Incidence and risk factors of posttransplant diabetes mellitus in lung transplant recipients: a retrospective cohort study

Introduction

Post-transplantation diabetes mellitus (PTDM) is a term introduced by an international expert committee to update the 2003 definition of “New-Onset Diabetes After Transplantation (NODAT)” (1,2). PTDM refers to diabetes diagnosed in individuals after organ transplantation who are in a stable immunosuppressive state and free of infections. These patients did not have diabetes prior to transplantation. Hyperglycemia is common during the early post-transplant period, defined as the first 45 days after surgery, and may be diagnosed as diabetes (3).

PTDM is a significant and common metabolic complication following solid organ transplantation. Its pathogenesis is multifactorial and complex, with a postoperative incidence ranging from 30% to 35% (4,5). PTDM increases the risk of infection, transplant rejection, and even graft loss, severely compromising patients’ postoperative quality of life, reducing survival time, elevating mortality, and contributing to poor long-term prognosis (6-9).

PTDM significantly affects the prognosis of lung transplant patients, making early identification and intervention essential (10). Research shows that the first 6 months post-transplant represent a high-risk period for PTDM. Consequently, follow-up care during this time is crucial for identifying risk factors, which can inform the development of personalized treatment plans and preventive strategies.

Although much of the research has focused on kidney, liver, and heart transplant recipients, fewer studies have specifically examined PTDM in lung transplantation. A retrospective study involving 22,991 lung transplant recipients identified age >50 years, higher body mass index (BMI), and cystic fibrosis as risk factors for PTDM post-transplantation (11). While previous international studies have provided valuable data, the incidence of obesity among Chinese patients is relatively low, and their diabetes and metabolic conditions differ from those in foreign populations. As a result, specific risk factors and treatment strategies may have distinct effects on the development of PTDM in Chinese lung transplant recipients. However, most existing studies have been conducted on populations outside of China, which may not fully reflect the unique body characteristics and treatment regimens of lung transplant recipients in China.

Previous studies have demonstrated that the incidence of PTDM peaks within the first 6 months after transplantation (12,13). Therefore, this study aims to provide valuable reference data through preoperative patient data collection and postoperative follow-up outcomes within the first 6 months with the goal of identifying risk factors for PTDM, enabling early prevention, enhancing postoperative quality of life, and reducing the risk of complications. We present this article in accordance with the STROBE reporting checklist (available at https://jtd.amegroups.com/article/view/10.21037/jtd-2025-1300/rc).

Methods

Study participants

This retrospective study examines allogeneic lung transplant recipients who were routinely followed at The First Affiliated Hospital of Guangzhou Medical University, a tertiary hospital in Guangzhou, China, between February 2023 and August 2024. The inclusion criteria were: (I) first-time lung transplantation; (II) age >18 years; (III) follow-up duration >6 months. Exclusion criteria included: (I) missing medical records >15%; (II) death within 6 months post-surgery or occurrence of multi-organ failure; (III) preoperative diagnosis of diabetes mellitus or abnormal glucose tolerance.

Sample size calculation

The sample size was estimated to be 5–10 times the number of positive outcomes, based on a prior study (14). A literature review indicated that the incidence of PTDM following lung transplantation was 38.57% for ≤9 positive outcomes (15). To account for potential missing data, the sample size was increased by 10%, resulting in an adjusted range of 128 to 257 cases. Ultimately, 158 cases were included in this study. The study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments. This study was approved by the Medical Ethics Committee of The First Affiliated Hospital of Guangzhou Medical University (approval number: ES-2023-225-01). Informed consent was obtained from all participants prior to their inclusion in the study. The patient selection flowchart is shown in Figure 1.

Figure 1 Flowchart for screening of the research subjects.

Data collection

Baseline data collection

Baseline data were retrospectively retrieved from the hospital’s health information system (HIS), covering factors such as primary disease, gender, age, BMI, and the presence of medical conditions, including hypertension, smoking, drinking, and cardiac insufficiency. In line with previous studies, both BMI and age were dichotomized. BMI was categorized into <24.0 and ≥24.0 kg/m² groups according to prior literature (16); age was dichotomized at 45 years (≤45 vs. >45 years), as commonly applied in previous research (8).

Laboratory indicators

The most recent preoperative laboratory indicators, collected within 2 weeks prior to surgery, included: white blood cell (WBC) count, red blood cell (RBC) count, total protein (TP), uric acid (UA), aspartate aminotransferase (AST), hemoglobin A1c (HbA1c), and other relevant markers.

Intraoperative and postoperative data

Intraoperative data included the type of transplantation, use of extracorporeal membrane oxygenation (ECMO), and urine output, and intraoperative blood loss.

