Clustering of adult-onset diabetes into novel subgroups guides therapy and improves prediction of outcome

Ahlqvist, Emma; Storm, Petter; A, Käräjämäki; Martinell, Mats; Dorkhan, Mozhgan; Carlsson, Annelie; Vikman, Petter; Prasad, Rashmi B.; Aly, Dina Mansour; Almgren, Peter; Wessman, Ylva; Shaat, Nael; Sartipy, Peter; Mulder, Hindrik; Lindholm, Eero; Melander, Olle; Hansson, Ola; Malmqvist, Ulf; Lernmark, Åke; Lahti, Kaj; Forsén, Tom; Tuomi, Tiinamaija; Rosengren, Anders H.; Groop, Leif

doi:10.1101/186387

Cited by 13 publications

(58 citation statements)

References 22 publications

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“…The responses to identical meals were assessed and high variability was shown between participants. Additionally, four sub‐types of type 2 diabetes have recently been identified, and Ahlqvist and colleagues have suggested that different sub‐types may respond differently to food, activity and medication, but acknowledged that further research is required to understand this fully. This is the first study to identify an individualised response to activity behaviour as suggested by Ahlqvist and colleagues …”

Section: Discussionmentioning

confidence: 99%

“…There is growing evidence to support a more personalised approach to diabetes care and incorporating mobile technology could provide a mechanism for this tailored approach. A recent pilot study examined the use of personalised glucose feedback to change activity behaviour in non‐diabetic adults (n=33) and found that the feedback resulted in reduced sedentary time at follow‐up.…”

Section: Discussionmentioning

confidence: 99%

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Measuring group and individual relationship between patterns in sedentary behaviour and glucose in type 2 diabetes adults

et al. 2020

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The aim of this study was to explore the relationship between patterns in sedentary behaviour and glucose in adults with type 2 diabetes. Thirty‐seven adults with type 2 diabetes managed with diet and/or anti‐diabetes medication (not insulin) were recruited. Participants wore an activPAL accelerometer and FreeStyle Libre flash glucose monitor for continuous measurement of activity and glucose for 24 hours/day for three to 14 days while documenting sleep, food and medication. The relationship between daily patterns of sedentary behaviour and sedentary breaks with glucose was investigated using correlation analysis. Regression analysis was used to investigate these relationships at an individual level. Participants (mean age 62.8±10.5 years, BMI 29.6±6.8kg/m2) spent 64% of their day sedentary, 44.7% of sedentary bouts were 30–60 minutes in duration and mean bout duration was 47.2 minutes. No association between mean glucose and sedentary proportion (total sedentary time) was identified. Mean glucose and glucose standard deviation were positively correlated with sedentary bout duration (both p<0.05). Individual regression analysis showed increased sedentary time is associated with increased mean glucose in 25 (68%) of the participants, with a negative association being shown in 12 (32%) of the participants. In analysis of the whole group, sedentary bout duration but not sedentary proportion was associated with mean glucose and glucose variability. Individual regression analysis identified a different relationship pattern for the majority of participants. This is the first study to identify an individualised response to activity behaviour and highlights the importance of conducting individual analysis when using continuous measurement methods.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Measuring group and individual relationship between patterns in sedentary behaviour and glucose in type 2 diabetes adults

et al. 2020

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“…1 Tkaczynski (2017) implies that two-step clustering has been performed in many researches since its first version. It has been used in many applications from tourism to health, marketing to transportation (Ahlqvist et al, 2017;Kent, Jensen, & Kongsted, 2014;Cerin, Leslie, Du Toit, Owen, & Frank, 2007;Grifin et al, 2014;Hsu, Kang, & LAM, 2006). Beside its user friendly usage it has also many scientific advantages too.…”

Section: Outlier Detection With Two-step Clusteringmentioning

confidence: 99%

How to detect illegal corporate insider trading? A data mining approach for detecting suspicious insider transactions

Esen

Bilgiç

Basdas³

2019

Intell Sys Acc Fin Mgmt

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Summary Only in the U.S. Stock Exchanges, the daily average trading volume is about 7 billion shares. This vast amount of trading shows the necessity of understanding the hidden insights in the data sets. In this study, a data mining technique, clustering based outlier analysis is applied to detect suspicious insider transactions. 1,244,815 transactions of 61,780 insiders are analysed, which are acquired from Thomson Financial, covering a period of January 2010–April 2017. In order to detect outliers, similar transactions are grouped into the same clusters by using a two‐step clustering based outlier detection technique, which is an integration of k‐means and hierarchical clustering. Then, it is shown that outlying transactions earn higher abnormal returns than non‐outlying transactions by using event study methodology.

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“…Despite the differences, these methods aim to produce subsets which have high intra-group similarities (objects within a group are similar), and low inter-group similarities (objects between groups are more dissimilar). Cluster analysis has widely been used to assess microarray data[35] and has seen success in EHR applications[11,36–41]. Additionally, hierarchical clustering has been used to assess the contribution of obesity (a trait with known heterogeneity, as seen in Figure 1) to respiratory conditions[42,43].…”

Section: Machine Learning Approaches For Establishing a Phenotypementioning

confidence: 99%

Informatics and machine learning to define the phenotype

Basile

Ritchie

2018

Expert Review of Molecular Diagnostics

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Introduction For the past decade, the focus of complex disease research has been the genotype. From technological advancements to the development of analysis methods, great progress has been made. However, advances in our definition of the phenotype have remained stagnant. Phenotype characterization has recently emerged as an exciting area of informatics and machine learning. The copious amounts of diverse biomedical data that have been collected may be leveraged with data-driven approaches to elucidate trait-related features and patterns. Areas covered In this review, the authors discuss the phenotype in traditional genetic associations and the challenges this has imposed. The authors address approaches for phenotype refinement that can aid in the more accurate characterization of traits. Further, the authors highlight promising machine learning approaches for establishing a phenotype and the challenges of electronic health record (EHR) derived data. Expert Commentary The authors hypothesize that through unsupervised machine learning, data-driven approaches can be used to define phenotypes rather than relying on expert clinician knowledge, which may be inaccurate. Through the use of machine learning and an unbiased set of features extracted from clinical repositories, researchers will have the potential to further understand complex traits and identify patient subgroups. This knowledge may lead to more preventative and precise clinical care.

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Clustering of adult-onset diabetes into novel subgroups guides therapy and improves prediction of outcome

Cited by 13 publications

References 22 publications

Measuring group and individual relationship between patterns in sedentary behaviour and glucose in type 2 diabetes adults

Measuring group and individual relationship between patterns in sedentary behaviour and glucose in type 2 diabetes adults

How to detect illegal corporate insider trading? A data mining approach for detecting suspicious insider transactions

Informatics and machine learning to define the phenotype

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