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Original Clinical Science—General

Comparing Glycaemic Benefits of Active Versus Passive Lifestyle Intervention in Kidney Allograft Recipients: A Randomized Controlled Trial

Kuningas, Kulli RN1; Driscoll, Joanne BSc2; Mair, Reena BSc2; Smith, Helen PhD3; Dutton, Mary MSc1; Day, Edward MA4; Sharif, and Adnan MD1,5

Author Information
doi: 10.1097/TP.0000000000002969



Kidney transplantation is the preferred modality of renal replacement therapy for suitable candidates with end-stage kidney failure. However, the need for lifelong immunosuppression to prevent allograft rejection is associated with significant side effects and complications. Cardiovascular disease remains a leading cause of morbidity and mortality after kidney transplantation,1 and its development is linked to both traditional and transplant-specific risk factors.2 The latter are predominantly because of the constellation of cardiometabolic side effects attributable to immunosuppression, among which the development of de novo posttransplantation diabetes mellitus (PTDM) is most significant.

PTDM is a major complication with underlying etiology related to both traditional and transplant-specific pathophysiology and can affect over a third of patients within the first year posttransplantation.3 PTDM is linked to cardiovascular events and all-cause mortality after kidney transplantation4 and is ranked as a leading concern for recipients themselves.5 Both generic6 and PTDM-specific7 international guidelines strongly advocate lifestyle intervention strategies to minimize the risk of PTDM but lack any strong evidence-base in support.

Previous work has shown the benefit of renal dietitian intervention to attenuate progression of abnormal glucose metabolism after kidney transplantation.8 However, this was a nonrandomized study with dietitian intervention only offered to recipients with abnormal postprandial glucose metabolism. Clinical trial evidence is lacking to support the efficacy of lifestyle intervention to reduce the risk of PTDM after kidney transplantation, compared with the general population where it is as effective as pharmacological therapy at preventing diabetes.9 Lifestyle interventions to delay or prevent PTDM have the potential to prolong kidney transplant survival, reduce the burden of healthcare costs, and improve the health and wellbeing of the kidney transplant population.

However, interventions to change health-related behaviors are complex and consist of many interacting components.10 Promoting lifestyle interventions in kidney transplant recipients is difficult as they compete for attention against a number of kidney- and transplant-specific complications, which rank higher for patient concern.5 The recent development of a taxonomy of behavior change techniques (BCTs) has identified interventions that are effective at promoting physical activity and healthy eating.11 Clearly defined BCTs are rarely embedded with clinical interventions after transplantation and have not been used in the development of lifestyle interventions posttransplant. In view of the significant clinical burden and patient anxiety related to PTDM, the need for evidence-based interventions to inform clinical practice is imperative. This led us to investigate the benefit of active versus passive lifestyle intervention after kidney transplantation to prevent abnormal glycemic control, developing a bespoke renal dietitian-led approach underpinned by effective BCTs.


Study Design and Participants

Details of the Comparing Glycaemic Benefits of Active Versus Passive Lifestyle Intervention in Kidney Allograft Recipients (CAVIAR) study objectives, design, methods, and analysis have been previously reported.12 Briefly, participants were recruited from a single transplant center and were eligible for inclusion if they were between 3 and 24 months after kidney transplantation, had no preexisting diabetes, and were deemed to have stable kidney function by their transplant clinician. Potential eligible patients were invited to participate by a member of the research team, who assessed eligibility and obtained written informed consent. Approval was obtained from the local Research Ethics Committee before enrollment, and the trial was registered with the registry (identifier: NCT02233491).


Patients were randomly assigned (1:1) to receive either active lifestyle intervention with the renal dietitian or passive lifestyle advice with no dietitian involvement. Randomization was done by the trial coordinator via a web-based randomization service ( stratified by age, body mass index, and ethnicity in random permutated blocks. In view of the nature of the intervention, patients and clinicians were aware of group allocation.


Kidney allograft recipients fulfilling the eligibility criteria and who gave informed consent were subsequently randomized into active versus passive intervention arms.

