FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W4284890192> ?p ?o ?g. }

• W4284890192 endingPage "1435" @default.
• W4284890192 startingPage "1424" @default.
• W4284890192 abstract "Data-driven diabetes subgroups have shown distinct clinical characteristics and disease progression, although there is a lack of evidence that this information can guide clinical decisions. We aimed to investigate whether diabetes subgroups, identified by data-driven clustering or supervised machine learning methods, respond differently to canagliflozin.We pooled data from five randomised, double-blinded clinical trials of canagliflozin at an individual level. We applied the coordinates from the All New Diabetics in Scania (ANDIS) study to form four subgroups: mild age-related diabetes (MARD); severe insulin-deficient diabetes (SIDD); mild obesity-related diabetes (MOD) and severe insulin-resistant diabetes (SIRD). Machine learning models for HbA1c lowering (ML-A1C) and albuminuria progression (ML-ACR) were developed. The primary efficacy endpoint was reduction in HbA1c at 52 weeks. Concordance of a model was defined as the difference between predicted HbA1c and actual HbA1c decline less than 3.28 mmol/mol (0.3%).The decline in HbA1c resulting from treatment was different among the four diabetes clusters (pinteraction=0.004). In MOD, canagliflozin showed a robust glucose-lowering effect at week 52, compared with other drugs, with least-squares mean of HbA1c decline [95% CI] being 6.6 mmol/mol (4.1, 9.2) (0.61% [0.38, 0.84]) for sitagliptin, 7.1 mmol/mol (4.7, 9.5) (0.65% [0.43, 0.87]) for glimepiride, and 9.8 mmol/mol (9.0, 10.5) (0.90% [0.83, 0.96]) for canagliflozin. This superiority persisted until 104 weeks. The proportion of individuals who achieved HbA1c <53 mmol/mol (<7.0%) was highest in sitagliptin-treated individuals with MARD but was similar among drugs in individuals with MOD. The ML-A1C model and the cluster algorithm showed a similar concordance rate in predicting HbA1c lowering (31.5% vs 31.4%, p=0.996). Individuals were divided into high-risk and low-risk groups using ML-ACR model according to their predicted progression risk for albuminuria. The effect of canagliflozin vs placebo on albuminuria progression differed significantly between the high-risk (HR 0.67 [95% CI 0.57, 0.80]) and low-risk groups (HR 0.91 [0.75, 1.11]) (pinteraction=0.016).Data-driven clusters of individuals with diabetes showed different responses to canagliflozin in glucose lowering but not renal outcome prevention. Canagliflozin reduced the risk of albumin progression in high-risk individuals identified by supervised machine learning. Further studies with larger sample sizes for external replication and subtype-specific clinical trials are necessary to determine the clinical utility of these stratification strategies in sodium-glucose cotransporter 2 inhibitor treatment.The application for the clinical trial data source is available on the YODA website ( http://yoda.yale.edu/ )." @default.
• W4284890192 created "2022-07-09" @default.
• W4284890192 creator A5041914033 @default.
• W4284890192 creator A5046517816 @default.
• W4284890192 creator A5054525661 @default.
• W4284890192 creator A5071824771 @default.
• W4284890192 creator A5075388616 @default.
• W4284890192 creator A5081953591 @default.
• W4284890192 creator A5091910110 @default.
• W4284890192 date "2022-07-08" @default.
• W4284890192 modified "2023-10-05" @default.
• W4284890192 title "The efficacy of canagliflozin in diabetes subgroups stratified by data-driven clustering or a supervised machine learning method: a post hoc analysis of canagliflozin clinical trial data" @default.
• W4284890192 cites W2066898102 @default.
• W4284890192 cites W2068171948 @default.
• W4284890192 cites W2130570549 @default.
• W4284890192 cites W2146264128 @default.
• W4284890192 cites W2168926920 @default.
• W4284890192 cites W2626446274 @default.
• W4284890192 cites W2790501791 @default.
