Cardiovascular-Related Polypharmacy and Its Association with Liver and Kidney Function: A Cross-Sectional Study Using Primary Care Data

Abstract

Background

Individuals at high risk of cardiovascular disease (CVD) often require multiple concurrent medications, resulting in polypharmacy. Although necessary for disease management, polypharmacy may increase the risk of adverse outcomes, including kidney and liver dysfunction. This study aimed to examine the association between CVD-related polypharmacy and biochemical indicators of kidney and liver dysfunction among patients prescribed lipid-lowering therapy in Australian primary care.

Methods

We conducted a retrospective cross-sectional study of electronic medical records of adults prescribed lipid lowering therapy between January 2013 and December 2022. CVD-related polypharmacy was defined as the concurrent use of more than five cardiovascular medications within a 365-day sliding window. Liver dysfunction was defined by elevated alanine aminotransferase (ALT), aspartate aminotransferase (AST), or bilirubin levels and kidney dysfunction was defined by reduced estimated glomerular filtration rate (eGFR). Multivariable logistic regression was used to assess associations, adjusting for age, sex, and comorbidities.

Results

Among 13,568 participants (median age 63 years, interquartile range [IQR] 54–72; 54% male), 33.7% had CVD-related polypharmacy. Diabetes (odds ratio [OR] 5.40, 95% confidence interval [CI] 4.96–5.93), chronic kidney disease (OR 2.39, 95% CI 2.00–2.86), and hypertension (OR 1.91, 95% CI 1.75–2.07) were significant predictors. Older adults (≥80 years) had higher odds of CVD-related polypharmacy (OR 8.17, 95% CI 5.47–12.21). CVD-related polypharmacy was significantly associated with kidney dysfunction (OR 1.45, 95% CI 1.30–1.62), but not statistically significantly associated with liver dysfunction (OR 1.19, 95% CI 0.59–2.37).

Conclusion

CVD-related polypharmacy is common among individuals receiving lipid-lowering therapy and is strongly linked to ageing and multimorbidity. Its association with kidney dysfunction suggests an interplay between cardiovascular risk, physiological decline, and treatment burden, highlighting the need for integrated, patient-centred prescribing in high-risk populations.

Supplementary Information

The online version contains supplementary material available at 10.1007/s40266-026-01297-7.

Key Points

Individuals with diabetes, chronic kidney disease, or hypertension demonstrated significantly higher odds of cardiovascular‑related polypharmacy. These conditions increase the likelihood of patients being prescribed multiple cardiovascular medicines.

Older adults, particularly those aged 80 years and above, were more likely to experience polypharmacy. This reflects the combined effects of ageing, multimorbidity, and complex prescribing needs.

Cardiovascular-related polypharmacy was significantly associated with elevated kidney biomarkers, suggesting potential kidney dysfunction. This finding underscores the importance of regular medication reviews and tailored prescribing strategies to reduce risks of kidney impairment in high-risk populations.

Introduction

Cardiovascular diseases (CVD) remain one of the leading causes of mortality and morbidity globally [1]. CVD contributed to approximately 12% of the burden of disease in Australia, contributing substantially to reduced quality of life and premature mortality [2]. Epidemiological projections indicate a continued rise in CVD prevalence between 2020 and 2029, estimating approximately 372,766 Australians will die from CVD, with the total cost to the healthcare system projected to exceed AU$61.89 billion [1]. Together, these trends highlight the growing importance of optimising long-term disease management strategies, including the safe and effective use of medications within primary care [3].

Individuals at risk of CVD often have multimorbid conditions such as diabetes mellitus, hypertension, hyperlipidaemia, chronic kidney disease (CKD), and obesity, all of which are established biomedical risk factors for CVD development and progression [4]. As a result, polypharmacy has become increasingly common in patients with or at risk of CVD, representing a complex and growing challenge in chronic disease management, particularly for older adults [5].

The World Health Organization (WHO) defines polypharmacy as the concurrent use of five or more unique medications, including prescribed, over-the-counter, traditional, and complementary therapies [6]. Research conducted within the context of the Australian Pharmaceutical Benefits Scheme (PBS) showed that the prevalence rose from 8.0% in 2013 to 9.2% with two million Australians exposed in 2024 [7], especially in older adults who are more vulnerable to adverse drug events and organ dysfunction [8], indicating a significant rise in the challenge.