Active Intervention Group

This group received active lifestyle modification led by a renal dietitian who facilitated individualized lifestyle intervention advice to prevent the risk of PTDM. These participants received 4 face-to-face appointments with the dietitian (lasting 45–60 min) at baseline, day 30, day 60, and day 120. Brief telephone reviews were conducted between appointments (2–4 wk after each face-to-face appointment) to review progress and provide additional support during the 6-month active intervention period (some appointments could be substituted with telephone support if requested). Patients had their dietary habits personally reviewed by the renal dietitian, and personalized healthy eating advice was given based upon current guidelines issued by Diabetes UK13 and Public Health England tailored to the individual. Briefly, the guidelines recommend a diet containing less saturated fat and sugar, with more fruit, vegetables, healthy protein sources, and whole grains. Patients were advised to keep food diaries to monitor compliance with initiated changes and were followed up by the renal dietitians prospectively as highlighted to monitor progress and reinforce the advice (running parallel with routine clinic visits). In addition, a graded exercise program was encouraged to increase physical activity (eg, endurance exercise, such as walking, jogging, or swimming) and an exercise diary encouraged to track progress.

Passive Control Group

This group received standard of care, which involved counseling about the risks of PTDM and leaflet advice outlining recommended lifestyle intervention (advice on healthy eating, exercise, and the importance of weight loss if required—see supplementary files (SDC, There was no renal dietitian input and no behavioral therapy intervention.

Both groups underwent assessment at baseline and end of the 6-month intervention. If any kidney allograft recipient developed PTDM during the study, they were treated in line with recommended international consensus guidelines.7

Behavior Change Techniques

The active intervention was underpinned by defined BCTs and overseen by a clinician with recognized expertise in behavioral change therapy. After development of the behavior change intervention in conjunction with the renal dietitians, ongoing support was provided to renal dietitians to support and refine their delivery of personalized interventions to study participants in the active intervention arm.

Research evidence in relation to BCTs for healthy eating and physical activity interventions10 suggests that interventions that combine self-monitoring with at least 1 other technique derived from control theory are significantly more effective than other interventions. Therefore, the intervention included the following BCTS:

  1. Providing information on the consequences of suboptimal diet and exercise levels on health in general.
  2. Providing specific feedback of personalized information (body mass index, body fat percentage, waist to hip ratio) and comparison with healthy range.
  3. Prompting intention formation (ie, encouraging the patient to make a resolution to change their diet or level of exercise).
  4. Setting Specific, Measurable, Achievable, Relevant, and Time-bound goals around diet, exercise, and weight.
  5. Setting graded tasks around the achievement of patient goals.
  6. Encouraging self-monitoring of goals through food and exercise diaries and other node-link maps.
  7. Regular reviews of specific behavioral goals and reinforcement of progress through praise and encouragement.
  8. Reviewing social support available from personal network of family/friends and linking support to the achievement of specific goals.

The intervention incorporated self-regulatory techniques congruent with control theory, encouraging individuals to decide to act (intention formation), prompting specific goal-setting, providing feedback on performance and self-monitoring of behavior, and continuous review of set goals or intentions. These techniques were combined with 2 other effective strategies to support the behavior change intervention: node-link mapping (use of visual representation for presenting the intervention) and elements of Social Behavior and Network Therapy (focus on building social network support for behavior change). Further details and references are detailed in our CAVIAR methodology article.12

Study Investigations

Clinical and biochemical data were collected at baseline (mo 0), midway (mo 3), and end (mo 6). Oral glucose tolerance tests were classified by current International Consensus recommendations for diagnosis of both prediabetes and PTDM.7 The BioPlex Pro Human Diabetes 10-plex assay (BioRad, California) was used to quantitate a number of diabetes- and obesity-related markers at each study timepoint, checked in a fasting state on the morning of the oral glucose tolerance test. Recorded outputs were the average of 2 measurements to improve precision.

