Early antenatal prediction of gestational diabetes in obese women: development of prediction tools for targeted intervention

All obese women are categorised as being of equally high risk of gestational diabetes (GDM) whereas the majority do not develop the disorder. Lifestyle and pharmacological interventions in unselected obese pregnant women have been unsuccessful in preventing GDM. Our aim was to develop a prediction tool for early identification of obese women at high risk of GDM to facilitate targeted interventions in those most likely to benefit. Clinical and anthropometric data and non-fasting blood samples were obtained at 15+0–18+6 weeks’ gestation in 1303 obese pregnant women from UPBEAT, a randomised controlled trial of a behavioural intervention. Twenty one candidate biomarkers associated with insulin resistance, and a targeted nuclear magnetic resonance (NMR) metabolome were measured. Prediction models were constructed using stepwise logistic regression. Twenty six percent of women (n = 337) developed GDM (International Association of Diabetes and Pregnancy Study Groups criteria). A model based on clinical and anthropometric variables (age, previous GDM, family history of type 2 diabetes, systolic blood pressure, sum of skinfold thicknesses, waist:height and neck:thigh ratios) provided an area under the curve of 0.71 (95%CI 0.68–0.74). This increased to 0.77 (95%CI 0.73–0.80) with addition of candidate biomarkers (random glucose, haemoglobin A1c (HbA1c), fructosamine, adiponectin, sex hormone binding globulin, triglycerides), but was not improved by addition of NMR metabolites (0.77; 95%CI 0.74–0.81). Clinically translatable models for GDM prediction including readily measurable variables e.g. mid-arm circumference, age, systolic blood pressure, HbA1c and adiponectin are described. Using a ≥35% risk threshold, all models identified a group of high risk obese women of whom approximately 50% (positive predictive value) later developed GDM, with a negative predictive value of 80%. Tools for early pregnancy identification of obese women at risk of GDM are described which could enable targeted interventions for GDM prevention in women who will benefit the most

Characteristics of the gut microbiome in women with gestational diabetes mellitus:A systematic review

BACKGROUND: The incidence of women developing gestational diabetes mellitus (GDM) is increasing, which is associated with an increased risk of type 2 diabetes mellitus (T2DM) for both mother and child. Gut microbiota dysbiosis may contribute to the pathogenesis of both GDM and the accompanying risk of T2DM. Thus, a better understanding of the microbial communities associated with GDM could offer a potential target for intervention and treatment in the future. Therefore, we performed a systematic review to investigate if the GDM women have a distinct gut microbiota composition compared to non-GDM women. METHODS: We identified 21 studies in a systematic literature search of Embase and PubMed up to February 24, 2021. Data on demographics, methodology and identified microbial metrics were extracted. The quality of each study was assessed according to the Newcastle-Ottawa Scale. RESULTS: Sixteen of the studies did find a GDM-associated gut microbiota, although no consistency could be seen. Only Collinsella and Blautia showed a tendency to be increased in GDM women, whereas the remaining genera were significantly different in opposing directions. CONCLUSION: Although most of the studies found an association between GDM and gut microbiota dysbiosis, no overall GDM-specific gut microbiota could be identified. All studies in the second trimester found a difference between GDM and non-GDM women, indicating that dysbiosis is present at the time of diagnosis. Nevertheless, it is still unclear when the dysbiosis develops, as no consensus could be seen between the studies investigating the gut microbiota in the first trimester of pregnancy. However, studies varied widely concerning methodology and study design, which might explain the highly heterogeneous gut microbiota compositions between studies. Therefore, future studies need to include multiple time points and consider possible confounding factors such as ethnicity, pre-pregnancy body mass index, and GDM treatment

Maternal thyroid function and child educational attainment: prospective cohort study

