Visceral fat is better related to impaired glucose metabolism than body mass index after kidney transplantation

New-Onset Diabetes Mellitus after Kidney Transplantation

New-Onset Diabetes Mellitus after Transplantation (NODAT) emerges as a prevalent complication post-kidney transplantation, with its incidence influenced by variations in NODAT definitions and follow-up periods. The condition's pathophysiology is marked by impaired insulin sensitivity and β-cell dysfunction. Significant risk factors encompass age, gender, obesity, and genetics, among others, with the use of post-transplant immunosuppressants intensifying the condition. NODAT's significant impact on patient survival and graft durability underscores the need for its prevention, early detection, and treatment. This review addresses the complexities of managing NODAT, including the challenges posed by various immunosuppressive regimens crucial for transplant success yet harmful to glucose metabolism. It discusses management strategies involving adjustments in immunosuppressive protocols, lifestyle modifications, and pharmacological interventions to minimize diabetes risk while maintaining transplant longevity. The importance of early detection and proactive, personalized intervention strategies to modify NODAT's trajectory is also emphasized, advocating for a shift towards more anticipatory post-transplant care.

Glucometabolism in Kidney Transplant Recipients with and without Posttransplant Diabetes: Focus on Beta-Cell Function

Posttransplant diabetes mellitus (PTDM) is a common complication after kidney transplantation. Pathophysiologically, whether beta-cell dysfunction rather than insulin resistance may be the predominant defect in PTDM has been a matter of debate. The aim of the present analysis was to compare glucometabolism in kidney transplant recipients with and without PTDM. To this aim, we included 191 patients from a randomized controlled trial who underwent oral glucose tolerance tests (OGTTs) 6 months after transplantation. We derived several basic indices of beta-cell function and insulin resistance as well as variables from mathematical modeling for a more robust beta-cell function assessment. Mean ± standard deviation of the insulin sensitivity parameter PREDIM was 3.65 ± 1.68 in PTDM versus 5.46 ± 2.57 in NON-PTDM. Model-based glucose sensitivity (indicator of beta-cell function) was 68.44 ± 57.82 pmol∙min-1∙m-2∙mM-1 in PTDM versus 143.73 ± 112.91 pmol∙min-1∙m-2∙mM-1 in NON-PTDM, respectively. Both basic indices and model-based parameters of beta-cell function were more than 50% lower in patients with PTDM, indicating severe beta-cell impairment. Nonetheless, some defects in insulin sensitivity were also present, although less marked. We conclude that in PTDM, the prominent defect appears to be beta-cell dysfunction. From a pathophysiological point of view, patients at high risk for developing PTDM may benefit from intensive treatment of hyperglycemia over the insulin secretion axis.

Changes in glucose metabolism among recipients with diabetes 1 year after kidney transplant: a multicenter 1-year prospective study

Background

Diabetes mellitus is a common and crucial metabolic complication in kidney transplantation. It is necessary to analyze the course of glucose metabolism in patients who already have diabetes after receiving a transplant. In this study, we investigated the changes in glucose metabolism after transplantation, and a detailed analysis was performed on some patients whose glycemic status improved.

Methods

The multicenter prospective cohort study was conducted between 1 April 2016 and 31 September 2018. Adult patients (aged 20 to 65 years) who received kidney allografts from living or deceased donors were included. Seventy-four subjects with pre-transplant diabetes were followed up for 1 year after kidney transplantation. Diabetes remission was defined as the results of the oral glucose tolerance test performed one year after transplantation and the presence or absence of diabetes medications. After 1-year post-transplant, 74 recipients were divided into the persistent diabetes group (n = 58) and the remission group (n = 16). Multivariable logistic regression was performed to identify clinical factors associated with diabetes remission.

Results

Of 74 recipients, 16 (21.6%) showed diabetes remission after 1-year post-transplant. The homeostatic model assessment for insulin resistance numerically increased in both groups throughout the first year after transplantation and significantly increased in the persistent diabetes group. The insulinogenic index (IGI₃₀) value significantly increased only in the remission group, and the IGI₃₀ value remained low in the persistent diabetes group. In univariate analysis, younger age, newly diagnosed diabetes before transplantation, low baseline hemoglobin A1c, and high baseline IGI₃₀ were significantly associated with remission of diabetes. After multivariate analysis, only newly diagnosed diabetes before transplantation and IGI₃₀ at baseline were associated with remission of diabetes (34.00 [1.192-969.84], P = 0.039, and 17.625 [1.412-220.001], P = 0.026, respectively).