The following formulae were utilized for determination of glucose metabolism parameters: insulin sensitivity, insulin secretion, and the disposition index. These surrogates were chosen on the basis of previous validation work showing them to be the best surrogates against gold-standard investigations in the setting of kidney transplantation14,15:

  • Insulin secretion = HOMAsec = insulin0 × [3.33/(glucose0–3.5)]
  • Insulin sensitivity = McAuley index = exp [2.63–(0.28 × ln {insulin0 / 6.945}) – (0.31 × ln trigycerides0)]
  • Disposition index = HOMAsec × McAuley index = Insulin0 × [3.33/(glucose0 – 3.5)] × exp [2.63 – (0.28 × ln {insulin0 / 6.945}) – (0.31 × ln trigycerides0)]


The primary endpoint for this trial was change in glucose metabolism as measured by change in insulin secretion, insulin sensitivity, and disposition index at the end of the 6-month study intervention. These indices were analyzed using surrogate measures as previously validated in the setting of kidney transplantation.14,15 The justification for using these indices as the primary outcome was based on previous work showing benefit of lifestyle intervention on glucose metabolism parameters and change in disposition index being the earliest detected glycemic abnormality in nondiabetic kidney transplant recipients. On this basis, it was hypothesized that we would be able to determine a beneficial impact of active versus passive lifestyle intervention based on these surrogate outcomes of abnormal glucose metabolism. A number of secondary endpoints relating to cardiometabolic function and profile, patient-reported outcomes, safety endpoints, and clinical outcomes were also collected.

This report deals only with the immediate primary and secondary outcome measurements at 6-months poststudy commencement. Long-term outcomes at 1, 3, 5, and 10 years are planned for collection and will be reported in due course.

Sample Size Calculation

The principle parameters being examined in this study are changes in insulin sensitivity and insulin secretion. The power calculation was performed with the assumption of a 20% participant drop-out rate. An anticipated change in the primary outcome measure of 5% in the control group and 25% in the intervention group was predicted. These figures are based on intrasubject variability of 25% for insulin secretion and 20% for insulin sensitivity, as observed in our previous work.16

Therefore, assuming 80% of the control group demonstrates a 5% change in the primary outcome measure (and 20% dropouts demonstrate no change), then the average change in the control group is 4%. Similarly, if it is assumed 80% of the intervention group will demonstrate a 25% change in the primary outcome measure (and 20% dropouts demonstrate no change), then the average change in the intervention group is 20%. To detect this difference of 16% change (assuming SD of change is 25%), it was calculated that a total of 130 patients were required for recruitment (65 per randomized arm) for 95% power (assuming a 5% significance level and a 2-sided test) to attain a high-powered sample size. All analyses were undertaken on an intention-to-treat basis.

Statistical Analysis

The planned primary analyses were done at the individual level, according to the intention-to-treat principle. For participants who did not attend the 6-month end of study assessment, secondary outcomes of clinical endpoints were determined from healthcare records unless participants had withdrawn consent for this access.

Statistical analysis was performed using SPSS Version 25 (Chicago, Illinois). Normality of data was assessed using the Kolmogorov-Smirnov tests. Descriptive statistics were used to estimate the frequencies, means (± SD) or medians (± interquartile range) of study variables as required. For continuous variables, Student t test or Mann-Whitney test was used for parametric and nonparametric data, respectively. Mean differences between continuous variables for groups were also reported, with 95% confidence intervals (CIs) of the difference. Difference between groups was assessed with 2-sided Fisher exact test or Pearson χ2 for categorical variables as appropriate. Correlation assessment was made with Pearson test or Spearman rank test for parametric and nonparametric variables, respectively. A P < 0.05 was considered significant in the statistical analysis.

Role of the Funding Source

The funders of this study had no role in study design, data collection, data analysis, data interpretation, or writing of the report. The corresponding author had full access to the data in the study and had final responsibility for the decision to submit for publication.


Study Cohort

Between August 17, 2015 and December 18, 2017, 130 individuals were recruited that comprised the intention-to-treat population at the start of the study. At the end of study (July 26, 2018), 103 individuals remained and completed follow-up investigations, reflecting a drop-out rate of 20.8%. Figure 1 provides a Consolidated Standards of Reporting Trials (CONSORT) flow diagram of the study.

