Towards personalized diabetes management: Identifying stability for efficient care using primary care data

Journal of Medicine Surgery and Public Health, vol. 7, pp. 100218

Abstract

Type 2 diabetes mellitus (T2DM) is largely managed in primary care, where Dutch guidelines recommend quarterly follow-ups. Many T2DM individuals maintain long-term glycaemic control and may not need such frequent visits. We conducted a retrospective cohort study using routinely collected data from the Extramural Leiden University Medical Center Academic Network (ELAN) linked to Statistics Netherlands to identify those with stable diabetes and predict ongoing control. We examined 47,801 adults with T2DM aged 40–79 years and formed two cohorts: the metformin cohort, which included individuals newly starting metformin (n = 21,307), and the random-time cohort, which included individuals using any glucose-lowering medication at a random point in their disease course (n = 22,245). Stability was defined as maintaining glycated hemoglobin (HbA1c) ≤53 mmol/mol (7%) for one year without additional diabetes medication or dose adjustment. Logistic regression models were used to examine whether first-year stability predicted glycaemic control in the second year. Additional models adjusted for demographic and clinical factors, including age, sex, body mass index, blood pressure, High-Density Lipoprotein (HDL) cholesterol, eGFR, smoking, migration status, and household income. Models performance was evaluated with the area under the receiver operating characteristic curve (AUC), and continuous variables were standardized to allow comparison across measures. All analyses were performed using R (version 4.2.1). In the metformin cohort, 23% of participants achieved glycaemic stability during the first year of follow-up. Among the individuals who achieved stability in the first year, 82% maintained this stable glycaemic control throughout the second year. First-year stability was strongly associated with continued control during the second year (odds ratio [OR] 89, 95% confidence interval [CI] 81–99) with excellent discrimination (AUC 0.89). Adding other predictors increased the AUC only slightly to 0.91. Findings were similar in the random time cohort: 46% were stable in the first year, and 80% of these remained stable in the second year. First-year stability alone was a strong predictor (OR 25, 95% CI 23–26) with an AUC of 0.84, rising modestly to 0.85 with additional predictors. One year of stable glycaemic control is a powerful indicator of future stability and could support a more tailored approach to diabetes follow-up. Reducing visit frequency for well-controlled individuals may help optimize primary care resources, reduce unnecessary workload, and lessen the burden of frequent clinic visits. These findings could inform updates to follow-up recommendations and support the use of remote monitoring and self-management to individualize diabetes care.

Authors 5

1. Sukainah Alfaraj corresponding Aachen Department of Public Health and Primary Care/Health Campus The Hague

   Leiden University Medical Center

   Affiliation as printed

   Department of Public Health and Primary Care/Health Campus the Hague, Leiden University Medical Center (LUMC), the Netherlands

2. Rimke Cathelijne Vos Aachen Department of Public Health and Primary Care/Health Campus The Hague

   Leiden University Medical Center

   Affiliation as printed

   Department of Public Health and Primary Care/Health Campus the Hague, Leiden University Medical Center (LUMC), the Netherlands

3. Marco René Spruit Aachen Department of Public Health and Primary Care/Health Campus The Hague Leiden Institute of Advanced Computer Science

   Leiden University · Leiden University Medical Center

   Affiliation as printed

   Department of Public Health and Primary Care/Health Campus the Hague, Leiden University Medical Center (LUMC), the Netherlands

   Leiden Institute of Advanced Computer Science (LIACS), Leiden University, the Netherlands

4. Rolf H. H. Groenwold Aachen Department of Biomedical Data Sciences Department of Clinical Epidemiology

   Leiden University Medical Center

   Affiliation as printed

   Department of Biomedical Data Sciences, LUMC, the Netherlands

   Department of Clinical Epidemiology, LUMC, the Netherlands

5. Dennis Owen Mook-Kanamori Aachen Department of Clinical Epidemiology Department of Public Health and Primary Care/Health Campus The Hague

   Leiden University Medical Center

   Affiliation as printed

   Department of Clinical Epidemiology, LUMC, the Netherlands

   Department of Public Health and Primary Care/Health Campus the Hague, Leiden University Medical Center (LUMC), the Netherlands