Most earlier investigations of the relationship between body mass index and outcomes in peritoneal dialysis have used BMI measured at enrollment or changes between two points. Those approaches cannot fully represent how long a patient remains at a given weight. The new study instead estimated the cumulative duration of BMI exposure at baseline, 12 months and 36 months. Its authors described time above a target range, or TAR, and time in a target range, or TTR, as longitudinal measures intended to capture sustained BMI rather than a single snapshot. The analysis used a landmark cohort design: the first three years defined the exposure period, and mortality was tracked only after participants reached the 36-month landmark.

The investigators scanned 15 integer BMI thresholds, from 18 to 32 kg/m², to identify the level most closely associated with lower mortality. Time above each threshold was calculated by Rosendaal linear interpolation. The lowest estimated hazard ratio occurred at 26 kg/m²: for each 10% increase in TAR above that threshold, the adjusted hazard ratio for death was 0.82, with a 95% confidence interval of 0.73 to 0.92 and a Bonferroni-corrected P value of 0.018. Although every threshold produced a point estimate below 1, only thresholds from 24 through 26 kg/m² remained statistically significant after correction for testing 15 cutoffs. The researchers therefore selected 26 kg/m² as the lower boundary of a candidate range and examined TTR in two prespecified intervals: 24–25.9 and 26–30 kg/m².

Participants entered the study if they were at least 18 years old, had received continuous ambulatory peritoneal dialysis for at least three months and attended a scheduled reassessment between January 2014 and December 2023. The investigators excluded 550 people, including two with missing baseline BMI, 287 with fewer than two BMI measurements during the first three years, 72 who died before the landmark, 75 who transferred to hemodialysis, 23 who received a kidney transplant, 18 who were lost to follow-up and 73 with other reasons for exiting before the landmark. The final group of 670 had a mean age of 48.2 years, and 54.5% were male. Its mean baseline BMI was 21.91 kg/m². Of these participants, 548 spent no time in the 26–30 kg/m² interval during the exposure period, while 122 spent at least some time there.

BMI was calculated from measured weight in kilograms divided by height in metres squared. Heights and weights were recorded by trained personnel using calibrated stadiometers and digital scales, with participants wearing light clothing and no shoes. The investigators used multiple imputation by chained equations to address missing data, generating 20 datasets and combining their estimates with Rubin’s rules. Survival models were fitted in stages: first without adjustment, then for age and diabetes, and finally for estimated glomerular filtration rate, serum albumin, high-density lipoprotein cholesterol, 24-hour urine volume and the cumulative number of peritonitis episodes during the 36-month exposure period. The fully adjusted model also included age and diabetes, making seven covariates in total. Post-hoc power for the primary TTR estimate was 63%.

The TTR results separated the two candidate intervals. Patients with any time in the 24–25.9 kg/m² range did not have a statistically significant association with mortality after full adjustment. The categorical hazard ratio for any versus no time in that lower range was 0.75, with a 95% confidence interval of 0.46 to 1.23, while the continuous hazard ratio per 10-percentage-point increase in TTR was 0.98, with a 95% confidence interval of 0.89 to 1.08. By contrast, any time in the 26–30 kg/m² range was associated with a hazard ratio of 0.27, with a 95% confidence interval of 0.11 to 0.68. For each 10% increase in TTR in that higher range, the adjusted hazard ratio was 0.77, with a 95% confidence interval of 0.62 to 0.96. A restricted cubic spline analysis found no evidence of non-linearity within the selected range, with a P value of 0.21 for non-linearity.

