Addressing Dyslipidaemia in Advanced CKD: Insights from a Secondary Care Cohort

Abstract

Background: Patients with chronic kidney disease (CKD) face an elevated risk of cardiovascular disease (CVD), particularly those with estimated glomerular filtration rate (eGFR) <30 mL/min/1.73 m². Aims: To assess low-density lipoprotein cholesterol (LDL-C) values and the proportion of pre-dialysis patients achieving national and international targets. Methods: This was a retrospective audit (May–October 2024) of 272 patients aged >18 years attending pre-dialysis clinic (estimated glomerular filtration rate <30 mL/min/1.73 m²) at the Renal Unit, Birmingham Heartlands Hospital. Data on age, sex, ethnicity, body mass index, smoking status, CVD status, hypertension, diabetes, lipids (including LDL-C using the Friedewald and Sampson algorithms) and lipid-lowering therapy were collected from the hospital electronic records. Statistical analyses evaluated factors that were associated with LDL-C (linear/multiple regression) and statin therapy (Chi square). Results: The median (interquartile range) calculated LDL-C values were 2.2 (1.7–2.8) mmol/L and 2.3 (1.7–2.9) mmol/L using the Friedewald and Sampson algorithms respectively. Age and statin therapy were independently associated with LDL-C. Using the Friedewald algorithm, 83.8%, 70.6% and 60.3% did not achieve LDL-C targets of 1.4 mmol/L, 1.8 mmol/L and 2.0 mmol/L respectively, these figures were higher when the Sampson algorithm was applied. Only 18 and 3 of the patients were on ezetimibe and inclisiran respectively, whilst not a single patient was on bempedoic acid or proprotein convertase subtilisin/kexin type 9 inhibitors. Conclusion: Our data highlight deficiencies in the management of LDL-C in advanced CKD. We would recommend greater awareness of LDL-C targets and the use of combination lipid-lowering therapy following optimisation of statin therapy.

1. Introduction

Cardiovascular risk (CVD) remains disproportionately high in patients suffering from chronic kidney disease (CKD), with traditional and non-traditional risk factors such as dyslipidaemia, hypertension and vascular calcification playing contributory roles [1,2]. The lipid hypothesis is based on randomised controlled trials demonstrating associations between LDL-C reduction and decrease in CVD [3,4,5].

Current lipid-lowering therapy (LLT) options for non-dialysis CKD patients include statins, either as monotherapy or in combination with other LDL-C reducing agents such as ezetimibe, proprotein convertase subtilisin/kexin type 9 (PCSK9) inhibitors, bempedoic acid and inclisiran [6,7,8]. Considerable variation exists in the recommended LDL-C targets for patients with advanced renal disease. The 2019 European Society of Cardiology/European Atherosclerosis Society (ESC/EAS) guidelines classify patients with an estimated glomerular filtration rate (eGFR) below 30 mL/min/1.73 m² as being at very high risk of CVD [9], while the Joint British Societies’ Consensus guideline in 2014 recommended a more moderate LDL-C target of <1.8 mmol/L [10]. The National Institute for Health and Care Excellence (NICE) guidelines, issued in 2023, set an LDL-C goal of <2.0 mmol/L for high-risk patients, perhaps reflecting a pragmatic approach weighing achievable risk reduction against practical treatment considerations [11]. This variability in LDL-C targets can add confusion to clinical decision-making; the local guidelines drawn by the dyslipidaemia clinics at University Hospitals Birmingham (including Queen Elizabeth Hospital, Birmingham Heartlands Hospital, Good Hope Hospital and Solihull Hospital) in January 2022 issued a LDL-C target of <1.8 mmol/L in secondary prevention, with the ESC/EAS LDL-C target of <1.4 mmol/L but acknowledged this as an aspirational target (as it was not mandated by a UK healthcare body) that healthcare professionals were encouraged to adopt.