Consolidated Standards of Reporting Trials (CONSORT) flow chart for Comparing Glycaemic Benefits of Active Vs Passive Lifestyle Intervention in Kidney Allograft Recipients study profile. PTDM, posttransplantation diabetes mellitus.

Table 1 outlines the baseline demographics of the study cohort, reflecting well-matched active and passive intervention groups. Of particular note, 32.3% of recruitment was from individuals of the Black, Asian, and Minority Ethnic community.

Baseline demographics of study cohort

Change in Metabolic Parameters

Table 2 shows metabolic surrogate outcomes for the study cohort. Active versus passive lifestyle intervention after kidney transplantation was not associated with any change in glucose metabolism, such as insulin secretion (mean difference, −446; 95% CI, −3184 to 2292; P = 0.748), insulin sensitivity (mean difference, −0.45; 95% CI, −1.34 to 0.44; P = 0.319), or disposition index (mean difference, −940; 95% CI, −5655 to 3775; P = 0.693). We did not observe any significant difference in diabetes- or obesity-related immunoassays.

Metabolic surrogate outcomes between baseline and 6-mo study end

In the total study cohort, fasting glucose levels (in mmol/L) after active versus passive lifestyle intervention at baseline were 5.5 versus 5.6, respectively (P = 0.566), and, after 6 months, were 5.4 versus 5.5, respectively (P = 0.795). Postprandial glucose levels (in mmol/L) after active versus passive lifestyle intervention at baseline were 7.5 versus 7.3, respectively (P = 0.603), and, after 6 months, were and after 6-months was 7.1 versus 7.1, respectively (0.992).

Change in postprandial glucose levels had weak correlation with change in insulin secretion (−0.288, P = 0.011) and disposition index (−2.23, P = 0.050) but not significantly for insulin sensitivity (−0.125, P = 0.277). There was no significant correlation between change in fasting glucose levels with change in insulin secretion (−0.111, P = 0.266), insulin sensitivity (−0.077, P = 0.440), and disposition index (−0.143, P = 0.152).

Patient-reported Outcomes

Table 3 outlines change in patient-reported outcomes including change in physical activity (Duke Activity Status Index and General Practice Physical Activity Questionnaire) and psychological wellbeing (EuroQol - 5 dimensions; quality of life and health status; Beck Depression Inventory; specific tool for depression; Situational Motivational Score; specific tool for assessment of situational motivation). No significant difference was observed at 6 months between the cohorts.

Patient-reported outcome measures between baseline and 6-mo study end

Safety and Clinical Outcomes

No significant safety concerns were identified when comparing the 2 cohorts, and no deaths or graft losses occurred over the 6-month study period (see Table 4). Immunosuppression was no different between the groups. Tacrolimus trough levels in ng/mL (± SD) were similar for active versus passive groups at baseline (8.0 ± 3.2 versus 7.2 ± 2.5, respectively, P = 0.197) and 6 months (7.5 ± 3.5 versus 7.0 ± 1.7, respectively, P = 0.466). There was no difference in cumulative exposure to mycophenolate or corticosteroids over the study period.

Clinical outcomes at 6-mo

From a clinical perspective, active lifestyle intervention was associated with a significant difference in weight change over the course of the 6-month follow-up (mean difference, −2.47 kg; 95% CI, −0.401 to −0.92; P = 0.002). Overall, weight loss was observed in 60.0% versus 38.3% of participants in active versus passive intervention arms (P = 0.023). There was a trend towards a significant difference in fat-free mass (mean difference, −1.540 kg; 95% CI, −3.24 to 0.16; P = 0.075) and a significant difference in fat mass (mean difference, −1.537 kg; 95% CI, −2.947 to −0.127; P = 0.033) over the course of the 6-month follow-up favoring active intervention.

Rates of new-onset posttransplantation diabetes were halved in the group receiving active intervention compared to standard of care (7·6% versus 15·6%, respectively, P = 0·123), a clinically significant reduction that failed to achieve statistical significance. Subanalyses of our recruited cohort (Supplementary Document [SDC,]) showed a more pronounced clinical difference in PTDM rates for recruits within 12 months of kidney transplantation (n = 82; 9.8% versus 18.4%, respectively, P = 0.216) or body mass index 25 mg/m2 or higher (n = 73; 11.1% versus 24.2%, respectively, P = 0.131) or both (n = 55; 10.7% versus 25.9%, respectively, P = 0.133).


