(X) Age and Sex Differences in Polypharmacy Patterns in New Users of Metformin: A UK-Based Descriptive Study

Publication Number: 647

Background: With increased concomitant chronic diseases in type 2 diabetes mellitus (T2DM), the use of multiple drugs increases as well as the risk of drug-drug interactions (DDI) and adverse drug reactions (ADR). Although age is a risk factor for polypharmacy (≥5 co-medications), how medication patterns vary in T2DM patients with multiple drugs across different sex and age groups is unclear.

Objectives: To identify and quantify common drug combinations at a drug compound level in first-time metformin users with polypharmacy in the UK, stratified by sex and age.

Methods: New users of metformin aged 18+ were identified from the IQVIA Medical Research Data incorporating data from THIN, A Cegedim Database of anonymized electronic health records (2016-2019). Patients with gestational diabetes, polycystic ovary syndrome, or insulin therapy were excluded. A descriptive cohort study explored prescription patterns in patients with polypharmacy (metformin + ≥4 co-prescribed drugs). The apriori algorithm was applied to identify and quantify drug combinations of up to 7 drugs to investigate potential harmful polypharmacy patterns stratified by sex and age.

Results: The cohort included 35,531 new-users of metformin, of which 20,962 (59.0%) received polypharmacy. Atorvastatin was the most frequently co-prescribed drug with metformin overall (38.3%), in women (33.6%) and men (42.6%), and adults ≥40 years old with polypharmacy (40-59 years [37.5%]; 60-74 years [45.6%]; ≥75 years [37.2%]). Combinations of ≤7 drugs mainly treat manifestations of metabolic syndrome. Four main polypharmacy patterns were identified after visual inspection based on drugs groups and indications, stratifying by sex (women: respiratory disease-related drugs and pain-related drugs and their associated effects; men: cardiac disease-related drugs and antithrombotic medication). Additionally, four main polypharmacy patterns were identified in adults in the analysis stratified by age groups ( <40 years old: pain-related drugs and their associated effects, folic acid medication, respiratory disease-related drugs; ≥ 60 years old: respiratory disease-related drugs).

Conclusions: This study is the first to identify and quantify commonly prescribed combinations of drug compounds in patients with polypharmacy using the apriori algorithm. The high prevalence of polypharmacy at all age strata indicates the need to aim for deprescribing to minimize DDI and ADR. Hypotheses regarding pathophysiological processes underlying polypharmacy patterns identified in this study should be interpreted with caution due to the limitations of the study design (i.e., descriptive). Longitudinal studies will be needed to investigate causal associations and validate potential DDI.