Introduction: The relationship between BMI and early renal function recovery after kidney transplantation is important due to the rising global obesity rates. Methods: A retrospective study on 320 patients who received allograft kidney transplantation at Guangxi Medical University Hospital explored the BMI-kidney function relationship using various statistical methods. Mendelian randomization (MR) was also employed to investigate causality. Results: Based on the univariate analysis, multivariate linear regression models, and trend analysis, it was found that there were significant positive correlations between BMI and creatinine, urea, and cystatin C on the 7th day after kidney transplantation (p < 0.05). The sensitivity analysis further confirmed these correlations in different gender stratification, adolescents, and adults. However, the positive correlation with cystatin C was only significant in males. Additionally, after conducting smooth curve fitting analysis and threshold saturation analysis, it was revealed that the negative correlation between early renal function recovery was most significant when BMI was between 22.0 and 25.5 kg/m2, and early postoperative renal function may be optimal when BMI was at 22.2 kg/m2. Finally, the MR analysis confirmed a causal relationship between BMI and renal failure, as indicated by the IVW method (p = 0.003), as well as the weighted median estimator (p = 0.004). Conclusion: This study on kidney transplant patients found that maintaining a BMI within the range of 22.0–25.5 kg/m2, with an optimal BMI of 22.2 kg/m2, improves early renal function recovery. This correlation holds true for different age-groups and genders. Monitoring and controlling BMI in high-risk patients can enhance post-transplantation renal function.

Renal transplantation is considered the best treatment for patients with end-stage renal disease and can improve their quality of life [1‒3]. Although kidney transplantation technology has gradually matured, attention is still being paid to the long-term survival rate after transplantation [3]. Research indicates that delayed recovery of renal function after kidney transplantation can affect the long-term survival rate. Delayed renal function after transplantation is commonly characterized by the need for one or more dialysis treatments within the first week, with an incidence ranging from 2 to 50% [4]. Therefore, it is necessary to explore further the factors that affect renal function recovery. Research has shown that obesity is a risk factor for early and late complications after kidney transplantation [5, 6]. Furthermore, with the increasing prevalence of overweight and obese patients, the proportion of obese kidney transplant recipients continues to rise [7]. However, previous studies have failed to clarify the relationship between body mass index (BMI) and early renal function recovery after transplantation.

Thus, to comprehensively evaluate the potential two-way relationship between BMI and renal function after kidney transplantation, we used an observational study method and a Mendelian randomization (MR) approach. This study aims to investigate the relationship between BMI and early renal function recovery after transplantation, aiming to provide more targeted measures to improve the long-term survival rate of kidney transplant patients.

Observational Study

Research Object

This retrospective study selected kidney transplant patients at the Second Affiliated Hospital of Guangxi Medical University from 2019 to 2021 for analysis. Inclusion criteria for donors were as follows: (1) No serious disease of other organ systems, (2) no urinary system malformation based on IVP examination, (3) radioisotope renogram indicating bilateral single kidney glomerular filtration rate ≥40 mL/min/1.73 m2, (4) meeting normal surgical criteria based on other examinations, and (5) bilateral single kidney GFR relaxed to ≥35 mL/min/1.73 m2 with normal serum creatinine for clarity. Receptor inclusion criteria were as follows: (1) First kidney transplant surgery, (2) age 18–65 years old. The exclusion criteria were as follows: (1) Patients undergoing combined transplantation, (2) patients who had failed transplantation because of rejection or surgical reasons.

According to the inclusion and exclusion criteria, this study included 320 kidney transplant patients aged 40.03 ± 10.39 years. This study was approved by the Ethics Committee of the Second Affiliated Hospital of Guangxi Medical University (KY-0385). This study was retrospective, and no informed consent was required.

Variables

Independent Variable. Demographic data of patients included age, sex, height, weight and BMI, laboratory indicators, and perioperative data: operation time, blood loss, crystal intake, direct bilirubin, indirect bilirubin, albumin, globulin, alanine aminotransferase, glutamine aminotransferase, glutamine aminotransferase/glutamine aminotransferase, white blood cells, red blood cells, hemoglobin hematocrit, the proportion of platelets, neutrophils, prothrombin time, coagulation time, and fibrinogen.

BMI. The recipient’s BMI is calculated based on the following formula: (weight [kg]/height [m]2). According to Chinese guidelines [8], patients are classified as lean (BMI ≤18.4 kg/m2), normal weight (BMI 18.5–23.9 kg/m2), overweight (24–27.9 kg/m2), and obese (BMI ≥28 kg/m2).

Outcome Variables. Creatinine, urea, and cystatin C levels on the seventh day after renal transplantation were the outcome variables.

MR Study

Acquired Genetic Variant Data

We acquired data from MR Base (http://app.Mrbase.org), a platform for MR research that compiles and summarizes data from genome-wide association studies (GWASs). We used publicly available summary statistics datasets from GWAS meta-analyses for BMI of Europeans (n = 339,224; GIANT) [9] as the exposure. Based on the p value threshold (genome-wide significance) of 5.00E−08, a two-sample MR study was conducted on the genetic variation related to BMI as an instrumental variable estimation for improved inference. We obtained summary statistical data of 78 single-nucleotide polymorphisms (SNPs) related to BMI (β coefficient and standard errors) as the instrumental variable estimation of GWAS on BMI. We used GWAS’s publicly aggregated statistical dataset: renal failure (case = 5,951, control = 212,841), as the result (Table 1).

Table 1.

Details of studies and datasets used in the study

Exposure/outcomesWeb sourceNumber of variantsNumber of instruments usedYearPopulation studied
BMI https://pubmed.ncbi.nlm.nih.gov/25673413/ 2,555,511 79 2015 European 
Renal failure https://app.mrbase.org/ 16,380,466 78 2021 European 
Exposure/outcomesWeb sourceNumber of variantsNumber of instruments usedYearPopulation studied
BMI https://pubmed.ncbi.nlm.nih.gov/25673413/ 2,555,511 79 2015 European 
Renal failure https://app.mrbase.org/ 16,380,466 78 2021 European 

Statistical Analysis

Observation of Correlation between BMI and Renal Function after Kidney Transplantation

Normal distribution variables are expressed as mean ± standard deviation (SD), non-normal distribution variables are expressed as median (quartile), and non-parametric tests are used. Qualitative data are represented as numbers and percentages (n, %). First, BMI classification is as follows: lean: BMI ≤18.4 kg/m2, normal weight: BMI 18.5–23.9 kg/m2, overweight and obese: BMI ≥24 kg/m2 (overweight and obesity are grouped together due to limited obese patients); then we described the demographic characteristics and related clinical data of the subjects (Table 2). Second, in the univariate analysis model (Table 3), we explored whether the patient’s BMI and other variables were related to renal function (creatinine, urea, cystatin C) after kidney transplantation. Then, in the first model, BMI was included as the variable and renal dysfunction was the dependent variable. The second model was further adjusted to include gender and age. The third model included all variables to analyze the relationship between BMI and short-term renal dysfunction after kidney transplantation. In the trend test, we stratified the BMI for analysis (Tables 4-6). In the sensitivity analysis, we stratified gender and age (youth: 18–35 years old, adulthood: 36–50 years old, and middle-aged and elderly: >50 years old), conducted multiple linear regression analysis on different genders and ages (Tables 4-6). Then, the relationship between BMI and the renal function after kidney transplantation (creatinine, urea, cystatin C) was explored by smoothing the curve fitting of potential confounding factors (Fig. 1a, b). Finally, we used a multivariate segmented linear regression model to evaluate the independent correlation between renal function and BMI after kidney transplantation (Table 7). p < 0.05 is considered statistically significant. All analyses were conducted using R software (R version: 4.0.3).

