Personalizing guidelines for diabetes management: twilight or dawn of the expert?

Paschou, Stavroula Α.; Leslie, Richard

doi:10.1186/1741-7015-11-161

Cited by 20 publications

(14 citation statements)

References 77 publications

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“…It is in the nature of guidelines that they are exactly thatguidelinesbut that non-specialists may treat them as instructions cast in stone [12]. There is a duty of care for medical guidelines to avoid taking firm positions when data are ambivalent and taking clear positions when data are unequivocal.…”

Section: Issues In the Production Of Guidelinesmentioning

confidence: 99%

NICE guidelines for Type 2 diabetes: revised but still not fit for purpose

et al. 2015

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Revised Type 2 diabetes guidelines (June 2015) have been released for further consultation after pressure from healthcare professionals and other stakeholders [1][2][3][4]. What NICE proposesThe National Institute for Health and Care Excellence (NICE) sets out a structure for the management of Type 2 diabetes. Advice is provided on appropriate lifestyle changes, including diet, exercise and patient education. It is proposed that oral monotherapy (metformin) be introduced when HbA 1c increases beyond 48 mmol/mol (6.5%). Failure to achieve that target should lead to the introduction of combination therapy at HbA 1c ≥ 58 mmol/mol (≥ 7.5%). Glucose monitoring should be limited to those on drugs that cause hypoglycaemia and be supported by regular auditing. The sequence of therapy proposed in the first draft ran: metformin, repaglinide, pioglitazone and then alternative fourth-line choices. In the revised proposal, the sequence runs, at least in the body of the text, as metformin then a choice of repaglinide, pioglitazone, sulfonylureas and dipeptidyl peptidase-4 (DPP-4) inhibitors (gliptins). These are followed by glucagon-like peptide 1 (GLP-1) receptor agonists and insulin. GLP-1 receptor agonists are to be considered only when the BMI is greater than prescribed levels (35 kg/m 2 ) or at lower BMI depending on ethnicity and risk assessment regarding weight loss benefit. Insulin is to be given as regular human insulin (isophane) for patients with Type 2 diabetes. Sodium/ glucose co-transporter 2 (SGLT-2) inhibitors are not formally included in the algorithm because HTA guidance is awaited later in the year.

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Section: Issues In the Production Of Guidelinesmentioning

confidence: 99%

NICE guidelines for Type 2 diabetes: revised but still not fit for purpose

et al. 2015

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“…Metformin, sulfonylureas, thiazolidinediones (TZDs), dipeptidyl peptidase-4 inhibitors (DPP-4i), glucagon-like peptide-1 receptor agonists (GLP-1RA), sodium-dependent glucose transporter-2 inhibitors (SGLT-2i) and insulin are the most commonly used medications. They have different metabolic, cardiovascular and bone effects [63][64][65], and the most suitable agent for any particular woman should be selected according to these different effects after taking into consideration the woman's specific characteristics and comorbidities. Metformin should be used as the first-line treatment, while DPP-4i and GLP-1RA may be useful second-line options because of their beneficial (or at least neutral) effects on bones.…”

Section: Management Of T2dm In Women During and After Menopausementioning

confidence: 99%

Menopause and diabetes: EMAS clinical guide

et al. 2018

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Introduction: Whether menopause increases the risk of type 2 diabetes mellitus (T2DM) independently of ageing has been a matter of debate. Controversy also exists about the benefits and risks of menopausal hormone therapy (MHT) in women with T2DM. Aims: To summarise the evidence on 1) the effect of menopause on metabolic parameters and the risk of T2DM, 2) the effect of T2DM on age at menopause, 3) the effect of MHT on the risk of T2DM, and 4) the management of postmenopausal women with T2DM. Materials and methods: Literature review and consensus of experts' opinions. Results and conclusion: Metabolic changes during the menopausal transition include an increase in and the central redistribution of adipose tissue, as well as a decrease in energy expenditure. In addition, there is impairment of insulin secretion and insulin sensitivity and an increase in the risk of T2DM. MHT has a favourable effect on glucose metabolism, both in women with and in women without T2DM, while it may delay the onset of T2DM. MHT in women with T2DM should be administered according to their risk of cardiovascular disease (CVD). In women with T2DM and low CVD risk, oral oestrogens may be preferred, while transdermal 17βoestradiol is preferred for women with T2DM and coexistent CVD risk factors, such as obesity. In any case, a progestogen with neutral effects on glucose metabolism should be used, such as progesterone, dydrogesterone or transdermal norethisterone. Postmenopausal women with T2DM should be managed primarily with lifestyle intervention, including diet and exercise. Most of them will eventually require pharmacological therapy. The

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“…The development of new insulin analogs led to a more predictable behavior of the drugs' blood glucose lowering effect [15,16]. The first type of oral antidiabetic agents were developed in France in the 1940s [6].…”

Section: Personalization Of Diabetes Therapymentioning

confidence: 99%

“…If an intensification of the diabetes therapy is necessary different strategies involving insulin are treatment options. Here, personalization is possible by setting different treatment goals for the different stages of intensification (stepwise approach) of the insulin therapy [4,16]. Less intensive insulin therapies comprise fixed insulin doses once a day, either adjusted by the physician at the next routine appointment or by the patient according to a schema.…”

Section: Personalization Of Diabetes Therapymentioning

confidence: 99%

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Towards Personalization of Diabetes Therapy Using Computerized Decision Support and Machine Learning: Some Open Problems and Challenges

Donsa

Spat

Beck

et al. 2015

Lecture Notes in Computer Science

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Abstract. Diabetes mellitus (DM) is a growing global disease which highly affects the individual patient and represents a global health burden with financial impact on national health care systems. Type 1 DM can only be treated with insulin, whereas for patients with type 2 DM a wide range of therapeutic options are available. These options include lifestyle changes such as change of diet and an increase of physical activity, but also administration of oral or injectable antidiabetic drugs. The diabetes therapy, especially with insulin, is complex. Therapy decisions include various medical and life-style related information. Computerized decision support systems (CDSS) aim to improve the treatment process in patient's self-management but also in institutional care. Therefore, the personalization of the patient's diabetes treatment is possible at different levels. It can provide medication support and therapy control, which aid to correctly estimate the personal medication requirements and improves the adherence to therapy goals. It also supports long-term disease management, aiming to develop a personalization of care according to the patient's risk stratification. Personalization of therapy is also facilitated by using new therapy aids like food and activity recognition systems, lifestyle support tools and pattern recognition for insulin therapy optimization. In this work we cover relevant parameters to personalize diabetes therapy, how CDSS can support the therapy process and the role of machine learning in this context. Moreover, we identify open problems and challenges for the personalization of diabetes therapy with focus on decision support systems and machine learning technology.

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Personalizing guidelines for diabetes management: twilight or dawn of the expert?

Cited by 20 publications

References 77 publications

NICE guidelines for Type 2 diabetes: revised but still not fit for purpose

NICE guidelines for Type 2 diabetes: revised but still not fit for purpose

Menopause and diabetes: EMAS clinical guide

Towards Personalization of Diabetes Therapy Using Computerized Decision Support and Machine Learning: Some Open Problems and Challenges

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