Conclusion

In conclusion, some kidney recipients with pre-transplant diabetes have diabetes remission 1 year after transplantation. Our prospective study revealed that preserved insulin secretory function and newly diagnosed diabetes at the time of kidney transplantation were favorable factors for which glucose metabolism did not worsen or improve 1 year after kidney transplantation.

Body Fat Distribution, Adipocytokines Levels and Variability in Associated Genes and Kidney Transplant Outcomes

Introduction: Body fat distribution is known to contribute to a variety of pathologies. Research Questions: We aimed to assess whether this distribution is associated with clinical outcomes in renal transplant recipients (RTR) and to examine its relationship with leptin and adiponectin gene variants and plasma concentrations. Design: Bioelectrical impedance analyses were performed in 236 RTR. Leptin/adiponectin levels were measured by immunoassay and relevant polymorphisms in the leptin receptor (LEPR) and adiponectin (ADIPOQ) genes were identified. Associations were assessed by logistic regression modeling. Results: The waist-to-height ratio (WHr) displayed a significant association with delayed graft function, acute rejection and post-transplant diabetes mellitus, with OR values of 2.04 (1.02-4.08) p = 0.045; 3.08 (1.22-7.79) p = 0.017 and 2.79 (1.16-6.74) p = 0.022, respectively. Waist circumference was linked to delayed graft function [OR = 1.03 (1.01-1.05), p = 0.025] and AR [OR = 1.041 (1.01-1.07), p = 0.009]. Leptin levels were significantly higher in patients who experienced rejection [19.91 ± 23.72 versus 11.22 ± 16.42 ng/ml; OR = 1.021 (1.01-1.04), p = 0.017]. The ADIPOQ rs1501299TT genotype showed a significant association with higher WHr (0.63 ± 0.11 vs 0.59 ± 0.87 for GG/GT genotypes; p = 0.015) and WC values (102.3 ± 14.12 vs 96.38 ± 14.65 for GG/GT genotypes; p = 0.021). Conclusion: WC, and especially WHr, are associated with adverse outcomes in renal transplantation and are affected by variability in the ADIPOQ gene.

Prevention of Post-Transplant Diabetes Mellitus: Towards a Personalized Approach

Post-transplant diabetes is a frequent complication after transplantation. Moreover, patients suffering from post-transplant diabetes have increased cardiovascular morbidity and reduced survival. Pathogenesis mainly involves beta-cell dysfunction in presence of insulin resistance. Both pre- and post-transplant risk factors are well-described, and some of them may be corrected or prevented. However, the frequency of post-transplant diabetes has not decreased in recent years. We realized a critical appraisal of preventive measures to reduce post-transplant diabetes.

Novel management of diabetes in kidney transplantation

PURPOSE OF REVIEW

Posttransplant diabetes mellitus (PTDM) is a prevalent complication in kidney transplant recipients, and has been associated with worse short-term and long-term outcomes.

RECENT FINDINGS

While hyperglycemia is frequently seen in the early posttransplant period because of surgical stress, infection, and use of high-dose steroids, the diagnosis of PTDM should be established after patients are clinically stable and on stable maintenance immunosuppression. In the early posttransplant period, hyperglycemia is typically treated with insulin, and pilot data have suggested potential benefit of lower vs. higher glycemic targets in this setting. Growing data indicate lifestyle modifications, including dietary interventions, physical activity, and mitigation of obesity, are associated with improved posttransplant outcomes. While there are limited data to support a first-line antidiabetic medication for PTDM, more established pharmacotherapies such as sulfonylureas, meglitinides, and dipetidyl peptidase IV inhibitors are commonly used. Given recent trials showing the benefits of sodium-glucose cotransporter 2 inhibitors and glucagon-like peptide 1 receptor agonists upon kidney outcomes in nontransplant patients, further study of these agents specifically in kidney transplant recipients are urgently needed.