Table 2.

Demographic characteristics, surgical information, and biochemical values of the subjects

BMILeanNormalOverweight and obesep value
(≤18.4 kg/m2)(18.5–23.9 kg/m2)(≥24 kg/m2)
N 51 190 79  
CR7, μmol/L 150.0 (109.5–206.0) 147.0 (107.2–251.0) 206.0 (133.0–468.5) <0.001* 
BUN7, mmol/L 14.0 (9.7–21.7) 13.8 (9.4–22.4) 18.6 (12.2–35.3) <0.001* 
Cysc7, mg/L 2.4 (1.9–3.4) 2.4 (1.8–3.6) 3.1 (2.2–5.0) 0.003* 
Saline, ml 1,325.0 (1,183.7–1,625.0) 1,350.0 (1,225.0–1,543.8) 1,325.0 (1,225.0–1,705.0) 0.963 
DIBL, μmol/L 2.4 (2.0–2.8) 2.4 (2.0–3.2) 2.3 (1.9–2.9) 0.346 
IBIL, μmol/L 3.3 (2.5–4.2) 3.2 (2.5–4.2) 3.2 (2.3–4.1) 0.55 
ALB, g/L 47.31±3.47 44.81±5.09 44.13±5.06 <0.001* 
GLB, g/L 28.78±5.55 28.30±4.84 29.08±4.60 0.47 
AST, U/L 12.0 (9.0–15.0) 13.0 (9.0–16.0) 13.0 (10.0–17.0) 0.487 
ALT, U/L 8.0 (6.0–13.0) 11.0 (8.0–14.0) 10.0 (7.5–16.0) 0.671 
AST/ALT 1.3 (0.9–1.6) 1.1 (0.8–1.5) 1.1 (0.7–1.4) 0.47 
WBC (×109/L) 6.2 (5.4–7.6) 6.2 (5.2–7.8) 6.8 (5.7–7.9) 0.535 
RBC (×1012/L) 3.93±0.83 3.93±0.85 3.79±0.81 0.404 
HGB, g/L 109.14±21.99 112.68±21.27 110.11±21.62 0.467 
HCT, % 0.34±0.07 0.34±0.06 0.34±0.07 0.621 
PLT (×109/L) 204.40±69.27 209.32±73.48 204.83±62.14 0.843 
NEU, % 0.69±0.09 0.70±0.10 0.68±0.09 0.416 
APTT, s 32.3 (30.0–33.8) 31.6 (29.9–34.3) 30.8 (28.9–33.0) 0.391 
FIB, g/L 3.0 (2.7–3.6) 3.4 (2.9–3.8) 3.9 (3.5–4.4) <0.001* 
PT, s 10.6 (10.3–11.0) 10.5 (10.1–11.0) 10.6 (10.1–10.9) 0.815 
Blood loss volume, mL 50.0 (50.0–80.0) 50.0 (50.0–60.0) 50.0 (50.0–60.0) 0.748 
Time of operation, min 175.0 (150.0–194.5) 174.0 (150.0–203.8) 180.0 (150.0–197.0) 0.885 
Sex, n (%)    <0.001* 
 Female 29 (56.86) 72 (37.89) 16 (20.25)  
 Male 22 (43.14) 118 (62.11) 63 (79.75)  
Age, n (%)    <0.001* 
 Teenagers (18–35 years) 36 (70.59) 67 (35.26) 18 (22.78)  
 Adults (36–50 years) 14 (27.45) 88 (46.32) 36 (45.57)  
 Middle aged and elderly (>50 years) 1 (1.96) 35 (18.42) 25 (31.65)  
BMILeanNormalOverweight and obesep value
(≤18.4 kg/m2)(18.5–23.9 kg/m2)(≥24 kg/m2)
N 51 190 79  
CR7, μmol/L 150.0 (109.5–206.0) 147.0 (107.2–251.0) 206.0 (133.0–468.5) <0.001* 
BUN7, mmol/L 14.0 (9.7–21.7) 13.8 (9.4–22.4) 18.6 (12.2–35.3) <0.001* 
Cysc7, mg/L 2.4 (1.9–3.4) 2.4 (1.8–3.6) 3.1 (2.2–5.0) 0.003* 
Saline, ml 1,325.0 (1,183.7–1,625.0) 1,350.0 (1,225.0–1,543.8) 1,325.0 (1,225.0–1,705.0) 0.963 
DIBL, μmol/L 2.4 (2.0–2.8) 2.4 (2.0–3.2) 2.3 (1.9–2.9) 0.346 
IBIL, μmol/L 3.3 (2.5–4.2) 3.2 (2.5–4.2) 3.2 (2.3–4.1) 0.55 
ALB, g/L 47.31±3.47 44.81±5.09 44.13±5.06 <0.001* 
GLB, g/L 28.78±5.55 28.30±4.84 29.08±4.60 0.47 
AST, U/L 12.0 (9.0–15.0) 13.0 (9.0–16.0) 13.0 (10.0–17.0) 0.487 
ALT, U/L 8.0 (6.0–13.0) 11.0 (8.0–14.0) 10.0 (7.5–16.0) 0.671 
AST/ALT 1.3 (0.9–1.6) 1.1 (0.8–1.5) 1.1 (0.7–1.4) 0.47 
WBC (×109/L) 6.2 (5.4–7.6) 6.2 (5.2–7.8) 6.8 (5.7–7.9) 0.535 
RBC (×1012/L) 3.93±0.83 3.93±0.85 3.79±0.81 0.404 
HGB, g/L 109.14±21.99 112.68±21.27 110.11±21.62 0.467 
HCT, % 0.34±0.07 0.34±0.06 0.34±0.07 0.621 
PLT (×109/L) 204.40±69.27 209.32±73.48 204.83±62.14 0.843 
NEU, % 0.69±0.09 0.70±0.10 0.68±0.09 0.416 
APTT, s 32.3 (30.0–33.8) 31.6 (29.9–34.3) 30.8 (28.9–33.0) 0.391 
FIB, g/L 3.0 (2.7–3.6) 3.4 (2.9–3.8) 3.9 (3.5–4.4) <0.001* 
PT, s 10.6 (10.3–11.0) 10.5 (10.1–11.0) 10.6 (10.1–10.9) 0.815 
Blood loss volume, mL 50.0 (50.0–80.0) 50.0 (50.0–60.0) 50.0 (50.0–60.0) 0.748 
Time of operation, min 175.0 (150.0–194.5) 174.0 (150.0–203.8) 180.0 (150.0–197.0) 0.885 
Sex, n (%)    <0.001* 
 Female 29 (56.86) 72 (37.89) 16 (20.25)  
 Male 22 (43.14) 118 (62.11) 63 (79.75)  
Age, n (%)    <0.001* 
 Teenagers (18–35 years) 36 (70.59) 67 (35.26) 18 (22.78)  
 Adults (36–50 years) 14 (27.45) 88 (46.32) 36 (45.57)  
 Middle aged and elderly (>50 years) 1 (1.96) 35 (18.42) 25 (31.65)  

