AbstractData linkage of cohort or RCT data with routinely collected data is becoming increasingly commonplace, and this often involves combining biomarker measurements from different sources. However, sources may have different biases due to differences in assay method and calibration. Combining these measurements, or diagnoses based on these measurements, is therefore not always valid. We highlight an example using glycated haemoglobin A1c (HbA1c) test results from two different sources in UK Biobank data.

AbstractAimsThe aim of this study was to evaluate longitudinal associations between the mean and variability of HbA1c levels in individuals with type 2 diabetes (T2D) and major depressive disorder (MDD).MethodsIndividuals with T2D from the UK Biobank with linked primary care records were analysed. An HbA1c measurement within +/- 6-months of T2D diagnosis was taken as baseline, with subsequent HbA1c measurements used as the outcome in generalised least squares regression to evaluate longitudinal associations with a three-level MDD diagnosis variable (MDD controls, pre-T2D MDD cases and post-T2D MDD cases).ResultsUsing 7,968 T2D individuals, we show that MDD has utility in explaining mean HbA1c levels (p=6.53E-08). This is attributable to MDD diagnosis interacting with baseline T2D medication (p=3.36E-04) and baseline HbA1c (p=2.66E-05), but not with time-when all else is equal, the temporal trend in expected HbA1c did not differ by MDD diagnosis. However, joint consideration with baseline T2D medication showed that each additional medication prescribed was associated with a +4 mmol/mol (2.5%) increase in expected HbA1c across follow up for post-T2D MDD cases, relative to pre-T2D MDD cases and MDD controls. Furthermore, variability in HbA1c increased across time for post-T2D MDD cases but decreased for MDD controls and pre-T2D MDD cases.ConclusionsThese findings suggest closer monitoring of individuals with both T2D and MDD is essential to improve their diabetic control, particularly for those who develop MDD after T2D diagnosis.Novelty statementWhat is already known?Comorbid T2D and MDD is associated with poorer diabetic control and worse prognosis.What this study has found?We demonstrate a strong complex relationship between MDD and diabetic control, influenced by diabetic medication and baseline HbA1c levels. We showed that individuals who develop MDD after their T2D diagnosis have greater variability in HbA1c levels over time.What are the implications of the study?This study shows the importance of closer monitoring of HbA1c in individuals with both T2D and MDD, particularly those who develop MDD after diabetes, to improve diabetic control and reduce complications associated comorbid T2D and MDD.

AbstractLinear mixed effects models are frequently used in biomedical statistics to model the trajectory of a repeatedly measured longitudinal variable, such as a biomarker, over time. However, population-level estimates may be biased by censoring bias resulting from exit criteria that depend on the variable in question. A joint longitudinal-survival model, in which the exit criteria and longitudinal variable are modelled simultaneously, may address this bias. Using blood glucose progression (change in HbA1c) in type 2 diabetes patients on metformin monotherapy as an example, we study the potential benefit of using joint models to model trajectory of a biomarker in observational data. 7,712 patients with type 2 diabetes initiating metformin monotherapy were identified in UK Biobank’s general practice (GP) linked records. Genetic information was extracted from UK Biobank, and prescription records, baseline clinical features and biomarkers, and longitudinal HbA1c measures were extracted from GP records. Exit criteria for follow-up for a patient was defined as progression to an additional glucose-lowering drug (which is more likely in patient with higher HbA1c). Estimates of HbA1c trajectory over time were compared using linear mixed effect model approaches (which do not account for censoring bias) and joint models. In the primary analysis, a 0.19 mmol/mol per year higher (p = 0.01) HbA1c gradient was estimated using the joint model compared to the linear mixed effects model. This difference between models was attenuated (0.13 mmol/mol per year higher, p=0.43) when baseline clinical features and biomarkers were included as additional covariates.Censoring bias should be carefully considered when modelling trajectories of repeatedly measured longitudinal variables in observational data. Joint longitudinal-survival models are a useful approach to identify and potentially correct for censoring bias when estimating population-level trajectories.Author SummaryModelling biomarkers that change over time in real world data is a challenging statistical problem due to many potential sources of bias. For example, when studying a chronic disease using a biomarker or other measurement that represents disease severity, medication intended to affect that measurement has a profound effect on how it will change over time. One common way to control for this is to study a cohort on the same treatment strategy. That way, results are not influenced by treatment change. If a patient progresses to stronger medication, then future data is no longer used. However, this approach introduces its own bias. Patients whose condition progress particularly quickly are more likely to change treatment more rapidly (and therefore be removed from further analysis, or ‘censored’), so the cohort is biased towards those whose condition progresses slower. In this paper we apply a technique called joint longitudinal-survival modelling which can adjust for this censoring bias and produce less biased estimates of progression rates. We use HbA1c (a widely used measure of glucose control) in type 2 diabetes as an example, however our methods are theoretically applicable to a range of problems across medicine in which a biomarker or feature is repeatedly measured in an individual.