The Clinical Biochemistry laboratories at the University Hospitals Birmingham currently calculate LDL-C using the Friedewald equation and plan to replace it with the Sampson equation shortly [12,13,14]. The Sampson algorithm appears to lead to more accurate calculated LDL-C in patients with low LDL-C as well as those with higher triglycerides (TG) [13,14]. It appears comparable to the Martin–Hopkins algorithm, but has the advantage of being based on the reference method, hence the reason for the planned switch of LDL-C calculation.

In this study, we aimed to assess the distribution of calculated LDL-C values (using both the Friedewald and Sampson algorithms) in CKD patients with eGFR <30 mL/min/1.73 m² attending the pre-dialysis clinic at the Birmingham Heartlands Hospital in the entire group as well as in subgroups stratified by CVD status (primary/secondary prevention) and LLT. The proportion of patients not achieving the above-mentioned LDL-C targets were estimated in the total cohort and each of the subgroups.

2. Methods

2.1. Audit Design

This retrospective audit was carried out between May–October 2024 using data from 272 patients attending the pre-dialysis chronic kidney disease clinic at the Renal Unit, Birmingham Heartlands Hospital, serving a population of around 1.14 million individuals in Birmingham and West Midlands, the United Kingdom (UK). All patients were aged over 18 years with an eGFR <30 mL/min/1.73 m². Demographic, clinical, treatment and biochemistry data from all 272 active patient records on the clinic’s secure electronic database were obtained and an anonymised secondary database was created. Data extraction was conducted from the Patient Information and Communications System (PICS) and Portal, which serve as the electronic medical record platforms for outpatient and inpatient documentation at Birmingham Heartlands Hospital. Approval for this clinical audit was obtained from University Hospitals Birmingham NHS Foundation Trust (Ref: CARMS-22516).

The downloaded variables included age, sex, ethnicity, body mass index (BMI), primary cause of CKD, hypertension (persistent elevation of blood pressure, with a systolic blood pressure (SBP) of 140 mmHg or greater and/or a diastolic blood pressure (DBP) of 90 mmHg or greater, based on multiple readings over time), diabetes status, prior CVD (primary and secondary with further details in the case of the latter), current LLT use (e.g., statin monotherapy, combination therapy with ezetimibe, PCSK9 inhibitors, bempedoic acid, inclisiran). Data regarding statin intolerance or contraindication were not available.

Table 1. Demographic data collected on the 272 pre-dialysis patients assessed in this study.

	Median (IQR)	n (%)
Total Cohort		272
Age (years)	72 (60–79)
eGFR (mL/min/m2)	16 (13–19)
BMI (kg/m2)	29.6 (25.4–34.0)
Sex
Male		159 (58.5%)
Female		113 (41.5%)
Smoking status
Non-smokers		140 (51.5%)
Ex-smokers		35 (12.9%)
Current smokers		54 (19.9%)
Unknown		43 (15.8%)
Ethnicity
White Caucasian		166 (61.0%)
South Asian		86 (31.6%)
Afro-Caribbean		16 (5.9%)
Other		4 (1.5%)
Hypertension
No		64 (23.5%)
Yes		208 (76.5%)
Diabetes
Non-diabetic		119 (43.8%)
Type 1 diabetes		12 (4.4%)
Type 2 diabetes		141 (51.8%)
CVD
Primary prevention		178 (65.4%)
Secondary prevention		94 (34.6%)

Abbreviations: IQR: interquartile range, eGFR: estimated glomerular filtration rate, BMI: body mass index, CVD: cardiovascular disease.

provides some characteristics of the 272 patients analysed in this study. Data were not available in 43 (15.8%) and 4 (1.5%) patients regarding smoking status and ethnicity. The most recent lipid parameters (total cholesterol (TC), high-density lipoprotein cholesterol (HDL-C) and triglycerides (TG) were retrieved from PICS, where LDL-C values had been calculated using the Friedewald equation for LDL-C in patients with triglyceride levels <4.5 mmol/L; this will be referred to as LDL-C-F [12].

