Seán R. Millar

DataSHIELD: taking the analysis to the data, not the data to the analysis

Background: Research in modern biomedicine and social science requires sample sizes so large that they can often only be achieved through a pooled co-analysis of data from several studies. But the pooling of information from individuals in a central database that may be queried by researchers raises important ethico-legal questions and can be controversial. In the UK this has been highlighted by recent debate and controversy relating to the UK’s proposed ‘care.data’ initiative, and these issues reflect important societal and professional concerns about privacy, confidentiality and intellectual property. DataSHIELD provides a novel technological solution that can circumvent some of the most basic challenges in facilitating the access of researchers and other healthcare professionals to individual-level data. Methods: Commands are sent from a central analysis computer (AC) to several data computers (DCs) storing the data to be co-analysed. The data sets are analysed simultaneously but in parallel. The separate parallelized analyses are linked by non-disclosive summary statistics and commands transmitted back and forth between the DCs and the AC. This paper describes the technical implementation of DataSHIELD using a modified R statistical environment linked to an Opal database deployed behind the computer firewall of each DC. Analysis is controlled through a standard R environment at the AC. Results: Based on this Opal/R implementation, DataSHIELD is currently used by the Healthy Obese Project and the Environmental Core Project (BioSHaRE-EU) for the federated analysis of 10 data sets across eight European countries, and this illustrates the opportunities and challenges presented by the DataSHIELD approach. Conclusions: DataSHIELD facilitates important research in settings where: (i) a co-analysis of individual-level data from several studies is scientifically necessary but governance restrictions prohibit the release or sharing of some of the required data, and/or render data access unacceptably slow; (ii) a research group (e.g. in a developing nation) is particularly vulnerable to loss of intellectual property—the researchers want to fully share the information held in their data with national and international collaborators, but do not wish to hand over the physical data themselves; and (iii) a data set is to be included in an individual-level co-analysis but the physical size of the data precludes direct transfer to a new site for analysis.

The Prevalence and Determinants of Undiagnosed and Diagnosed Type 2 Diabetes in Middle-Aged Irish Adults

Background The prevalence of type 2 diabetes within the Republic of Ireland is poorly defined, although a recent report suggested 135,000 cases in adults aged 45+, with approximately one-third of these undiagnosed. This study aims to assess the prevalence of undiagnosed and diagnosed diabetes in middle-aged adults, and compare features related to either condition, in order to investigate why certain individuals remain undetected. Methods This was a cross-sectional study involving a sample of 2,047 men and women, aged between 50–69 years, randomly selected from a large primary care centre. Univariate logistic regression was used to explore socio-economic, metabolic and other health related variable associations with undiagnosed or diagnosed diabetes. A final multivariate analysis was used to determine odds ratios and 95% confidence intervals for having undiagnosed compared to diagnosed diabetes, adjusted for gender, age and significant covariates determined from univariate models. Principle Findings The total prevalence of diabetes was 8.5% (95% CI: 7.4%–8.8%); 72 subjects (3.5%) had undiagnosed diabetes (95% CI: 2.8%–4.4%) and 102 subjects (5.0%) had diagnosed diabetes (95% CI: 4.1%–6.0%). Obesity, dyslipidaemia, and family history of diabetes were positively associated with both undiagnosed and diagnosed type 2 diabetes. Compared with diagnosed subjects, study participants with undiagnosed diabetes were significantly more likely to have low levels of physical activity and were less likely to be on treatment for diabetes-related conditions or to have private medical insurance. Conclusions The prevalence of diabetes within the Cork and Kerry Diabetes and Heart Disease Study is comparable to recent estimates from the Slán National Health and Lifestyle Survey, a study which was nationally representative of the general population. A considerable proportion of diabetes cases were undiagnosed (41%), emphasising the need for more effective detection strategies and equitable access to primary healthcare.

Surrogate Measures of Adiposity and Cardiometabolic Risk - Why the Uncertainty? A Review of Recent Meta-Analytic Studies

The increasing obesity epidemic has become a significant public health concern worldwide, as excess body fat has been shown to be strongly related to cardiometabolic risk. Measurement of adiposity is commonly used as an indicator of health and various anthropometric measurement procedures have been proposed to characterise individual susceptibility to cardiometabolic conditions. Although extensive research has attempted to quantify relationships between different adiposity measures and morbidity, results have been conflicting and inconclusive, and considerable controversy still exists as to which anthropometric measurement most accurately defines nonoptimal body fat distribution.In this review we describe the most commonly used indices of general and central adiposity and review the most recently completed meta-analytic studies to determine which adiposity measure is most strongly associated with, and the best discriminator of, type 2 diabetes, cardiovascular disease and mortality.