Mean ± SD/median (Q1-Q3)/n (%). CR7, creatinine on the 7th day after transplantation; BUN7, urea on the 7th day after transplantation; CysC7, cystatin C on the 7th day after transplantation; HGB, hemoglobin; RBC, red blood cells; WBC, white blood cells; PLT, platelets; HCT, erythrocyte specific volume; FIB, fibrinogen; NEU%, centrocyte ratio; APTT, activated partial thromboplastin time; PT, prothrombin time; ALB, leukocyte; GLB, globulin; DBIL, direct bilirubin; IBIL, indirect bilirubin; AST, aspartate aminotransferase; ALT, alanine aminotransferase; p value: if the variable is continuous, it is obtained by Kruskal-Wallis rank-sum test; if the counting variable has a theoretical number <10, it is obtained by Fisher exact probability test.

*p < 0.05.

Table 3.

Single-factor analysis

VariablesCR7BUN7CysC7
β (95% CI) p valueβ (95% CI) p valueβ (95% CI) p value
Age (years) −0.69 (−3.41, 2.02) 0.616 0.1 (−0.03, 0.22) 0.121 0.01 (−0.01, 0.03) 0.323 
Height (cm) 7.45 (3.81, 11.10) 0.000* 0.28 (0.11, 0.44) 0.001* 0.03 (0.01, 0.06) 0.010* 
Weight (kg) 6.29 (3.89, 8.69) 0.000* 0.29 (0.18, 0.40) <0.0001* 0.03 (0.02, 0.05) <0.0001* 
BMI (kg/m216.03 (7.72, 24.33) 0.000* 0.82 (0.44, 1.20) <0.0001* 0.09 (0.04, 0.15) 0.002* 
Time of operation (min) 0.09 (−0.57, 0.74) 0.792 0.01 (−0.02, 0.04) 0.397 0.00 (−0.00, 0.01) 0.670 
Blood loss volume (mL) −0.19 (−0.60, 0.23) 0.383 −0.002 (−0.02, 0.02) 0.740 0.00 (−0.00, 0.00) 0.443 
Saline (mL) −0.05 (−0.13, 0.03) 0.194 −0.00 (−0.01, 0.00) 0.378 0.00 (−0.00, 0.00) 0.066 
DBIL (μmol/L) 6.80 (−11.73, 25.33) 0.471 0.66 (−0.20, 1.51) 0.133 0.08 (−0.05, 0.21) 0.2109 
IBIL (μmol/L) 9.38 (−9.29, 28.06) 0.324 0.56 (−0.28, 1.41) 0.193 0.05 (−0.08, 0.18) 0.4613 
ALB (g/L) 3.25 (−2.42, 8.93) 0.260 0.11 (−0.15, 0.37) 0.398 0.01 (−0.03, 0.05) 0.5381 
GLB (g/L) 3.56 (−2.18, 9.31) 0.223 0.06 (−0.20, 0.32) 0.653 0.02 (−0.02, 0.06) 0.3625 
AST (U/L) 1.59 (−1.74, 4.92) 0.348 0.02 (−0.14, 0.17) 0.821 0.01 (−0.02, 0.03) 0.6119 
ALT (U/L) 4.98 (2.35, 7.61) 0.000* 0.18 (0.06, 0.30) 0.004* 0.03 (0.01, 0.05) 0.0026* 
AST/ALT −13.08 (−30.81, 4.66) 0.148 −0.69 (−1.50, 0.12) 0.098 −0.07 (−0.20, 0.05) 0.235 
WBC (×109/L) 6.07 (−7.29, 19.44) 0.372 0.29 (−0.32, 0.90) 0.356 0.06 (−0.03, 0.15) 0.1931 
RBC (×1012/L) 31.75 (−1.78, 65.28) 0.063 0.70 (−0.85, 2.24) 0.376 0.19 (−0.04, 0.42) 0.1026 
HGB (g/L) 0.81 (−0.51, 2.12) 0.227 0.03 (−0.03, 0.09) 0.355 0.01 (−0.00, 0.02) 0.1585 
HCT (%) 318.73 (−107.36, 744.82) 0.142 10.68 (−8.89, 30.24) 0.286 2.48 (−0.45, 5.40) 0.0978 
PLT (×109/L) −0.06 (−0.46, 0.35) 0.778 0.00 (−0.02, 0.02) 0.913 0.00 (−0.00, 0.00) 0.6946 
NEU (%) −105.30 (−410.32, 199.71) 0.497 −0.31 (−14.30, 13.68) 0.965 −0.10 (−2.20, 1.99) 0.9230 
APTT (s) −0.15 (−0.93, 0.64) 0.715 0.00 (−0.02, 0.02) 0.913 0.00 (−0.00, 0.01) 0.2670 
PT (s) −0.60 (−4.46, 3.26) 0.760 0.06 (−0.11, 0.24) 0.492 −0.00 (−0.03, 0.02) 0.8591 
FIB (g/L) 11.20 (−26.64, 49.04) 0.561 0.60 (−1.14, 2.33) 0.502 0.14 (−0.12, 0.40) 0.2884 
Sex 
 Female Reference Reference Reference 
 Male 76.62 (18.77, 134.47) 0.010* 2.21 (−0.46, 4.88) 0.105 0.23 (−0.17, 0.63) 0.257 
VariablesCR7BUN7CysC7
β (95% CI) p valueβ (95% CI) p valueβ (95% CI) p value
Age (years) −0.69 (−3.41, 2.02) 0.616 0.1 (−0.03, 0.22) 0.121 0.01 (−0.01, 0.03) 0.323 
Height (cm) 7.45 (3.81, 11.10) 0.000* 0.28 (0.11, 0.44) 0.001* 0.03 (0.01, 0.06) 0.010* 
Weight (kg) 6.29 (3.89, 8.69) 0.000* 0.29 (0.18, 0.40) <0.0001* 0.03 (0.02, 0.05) <0.0001* 
BMI (kg/m216.03 (7.72, 24.33) 0.000* 0.82 (0.44, 1.20) <0.0001* 0.09 (0.04, 0.15) 0.002* 
Time of operation (min) 0.09 (−0.57, 0.74) 0.792 0.01 (−0.02, 0.04) 0.397 0.00 (−0.00, 0.01) 0.670 
Blood loss volume (mL) −0.19 (−0.60, 0.23) 0.383 −0.002 (−0.02, 0.02) 0.740 0.00 (−0.00, 0.00) 0.443 
Saline (mL) −0.05 (−0.13, 0.03) 0.194 −0.00 (−0.01, 0.00) 0.378 0.00 (−0.00, 0.00) 0.066 
DBIL (μmol/L) 6.80 (−11.73, 25.33) 0.471 0.66 (−0.20, 1.51) 0.133 0.08 (−0.05, 0.21) 0.2109 
IBIL (μmol/L) 9.38 (−9.29, 28.06) 0.324 0.56 (−0.28, 1.41) 0.193 0.05 (−0.08, 0.18) 0.4613 
ALB (g/L) 3.25 (−2.42, 8.93) 0.260 0.11 (−0.15, 0.37) 0.398 0.01 (−0.03, 0.05) 0.5381 
GLB (g/L) 3.56 (−2.18, 9.31) 0.223 0.06 (−0.20, 0.32) 0.653 0.02 (−0.02, 0.06) 0.3625 
AST (U/L) 1.59 (−1.74, 4.92) 0.348 0.02 (−0.14, 0.17) 0.821 0.01 (−0.02, 0.03) 0.6119 
ALT (U/L) 4.98 (2.35, 7.61) 0.000* 0.18 (0.06, 0.30) 0.004* 0.03 (0.01, 0.05) 0.0026* 
AST/ALT −13.08 (−30.81, 4.66) 0.148 −0.69 (−1.50, 0.12) 0.098 −0.07 (−0.20, 0.05) 0.235 
WBC (×109/L) 6.07 (−7.29, 19.44) 0.372 0.29 (−0.32, 0.90) 0.356 0.06 (−0.03, 0.15) 0.1931 
RBC (×1012/L) 31.75 (−1.78, 65.28) 0.063 0.70 (−0.85, 2.24) 0.376 0.19 (−0.04, 0.42) 0.1026 
HGB (g/L) 0.81 (−0.51, 2.12) 0.227 0.03 (−0.03, 0.09) 0.355 0.01 (−0.00, 0.02) 0.1585 
HCT (%) 318.73 (−107.36, 744.82) 0.142 10.68 (−8.89, 30.24) 0.286 2.48 (−0.45, 5.40) 0.0978 
PLT (×109/L) −0.06 (−0.46, 0.35) 0.778 0.00 (−0.02, 0.02) 0.913 0.00 (−0.00, 0.00) 0.6946 
NEU (%) −105.30 (−410.32, 199.71) 0.497 −0.31 (−14.30, 13.68) 0.965 −0.10 (−2.20, 1.99) 0.9230 
APTT (s) −0.15 (−0.93, 0.64) 0.715 0.00 (−0.02, 0.02) 0.913 0.00 (−0.00, 0.01) 0.2670 
PT (s) −0.60 (−4.46, 3.26) 0.760 0.06 (−0.11, 0.24) 0.492 −0.00 (−0.03, 0.02) 0.8591 
FIB (g/L) 11.20 (−26.64, 49.04) 0.561 0.60 (−1.14, 2.33) 0.502 0.14 (−0.12, 0.40) 0.2884 
Sex 
 Female Reference Reference Reference 
 Male 76.62 (18.77, 134.47) 0.010* 2.21 (−0.46, 4.88) 0.105 0.23 (−0.17, 0.63) 0.257 