Geriatric nutritional risk index (GNRI)

GNRI is an advanced version of the nutritional risk index (NRI). It is simple and convenient to calculate, addressing the limitations of case systems that do not consider daily body weight. The GNRI provides superior predictive value for assessing muscle mass in transplant patients (17). The GNRI calculation method is outlined in Table 1.

Table 1 outlines the indicators and steps used to calculate GNRI scores. Based on this calculation method, GNRI classifications can be further clarified. The GNRI classification method is detailed in Table 2.

Diagnostic criteria for PTDM

The diagnostic criteria for PTDM should be assessed after patient stabilization, typically 45 days post-transplantation. PTDM is diagnosed based on the presence of diabetes symptoms along with one or more of the following criteria (3). PTDM diagnostic methods are shown in Table 3.

Statistical analysis

Data were analyzed using SPSS version 25.0 and R 4.4.1. To prevent the introduction of substantial bias, variables with more than 15% missing data were excluded, while those with less than 15% missing data were imputed using multiple imputation. The glmnet function in R was employed for variable selection. For normally distributed data, results are presented as mean ± standard deviation, with group comparisons made using the independent-samples t-test. For non-normally distributed data, values are expressed as the median (Q1, Q3), and group comparisons are performed using the non-parametric rank-sum test. Categorical data are presented as frequencies or percentages (%), with group comparisons conducted using the chi-square test or Fisher’s exact test. Univariate analysis was used to identify risk factors for PTDM after lung transplantation, with a significance level set at α =0.05 (two-sided).

To identify the most relevant predictors for PTDM following lung transplantation, least absolute shrinkage and selection operator (LASSO) regression was performed, and the results are illustrated in Figure 2.

Figure 2 LASSO-selected predictors of PTDM following lung transplantation. (A) Trajectory diagram illustrating the influence of each variable on PTDM [x-axis: Log(λ)]. (B) Variable selection process for PTDM based on LASSO regression [x-axis: Log(λ)]. LASSO, least absolute shrinkage and selection operator; PTDM, post-transplantation diabetes mellitus.

Results

LASSO regression screening for factors influencing the occurrence of PTDM after lung transplantation.

Using the occurrence of PTDM postoperatively as the dependent variable, LASSO regression was applied to identify the optimal minimum lambda (λ) value for the model, with λ =0.044. After variable screening, the following factors were identified as potential risk factors for PTDM development after lung transplantation: cardiac insufficiency, hyperlipidemia, age >45 years, BMI ≥24 kg/m², smoking history, AST, HbA1c, intraoperative blood loss, and GNRI ≤92 (Table 4).

Informed by the literature, the following variables were included as independent predictors using binary coding: HbA1c (>5.7 mmol/L =1, ≤5.7 mmol/L =0) (20).

Based on the results of univariate logistic analysis, a subsequent multivariate logistic regression was performed. The analysis revealed that cardiac insufficiency, hyperlipidemia, age >45 years, GNRI ≤92, and HbA1c >5.7% are potential independent risk factors for the development of PTDM following lung transplantation, as detailed in Figure 3. Collinearity diagnostics showed that the variance inflation factors (VIFs) for cardiac insufficiency, hyperlipidemia, age >45 years, GNRI ≤92, and HbA1c >5.7% were 1.042, 1.013, 1.049, 1.021, and 1.046, respectively, with corresponding tolerances of 0.960, 0.987, 0.954, 0.979, and 0.956, indicating no multicollinearity among the five variables. The Hosmer-Lemeshow test confirmed that the logistic regression model had a good fit (χ²=0.306, df =5, P=0.70).

Figure 3 Multivariable analysis of risk factors for PTDM (n=158). BMI ≥24 kg/m² indicates overweight; GNRI ≤92 indicates moderate to severe malnutrition risk; HbA1c >5.7% indicates prediabetes or higher. BMI, body mass index; CI, confidence interval; GNRI, geriatric nutritional risk index; HbA1c, hemoglobin A1c; OR, odds ratio; PTDM, post-transplantation diabetes mellitus.

Discussion

Risk factors for PTDM identified in our study include: (I) BMI ≥24 kg/m²: this study found a marginal association between BMI and diabetes risk, suggesting that BMI could be a risk factor for PTDM. (II) Preoperative malnutrition and HbA1c: patients with preoperative malnutrition or those with HbA1c levels slightly above the normal range were found to be at a higher risk of developing PTDM after lung transplantation. (III) Age >45 years and hyperlipidemia: in our study, both age and hyperlipidemia may increase the incidence of PTDM after lung transplantation.