BackgroundFabry disease is an X-linked lysosomal storage disorder resulting from deficiency of the alpha-galactosidase a enzyme leading to accumulation of globotriaosylceramide in multiple organ sites with prominent cardiovascular and renal involvement. Global prevalence estimates of Fabry disease based on clinical ascertainment range from 1 in 40 000 to 1 in 170 000. We aimed to determine the prevalence of Fabry disease-causing variants in UK Biobank.MethodsWe sought GLA gene variants in exome sequencing data from 200 643 individuals from UK Biobank. We used ACMG/AMP guidelines (American College of Medical Genetics/Association for Molecular Pathology) to classify pathogenicity and compared baseline biomarker data, hospital ICD-10 (International Classification of Diseases version-10) codes, general practitioner records and self-reported health data with those without pathogenic variants.ResultsWe identified 81 GLA coding variants. We identified eight likely pathogenic variants on the basis of being rare (<1/10 000 individuals) and either previously reported to cause Fabry disease, or being protein-truncating variants. Thirty-six individuals carried one of these variants. In the UK Biobank, the prevalence of likely pathogenic Fabry disease-causing variants is 1/5732 for late-onset disease-causing variants and 1/200 643 for variants causing classic Fabry disease.ConclusionFabry disease-causing GLA variants are more prevalent in an unselected population sample than the reported prevalence of Fabry disease. These are overwhelmingly variants associated with later onset. It is possible the prevalence of later-onset Fabry disease exceeds current estimates.

AbstractData linkage of cohort or RCT data with routinely collected data is becoming increasingly commonplace, and this often involves combining biomarker measurements from different sources. However, sources may have different biases due to differences in assay method and calibration. Combining these measurements, or diagnoses based on these measurements, is therefore not always valid. We highlight an example using glycated haemoglobin A1c (HbA1c) test results from two different sources in UK Biobank data.

AbstractAimsThe aim of this study was to evaluate longitudinal associations between the mean and variability of HbA1c levels in individuals with type 2 diabetes (T2D) and major depressive disorder (MDD).MethodsIndividuals with T2D from the UK Biobank with linked primary care records were analysed. An HbA1c measurement within +/- 6-months of T2D diagnosis was taken as baseline, with subsequent HbA1c measurements used as the outcome in generalised least squares regression to evaluate longitudinal associations with a three-level MDD diagnosis variable (MDD controls, pre-T2D MDD cases and post-T2D MDD cases).ResultsUsing 7,968 T2D individuals, we show that MDD has utility in explaining mean HbA1c levels (p=6.53E-08). This is attributable to MDD diagnosis interacting with baseline T2D medication (p=3.36E-04) and baseline HbA1c (p=2.66E-05), but not with time-when all else is equal, the temporal trend in expected HbA1c did not differ by MDD diagnosis. However, joint consideration with baseline T2D medication showed that each additional medication prescribed was associated with a +4 mmol/mol (2.5%) increase in expected HbA1c across follow up for post-T2D MDD cases, relative to pre-T2D MDD cases and MDD controls. Furthermore, variability in HbA1c increased across time for post-T2D MDD cases but decreased for MDD controls and pre-T2D MDD cases.ConclusionsThese findings suggest closer monitoring of individuals with both T2D and MDD is essential to improve their diabetic control, particularly for those who develop MDD after T2D diagnosis.Novelty statementWhat is already known?Comorbid T2D and MDD is associated with poorer diabetic control and worse prognosis.What this study has found?We demonstrate a strong complex relationship between MDD and diabetic control, influenced by diabetic medication and baseline HbA1c levels. We showed that individuals who develop MDD after their T2D diagnosis have greater variability in HbA1c levels over time.What are the implications of the study?This study shows the importance of closer monitoring of HbA1c in individuals with both T2D and MDD, particularly those who develop MDD after diabetes, to improve diabetic control and reduce complications associated comorbid T2D and MDD.

