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Diabetes complications worsen prognosis for colorectal cancer patients

Diabetes News


Complications of diabetes can have numerous negative health effects, from impaired vision and nerve damage to kidney dysfunction and heart disease. In an analysis of information on adults with colorectal cancer, patients who also had diabetes—particularly those with diabetic complications—faced a higher risk of dying early. The results are published by Wiley online in CANCER, a peer-reviewed journal of the American Cancer Society.

For the study, Kuo‐Liong Chien, MD, PhD, of National Taiwan University, and his colleagues examined data registered between 2007 and 2015 in the Taiwan Cancer Registry Database, which is linked to health insurance and death records. Their analysis included 59,202 individuals with stage I–III colorectal cancer who underwent potentially curative surgery to remove their tumors. Among these patients, 9,448 experienced a cancer recurrence and 21,031 died from any cause during the study period.

Compared with individuals without diabetes, those with uncomplicated diabetes were at a minimally or insignificantly higher risk of all‐cause and cancer‐specific death, whereas those with complicated diabetes had 85% higher odds of death from any cause and 41% higher odds of death from cancer. These associations were more pronounced in women and in patients with early‐stage colorectal cancer.

Also, compared to patients without diabetes, patients with uncomplicated or complicated diabetes had a 10–11% higher risk of colorectal cancer recurrence.

The mechanisms behind the relationship between diabetic severity and poor colorectal cancer prognosis could involve various pathways and responses triggered by high insulin and glucose levels in the blood, as well as elevated inflammatory states, which are characteristic of type 2 diabetes.

While a higher diabetes prevalence was noted in patients with colorectal cancer, the study suggests that coordinated medical care involving multiple specialists can help prevent diabetes complications, potentially improving long-term colorectal cancer oncological outcomes, particularly in women and patients with early-stage cancer.”

Kuo‐Liong Chien, MD, PhD, National Taiwan University

Source:

Journal reference:

Hsu, H. Y., et al. (2023) Diabetic severity and oncological outcomes of colorectal cancer following curative resection: A population-based cohort study in Taiwan diabetes and colorectal cancer prognosis. Cancer. doi.org/10.1002/cncr.34975.



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Prediction of Diabetic Kidney Disease in Newly Diagnosed Type 2 Diabet

Diabetes News


Introduction

The latest global diabetes mellitus (DM) map demonstrated by the International Diabetes Federation (IDF) showed that the incidence of DM patients (aged 20–79) would rise to 12.2% (783.2 million people) in 2045.1 China has the most DM currently, and the number is expected to exceed 174 million by 2045.1 Diabetic kidney disease (DKD), characterized by persistent microalbuminuria or decreased estimated glomerular filtration rate (eGFR), is one of the most significant microvascular complications of diabetes. DKD is now more common than primary glomerulonephritis and is the major cause of chronic kidney disease (CKD) in China, where it affects 40% of those with type 2 diabetes mellitus (T2DM) who are hospitalized.2–4

DKD is always asymptomatic until it develops to the advanced stage. Currently, the main methods for early diagnosis of DKD are urinary albumin creatinine ratio (UACR) and eGFR. However, on the one hand, these diagnostic methods have their limitations. UACR does not increase in the early stage (stages I and II) of DKD,5 meanwhile eGFR increases in the early stage.6 On the other hand, the increasing incidence of DKD is placing a burden on the healthcare system, making it essential to find an approach to screen DKD in the medical environment. Hence, early detection and treatment of DKD are critical.

Previous studies have demonstrated multiple clinically related factors of DKD, such as age, UACR, haemoglobin A1c (HbA1c) and high-density lipoprotein (HDL).7,8 Furthermore, related studies have shown that the development of DKD is strongly associated with insulin resistance (IR), which may accelerate the progression of DKD.9–11 Hyperinsulinemic euglycemic glucose clamp (HEGC), a significant method for evaluating IR, is expensive and difficult to operate.12 Additionally, the homeostasis model for insulin resistance (HOMA-IR) index is also a common tool to assess IR in clinical practice.13 However, serum insulin or C-peptide tests are not appropriate for all inpatients or outpatients owing to the impact of insulin treatment. Triglyceride-glucose index (TyG index), as well as triglyceride and fasting glucose products, demonstrates greater potential in evaluating IR.14,15 Previous clinical studies have demonstrated the performance of the TyG index in evaluating IR, and it was superior to the HOMA-IR index.10 In conclusion, these easily available clinical risk factors may be used to build an accurate prediction model of DKD development in individuals with newly diagnosed T2DM, facilitating DKD screening. In addition, we intended to use the least absolute shrinkage and selection operator (LASSO) regression to screen variables and construct a predictive model. LASSO regression, a compressive estimation method for selecting relevant features for various classes of data, helps deal with biased estimates with complex collinearity data.16 It can perform variable screening and complexity adjustment while fitting generalized linear models, and is widely applied in variable screening of various prediction models, including commonly used clinical variables and related omics data.17,18 LASSO regression has contributed to constructing some prediction models, such as tumors, metabolic diseases, kidney diseases, etc.19,20

In this study, we designed a long-term cohort trial in patients with newly diagnosed T2DM for more than 10 years, aiming to explore the risk factors related to DKD, building and validating the prediction model.

Materials and Methods

Study Population

This was a retrospective analysis on prospectively collected T2DM patients’ data from the Third Affiliated Hospital of Soochow University. We consecutively incorporated 863 newly diagnosed T2DM patients in our hospital from December 2010 to January 2014 for long-term follow-up.

Inclusion criteria: (1) meet the American Diabetes Association (ADA) classification criteria for T2DM and were newly diagnosed patients;14 (2) age > 18 years old.

Exclusion criteria: (1) a history of DKD or other kidney diseases; (2) UACR ≥ 30mg/g, eGFR < 60 mL/min/1.73m2 (CKD-EPI),21 or urine dipstick test ≥ 1 +; (3) presence of an acute infection; (4) presence of a malignancy or pregnancy; (5) incomplete clinical records.

