Big data and machine learning to tackle diabetes management

Pina, Ana; Meneses, Maria João; Sousa‐Lima, Inês; Henriques, Roberto; Raposo, João Filipe; Macedo, M. Paula

doi:10.1111/eci.13890

Cited by 18 publications

(10 citation statements)

References 60 publications

(188 reference statements)

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“…Wang et al provided a commentary on the previously published manuscript by Riedel et al (1) calling attention to the need to look to T2D beyond hyperglycemia. Indeed, the authors support that T2D is more likely to be a syndrome leading to hyperglycemia, systematic inflammation, insulin resistance, and intestinal bowel disease, indeed a systematic disease as previously supported by Pina et al (2,3). As such, hyperglycemia could be treated as a coexistent symptom rather than the central one.…”

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confidence: 52%

Editorial: Metabolic miscommunication among organs: The missing links

Meneses

Macedo

2023

Front. Endocrinol.

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Editorial on the Research TopicMetabolic miscommunication among organs: The missing links Type 2 diabetes (T2D) is one of the modern metabolic diseases that involve impaired or faulty communication between tissues and/or organs. Extracellular vesicles, endocrine hormones, and cytokines are just some of the communication/signaling molecules that may be affected, involving both commensal microbiota and constitutive tissues. For these processes to be identified and characterized in a clinical setting, various disciplines and methods of analysis and diagnosis must be integrated. As an example, the imaging of liver fat and fibrosis could be analyzed along with the characterization of intestinal microbiome activity to understand the role of intestinal dysbiosis in fatty liver disease development.The goal of this Research Topic was to present new progress in the integration of epidemiology and pathogenic mechanisms that improve the understanding of biological crosstalk within diabetes and related disorders. Indeed, the authors replied to our call and the manuscripts within this Research Topic include the link between diabetes and intestinal disorders, kidney disease, bone metabolism, nocturia, iron and nitrogen metabolism, exercise as well as inflammation and liver disease.Meneses et al. studied the impact of high-fat and high-fructose feeding on the metabolome of the liver, muscle, white and brown adipose tissue. By feeding male mice with these diets for 12 weeks, it was possible to disclose that although both diets have deleterious effects on the liver, causing non-alcoholic fatty liver disease (NAFLD), high-fat resulted in a higher impact on hepatic glucose and lipid metabolism than high-fructose, leading namely to glucose intolerance. Moreover, while high-fat had alterations in brown adipose tissue metabolites that indicate increased thermogenesis, high-fructose led to increased levels of betaine, known to be a shielding metabolite against fructose-induced inflammation. Overall, this study indicates that high-fat and high-fructose feeding have a negative but distinct effect on the metabolome of the abovementioned organs.Besides high circulating lipid and fructose levels, iron levels also impact metabolic homeostasis. Wang et al. aimed at exploring the association of serum iron levels with metabolic dysfunction-associated fatty liver disease (MAFLD) in Chinese patients with T2D in a cross-sectional study with 1,467 individuals. Indeed, the association between iron metabolism and insulin resistance has been explored in the last few years, and the present study found a positive correlation between the presence of MAFLD and serum iron levels in Frontiers in Endocrinology frontiersin.org 01

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confidence: 52%

Editorial: Metabolic miscommunication among organs: The missing links

Meneses

Macedo

2023

Front. Endocrinol.

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“…As summarized in many review studies, the application of ML in the general field of diabetes has been widely validated and recognized ( 46 ). However, compared to other diabetes-related complications such as diabetic retinopathy, relatively few studies have been conducted on diabetic foot syndrome ( 47 ).…”

Section: Advancements In Diabetic Foot Care: Exploring Machine Learni...mentioning

confidence: 99%

The role of machine learning in advancing diabetic foot: a review

Guan,

Wang,

Niu

et al. 2024

Front. Endocrinol.