SUMMARY

Increasing evidence supports a multidisciplinary approach, including lifestyle modification, obesity treatment, judicious immunosuppression selection, and careful utilization of novel antidiabetic therapies in PTDM patients.

Diabetes in Kidney Transplantation

Diabetes mellitus (DM) is one of the most common complications after kidney transplantation and is associated with unfavorable outcomes including death. DM can be present before transplant but post-transplant DM (PTDM) refers to diabetes that is diagnosed after solid organ transplantation. Despite its high prevalence, optimal treatment to prevent complications of PTDM is unknown. Medical therapy of pre-existent DM or PTDM after transplant is challenging because of frequent interactions between antidiabetic and immunosuppressive agents. There is also frequent need for medication dose adjustments due to residual kidney disease and a higher risk of medication side effects in patients treated with immunosuppressive agents. Sodium-glucose cotransporter 2 inhibitors have demonstrated a favorable cardio-renal profile in patients with DM without a transplant and hence hold great promise in this patient population although there is concern about the higher risk of urinary tract infections. The significant gaps in our understanding of the pathophysiology, diagnosis, and management of DM after kidney transplantation need to be urgently addressed.

Deep Learning-Based Quantification of Visceral Fat Volumes Predicts Posttransplant Diabetes Mellitus in Kidney Transplant Recipients

Background: Because obesity is associated with the risk of posttransplant diabetes mellitus (PTDM), the precise estimation of visceral fat mass before transplantation may be helpful. Herein, we addressed whether a deep-learning based volumetric fat quantification on pretransplant computed tomographic images predicted the risk of PTDM more precisely than body mass index (BMI).

Methods: We retrospectively included a total of 718 nondiabetic kidney recipients who underwent pretransplant abdominal computed tomography. The 2D (waist) and 3D (waist or abdominal) volumes of visceral, subcutaneous, and total fat masses were automatically quantified using the deep neural network. The predictability of the PTDM risk was estimated using a multivariate Cox model and compared among the fat parameters using the areas under the receiver operating characteristic curves (AUROCs).

Results: PTDM occurred in 179 patients (24.9%) during the median follow-up period of 5 years (interquartile range, 2.5–8.6 years). All the fat parameters predicted the risk of PTDM, but the visceral and total fat volumes from 2D and 3D evaluations had higher AUROC values than BMI did, and the best predictor of PTDM was the 3D abdominal visceral fat volumes [AUROC, 0.688 (0.636–0.741)]. The addition of the 3D abdominal VF volume to the model with clinical risk factors increased the predictability of PTDM, but BMI did not.

Conclusions: A deep-learning based quantification of visceral fat volumes on computed tomographic images better predicts the risk of PTDM after kidney transplantation than BMI.

Metabolic Consequences of Solid Organ Transplantation

Metabolic complications affect over 50% of solid organ transplant recipients. These include posttransplant diabetes, nonalcoholic fatty liver disease, dyslipidemia, and obesity. Preexisting metabolic disease is further exacerbated with immunosuppression and posttransplant weight gain. Patients transition from a state of cachexia induced by end-organ disease to a pro-anabolic state after transplant due to weight gain, sedentary lifestyle, and suboptimal dietary habits in the setting of immunosuppression. Specific immunosuppressants have different metabolic effects, although all the foundation/maintenance immunosuppressants (calcineurin inhibitors, mTOR inhibitors) increase the risk of metabolic disease. In this comprehensive review, we summarize the emerging knowledge of the molecular pathogenesis of these different metabolic complications, and the potential genetic contribution (recipient +/- donor) to these conditions. These metabolic complications impact both graft and patient survival, particularly increasing the risk of cardiovascular and cancer-associated mortality. The current evidence for prevention and therapeutic management of posttransplant metabolic conditions is provided while highlighting gaps for future avenues in translational research.

