Abstract
Aim
This study aims to assess the impact of weight gain on patient and graft survival in renal transplant recipients over a 10-year period.
Materials and Methods
A retrospective cohort study was conducted on 716 renal transplant recipients aged 18–67 at a tertiary care center in South India. Weight change was assessed at 1 year and at the last follow-up, with significant weight gain defined as an increase of more than 5%. Data on baseline characteristics, comorbidities, and transplant-related factors were collected. Statistical analyses, including Log Rank and Mantel–Cox tests, were used to evaluate the impact of weight gain on survival outcomes, while logistic regression identified factors influencing weight gain and loss.
Results
At 1-year posttransplant, 70% of patients experienced weight gain, with 54.1% showing significant increases. Significant weight gain at 1 year was not associated with improved patient survival. However, at the last follow-up, 79.3% of patients had gained weight, with 68.7% showing significant increases. Significant weight gain at the last follow-up was associated with improved graft and patient survival. Factors influencing weight gain included age at transplant (odds ratio [OR] =1.05), female gender (OR = 1.40), and cytomegalovirus (CMV) viremia (OR = 1.25). Factors influencing weight loss included age (OR = 1.07), pretransplant hypertension (OR = 1.50), posttransplant hypertension (OR = 1.45), and CMV viremia (OR = 1.30). However, excessive weight gain culminating in Class II obesity was associated with reduced patient survival.
Conclusion
Significant weight gain after renal transplantation is common and linked to improved graft and patient survival at the last follow-up. These findings emphasize the need for monitoring weight posttransplant and implementing targeted interventions to manage weight gain, potentially enhancing long-term outcomes for transplant recipients.
Keywords: Graft survival, patient survival, renal transplantation, weight gain, weight management
Introduction
The “Transplant Outcomes and Weight Management in Renal Disease Study” focuses on exploring the relationship between weight gain and survival outcomes in patients who have undergone renal transplants. Renal transplantation is a crucial treatment option for individuals with end-stage renal disease, providing them with a chance at improved quality of life and increased longevity. However, posttransplant weight gain has been observed in many patients and is considered a significant concern due to its potential impact on overall health and survival outcomes.
Obesity is a growing health issue worldwide and is known to be associated with an increased risk of various comorbidities, such as cardiovascular diseases (CVDs), diabetes, and hypertension. In the context of renal transplantation, excessive weight gain may exacerbate these comorbidities and adversely affect the long-term survival of transplant recipients.
The study aims to shed light on the prevalence and impact of weight gain in renal transplant recipients over a substantial period of 10 years. By analyzing data from a tertiary care center in South India, we seek to investigate the factors contributing to weight change, identify potential risk factors, and understand the influence of weight gain/loss on patient survival.
Materials and Methods
Aim
We aim to analyze and understand the relationship between weight change and survival outcomes in renal transplant recipients over a significant period of 10 years.
Objective
The main objectives of this study are twofold:
To investigate the weight gain in patients who have undergone renal transplants and its potential impact on overall survival over 10 years at a tertiary care center in South India
To examine the various factors that may influence weight gain in patients undergoing renal transplants.
Subjects
The sample population consisted of 716 kidney transplant patients, 560 males (78.2%) and 156 (21.8%) females of 18–67 years of age, who underwent kidney transplant consultation at Christian Medical College (CMC) Vellore over 10 years. Exclusion criteria were age at transplant <18 years, follow-up duration <12 months, and retransplant. BMI was calculated as weight in kilograms divided by the square of the height in meters (kg/m2). The study was reviewed and approved by the Institutional Ethics Review Board of CMC (IRB 12484).
Data collection
Data were collected from the CMC transplant files and electronic medical records. During routine follow-up visits, all recipients had their body weight and height measured. Baseline dry weight on the day of the transplant, weight at 1 year after transplantation, and weight at the last follow-up were recorded. Baseline characteristics, such as age, gender, comorbidities, past medical history, immunosuppressant medications, and graft function, were documented. The total duration of follow-up was also recorded.
Weight gain calculation
Weight gain at 1 year after transplantation was calculated. Weight gain velocity at 1 year was determined by dividing the weight gain by 12 months. Weight gain at the last follow-up was calculated by subtracting the baseline weight from the weight at the last follow-up. The weight gain velocity at the last follow-up was calculated by dividing the weight gain until the last follow-up by the total follow-up duration in months.
Body mass index gain calculation
Similarly, the body mass index (BMI) gain and BMI gain velocity were calculated at 1 year after transplantation and at the last follow-up.
Significant weight gain was defined as an increase of more than 5% in body weight.