AbstractLinear mixed effects models are frequently used in biomedical statistics to model the trajectory of a repeatedly measured longitudinal variable, such as a biomarker, over time. However, population-level estimates may be biased by censoring bias resulting from exit criteria that depend on the variable in question. A joint longitudinal-survival model, in which the exit criteria and longitudinal variable are modelled simultaneously, may address this bias. Using blood glucose progression (change in HbA1c) in type 2 diabetes patients on metformin monotherapy as an example, we study the potential benefit of using joint models to model trajectory of a biomarker in observational data. 7,712 patients with type 2 diabetes initiating metformin monotherapy were identified in UK Biobank’s general practice (GP) linked records. Genetic information was extracted from UK Biobank, and prescription records, baseline clinical features and biomarkers, and longitudinal HbA1c measures were extracted from GP records. Exit criteria for follow-up for a patient was defined as progression to an additional glucose-lowering drug (which is more likely in patient with higher HbA1c). Estimates of HbA1c trajectory over time were compared using linear mixed effect model approaches (which do not account for censoring bias) and joint models. In the primary analysis, a 0.19 mmol/mol per year higher (p = 0.01) HbA1c gradient was estimated using the joint model compared to the linear mixed effects model. This difference between models was attenuated (0.13 mmol/mol per year higher, p=0.43) when baseline clinical features and biomarkers were included as additional covariates.Censoring bias should be carefully considered when modelling trajectories of repeatedly measured longitudinal variables in observational data. Joint longitudinal-survival models are a useful approach to identify and potentially correct for censoring bias when estimating population-level trajectories.Author SummaryModelling biomarkers that change over time in real world data is a challenging statistical problem due to many potential sources of bias. For example, when studying a chronic disease using a biomarker or other measurement that represents disease severity, medication intended to affect that measurement has a profound effect on how it will change over time. One common way to control for this is to study a cohort on the same treatment strategy. That way, results are not influenced by treatment change. If a patient progresses to stronger medication, then future data is no longer used. However, this approach introduces its own bias. Patients whose condition progress particularly quickly are more likely to change treatment more rapidly (and therefore be removed from further analysis, or ‘censored’), so the cohort is biased towards those whose condition progresses slower. In this paper we apply a technique called joint longitudinal-survival modelling which can adjust for this censoring bias and produce less biased estimates of progression rates. We use HbA1c (a widely used measure of glucose control) in type 2 diabetes as an example, however our methods are theoretically applicable to a range of problems across medicine in which a biomarker or feature is repeatedly measured in an individual.