This study demonstrates kidney transplant recipients can be encouraged to undertake lifestyle modification under the supervision of a renal dietitian with proactive intervention underpinned by defined BCTs, which may improve their cardiometabolic risk profile. Our study did not identify any influence of active versus passive lifestyle intervention on our primary outcome of surrogate glucose metabolism measures, but there were encouraging improvements in secondary outcomes, such as weight difference, fat mass, and trend towards less PTDM between study arms. This study is the first lifestyle intervention trial designed to improve glycemic metabolism after kidney transplantation and introduces the concept of incorporating evidence-based BCTs into posttransplant care, but further research investigation is warranted to determine beneficial effects on clinical outcomes.

The immediate interpretation of our negative study suggests 1 of 3 conclusions: (1) active lifestyle intervention was ineffective; (2) study intervention was too short; or (3) the chosen primary outcome for analysis was inappropriate. It is possible the study intervention period was too short, and longer exposure could convert observed trends into significant differences with longer follow-up. A recent systematic review of clinical trials suggests time-limited lifestyle interventions may have variable efficacy for prevention of diabetes.17 With regards to the primary outcome, the negligible effects of active lifestyle intervention on glucose metabolism seem to contrast with clinically meaningful reduction in PTDM rates. This paradoxical observation seems contradictory and requires explanation. First, previous work validating use of surrogate measures of glucose metabolism was conducted exclusively in kidney transplant recipients of white ethnicity,14,15 whereas approximately a third of our study participants were nonwhite. High participation rates from black, Asian, and minority ethnic individuals are a significant strength of this study but may have an impact on the final analysis. Kodama et al,18 in their systematic review and meta-analysis of 74 study cohorts, demonstrated significant differences in the hypothesized hyperbolic relationship between insulin secretion and sensitivity among Africans, Caucasians, and East-Asian individuals. Rasouli et al19 also observed African-American individuals paradoxically have an approximate 25% increase in disposition index compared with white individuals (secondary to greater compensatory increase in insulin secretion in relation to increased insulin resistance). Another limitation to the interpretation of insulin-based parameters in this study is the lack of data relating to influences which impact circulating insulin levels such as hepatic insulin extraction. The complex interplay between insulin secretion, insulin sensitivity, and hepatic insulin extraction has led to significant debate about the strengths and limitations of calculating the disposition index. Although the hyperbolic relationship between insulin secretion and sensitivity remains a convenient conceptual framework, it continues to be refined in light of emerging research evidence. In addition, the power calculation was not adjusted for baseline glucose metabolism, which may have interfered in our sample size estimation. Therefore, it is possible that the observed power may have differed from assumed power, leading to an underpowered sample.

The lack of improvement in surrogates of glucose metabolism may also reflect the volatile nature of posttransplantation glycemia. First, postoperative hyperglycemia consistent with diagnostic criteria for diabetes is ubiquitous post-kidney transplantation among nondiabetic recipients.20 Although this frequently improves, approximately half of kidney transplant recipients remain with PTDM or prediabetes as demonstrated in a Spanish cohort study of 672 kidney transplant recipients.21 The dynamic and bimodal nature of posttransplant glycemia may explain the lack of significant change in short-term glucose metabolism indices in our study. We relied on surrogates assessing baseline glucose metabolism rather than postprandial glucose metabolism because of previously validated work, but this reliance on static versus dynamic measurements may underestimate intervention benefits. The utility of the disposition index has also been questioned in recent studies, with beta-cell sensitivity or beta-cell response to rate of change in plasma glucose concentration competing for importance as determinants of beta-cell function.22,23 It is clear from this study that our understanding of the pathophysiology of PTDM remains suboptimal and requires further investigation, especially in light of fundamental differences compared with alternative forms of diabetes24 and justifies PTDM to be considered as a unique subset within diabetes classification systems.