CR7, creatinine on the 7th day after transplantation; BUN7, urea on the 7th day after transplantation; CysC7, cystatin C on the 7th day after transplantation; HGB, hemoglobin, RBC, red blood cells; WBC, white blood cells; PLT, platelets; HCT, erythrocyte specific volume; FIB, fibrinogen; NEU%, centrocyte ratio; APTT, activated partial thromboplastin time; PT, prothrombin time; ALB, leukocyte; GLB, globulin; DBIL, direct bilirubin; IBIL, indirect bilirubin; AST, aspartate aminotransferase; ALT, alanine aminotransferase.

*p < 0.05.

Table 4.

Relationship between BMI and creatinine on the 7th day after renal transplantation in the same model, trend, and sensitivity analysis

Crude modelMinimally adjusted modelFully adjusted model
β (95% CI) p valueβ (95% CI) p valueβ (95% CI) p value
BMI 16.03 (7.72, 24.34) 0.000* 17.54 (8.44, 26.64), 0.000 * 17.41 (7.45, 27.36) 0.001* 
BMI groups 
 Normal (18.5–23.9 kg/m2Reference Reference Reference 
 Lean (≤18.4 kg/m2−17.94 (−95.19, 59.30) 0.649 −26.91 (−108.09, 54.27) 0.516 −31.58 (−118.00, 54.84) 0.475 
 Overweight and obese (≥24 kg/m2127.87 (62.30, 193.44) 0.000* 127.28 (60.35, 194.20) 0.000* 128.42 (56.05, 200.80) 0.001* 
p for trend <0.001* <0.001* 0.001* 
Stratified by gender 
 Male 14.54 (2.65, 26.43) 0.018* 19.65 (6.76, 32.53) 0.003* 22.63 (7.22, 38.04) 0.005* 
 Female 13.71 (3.05, 24.38) 0.013* 14.61 (3.36, 25.87) 0.012* 15.86 (1.82, 29.90) 0.030* 
Stratified by age 
 Teenagers (18–35 years) 22.87 (7.21, 38.52) 0.005* 20.74 (4.91, 36.57) 0.012* 24.14 (3.43, 44.86) 0.025* 
 Adults (36–50 years) 20.81 (9.55, 32.06) 0.000* 19.50 (7.77, 31.23) 0.001* 24.24 (10.07, 38.42) 0.001* 
 Middle aged and elderly (>50 years) 6.95 (−15.32, 29.23) 0.543 5.50 (−17.65, 28.65) 0.643 10.07 (−24.86, 5.00) 0.576 
Crude modelMinimally adjusted modelFully adjusted model
β (95% CI) p valueβ (95% CI) p valueβ (95% CI) p value
BMI 16.03 (7.72, 24.34) 0.000* 17.54 (8.44, 26.64), 0.000 * 17.41 (7.45, 27.36) 0.001* 
BMI groups 
 Normal (18.5–23.9 kg/m2Reference Reference Reference 
 Lean (≤18.4 kg/m2−17.94 (−95.19, 59.30) 0.649 −26.91 (−108.09, 54.27) 0.516 −31.58 (−118.00, 54.84) 0.475 
 Overweight and obese (≥24 kg/m2127.87 (62.30, 193.44) 0.000* 127.28 (60.35, 194.20) 0.000* 128.42 (56.05, 200.80) 0.001* 
p for trend <0.001* <0.001* 0.001* 
Stratified by gender 
 Male 14.54 (2.65, 26.43) 0.018* 19.65 (6.76, 32.53) 0.003* 22.63 (7.22, 38.04) 0.005* 
 Female 13.71 (3.05, 24.38) 0.013* 14.61 (3.36, 25.87) 0.012* 15.86 (1.82, 29.90) 0.030* 
Stratified by age 
 Teenagers (18–35 years) 22.87 (7.21, 38.52) 0.005* 20.74 (4.91, 36.57) 0.012* 24.14 (3.43, 44.86) 0.025* 
 Adults (36–50 years) 20.81 (9.55, 32.06) 0.000* 19.50 (7.77, 31.23) 0.001* 24.24 (10.07, 38.42) 0.001* 
 Middle aged and elderly (>50 years) 6.95 (−15.32, 29.23) 0.543 5.50 (−17.65, 28.65) 0.643 10.07 (−24.86, 5.00) 0.576 