Obesity is defined as a BMI >25 kg/m2, according to the Indian Consensus Guidelines and the Asia-Pacific Guidelines.[1]
Statistical analysis
Data were analyzed using IBM SPSS statistics software (version 24.0) (SPSS Inc., Chicago, IL, USA). Continuous variables were reported as mean ± standard deviation for normally distributed data and median with interquartile range (IQR) for nonnormally distributed data.
Declaration of patient consent
The patient consent has been taken for participation in the study and for publication of clinical details and images. Patients understand that the names and initials would not be published, and all standard protocols will be followed to conceal their identity.
Ethics statement
The study was performed according to the guidelines in Declaration of Helsinki.
The study was reviewed and approved by the Institutional Review Board, Christian Medical College, Vellore (IRB number: 12484).
Results
Seven hundred and ninety-four patients underwent kidney transplants in CMC between January 2009 and December 2018. After excluding the patients who had <1 year of follow-up and age <18 years, 716 patients were included in the study.
Baseline characteristics
At the time of the transplant
Among the cohort of 716 patients, 156 individuals were female, constituting 21.8% of the total participants. The median age was 34 years (IQR: 37–45). The prevalence of diabetes was 11%, and 658 individuals (91.9%) had hypertension. Macrovascular complications included coronary artery disease in 45 cases (6.3%) and CVD in 13 cases (1.8%). Hepatitis C virus infection was observed in 4.9% of the participants (35 cases), whereas Hepatitis B virus infection affected 3.4% (24 cases) [Table 1].
Table 1. Baseline characteristics of study participants stratified by significant weight gain.
| Variables | Overall (n=716), n (%) | Significant weight gain | ||
|---|---|---|---|---|
| No (<5%) (n=224), n (%) | Yes (>5%) (n=492), n (%) | P | ||
| Age (years) | 36±11 | 38±12 | 36±11 | 0.027* |
| Gender | ||||
| Male | 560 (78.2) | 190 (84.8) | 370 (75.2) | 0.004* |
| Female | 156 (21.8) | 34 (15.2) | 122 (24.8) | |
| Donor BMI | 24.46±14.26 | 23.35±4.2 | 24.94±16.85 | 0.193 |
| Baseline weight (kg) | 56.21±10.55 | 58.97±10.35 | 54.96±10.4 | <0.001* |
| Baseline height | 163±8 | 163±8 | 163±8 | 0.732 |
| Recipient BMI | 21.16±3.45 | 22.19±3.54 | 20.69±3.3 | <0.001* |
| Pretransplant diabetes | 79 (11) | 31 (13.8) | 48 (9.8) | 0.106 |
| Pretransplant hypertension | 658 (91.9) | 200 (89.3) | 458 (93.1) | 0.084 |
| Pretransplant CAD | 45 (6.3) | 14 (6.3) | 31 (6.3) | 0.979 |
| Pretransplant CVA | 13 (1.8) | 6 (2.7) | 7 (1.4) | 0.243 |
| Pretransplant HBV | 24 (3.4) | 8 (3.6) | 16 (3.3) | 0.826 |
| Pretransplant HCV | 35 (4.9) | 10 (4.5) | 25 (5.1) | 0.723 |
| Type of HD | ||||
| Premptive | 75 (10.5) | 22 (9.8) | 53 (10.8) | 0.204 |
| Hemodialysis | 613 (85.6) | 189 (84.4) | 424 (86.2) | |
| Peritoneal dialysis | 28 (3.9) | 13 (5.8) | 15 (3) | |
| Dialysis vintage (months) | 6 (4–11) | 6 (4–10) | 7 (4–11) | 0.764 |
| HLA mismatch >3 | 387 (57.8) | 130 (63.4) | 257 (55.4) | 0.053 |
| ABO compatibility | ||||
| ABO compatible | 697 (97.3) | 218 (97.3) | 479 (97.4) | 0.978 |
| ABO incompatible | 19 (2.7) | 6 (2.7) | 13 (2.6) | |
| Donor | ||||
| Living | 644 (89.9) | 196 (87.5) | 448 (91.1) | 0.142 |
| Deceased | 72 (10.1) | 28 (12.5) | 44 (8.9) | |
| Immunosuppression | ||||
| No IMS | 18 (2.5) | 2 (0.9) | 16 (3.3) | 0.015* |
| Basiliximab | 519 (72.5) | 151 (67.4) | 368 (74.8) | |
| Grafalon | 59 (8.2) | 24 (10.7) | 35 (7.1) | |
| ATG | 120 (16.8) | 47 (21) | 73 (14.8) | |
| CIT (min) | 75 (62–93) | 78 (62–97) | 73 (62–91) | 0.168 |
| WIT | 210 (135–315) | 210 (135–315) | 214 (135–315) | 0.825 |
CAD: Coronary artery disease, CVA: Cerebrovascular accident, HBV: Hepatitis B virus, HCV: Hepatitis C virus, HD: Hemodialysis, HLA: Human leukocyte antigen, ABO: Blood group compatibility, IMS: Induction immunosuppression, CIT: Cold ischemia time, WIT: Warm ischemia time, ATG: Anti-thymocyte globulin. *P < 0.05 considered statistically significant
A significant majority of the patients, 613 individuals (85.6%), were on hemodialysis, while only 3.9% (28 patients) were on peritoneal dialysis, and 10.5% (75 patients) underwent pre-emptive transplantation. The median duration of dialysis before transplant was 6 months (IQR: 4–11 months). The majority of renal transplant operations utilized grafts from living donors, constituting 644 cases (89.9%). ABO compatibility was present in 97.3% of the transplants (697 cases).