Thirty Years of Tacrolimus in Clinical Practice

Tacrolimus was discovered in 1984 and entered clinical use shortly thereafter, contributing to successful solid organ transplantation across the globe. In this review, we cover development of tacrolimus, its evolving clinical utility, and issues affecting its current usage. Since earliest use of this class of immunosuppressant, concerns for calcineurin-inhibitor toxicity have led to efforts to minimize or eliminate these agents in clinical regimens but with limited success. Current understanding of the role of tacrolimus focuses more on its efficacy in preventing graft rejection and graft loss. As we enter the fourth decade of tacrolimus use, newer studies utilizing novel combinations (as with the mammalian target of rapamycin inhibitor, everolimus, and T-cell costimulation blockade with belatacept) offer potential for enhanced benefits.

The effect of pretreatment BMI on the prognosis and serum immune cells in advanced LSCC patients who received ICI therapy

This study aims to evaluate the prognosis and serum immune cells of patients with different pretreatment body mass index (BMI) values. The data of 61 newly diagnosed patients with advanced lung squamous cell carcinoma (LSCC) who received immune checkpoint inhibitors (ICIs) combined with chemotherapy were obtained from the database of Rizhao People's Hospital (Rizhao, Shandong). According to the cutoff value of BMI (23.2 kg/m2), 32 patients had a high BMI and the remaining 29 patients had a low BMI. The effects of different BMIs on the prognosis and serum immune cells of patients were analyzed. The median progression-free survival (PFS) times were 7.72 months in the high BMI group and 4.83 months in the low BMI group [adjusted hazard ratio (HR), 0.23; 95% confidence interval (CI), 0.11-0.48; P < .001]. In terms of the overall survival (OS), the median times of the high BMI group and low BMI group were 18.10 and 13.90 months, respectively (adjusted HR, 0.15; 95% CI, 0.07-0.32; P < .001). After 4 cycles of ICI therapy combined with chemotherapy, the objective response rate was 59.4% for the high BMI group and 20.7% for the low BMI group (P = .002). In addition, the number of serum immune cells in patients with high BMI was significantly higher than that in patients with low BMI (all P < .001). There was a linear relationship between BMI value and the number of serum immune cells (all R2 > 0.7). The current results showed that high BMI is associated with better prognosis in LSCC patients who received ICIs, which may be related to higher levels of serum immune cells.

Management of post-transplant diabetes: immunosuppression, early prevention, and novel antidiabetics

Post‐transplant diabetes mellitus (PTDM) shows a relationship with risk factors including obesity and tacrolimus‐based immunosuppression, which decreases pancreatic insulin secretion. Several of the sodium–glucose‐linked transporter 2 inhibitors (SGLT2is) and glucagon‐like peptide 1 receptor agonists (GLP1‐RAs) dramatically improve outcomes of individuals with type 2 diabetes with and without chronic kidney disease, which is, as heart failure and atherosclerotic cardiovascular disease, differentially affected by both drug classes (presumably). Here, we discuss SGLT2is and GLP1‐RAs in context with other PTDM management strategies, including modification of immunosuppression, active lifestyle intervention, and early postoperative insulin administration. We also review recent studies with SGLT2is in PTDM, reporting their safety and antihyperglycemic efficacy, which is moderate to low, depending on kidney function. Finally, we reference retrospective case reports with GLP1‐RAs that have not brought forth major concerns, likely indicating that GLP1‐RAs are ideal for PTDM patients suffering from obesity. Although our article encompasses PTDM after solid organ transplantation in general, data from kidney transplant recipients constitute the largest proportion. The PTDM research community still requires data that treating and preventing PTDM will improve clinical conditions beyond hyperglycemia. We therefore suggest that it is time to collaborate, in testing novel antidiabetics among patients of all transplant disciplines.

Post-Transplantation Diabetes Mellitus

Solid organ transplantation (SOT) is an established therapeutic option for chronic disease resulting from end-stage organ dysfunction. Long-term use of immunosuppression is associated with post-transplantation diabetes mellitus (PTDM), placing patients at increased risk of infections, cardiovascular disease and mortality. The incidence rates for PTDM have varied from 10 to 40% between different studies. Diagnostic criteria have evolved over the years, as a greater understating of PTDM has been reached. There are differences in pathophysiology and clinical course of type 2 diabetes and PTDM. Hence, managing this condition can be a challenge for a diabetes physician, as there are several factors to consider when tailoring therapy for post-transplant patients to achieve better glycaemic as well as long-term transplant outcomes. This article is a detailed review of PTDM, examining the pathogenesis, diagnostic criteria and management in light of the current evidence. The therapeutic options are discussed in the context of their safety and potential drug-drug interactions with immunosuppressive agents.