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BackgroundDiabetic foot complications impose a significant strain on healthcare systems worldwide, acting as a principal cause of morbidity and mortality in individuals with diabetes mellitus. While traditional methods in diagnosing and treating these conditions have faced limitations, the emergence of Machine Learning (ML) technologies heralds a new era, offering the promise of revolutionizing diabetic foot care through enhanced precision and tailored treatment strategies.ObjectiveThis review aims to explore the transformative impact of ML on managing diabetic foot complications, highlighting its potential to advance diagnostic accuracy and therapeutic approaches by leveraging developments in medical imaging, biomarker detection, and clinical biomechanics.MethodsA meticulous literature search was executed across PubMed, Scopus, and Google Scholar databases to identify pertinent articles published up to March 2024. The search strategy was carefully crafted, employing a combination of keywords such as “Machine Learning,” “Diabetic Foot,” “Diabetic Foot Ulcers,” “Diabetic Foot Care,” “Artificial Intelligence,” and “Predictive Modeling.” This review offers an in-depth analysis of the foundational principles and algorithms that constitute ML, placing a special emphasis on their relevance to the medical sciences, particularly within the specialized domain of diabetic foot pathology. Through the incorporation of illustrative case studies and schematic diagrams, the review endeavors to elucidate the intricate computational methodologies involved.ResultsML has proven to be invaluable in deriving critical insights from complex datasets, enhancing both the diagnostic precision and therapeutic planning for diabetic foot management. This review highlights the efficacy of ML in clinical decision-making, underscored by comparative analyses of ML algorithms in prognostic assessments and diagnostic applications within diabetic foot care.ConclusionThe review culminates in a prospective assessment of the trajectory of ML applications in the realm of diabetic foot care. We believe that despite challenges such as computational limitations and ethical considerations, ML remains at the forefront of revolutionizing treatment paradigms for the management of diabetic foot complications that are globally applicable and precision-oriented. This technological evolution heralds unprecedented possibilities for treatment and opportunities for enhancing patient care.

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“…Machine learning (ML) can analyze a large amount of data and identify meaningful patterns that correspond to the diabetes risk level of individuals and predict their diabetes outcome risk [8][9][10][11]. The objective of this study was to conceptualize and develop a novel ML approach to proactively identify participants enrolled in a large-scale RDMP who were at risk of uncontrolled diabetes at 12 months in the program.…”

Section: Introductionmentioning

confidence: 99%

Proactive Identification of Patients with Diabetes at Risk of Uncontrolled Outcomes during a Diabetes Management Program: Conceptualization and Development Study Using Machine Learning

Khalilnejad,

Sun,

Kompala

et al. 2024

JMIR Form Res

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Background The growth in the capabilities of telehealth have made it possible to identify individuals with a higher risk of uncontrolled diabetes and provide them with targeted support and resources to help them manage their condition. Thus, predictive modeling has emerged as a valuable tool for the advancement of diabetes management. Objective This study aimed to conceptualize and develop a novel machine learning (ML) approach to proactively identify participants enrolled in a remote diabetes monitoring program (RDMP) who were at risk of uncontrolled diabetes at 12 months in the program. Methods Registry data from the Livongo for Diabetes RDMP were used to design separate dynamic predictive ML models to predict participant outcomes at each monthly checkpoint of the participants’ program journey (month-n models) from the first day of onboarding (month-0 model) up to the 11th month (month-11 model). A participant’s program journey began upon onboarding into the RDMP and monitoring their own blood glucose (BG) levels through the RDMP-provided BG meter. Each participant passed through 12 predicative models through their first year enrolled in the RDMP. Four categories of participant attributes (ie, survey data, BG data, medication fills, and health signals) were used for feature construction. The models were trained using the light gradient boosting machine and underwent hyperparameter tuning. The performance of the models was evaluated using standard metrics, including precision, recall, specificity, the area under the curve, the F1-score, and accuracy. Results The ML models exhibited strong performance, accurately identifying observable at-risk participants, with recall ranging from 70% to 94% and precision from 40% to 88% across the 12-month program journey. Unobservable at-risk participants also showed promising performance, with recall ranging from 61% to 82% and precision from 42% to 61%. Overall, model performance improved as participants progressed through their program journey, demonstrating the importance of engagement data in predicting long-term clinical outcomes. Conclusions This study explored the Livongo for Diabetes RDMP participants’ temporal and static attributes, identification of diabetes management patterns and characteristics, and their relationship to predict diabetes management outcomes. Proactive targeting ML models accurately identified participants at risk of uncontrolled diabetes with a high level of precision that was generalizable through future years within the RDMP. The ability to identify participants who are at risk at various time points throughout the program journey allows for personalized interventions to improve outcomes. This approach offers significant advancements in the feasibility of large-scale implementation in remote monitoring programs and can help prevent uncontrolled glycemic levels and diabetes-related complications. Future research should include the impact of significant changes that can affect a participant’s diabetes management.

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Big data and machine learning to tackle diabetes management

Cited by 18 publications

References 60 publications

Editorial: Metabolic miscommunication among organs: The missing links

Editorial: Metabolic miscommunication among organs: The missing links

The role of machine learning in advancing diabetic foot: a review

Proactive Identification of Patients with Diabetes at Risk of Uncontrolled Outcomes during a Diabetes Management Program: Conceptualization and Development Study Using Machine Learning

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