At the time of transplant, the median weight (dry weight) was 55 kg (IQR: 49–63), and the median height was 164 cm (IQR: 158–168). The median BMI was 18.72–23.28.13% of renal transplant recipients were obese at the time of transplant.
Transplant and immunosuppression
In this study, 72.5% of the patients received Basiliximab induction therapy. The majority (94.6% or 677 out of 716) were given prednisolone, tacrolimus, and mycophenolate mofetil as primary immunosuppression. Pretransplant nephrectomy was performed on 3.6% of the patients. The median warm ischemia time was 210 s (with an IQR of 135–315 s), and the median cold ischemia time was 74.5 min (with an IQR of 62–92.75 min). Median blood loss during the procedure was 300 ml (with an IQR of 250–400 ml). Posttransplant, 7.7% experienced slow graft function, and 6.1% had delayed graft function.
Weight change at 1 year
In the study, at the 1-year follow-up after transplantation, the prevalence of obesity among patients increased significantly from the initial 13% to 23.1% [Table 2].
Table 2. Obesity prevalence in percentage.
| Classification | BMI | Baseline | At 1 year | At last follow up |
|---|---|---|---|---|
| Underweight | <18 | 22.8 | 11.6 | 9.4 |
| Normal | 18–22.9 | 49.4 | 45.0 | 32.8 |
| Overweight | 23–24.9 | 14.8 | 20.4 | 21.8 |
| Obese 1 | 25–29.9 | 11.9 | 20.0 | 29.9 |
| Obese 2 | >30 | 1.1 | 3.1 | 6.1 |
BMI: Body mass index
At this 1-year mark, weight gain was observed in a substantial portion of the patient population, with 70% (501 out of 716) experiencing weight gain. Among those who gained weight, 54.1% (387 patients) exhibited a clinically significant increase. A smaller proportion of patients, approximately 4.6% (33 individuals), reported stable weight without significant changes. On the other hand, 25.4% (182 out of 716) of patients experienced weight loss during the same period. It is worth noting that only a minority of patients, specifically 14.1% (101 out of 716), who experienced weight loss, achieved a significant reduction in weight.
The median weight of patients at the 1-year follow-up was 59 kg, with an IQR of 52 to 68 kg. The median BMI at the same time point was 22.3 kg/m2, with an IQR ranging from 20.12 kg/m2 to 20.76 kg/m2.
Furthermore, the weight gain velocity during the 1st-year posttransplantation showed a median value of 0.58 kg per month, ranging from 0.02 kg/month to 0.70 kg/month.
Weight change at last follow-up
In the study, after transplantation, the prevalence of obesity among patients increased to 36% [Table 2], representing a substantial 176.9% rise in obesity at the last follow-up. The median follow-up duration was 51 months, with an IQR of 31–80 months.
A significant proportion of the patient population experienced weight gain, with 79.3% (568 out of 716) reporting an increase in weight. Among those who gained weight, 68.7% (492 patients) exhibited a notable and clinically significant increase. A smaller group of patients, approximately 2.4% (17 individuals), had stable weight without significant changes. Conversely, during the same period, 18.3% (131 out of 716) of patients experienced weight loss, but only a minority of them, specifically 9.8% (70 out of 716), achieved a significant reduction in weight.
At the last follow-up, the median weight of patients was 62 kg, with an IQR of 50 kg to 70.75 kg. In addition, the median BMI at that time point was 23.60 kg/m2, with an IQR of 21.12–26.40 kg/m2.
Furthermore, the weight gain velocity during the follow-up period had a median value of 0.12 kg/month, with an IQR of 0.03–0.24 kg/month. Overall, the weight gain velocity during this period was lower than the weight gain velocity observed in the first year after transplantation. This suggests that weight gain may slow down as more time elapses after transplantation.