Polypharmacy presents a growing concern within the management of patients at risk of CVD. The co-existence of multimorbidity necessitates multi-medication use, further increasing the risk of polypharmacy and its associated negative health outcomes [5, 9, 10]. Inappropriate polypharmacy is associated with a range of adverse outcomes including reduced medication adherence [13], drug-to-drug interactions [5], medication dosage errors [5], increased impaired renal function [5], adverse events [11], increased healthcare expenditures [12], and even a higher mortality rate [11]. Patients receiving lipid-lowering therapy (LLT) are a key focus of this study, as these medications are commonly prescribed in Australia for cardiovascular disease management [13]. Given their reliance on hepatic and renal pathways for metabolism and clearance, examining their use may provide important insights into the potential implications of medication burden on organ function. Emerging evidence suggests that polypharmacy may contribute to abnormal liver and kidney function [9, 14–17]. However, despite existing evidence linking polypharmacy to organ dysfunction, there is a lack of real-world data from Australian primary care settings, particularly among patients receiving LLT, a key population in CVD prevention. Therefore this study aims to (1) quantify the prevalence of CVD-related polypharmacy; (2) identify key demographic and clinical predictors; and (3) evaluate its association with liver and kidney function among patients receiving LLT in Australian primary care.

Methods

Study Design and Setting

This study was reported in accordance with the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guidelines (http://www.strobe-statement.org). A completed STROBE checklist is provided as Supplementary Material 3 (see electronic supplementary material [ESM]).

We conducted a cross-sectional study (data analysed from January 2013 to December 2022) using electronic data from general practitioners (GPs) across Australia. Data were obtained from the IQVIA ‘general practice electronic medical record’ (GP EMR) database, which contains de-identified in-patient records across the Australian primary care setting. IQVIA is a health data analytics company that provides de-identified patient records used for research and quality purposes. The GP EMR dataset represents approximately 12% of Australian primary care practices, based on national GP workforce data. Ethical approval for this study was obtained from the Monash University Human Research Ethics Committee (MUHREC project ID: 44395).

Participants and Data

The study population consisted of people aged 18 years and older, who were prescribed at least one LLT based on the Anatomical Therapeutic Chemical Classification System (ATC) ‘C10’ code [18]. Diagnoses were clinician-entered and mapped to ICD-10-AM codes using standardised algorithms of the International Classification of Diseases Australian Modification, 10th revision (ICD-10-AM) using standardised coding algorithms.

Variables included demographic data (age, sex, smoking status), clinical diagnoses (hypertension, hyperlipidaemia, CKD, diabetes), and medication classes (antithrombotics, antihypertensives, LLTs, smoking cessation therapies). Pathology data included biomarkers such as alanine transaminase (ALT), aspartate transaminase (AST), and bilirubin, for testing of liver function, and estimated glomerular filtration rate (eGFR) for testing of kidney function. Biomarker values were extracted from routine pathology records linked to the EMR. Patients missing any of the key biomarkers (ALT, AST, bilirubin, or eGFR) were excluded from the analysis.

Exposure

Polypharmacy was examined both as an exposure and as an outcome in this study. First, CVD-related polypharmacy was treated as an outcome to identify demographic and clinical predictors associated with its prevalence, using multivariable logistic regression. Second, polypharmacy was also analysed as an exposure to assess its association with liver and kidney dysfunction.

CVD-related polypharmacy was defined as the concurrent use of five or more CVD-related prescription medications including LLT, antithrombotics, antihypertensives, and other cardiovascular therapies, within a 365-day sliding window between 2013 and 2022. The sliding window approach allowed for periodic recalculation of medication counts by shifting the start and end dates, thereby capturing temporal variation in prescribing and better reflecting real-world medication exposure. To account for medication substitutions within the same therapeutic class, medication types were defined using the first four digits of the Anatomical Therapeutic Chemical (ATC) classification code. This approach ensured that medications within the same therapeutic class were not counted multiple times and enabled the calculation of medication class counts. A sensitivity analysis was conducted using shorter sliding windows of 90 and 180 days to test the robustness of the polypharmacy definition (Supplementary Material 1, ESM). A list of the CVD-related medications including medication classes is provided in Supplementary Material 2 (ESM). The analysis was restricted to CVD-related prescriptions available within the dataset.

