Clinical features and treatment of maturity onset diabetes of the young (MODY)

Maturity onset diabetes of the young (MODY) is a heterogeneous group of disorders that result in β-cell dysfunction. It is rare, accounting for just 1%–2% of all diabetes. It is often misdiagnosed as type 1 or type 2 diabetes, as it is often difficult to distinguish MODY from these two forms. However, diagnosis allows appropriate individualized care, depending on the genetic etiology, and allows prognostication in family members. In this review, we discuss features of the common causes of MODY, as well as the treatment and diagnosis of MODY

Epidemiology of palsma lipids and the metabolic syndrome in multi-ethnic population

Ph.DDOCTOR OF PHILOSOPH

Health status of older adults with Type 2 diabetes mellitus after aerobic or resistance training: A randomised trial

10.1186/1477-7525-9-59Health and Quality of Life Outcomes9

Personalised nutrition and health

This article is one of a series commissioned by The BMJ. Open access fees for the series were funded by SwissRe, which had no input into the commissioning or peer review of the articles.S

Adverse Associations between Visceral Adiposity, Brain Structure, and Cognitive Performance in Healthy Elderly

The link between central adiposity and cognition has been established by indirect measures such as body mass index (BMI) or waist–hip ratio. Magnetic resonance imaging (MRI) quantification of central abdominal fat has been linked to elevated risk of cardiovascular and cerebro-vascular disease. However it is not known how quantification of visceral fat correlates with cognitive performance and measures of brain structure. We filled this gap by characterizing the relationships between MRI measures of abdominal adiposity, brain morphometry, and cognition, in healthy elderly. Methods: A total of 184 healthy community dwelling elderly subjects without cognitive impairment participated in this study. Anthropometric and biochemical markers of cardiovascular risk, neuropsychological measurements as well as MRI of the brain and abdomen fat were obtained. Abdominal images were segmented into subcutaneous adipose tissue and visceral adipose tissue (VAT) adipose tissue compartments. Brain MRI measures were analyzed quantitatively to determine total brain volume, hippocampal volume, ventricular volume, and cortical thickness. Results: VAT showed negative association with verbal memory (r = 0.21, p = 0.005) and attention (r = 0.18, p = 0.01). Higher VAT was associated with lower hippocampal volume (F = 5.39, p = 0.02) and larger ventricular volume (F = 6.07, p = 0.02). The participants in the upper quartile of VAT had the lowest hippocampal volume even after adjusting for age, gender, hypertension, and BMI (b = −0.28, p = 0.005). There was a significant age by VAT interaction for cortical thickness in the left prefrontal region. Conclusion: In healthy older adults, elevated VAT is associated with negative effects on cognition, and brain morphometry

Precision Medicine and Big Data: The Application of an Ethics Framework for Big Data in Health and Research

As opposed to a ‘one size fits all’ approach, precision medicine uses relevant biological (including genetic), medical, behavioural and environmental information about a person to further personalize their healthcare. This could mean better prediction of someone’s disease risk and more effective diagnosis and treatment if they have a condition. Big data allows for far more precision and tailoring than was ever before possible by linking together diverse datasets to reveal hitherto-unknown correlations and causal pathways. But it also raises ethical issues relating to the balancing of interests, viability of anonymization, familial and group implications, as well as genetic discrimination. This article analyses these issues in light of the values of public benefit, justice, harm minimization, transparency, engagement and reflexivity and applies the deliberative balancing approach found in the Ethical Framework for Big Data in Health and Research (Xafis et al. 2019) to a case study on clinical genomic data sharing. Please refer to that article for an explanation of how this framework is to be used, including a full explanation of the key values involved and the balancing approach used in the case study at the end. Our discussion is meant to be of use to those involved in the practice as well as governance and oversight of precision medicine to address ethical concerns that arise in a coherent and systematic manner.National Medical Research Council (NMRC)Published versionThe development of the Framework and its application to the six Domain papers was funded and supported by the Singapore National Medical Research Council Research, Innovation and Enterprise 2020 Gran

Implementing and evaluating Care and Support Planning : a qualitative study of health professionals’ experiences in public polyclinics in Singapore