Post-transplant diabetes mellitus in patients with solid organ transplants

Solid organ transplantation (SOT) is a life-saving procedure and an established treatment for patients with end-stage organ failure. However, transplantation is also accompanied by associated cardiovascular risk factors, of which post-transplant diabetes mellitus (PTDM) is one of the most important. PTDM develops in 10-20% of patients with kidney transplants and in 20-40% of patients who have undergone other SOT. PTDM increases mortality, which is best documented in patients who have received kidney and heart transplants. PTDM results from predisposing factors (similar to type 2 diabetes mellitus) but also as a result of specific post-transplant risk factors. Although PTDM has many characteristics in common with type 2 diabetes mellitus, the prevention and treatment of the two disorders are often different. Over the past 20 years, the lifespan of patients who have undergone SOT has increased, and PTDM becomes more common over the lifespan of these patients. Accordingly, PTDM becomes an important condition not only to be aware of but also to treat. This Review presents the current knowledge on PTDM in patients receiving kidney, heart, liver and lung transplants. This information is not only for transplant health providers but also for endocrinologists and others who will meet these patients in their clinics.

Temporal Changes on the Risks and Complications of Posttransplantion Diabetes Mellitus Following Cardiac Transplantation

Background

Recent changes in the demographic of cardiac donors and recipients have modulated the rate and risk, associated with posttransplant diabetes mellitus (PTDM). We investigated the secular trends of the risk of PTDM at 1 year and 3 years after transplantation over 30 years and explored its effect on major outcomes.

Methods

Three hundred and three nondiabetic patients were followed for a minimum of 36 months, after a first cardiac transplantation performed between 1983 and 2011. Based on the year of their transplantation, the patients were divided into 3 eras: (1983-1992 [era 1], 1993-2002 [era 2], and 2003-2011 [era 3]).

Results

In eras 1, 2, and 3, the proportions of patients with PTDM at 1 versus 3 years were 23% versus 39%, 21% versus 26%, and 33% versus 38%, respectively. Independent risk factors predicting PTDM at one year were recipient's age, duration of cold ischemic time, treatment with furosemide, and tacrolimus. There was a trend for overall survival being worse for patients with PTDM in comparison to patients without PTDM (p = 0.08). Patients with PTDM exhibited a significantly higher rate of renal failure over a median follow-up of 10 years (p = 0.03).

Conclusion

The development of PTDM following cardiac transplantation approaches 40% at 3 years and has not significantly changed over thirty years. The presence of PTDM is weakly associated with an increased mortality and is significantly associated with a worsening in renal function long-term following cardiac transplantation.

Arterial stiffness is associated with visceral fat mass in kidney transplanted patients-A nationwide cohort study

Arterial stiffness, visceral fat, and hyperglycemia are acknowledged risk factors for adverse outcomes after transplantation, but whether arterial stiffness is associated with visceral adipose tissue and hyperglycemia is unknown. We studied 162 non-diabetic kidney transplant recipients 8-10 weeks after transplantation. Arterial stiffness was measured as pulse wave velocity (PWV) by SphygmoCor and visceral fat using a validated software applied on DXA scans. Also a standard oral glucose tolerance test was performed. Median PWV was 8.6 m/s (IQR 7.3-10.4 m/s). Patients in the upper quartile of PWV had 31%-106% higher visceral fat percentage (P < 0.001), they were older (P < 0.001) and had a fasting plasma glucose of 5.8 mmol/L that was higher than in the other quartiles (P = 0.006). In univariate analysis, visceral fat percentage and age were the parameters strongest associated with PWV (P < 0.001), but cholesterol and glucose were also significant (P < 0.05). In multivariate analysis, visceral fat was the only significant predictor of PWV along with age (P < 0.001). In conclusion, arterial stiffness is significantly associated with visceral fat but not hyperglycemia in non-diabetic kidney transplant patients. We identified age and VAT as risk variables for arterial stiffness. A potential reversibility of arterial wall stiffness with reduction in VAT needs further study.