Outcomes

The main outcomes were CVD-related polypharmacy described in the previous section and the prevalence of liver and kidney dysfunction. Kidney and liver function were assessed using median values across all available biomarker measurements to reduce the influence of outliers and reflect typical levels over time [19–22]. Kidney function was evaluated using eGFR [23]. Kidney dysfunction was defined as eGFR <90 mL/min/1.73 m². Liver function was assessed using bilirubin, alanine transaminase (ALT), or aspartate transaminase (AST). Reference ranges were defined as bilirubin 1–20 µmol/L, ALT 5–40 U/L for males and 5–35 U/L for females, and AST 5–35 U/L for males and 5–30 U/L for females. Biomarker values were extracted from routine pathology records recorded between 2013 and 2022. Where multiple measurements were available, patient-level median biomarker values were calculated across the observation period to summarise typical liver and kidney function and minimise the influence of extreme values. Detailed reference thresholds and classification criteria are provided in Supplementary Material 2 (ESM).

Statistical Analyses

Descriptive statistics were used to characterise the study population. Continuous variables were summarised as medians with interquartile ranges (IQR) for non-normally distributed data. Categorical variables were presented as frequencies and percentages. Normality of continuous variables was assessed using the Kolmogorov Smirnov test in conjunction with visual inspection of histograms and Q to Q plots. For between-group comparisons, we utilised Student’s t-test for normally distributed continuous variables and the Mann–Whitney U test for non-normally distributed continuous variables. For categorical variables, the chi-square test was applied. Pearson’s chi square test was used to compare categorical variables.

Six logistic regression models were constructed. Models 1 and 2 examined predictors of CVD-related polypharmacy, with polypharmacy defined as the outcome variable. Model 1 included age and sex. Model 2 additionally adjusted for hyperlipidaemia, hypertension, diabetes, and smoking status.

Models 3 and 4 assessed the association between polypharmacy and kidney dysfunction, with kidney dysfunction defined as the outcome variable and polypharmacy as the primary exposure. Model 3 adjusted for age and sex, while Model 4 additionally adjusted for hyperlipidaemia, hypertension, diabetes, and smoking status.

Models 5 and 6 evaluated the association between polypharmacy and liver dysfunction, with liver dysfunction defined as the outcome variable and polypharmacy as the primary exposure. Model 5 included age and sex, and Model 6 additionally adjusted for hyperlipidaemia, hypertension, diabetes, and smoking status.

Covariates were selected based on clinical relevance and established associations with cardiovascular disease and medication burden. Multicollinearity was assessed using variance inflation factors, with no evidence of problematic collinearity identified. Adjusted and unadjusted odds ratios (ORs) with 95 percent confidence intervals (CIs) were reported. Statistical significance was defined as a two-sided p-value <0.05.

Given the use of a large national electronic medical record dataset, all eligible patients meeting inclusion criteria during the study period were included and no formal sample size calculation was performed. Sensitivity analyses were conducted using alternative polypharmacy definitions based on 90-day and 180-day sliding windows to assess the robustness of the findings.

Sensitivity analyses were conducted to evaluate the utility of the ALT/AST ratio in identifying hepatocellular damage and its association with polypharmacy. Receiver operating characteristic (ROC) curve analyses were performed to compare the discriminatory performance of the ALT/AST ratio with individual biomarkers (ALT, AST, and total bilirubin), with full details provided in Supplementary Material 4 (ESM).

All statistical analyses were performed using Stata version 14.1 (StataCorp, College Station, Texas, USA).

To explore associations, six logistic regression models were constructed:

Model I (crude polypharmacy): Polypharmacy as the outcome, with age and sex as predictors.

Model II (adjusted polypharmacy): Polypharmacy as the outcome, with age, sex, hyperlipidaemia, hypertension, diabetes, and smoking status as predictors.

Model III (crude kidney): Kidney dysfunction as the outcome, with polypharmacy as the primary exposure and adjustment for age and sex.