During the initial univariate analysis, the impact of 31 different factors on significant weight gain at the last follow-up was assessed. Among these factors, recipient age at the time of transplant, estimated glomerular filtration rate at 12 months, recipient 24-h urinary protein at 12 months, recipient gender (specifically female), presence of CMV viremia, bacterial pneumonia, and leukopenia were identified as the factors significantly affecting weight gain [Table 3].
Table 3. Unadjusted factors affecting significant weight gain and weight loss.
| Variables | Weight gain (>5%) | Weight loss (>5%) | |||
|---|---|---|---|---|---|
| Unadjusted OR (95% CI) | P | Unadjusted OR (95% CI) | P | ||
| Recipient age (years) | 0.99 (0.97–0.99) | 0.027* | 1.024 (1.003–1.046) | 0.026* | |
| Recipient gender | |||||
| Male | Reference | Reference | |||
| Female | 1.83 (1.20–2.77) | 0.005* | 0.744 (0.392–1.411) | 0.365 | |
| Country | |||||
| India | Reference | Reference | |||
| Bangladesh | 0.94 (0.56–1.57) | 0.804 | 1.75 (0.876–3.496) | 0.113 | |
| Bhutan | 1.27 (0.72–2.23) | 0.411 | 0.824 (0.328–2.069) | 0.680 | |
| Nepal | 0.81 (0.08–8.49) | 0.857 | 1.328 (0.042–42.376) | 0.872 | |
| Recipient occupation class | |||||
| Unemployed | Reference | Reference | |||
| Unskilled | 0.30 (0.08–1.16) | 0.082 | 2.159 (0.326–14.315) | 0.425 | |
| Semiskilled | 0.79 (0.44–1.43) | 0.437 | 1.636 (0.668–4.009) | 0.281 | |
| Skilled | 0.66 (0.37–1.17) | 0.155 | 2.373 (1.055–5.336) | 0.037* | |
| Clerical/farm/shop | 0.64 (0.40–1.00) | 0.051 | 1.603 (0.782–3.286) | 0.198 | |
| Semi professional | 1.06 (0.64–1.74) | 0.832 | 1.215 (0.553–2.668) | 0.628 | |
| Professional | 0.62 (0.36–1.05) | 0.072 | 1.638 (0.719–3.73) | 0.240 | |
| Obesity - 1 year | |||||
| Underweight | Reference | 2.17 (0.96–4.93) | 0.064 | ||
| Normal | 2.06 (1.26–3.36) | 0.004* | 1.34 (0.68–2.61) | 0.399 | |
| Overweight | 3.37 (1.89–6.01) | <0.001* | 0.95 (0.41–2.19) | 0.953 | |
| Obese | 3.09 (1.78–5.41) | <0.001* | Reference | ||
| Dialysis vintage duration (months) | 0.99 (0.98–1.01) | 0.347 | 0.99 (0.97–1.02) | 0.719 | |
| eGFR at 12 months (mL/min/1.73 m2) | 1.01 (1.00–1.02) | 0.032* | 0.989 (0.977–1) | 0.059 | |
| Recipient 24 h urinary protein at 12 months (mg per 24 h) | 1 (0.99–1) | 0.049* | 1 (0.999–1.001) | 0.883 | |
| Recipient pretransplant CAD | 1 (0.52–1.92) | >0.999 | 0.742 (0.24–2.291) | 0.604 | |
| Recipient pretransplant diabetes mellitus | 0.67 (0.41–1.09) | 0.103 | 1.265 (0.609–2.625) | 0.529 | |
| Recipient pretransplant hypertension | 1.63 (0.94–2.81) | 0.083 | 0.467 (0.227–0.962) | 0.039* | |
| Recipient pretransplant CVA | 0.52 (0.17–1.56) | 0.244 | 4.57 (1.391–15.02) | 0.012* | |
| Type of dialysis | |||||
| Without HD | Reference | Reference | |||
| With HD | 1.17 (0.75–1.81) | 0.496 | 1.107 (0.539–2.275) | 0.782 | |
| Recipient pretransplant HBV | 0.91 (0.38–2.16) | 0.827 | 1.327 (0.386–4.556) | 0.653 | |
| Recipient pretransplant HCV | 1.14 (0.54–2.42) | 0.727 | 0.678 (0.181–2.535) | 0.563 | |
| Donor | |||||
| Live | Reference | Reference | |||
| Deceased | 0.68 (0.41–1.13) | 0.138 | 1.167 (0.536–2.542) | 0.698 | |
| ABO transplant | |||||
| ABO compatible | Reference | Reference | |||
| ABO incompatible | 0.94 (0.36–2.49) | 0.906 | 1.378 (0.354–5.361) | 0.643 | |
| DGF | 1.09 (0.56–2.12) | 0.802 | 1 (0.358–2.792) | >0.999 | |
| SGF | 0.92 (0.51–1.65) | 0.777 | 1.719 (0.787–3.753) | 0.174 | |
| Posttransplant TB | 0.53 (0.26–1.07) | 0.074 | 1.426 (0.506–4.018) | 0.502 | |
| Posttransplant hypertension | 1.44 (0.97–2.13) | 0.069 | 0.514 (0.295–0.895) | 0.019* | |