Funding. The PACE-D programme is funded by Singapore’s Ministry of Health. This study was funded by the Centre for Chronic Disease Prevention and Management of the National University Health System, Singapore. SM was supported by a Wellcome Trust Institutional Strategic Support Fund flexible returners award (University of Aberdeen, RG13795-18). VAE’s contribution was also supported by a Wellcome Trust Collaborative Award: This research was funded in whole, or in part, by the Wellcome Trust [209811/Z/17/Z]. For the purpose of open access, the author has applied a CC BY public copyright licence to any Author Accepted Manuscript version arising from this submission. Acknowledgements We are extremely grateful to the health professionals who took part in interviews for this study and allowed observation of their team huddles. We also thank LIM Mui Eng and ANG Shu Lin for supporting the recruitment of health professionals and arrangement of interviews, Monica ASHWINI for arranging transcription and discussion of patients’ experiences of PACE-D, Matthavi SENGUTTUVAN for contributions to early analytic discussions, Marlie FERENCZI and LOY En Yun for support with grant administration, data sharing agreements and helpful suggestions, and the Year of Care Partnerships team, especially Lindsay OLIVER and Nick LEWIS-BARNED for training health professionals and trainers in Singapore and for ongoing advice and support, including helpful comments on a draft of this manuscript.Peer reviewedPublisher PD

Plasma lipoprotein subfraction concentrations are associated with lipid metabolism and age-related macular degeneration

10.1194/jlr.M073684Journal of Lipid Research5891785-179

Allele frequencies of variants in Ultra Conserved Elements identify selective pressure on transcription factor binding

Ultra-conserved genes or elements (UCGs/UCEs) in the human genome are extreme examples of conservation. We characterized natural variations in 2884 UCEs and UCGs in two distinct populations ; Singaporean Chinese (n=280) and Italian (n=501) by using a pooled sample, targeted capture, sequencing approach. We identify, with high confidence, in these regions the abundance of rare SNVs (MAF<0.5%) of which 75% is not present in dbSNP137. UCEs association studies for complex human traits can use this information to model expected background variation and thus necessary power for association studies. By combining our data with 1000 Genome Project data, we show in three independent datasets that prevalent UCE variants (MAF>5%) are more often found in relatively less-conserved nucleotides within UCEs, compared to rare variants. Moreover, prevalent variants are less likely to overlap transcription factor binding site. Using SNPfold we found no significant influence of RNA secondary structure on UCE conservation. All together, these results suggest UCEs are not under selective pressure as a stretch of DNA but are under differential evolutionary pressure on the single nucleotide level

Exploring the potential of mobile health interventions to address behavioural risk factors for the prevention of non-communicable diseases in Asian populations: a qualitative study

Background Changing lifestyle patterns over the last decades have seen growing numbers of people in Asia affected by non-communicable diseases and common mental health disorders, including diabetes, cancer, and/or depression. Interventions targeting healthy lifestyle behaviours through mobile technologies, including new approaches such as chatbots, may be an effective, low-cost approach to prevent these conditions. To ensure uptake and engagement with mobile health interventions, however, it is essential to understand the end-users’ perspectives on using such interventions. The aim of this study was to explore perceptions, barriers, and facilitators to the use of mobile health interventions for lifestyle behaviour change in Singapore. Methods Six virtual focus group discussions were conducted with a total of 34 participants (mean ± SD; aged 45 ± 3.6 years; 64.7% females). Focus group recordings were transcribed verbatim and analysed using an inductive thematic analysis approach, followed by deductive mapping according to perceptions, barriers, facilitators, mixed factors, or strategies. Results Five themes were identified: (i) holistic wellbeing is central to healthy living (i.e., the importance of both physical and mental health); (ii) encouraging uptake of a mobile health intervention is influenced by factors such as incentives and government backing; (iii) trying out a mobile health intervention is one thing, sticking to it long term is another and there are key factors, such as personalisation and ease of use that influence sustained engagement with mobile health interventions; (iv) perceptions of chatbots as a tool to support healthy lifestyle behaviour are influenced by previous negative experiences with chatbots, which might hamper uptake; and (v) sharing health-related data is OK, but with conditions such as clarity on who will have access to the data, how it will be stored, and for what purpose it will be used. Conclusions Findings highlight several factors that are relevant for the development and implementation of mobile health interventions in Singapore and other Asian countries. Recommendations include: (i) targeting holistic wellbeing, (ii) tailoring content to address environment-specific barriers, (iii) partnering with government and/or local (non-profit) institutions in the development and/or promotion of mobile health interventions, (iv) managing expectations regarding the use of incentives, and (iv) identifying potential alternatives or complementary approaches to the use of chatbots, particularly for mental health