Model IV (adjusted kidney): Kidney dysfunction as the outcome, with polypharmacy as the primary exposure and additional adjustment for age, sex, hyperlipidaemia, hypertension, diabetes, and smoking status.

Model V (crude liver): Liver dysfunction as the outcome, with polypharmacy as the primary exposure and adjustment for age and sex.

Model VI (adjusted liver): Liver dysfunction as the outcome, with polypharmacy as the primary exposure and additional adjustment for age, sex, hyperlipidaemia, hypertension, diabetes, and smoking status.

Results

Participant Selection

Figure 1 presents the STROBE diagram outlining the data extraction and participant inclusion process. Data were sourced from the GP EMR database, including records from 32,059 GPs across Australian primary care settings. From this, three primary datasets were obtained: pathology (N = 198,198), diagnosis (N = 115,230), and medication records (N = 198,198). After merging these datasets, a total of 105,385 individuals were identified. Of these, 89,666 were excluded due to missing biomarker data required for liver and kidney function assessment, specifically ALT (N = 9778), AST (N = 74,146), bilirubin (N = 1082), and eGFR (N = 4660). The final analytic cohort comprised 13,568 individuals who had complete data for all relevant biomarkers and met the inclusion criteria.

Fig. 1.

STROBE diagram outlining participants’ inclusion and eligibility criteria. ALT alanine aminotransferase, AST aspartate aminotransferase, eGFR estimated glomerular filtration rate, EMR electronic medical record, GP general practice, Gps general practitioners

CVD-Related Polypharmacy

Among individuals prescribed at least two LLT, 33.7% met the criteria for CVD-related polypharmacy (Table 1). The median age of individuals in the polypharmacy group was 68 years (IQR 59–76), compared with 61 years (IQR 52–70) in the non-polypharmacy group; 74% of those with CVD-related polypharmacy were aged ≥60 years, highlighting the burden of medication use in older adults. Age distribution was skewed toward older age groups, with only 0.7% of affected individuals aged 18–35 years. Polypharmacy was more prevalent among males (55.6%) than females (44.4%).

Table 1.

Patient characteristics

Variables	Total cohort, N (%)	CVD polypharmacy, N (%)	No CVD polypharmacy, N (%)
Total no. of patients, n (%)	13,568	4576 (33.7)	8992 (66.3)
Demographics
Females	6256 (46.1)	2032 (44.4)	4224 (47.0)
Males	7312 (53.9)	2544 (55.6)	4768 (53.0)
Age, median (IQR)		68.0 (59.0–76.0)	61.0 (52.0–70.0)
Age group [years], n (%)
18–35	296 (2.2)	34 (0.7)	262 (2.9)
36–50	2097 (15.5)	387 (8.4)	1710 (19.0)
51–65	3002 (22.1)	771 (16.9)	2231 (24.8)
60+	8173 (60.2)	3384 (74.0)	4789 (53.3)
Clinical biomarkers
Kidney biomarkers, median (IQR)
eGFR^ (mL/min)	79.1 (65.3–88.5)	71.0 (55.0–83.8)	82.2 (70.8–89.8)
Liver biomarkers, median (IQR)
Bilirubin (µmol/L)	8.7 (6.8–11.2)	8.5 (6.6–11.2)	8.7 (7.0–11.2)
AST^ levels (μ/L)	22.0 (18.5–27.0)	22.0 (18.0–27.7)	22.2 (19.0–27.0)
ALT^ levels (μ/L)	23.0 (17.0–31.7)	22.0 (16.3–30.7)	23.5 (17.75–32.1)
Clinical factors, n (%)
Diabetes	4666 (34.4)	1936 (42.3)	2026 (22.5)
Smoking status	6235 (45.9)	2101 (45.9)	4134 (46.0)
Hyperlipidaemia	7256 (53.5)	2786 (60.9)	5466 (60.8)
Hypertension	7467 (55.0)	2950 (64.5)	4517 (50.2)
CKD	671 (4.9)	424 (9.3)	247 (2.8)

ALT alanine aminotransferase, AST aspartate aminotransferase, CKD chronic kidney disease, CVD cardiovascular disease, eGFR estimated glomerular filtration rate

Among patients with available biomarker data (n = 13,568), 39.5% of those with CVD-related polypharmacy had indications of liver dysfunction, defined by median values exceeding reference ranges for ALT, AST, or bilirubin. Similarly, 39.0% had indications of kidney dysfunction, based on median eGFR values <60 mL/min/1.73 m².