Optimal Central Obesity Measurement Site for Assessing Cardiometabolic and Type 2 Diabetes Risk in Middle-Aged Adults

Objectives Despite recommendations that central obesity assessment should be employed as a marker of cardiometabolic health, no consensus exists regarding measurement protocol. This study examined a range of anthropometric variables and their relationships with cardiometabolic features and type 2 diabetes in order to ascertain whether measurement site influences discriminatory accuracy. In particular, we compared waist circumference (WC) measured at two sites: (1) immediately below the lowest rib (WC rib) and (2) between the lowest rib and iliac crest (WC midway), which has been recommended by the World Health Organisation and International Diabetes Federation. Materials and Methods This was a cross-sectional study involving a random sample of 2,002 men and women aged 46-73 years. Metabolic profiles and WC, hip circumference, pelvic width and body mass index (BMI) were determined. Correlation, logistic regression and area under the receiver operating characteristic curve analyses were used to evaluate obesity measurement relationships with metabolic risk phenotypes and type 2 diabetes. Results WC rib measures displayed the strongest associations with non-optimal lipid and lipoprotein levels, high blood pressure, insulin resistance, impaired fasting glucose, a clustering of metabolic risk features and type 2 diabetes, in both genders. Rib-derived indices improved discrimination of type 2 diabetes by 3-7% compared to BMI and 2-6% compared to WC midway (in men) and 5-7% compared to BMI and 4-6% compared to WC midway (in women). A prediction model including BMI and central obesity displayed a significantly higher area under the curve for WC rib (0.78, P=0.003), Rib/height ratio (0.80, P<0.001), Rib/pelvis ratio (0.79, P<0.001), but not for WC midway (0.75, P=0.127), when compared to one with BMI alone (0.74). Conclusions WC rib is easier to assess and our data suggest that it is a better method for determining obesity-related cardiometabolic risk than WC midway. The clinical utility of rib-derived indices, or alternative WC measurements, deserves further investigation.

HbA1c Alone Is a Poor Indicator of Cardiometabolic Risk in Middle-Aged Subjects with Pre-Diabetes but Is Suitable for Type 2 Diabetes Diagnosis: A Cross-Sectional Study

Objectives Glycated haemoglobin A1c (HbA1c) measurement is recommended as an alternative to fasting plasma glucose (FPG) for the diagnosis of pre-diabetes and type 2 diabetes. However, evidence suggests discordance between HbA1c and FPG. In this study we examine a range of metabolic risk features, pro-inflammatory cytokines, acute-phase response proteins, coagulation factors and white blood cell counts to determine which assay more accurately identifies individuals at increased cardiometabolic risk. Materials and Methods This was a cross-sectional study involving a random sample of 2,047 men and women aged 46-73 years. Binary and multinomial logistic regression were employed to examine risk feature associations with pre-diabetes [either HbA1c levels 5.7-6.4% (39-46 mmol/mol) or impaired FPG levels 5.6-6.9 mmol/l] and type 2 diabetes [either HbA1c levels >6.5% (>48 mmol/mol) or FPG levels >7.0 mmol/l]. Receiver operating characteristic curve analysis was used to evaluate the ability of HbA1c to discriminate pre-diabetes and diabetes defined by FPG. Results Stronger associations with diabetes-related phenotypes were observed in pre-diabetic subjects diagnosed by FPG compared to those detected by HbA1c. Individuals with type 2 diabetes exhibited cardiometabolic profiles that were broadly similar according to diagnosis by either assay. Pre-diabetic participants classified by both assays displayed a more pro-inflammatory, pro-atherogenic, hypertensive and insulin resistant profile. Odds ratios of having three or more metabolic syndrome features were also noticeably increased (OR: 4.0, 95% CI: 2.8-5.8) when compared to subjects diagnosed by either HbA1c (OR: 1.4, 95% CI: 1.2-1.8) or FPG (OR: 3.0, 95% CI: 1.7-5.1) separately. Conclusions In middle-aged Caucasian-Europeans, HbA1c alone is a poor indicator of cardiometabolic risk but is suitable for diagnosing diabetes. Combined use of HbA1c and FPG may be of additional benefit for detecting individuals at highest odds of type 2 diabetes development.

Smoking as an independent risk factor for macrocytosis in middle-aged adults: A population-based observational study

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RF34 Parent and child misperception of child weight status: a cross-sectional analysis of the cork children’s lifestyle study (CCLaS)

Background Despite the increased global awareness of childhood obesity, a high proportion of parents and children continue to misclassify child weight status. The aim of this study was to determine parent and child misperception of child weight and identify the determinants influencing this misperception. Methods A cross-sectional study involving 1 075 children, aged 8–11 years, drawn from primary schools in Cork city and county in Ireland. Data were collected using child and parent self-administered questionnaires. Physical measurements were taken by trained researchers according to standard procedures. Univariate and multivariable logistic regression analysis was used to examine factors influencing parental and child perceptions regarding child weight. Results Almost one-quarter of parents of all children misclassified their child’s weight status. Forty four per cent of parents of overweight or obese children underestimated their child’s weight. In all children, factors associated with parental misperception of child weight included the child being female (OR=1.95; 95% CI 1.36 to 2.81, p<0.001), being overweight or obese (OR=2.84; 95% CI 1.95 to 4.15, p<0.001), child misclassification of own weight (OR=3.28; 95% CI 2.26 to 4.78, p<0.001) and parent reported child computer use (OR=1.64; 95% CI 1.12 to 2.39, p=0.01). In overweight or obese children, accuracy in parental perception of weight improved with increasing child age (OR=0.49; 95% CI 0.27 to 0.88, p=0.02). Of children who were overweight/obese, 76% (n=213) underestimated their weight. These children had increased odds of misperceiving their own weight status if their parents misclassified their child’s weight (OR=3.98; 95% CI 1.95 to 8.10, p<0.001). Conclusion Findings suggest that in an obesogenic society, where overweight and obesity has become the norm, the capacity of both parents and children to correctly classify child weight status is significantly impaired. Health care professionals should be aware of the frequent misperception of weight status, especially when dealing with parents of younger children and children who are overweight or obese.