BackgroundFabry disease is an X-linked lysosomal storage disorder resulting from deficiency of the alpha-galactosidase a enzyme leading to accumulation of globotriaosylceramide in multiple organ sites with prominent cardiovascular and renal involvement. Global prevalence estimates of Fabry disease based on clinical ascertainment range from 1 in 40 000 to 1 in 170 000. We aimed to determine the prevalence of Fabry disease-causing variants in UK Biobank.MethodsWe sought GLA gene variants in exome sequencing data from 200 643 individuals from UK Biobank. We used ACMG/AMP guidelines (American College of Medical Genetics/Association for Molecular Pathology) to classify pathogenicity and compared baseline biomarker data, hospital ICD-10 (International Classification of Diseases version-10) codes, general practitioner records and self-reported health data with those without pathogenic variants.ResultsWe identified 81 GLA coding variants. We identified eight likely pathogenic variants on the basis of being rare (<1/10 000 individuals) and either previously reported to cause Fabry disease, or being protein-truncating variants. Thirty-six individuals carried one of these variants. In the UK Biobank, the prevalence of likely pathogenic Fabry disease-causing variants is 1/5732 for late-onset disease-causing variants and 1/200 643 for variants causing classic Fabry disease.ConclusionFabry disease-causing GLA variants are more prevalent in an unselected population sample than the reported prevalence of Fabry disease. These are overwhelmingly variants associated with later onset. It is possible the prevalence of later-onset Fabry disease exceeds current estimates.

Abstract
. Aims/hypothesis
. Screening programmes can detect cases of undiagnosed diabetes earlier than symptomatic or incidental diagnosis. However, the improvement in time to diagnosis achieved by screening programmes compared with routine clinical care is unclear. We aimed to use the UK Biobank population-based study to provide the first population-based estimate of the reduction in time to diabetes diagnosis that could be achieved by HbA1c-based screening in middle-aged adults.
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. Methods
. We studied UK Biobank participants aged 40–70 years with HbA1c measured at enrolment (but not fed back to participants/clinicians) and linked primary and secondary healthcare data (n=179,923) and identified those with a pre-existing diabetes diagnosis (n=13,077, 7.3%). Among the remaining participants (n=166,846) without a diabetes diagnosis, we used an elevated enrolment HbA1c level (≥48 mmol/mol [≥6.5%]) to identify those with undiagnosed diabetes. For this group, we used Kaplan–Meier analysis to assess the time between enrolment HbA1c measurement and subsequent clinical diabetes diagnosis up to 10 years, and Cox regression to identify clinical factors associated with delayed diabetes diagnosis.
.
. Results
. In total, 1.0% (1703/166,846) of participants without a diabetes diagnosis had undiagnosed diabetes based on calibrated HbA1c levels at UK Biobank enrolment, with a median HbA1c level of 51.3 mmol/mol (IQR 49.1–57.2) (6.8% [6.6–7.4]). These participants represented an additional 13.0% of diabetes cases in the study population relative to the 13,077 participants with a diabetes diagnosis. The median time to clinical diagnosis for those with undiagnosed diabetes was 2.2 years, with a median HbA1c at clinical diagnosis of 58.2 mmol/mol (IQR 51.0–80.0) (7.5% [6.8–9.5]). Female participants with lower HbA1c and BMI measurements at enrolment experienced the longest delay to clinical diagnosis.
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. Conclusions/interpretation
. Our population-based study shows that HbA1c screening in adults aged 40–70 years can reduce the time to diabetes diagnosis by a median of 2.2 years compared with routine clinical care. The findings support the use of HbA1c screening to reduce the time for which individuals are living with undiagnosed diabetes.
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. Graphical abstract
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The success of DNA nanotechnology has been driven by the discovery of novel
structural motifs with a wide range of shapes and uses. We present a
comprehensive study of the T-motif, a 3-armed, planar, right-angled junction
that has been used in the self-assembly of DNA polyhedra and periodic
structures. The motif is formed through the interaction of a bulge loop in one
duplex and a sticky end of another. The polarity of the sticky end has
significant consequences for the thermodynamic and geometrical properties of
the T-motif: different polarities create junctions spanning different grooves
of the duplex. We compare experimental binding strengths with predictions of
oxDNA, a coarse-grained model of DNA, for various loop sizes. We find that,
although both sticky-end polarities can create stable junctions, junctions
resulting from 5$'$ sticky ends are stable over a wider range of bulge loop
sizes. We highlight the importance of possible coaxial stacking interactions
within the motif and investigate how each coaxial stacking interaction
stabilises the structure and favours a particular geometry.