The endpoint event was the occurrence of DKD,22 defined as (1) persistent albuminuria (UACR ≥ 30mg/g) over 3 months (2) eGFR < 60 mL/min/1.73 m2; (3) excluded other kidney diseases.

Participants were followed up until DKD was diagnosed for the first time. Otherwise, patients without DKD events were followed up until June 2022. Figure 1 shows the procession of subject inclusion.

Figure 1 Flow chart of participants.

Abbreviations: T2DM, type 2 diabetes mellitus; DKD, diabetic kidney disease.

Note: The flow chart shows the entire research process.

Data Collection

Baseline demographic data (sex and age) and related clinical data were from the electronic medical record system. The risk prediction model was trained using variables selected from medical record system and published literature,23,24 including age, sex, body mass index (BMI), history of smoking and hypertension, systolic blood pressure (SBP), fasting blood glucose (FBG), postprandial blood glucose (PBG), haemoglobin A1c (Hb1Ac), fasting C-peptide (FCP), white blood cells (WBC), hemoglobin (Hb), neutrophil to lymphocyte ratio (NLR), fibrinogen (FIB), D-dimer, alanine aminotransferase (ALT), aspartate aminotransferase (AST), serum albumin (ALB), bile cid (BA), bicarbonate, blood urea nitrogen (BUN), uric acid (UA), eGFR, total cholesterol (TC), triglyceride (TG), HDL, low density lipoprotein (LDL), apolipoprotein a1 (Apoa1), apolipoprotein b (Apob), UACR, TyG index, HOMA-IR, thyroid stimulating hormone (TSH), free triiodothyronine (FT3), free tetraiodothyronine (FT4), diagnosis of diabetic retinopathy (DR) and diabetic peripheral neuropathy (DPN), as well as antidiabetic and antihypertensive treatment. The superior indicator of the HOMA-IR and TyG index for predicting DKD was also determined by comparing the area under the curve (AUC).

Statistical Analysis

Statistical analyses were performed using SPSS software version 25.0, R software version 4.2.2 and GraphPad Prism software version 9.0.0. The median (interquartile range) [M (P25, P75)] was used to represent data with a non-normal distribution, whereas the mean ± standard deviation (SD) was used to express data with a quantitative normal distribution. The qualitative variable was selected as percentages (%). A random number table generated using R software was assigned to the training and validation set in a ratio of 7: 3. There was no statistical difference between the training and validation set for all variables, so the validation set can fully validate the prediction model based on the training set. We screened the variables and built the model based on the training set, and presented the model in a nomogram visualization. In addition, we utilized the validation set to internally validate the performance and clinical utility of the model constructed based on the training set. Spearman correlation was employed for correlation analysis. For further analysis, a nomogram was developed based on multivariate analysis. The logistic regression results were presented as odds ratio (OR), 95% confidence intervals (95% CI), and P values. To obtain the perfect prediction model, we used “glmnet” package for LASSO regression screening to to further filter the predictor variables derived from the traditional univariate logistic regression. The nomogram was drawn using the R package “regplot”, and receiver operating characteristic (ROC) analysis was performed to assess how well the nomogram predicted the risk of developing DKD. Use 1000 bootstrap replications for validation. Calibration curves were conducted using the “calibrate” R package. Draw a decision curve using the “rmda” R package. P < 0.05 was considered statistically significant.

Results

Baseline Characteristics

The final queue size was 521 persons after follow-up (Figure 1). The average follow-up time of the T2DM participants was 7.8 ± 1.8 years. There were 127 DKD events (24.4%) among the 521 individuals identified in our long-time follow-up cohort.

Table 1 presents the baseline demographic and medication of 521 T2DM patients, meanwhile, Table 2 presents the laboratory measurement indicators. The R package randomly divided the whole individuals into the training set and validation set in a ratio of 7: 3. 365 participants (70%) were randomly assigned to the training set and 156 participants (30%) to the validation set. In the training set, 90 patients (24.7%) progressed to DKD, while 37 patients (23.7%) progressed to DKD in the validation set. There was no significant difference in demographic and clinical characteristics between the training and the validation set (P > 0.05). However, variables of significance include DKD participants being on average older in age, having higher FCP, higher BMI, higher BA, higher TG, higher Apob, higher UACR, higher HOMA-IR, and TyG index, and having greater proportion with a history of smoking and hypertension, a higher incidence of DPN, and a higher utilization rate of some antidiabetic and antihypertensive drugs while having lower eGFR and HDL (Table 1 and Table 2). In addition, we also develop logistic and LASSO regression, aiming to identify the related factors with DKD.

Table 1 Demographic and Medication of Newly Diagnosed T2DM Patients

Table 2 Laboratory Measurement Indicators of Newly Diagnosed T2DM Patients

Comparison of TyG Index and HOMA-IR

To evaluate the performance of the HOMA-IR and TyG index in predicting DKD, we plotted AUC. HOMA-IR and TyG index are both tools to assess IR. We determined the AUCs for the TyG index and HOMA-IR in the training set, were 0.694 (95% CI 0.630 to 0.758) and 0.640 (95% CI 0.575 to 0.705), respectively (Figure 2). The findings were following those reported in other studies, which found that TyG index was more accurate than HOMA-IR at predicting the risk of IR and DM-related complications.25,26 In addition, multivariate analysis revealed that the HOMA-IR was no statistical significance after adjusting confounding variables (Table 3).

Table 3 Logistic Regression Analysis of DKD-Related Risk Factor

Figure 2 ROC curve of TyG index and HOMA-IR.

Abbreviations: ROC, receiver operating characteristic; AUC, area under the curve; TyG index, triglyceride-glucose index; HOMA-IR, homeostasis model assessment for insulin resistance.

Notes: ROC curve of TyG index and HOMA-IR drawn according to the training set. We calculated the AUC of the TyG index and HOMA-IR, which were 0.694 (95% CI 0.630 to 0.758) and 0.640 (95% CI 0.575 to 0.705), respectively.

