Aims: To evaluate the performance of the WATCH-DM risk score, a clinical risk score for heart failure (HF), in patients with dysglycaemia and in combination with natriuretic peptides (NPs).
Methods And Results: Adults with diabetes/pre-diabetes free of HF at baseline from four cohort studies (ARIC, CHS, FHS, and MESA) were included. The machine learning- [WATCH-DM(ml)] and integer-based [WATCH-DM(i)] scores were used to estimate the 5-year risk of incident HF. Discrimination was assessed by Harrell's concordance index (C-index) and calibration by the Greenwood-Nam-D'Agostino (GND) statistic. Improvement in model performance with the addition of NP levels was assessed by C-index and continuous net reclassification improvement (NRI). Of the 8938 participants included, 3554 (39.8%) had diabetes and 432 (4.8%) developed HF within 5 years. The WATCH-DM(ml) and WATCH-DM(i) scores demonstrated high discrimination for predicting HF risk among individuals with dysglycaemia (C-indices = 0.80 and 0.71, respectively), with no evidence of miscalibration (GND P ≥0.10). The C-index of elevated NP levels alone for predicting incident HF among individuals with dysglycaemia was significantly higher among participants with low/intermediate (<13) vs. high (≥13) WATCH-DM(i) scores [0.71 (95% confidence interval 0.68-0.74) vs. 0.64 (95% confidence interval 0.61-0.66)]. When NP levels were combined with the WATCH-DM(i) score, HF risk discrimination improvement and NRI varied across the spectrum of risk with greater improvement observed at low/intermediate risk [WATCH-DM(i) <13] vs. high risk [WATCH-DM(i) ≥13] (C-index = 0.73 vs. 0.71; NRI = 0.45 vs. 0.17).
Conclusion: The WATCH-DM risk score can accurately predict incident HF risk in community-based individuals with dysglycaemia. The addition of NP levels is associated with greater improvement in the HF risk prediction performance among individuals with low/intermediate risk than those with high risk.
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http://dx.doi.org/10.1002/ejhf.2375 | DOI Listing |
Pigment Cell Melanoma Res
January 2025
Department of Dermatology, Union Hospital, Hubei Engineering Research Center of Skin Disease Theranostics and Health, Tongji Medical College, Huazhong University of Science and Technology (HUST), Wuhan, China.
Metabolic syndrome (MetS) remains a significant global public health concern. However, the relationship between MetS, its individual components and melanoma metastasis remains unexplored. We analysed the clinical data of 258 Chinese melanoma patients who had not undergo systemic therapy.
View Article and Find Full Text PDFLancet Diabetes Endocrinol
December 2024
Department of Paediatrics, Diabetes Center, University of California San Francisco, San Francisco, California, USA.
Diabetes Obes Metab
February 2025
Division of Endocrinology, Department of Medicine, National University Health Systems, National University Hospital, Singapore.
Aims: Women with gestational diabetes (GDM) have increased lifetime risk of developing diabetes. We aim to determine the factors contributing to poor adherence of the postpartum oral glucose tolerance test (OGTT) and identify key predictors to postpartum dysglycaemia in our Asian cohort.
Methods: We conducted a retrospective cohort study of women with high-risk GDM (n = 561).
Diabetes Res Clin Pract
December 2024
Western Sydney University, Campbelltown, NSW, Australia. Electronic address:
Aim: To evaluate the incidence and predictors of postpartum dysglycaemia among high-risk women who develop early gestational diabetes (eGDM) prior to 20 weeks' gestation.
Methods: This is a sub-study of the Treatment of Booking Gestational Diabetes (TOBOGM) Study, a randomised controlled trial of early or deferred treatment for women with risk factors for gestational diabetes diagnosed with eGDM, using current WHO criteria. Overt diabetes in pregnancy was excluded.
Diabetes Obes Metab
January 2025
Research Institute of the Diabetes-Academy Mergentheim (FIDAM), Bad Mergentheim, Germany.
Aim: To analyse the potential drivers (glucose level, complications, diabetes type, gender, age and mental health) of diabetes symptoms using continuous glucose monitoring (CGM) and ecological momentary assessment.
Materials And Methods: Participants used a smartphone application to rate 25 diabetes symptoms in their daily lives over 8 days. These symptoms were grouped into four blocks so that each symptom was rated six times on 2 days (noon, afternoon and evening).
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