This study area is important as cardiovascular disease remains a leading cause of morbidity and mortality after kidney transplantation1 and evidence-based strategies to attenuate cardiometabolic risk profiles are limited.2 Only 2 randomized controlled trials to reduce cardiovascular risk posttransplantation have ever been conducted, Assessment of LEscol in Renal Transplantation (ALERT)25 and Folic Acid for Vascular Outcome Reduction in Transplantation Trial (FAVORIT),26 but the benefit of lifestyle modification posttransplantation has never been robustly explored. In the general population, lifestyle intervention is effective at preventing type 2 diabetes but does not reduce all-cause mortality among individuals with type 2 diabetes.27 Trials exploring the benefits of lifestyle intervention after kidney transplantation are limited. The Intensive Nutrition Interventions on Weight Gain after Kidney Transplantation study compared early intensive nutritional/exercise advice versus standard of care in 36 kidney transplant recipients in New Zealand, with the primary outcome change in weight after 6 months.28 No difference was observed between the cohorts at 6 months, which differs from our study results. This could be explained by methodological variations in the intervention, higher-than-expected attrition rate for study participation and different behavior change components. The Active Care after Transplantation study is a multicenter randomized controlled trial currently in progress across 3 centers in the Netherlands, comparing 3 arms (exercise versus exercise/diet versus standard of care) among 219 kidney transplant recipients, with the primary outcome change in physical functioning of quality of life.29 Lifestyle counseling and motivational techniques, in line with the self-determination theory, will underpin the delivery of interventions. Recently published taxonomy of BCT has assessed the effectiveness of behavior change intervention to promote healthy eating and physical activity.10 With this knowledge in mind, CAVIAR incorporated self-regulatory techniques congruent with Control Theory, combined with node-link mapping and elements of social behavior and network therapy in support. Although the pharmacological management of PTDM is slowly developing a growing evidence base,30 we believe behavior change and lifestyle intervention remain critical after kidney transplantation, and seeking clinical evidence for its efficacy remains desirable.

Additional limitations of this study, distinct from the methodological considerations already discussed regarding study intervention period and primary outcome, should be noted. The study participant attrition rate of 20.8% was marginally above our estimated 20.0% that was factored into our power calculations (aiming for 95% power). However, it is unlikely to have made any significant difference to the final analysis. The active intervention arm was designed pragmatically in an attempt to minimize participant attrition rates, with flexibility for some study visits to be telephone-based, but posttrial participant feedback will help to develop any refinements or improvements to future work. Although an excellent proportion of nonwhite kidney transplant recipients was achieved, a number of potential recruits could not be recruited because of language barriers, and it is important to overcome such inequalities in access to research to ensure study findings are genuinely representative of patient cohorts.

Despite our frank discussion of limitations, we believe this study identifies potential benefits of active lifestyle modification and supports the encouragement of active lifestyles after transplantation. However, in line with a recent meeting report on the benefits of sport and exercise posttransplantation,31 evidence for improved hard clinical outcomes remain lacking. Our experience should not dissuade further research but guide methodological considerations for future work. For example, the active intervention may require more frequent visits to improve intensity (but needs balancing against risk for dropouts). Future study recruitment should also select an at-risk group for development of PTDM (eg, older age, nonwhite ethnicity, overweight, family history) for investigation. Assessing change in dynamic glucose metabolism (using postprandial samples) rather than static physiological markers may be more beneficial. However, this study will allow adequate power calculations to be made for clinically meaningful outcomes like development of PTDM.

In conclusion, our renal dietitian-led lifestyle intervention utilizing defined BCTs after kidney transplantation failed to demonstrate improvement in parameters of glucose metabolism but conflictingly suggested some improvement in clinical outcomes, such as weight and risk for PTDM. Rather than failing to show the benefit of active lifestyle intervention, we believe our work highlights methodological considerations that should be corrected for any future work in this area. Further research is needed to determine if lifestyle modification after kidney transplantation has benefit upon clinical outcomes, such as prevention of PTDM, and this study provides adequate event rates and practical experience to develop a more refined well-powered clinical trial across multiple centers.


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