LASSO Regression Analysis

To identify the independent significant factors, we used logistic regression and LASSO regression for analysis. We conducted a correlation analysis and univariate logistic regression (Figure 3 and Table 3). Correlation analysis found a strong collinearity correlation among some variables. LASSO regression, a greedy algorithm, can be used to select the most significant variables to avoid overfitting, then establish a more accurate linear regression model.27 According to previous reports and univariate analysis, we selected 37 variables related to DKD (age, gender, BMI, smoking, hypertension, SBP, HbA1c, FBG, PBG, ALB, BUN, UA, TC, TG, HDL, LDL, Apoa1, Apob, UACR, eGFR, FCP, FT4, TyG index, HOMA-IR, DR, DPN, antihypertensive and antidiabetic treatment) for LASSO analysis. Figure 4A shows the cross-validation curve result, and the coefficient distribution plot is shown in Figure 4B. Through 10-fold cross-validation, we chose the minimum value within 1 standard error and obtained 5 independent variables that were finally screened out (Table 4).

Table 4 LASSO Regression Analysis of DKD-Related Risk Factor

Figure 3 Clinical indicators correlation analysis.

Abbreviations: BMI, body mass index; SBP, systolic blood pressure; FBG, fasting blood glucose; PBG, postprandial blood glucose; HbA1c, haemoglobin A1c; FCP, fasting c-peptide; WBC, white blood cell; Hb, haemoglobin; NLR, neutrophil to lymphocyte ratio; FIB, fibrinogen; ALT, alanine aminotransferase; AST, aspartate aminotransferase; ALB, albumin; BA, bile acid; BUN, blood urea nitrogen; UA, uric acid; eGFR, estimated glomerular filtration rate; TC, total cholesterol; TG, triglyceride; HDL, high-density lipoprotein; LDL, low-density lipoprotein; Apoa1, apolipoprotein a1; Apob, apolipoprotein b; UACR, urinary albumin creatinine ratio; TyG index, triglyceride-glucose index; TSH, thyroid stimulating hormone; FT3, free triiodothyronine; FT4, free thyroxine; DR, diabetic retinopathy; DPN, diabetic peripheral neuropathy; CCB, calcium channel blocker; ACEI, angiotensin-converting enzyme inhibitor; ARB, angiotensin receptor blocker.

Notes: *P value < 0.05, **P value < 0.001, significant correlation between variables.

Figure 4 LASSO regression analysis.

Abbreviation: LASSO, less absolute shrinkage and selection operator.

Notes: (A) Cross-validation curve. (B) Coefficient distribution plot of 37 variables. The cross-validated error within one standard error of the minimum is shown on the right vertical line, while the minimum error is shown on the left vertical line.

Development of an Individualized Prediction Model

To display the prediction model vividly, we applied an individualized dynamic nomogram to visualize it (Figure 5). Then, we applied ROC to analyze the performance of the prediction model. Figure 6 shows the AUC of the prediction model. For the training set, the predicted AUC of the nomogram was 0.826 (95% CI 0.775 to 0.876), meanwhile, the AUC of the validation set was 0.803 (95% CI 0.719 to 0.887).

Figure 5 Dynamic nomogram developed for predicting DKD in patients with newly diagnosed T2DM.

Abbreviations: TG, triglyceride; eGFR, estimated glomerular filtration rate; UACR, urinary albumin creatinine ratio; TyG index, triglyceride-glucose index; DKD, diabetic kidney disease; T2DM, type 2 diabetes mellitus.

Notes: Dynamic nomogram can reflect the score of each variable of patients in actual situation, so as to obtain the corresponding DKD risk probability. The risk probability of DKD calculated for the first patient in the cohort by each variable score is shown in the figure above.

Figure 6 ROC curves of the predictive model.

Abbreviations: ROC, Receiver operating characteristic; AUC, area under the curve; V set, validation set; T set, training set.

Notes: For the training set, the predicted AUC of the nomogram was 0.826 (95% CI 0.775 to 0.876), meanwhile, the AUC of the validation set was 0.803 (95% CI 0.719 to 0.887).

Calibration of the Model

To validate the nomogram, we used 1000 bootstrap analysis and draw a calibration curve. The calibration curves of the model revealed a strong correlation between the predicted probability and the actual probability, indicating that our model had been properly calibrated (Figure 7).

Figure 7 Calibration curves of the nomogram.

Abbreviation: DKD, diabetic kidney disease.

Notes: (A) Calibration curve of the model in the training group. (B) Calibration curve of the model in the validation group. The calibration curve reflects the consistency between the predicted probability and the actual probability. Y-axis = actual probability of DKD. X-axis = predicted probability of DKD. The shadow line represents a perfect prediction by an ideal model. The red line represents the performance of our model, which coincides well with the shadow line.

Clinical Use of the Model

To prove the clinical practicability of the nomogram, we carried out a decision curve analysis (DCA). Figure 8 presents the DCA for the DKD nomogram. Between 20% and 40% of T2DM patients progressed to DKD,28 and within this cutoff, both decision curves were above the None and All lines. This demonstrated that predicting DKD in newly diagnosed T2DM using the nomogram model provides more clinical benefits than either treatment or no treatment for all patients.

Figure 8 DCA for the nomogram.

Abbreviations: DCA, decision curve analysis; DKD, diabetic kidney disease.

Notes: (A) Decision curve of the model in the training group. (B) Decision curve of the model in the validation group. The y-axis measures the net benefit. The solid red line represents the nomogram. The solid gray line represents the assumption that all patients have DKD. The solid black line represents the assumption that no patients have DKD.

Discussion

Our study was a single-center analysis of prospectively collected data from 521 newly diagnosed T2DM patients, 127 of whom had a DKD event. We screened variables through LASSO regression and incorporated 5 variables to construct a prediction model. Furthermore, we carried out internal validation, discrimination ability, calibration ability, and clinical practicability of the model. These results revealed that the prediction model performed well and had clinical applicability.