Objective: To determine if first trimester maternal thyroid dysfunction is a critical determinant of child scholastic performance and overall educational attainment. Design: Prospective cohort study. Setting: Avon Longitudinal Study of Parents and Children cohort in the UK. Participants: 4615 mother-child pairs with an available first trimester sample (median 10 weeks gestation, interquartile range 8-12). Exposures: Free thyroxine, thyroid stimulating hormone, and thyroid peroxidase antibodies assessed as continuous measures and the seven clinical categories of maternal thyroid function. Main outcome measures: Five age-specific national curriculum assessments in 3580 children at entry stage assessment at 54 months, increasing up to 4461 children at their final school assessment at age 15. Results: No strong evidence of clinically meaningful associations of first trimester free thyroxine and thyroid stimulating hormone levels with entry stage assessment score or Standard Assessment Test scores at any of the key stages was found. Associations of maternal free thyroxine or thyroid stimulating hormone with the total number of General Certificates of Secondary Education (GCSEs) passed (range 0-16) were all close to the null: free thyroxine, rate ratio per pmol/L 1.00 (95% confidence interval 1.00 to 1.01); and thyroid stimulating hormone, rate ratio 0.98 (0.94 to 1.02). No important relationship was observed when more detailed capped scores of GCSEs allowing for both the number and grade of pass or when language, mathematics, and science performance were examined individually or when all educational assessments undertaken by an individual from school entry to leaving were considered. 200 (4.3%) mothers were newly identified as having hypothyroidism or subclinical hypothyroidism and 97 (2.1%) subclinical hyperthyroidism or hyperthyroidism. Children of mothers with thyroid dysfunction attained an equivalent number of GCSEs and equivalent grades as children of mothers with euthyroidism. Conclusions: Maternal thyroid dysfunction in early pregnancy does not have a clinically important association with impaired child performance at school or educational achievement

2nd International Consensus Report on Gaps & Opportunities for the Clinical Translation of Precision Diabetes Medicine

Precision medicine is part of the logical evolution of contemporary evidence-based medicine that seeks to reduce errors and optimize outcomes when making medical decisions and health recommendations. Diabetes affects hundreds of millions worldwide, many of whom will develop life-threatening complications and die prematurely. Precision medicine can potentially address this enormous problem by accounting for the heterogeneous etiology, clinical presentation, and pathogenesis of common forms of diabetes and risk of complications. This 2nd International Consensus Report on Precision Diabetes Medicine summarize the findings from a systematic evidence review across the key pillars of precision medicine (prevention, diagnosis, treatment, prognosis) in four recognized forms of diabetes (monogenic, gestational, type 1, type 2). These reviews address key questions about translation of precision medicine research into practice. Although not complete, owing to the vast literature on this topic, they revealed opportunities for the immediate or near-term clinical implementation of precision diabetes medicine; further, we expose important gaps in knowledge, focusing on the need to obtain new clinically relevant evidence. Gaps include the need for common standards for clinical readiness, including consideration of cost-effectiveness, health equity, predictive accuracy, liability, and accessibility. Key milestones are outlined for the broad clinical implementation of precision diabetes medicine

Risk models and scores for type 2 diabetes: Systematic review

This article is published under a Creative Commons Attribution Non Commercial (CC BY-NC 3.0) licence that allows reuse subject only to the use being non-commercial and to the article being fully attributed (http://creativecommons.org/licenses/by-nc/3.0).Objective - To evaluate current risk models and scores for type 2 diabetes and inform selection and implementation of these in practice. Design - Systematic review using standard (quantitative) and realist (mainly qualitative) methodology. Inclusion - criteria Papers in any language describing the development or external validation, or both, of models and scores to predict the risk of an adult developing type 2 diabetes. Data sources - Medline, PreMedline, Embase, and Cochrane databases were searched. Included studies were citation tracked in Google Scholar to identify follow-on studies of usability or impact. Data extraction - Data were extracted on statistical properties of models, details of internal or external validation, and use of risk scores beyond the studies that developed them. Quantitative data were tabulated to compare model components and statistical properties. Qualitative data were analysed thematically to identify mechanisms by which use of the risk model or score might improve patient outcomes. Results - 8864 titles were scanned, 115 full text papers considered, and 43 papers included in the final sample. These described the prospective development or validation, or both, of 145 risk prediction models and scores, 94 of which were studied in detail here. They had been tested on 6.88 million participants followed for up to 28 years. Heterogeneity of primary studies precluded meta-analysis. Some but not all risk models or scores had robust statistical properties (for example, good discrimination and calibration) and had been externally validated on a different population. Genetic markers added nothing to models over clinical and sociodemographic factors. Most authors described their score as “simple” or “easily implemented,” although few were specific about the intended users and under what circumstances. Ten mechanisms were identified by which measuring diabetes risk might improve outcomes. Follow-on studies that applied a risk score as part of an intervention aimed at reducing actual risk in people were sparse. Conclusion - Much work has been done to develop diabetes risk models and scores, but most are rarely used because they require tests not routinely available or they were developed without a specific user or clear use in mind. Encouragingly, recent research has begun to tackle usability and the impact of diabetes risk scores. Two promising areas for further research are interventions that prompt lay people to check their own diabetes risk and use of risk scores on population datasets to identify high risk “hotspots” for targeted public health interventions.Tower Hamlets, Newham, and City and Hackney primary care trusts and National Institute of Health Research

