IHD, irrespective of age.

Data integration in eHealth: a domain/disease specific roadmap.

The paper documents a series of data integration workshops held in 2006 at the UK National e-Science Centre, summarizing a range of the problem/solution scenarios in multi-site and multi-scale data integration with six HealthGrid projects using schizophrenia as a domain-specific test case. It outlines emerging strategies, recommendations and objectives for collaboration on shared ontology-building and harmonization of data for multi-site trials in this domain.