The results of this study revealed a PTDM incidence of 36.08% among lung transplant recipients, underscoring PTDM as a common metabolic complication in this population. This finding is consistent with the study by Ye et al. (11), which reported an incidence of 40.8%. However, our study observed a slightly lower prevalence, which can primarily be attributed to differences in age distribution across the populations. These differences may also be influenced by variations in patient characteristics and treatment protocols across countries.

Moreover, preventive medical management are essential for reducing the risk of PTDM. Early implementation of tailored interventions including dietary management, moderate physical activity, and strict medication adherence, can significantly lower the incidence of PTDM and related cardiovascular complications (21). These measures not only reduce PTDM risk but also improve patients’ overall quality of life and psychological well-being (22,23).

This study suggests that a GNRI ≤92 may be associated with an increased risk of PTDM following lung transplantation. Previous research has demonstrated that optimizing preoperative nutritional status not only improves patient prognosis but also reduces hospital stay duration (24,25).

Patients with a GNRI ≤92 frequently experience malnutrition or poor nutritional status, both of which significantly impair insulin metabolism. Malnutrition often contributes to increased insulin resistance, likely due to prolonged nutrient deficiency, which results in the loss of fat and muscle mass. This, in turn, disrupts insulin receptor function and leads to disorders in glucose metabolism.

A large-scale cohort study has shown that lipid- and blood pressure-lowering therapies significantly reduce cardiovascular risk and all-cause mortality in diabetic populations (26). In contrast, the absence of these interventions is associated with increased mortality in patients with PTDM (27). International guidelines for kidney transplantation recommend maintaining low-density lipoprotein cholesterol (LDL-C) levels below 2.59 mmol/L in post-transplant patients (28), which is consistent with our fundings.

In lung transplant recipients with preoperative dyslipidemia, clinicians should conduct comprehensive metabolic evaluations and implement early lipid-lowering strategies to optimize glycemic control. Moreover, a multidisciplinary approach involving transplant physicians, endocrinologists, nutritionists, and nursing staff is crucial for optimizing long-term prognosis in this high-risk population (29,30).

In this study, the association between a BMI ≥24 kg/m² and the risk of diabetes was marginally significant (P=0.052). This outcome may be attributed to the BMI distribution characteristics of the study population, as the overall low BMI of the sample likely weakened the comparative effects between different BMI strata. Notably, previous studies have reported similar findings, suggesting that the relationship between BMI and diabetes risk may differ across specific populations, and that baseline characteristics could influence the results (31,32).

Both underweight and obesity have been linked to increased cause-specific mortality following transplantation, highlighting the importance of maintaining a moderate BMI to optimize clinical outcomes (33,34). Recent reviews further emphasize obesity as a major risk factor for PTDM, stressing the need for vigilant BMI monitoring and management in transplant recipients (35).

This study suggests that preoperative cardiac insufficiency may be associated with an increased risk of developing PTDM following lung transplantation. Previous research has demonstrated that cardiac insufficiency activates the renin–angiotensin system, leading to elevated levels of angiotensin II. This, in turn, promotes pulmonary capillary vasoconstriction and increases pulmonary vascular resistance. These systemic hemodynamic and metabolic disturbances may contribute to insulin resistance, thereby elevating the risk of PTDM. Furthermore, many lung transplant recipients have preexisting pulmonary hypertension, which increases right ventricular afterload and predisposes them to right heart dysfunction. Surgical stress and postoperative inflammatory responses may further exacerbate metabolic dysregulation and insulin resistance in this population.

Our study suggests that a preoperative HbA1c >5.7% may be associated with an increased risk of developing postoperative PTDM in lung transplant patients, which is consistent with findings from previous studies (20,36,37). Elevated HbA1c levels indicate underlying insulin resistance or impaired glucose metabolism. Chronic hyperglycemia over time can lead to β-cell dysfunction or failure, which worsens insulin resistance and β-cell impairment. This combination increases the risk of PTDM following transplantation (38). We acknowledge that different studies have proposed varying HbA1c thresholds, which may be influenced by factors such as demographic differences or variations in measurement techniques (39,40).

According to the Chinese consensus paper, in the early postoperative phase (within 45 days), continuous glucose monitoring is recommended to promptly detect fluctuations in blood glucose levels. During the stable phase (after 45 days post-transplant), glycemic control targets should include fasting plasma glucose (FPG) <7.0 mmol/L, postprandial 2-hour blood glucose <10.0 mmol/L, and HbA1c <7.0%. Monitoring should occur at 3, 6, and 12 months postoperatively. For high-risk patients, more frequent monitoring and oral glucose tolerance test (OGTT) should be considered (41).