*p < 0.05.

Table 5.

Relationship between BMI and urea on the 7th day after renal transplantation in the same model, trend, and sensitivity analysis

Crude modelMinimally adjusted modelFully adjusted model
β (95% CI) p valueβ (95% CI) p valueβ (95% CI) p value
BMI 0.82 (0.44, 1.20) 0.000* 0.78 (0.36, 1.20) 0.000 * 0.84 (0.38, 1.31) 0.000* 
BMI groups 
 Normal (18.5–23.9 kg/m2Reference Reference Reference 
 Lean (≤18.4 kg/m2−0.53 (−4.05, 2.99) 0.768 −0.07 (−3.80, 3.66) 0.972 0.52 (−3.47, 4.50) 0.790 
 Overweight and obese (≥24 kg/m26.99 (4.00, 9.98) <0.0001* 6.69 (3.61, 9.76) <0.0001* 7.21 (3.88, 10.55) <0.0001* 
p for trend <0.001 <0.001 0.002 
Stratified by gender 
 Male 0.79 (0.28, 1.30) 0.003* 0.79 (0.23, 1.35) 0.006* 0.94 (0.28, 1.61) 0.006* 
 Female 0.79 (0.20, 1.38) 0.010* 0.77 (0.14, 1.39) 0.018* 0.87 (0.11, 1.64) 0.028* 
Stratified by age 
 Teenagers (18–35 years) 0.97 (0.36, 1.57) 0.002* 0.92 (0.31, 1.53) 0.004* 1.02 (0.20, 1.83) 0.016* 
 Adults (36–50 years) 0.71 (0.14, 1.28) 0.016* 0.67 (0.07, 1.27) 0.029* 0.79 (0.06, 1.52) 0.037* 
 Middle aged and elderly (>50 years) 0.79 (−0.50, 2.07) 0.234 0.81 (−0.53, 2.14) 0.241 1.90 (−0.12, 3.92) 0.075 
Crude modelMinimally adjusted modelFully adjusted model
β (95% CI) p valueβ (95% CI) p valueβ (95% CI) p value
BMI 0.82 (0.44, 1.20) 0.000* 0.78 (0.36, 1.20) 0.000 * 0.84 (0.38, 1.31) 0.000* 
BMI groups 
 Normal (18.5–23.9 kg/m2Reference Reference Reference 
 Lean (≤18.4 kg/m2−0.53 (−4.05, 2.99) 0.768 −0.07 (−3.80, 3.66) 0.972 0.52 (−3.47, 4.50) 0.790 
 Overweight and obese (≥24 kg/m26.99 (4.00, 9.98) <0.0001* 6.69 (3.61, 9.76) <0.0001* 7.21 (3.88, 10.55) <0.0001* 
p for trend <0.001 <0.001 0.002 
Stratified by gender 
 Male 0.79 (0.28, 1.30) 0.003* 0.79 (0.23, 1.35) 0.006* 0.94 (0.28, 1.61) 0.006* 
 Female 0.79 (0.20, 1.38) 0.010* 0.77 (0.14, 1.39) 0.018* 0.87 (0.11, 1.64) 0.028* 
Stratified by age 
 Teenagers (18–35 years) 0.97 (0.36, 1.57) 0.002* 0.92 (0.31, 1.53) 0.004* 1.02 (0.20, 1.83) 0.016* 
 Adults (36–50 years) 0.71 (0.14, 1.28) 0.016* 0.67 (0.07, 1.27) 0.029* 0.79 (0.06, 1.52) 0.037* 
 Middle aged and elderly (>50 years) 0.79 (−0.50, 2.07) 0.234 0.81 (−0.53, 2.14) 0.241 1.90 (−0.12, 3.92) 0.075 

*p < 0.05.

Table 6.

Relationship between BMI and cystatin C on the 7th day after renal transplantation in the same model, trend, and sensitivity analysis

Crude modelMinimally adjusted modelFully adjusted model
β (95% CI) p valueβ (95% CI) p valueβ (95% CI) p value
BMI 0.09 (0.04, 0.15) 0.001 0.09 (0.03, 0.16) 0.004 0.09 (0.02, 0.15) 0.017 
BMI groups 
 Normal (18.5–23.9 kg/m2Reference Reference Reference 
 Lean (≤18.4 kg/m2−0.09 (−0.62, 0.45) 0.217 −0.05 (−0.62, 0.51) 0.851 0.08 (−0.52, 0.68) 0.793 
 Overweight and obese (≥24 kg/m20.75 (0.30, 1.21) 0.001* 0.73 (0.26, 1.20) 0.003* 0.75 (0.25, 1.26) 0.004* 
p for trend 0.003* 0.009* 0.031* 
Stratified by gender 
 Male 0.09 (0.01, 0.16) 0.023* 0.09 (0.01, 0.17) 0.024* 0.10 (0.00, 0.20) 0.041* 
 Female 0.10 (0.00, 0.20) 0.050* 0.09 (−0.01, 0.20) 0.080* 0.09 (−0.04, 0.22) 0.181 
Stratified by age 
 Teenagers (18–35 years) 0.09 (0.00, 0.18) 0.050 0.09 (−0.01, 0.18) 0.075 0.09 (−0.03, 0.21) 0.160 
 Adults (36–50 years) 0.10 (0.01, 0.19) 0.031 0.10 (0.01, 0.19) 0.039 0.11 (−0.01, 0.22) 0.0680 
 Middle aged and elderly (>50 years) 0.11 (−0.08, 0.30) 0.250 0.12 (−0.08, 0.31) 0.240 0.19 (−0.10, 0.47) 0.206 
Crude modelMinimally adjusted modelFully adjusted model
β (95% CI) p valueβ (95% CI) p valueβ (95% CI) p value
BMI 0.09 (0.04, 0.15) 0.001 0.09 (0.03, 0.16) 0.004 0.09 (0.02, 0.15) 0.017 
BMI groups 
 Normal (18.5–23.9 kg/m2Reference Reference Reference 
 Lean (≤18.4 kg/m2−0.09 (−0.62, 0.45) 0.217 −0.05 (−0.62, 0.51) 0.851 0.08 (−0.52, 0.68) 0.793 
 Overweight and obese (≥24 kg/m20.75 (0.30, 1.21) 0.001* 0.73 (0.26, 1.20) 0.003* 0.75 (0.25, 1.26) 0.004* 
p for trend 0.003* 0.009* 0.031* 
Stratified by gender 
 Male 0.09 (0.01, 0.16) 0.023* 0.09 (0.01, 0.17) 0.024* 0.10 (0.00, 0.20) 0.041* 
 Female 0.10 (0.00, 0.20) 0.050* 0.09 (−0.01, 0.20) 0.080* 0.09 (−0.04, 0.22) 0.181 
Stratified by age 
 Teenagers (18–35 years) 0.09 (0.00, 0.18) 0.050 0.09 (−0.01, 0.18) 0.075 0.09 (−0.03, 0.21) 0.160 
 Adults (36–50 years) 0.10 (0.01, 0.19) 0.031 0.10 (0.01, 0.19) 0.039 0.11 (−0.01, 0.22) 0.0680 
 Middle aged and elderly (>50 years) 0.11 (−0.08, 0.30) 0.250 0.12 (−0.08, 0.31) 0.240 0.19 (−0.10, 0.47) 0.206 