Comorbidity profiles were consistent with high cardiovascular risk: 42.3% had diabetes, 45.9% were current or former smokers, 60.9% had hyperlipidaemia, 64.5% had hypertension, and 9.3% had chronic kidney disease (CKD). Median biomarker values in the polypharmacy group were as follows: eGFR 71.0 mL/min/1.73m² (IQR 55.0–83.8), bilirubin 8.5 μmol/L (IQR 6.6–11.2), AST 22.0 IU/L (IQR 18.0–27.7), and ALT 22.0 IU/L (IQR 16.3–30.7). In comparison, the non-polypharmacy group had higher median eGFR (82.2, IQR 70.8–89.8) and slightly higher ALT (23.5, IQR 17.75–32.1), with similar bilirubin and AST levels. While these differences are modest, they may warrant further investigation into prescribing practices and organ monitoring in multimorbid patients.

Factors Associated With CVD-Related Polypharmacy

Multivariable logistic regression analyses (Fig. 1; Table 2, Model II) identified several demographic and clinical factors significantly associated with CVD-related polypharmacy. Compared with females, males were slightly more likely to experience CVD-related polypharmacy (OR 1.11, 95% CI 1.02–1.20). Age was a strong predictor: individuals aged 36–50 years (OR 1.54, 95% CI 1.05–2.32), 51–65 years (OR 2.41, 95% CI 1.66–3.61), and ≥60 years (OR 4.75, 95% CI 3.29–7.07) were significantly more likely to be exposed to polypharmacy compared with those aged 18–35 years. Clinical comorbidities were also strongly associated with polypharmacy. The presence of diabetes was the most prominent predictor (OR 5.19, 95% CI 4.78–5.65), followed by CKD (OR 2.69, 95% CI 2.25–3.21) and hypertension (OR 1.91, 95% CI 1.75–2.07). Smoking status showed a marginal and non-significant association (OR 1.04, 95% CI 0.95–1.12) (Fig. 2).

Table 2.

Factors associated with CVD-related polypharmacy and factors associated with liver and kidney dysfunction

	Model I (crude polypharmacy)	Model II (adjusted polypharmacy)	Model III (crude kidney)	Model IV (adjusted kidney)	Model V (crude liver)	Model VI (adjusted liver)
CVD polypharmacy (no polypharmacy)			1.6 [1.45–1.76]*	1.67 [1.51–1.85]*	1.08 [0.98–1.18]	0.99 [0.9–1.09]
Intercept	0.11 [0.08–0.16]	0.06 [0.04–0.09]	0.13 [0.09–0.18]	0.16 [0.11–0.22]	0.68 [0.53–0.86]	0.65 [0.51–0.84]
Age groups, years (18–35)	1 [1–1]	1 [1–1]	1 [1–1]	1 [1–1]	1 [1–1]	1 [1–1]
36–50	1.76 [1.23–2.61]*	1.54 [1.05–2.32]*	3.54 [2.53–5.1]*	3.54 [2.53–5.1]*	0.49 [0.38–0.63]*	0.48 [0.38–0.62]*
51–59	2.75 [1.93–4.03]*	2.41 [1.66–3.61]*	7.39 [5.3–10.59]*	7.26 [5.2–10.41]*	0.41 [0.32–0.53]*	0.4 [0.31–0.51]*
60+	5.7 [4.04–8.32]*	4.75 [3.29–7.07]*	37.83 [27.14–54.15]*	36.1 [25.86–51.74]*	0.28 [0.22–0.35]*	0.27 [0.21–0.34]*
Sex (females)	1.24 [1.15–1.34]*	1.11 [1.02–1.2]*	1.18 [1.09–1.29]*	1.19 [1.1–1.3]*
Smoking (no smoking)		1.04 [0.95–1.12]		0.91 [0.84–0.99]*		0.95 [0.87–1.03]
Diabetes (no diabetes)		5.19 [4.78–5.65]*		0.8 [0.73–0.89]*		1.19 [1.08–1.31]*
Hyperlipidaemia (no hyperlipidaemia)		0.57 [0.52–0.61]*		0.83 [0.76–0.91]*		0.98 [0.9–1.07]
Hypertension (no hypertension)		1.91 [1.75–2.07]*		1.04 [0.95–1.13]		1.11 [1.02–1.21]*
CKD (no CKD)		2.69 [2.25–3.21]*