This study has several limitations. First, we did not collect detailed information on the use of immunosuppressive agents in lung transplant patients postoperatively, preventing a full assessment of how the type, dose, and duration of these drugs might influence the occurrence of PTDM (36). Additionally, the lack of primary graft dysfunction (PGD) data within 72 hours hindered our ability to explore the effects of initial transplant-related lung injury on glucose metabolism, especially in PGD patients, who may be at higher risk for diabetes. The absence of donor information further limited our capacity to evaluate how donor characteristics (such as age and health status) might impact postoperative complications and the development of PTDM. Moreover, the lack of perioperative cytomegalovirus (CMV) infection data prevented an analysis of CMV’s role in immunosuppression, inflammatory responses, and the onset of PTDM. These gaps in data may introduce bias and prevent a comprehensive assessment of all potential influencing factors.

In this study, data on the use of postoperative immunosuppressive drugs, donor-related factors, PGD, and CMV infection were not collected, which limits the ability to fully assess the mechanisms underlying postoperative PTDM. Postoperative immunosuppressive drugs, particularly steroids and calcineurin inhibitors, have been well-documented as significant contributors to diabetes development (36). As one of the common complications following transplantation, PTDM is influenced by various pathophysiological factors, including peripheral insulin resistance, immunosuppressive drugs, and infections (35). Due to the absence of detailed data on these drugs, the study could not comprehensively evaluate their specific role in the onset of postoperative diabetes. Furthermore, donor preservation conditions, processing methods, and genetic factors are crucial in influencing organ function after transplantation, which may indirectly contribute to the development of PTDM (42). Factors such as PGD and CMV infection could also play a role in the development of PTDM, but the lack of data on these variables prevents an exploration of their potential contributions (36). Besides, all patients had a minimum postoperative follow-up of more than 6 months. However, the subsequent duration of follow-up varied among patients, so the follow-up period of this study was limited to 6 months post-surgery, it may have failed to capture cases of PTDM that occur later. Since some patients develop PTDM beyond 6 months, focusing solely on short-term outcomes may underestimate the true long-term risk.

Our findings suggest that preoperative cardiac insufficiency, dyslipidemia, elevated HbA1c, advanced age, and low GNRI may identify lung transplant recipients at higher risk for PTDM. These observations highlight the potential value of comprehensive preoperative metabolic and nutritional assessment, targeted perioperative monitoring, and multidisciplinary follow-up, including nursing-led interventions such as patient education on blood glucose monitoring, individualized dietary counseling, and reinforcement of medication adherence. However, as this study is retrospective, prospective studies are needed to confirm these associations and determine whether interventions targeting these risk factors can effectively reduce PTDM incidence.

To address missing data, this study employed multiple imputation methods, which help mitigate bias. However, this approach cannot completely eliminate the potential impact of missing data on the results. Additionally, as a retrospective study relying on existing clinical records, it is susceptible to selection and information biases. Consequently, while correlations between variables are described, causal relationships cannot be established. These data gaps may introduce bias, limiting the ability to conduct a comprehensive assessment of all potential influencing factors. Data incompleteness may introduce bias, limiting the ability to conduct a comprehensive assessment of all potential influencing factors. This study was conducted at a single-center, which may limit the generalizability of our findings; therefore, further multi-studies are warranted to validate these findings in broader populations.

Conclusions

This study identified several factors that may be associated with an increased risk of developing PTDM following lung transplantation. These findings highlight the multifactorial nature of PTDM pathogenesis in lung transplant recipients and underscore the need for comprehensive perioperative management. Although causality cannot be established due to the retrospective design, the study highlights key factors linked to PTDM after lung transplantation. But early identification and targeted interventions addressing modifiable risk factors may improve postoperative outcomes and reduce the incidence of PTDM in this high-risk patient population.

Acknowledgments

None.

Footnote

Reporting Checklist: The authors have completed the STROBE reporting checklist. Available at https://jtd.amegroups.com/article/view/10.21037/jtd-2025-1300/rc

Data Sharing Statement: Available at https://jtd.amegroups.com/article/view/10.21037/jtd-2025-1300/dss

Peer Review File: Available at https://jtd.amegroups.com/article/view/10.21037/jtd-2025-1300/prf

Funding: This work was supported by the Guangdong Provincial Medical Science and Technology Research Fund, China (grant No. A2023415).

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://jtd.amegroups.com/article/view/10.21037/jtd-2025-1300/coif). The authors have no conflicts of interest to declare.

Ethical Statement: The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. The study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments. The study was approved by the Medical Ethics Committee of The First Affiliated Hospital of Guangzhou Medical University (approval number: ES-2023-225-01). Informed consent was obtained from all participants prior to their inclusion in the study.

Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0/.

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