For each patient, we also calculated the LDL-C levels using the Sampson method which has an extended TG window of <9.0 mmol/L; this will be termed LDL-C-S [13]. TC, HDL-C and TG concentrations were measured on the Abbott Alinity c system utilising the supplied kit reagents. The analytical performance of the Abbott Alinity c system has previously been extensively evaluated and reported [15,16].

2.2. Statistical Analysis

TC, HDL-C, TG, LDL-C-F and LDL-C-S values did not demonstrate normal distribution (skewness kurtosis test, p < 0.05). Hence, all lipids in this manuscript are reported as median and interquartile range (IQR) values. Much of this audit’s results are descriptive as we established the proportions in our cohort did not meet the established LDL-C targets, based on ESC/EAS, Joint British Societies’ Consensus and NICE guidelines, to assess adherence. In accordance with the publication by Schmidt and Finan, linear/multiple regression analyses were used to identify whether significant differences existed in the LDL-C-F and LDL-C-S distributions (dependent variables) and age, sex, hypertension, CVD status, statin treatment (we did not include ezetimibe therapy as the number of patients (n = 18) were low), BMI, eGFR, ethnicity, diabetes status and smoking status (discrete variables were factorised with one category compared as reference) as independent variables [17].

All significant (on linear regression) independent variables were then entered into a single multiple regression model to check for confounding variables. Associations between the significant factors were checked using either linear regression or Chi square. Chi square was also carried out to establish associations between statin use and achievement of LDL-C targets. All statistical analyses were performed using Stata version 14 (College Station, TX, USA).

3. Results

LDL-C Values in the Total Cohort

Table 2. Lipid values in the total cohort and subgroups stratified by CVD status and treatment. LDL-C-F and LDL-C-S data are both presented.

		Median (IQR)
	n	TC (mmol/L)	TG (mmol/L)	LDL-C-F (mmol/L)	LDL-C-S (mmol/L)
Total Cohort	272	4.2 (3.5–4.9)	1.6 (1.1–2.4)	2.2 (1.7–2.8)	2.3 (1.7–2.9)
Primary Prevention	178	4.3 (3.6–5.1)	1.6 (1.1–2.3)	2.3 (1.8–3.0)	2.3 (1.9–3.1)
Secondary Prevention	94	3.9 (3.3–4.7)	1.5 (1.1–2.5)	2.0 (1.6–2.5)	2.1 (1.6–2.6)
Primary Prevention
On statins	112	4.2 (3.5–5.0)	1.7 (1.3–2.5)	2.1 (1.7–2.6)	2.2 (1.7–2.7)
Not on statins	66	4.5 (4.0–5.3)	1.5 (1.0–2.2)	2.6 (2.2–3.4)	2.7 (2.3–3.5)
Secondary Prevention
On statins	78	3.7 (3.2–4.5)	1.5 (1.1–2.6)	1.9 (1.4–2.4)	2.1 (1.5–2.5)
Not on statins	16	4.6 (3.6–5.6)	1.6 (1.3–2.0)	2.4 (1.8–3.3)	2.5 (2.0–3.4)
Primary Prevention
On statin monotherapy	105	4.1 (3.5–4.8)	1.7 (1.3–2.5)	2.1 (1.7–2.5)	2.2 (1.7–2.6)
On statins & Ezetimibe	7	5.4 (3.5–5.8)	1.9 (1.1–2.1)	2.9 (1.4–3.6)	3.3 (1.5–3.7)
Ezetimibe monotherapy	4	4.7 (4.5–5.1)	2.0 (1.8–2.1)	2.5 (2.4–3.1)	2.6 (2.5–3.2)
Not on statins or ezetimibe	62	4.5 (4.0–5.3)	1.4 (0.9–2.2)	2.5 (2.1–3.4)	2.7 (2.2–3.5)
Secondary Prevention
On statin monotherapy	73	3.7 (3.3–4.5)	1.5 (1.2–2.6)	2.0 (1.6–2.4)	2.1 (1.6–2.5)
On statins & Ezetimibe	5	2.8 (2.5–4.7)	1.3 (1.1–2.2)	1.2 (1.1–2.9)	1.2 (1.1–3.0)
Ezetimibe monotherapy	2	6.4 (5.8–6.9)	3.0 (1.6–4.4)	3.8 (3.6–4.0)	3.9 (3.7–4.1)
Not on statins or ezetimibe	14	4.3 (3.5–4.9)	1.5 (1.1–1.9)	2.2 (1.6–3.0)	2.3 (1.9–3.1)