Independent prediction indicators were screened by logistic and LASSO regression, and finally, a DKD prediction model for predicting newly diagnosed T2DM was developed. Nomogram revealed that age, renal excretion, lipid metabolism, and IR may be potential good predictors of DKD. Age is recognized as an independent risk factor for T2DM and DKD.29 Persistent microalbuminuria (UACR > 30mg/g) can appear in the early stage of the DKD hyperfiltration state, and metabolic substances such as serum creatinine will accumulate with the decrease of renal filtration level in the later period.30 At present, some scholars considered that mitochondrial dysfunction played an indispensable role in DN, which will cause fatty acid oxidation disorder, resulting in abnormal lipid deposition in the kidney.31 Furthermore, many lipid metabolites have been confirmed to be related to the complications of DM. As an index of IR, the TyG index performs excellently in predicting complications of DM.32,33 TyG index can accurately evaluate whether IR exists only by combining two simple indexes: serum lipid and glucose. Moreover, an Australian study investigated the relationship between the TyG index and end-stage renal disease (ESRD). The results revealed a positive correlation between the TyG index and ESRD risk.9 Therefore, researchers should not ignore the relationship between IR and DKD in the future.

The onset of DKD is hidden, and it may be difficult for patients newly diagnosed with T2DM to realize the risk of developing DKD. However, if DKD occurs, we can only take symptomatic treatment (control albuminuria and serum glucose), then wait for ESRD to evolve slowly. Therefore, incorporating the clinical variables described above into the model and developing a nomogram as a screening tool can well identify newly diagnosed T2DM patients with DKD risk. For the identification of our model, the AUC of the training set and the validation set were 0.826 and 0.803, respectively. These results revealed that the model had an excellent predictive ability in identifying DKD and non-DKD in T2DM patients. The model’s calibration curve revealed a strong correlation between the predicted probability and the actual probability, indicating that our model had been properly calibrated. DCA results showed that using this model to predict DKD had clinical practicability and benefits patients.

In comparison to previous studies on developing DKD prediction models, our study had some strengths. First, our study was a retrospective cohort study with a long-time follow-up. Some patients with DKD have a DM duration of 10 years or more, so it may be difficult to collect enough participants with positive outcomes in short-term follow-up and underestimate the prevalence rate, which ultimately affects the predictive performance. Secondly, the participants in our study were newly diagnosed T2DM patients compared with other research focus individuals, who frequently overlooked the danger of developing DKD. Newly diagnosed T2DM patients are a concern in this study, and the prediction model of DKD is constructed to find all DKD events in DM patients earlier. Finally, to incorporate all related variables, we initially investigated the pathological process and risk factors for T2DM and its complications. IR is a major pathogenic factor and characteristic of T2DM, causing concomitant injury to various target organs, and DKD is no exception.34,35 It is crucial to find an index for assessing IR since it is the chief culprit of glucose metabolism disorders. The performance of the TyG index and the HOMA-IR as IR evaluation indexes varies throughout other studies. Hence, we used AUC to distinguish the performance of the TyG index from the HOMA-IR and finally revealed that the TyG index performs better. Therefore, we incorporated significant correlation factors including the TyG index, constructing a more accurate prediction model.

There were still some limitations in this trial. First, the lack of external validation was one of the important limitations of our study. To duplicate and externally verify the results of this study, more investigation is required. Secondly, this was a cohort study with limited sample size, so the endpoint event was defined as the occurrence of DKD, and no subgroup analysis was carried out. If DKD was divided into subgroups with different renal function levels according to eGFR stages, the research results would be more abundant. In addition, the family history of DM was missing from baseline data, and it is undeniable that it is also a significant factor in the development of DKD. Finally, since this was a retrospective cohort study, it may suffer from recollection bias and loss of follow-up. In this cohort study, there were 82 participants with loss to follow-up, which was controlled within 10% and had no significant impact on the outcome.

Conclusion

In conclusion, the new DKD progression model for T2DM patients constructed in this study had excellent identification and calibration ability, which was helpful for clinical practice. In the future, we need to integrate large-scale T2DM cohort studies to further verify this nomogram prediction model.

Data Sharing Statement

The data underlying this article will be shared on reasonable request to the corresponding author.

Ethics Statement

This research study was complied with the Declaration of Helsinki, and approved by the Ethics Committee of the Third Affiliated Hospital of Soochow University (2013#27). All individuals provided written informed consent for this research study.

Author Contributions

All authors made a significant contribution to the work reported, whether that is in the conception, study design, execution, acquisition of data, analysis and interpretation, or in all these areas; took part in drafting, revising or critically reviewing the article; gave final approval of the version to be published; have agreed on the journal to which the article has been submitted; and agree to be accountable for all aspects of the work.

Funding

This work was supported by grants from the National Natural Science Foundation of China (82000684) and Changzhou Sci & Tech Program (CE20215024) and Top Talent of Changzhou “The 14th Five-Year Plan” High-Level Health Talents Training Project (2022260).

Disclosure

The authors report no conflicts of interest in this work.

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Statin initiation and risk of incident kidney disease in patients with diabetes

Diabetes News


Abstract

Background: The role of statin therapy in the development of kidney disease in patients with type 2 diabetes mellitus (DM) remains uncertain. We aimed to determine the relationships between statin initiation and kidney outcomes in patients with type 2 DM.

Methods: Through a new-user design, we conducted a multicentre retrospective cohort study using the China Renal Data System database (which includes inpatient and outpatient data from 19 urban academic centres across China). We included patients with type 2 DM who were aged 40 years or older and admitted to hospital between Jan. 1, 2000, and May 26, 2021, and excluded those with pre-existing chronic kidney disease and those who were already on statins or without follow-up at an affiliated outpatient clinic within 90 days after discharge. The primary exposure was initiation of a statin. The primary outcome was the development of diabetic kidney disease (DKD), defined as a composite of the occurrence of kidney dysfunction (estimated glomerular filtration rate [eGFR] < 60 mL/min/1.73 m2 and > 25% decline from baseline) and proteinuria (a urinary albumin-to-creatinine ratio ≥ 30 mg/g and > 50% increase from baseline), sustained for at least 90 days; secondary outcomes included development of kidney function decline (a sustained > 40% decline in eGFR). We used Cox proportional hazards regression to evaluate the relationships between statin initiation and kidney outcomes, as well as to conduct subgroup analyses according to patient characteristics, presence or absence of dyslipidemia, and pattern of dyslipidemia. For statin initiators, we explored the association between different levels of lipid control and outcomes. We conducted analyses using propensity overlap weighting to balance the participant characteristics.