*p < 0.05.

Fig. 1.

Smoothed fit curves, association between BMI and early postoperative renal function (creatinine, urea, cystatin C). a Smooth-fitting line of BMI and creatinine on day 7 after renal transplantation. b Smooth fitting of BMI and urea on the 7th day after renal transplantation. c Smooth fitting of BMI and cystatin C on the 7th day after renal transplantation.

Fig. 1.

Smoothed fit curves, association between BMI and early postoperative renal function (creatinine, urea, cystatin C). a Smooth-fitting line of BMI and creatinine on day 7 after renal transplantation. b Smooth fitting of BMI and urea on the 7th day after renal transplantation. c Smooth fitting of BMI and cystatin C on the 7th day after renal transplantation.

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Table 7.

Threshold effects of CR7, BUN7, and Cysc7 on BMI were analyzed by piecewise linear regression

Dependent variableVariableβ(95% CI)p value
CR7 BMI 17.79 (8.98, 26.60) 0.000* 
BUN7 BMI 
 <22.2 kg/m2 −0.24 (−1.03, 0.55) 0.545 
 22.2–25.5 kg/m2 2.39 (0.64, 4.13) 0.008* 
 >25.5 kg/m2 −0.13 (−2.12, 1.86) 0.899 
Cysc7 BMI 
 <21.2 kg/m2 −0.04 (−0.19, 0.10) 0.552 
 21.2–25.9 kg/m2 0.19 (0.03, 0.35) 0.023* 
 >25.9 kg/m2 −0.18 (−0.52, 0.17) 0.324 
Dependent variableVariableβ(95% CI)p value
CR7 BMI 17.79 (8.98, 26.60) 0.000* 
BUN7 BMI 
 <22.2 kg/m2 −0.24 (−1.03, 0.55) 0.545 
 22.2–25.5 kg/m2 2.39 (0.64, 4.13) 0.008* 
 >25.5 kg/m2 −0.13 (−2.12, 1.86) 0.899 
Cysc7 BMI 
 <21.2 kg/m2 −0.04 (−0.19, 0.10) 0.552 
 21.2–25.9 kg/m2 0.19 (0.03, 0.35) 0.023* 
 >25.9 kg/m2 −0.18 (−0.52, 0.17) 0.324 

CR7, creatinine on the 7th day after transplantation; BUN7, urea on the 7th day after transplantation; CysC7, cystatin C on the 7th day after transplantation.

*p < 0.05.

MR Analysis

MR analysis requires a genetic variation related to the exposure factor but not to the potential confounding factors [10]. After data acquisition, we evaluated the association between the SNPs and BMI. We examined the association between each SNP and renal failure separately. Finally, combined with the results, we used MR analysis to evaluate the causal association between BMI and renal failure. Three statistical methods were used to investigate the relationship between BMI and renal failure: MR-Egger regression, weighted median estimator, and inverse variance weighting (IVW) [11]. MR-Egger regression is a statistical technique applied in MR studies to examine causal associations between exposure and outcome [12]. This method is commonly used to identify and adjust for pleiotropic effects in which a genetic variant influences outcomes through various paths [13]. The MR-Egger approach utilizes instrumental variables to estimate the causal effect of the exposure on the outcome, taking into account any potential effects resulting from pleiotropy [13]. The weighted median estimator is a statistical method used to estimate a population’s typical value by computing the median score of a weighted sample. In this technique, each data point in the sample is assigned a weight that denotes its relative significance or impact on the overall estimation of the population parameters. The weighted median estimator is instrumental when outliers are present in the data distribution as it provides a more robust measure of central tendency than traditional techniques such as the arithmetic mean [14]. IVW is a statistical analysis technique commonly employed in meta-analyses to integrate the effect size estimates derived from numerous studies. In the IVW, the weight assigned to each study is proportional to its inverse variance, which signifies the accuracy of its effect estimate. By considering both the sample size and the variation in effect sizes across studies, the IVW approach constructs a pooled effect size [11]. All MR analyses were performed using the MR Base platform (App version 1.4.3 8a77eb [October 25, 2020], R version 4.0.3) [15].

Sensitivity Testing

We conducted a “leave one” analysis to explore the potential causal relationships driven by a single SNP.

Observation Correlation between BMI and Renal Function after Kidney Transplantation

The study included 320 participants aged 18–65. Based on the BMI classification, there were significant differences in creatinine, urea, cystatin C, ALB, FIB, sex, and age between different BMI grades. In fact, the majority of overweight and obese patients were men (79.75%). Participants with higher BMI grades had higher levels of creatinine, urea, cystatin C, FIB and significantly lower levels of ALB (Table 2).

Correlation between Short-Term Renal Function, BMI, and Other Variables after Kidney Transplantation

Univariate analysis was conducted to analyze the relationship between renal function, BMI, and other variables after kidney transplantation. Table 3 shows a significant correlation between BMI and creatinine, urea, and Cystatin C on the 7th day after kidney transplantation (p = 0.011, p ≤ 0.0001, p = 0.002). The other variables significantly related to creatinine were sex, height, weight, and ALT (p ≤ 0.05), and the other variables significantly related to urea and cystatin C were height, weight, and ALT (p ≤ 0.05). The other variables were not significantly related.