provides the median (IQR) lipid values in the total cohort and subgroups stratified by CVD status and LLT. There was a good correlation on linear regression analysis between LDL-C-F (independent variable) and LDL-C-S (dependent variable) in the total cohort; coefficient: 1.03 (95% confidence interval: 1.02–1.04, p < 0.001, R² = 1.00). LDL-C-F was the main outcome used in this retrospective audit, whilst we included LDL-C-S in the data, as that would lead to further challenges in view of the slight positive bias. The median total cholesterol in the total cohort was 4.2 (3.5–4.9) mmol/L, while the median triglyceride level was 1.6 (1.1–2.35) mmol/L. The median LDL-C in the total cohort was 2.2 (1.7–2.8) mmol/L and 2.3 (1.7–2.9) mmol/L for LDL-C-F and LDL-C-S respectively. Statins were prescribed in 190 (69.9%) and ezetimibe in 18 (6.6%) of the patients. Importantly, only three individuals were on inclisiran (a patient each on inclisiran monotherapy, inclisiran/ezetimibe and inclisiran/statin/ezetimibe), but none on bempedoic acid or PCSK9 inhibitors. The data are now presented in two sections to (1) identify factors (especially modifiable) associated with LDL-C and (2) evaluate the proportion of patients not meeting the various targets.

(1)

Factors Associated with LDL-C Values

LDL-C (both LDL-C-F and LDL-C-S) was significantly associated with age (inversely), sex (males had lower LDL-C than females), diabetes (patients with type 1 diabetes and type 2 diabetes had higher and lower LDL-C values respectively compared to patients without diabetes), secondary prevention (lower LDL-C than primary prevention) and statin therapy (lower LDL-C compared to patients not on statins). Ethnicity, hypertension, smoking status and eGFR were not associated with LDL-C-F or LDL-C-S levels. When all the significant factors were entered into a single multiple regression model, only statin therapy and age (type 2 diabetes and secondary prevention were not significant in the multifactorial model) remained significantly and independently associated (inversely) with LDL-C-F and LDL-C-S.

Statin therapy appeared to be the only modifiable factor associated with LDL-C and was significantly greater in secondary prevention (83.0%, p = 0.001, Chi square) compared to primary prevention (65.4%) and type 2 diabetes (82.3%, p < 0.001, Chi square) compared to type 1 diabetes (58.3%) and patients without diabetes (56.3%); thus, both these may be considered confounders. Interestingly, age (linear regression with age as the dependent variable) or sex (Chi square) were not associated with statin therapy.

(2)

Evaluating the Proportion of Patients Not Meeting the Various LDL-C Targets

The principal aim of this audit was to estimate the proportion of patients with LDL-C not achieving target.

Table 3. Proportion of patients not meeting the various LDL-C targets in the total cohort and subgroups stratified by CVD status and treatment. Both LDL-C-F and LDL-C-S data are presented.

