Journal of cystic fibrosis : official journal of the European Cystic Fibrosis Society · 2026
Machine learning analysis of continuous glucose monitoring identifies a novel dysglycemic phenotype found in most people with cystic fibrosis.
СтатьяПоддержка службы здравоохранения СШАПоддержка NIH: внешняя программаНазвание и аннотация: Перевод пока не подготовлен
Обработка статьи · этапы, ошибки и управление
Загрузка состояния…
Ошибки и ограничения
История · было → стало
Состояние обновляется каждые 5 секунд, пока блок открыт. Процент показывается только там, где известен объём работы. Пауза останавливает продолжение этапа; текущий запрос к источнику может завершиться.
Song Jiafeng, Alvarez Jessica, Gent Alasdair, Gillespie Scott, Harris Ryan A, McNeany Jocelyn, Daley Tanicia, Kamaleswaran Rishikesan, Stecenko Arlene
Аннотация
BACKGROUND: Cystic fibrosis related diabetes (CFRD) is a common complication in people with cystic fibrosis (PwCF), yet traditional diagnostic tools such as fasting glucose, HbA1c, and the oral glucose tolerance test (OGTT) often fail to detect early dysglycemia. Continuous glucose monitoring (CGM) generates high resolution glucose data, but analytic methods for extracting meaningful phenotypes remain limited.
METHODS: CGM data from 82 PwCF aged 6 to 78 years were compared with 166 healthy controls (HC). Thirty-two glycemic features were extracted from 24-hour CGM segments. Uniform Manifold Approximation and Projection (UMAP) was trained using HC and CFRD data, and Silhouette scores quantified the alignment of each daily profile with these clusters. Group differences were evaluated using linear mixed effects models.
RESULTS: UMAP showed complete separation between HC and CFRD. Mean Silhouette score values were +0.35 (95% CI: 0.31 to 0.38) for HC and -0.77 (95% CI: -0.83 to -0.71) for CFRD. PwCF classified as normal glucose tolerance (NGT) or impaired glucose tolerance (IGT) had negative Silhouette score values, -0.58 (95% CI: -0.67 to -0.49) and -0.56 (95% CI: -0.63 to -0.49), with no difference between groups.
CONCLUSIONS: Machine learning analysis of CGM data revealed a pervasive dysglycemic phenotype in PwCF. NGT and IGT individuals showed similar glycemic profiles shifted toward the CFRD phenotype, indicating that OGTT categories underestimate early metabolic dysfunction. CGM based digital phenotyping offers a more sensitive and continuous assessment of dysglycemia and may improve early detection and risk stratification in PwCF.
METHODS: CGM data from 82 PwCF aged 6 to 78 years were compared with 166 healthy controls (HC). Thirty-two glycemic features were extracted from 24-hour CGM segments. Uniform Manifold Approximation and Projection (UMAP) was trained using HC and CFRD data, and Silhouette scores quantified the alignment of each daily profile with these clusters. Group differences were evaluated using linear mixed effects models.
RESULTS: UMAP showed complete separation between HC and CFRD. Mean Silhouette score values were +0.35 (95% CI: 0.31 to 0.38) for HC and -0.77 (95% CI: -0.83 to -0.71) for CFRD. PwCF classified as normal glucose tolerance (NGT) or impaired glucose tolerance (IGT) had negative Silhouette score values, -0.58 (95% CI: -0.67 to -0.49) and -0.56 (95% CI: -0.63 to -0.49), with no difference between groups.
CONCLUSIONS: Machine learning analysis of CGM data revealed a pervasive dysglycemic phenotype in PwCF. NGT and IGT individuals showed similar glycemic profiles shifted toward the CFRD phenotype, indicating that OGTT categories underestimate early metabolic dysfunction. CGM based digital phenotyping offers a more sensitive and continuous assessment of dysglycemia and may improve early detection and risk stratification in PwCF.