Model I (crude polypharmacy): This model examined the association of CVD-related polypharmacy with sex and age. Model II (adjusted polypharmacy): This model examined the association between CVD-related polypharmacy and adjustments expanded to include age, sex, hyperlipidaemia, hypertension, diabetes and smoking status. Model III (crude kidney): This model investigated the association between CVD-related polypharmacy and liver dysfunction and was adjusted for age and sex. Model IV (adjusted kidney): This model investigated the association between CVD-related polypharmacy and liver dysfunction and was adjusted for age, sex, hyperlipidaemia, hypertension, diabetes and smoking status. Model V (crude liver): This model investigated the association between CVD-related polypharmacy and kidney dysfunction and was adjusted for age and sex. Model VI (adjusted liver): This model investigated the association between CVD-related polypharmacy and kidney dysfunction and was adjusted for age, sex, hyperlipidaemia, hypertension, diabetes and smoking status

CKD chronic kidney disease, CVD cardiovascular disease

*Statistically significant (p < 0.05)

Fig. 2.

Risk factors associated with CVD-related polypharmacy. a Crude multiple regression model adjusted for age groups and sex. b Adjusted multiple regression model adjusted for age group, sex and smoking status, hyperlipidaemia, hypertension, diabetes, and CKD). CKD chronic kidney disease, CVD cardiovascular disease. ^Age group 18–35 years is a reference group for age group

Factors Associated with Kidney and Liver Dysfunction

A multivariate analysis showed that CVD-related polypharmacy was strongly associated with kidney dysfunction (OR 1.67, 95% Cl 1.51–1.85). Compared with females, males were more likely to have kidney dysfunction (OR 1.19, 95% Cl 1.10–1.30). Compared with the 18–35 years age group, those in older age groups had a higher likelihood of having kidney dysfunction (age groups: 36–50 years: OR 3.54 [95% CI 2.53–5.10]; 51–59 years: OR 7.26 [95% CI 5.20–10.41]; 60 years and above: OR 36.1 [95% CI 25.86–51.74]). Compared with non-smokers, smokers (OR 0.91, 95% Cl 0.84–0.99) were less likely to have kidney dysfunction. Individuals who had been previously diagnosed with diabetes (OR 0.80, 95% Cl 0.73–0.89), hyperlipidaemia (OR 0.83, 95% Cl 0.76–0.91), or hypertension (OR 1.04, 95% Cl 0.95–1.13) were less likely to have kidney dysfunction compared with those who have not been previously diagnosed with diabetes, hyperlipidaemia or hypertension (Fig. 3).

Fig. 3.

Risk factors associated with kidney dysfunction. a Crude multiple regression model adjusted for polypharmacy, age group, sex. b Adjusted multiple regression model adjusted for age groups, sex, smoking, diabetes, hyperlipidaemia, hypertension for risk associated with kidney dysfunction. *Polypharmacy is defined as five or more drugs within a 365-day sliding window. ^Age group 18–35 years is a reference group for age group

A multiple logistic regression analysis showed that CVD-related polypharmacy was not statistically significantly associated with liver dysfunction (OR 0.99, 95% Cl 0.90–1.09). Compared with females, being a male was associated with having a liver dysfunction (OR 1.29, 95% Cl 1.19–1.41). Individuals with hypertension (OR 1.11, 95% CI 1.02–1.21) or with diabetes (OR 1.19, 95% Cl 1.08–1.31) were also associated with having liver dysfunction. Being a current smoker (OR 0.95, 95% Cl 0.87–1.03) or previously diagnosed with hyperlipidaemia (OR 0.98, 95% Cl 0.90–1.07) did not seem to be risk factors associated with liver dysfunction compared with individuals who were not smokers, or had not been previously diagnosed with hyperlipidaemia.