	Proportion of Patients Not Meeting the Various LDL-C Thresholds (%)
	LDL-C-F > 1.4 mmol/L	LDL-C-F > 1.8 mmol/L	LDL-C-F > 2.0 mmol/L)	LDL-C-S > 1.4 mmol/L	LDL-C-S > 1.8 mmol/L	LDL-C-S > 2.0 mmol/L)
Total Cohort	228 (83.8%)	192 (70.6%)	164 (60.3%)	242 (89.0%)	199 (73.2%)	177 (65.1%)
Primary Prevention	156 (87.6%)	132 (74.2%)	116 (65.2%)	165 (92.7%)	136 (76.4%)	125 (70.2%)
Secondary Prevention	72 (76.6%)	60 (63.8%)	48 (51.1%)	77 (81.9%)	63 (67.0%)	52 (55.3%)
Primary Prevention
On statins	96 (85.7%)	78 (69.6%)	64 (57.1%)	103 (92.0%)	82 (73.2%)	71 (63.4%)
Not on statins	60 (90.9%)	54 (81.8%)	52 (78.8%)	62 (93.9%)	54 (81.8%)	54 (81.8%)
Secondary Prevention
On statins	58 (74.3%)	48 (61.5%)	37 (47.4%)	63 (80.8%)	50 (64.1%)	40 (51.3%)
Not on statins	14 (87.5%)	12 (75.0%)	11 (68.8%)	14 (87.5%)	13 (81.3%)	12 (75.0%)
Primary Prevention
On statin monotherapy	91 (86.7%)	73 (69.5%)	59 (56.2%)	97 (92.4%)	77 (73.3%)	66 (62.9%)
On statins & Ezetimibe	5 (71.4%)	5 (71.4%)	5 (71.4%)	6 (85.7%)	5 (71.4%)	5 (71.4%)
Ezetimibe monotherapy	4 (100%)	4 (100%)	4 (100%)	4 (100%)	4 (100%)	4 (100%)
Not on statins or ezetimibe	56 (90.3%)	50 (80.7%)	48 (77.4%)	58 (93.6%)	50 (80.7%)	50 (80.7%)
Secondary Prevention
On statin monotherapy	56 (76.7%)	46 (63.0%)	35 (48.0%)	61 (83.6%)	48 (65.8%)	38 (52.1%)
On statins & Ezetimibe	2 (40.0%)	2 (40.0%)	2 (40.0%)	2 (40.0%)	2 (40.0%)	2 (40.0%)
Ezetimibe monotherapy	2 (100%)	2 (100%)	2 (100%)	2 (100%)	2 (100%)	2 (100%)
Not on statins or ezetimibe	12 (85.7%)	10 (71.4%)	9 (64.3%)	12 (85.7%)	10 (71.4%)	10 (71.4%)

provides these data for various LDL-C levels (1.4 mmol/L, 1.8 mmol/L and 2.0 mmol/L). LDL-C-F data were presented, as it indicates the current state of lipid-lowering in the study cohort. Additionally, we presented LDL-C-S data, as the laboratory was moving to the Sampson LDL-C algorithm. Table 3 demonstrates the proportion of individuals not meeting target LDL-C levels was marginally higher when LDL-C-S was considered; this was expected in view of the previously demonstrated positive bias (Table 3).

Regardless of the LDL-C target, more than 50% of the patients with established CVD (secondary prevention) did not achieve it (Table 3 LDL-C-F). Statin therapy (using LDL-C-F values) appeared to lead to significantly greater (Chi square) achievement of the LDL-C targets of 1.8 mmol/L (p = 0.019) and 2.0 mmol/L (p < 0.001) in the total cohort, but not when the 1.4 mmol/L target (p = 0.31) was considered. As only 18 of the 272 patients were on ezetimibe (Table 2), we refrained from carrying out statistical analyses in this group or when combined with a statin. The three patients on inclisiran had the following LDL-C-F values: 0.8 mmol/L, 3.05 mmol/L and 3.63 mmol/L; thus, only a single patient achieving secondary prevention targets.

4. Discussion

The aim of this study was to evaluate the LDL-C distribution, use of LLT and achievement of LDL-C targets in the cohort attending the pre-dialysis clinic at Birmingham Heartland Hospital. LDL-C lowering is a cornerstone of CVD prevention and essential in this high-risk cohort with eGFR <30 mL/min/m².