Maternal ABO Blood Phenotype and Factors Associated with Preeclampsia Subtype

Preeclampsia affects 3-8% of all pregnancies and is a global issue that significantly effects the short and long-term health of women and neonates. The pathophysiology of preeclampsia remains unclear and there seems to be two distinct subtypes, early and late onset. Each subtype may have a unique pathophysiology and set of risk factors. Preeclampsia is linked to long-term risk of cardiovascular disease in previously affected women. Subsequently, risk factors shared between preeclampsia and cardiovascular disease should be explored. The main aim of this study was to determine the strength of association between maternal ABO blood type and preeclampsia subtype. This hospital-based case control study was completed at one community hospital in the Mid Atlantic, United States. The study included 126 female subjects with early onset preeclampsia (≤ 33 6/7 weeks gestation), 126 female subjects with late onset preeclampsia (≥ 34 weeks gestation) and 259 control subjects with no history of preeclampsia. Strict diagnostic criteria were used and preeclamptic subjects were classified by subtype based on gestational age at diagnosis. Data on ABO blood type, as well other physical and socio-demographic variables were extracted from the electronic health record. No significant association was noted between preeclampsia subtype and non-O blood type (p=.456) and ABO blood phenotype trended towards significance (p=.062). After exclusion of subjects with comorbidities (CHTN, GDM and DM) from the sample (n=403), there was a significant association noted between ABO blood type and preeclampsia subtype (p=.001). A significant association was also noted between preeclamptic subjects with growth restriction and ABO blood type (p= \u3c.001). Preeclamptic subjects with the B blood type had OR=3.44, 95% CI 1.58, 7.50 of having a growth-restricted fetus than did those with other blood types. Finally, when adjusting for race only, subjects with AB blood type had the following odds (OR=3.03, 95% CI 1.04, 8.80; OR=3.35, 95% CI 1.02, 11.03.) of developing pre-eclampsia and late onset preeclampsia respectively. When other clinical risk factors of preeclampsia are included in the model, AB blood type significantly predicts membership in the early onset preeclampsia subtype (OR=5.41, 95% CI, 1.19, 24.55) and was trend-level in the late onset group (p=.053). Preeclamptic women with B blood type had three times the odds of having a growth-restricted fetus, subsequently; they may require close ultrasound surveillance. AB blood type was significantly associated with three times increased odds of late onset preeclampsia. When included in a model with other common risk factors of preeclampsia, ABO blood type only accounted for a small amount of variability in the model. ABO blood type may not be a valuable addition to a preeclampsia-screening algorithm that already includes common clinical risk factors of preeclampsia. However, when controlling for other common clinical risk factors of preeclampsia, women with AB blood type had over 5 times the odds of developing early onset preeclampsia. Further research is necessary to examine if blood type regulates biomarkers that mediate the development of each preeclampsia subtype or in some way is associated with severe features of the disease