Is there relationship between epicardial fat and cardiovascular parameters in incident kidney transplant patients? A post-hoc analysis

Background

Epicardial fat (EF) has been related to increased cardiovascular risk in chronic kidney disease patients. Kidney transplantation is associated with weight gain, especially within the first 12 months. Recently an association between EF and left ventricular mass (LVM) has been suggested in kidney transplant (KTX) recipients.

Objective

Evaluate the EF in KTX recipients and its association with cardiovascular parameters in a 12-month follow-up study.

Methods

EF volume was determined using thoracic computed tomography. The EF progressor group (EF gain) was defined by any increment in EF after 12 months. LVM and LVM index were calculated by echocardiography.

Results

Ninety-eight incident KTX patients [57% men, 41.2 ± 10.1 years, mean dialysis time prior to transplant of 24 (11–60) months] were analyzed. At baseline and after 12 months, EF was 318.6 (275.2–392.6) ml and 329.5 (271.7–384.8) ml, respectively (p = 0.03). When compared to patients who EF decreased (n = 33), those with EF gain (n = 65) had a greater increase of body mass index, abdominal circumference and blood glucose. These patients also had a lower reduction of LVM index. However in the multivariate analysis, there was no difference in LVM index change between groups (interaction p = 0.565), even after adjustment for hypertension, glucose and coronary calcium score (interaction p = 0.538).

Conclusion

The impact of EF gain on ventricular mass after KTX could not be definitely confirmed. Further prospective studies in a large sample of KTX patients should be considered to address a possible causal relationship between EF gain and cardiac hypertrophy in this population.

Five myths about variable selection

Multivariable regression models are often used in transplantation research to identify or to confirm baseline variables which have an independent association, causally or only evidenced by statistical correlation, with transplantation outcome. Although sound theory is lacking, variable selection is a popular statistical method which seemingly reduces the complexity of such models. However, in fact, variable selection often complicates analysis as it invalidates common tools of statistical inference such as P-values and confidence intervals. This is a particular problem in transplantation research where sample sizes are often only small to moderate. Furthermore, variable selection requires computer-intensive stability investigations and a particularly cautious interpretation of results. We discuss how five common misconceptions often lead to inappropriate application of variable selection. We emphasize that variable selection and all problems related with it can often be avoided by the use of expert knowledge.

Diabetes Mellitus Following Renal Transplantation: Clinical and Pharmacological Considerations for the Elderly Patient

Post-transplant diabetes mellitus occurs in 30-50% of cases during the first year post-renal transplantation. It is associated with increased morbidity, mortality and healthcare costs. Risk factors include age and specific immunosuppression regimens. At the same time, renal transplantation is increasingly indicated in elderly (aged >65 years) patients as this proportion of older patients in the prevalent dialysis population has increased. The immune system and β cells undergo senescence and this impacts on the risk for developing post-transplant diabetes and our ability to prevent such development. It may, however, be possible to identify patients at risk of developing post-transplant diabetes, enabling treatment protocols that prevent or reduce the impact of post-transplant diabetes. Much work remains to be completed in this area and is facilitated by the growing base of knowledge regarding the pathophysiology of post-transplant diabetes. Should post-transplant diabetes develop, there are a range of treatment options available. There is increasing interest in using newer agents, although their safety and efficacy in transplant recipients remains to be conclusively established.

Characterization of Body Composition and Fat Mass Distribution 1 Year After Kidney Transplantation

PURPOSE

In some recipients, significant weight gain occurs after kidney transplantation. Weight gain is associated with poor outcomes, particularly increased cardiovascular risk. In this study, we characterized changes in body mass index and body fat mass and compared them based on gender and race.

METHODS

Fifty-two kidney transplant recipients (aged ≥18 years old, 50% men, 58% African American) were enrolled into a prospective study. Body mass index and body fat mass were measured at baseline and 12 months posttransplant. Body fat mass was determined by dual-energy X-ray absorptiometry.

RESULTS

The mean increase in body weight was 3.7kg at 12 months; 36.5% (n=19) gained at least 10% of their baseline body weight. Body mass index, percentage of total body fat, and trunk fat were significantly increased. In subgroups, women and African American showed significant increases in body mass index and body fat measures. More participants were classified as obese based on total body fat compared to body mass index. Calories from fat were significantly increased at 12 months and associated with increased body mass index, total body fat, and trunk fat. Days of physical activity were significantly increased.