A meta-analysis/systematic review of 26 randomised controlled trials suggested that a 1 mmol/L reduction in LDL-C following statin therapy was associated with a 22% reduction in CVD (rate ratio: 0.78, 95% confidence interval: 0.76−0.80, p < 0.0001) [3]. In patients with eGFR values <60 mL/min/1.73 m², the relative risk reduction was similar (rate ratio: 0.77, 95% confidence interval: 0.72−0.83) [3]. The results of our work emphasised critical gaps in the management of dyslipidaemia in patients with advanced CKD. Despite their high risk for cardiovascular complications, a significant proportion of patients in both primary and secondary prevention groups failed to achieve LDL-C targets, particularly the <1.4 mmol/L and <1.8 mmol/L thresholds established by international guidelines [9,10]. Given the importance of LDL-C reduction in mitigating CVD risk, these findings suggest an urgent need to understand the possible reasons for the inadequate lipid lowering we have observed and then propose suggestions for improving performance.

One notable finding is the underutilization of combination lipid-lowering therapies, such as ezetimibe and bempedoic acid, in our CKD patient cohort. These therapies have demonstrated safety and efficacy in patients with renal impairment and are specifically recommended in cases where statin monotherapy does not achieve target LDL-C levels [18,19,20,21]. Importantly, these agents have been available in our secondary care centre for a long period of time: ezetimibe (since 2002), bempedoic acid (since 2020), PCSK9 inhibitors (since 2016) and inclisiran (since 2021). However, in our study population, combination therapies were infrequently prescribed, suggesting a need for greater clinician awareness and adherence to current guidelines, which endorse combination LLT as a viable option for achieving LDL-C goals [22]. Remarkably, no patients were on bempedoic acid, a newer LLT with demonstrated benefits, which is prescribed in patients intolerant to statins or those unable to reach LDL-C targets on statin monotherapy [7]. Recently, a real-world study of bempedoic acid was led by our centre and demonstrated LDL-C efficacy comparable with other efficacy-determining studies [23].

Furthermore, a considerable number of CKD patients in the primary prevention group may have undiagnosed subclinical atherosclerosis. In these cases, coronary artery calcium (CAC) scoring could serve as a valuable tool for reclassifying risk and guiding treatment. CAC scoring has been shown to identify higher risk individuals effectively, warranting more intensive lipid management strategies. This aligns with current recommendations advocating for more personalised cardiovascular risk assessments in CKD, where traditional risk scores may underestimate atherosclerotic burden [24]. Inclisiran, a novel RNA interference therapy that significantly reduces LDL-C with biannual dosing, offers another promising avenue for improved lipid management in CKD. The biannual dosing regimen may be especially beneficial for CKD patients who face challenges with daily oral medication adherence. Despite 24 eligible patients in our cohort, only 3 were receiving inclisiran, pointing to an opportunity for wider utilisation of this novel therapy [8]. Expanding the use of inclisiran use could potentially improve adherence and reduce CVD risk over time in CKD patients.

Our study also compared LDL-C calculations using the Friedewald and Sampson equations, with Sampson's method revealing slightly higher LDL-C levels. Evidence suggests that the Sampson formula may provide a more accurate LDL-C estimation, particularly in CKD patients with elevated triglycerides, where traditional calculations often yield underestimates [13,14]. Utilising the Sampson equation in clinical practice could therefore lead to more accurate lipid-lowering strategies and help guide clinicians in optimising LDL-C management in CKD.

Finally, differences among international guidelines on lipid management in CKD present further complexities in clinical practice. The ESC/EAS guidelines advocate for the most stringent LDL-C targets for high-risk populations, including CKD patients, compared to the Joint British Societies and NICE guidelines [9,10,11]. Adoption of these lower targets could potentially improve cardiovascular outcomes, as CKD patients have a heightened susceptibility to atherosclerotic cardiovascular disease. Tailoring treatment based on these stricter LDL-C targets as suggested by the ESC/EAS in 2019 [9] may enhance clinical outcomes and provide better long-term protection against cardiovascular events in CKD patients.