Performance Evaluation of Smart Decision Support Systems on Healthcare

Medical activity requires responsibility not only from clinical knowledge and skill but also on the management of an enormous amount of information related to patient care. It is through proper treatment of information that experts can consistently build a healthy wellness policy. The primary objective for the development of decision support systems (DSSs) is to provide information to specialists when and where they are needed. These systems provide information, models, and data manipulation tools to help experts make better decisions in a variety of situations. Most of the challenges that smart DSSs face come from the great difficulty of dealing with large volumes of information, which is continuously generated by the most diverse types of devices and equipment, requiring high computational resources. This situation makes this type of system susceptible to not recovering information quickly for the decision making. As a result of this adversity, the information quality and the provision of an infrastructure capable of promoting the integration and articulation among different health information systems (HIS) become promising research topics in the field of electronic health (e-health) and that, for this same reason, are addressed in this research. The work described in this thesis is motivated by the need to propose novel approaches to deal with problems inherent to the acquisition, cleaning, integration, and aggregation of data obtained from different sources in e-health environments, as well as their analysis. To ensure the success of data integration and analysis in e-health environments, it is essential that machine-learning (ML) algorithms ensure system reliability. However, in this type of environment, it is not possible to guarantee a reliable scenario. This scenario makes intelligent SAD susceptible to predictive failures, which severely compromise overall system performance. On the other hand, systems can have their performance compromised due to the overload of information they can support. To solve some of these problems, this thesis presents several proposals and studies on the impact of ML algorithms in the monitoring and management of hypertensive disorders related to pregnancy of risk. The primary goals of the proposals presented in this thesis are to improve the overall performance of health information systems. In particular, ML-based methods are exploited to improve the prediction accuracy and optimize the use of monitoring device resources. It was demonstrated that the use of this type of strategy and methodology contributes to a significant increase in the performance of smart DSSs, not only concerning precision but also in the computational cost reduction used in the classification process. The observed results seek to contribute to the advance of state of the art in methods and strategies based on AI that aim to surpass some challenges that emerge from the integration and performance of the smart DSSs. With the use of algorithms based on AI, it is possible to quickly and automatically analyze a larger volume of complex data and focus on more accurate results, providing high-value predictions for a better decision making in real time and without human intervention.A atividade médica requer responsabilidade não apenas com base no conhecimento e na habilidade clínica, mas também na gestão de uma enorme quantidade de informações relacionadas ao atendimento ao paciente. É através do tratamento adequado das informações que os especialistas podem consistentemente construir uma política saudável de bem-estar. O principal objetivo para o desenvolvimento de sistemas de apoio à decisão (SAD) é fornecer informações aos especialistas onde e quando são necessárias. Esses sistemas fornecem informações, modelos e ferramentas de manipulação de dados para ajudar os especialistas a tomar melhores decisões em diversas situações. A maioria dos desafios que os SAD inteligentes enfrentam advêm da grande dificuldade de lidar com grandes volumes de dados, que é gerada constantemente pelos mais diversos tipos de dispositivos e equipamentos, exigindo elevados recursos computacionais. Essa situação torna este tipo de sistemas suscetível a não recuperar a informação rapidamente para a tomada de decisão. Como resultado dessa adversidade, a qualidade da informação e a provisão de uma infraestrutura capaz de promover a integração e a articulação entre diferentes sistemas de informação em saúde (SIS) tornam-se promissores tópicos de pesquisa no campo da saúde eletrônica (e-saúde) e que, por essa mesma razão, são abordadas nesta investigação. O trabalho descrito nesta tese é motivado pela necessidade de propor novas abordagens para lidar com os problemas inerentes à aquisição, limpeza, integração e agregação de dados obtidos de diferentes fontes em ambientes de e-saúde, bem como sua análise. Para garantir o sucesso da integração e análise de dados em ambientes e-saúde é importante que os algoritmos baseados em aprendizagem de máquina (AM) garantam a confiabilidade do sistema. No entanto, neste tipo de ambiente, não é possível garantir um cenário totalmente confiável. Esse cenário torna os SAD inteligentes suscetíveis à presença de falhas de predição que comprometem seriamente o desempenho geral do sistema. Por outro lado, os sistemas podem ter seu desempenho comprometido devido à sobrecarga de informações que podem suportar. Para tentar resolver alguns destes problemas, esta tese apresenta várias propostas e estudos sobre o impacto de algoritmos de AM na monitoria e gestão de transtornos hipertensivos relacionados com a gravidez (gestação) de risco. O objetivo das propostas apresentadas nesta tese é melhorar o desempenho global de sistemas de informação em saúde. Em particular, os métodos baseados em AM são explorados para melhorar a precisão da predição e otimizar o uso dos recursos dos dispositivos de monitorização. Ficou demonstrado que o uso deste tipo de estratégia e metodologia contribui para um aumento significativo do desempenho dos SAD inteligentes, não só em termos de precisão, mas também na diminuição do custo computacional utilizado no processo de classificação. Os resultados observados buscam contribuir para o avanço do estado da arte em métodos e estratégias baseadas em inteligência artificial que visam ultrapassar alguns desafios que advêm da integração e desempenho dos SAD inteligentes. Como o uso de algoritmos baseados em inteligência artificial é possível analisar de forma rápida e automática um volume maior de dados complexos e focar em resultados mais precisos, fornecendo previsões de alto valor para uma melhor tomada de decisão em tempo real e sem intervenção humana