Sensitivity analyses showed that the ALT/AST ratio was strongly associated with hepatocellular damage, although its discriminatory performance was attenuated in patients with polypharmacy, and it did not outperform ALT alone. These additional analyses, including ROC findings and optimal cut-off thresholds, are provided in Supplementary Material 4 (see ESM) (Fig. 4).

Fig. 4.

a Crude multiple regression liver dysfunction model adjusting for age group, and sex. b Adjusted multiple regression model adjusted for age groups, sex, smoking, diabetes, hyperlipidaemia, hypertension for risk associated with kidney dysfunction. *Polypharmacy is defined as five or more drugs within a 365-day sliding window. ^Age group 18–35 years is a reference group for age group

Discussion

This study utilised a large, contemporary primary care dataset to examine both the predictors of CVD-related polypharmacy and its associations with liver and kidney function. Older age and clinical diagnoses such as diabetes, hypertension, and CKD were strongly associated with CVD-related polypharmacy, whilst a diagnosis of hyperlipidaemia was less likely associated with CVD-related polypharmacy. Additionally, CVD-related polypharmacy was significantly associated with kidney dysfunction. Our findings demonstrate a strong association between increasing age, particularly in individuals aged 60 years and older, and CVD-related polypharmacy. A previous review by the European Society of Cardiology Working Group confirms and further indicates that medication management in this population is complicated by age-related physiological changes that affect drug pharmacokinetics and pharmacodynamics, including alterations in drug absorption and metabolism [24]. These changes increase susceptibility to drug–drug and drug–disease interactions, raising the risk of adverse effects, reduced medication adherence, and poorer clinical outcomes, including increased morbidity and mortality [24]. Together, these findings reinforce older age as a key determinant of polypharmacy and underscore the need for more targeted and cautious prescribing practices in older adults, including regular medication review and careful consideration of potential interactions in cardiovascular care.

CVD risk factors, including diabetes and hypertension, were also significant predictors of polypharmacy. Type 2 diabetes is a well-established driver of polypharmacy due to its complex management and frequent comorbidities, including microvascular and macrovascular complications [25]. Similarly, patients with hypertension often present with overlapping conditions such as obesity, and CKD, necessitating multiple medications [26]. Regular monitoring of these patients is critical to mitigate the risks of polypharmacy.

An inverse association between hyperlipidaemia and polypharmacy was observed, though interpretation should remain cautious. LLT is not entirely limited to hyperlipidaemia and is frequently prescribed for cardiovascular prevention in patients with greater multimorbidity [27, 28], which may explain the higher likelihood of polypharmacy among those without a recorded diagnosis. This finding highlights that reliance on diagnostic coding alone may underestimate patient complexity, and that medication exposure may provide a more accurate indicator of polypharmacy risk in CVD care. We observed a robust association between CVD-related polypharmacy and kidney dysfunction, independent of CKD diagnosis. This aligns with evidence describing the bidirectional relationship between the cardiovascular and renal systems, where dysfunction in one organ can adversely affect the other. This reflects a broader interplay between ageing, CVD progression, and renal physiology. In ageing populations, increasing CVD risk is accompanied by multimorbidity, treatment intensification, and declining renal function, all of which may compound vulnerability to kidney impairment. In this context, exposure to multiple CVD medications may further contribute through haemodynamic effects, drug interactions, and cumulative toxicity, as supported by a prior study [29]. Together with our findings, this may suggest that kidney dysfunction may reflect not only disease progression but also treatment burden, underscoring the importance of ongoing renal monitoring and more integrated, patient-centred prescribing in complex patients. Although no statistically significant association was found between CVD-related polypharmacy and liver dysfunction, this does not exclude a potential biological relationship. Polypharmacy has been associated with drug-induced liver injury, particularly in the context of long-term exposure to multiple medications and altered hepatic metabolism in older adults [19]. The absence of such results in this study may reflect limitations in the dataset, including lack of information on alcohol use, dietary factors, and other hepatotoxic exposures. Further research is needed to clarify these relationships and their clinical relevance.

This study has several strengths. The use of a large dataset representing approximately 12% of Australian primary care practices, and the ability to classify CVD-related medications using ATC codes, are key strengths of this study. Linking prescription data with clinical diagnoses and biomarker results enabled a comprehensive assessment of polypharmacy and its potential impact on organ function.