This was a retrospective audit and not a hypothesis-led prospective study, which could have investigated the clinical outcomes and mechanisms leading to benefits. Hence, the findings are restricted to the effectiveness of the care provided. All the patients in this audit were pre-dialysis and this patient cohort was selected as opposed to patients on dialysis, as robust evidence regarding outcome benefit(s) in the latter group is not evident [25]. Hence, it was reasonable to evaluate the use of LLT and attainment of evidence-based targets in these patients. LLT is prescribed in the Dyslipidaemia Clinics and the Renal Unit based at the University Hospitals Birmingham in patients with CKD, but not initiated (but continued when already on LLT) in those on dialysis, in accordance with the national guidelines and evidence [10,11,25]. In addition to statins and ezetimibe that show CVD benefit, we also consider use of PCSK9 inhibitors, bempedoic acid and inclisiran in non-dialysis CKD patients, based on their safety data [26,27,28,29].

One of the strengths of this study is its focus on a high-risk population with advanced CKD, providing valuable insights into LDL-C management in this group. The use of both Friedewald and Sampson equations for LDL-C calculation allows for a comparative analysis that could inform future practice, especially considering the potential inaccuracies associated with traditional methods in patients with high triglycerides. Another strength is the subgroup analysis based on CVD status and LLT, which helps in understanding the effectiveness of current lipid management strategies across different patient profiles.

However, the study also has several limitations. Its applicability was restricted to the cohort of patients in a large secondary care renal unit in the UK. However, the poor achievement of LDL-C target should encourage similar units to evaluate their lipid management. The retrospective audit design limits the ability to establish causation between LDL-C management and patient outcomes. Furthermore, the absence of data on statin intolerance or contraindications limits the interpretation of lipid-lowering therapy utilisation. Although we do not collect data on other factors affecting the LDL-C values, such as thyroid function, checking secondary causes of dyslipidaemia is part of the treatment pathway prior to LLT initiation. The study also lacks a prospective intervention component that could provide insights into improving adherence to LDL-C targets. Additionally, the small sample size of patients on newer therapies like inclisiran and PCSK9 inhibitors restricts the generalizability of findings related to these treatments. Although we collected data on the different statins and doses, the small sample size made it difficult to subgroup the cohort based on these factors.

Importantly, as it was a retrospective audit analysing a relatively small sample, we were unable to evaluate clinical outcomes such as CVD, renal function and hospitalisation. A large prospective longitudinal study with patients recruited at the point of LLT initiation would be required to determine associations between clinical CVD-related endpoints and achieved LDL-C values, and also CVD incidence in patients achieving/not achieving the various LDL-C targets. It would also have been interesting to compare the observed benefit against the expected benefit based on the Cholesterol Treatment Trialists’ Collaboration data for each of the drug groups [3,4]. This may provide a hint as to the presence of drug-specific pleiotropic effects. A recent in-vitro study on the erythrocyte deformability following incubation of the cells showed that statins increased erythrocyte deformability, with atorvastatin having a greater impact than rosuvastatin [30]. The authors speculated that this could potentially benefit microvascular blood flow. Microvascular dysfunction may be important in the progression of CKD and CVD comorbidities, and hence, any improvement in blood flow could have a positive impact [31].

5. Conclusions

In conclusion, this study highlights significant gaps in the management of dyslipidaemia in patients with advanced CKD, with a considerable proportion of patients not achieving recommended LDL-C targets. The underutilisation of combination lipid-lowering therapies, such as ezetimibe and bempedoic acid, suggests a need for increased clinician awareness and adherence to guideline-recommended treatments. The planned shift from the Friedewald to the Sampson equation for LDL-C calculation may improve accuracy, particularly in patients with elevated triglycerides, potentially leading to more effective lipid management. Overall, there is an urgent need to enhance lipid-lowering strategies and optimise treatment regimens to reduce cardiovascular risk in this vulnerable population.