Best practice guidelines for the molecular genetic diagnosis of maturity-onset diabetes of the young

Member of the EMQN MODY group: Gisela GasparAIMS/HYPOTHESIS: Mutations in the GCK and HNF1A genes are the most common cause of the monogenic forms of diabetes known as 'maturity-onset diabetes of the young'. GCK encodes the glucokinase enzyme, which acts as the pancreatic glucose sensor, and mutations result in stable, mild fasting hyperglycaemia. A progressive insulin secretory defect is seen in patients with mutations in the HNF1A and HNF4A genes encoding the transcription factors hepatocyte nuclear factor-1 alpha and -4 alpha. A molecular genetic diagnosis often changes management, since patients with GCK mutations rarely require pharmacological treatment and HNF1A/4A mutation carriers are sensitive to sulfonylureas. These monogenic forms of diabetes are often misdiagnosed as type 1 or 2 diabetes. Best practice guidelines for genetic testing were developed to guide testing and reporting of results

Gestational diabetes self-management and remote monitoring mobile platform

There is a high prevalence of gestational diabetes (GD) in South Africa, which is continually growing. South African women with GD are not effectively managed or educated about selfcare, do not self-monitor frequently enough and, therefore, often succumb to various GD induced complications. The ineffective management of GD is largely due to financial and time constraints caused by the regularly required outpatient services. On the other hand, healthcare professionals do not monitor their patients frequently enough because of accessibility issues, which means they cannot intervene timeously to prevent diabetes complications. The aim of this project was to develop a mobile health (mHealth) platform for GD self-management and for remote monitoring to improve the GD cycle of care in South Africa. The objectives were to assess the current GD management practices in South Africa, to assess the existing mHealth solutions for GD and to design, develop and test a GD mHealth platform. The existing GD management practices and current GD mHealth solutions were investigated. The results of the investigation informed the design of low-fidelity and high-fidelity mock-ups of the platform. The high-fidelity mock-up underwent usability testing and the insights gained were used to develop a working prototype of the new mHealth platform, which was then ready for in-lab testing. It was found that GD had a prevalence of up to 25% in parts of South Africa. Over 70% of patients in both private and public healthcare sectors did not meet their diabetic goals, which directly correlated with diabetes induced complications. However, previous research found that using mHealth as an intervention caused a statistically significant decrease of 0.38 mmol/L (95% confidence interval (CI) 0.52 mmol/L to 0.23 mmol/L) in overall blood glucose levels during pregnancy when compared to a control group. There was a higher probability of vaginal deliveries in the intervention group than in the control group (risk ratio = 1.18). It was less likely for new-borns from the intervention group to be diagnosed with hypoglycaemia than new-borns from the control group (risk ratio = 0.67). Based on the research and usability studies conducted, an alpha version of the GD mHealth platform was developed, including a mobile app used to track the patient’s blood glucose levels via a Bluetooth-enabled glucose meter. The food intake, exercise and weight gain during pregnancy were manually captured by the patient. The app reminded the patient to take medication, measure glucose levels and attend appointments. A GD educational component was available for the patient throughout the pregnancy. The platform included a web app which allowed healthcare professionals to remotely monitor and communicate with their patients so that they could analyse trends in the data and intervene when necessary. The testing done on the prototype resulted in positive feedback with 60% of participants saying that they would use the GooDMoM mobile app to manage their GD and 70% of participants saying that they would use the GooDMoM web app to manage their patients with GD. This put the platform in a good position for beta development. The solution has the potential to benefit patients both financially and timewise, by reducing the frequency of hospital visits required. It also has the potential to positively impact the healthcare professionals by reducing the tediousness of their workload and allowing for remote monitoring of patients. The platform can, thus, optimise the GD management process in South Africa and worldwide