Results: Among 7272 statin initiators and 12 586 noninitiators in the weighted cohort, statin initiation was associated with lower risks of incident DKD (hazard ratio [HR] 0.72, 95% confidence interval [CI] 0.62–0.83) and kidney function decline (HR 0.60, 95% CI 0.44–0.81). We obtained similar results to the primary analyses for participants with differing patterns of dyslipidemia, those prescribed different statins, and after stratification according to participant characteristics. Among statin initiators, those with intensive control of high-density lipoprotein cholesterol (LDL-C) (< 1.8 mmol/L) had a lower risk of incident DKD (HR 0.51, 95% CI 0.32–0.81) than those with inadequate lipid control (LDL-C ≥ 3.4 mmol/L).

Interpretation: For patients with type 2 DM admitted to and followed up in academic centres, statin initiation was associated with a lower risk of kidney disease development, particularly in those with intensive control of LDL-C. These findings suggest that statin initiation may be an effective and reasonable approach for preventing kidney disease in patients with type 2 DM.

Statins are among the most commonly prescribed medications, administered to 146 million people worldwide.1 Statin therapy reduces the risk of cardiovascular disease in patients with type 2 diabetes mellitus (DM) and in patients with hypertension. 2–5 Current guidelines from the Canadian Cardiovascular Society and the American Diabetes Association recommend statin therapy for patients with diabetes aged 40 years or older.6–9 However, some recent studies have shown that treatment with a statin alters glucose metabolism and affects glycemic control in such patients.10–15 Given that a worsening of glycemic control is associated with the development or progression of microvascular disease,16 patients with diabetes who are undergoing statin treatment might be at higher risk of developing microvascular complications.

Diabetic kidney disease (DKD) is a common microvascular complication in patients with type 2 DM, is the leading cause of end-stage kidney disease, and imposes enormous health care and financial burdens in both low- and high-income countries.17,18 Although multiple experimental and epidemiologic studies have shown that dyslipidemia is a risk factor for kidney disease in patients with diabetes, the role of lipid-lowering therapy in the development of kidney disease in patients with type 2 DM remains unclear.19–21 Previous studies suggested that statins might have protective effects against diabetes-induced oxidative stress and podocyte injury in the kidney. 22,23 However, several population-based studies have shown that the use of statins does not reduce the risk of kidney disease24,25 and, as noted earlier, may even have adverse effects in patients with diabetes.26,27 Thus, it is uncertain whether the administration of statins represents an appropriate means of preventing DKD.

To address this knowledge gap, we performed a retrospective observational cohort study of patients with type 2 DM across China to determine the effect of statin initiation on the development of DKD and kidney function decline.

Methods

Study design and setting

This is a multicentre retrospective cohort study using a new-user design and de-identified data collected from the China Renal Data System (CRDS) database from Jan. 1, 2000, to May 26, 2021.28 At present, the government’s basic medical insurance provides coverage for more than 95% of the population in China, including access to care at academic health care centres.29

We reported the study according to the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) reporting guideline.30

Data source

The CRDS database is a joint initiative of the National Clinical Research Center for Kidney Disease and the China Center for Disease Control and Prevention. This database contains data for more than 7 million inpatients and outpatients from 19 large, urban academic centres that cover the major geographic regions across China (Appendix 1, Supplementary Methods 1, available at www.cmaj.ca/lookup/doi/10.1503/cmaj.230093/tab-related-content). The accuracy and completeness of this database have been verified in our previous studies31,32 and by other validation activities (Appendix 1, Supplementary Methods 1).

Participants

We selected patients with type 2 DM admitted to hospital between Jan. 1, 2000, and May 26, 2021, and aged 40 years or older, for inclusion in the present study. The diagnosis of type 2 DM was based on the International Classification of Diseases, 10th Revision (ICD-10) code E11,33 and all hospital admissions for diabetes-related complications or general admissions with diabetes as a comorbidity were included.

We assigned the date of the first statin prescription as the index date for statin initiators. For noninitiators, we assigned the index date as a randomly selected date of any admission (i.e., not limited to hospital admissions for diabetes or related complications). All study participants (both initiators and noninitiators) had not received a statin prescription within the previous year before the index date.

We excluded patients for whom baseline serum creatinine or urinary protein concentrations were not available, and those with a diagnosis of chronic kidney disease (as defined by ICD-10 code N18) or an estimated glomerular filtration rate (eGFR) of < 60 mL/min/1.73 m2 or proteinuria (defined as a urinary albumin-to-creatinine ratio ≥ 30 mg/g) before the index date.34 We also excluded patients with identified or suspected acute kidney injury at the index date. We based the diagnosis of acute kidney injury on ICD-10 codes N10/N17/O90.4 or Kidney Disease Improving Global Outcomes creatinine criteria.35 We defined suspected acute kidney injury as a 50% or greater change in serum creatinine within 1 month.36

We defined a 1-year observational period before the index date and excluded those who did not have any records of prescriptions from an affiliated outpatient clinic or during a hospital admission during this period. We also excluded patients without follow-up at an affiliated outpatient clinic within 90 days after discharge or who were prescribed a statin and 1 or more other lipid-lowering drugs. The follow-up period started on the index date and continued until any outcome occurred, the participant was lost to follow-up, or the date of the final serum creatinine measurement or urinary protein test, whichever came first.

Exposure

The primary exposure was the initiation of statin treatment (Appendix 1, Supplementary Methods 2), as defined by a statin being prescribed. We extracted details of statin prescriptions — including dose, usage, and starting and ending times of statins during inpatient and outpatient periods — from the CRDS database and further validated them by accessing the relevant individual academic centre’s information system to extract the electronic medical records data. For comparison, we defined noninitiators as those who did not receive a statin prescription or a prescription for any nonstatin lipid-lowering drugs (e.g., fibrate, ezetimibe and nicotine acid).

