ArticleNPJ genomic medicine2016
Ranking factors involved in diabetes remission after bariatric surgery using machine-learning integrating clinical and genomic biomarkers.
Article in NPJ genomic medicine, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers, 1 of them a synthesis that pooled it.
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Who cites it
16 citing papers in PubMed, 1 synthesis or guideline pooled it, 30 citations in OpenAlex.
- The Evolution of Bariatric and Metabolic Surgery in the Artificial Intelligence Era: A Comprehensive Systematic Review of Current Applications and Clinical Implications.Obesity surgery · 2026Pooled it
- Proteomic signature of dementia risk in type 2 diabetes.Journal of advanced research · 2026Article
- What is in a name? Rethinking SMUG1 in genome maintenance.NAR cancer · 2025Review
- Type 2 Diabetes Mellitus Remission, Dream or Reality? A Narrative Review of Current Evidence and Integrated Care Strategies.Diabetes therapy : research, treatment and education of diabetes and related disorders · 2025Review
- Predicting pregnancy at the first year following metabolic-bariatric surgery: development and validation of machine learning models.Surgical endoscopy · 2025Article
- International expert consensus on the current status and future prospects of artificial intelligence in metabolic and bariatric surgery.Scientific reports · 2025Article
- Bariatric Surgery: Targeting pancreatic β cells to treat type II diabetes.Frontiers in endocrinology · 2023Review
- Current Applications of Artificial Intelligence in Bariatric Surgery.Obesity surgery · 2022Review
- Exploring microRNAs as predictive biomarkers for type 2 diabetes mellitus remission after sleeve gastrectomy: A pilot study.Obesity (Silver Spring, Md.) · 2022Article
- Data assimilation on mechanistic models of glucose metabolism predicts glycemic states in adolescents following bariatric surgery.Frontiers in physiology · 2022Article
- A Scoping Review of Artificial Intelligence and Machine Learning in Bariatric and Metabolic Surgery: Current Status and Future Perspectives.Obesity surgery · 2021Article
- Using a Convolutional Neural Network to Predict Remission of Diabetes After Gastric Bypass Surgery: Machine Learning Study From the Scandinavian Obesity Surgery Register.JMIR medical informatics · 2021Article
- Who will benefit from bariatric surgery for diabetes? A protocol for an observational cohort study.BMJ open · 2021Article
- Deep Learning Neural Networks to Predict Serious Complications After Bariatric Surgery: Analysis of Scandinavian Obesity Surgery Registry Data.JMIR medical informatics · 2020Article
- A Comparative Study of Machine Learning Algorithms in Predicting Severe Complications after Bariatric Surgery.Journal of clinical medicine · 2019Article
- Weight-Independent Mechanisms of Glucose Control After Roux-en-Y Gastric Bypass.Frontiers in endocrinology · 2018Review
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Authors and funding
6 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
As weight-loss surgery is an effective treatment for the glycaemic control of type 2 diabetes in obese patients, yet not all patients benefit, it is valuable to find predictive factors for this diabetic remission. This will help elucidating possible mechanistic insights and form the basis for prioritising obese patients with dysregulated diabetes for surgery where diabetes remission is of interest. In this study, we combine both clinical and genomic factors using heuristic methods, informed by prior biological knowledge in order to rank factors that would have a role in predicting diabetes remission, and indeed in identifying patients who may have low likelihood in responding to bariatric surgery for improved glycaemic control. Genetic variants from the Illumina CardioMetaboChip were prioritised through single-association tests and then seeded a larger selection from protein-protein interaction networks. Artificial neural networks allowing nonlinear correlations were trained to discriminate patients with and without surgery-induced diabetes remission, and the importance of each clinical and genetic parameter was evaluated. The approach highlighted insulin treatment, baseline HbA1c levels, use of insulin-sensitising agents and baseline serum insulin levels, as the most informative variables with a decent internal validation performance (74% accuracy, area under the curve (AUC) 0.81). Adding information for the eight top-ranked single nucleotide polymorphisms (SNPs) significantly boosted classification performance to 84% accuracy (AUC 0.92). The eight SNPs mapped to eight genes -
Identifiers
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.