• W4284890192 cites W2810075315 @default.
• W4284890192 cites W2900413769 @default.
• W4284890192 cites W2900940968 @default.
• W4284890192 cites W2905259954 @default.
• W4284890192 cites W2939222610 @default.
• W4284890192 cites W2942642824 @default.
• W4284890192 cites W2964211103 @default.
• W4284890192 cites W2968847082 @default.
• W4284890192 cites W2969294640 @default.
• W4284890192 cites W3036199105 @default.
• W4284890192 cites W3216079245 @default.
• W4284890192 doi "https://doi.org/10.1007/s00125-022-05748-9" @default.
• W4284890192 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/35802168" @default.
• W4284890192 hasPublicationYear "2022" @default.
• W4284890192 type Work @default.
• W4284890192 citedByCount "6" @default.
• W4284890192 countsByYear W42848901922022 @default.
• W4284890192 countsByYear W42848901922023 @default.
• W4284890192 crossrefType "journal-article" @default.
• W4284890192 hasAuthorship W4284890192A5041914033 @default.
• W4284890192 hasAuthorship W4284890192A5046517816 @default.
• W4284890192 hasAuthorship W4284890192A5054525661 @default.
• W4284890192 hasAuthorship W4284890192A5071824771 @default.
• W4284890192 hasAuthorship W4284890192A5075388616 @default.
• W4284890192 hasAuthorship W4284890192A5081953591 @default.
• W4284890192 hasAuthorship W4284890192A5091910110 @default.
• W4284890192 hasBestOaLocation W42848901921 @default.
• W4284890192 hasConcept C126322002 @default.
• W4284890192 hasConcept C134018914 @default.
• W4284890192 hasConcept C2776174234 @default.
• W4284890192 hasConcept C2776307423 @default.
• W4284890192 hasConcept C2777180221 @default.
• W4284890192 hasConcept C2777451236 @default.
• W4284890192 hasConcept C555293320 @default.
• W4284890192 hasConcept C67761136 @default.
• W4284890192 hasConcept C71924100 @default.
• W4284890192 hasConceptScore W4284890192C126322002 @default.
• W4284890192 hasConceptScore W4284890192C134018914 @default.
• W4284890192 hasConceptScore W4284890192C2776174234 @default.
• W4284890192 hasConceptScore W4284890192C2776307423 @default.
• W4284890192 hasConceptScore W4284890192C2777180221 @default.
• W4284890192 hasConceptScore W4284890192C2777451236 @default.
• W4284890192 hasConceptScore W4284890192C555293320 @default.
• W4284890192 hasConceptScore W4284890192C67761136 @default.
• W4284890192 hasConceptScore W4284890192C71924100 @default.
• W4284890192 hasFunder F4320321001 @default.
• W4284890192 hasFunder F4320325902 @default.
• W4284890192 hasFunder F4320335777 @default.
• W4284890192 hasIssue "9" @default.
• W4284890192 hasLocation W42848901921 @default.
• W4284890192 hasLocation W42848901922 @default.
• W4284890192 hasOpenAccess W4284890192 @default.
• W4284890192 hasPrimaryLocation W42848901921 @default.
• W4284890192 hasRelatedWork W1856636502 @default.
• W4284890192 hasRelatedWork W2077747914 @default.
• W4284890192 hasRelatedWork W2130570549 @default.
• W4284890192 hasRelatedWork W2500481180 @default.
• W4284890192 hasRelatedWork W2512006388 @default.
• W4284890192 hasRelatedWork W2899064970 @default.
• W4284890192 hasRelatedWork W2973242786 @default.
• W4284890192 hasRelatedWork W3013718006 @default.
• W4284890192 hasRelatedWork W4284890192 @default.
• W4284890192 hasRelatedWork W4285005095 @default.
• W4284890192 hasVolume "65" @default.
• W4284890192 isParatext "false" @default.
• W4284890192 isRetracted "false" @default.
• W4284890192 workType "article" @default.