Outcomes

The primary outcome was the development of DKD, defined as a composite of the occurrence of kidney dysfunction37 (defined as an eGFR < 60 mL/min/1.73 m2 and > 25% decline from baseline) and proteinuria (defined as a urinary albumin-to-creatinine ratio ≥ 30 mg/g and > 50% increase from baseline), sustained for at least 90 days. The secondary outcomes were individual indices indicating the development of DKD and the development of kidney function decline (defined as a sustained > 40% decline in eGFR).38 We calculated the eGFR using serum creatinine and the Chronic Kidney Disease Epidemiology Collaboration equation.39

Covariates

We collected demographic information (age, sex) and comorbidities determined using ICD-10 codes at baseline (defined as within a 3-month period before the index date). We calculated the age-adjusted Charlson Comorbidity Index to quantify the overall comorbidity status.40 We identified antihypertensive drugs and glucose-lowering drugs during the observational period (i.e., within 1 year before the index date). The relevant Anatomic Therapeutic Chemical codes are summarized in Appendix 1, Supplementary Table 1.

We extracted physical examination and laboratory test results, including blood pressure measurement, body mass index, serum lipid concentrations (total cholesterol [TC], low-density lipoprotein cholesterol [LDL-C], high-density lipoprotein cholesterol [HDL-C] and triglyceride [TG]), eGFR, glycosylated hemoglobin (HbA1c), serum uric acid, alanine aminotransaminase and aspartate aminotransaminase, hemoglobin and leukocytes. We included the most recent measurements of these parameters within 3 months before the index date. All covariates were considered clinically relevant based on biological mechanism or evidence from previously published data.10,41

Statistical analysis

We summarized the clinical parameters and baseline characteristics of the statin initiator and noninitiator groups and used standardized mean differences (SMDs) to evaluate the balance of the 2 groups.

We used propensity score overlap weighting to balance the characteristics of the statin initiators and noninitiators to mimic randomized clinical trials.42 We identified a total of 38 covariates and modelled their hypothetical causal pathways using a directed acyclic graph (Appendix 1, Supplementary Figure 1); we included these in the propensity score overlap weighting. Overlap weight for each participant was calculated by multivariable logistic regression and assigned to each participant proportionally to the probability of that patient belonging to the opposite treatment group. This method created balance between the exposure groups with regard to all the covariates included in the propensity score. After weighting, parameters with an SMD higher than 0.1 were regarded as unbalanced between the groups.43

We evaluated the relationships between statin initiation and kidney outcomes using Cox proportional hazards regression after weighting. Hazard ratios (HRs) and the corresponding 95% confidence intervals (CIs) were reported. We tested the proportional hazards assumption using Schoenfeld residuals.

We conducted a predefined subgroup analysis according to the presence or absence of dyslipidemia (defined as LDL-C ≥ 3.4 mmol/L or TC ≥ 5.2 mmol/L or TG ≥ 1.7 mmol/L) and the pattern of dyslipidemia (high TC or LDL-C, high TG, or both).44 We also determined the potential effect modification associated with statin initiation on the development of DKD using the same Cox model after weighting and after stratification according to age, sex, HbA1c, comorbidity, use of insulin, use of metformin, and treatment with antihypertensive drugs (renin–angiotensin system inhibitors, β-blockers, calcium channel blockers and diuretics).

Additional analyses

We performed additional analyses to compare the effects of statin initiation on kidney outcomes in patients with different levels of lipid control. We evaluated the first serum LDL-C concentration recorded for statin initiators between 90 and 365 days after the index date. Those whose serum LDL-C concentrations were less than 1.8 mmol/L, 1.8 to less than 3.4 mmol/L, and 3.4 mmol/L or higher were defined as belonging to the intensive lipid control, moderate lipid control and inadequate lipid control groups, respectively.44,45 Furthermore, we evaluated the relationships between the various types of statins with kidney outcomes. We also conducted a multivariable logistic regression to assess the association between statin initiation and the use of glucose-lowering drugs during follow-up, after weighting.

Sensitivity analyses

We performed a series of sensitivity analyses to evaluate the robustness of the findings. We developed a long-term follow-up cohort from our study population, which excluded those with loss of follow-up (i.e., not seen at an affiliated outpatient clinic) within the first 3 years. We fitted propensity score matching and inverse probability treatment weighting (stable weighting) models to evaluate the relationship between statin initiation and kidney outcomes, in place of overlap weighting (Appendix 1, Supplementary Methods 3). We used a time-varying Cox model with statin initiation treated as a time-varying exposure. To evaluate the bias associated with reverse causality, we excluded participants who developed a kidney outcome within the first year of the study.

Finally, to account for the bias introduced by unmeasured confounders, we calculated the E-value for the kidney outcomes. The E-value represents the minimum magnitude of association required for an unmeasured confounder to reverse the observed association toward a null. In brief, if the relative risk between unmeasured confounders, kidney outcomes and statin initiation is greater than the estimated E-value, residual confounders may be sufficient to explain the identified association.46

We managed missing data in all analyses using multiple imputation, with an assumption of missing at random. We performed all statistical analyses using SAS version 9.4 (SAS Institute, Cary, NY, USA) and R 4.1.1 (R Foundation for Statistical Computing, Vienna, Austria) and with a significance level of 0.05 (2-sided).

Ethics approval

The study was approved by the China Office of Human Genetic Resources for Data Preservation Application (approval no.: 2021-BC0037). The protocol was approved by the Medical Ethics Committee of Nanfang Hospital, Southern Medical University (approval no.: NFEC-2019–213), and the requirement for informed consent was waived.

Results

We selected a total of 19 858 participants from the original cohort of 455 493 patients with type 2 DM admitted to a participating hospital during the study period (Figure 1). The baseline characteristics and weighting of the groups are summarized in Table 1 and Appendix 1, Supplementary Table 2. Among 7272 statin initiators and 12 586 noninitiators, 11 012 (55.5%) were male and their median age was 62.2 years (interquartile range 54.5–69.4 yr). The mean duration of follow-up was 1.6 years.