| Posttransplant BK virus | 0.99 (0.58–1.69) | 0.982 | 0.747 (0.3–1.859) | 0.530 | |
| Posttransplant fungal infection | 0.48 (0.22–1.07) | 0.071 | 1.981 (0.683–5.745) | 0.208 | |
| Posttransplant CMV Viremia | |||||
| No CMV | Reference | Reference | |||
| CMV viremia | 0.45 (0.29–0.69) | <0.001* | 2.336 (1.293–4.22) | 0.005* | |
| CMV disease with viremia | 0.62 (0.2–1.91) | 0.404 | 3.581 (0.979–13.091) | 0.054 | |
| Any rejection | 0.80 (0.58–1.11) | 0.179 | 1.235 (0.752–2.026) | 0.404 | |
| Posttransplant PCP | 0.45 (0.11–1.83) | 0.266 | 1.315 (0.159–10.859) | 0.799 | |
| Nocardiosis | 0.29 (0.03–2.97) | 0.298 | 1 (0.022–45.518) | >0.999 | |
| Recipient posttransplant malignancy | 1 (0.31–3.27) | >0.999 | 1 (0.158–6.342) | >0.999 | |
| Bacterial pneumonia | 0.41 (0.21–0.80) | 0.009* | 1.594 (0.617–4.12) | 0.335 | |
| Leukopenia | 0.65 (0.47–0.90) | 0.009* | 1.796 (1.097–2.941) | 0.020* | |
Weight gain and loss are defined as >5% change from baseline weight at 12 months posttransplant. CAD: Coronary artery disease, CVA: Cerebrovascular accident, DGF: Delayed graft function (defined as the requirement of dialysis within the 1st-week posttransplant), SGF: Slow graft function (defined as serum creatinine >3.0 mg/dL on posttransplant day 5 without dialysis requirement), HBV: Hepatitis B virus, HCV: Hepatitis C virus, CMV: Cytomegalovirus, PCP: Pneumocystis jirovecii pneumonia, BK virus: BK polyomavirus, TB: Tuberculosis, ABO: Blood group system, HD: Hemodialysis, OR: Odds ratio, CI: Confidence interval, eGFR: Estimated glomerular filtration rate. *P < 0.05 considered statistically significant
After the comprehensive multivariate analysis, it was found that only three factors demonstrated statistically significant and independent associations with significant weight gain at the last follow-up. These significant factors were the recipient’s age at the time of transplant, the recipient’s gender (female), and the presence of CMV viremia [Table 4].
Table 4. Adjusted factors affecting significant weight gain.
| Variables | Adjusted OR (95% CI) | P |
|---|---|---|
| Age | 0.97 (0.96–0.99) | 0.002* |
| Gender | ||
| Male | Reference | |
| Female | 1.51 (0.96–2.39) | 0.078 |
| Obesity 1 year | ||
| Underweight | Reference | |
| Normal | 2.52 (1.47–4.34) | 0.001* |
| Overweight | 3.76 (1.97–7.19) | <0.001 |
| Obese | 8.37 (2.11–33.25) | 0.003* |
| eGFR at 12 months (mL/min/1.73 m2) | 1.00 (0.99–1.01) | 0.542 |
| Recipient 24 h urinary protein at 12 months (mg per 24 h) | 1.00 (0.99–1.00) | 0.281 |
| Posttransplant CMV viremia | ||
| No CMV | Reference | |
| CMV viremia | 0.52 (0.32–0.83) | 0.006* |
| CMV disease with viremia | 0.65 (0.19–2.18) | 0.482 |
| Bacterial pneumonia | 0.78 (0.35–1.73) | 0.540 |
| Leukopenia | 0.90 (0.62–1.31) | 0.592 |
eGFR: Estimated glomerular filtration rate, CMV: Cytomegalovirus, OR: Odds ratio, CI: Confidence interval. *P < 0.05 considered statistically significant
Survival analysis
Patient survival
Weight gain: Effect of significant weight gain at 1 year
The effect of significant weight gain at 1 year was examined on the mean survival times of patients. Patients with significant weight gain at 1 year had a mean survival time of 138.92 ± 1.7 (mean ± standard error), whereas patients without significant weight gain had a mean survival time of 128.06 ± 1.57 (mean ± standard error). Although the overall mean survival time (138.67) appeared slightly higher than both groups, the statistical tests (Log Rank) did not find any significant differences (P > 0.05) in survival between individuals with and without significant weight gain at 1 year [Figure1].