Relationship, Trend, and Sensitivity Analysis of BMI and Short-Term Renal Function after Kidney Transplantation in Different Models

According to the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) statement guidelines, accurately present the results of unadjusted, minimally adjusted, and fully adjusted analyses simultaneously. In the fully adjusted model, BMI and creatinine after kidney transplantation (β = 17.41, 95% CI = 7.45, 27.36, p = 0.001), urea (β = 0.84, 95% CI = 0.38, 1.31, p = 0.000), and cystatin C (β = 0.09, 95% CI = 0.02, 0.15, p = 0.017) showed a significant positive correlation (Tables 4-6).

BMI was converted into a categorical variable. Stratified analysis showed that the creatinine, urea, and cystatin C levels of overweight and obese patients were significantly higher than those of normal weight patients (p = 0.001), while there was no significant difference between lean patients and normal weight patients (p = 0.475). The results showed that renal function indicators gradually increased with the increase in BMI, all of which had statistical significance (p < 0.05) (Tables 4-6).

Sensitivity analysis showed that in a fully adjusted model, the positive correlation between BMI and creatinine and urea after kidney transplantation was still significant in sex-stratified subgroups, as well as in adolescents and adults. The positive correlation between BMI and cystatin C after kidney transplantation was only significant in men (Tables 4-6).

Linear Regression Results of BMI and Creatinine Levels after Kidney Transplantation

Covariate screening was used to screen for possible confounding factors, including introducing covariates in the basic model or eliminating factors that produced >10% changes when introducing covariates in the regression model. After adjusting for possible confounding factors (AST, ALT, RBC, and age), a smooth fitting curve was plotted (Fig. 1a). A linear relationship was observed between BMI and creatinine levels on the 7th day after kidney transplantation. Adjusted linear regression was performed (Table 7), and the results showed a positive correlation between BMI and creatinine on the 7th day after kidney transplantation (β = 17.788; 95% CI = 8.981, 26.596; p = 0.000).

Independent Correlation between BMI and Urea after Kidney Transplantation Analyzed through Multiple Segmented Linear Regression

Similarly, after adjusting for possible confounding factors (AST and ALT), a smooth fitting curve was plotted (Fig. 1b). A non-linear relationship existed between BMI and uric acid levels on the 7th day after kidney transplantation. Consequently, three stages and two breakpoints were identified. Changes in threshold saturation were analyzed using a smooth curve, and the results are presented in Table 7. The inflection points of BMI are 22.2 and 25.5 kg/m2. Specifically, the urea level increases with the increase of BMI, and the difference is statistically significant between 22.2 and 25.5 kg/m2 (β = 2.4; 95% CI = 0.6, 4.1; p = 0.023. However, there was no statistically significant correlation between BMI levels below 22.2 kg/m2 (p = 0.545) or above 25.5 kg/m2 (p = 0.899) and urea levels.

Analyze the Independent Correlation between BMI and Cystatin C after Kidney Transplantation by Multiple Piecewise Linear Regression

Similarly, a smooth fitting curve was plotted after adjusting for possible confounding factors (AST, ALT, RBC, and crystal input) (Fig. 1c). There was a non-linear relationship between BMI and uric acid levels on the 7th day after kidney transplantation. Thus, three stages and two breakpoints were generated. The change of threshold saturation is analyzed according to the smooth curve, and the results indicate that the inflection point of BMI is 21.2, 25.9 kg/m2 (Table 7). Specifically, the level of cystatin C increases with an increase in BMI. At 21.2–25.9 kg/m2, the difference is statistically significant (β = 0.2; 95% CI = 0.03, 0.35; p = 0.023). However, when the BMI level was lower than 21.2 kg/m2 (p = 0.551) or higher than 25.9 kg/m2 (p = 0.324), there was a negative correlation with the level of cystatin C, and the difference was not statistically significant.

Studies Included in Mendelian Analysis

Instrumental Variable of MR

We identified 78 independent SNPs from the BMI GWAS, all of which were statistically significant for BMI (Table 8, Fig. 2). Of these SNPs, 48 showed a positive association with renal failure, although not statistically significant (p value of 5.00E−08 corresponding to F >30 for each variable). We used a threshold of F <10 to define “weak IVs” and account for weak instrumental bias. Hence, the impact of weak instrumental bias in our analysis is negligible.

Table 8.

Causal associations between genetically determined BMI and renal failure

MR methodNumber of SNPsBetaSEOR (95% CI)Association p value
MR-Egger 78 0.36 0.26 1.44 (0.87, 2.38) 0.164 
Weighted median 78 0.46 0.16 1.58 (1.16, 2.15) 0.003* 
Inverse variance weighted 78 0.32 0.11 1.37 (1.12, 1.69) 0.002* 
MR methodNumber of SNPsBetaSEOR (95% CI)Association p value
MR-Egger 78 0.36 0.26 1.44 (0.87, 2.38) 0.164 
Weighted median 78 0.46 0.16 1.58 (1.16, 2.15) 0.003* 
Inverse variance weighted 78 0.32 0.11 1.37 (1.12, 1.69) 0.002* 

*p < 0.05.

Fig. 2.

Forest map of causality of SNPs associated with BMI for renal failure. The meaning of the red line is the MR result of the MR-Egger test and IVW method. Scatter plot of genetic association between BMI and kidney failure.

Fig. 2.

Forest map of causality of SNPs associated with BMI for renal failure. The meaning of the red line is the MR result of the MR-Egger test and IVW method. Scatter plot of genetic association between BMI and kidney failure.

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Results of MR

MR-Egger regression, weighted median estimator, and IVW results are presented in Table 8 and Figures 2 and 3. IVW and weighted median methods showed that BMI had a protective effect on renal failure (IVW: β = 0.318, SE = 0.105, p = 0.003; with the weighted median estimator, these values were β = 0.458, SE = 0.158, p = 0.004). On the other hand, MR-Egger analysis suggested no causal association between BMI and renal failure (β = 0.361, SE = 0.257, p = 0.164). However, the weighted median estimator and IVW allow for greater precision in estimates than MR-Egger analysis [14]. Therefore, our results may support a potential causal association between BMI and renal failure.

Fig. 3.

Scatter plot of genetic association between BMI and kidney failure. The slope of each line indicates the causality of each method. The blue line represents the inverse weighted estimate of variance, the green line represents the weighted median estimate, and the dark blue line represents MR-Egger estimate.

Fig. 3.

Scatter plot of genetic association between BMI and kidney failure. The slope of each line indicates the causality of each method. The blue line represents the inverse weighted estimate of variance, the green line represents the weighted median estimate, and the dark blue line represents MR-Egger estimate.

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Sensitivity Analysis

There was evidence that the results were influenced by genetic pleiotropy (MR-Egger regression intercept = −0.0013, SE = 0.0071, p = 0.854). The funnel plot shows no evidence of asymmetry (Fig. 4). According to the leave-out method, no single SNP is decisive in causal inference (Fig. 5).