CONCLUSION

Both body mass index and total body fat mass were significantly increased at 12 months following kidney transplantation, especially for women and African Americans. Importantly, more participants were classified as obese based on total body fat compared to body mass index. Relevant nutrition and physical intervention should be provided, and both body mass index and body fat mass should be evaluated when monitoring weight gain.

Higher visceral to subcutaneous fat ratio is associated with small intestinal bacterial overgrowth

BACKGROUND AND AIMS

There is a lack of studies evaluating the association between small intestinal bacterial overgrowth (SIBO) and abdominal fat. The aim of this study was to evaluate whether visceral fat area (VFA), subcutaneous fat area (SFA) or visceral to subcutaneous fat ratio (VFA/SFA ratio) were associated with SIBO.

METHODS AND RESULTS

In this case-control study, 152 eligible patients submitted to glucose hydrogen/methane breath test who also had computed tomography (CT) of the abdomen performed were included. Clinical and demographic information was obtained. VFA and SFA were measured using Image J software at lumbar 3 level on CT cross-sectional image of the 152 patients included in this study, 68 patients (44.7%) tested positive for SIBO. In the univariate analysis, the presence of SIBO was associated with older age (65.2 ± 1.5 vs. 59.3 ± 1.5, p = 0.007); type 2 diabetes mellitus (33.8% vs. 17.9%; p = 0.019); hypertension (63.2% vs. 39.3%; p = 0.003); metabolic syndrome (85.3% vs. 64.3%; p = 0.003); and higher VFA/SFA ratio (1.0 ± 0.1 vs. 0.7 ± 0.1; p < 0.001). In multivariate analysis, metabolic syndrome (odds ratio [OR]: 2.5; 95% confidence interval [CI]: 1.1-5.7; p = 0.035) and higher VFA/SFA ratio (OR: 3.3; 95% CI: 1.6-7.2; p = 0.002) remained independently associated with SIBO.

CONCLUSION

The presence of SIBO was found to be associated with high VFA/SFA ratio measured from cross-sectional CT image.

Prediction of Fat-Free Mass in Kidney Transplant Recipients

BACKGROUND

Individualization of drug doses is essential in kidney transplant recipients. For many drugs, the individual dose is better predicted when using fat-free mass (FFM) as a scaling factor. Multiple equations have been developed to predict FFM based on healthy subjects. These equations have not been evaluated in kidney transplant recipients. The objectives of this study were to develop a kidney transplant specific equation for FFM prediction and to evaluate its predictive performance compared with previously published equations.

METHODS

Ten weeks after transplantation, FFM was measured by dual-energy X-ray absorptiometry. Data from a consecutive cohort of 369 kidney transplant recipients were randomly assigned to an equation development data set (n = 245) or an evaluation data set (n = 124). Prediction equations were developed using linear and nonlinear regression analysis. The predictive performance of the developed equation and previously published equations in the evaluation data set was assessed.

RESULTS

The following equation was developed: FFM (kg) = {FFMmax × body weight (kg)/[81.3 + body weight (kg)]} × [1 + height (cm) × 0.052] × [1-age (years) × 0.0007], where FFMmax was estimated to be 11.4 in males and 10.2 in females. This equation provided an unbiased, precise prediction of FFM in the evaluation data set: mean error (ME) (95% CI), -0.71 kg (-1.60 to 0.19 kg) in males and -0.36 kg (-1.52 to 0.80 kg) in females, root mean squared error 4.21 kg (1.65-6.77 kg) in males and 3.49 kg (1.15-5.84 kg) in females. Using previously published equations, FFM was systematically overpredicted in kidney-transplanted males [ME +1.33 kg (0.40-2.25 kg) to +5.01 kg (4.06-5.95 kg)], but not in females [ME -2.99 kg (-4.07 to -1.90 kg) to +3.45 kg (2.29-4.61) kg].

CONCLUSIONS

A new equation for FFM prediction in kidney transplant recipients has been developed. The equation may be used for population pharmacokinetic modeling and clinical dose selection in kidney transplant recipients.