Figure 1:
Figure 1:

Flow chart of study population. *We assigned the date of the first statin prescription as the index date for statin initiators. For noninitiators, we assigned the index date as a randomly selected date of any admission. AKI = acute kidney injury, CKD = chronic kidney disease, DM = diabetes mellitus.

Table 1:

Baseline characteristics of study population*

Association of statin initiation with risk of kidney outcomes

The weighted cumulative incidences of kidney outcomes among statin initiators and noninitiators are shown in Figure 2 and Appendix 1, Supplementary Table 3. Statin initiation was significantly associated with lower cumulative incidences of incident DKD, new-onset proteinuria and more than 40% decline in eGFR (p < 0.05 for all), but not with new-onset eGFR lower than 60 mL/min/1.73 m2 (p = 0.34). After weighting, the risk of incident DKD was significantly lower for statin initiators than for noninitiators (HR 0.72, 95% CI 0.62–0.83) (Figure 3).

Figure 2:
Figure 2:

Statin initiation and weighted cumulative incidence of kidney outcomes in patients with type 2 diabetes mellitus. *We defined incident diabetic kidney disease (DKD) as an estimated glomerular filtration rate (eGFR) < 60 mL/min/1.73 m2 and > 25% decline from baseline and new-onset proteinuria. 37 †We defined new-onset proteinuria as a urinary albumin-to-creatinine ratio ≥ 30 mg/g and > 50% increase from baseline).

Figure 3:
Figure 3:

Adjusted hazard ratio (HR) of statin initiation associated with kidney outcomes among all patients and patients with or without dyslipidemia. We defined dyslipidemia as low-density lipoprotein cholesterol (LDL-C) ≥ 3.4 mmol/L or total cholesterol (TC) ≥ 5.2 mmol/L or triglyceride (TG) ≥ 1.7 mmol/L.44 We estimated HRs by Cox proportional model after propensity score overlap weighting with confounders adjusted for all characteristics in Table 1. Schoenfeld residuals validated the proportional hazards assumption (p = 0.35). Note: CI = confidence interval, DKD = diabetic kidney disease, eGFR = estimated glomerular filtration rate.

Using Cox proportional hazards regression after weighting, statin initiation was found to be associated with lower risks of new-onset eGFR lower than 60 mL/min/1.73 m2 (HR 0.63, 95% CI 0.50–0.79), new-onset proteinuria (HR 0.70, 95% CI 0.59–0.84) and kidney function decline (HR 0.60, 95% CI 0.44–0.81) (Figure 3).

In subgroup analyses, we also obtained consistent findings for participants with or without dyslipidemia and with different patterns of dyslipidemia (high TG, high TC or LDL-C, or both) (Figure 3 and Appendix 1, Supplementary Figure 2). We did not find any significant effect modifiers among the other clinical characteristics (all pinteraction > 0.05) (Appendix 1, Supplementary Figure 3).

Additional analyses

We performed additional analyses to compare the effects of statin initiation on kidney outcomes in those with different levels of LDL-C control (Appendix 1, Supplementary Figure 4). By comparison with the inadequate lipid control group (LDL-C ≥ 3.4 mmol/L), patients with intensive control (LDL-C < 1.8 mmol/L) had the lowest risk of developing DKD (HR 0.51, 95% CI 0.32–0.81). Similar results were obtained for the risk of new-onset eGFR lower than 60 mL/min/1.73 m2 and proteinuria. Among those with moderate control (LDL-C 1.8 to < 3.4 mmol/L), there was no significant association with the development of DKD when compared with the inadequate lipid control group (HR 0.84, 95% CI 0.61–1.11).

Atorvastatin (n = 4424 [60.8%]) and rosuvastatin (n = 1135 [15.6%]) were the most frequently used statins (Appendix 1, Supplementary Table 4). The hazard ratios for DKD ranged from 0.65 to 0.88 and from 0.46 to 0.75 for patients taking lipophilic (simvastatin, pitavastatin, fluvastatin and atorvastatin) and hydrophilic (pravastatin and rosuvastatin) statins, respectively.

With respect to glucose metabolism and glycemic control (Appendix 1, Supplementary Table 5), there was a significant positive association between statin initiation and an increase in the use of oral glucose-lowering drugs (OR 1.75, 95% CI 1.50–2.05).

Sensitivity analyses

About 1 in 5 study participants (4107/19 858, 20.7%) were followed up for longer than 3 years, with a mean follow-up of 3.5 years. The associations of statin initiation with kidney outcomes remained in these patients (Appendix 1, Supplementary Table 6). We also found similar results for the associations of statin initiation with the risks of kidney outcomes when using the stable weighting, propensity score matching and time-varying Cox models (Appendix 1, Supplementary Table 7 and 8).

With respect to the reverse causality (Appendix 1, Supplementary Table 9), we found consistent results after excluding participants who developed DKD within 1 year. The E-value for kidney outcomes ranged from 2.1 to 2.7 in the primary analyses.

Interpretation

In this multicentre cohort study of patients with type 2 DM admitted to and followed up in an academic centre in China, we found that statin initiation was associated with significantly lower risks of developing DKD and kidney function decline. These associations were robust, being unaffected by differences in clinical characteristics or the pattern of dyslipidemia. The similar results in multiple sensitivity analyses that evaluated reverse causality and unmeasured confounders also suggest that the findings of the present study are robust.

Several animal and epidemiologic studies have shown that dyslipidemia plays a role in the development and progression of DKD.47,48 Because dyslipidemia is both a risk factor and potential target for the treatment of DKD, further research into the clinical benefits of lipid-lowering drugs is required. However, given that the cardiovascular benefits of statins in patients with diabetes have been well established,2,49 it is difficult to conduct a randomized controlled trial (RCT) to compare the kidney outcomes of patients undergoing statin therapy with placebo-treated controls. Therefore, the use of medical data obtained from real-world clinical practice represents a rational means of studying the effect of statins to prevent kidney disease in patients with diabetes. Our findings provide evidence that statins may be reno-protective in patients with type 2 DM in a real-world setting and may help physicians to optimize disease management.