Fig. 4.

Heterogeneity was assessed by funnel plot. The blue line represents the inverse weighted estimate of variance, and the dark blue line represents the MR-Egger estimate.

Fig. 4.

Heterogeneity was assessed by funnel plot. The blue line represents the inverse weighted estimate of variance, and the dark blue line represents the MR-Egger estimate.

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Fig. 5.

Sensitivity analysis of the causal relationship between BMI and renal failure. From the results of the leave-out method, no single SNP plays a decisive role in causal inference.

Fig. 5.

Sensitivity analysis of the causal relationship between BMI and renal failure. From the results of the leave-out method, no single SNP plays a decisive role in causal inference.

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Renal transplantation is the best treatment option for patients with end-stage renal disease. However, obesity, as a risk factor, may affect the recovery of renal function after transplantation. Therefore, we plan to explore the relationship between obesity and renal function after transplantation through an observational study and the MR method.

In this study, we observed a significant correlation between BMI and creatinine, urea, and cystatin C levels on the seventh day after kidney transplantation, indicating a potential association with early renal function. Furthermore, ALT levels were also found to be significantly related to creatinine, urea, and cystatin C levels, possibly due to immune responses following viral infection [16, 17]. Additionally, we found a correlation between sex and creatinine levels but not with urea and cystatin C. This may be attributed to the higher muscle mass in men, resulting in increased creatinine production [18]. On the other hand, the production of cystatin C and urea is primarily influenced by factors such as diet and underlying medical conditions [19, 20].

After adjusting for various factors, we still found a significant correlation between BMI and creatinine, urea, and cystatin C levels on the seventh day after kidney transplantation. This suggests an independent correlation between BMI and early renal function, unaffected by factors such as sex, age, and liver function. Hierarchical analysis of BMI revealed that overweight and obese patients had significantly higher creatinine, urea, and cystatin C levels compared to normal-weight patients. There was no significant difference between lean patients and normal-weight patients. This indicates a negative correlation between BMI and early postoperative renal function in overweight and obese patients. In other words, an increase in BMI is associated with a decrease in early renal function in kidney transplant patients. Trend analysis showed that as BMI increased, renal function indicators also increased, further supporting the negative correlation between BMI and early postoperative renal function. These findings align with the conclusions of Montero’s study [21]. Possible factors contributing to this correlation include obesity-related chronic diseases such as hypertension and diabetes, which can cause renal microvascular injury, glomerulosclerosis, and abnormal renal tubular function [22]. Chronic inflammation and oxidative stress, often present in obesity, may also have toxic effects on the kidneys [23]. Additionally, obesity can increase the surgical difficulty and risk of complications, impacting the recovery and function of transplanted kidneys [24, 25].

We further drew a smooth fitting curve to more intuitively display the relationship between BMI and short-term renal function after kidney transplantation. According to the results of the study, there is a positive correlation between BMI and early renal function indicators after kidney transplantation. Specifically, there is a linear relationship between BMI and creatinine level on the 7th day after transplantation. Additionally, there is a non-linear relationship between BMI and urea and cystatin C levels on the 7th day after transplantation. Obesity, often accompanied by metabolic abnormalities [26, 27], such as hyperlipidemia and hypertension, may contribute to this non-linear relationship. There may also be an interaction between obesity and kidney damage [28], leading to non-linear changes in uric acid and cystatin C levels across different BMI ranges. The analysis suggests that there is a significant positive correlation between BMI and urea levels when BMI is between 22.2 and 25.5 kg/m2. Similarly, when BMI is in the range of 21.2–25.9 kg/m2, there is a positive correlation between BMI and cystatin C level. In conclusion, the study suggests that BMI has a significant impact on short-term renal function when it is between 22.2 and 25.5 kg/m2. Preoperative weight management intervention targeting recipients with a BMI around 22.2 kg/m2 may improve the early postoperative recovery of renal function.

Our findings, along with current clinical studies, demonstrate that obesity affects early renal function recovery after kidney transplantation and is a risk factor for early and late complications after transplantation [5, 6]. However, some studies have reported no significant correlation between obesity and post-transplant complications [29]. Given that most of the current evidence relies on cross-sectional studies, interpretation of the results may be limited by clinical referral bias [30]. We further explored the relationship between BMI and renal function using MR analysis to address confounding factors. Ultimately, the results of the MR analysis also support a potential causal relationship between BMI and renal failure.

Advantages and Limitations

Our study utilized three renal function indicators to assess the correlation between renal function and BMI on the seventh day following kidney transplantation. We employed the MR method to eliminate confounding factors and ensure the reliability of our findings. The MR analysis was conducted using a large-scale genetic alliance and a substantial sample size, which enhanced the statistical power of our study. To minimize potential biases, the MR analysis focused on a European population database and addressed population stratification. However, it is important to acknowledge that our study has certain limitations, including its retrospective non-randomized design and the possibility of single-center effects. Future studies should incorporate controlled trials and prospective cohort studies, taking into account long-term renal function recovery after surgery. Additionally, the analysis of different BMI cutoff points could not be further explored due to data limitations. It is necessary to improve the Chinese population database for MR analysis.

This retrospective study and MR analysis of kidney transplant patients found a significant negative correlation between BMI and early renal function recovery after kidney transplantation. When BMI ranges from 22.0 to 25.5 kg/m2, the negative correlation for early renal function recovery is most significant. When BMI is 22.2 kg/m2, the early renal function recovery after surgery may be optimal. Interestingly, this study also found a significant correlation between BMI and short-term renal function recovery in youth, adulthood, and males. This finding suggests that preoperative identification of high-risk patients and appropriate control of recipient BMI may be beneficial for the early recovery of renal function after transplantation.

We thank the study participants for their contribution.

This study was approved by the Ethics Committee of the Second Affiliated Hospital of Guangxi Medical University (KY-0385). Patient consent were not required as this study was based on publicly available data. The need for informed consent was waived by the Ethics Committee of the Second Affiliated Hospital of Guangxi Medical University.

The authors have no conflicts of interest to declare.

This work was supported by 2020 Guangxi Medical and Health Appropriate Technology Development and Application Program, that is, study on the protective effect of dexmedetomidine in perioperative patients undergoing kidney transplantation (Grant No. S2020014), and the other is the Guangxi Medical and Health Key Discipline Construction Project.

Shaopeng Ming and Chunrong Zeng participated in the design of the study and wrote the paper as co-first authors, Ke Qin and Hongtao Liu participated in research design and contributed new reagents or analytic tools, and Haiming Wen and Zhaoyu Li participated in data analysis.

The data of observational studies cannot be made publicly available because of ethical and legal considerations. It may be provided upon reasonable request to corresponding author Dr. Qin Ke. The data for Mendelian randomization are openly available in http://app.mrbase.Org.

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