Abstract
Background: Previous UK Biobank studies showed that symptoms and physical measurements had excellent prediction on long-term clinical outcomes in general population. Symptoms and signs could intuitively and non-invasively predict and monitor disease progression, especially for telemedicine, but related research is limited in diabetes and renal medicine.Methods: This retrospective cohort study aimed to evaluate the predictive power of a symptom-based stratification framework and individual symptoms for diabetes. Three hundred two adult diabetic patients were consecutively sampled from outpatient clinics in Hong Kong for prospective symptom assessment. Demographics and longitudinal measures of biochemical parameters were retrospectively extracted from linked medical records. The association between estimated glomerular filtration rate (GFR) (independent variable) and biochemistry, epidemiological factors, and individual symptoms was assessed by mixed regression analyses. A symptom-based stratification framework of diabetes using symptom clusters was formulated by Delphi consensus method. Akaike information criterion (AIC) and Bayesian information criterion (BIC) were compared between statistical models with different combinations of biochemical, epidemiological, and symptom variables.Results: In the 4.2-year follow-up period, baseline presentation of edema (−1.8 ml/min/1.73m2, 95%CI: −2.5 to −1.2, p < 0.001), epigastric bloating (−0.8 ml/min/1.73m2, 95%CI: −1.4 to −0.2, p = 0.014) and alternating dry and loose stool (−1.1 ml/min/1.73m2, 95%CI: −1.9 to −0.4, p = 0.004) were independently associated with faster annual GFR decline. Eleven symptom clusters were identified from literature, stratifying diabetes predominantly by gastrointestinal phenotypes. Using symptom clusters synchronized by Delphi consensus as the independent variable in statistical models reduced complexity and improved explanatory power when compared to using individual symptoms. Symptom-biologic-epidemiologic combined model had the lowest AIC (4,478 vs. 5,824 vs. 4,966 vs. 7,926) and BIC (4,597 vs. 5,870 vs. 5,065 vs. 8,026) compared to the symptom, symptom-epidemiologic and biologic-epidemiologic models, respectively. Patients co-presenting with a constellation of fatigue, malaise, dry mouth, and dry throat were independently associated with faster annual GFR decline (−1.1 ml/min/1.73m2, 95%CI: −1.9 to −0.2, p = 0.011).Conclusions: Add-on symptom-based diagnosis improves the predictive power on renal function decline among diabetic patients based on key biochemical and epidemiological factors. Dynamic change of symptoms should be considered in clinical practice and research design.
Highlights
The pathogenesis of diabetes is heterogeneous and there is a constant call for more personalized management [1, 2]
This study aimed to investigate the association between the presence of symptoms, symptom-based subtype (SS) and renal function deterioration among diabetic patients, and to establish a synchronized symptom-based framework for stratifying diabetes based on the quality of different statistical models in predicting renal function
Most presented symptoms and signs were susceptible to infections (80.8%), nocturnal polyuria (67.4%), forgetfulness (65.6%), past frequent intake of high fat diet (59.6%), malar flush (58.3%), malaise (56.3%) knee buckling (54.6%), lumbago (54.0%), dry skin (52.3%), and fatigue (49.7%) (Supplementary Material 6)
Summary
The pathogenesis of diabetes is heterogeneous and there is a constant call for more personalized management [1, 2]. Previous studies of UK Biobank suggested that phenomes, including symptoms and physical measures, had an excellent prediction of long-term clinical outcome in general population [3]. Patient reported outcomes and symptom-based diagnosis are increasingly used in all levels of healthcare [6] including evaluation of clinical interventions [10,11,12], disease diagnosis, surveillance [13], and stratification [7, 14,15,16,17]. Previous UK Biobank studies showed that symptoms and physical measurements had excellent prediction on long-term clinical outcomes in general population. Symptoms and signs could intuitively and non-invasively predict and monitor disease progression, especially for telemedicine, but related research is limited in diabetes and renal medicine
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