However, there are limitations. The prevalence of polypharmacy may be underestimated due to the exclusion of over-the-counter and complementary medicines, and reliance on GP prescribing data, which may not capture all medication use. Inherent limitations of real-world data, particularly EMR, may also introduce misclassification and missing data. As biomarkers were not routinely captured across all patients, summary measures (for example, median values) were used, which may reduce precision and limit interpretability. Incomplete or inconsistently recorded clinical information further reflects the constraints of EMR-based research. Additionally, medication records may be incomplete due to care fragmentation, including the use of multiple providers or “doctor shopping,” and the use of different prescribing systems. As this study is based on primary care EMR data within the Australian context, it does not capture prescribing or clinical events occurring in hospital settings, and therefore may not fully reflect the overall complexity of patient care. Additionally, the retrospective cross-sectional design limits causal inference. Future longitudinal studies are needed to explore the temporal relationship between polypharmacy and organ dysfunction.

Despite these limitations, we identified significant associations between increasing age, diabetes, hypertension, CKD, hyperlipidaemia, and CVD-related polypharmacy, as well as a consistent link between polypharmacy and kidney dysfunction. These findings suggest that polypharmacy, particularly in older adults, may reflect not only disease burden but also accumulated physiological vulnerability, where ageing, increasing cardiovascular risk, and treatment intensity converge to amplify the risk of renal impairment.

Rather than viewing polypharmacy solely as a consequence of multimorbidity, our results support a more integrated perspective in which medication burden itself may act as a contributor to organ dysfunction. This highlights the importance of shifting from single-disease management to a systems-based, patient-centred approach, where prescribing decisions are made within the broader context of ageing physiology, multimorbidity, and competing clinical priorities. Clinically, this underscores the need for proactive and interdisciplinary medication management, particularly for older adults at highest risk. Regular medication review, deprescribing, careful monitoring of renal function, and structured risk–benefit assessment should be prioritised, ideally within multidisciplinary care models [30]. Future research should focus on identifying patients most vulnerable to treatment-related harm and developing strategies that optimise therapeutic benefit while minimising cumulative medication burden.

Conclusion

CVD-related polypharmacy is independently associated with kidney dysfunction, indicating that treatment burden may contribute to adverse outcomes beyond disease alone. Routine medication review, renal monitoring, and targeted deprescribing are particularly important in older adults and those with diabetes, CKD and hypertension.

Supplementary Information

Below is the link to the electronic supplementary material.

Funding

Open Access funding enabled and organized by CAUL and its Member Institutions. No funding was available for this study. This project was part of students undertaking research placement/practicum in the Master of Public Health program.

Declarations

Conflict of interest

Caroline Trin, Harvey Jia Wei Koh, Zhomart Orman, Dianna J. Magliano, Ella Zomer, Zanfina Ademi, and Stella Talic declare no competing interests. Stella Talic is an Editorial Board member of Drugs & Aging. Stella Talic was not involved in the selection of peer reviewers for the manuscript nor any of the subsequent editorial decisions.

Ethics approval

The study was approved by the Monash University Human Research Ethics Committee (Project ID 27229). Data utilised in the study was completely de-identified and managed in compliance with the ethical principles outlined in the Declaration of Helsinki.

Informed consent

Consent was not required because data were de-identified.

Data availability

The data are not publicly available due to confidentiality, but may be made available from the corresponding author on reasonable request.

Consent to publish

Not applicable.

Code availability

Not applicable.

Author contributions

Conceptualisation: Caroline Trin, Stella Talic; Methodology: Caroline Trin, Stella Talic, Harvey Jia Wei Koh; Formal analysis and investigation: Caroline Trin, Stella Talic, Harvey Jia Wei Koh; Writing original draft: Caroline Trin; Writing review and editing: Caroline Trin, Stella Talic, Harvey Jia Wei Koh, Zhomart Orman, Dianna J. Magliano, Ella Zomer, Zanfina Ademi; Supervision: Stella Talic; Funding acquisition: Stella Talic. All authors made substantial contributions to the conception or design of the work, or the acquisition, analysis, or interpretation of data. All authors were involved in drafting the work or revising it critically for important intellectual content, approved the final version to be published, and agree to be accountable for all aspects of the work to ensure its integrity.