Figure 1. Kaplan Meier survival analysis showing patient and graft survival.
Weight gain: Effect of significant weight gain at last follow-up
Furthermore, the effect of significant weight gain at the last follow-up was assessed. The mean survival time for all patients was 138.67 ± 1.27 months (mean ± standard error). Patients with significant weight gain at the last follow-up had a mean survival time of 141.61 ± 1.15 months (mean ± standard error), whereas patients without significant weight gain had a mean survival time of 112.50 ± 2.50 (mean ± standard error). In contrast to the earlier analysis, the statistical tests (Log Rank, Breslow, and Tarone-Ware) now showed highly significant differences in survival between the two groups, with all P values being close to zero. This suggests that the disparities in survival times between patients with and without significant weight gain at the last follow-up are unlikely to be due to chance [Table 5].
Table 5. Effect of significant weight gain on patient survival.
| Significant weight gain at 1 year Meana | Significant weight gain at last follow-up Mean survival | ||||||||
|---|---|---|---|---|---|---|---|---|---|
| Estimate | SE | 95% CI | Estimate | SE | 95% CI | ||||
| Lower bound | Upper bound | Lower bound | Upper bound | ||||||
| Absent | 128.069 | 1.572 | 124.988 | 131.150 | 112.502 | 2.504 | 107.594 | 117.409 | |
| Present | 138.920 | 1.797 | 135.397 | 142.443 | 141.616 | 1.155 | 139.353 | 143.879 | |
| Overall | 138.667 | 1.274 | 136.171 | 141.164 | 138.667 | 1.274 | 136.171 | 141.164 | |
| Log rank (Mantel–Cox) | 0.355 | 0.00 | |||||||
Significant weight gain status, Mean survival estimate (months), Standard error (SE), 95% confidence interval (lower bound – upper bound). CI: Confidence interval, SE: Standard error
Graft Survival
Means for graft survival time in patients with or without significant weight gain at 1 year
-
Effect of significant weight gain at 1 year
The effect of significant weight gain at 1 year on the mean graft survival times of patients was examined. Patients without significant weight gain at 1 year had a mean graft survival time of 120.91 months (±2.345 standard error), with a 95% confidence interval ranging from 116.314 to 125.506 months. In contrast, patients with significant weight gain at 1 year had a mean graft survival time of 135.597 months (±2.134 standard error), with a 95% confidence interval from 131.415 to 139.778 months. The overall mean graft survival time was 133.173 months (±1.680 standard error), with a 95% confidence interval between 129.880 and 136.466 months. The Log Rank (Mantel–Cox) test revealed a statistically significant difference in graft survival distributions between patients with and without significant weight gain at 1 year, with a Chi-square value of 4.350 and P = 0.037.
-
Effect of significant weight gain at last follow-up
The effect of significant weight gain at the last follow-up on the mean graft survival times of patients was also assessed. Patients without significant weight gain at the last follow-up had a mean graft survival time of 103.961 months (±3.182 standard error), with a 95% confidence interval ranging from 97.724 to 110.198 months. Patients with significant weight gain at the last follow-up had a mean graft survival time of 138.482 months (±1.525 standard error), with a 95% confidence interval from 135.492 to 141.471 months. The overall mean graft survival time was 133.173 months (±1.680 standard error), with a 95% confidence interval between 129.880 and 136.466 months.
The analysis of graft survival times in relation to significant weight gain reveals noteworthy trends. At 1 year posttransplant, patients who experienced significant weight gain had a mean graft survival time of 135.5 months, compared to 120.7 months for those without such weight gain. This difference is statistically significant (P = 0.038). At the last follow-up, the disparity was even more pronounced, with patients who had significant weight gain showing a mean graft survival time of 138.5 months, while those without had only 103.8 months – a highly significant difference (P < 0.001). The overall mean graft survival time across groups was 133.0 months. These findings suggest a clear and consistent pattern: Significant weight gain is associated with improved graft survival outcomes, both at 1 year and at the final follow-up.