The results of previous studies assessing the reno-protective effect of statins in patients with diabetes have been contradictory. Two large population-based studies (n = 43 438 and 62 716)24,27 showed that statin use did not have beneficial effects on kidney outcomes and may possibly have had adverse effects. However, the kidney outcomes used in these studies were based on ICD codes, which lack sensitivity, and therefore the incidence of the outcomes may have been underestimated. Also, given the lack of inclusion of laboratory data, such as eGFR, proteinuria (urinary albumin-to-creatinine ratio), HbA1c and cholesterol, the baseline characteristics of the participants who were or were not taking a statin could not be well matched, and this represents a major limitation of these studies.

Consistent with our findings, several previous studies have shown that statins may be reno-protective in patients with diabetes. 50–52 However, these studies were limited by small sample sizes, inconsistent effects on proteinuria and renal function, or the use of an ICD-based method of diagnosis of DKD. More importantly, one of the basic principles of examining effectiveness of medications in cohort studies is excluding prevalent users.53 The exposure in these observational studies including prevalent users of a statin raises concern for bias toward better outcomes.

The strengths of the present study include its real world–based data source, new-user design, large sample size, inclusion of individuals with a wide range of disease phenotypes, and use of hard kidney outcomes. The comprehensive patient-level data with time stamps ensure that thorough weighting of the groups was possible. In addition, we adjusted for important potential confounders, such as comorbidities, concomitant drug administration, the type of statin used and the level of lipid control. Additional strengths include the use of sophisticated statistical methods to reduce the risk of confounding and indication bias. Given the E-values for kidney outcomes in the primary analyses (2.1–2.7), the robustness of the study results do not appear to be substantially affected by the presence of unassessed confounders.46

In our study, we found that various specific statins may have variable effects on kidney outcomes in patients with type 2 DM. A previous RCT that compared the renal effects of atorvastatin and rosuvastatin in patients with diabetes who had proteinuria showed that atorvastatin might be more reno-protective.54 Recently, a real-world study also found that rosuvastatin was associated with increased risk of proteinuria compared with atorvastatin. 55 However, these studies did not include a placebo control; therefore, it could not be determined whether atorvastatin protected the kidney or rosuvastatin harmed the kidney. In the present study, atorvastatin appeared to be most reno-protective, across all kidney outcomes.

Whether the potential reno-protective effect of statins is independent of their lipid-lowering effects remains unclear.56,57 In animal models of diabetes, statin therapy has been shown to cause increases in antioxidant enzyme levels, reduce the accumulation of advanced glycation end products, and reverse diabetes-related podocyte injury, which may prevent or slow the development of kidney disease, independent of the effects of reducing lipid concentrations.58–60 In a post hoc analysis of data from randomized trials,61 the effect of statin treatment on proteinuria was shown to be inconsistent with the degree of control of hypercholesterolemia in individual patients. However, in the present study, intensive control of LDL-C tended to be associated with a better kidney outcome, implying a reno-protective effect driven at least partly by the lipid-lowering effects. Considering the relatively few participants taking other lipid-lowering drugs, which limited the feasibility and statistical power of the comparison between statin and other lipid-lowering drugs, we could not preclude the potential reno-protective effect of statins independent of their lipid-lowering effect.

Although the management of type 2 DM has improved substantially in recent decades, patients with diabetes in China are still at substantial risk of kidney disease and progressive loss of renal function. Our study found that only 36.6% of people with type 2 DM who were age 40 years or older were prescribed statins during the study period, which is lower than that in Canada (54.0% for males and 45.3% for females)62 and the United States (41.6%).63 The current national guideline in China recommends statin therapy for patients with diabetes who are aged 40 years or older.64 The suboptimal accordance with this recommendation might contribute to the higher risk of kidney disease progression we observed in our study population. Our findings suggest that there is an urgent need to promote guideline-concordant care in real-world clinical practice in China.65

Limitations

Although we performed propensity score overlap weighting to balance the baseline characteristics of the statin initiators and noninitiators, other uncontrolled factors could have affected the kidney outcomes. We performed multiple sensitivity analyses to adjust for these residual confounders.

Statin initiation may have represented a marker of atherosclerosis, high health awareness or high frequency of hospital visits, all of which could have resulted in ascertainment bias and influenced the results. However, the number of outpatient visits by the weighted cohort during the follow-up period was similar for statin initiators and noninitiators. We selected our study population from patients with type 2 DM who were admitted to 1 of the 19 urban academic centres in the CRDS and who received follow-up at 1 of the affiliated clinics. These patients might have been sicker and had poorer glycemic control and higher risk of diabetic complications than those who were not admitted to hospital and those without follow-up. As such, caution is needed when generalizing our results to all patients with type 2 DM.

Finally, patients whose data are included in the CRDS are predominantly Chinese; therefore, whether there are ethnic differences in the potential reno-protective effect of statins warrants further research. Additionally, we found that only one-third of inpatients used metformin and statins in our study, reflecting poor adherence to guidelines in China.65 Our findings need to be validated in other countries in which a higher proportion of patients with type 2 DM are receiving guideline-concordant care.

Conclusion

We found that statin initiation was associated with significantly lower risks of incident DKD and kidney function decline among patients with type 2 DM admitted to and followed up in academic centres. We obtained similar results for participants with differing patterns of dyslipidemia, those prescribed different statins, and after stratification according to participant characteristics. Among statin initiators, these associations were more pronounced in those with intensive LDL-C control (< 1.8 mmol/L).

These findings suggest that statin initiation may be an effective approach for preventing kidney disease in patients with type 2 DM. Further research is needed to compare the reno-protective effects of specific statins and newer lipid-lowering drugs, such as proprotein convertase subtilisin/kexin type 9 inhibitors, on kidney outcomes.



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