The β-Cell Genomic Landscape in T1D: Implications for Disease Pathogenesis

Ramos-Rodríguez, Mireia; Pérez-González, Beatriz; Pasquali, Lorenzo

doi:10.1007/s11892-020-01370-4

Cited by 16 publications

(8 citation statements)

References 83 publications

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“…With time and further validation, clinicians may feel more confident in prescribing insulin algorithmically while avoiding hypoglycemia, thus achieving glycemic control faster and potentiating the benefits to patients and the healthcare system. 43 …”

Section: Discussionmentioning

confidence: 99%

Machine learning for initial insulin estimation in hospitalized patients

Nguyen

Janković

Kalesinskas

et al. 2021

Journal of the American Medical Informatics Association

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Objective The study sought to determine whether machine learning can predict initial inpatient total daily dose (TDD) of insulin from electronic health records more accurately than existing guideline-based dosing recommendations. Materials and Methods Using electronic health records from a tertiary academic center between 2008 and 2020 of 16 848 inpatients receiving subcutaneous insulin who achieved target blood glucose control of 100-180 mg/dL on a calendar day, we trained an ensemble machine learning algorithm consisting of regularized regression, random forest, and gradient boosted tree models for 2-stage TDD prediction. We evaluated the ability to predict patients requiring more than 6 units TDD and their point-value TDDs to achieve target glucose control. Results The method achieves an area under the receiver-operating characteristic curve of 0.85 (95% confidence interval [CI], 0.84-0.87) and area under the precision-recall curve of 0.65 (95% CI, 0.64-0.67) for classifying patients who require more than 6 units TDD. For patients requiring more than 6 units TDD, the mean absolute percent error in dose prediction based on standard clinical calculators using patient weight is in the range of 136%-329%, while the regression model based on weight improves to 60% (95% CI, 57%-63%), and the full ensemble model further improves to 51% (95% CI, 48%-54%). Discussion Owing to the narrow therapeutic window and wide individual variability, insulin dosing requires adaptive and predictive approaches that can be supported through data-driven analytic tools. Conclusions Machine learning approaches based on readily available electronic medical records can discriminate which inpatients will require more than 6 units TDD and estimate individual doses more accurately than standard guidelines and practices.

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Section: Discussionmentioning

confidence: 99%

Machine learning for initial insulin estimation in hospitalized patients

Nguyen

Janković

Kalesinskas

et al. 2021

Journal of the American Medical Informatics Association

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“…As mentioned earlier, Latinx people are more likely to develop diabetes and to be diagnosed at younger ages ( Avilés-Santa et al, 2017 ; Haw, Shah, Turbow, Egeolu, & Umpierrez, 2021 ). They are more likely to have disabilities related to diabetes and less likely to access and receive high quality care for diabetes; they are more likely to die from diabetes and diabetes-related conditions (such as end-stage renal disease) ( Haw et al, 2021 ; Marquez et al, 2019 ; Rodríguez & Campbell, 2017 ). Though diabetes can be prevented and mitigated at many stages, Latinx communities face disparities at each of these stages.…”

mentioning

confidence: 88%

“…In turn, the evidence contributes to the development of social and health policy that can best be implemented to improve equity efforts and decrease disease burden ( Arcaya, Arcaya, & Subramanian, 2015 ; Solar & Irwin, 2010 ). As both the prevalence and severity of diabetes is linked with many social determinants of health, it is a condition that meets the assumptions for this framework ( Avilés-Santa et al, 2017 ; Haw et al, 2021 ; Marquez et al, 2019 ; Rodríguez & Campbell, 2017 ). This paper uses Oaxaca-Blinder decomposition to further examine the social factors contributing to the diabetes disparity between Latinx and non-Latinx populations in the U.S.…”

mentioning

confidence: 99%

Social determinants of the Latinx diabetes health disparity: A Oaxaca-Blinder decomposition analysis

Cartwright¹

2021

SSM - Population Health

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Latinx people living in the U.S. report a disproportionately high prevalence of diabetes. This project builds on the existing social determinants of diabetes literature by examining factors associated with a greater likelihood of diabetes and investigates factors correlated with the Latinx/non-Latinx disparity. This project studies the adult sample (18 and older) from the 2010–2018 IPUMS Health: National Health Interview Survey (NHIS) data. Logistic regression analyses are used to examine the patterns between reporting Latinx identity and reporting diabetes with additional subgroup analyses of the Latinx and non-Latinx groups. Then, Oaxaca-Blinder decomposition is used to examine the patterns explaining the difference in self-reported diabetes between the Latinx and non-Latinx population for the whole sample and by age group. The logistic regression analyses show that after adjusting for age and other key social determinants of health, Latinx individuals are approximately 64.5% (OR 1.645, [95% CI, 1.536–1.760]) more likely to report being diagnosed with diabetes than non-Latinx individuals. Individual characteristics of age, race, and smoking behaviors are identified as suppressors of the gap, and conversely, characteristics of income, education, and BMI all contribute to the Latinx diabetes disparity gap. The Oaxaca-Blinder decomposition results show that the measured social determinants of health characteristics explain a meaningful amount of the Latinx diabetes gap. Importantly, differences in education and income (which are more immediately actionable policy areas) make larger contributions to the gap than BMI or other health behaviors.

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“…T1D is a multifactorial disease of polygenic inheritance. A number of T1D susceptibility genes have been identified [11], and several are known to play critical roles in autophagic degradation pathways. For example, the E3 ligase, CLEC16A, plays a crucial role in the selective degradation of mitochondria by autophagy (a process known as mitophagy) and also in insulin secretion in rodent beta cells [12].…”

Section: T1d Genetic Susceptibility Loci With Roles In Autophagymentioning

confidence: 99%

β-Cell autophagy in the pathogenesis of type 1 diabetes

Muralidharan

Linnemann

2021

American Journal of Physiology-Endocrinology and Metabolism

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Type 1 diabetes is an insulin-dependent, autoimmune disease where the pancreatic beta cells are destroyed resulting in hyperglycemia. This multi-factorial disease involves multiple environmental and genetic factors, and has no clear etiology. Accumulating evidence suggests that early signaling defects within the beta cells may promote a change in the local immune mileu, contributing to autoimmunity. Therefore, many studies have been focused on intrinsic beta cell mechanisms that aid in restoration of cellular homeostasis under environmental conditions that cause dysfunction. One of these intrinsic mechanisms to promote homeostasis is autophagy, defects in which are clearly linked with beta cell dysfunction in the context of type 2 diabetes. Recent studies have now also pointed towards beta cell autophagy defects in the context of type 1 diabetes. In this perspectives review, we will discuss the evidence supporting a role for beta cell autophagy in the pathogenesis of type 1 diabetes, including a potential role for unconventional secretion of autophagosomes/lysosomes in the changing dialogue between the beta cell and immune cells.

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The β-Cell Genomic Landscape in T1D: Implications for Disease Pathogenesis

Cited by 16 publications

References 83 publications

Machine learning for initial insulin estimation in hospitalized patients

Machine learning for initial insulin estimation in hospitalized patients

Social determinants of the Latinx diabetes health disparity: A Oaxaca-Blinder decomposition analysis

β-Cell autophagy in the pathogenesis of type 1 diabetes

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