Patient and graft survival as per body mass index categories
Kaplan–Meier survival analysis showed that overall patient survival was high across all BMI categories during the early posttransplant period (0–24 months). However, long-term differences became evident beyond 60 months. Patients in the Obese 2 (BMI category 4) and underweight (category 0) groups exhibited the lowest survival, while those in the overweight (category 2) and Obese 1 (category 3) groups had better long-term survival. The normal BMI group (category 1) showed intermediate outcomes. These differences were found to be statistically significant, with the Log-Rank (Mantel–Cox) test yielding χ2 = 9.726, df = 4, P = 0.045, indicating a significant variation in patient survival distributions across BMI categories.
In contrast, graft survival curves showed more pronounced differences across BMI groups. Patients in the Obese 2 and underweight categories experienced earlier and steeper declines in graft survival, while those in the overweight and Obese 1 groups maintained higher cumulative graft survival over time. Again, the normal BMI group demonstrated intermediate outcomes. The differences in graft survival between BMI categories were highly significant, as indicated by the Log-Rank (Mantel–Cox) test (χ2 = 16.316, df = 4, P = 0.003).
Discussion
Most patients gain weight after the transplant and predominant weight gain is in the 1st year after the transplant. Weight change following renal transplantation is a significant concern as it can impact both patient and graft outcomes. Our study is the largest retrospective study which deals with weight gain.
Incidence
At 6 months postrenal transplant, Gibson et al. observed that 26% of recipients experienced weight gain, with an average increase of 11.9% in body weight.[2] Concurrently, weight gain was frequently reported, with an average increase ranging from 6 to 10 kg during this period.[3]
Transitioning to the 1-year mark posttransplantation, several studies shed light on the incidence of weight gain. Johnson et al. found that weight gain equaled 10% of recipients’ body weight within the 1st year posttransplantation.[4] In addition, the prevalence of weight gain (>5%) ranged from 54.6% to 57% at this stage.[4,5]. Similarly, Cristina et al. observed weight gain in as high as 72.7% of patients after one renal transplantation, with an average increase of 7.12 ± 5.9 kg.[6]
In contrast to previous research, our study unearthed distinct findings. Specifically, at the 1-year posttransplantation juncture, 70% of patients manifested weight gain, with 54.1% demonstrating clinically significant increments akin to extant literature. In addition, our investigation showed a divergence in obesity prevalence at the last follow-up; we observed a notable increase to 36%, constituting a remarkable 176.9% surge from baseline. Over a median follow-up period of 51 months, 79.3% of patients reported experiencing weight gain.
Factors affecting weight gain
Weight gain following renal transplantation is influenced by various factors, including demographic characteristics, transplant-related variables, lifestyle choices, and health implications. Younger age at transplantation, black race, and female sex are associated with higher weight gain, while lower pretransplant body weight and receiving a kidney from a living donor contribute to more significant initial weight gain.[4,5,7] Patients without a rejection history tend to gain more weight. A sedentary lifestyle and higher energy intake are linked to weight gain posttransplantation.[8] Elevated oxidative stress may play a role in posttransplant weight gain.[9] In our study, we observed that recipient age at the time of transplant, recipient gender (specifically female), and the presence of CMV viremia were significant factors. Interestingly, contrary to findings in other studies, we found that younger recipients were more prone to weight gain. However, the relationship with the female sex mirrored previous research. Notably, our study revealed a novel finding regarding CMV viremia, wherein patients with CMV exhibited less weight gain. This association could be attributed to CMV infection predisposing the host to infections,[10] leading to increased inflammation and consequently less weight gain in an inflammatory state. Understanding these factors is paramount for devising targeted interventions to prevent excessive weight gain and mitigate associated health risks for renal transplant recipients.
Our investigation unveiled a notable prevalence of weight gain among renal transplant recipients, observed both at the 1-year milestone and during the last follow-up. This weight gain exhibited associations with recipient age, gender, and the presence of certain comorbidities, notably CMV viremia. Such findings underscore the imperative of vigilant weight monitoring posttransplantation and the implementation of interventions to mitigate excessive weight gain, potentially fostering enhanced long-term survival outcomes.
Curiously, although significant weight gain at 1 year did not manifest a discernible impact on patient survival, significant weight gain at the last follow-up markedly correlated with improved graft survival time. This suggests a temporal variance in the effects of weight gain on survival outcomes post-transplantation.
Weight gain and survival
This study sheds new light on the nuanced relationship between body composition and posttransplant outcomes in a large Indian renal transplant cohort. While prior research, including that by El-Agroudy[11] and Ducloux,[12] has associated posttransplant weight gain with adverse metabolic outcomes such as new-onset diabetes (NODAT) and diminished graft survival. However, Ducloux observed that the association between weight gain(>5%)[12] and graft loss persisted even after adjusting for NODAT and metabolic syndrome, suggesting that weight gain may reflect broader pathophysiologic risks. In contrast, our findings diverge: In an Indian population with a sample size nearly five times larger and a longer median follow-up of 4.3 years, weight gain exceeding 5% was consistently associated with improved patient and graft survival. Our analysis differentiates between BMI at the last follow-up, a static snapshot, and longitudinal weight gain, a dynamic marker of patient trajectory.
Patient survival was uniformly high across BMI groups in the early posttransplant period; however, distinctions emerged with extended follow-up. Underweight patients had the poorest survival, likely reflecting ongoing illness or nutritional compromise. Interestingly, overweight and Obese 1 categories displayed survival comparable to or better than the normal-weight group, and even Obese 2 patients did not experience early survival detriment. This pattern echoes the “obesity paradox,”[13] where moderate-to-severe obesity, assessed years after transplantation, may indicate preserved nutritional and physiological reserves rather than risk. Graft survival followed a similar but more pronounced trend. Underweight and normal-weight individuals demonstrated earlier and steeper declines in graft function, but the obese 2 group exhibited the highest cumulative graft survival.
Crucially, weight gain over time – especially when assessed at last follow-up – was a powerful predictor of positive outcomes. Patients with significant weight gain had superior patient survival (141.6 vs. 112.5 months, P < 0.001) and graft survival (138.5 vs. 103.8 months, P < 0.001). Although early weight gain at 1 year trended toward benefit, it was sustained weight gain that most clearly aligned with favorable trajectories. These findings support the concept of weight gain as a surrogate for recovery, metabolic adequacy, and long-term graft viability.
Taken together, these data suggest a clinical tipping point: moderate weight gain and mid-range BMI levels confer benefit, while persistent elevation into obese 2 territory may eventually become harmful. Despite obese patients maintaining graft function, their decline in patient survival signals a potential long-term metabolic burden. Thus, the clinical implications are twofold: First, encouraging early posttransplant recovery and nutritional rehabilitation may improve outcomes. Second, long-term weight trajectories should be monitored to avoid progression into high-risk obesity zones that may negate initial survival benefits.
Limitations
This study has several important limitations that merit consideration: First, the retrospective design inherently introduces potential biases, including missing or inconsistent data. Although efforts were made to cross-verify weight measurements using both transplant and nursing records, retrospective data collection is subject to limitations in accuracy and completeness, which may affect the reliability of certain variables.
Second, this was a single-center study conducted at a tertiary care institution in South India. As such, the findings may not be fully generalizable to other geographic regions or healthcare settings, especially given regional variations in nutritional practices, immunosuppression protocols, and posttransplant follow-up care.
Third, several potentially relevant confounding factors were not captured due to the limitations of available medical records. Specifically, data on dietary intake, physical activity levels, socioeconomic status, psychosocial stressors, and family history of obesity were unavailable. These factors may significantly influence posttransplant weight change and could not be adjusted for, limiting the ability to interpret the observed associations.
Fourth, weight gain was defined as an increase of more than 5% from baseline. While this threshold is supported by some prior transplant studies, it lacks universal validation and may not fully capture the complexity of posttransplant metabolic changes. We acknowledge that more nuanced approaches – such as BMI trajectory analysis or formal diagnosis of metabolic syndrome – would provide a deeper understanding of weight-related risk but were beyond the scope of this retrospective study.
Fifth, the study did not distinguish between gains in fat mass versus lean mass. This distinction is clinically relevant, as increases in adiposity may have different implications for cardiovascular and graft outcomes compared to increases in muscle mass. However, advanced body composition assessment tools such as DEXA were not routinely available or feasible in our setting during the follow-up period.
Sixth, although nearly all patients received corticosteroids as part of immunosuppression, the study did not examine the impact of cumulative steroid exposure or the use of high-dose pulse methylprednisolone for rejection episodes. Inconsistent documentation of dosing regimens precluded subgroup analysis, and this remains a limitation, as steroid exposure is a well-established contributor to post-transplant weight gain and metabolic complications.
Finally, although we accounted for some posttransplant complications such as CMV viremia and other infections, unmeasured factors – such as undiagnosed malignancies, chronic inflammation, or noninfectious causes of cachexia – could also influence both weight change and survival, introducing potential residual confounding.
Conclusion
Despite these limitations, our study contributes valuable real-world data from one of the largest single-center transplant cohorts in the region, with a median follow-up of over four years. The findings underscore the importance of weight monitoring in renal transplant recipients and provide a foundation for future prospective, multicenter studies that can incorporate detailed metabolic profiling and body composition analysis to better understand the long-term implications of posttransplant weight trajectories.
Footnotes
Financial support and sponsorship
Nil.
Conflicts of interest
There are no conflicts of interest.
References
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