Sensitivity analyses broadly supported the main association. Complete-case analyses, recalculation of TTR with a step-function rather than linear interpolation, and Firth penalized Cox regression produced consistent results. In exploratory competing-risks analyses, each 10% increase in TTR within the 26–30 kg/m² range was associated with lower cardiovascular mortality, with a subdistribution hazard ratio of 0.68 and a 95% confidence interval of 0.53 to 0.88. The same exposure was not significantly associated with infection-related mortality; its subdistribution hazard ratio was 0.93, with a 95% confidence interval of 0.71 to 1.21. Those cause-specific analyses were underpowered and should not be treated as independent proof of a cardiovascular mechanism. The study also calculated E-values of 1.9 for the continuous association and 6.8 for the categorical comparison, estimating how strong an unmeasured confounder would need to be to explain each result.

Several potential explanations were discussed, but none was directly tested. A higher BMI could partly reflect greater nutritional reserve and muscle mass, potentially helping patients withstand protein-energy wasting, a common and dangerous complication of peritoneal dialysis. Other possibilities included metabolic effects of adipose tissue, inflammation and fluid status. BMI cannot distinguish fat, lean tissue and retained fluid, and the investigators had no direct body-composition or overhydration measurements. Twenty-four-hour urine volume served only as a partial adjustment for fluid status. Peritoneal transport status was also missing for roughly one-fifth of the cohort and was omitted from the primary model because the limited number of deaths constrained the number of supported covariates. The findings therefore cannot show that excess adiposity itself improved survival.

Important design limitations constrain the interpretation. The retrospective, single-center design cannot establish causality, and 75 deaths provided only 10.7 events per covariate and about 63% post-hoc power for the primary effect estimate. Survivor bias was possible because the landmark design excluded patients who died, transferred dialysis modality, received a transplant or otherwise left before 36 months; these exclusions may have selected relatively healthier survivors. Unmeasured confounding and measurement error also remain plausible, particularly because diet, physical activity, dialysis adequacy and medication data were unavailable. BMI was measured at only three time points, so interpolation may have missed short-term fluctuations. Exploratory subgroup and interaction analyses were underpowered and should not be used to identify patient subgroups. Larger multicenter cohorts with more frequent BMI measurements, validated body-composition data and adequate numbers of deaths are needed before the observed 26–30 kg/m² range can be considered a clinical target or generalized beyond this Chinese population.

Subject of Research: Longitudinal BMI time-in-range and mortality in adults receiving peritoneal dialysis

Article Title: The obesity paradox in peritoneal dialysis: reassessing optimal BMI via longitudinal time in range analysis in a single-center cohort

Article References: Wang, M., Zeng, X., Liu, D., Chen, P., & Peng, Z. (2026). The obesity paradox in peritoneal dialysis: reassessing optimal BMI via longitudinal time in range analysis in a single-center cohort. Frontiers in Nutrition, 13, 1982039. https://doi.org/10.3389/fnut.2026.1982039

Image Credits: AI Generated

DOI: 10.3389/fnut.2026.1982039

Keywords: peritoneal dialysis, body mass index, time in range, all-cause mortality, obesity paradox, retrospective cohort, Chinese patients, nutritional reserve, landmark analysis, obesity, paradox, peritoneal

Kidney failure creates an unusual internal environment. As renal clearance declines, nitrogenous waste accumulates and changes the chemistry of the gut lumen. Protein-bound toxins such as indoxyl sulfate, p-cresyl sulfate and trimethylamine N-oxide can reach concentrations 10 to 50 times those in healthy controls, according to the review. These compounds are not simply passengers carried by the blood. Indoxyl sulfate activates the aryl hydrocarbon receptor and promotes oxidative stress, while microbial metabolism of tryptophan and tyrosine supplies additional precursors for toxin production. The resulting chemical environment favors pathobionts and disadvantages commensal organisms, including short-chain-fatty-acid producers such as Faecalibacterium and Roseburia.

That shift has structural consequences for the intestine. Butyrate normally supplies energy to colon cells and signals through receptors involved in metabolic and immune regulation. As butyrate-producing bacteria decline, epithelial integrity and anti-inflammatory signaling may weaken. Uremic toxins can also disturb tight-junction proteins, including occludin, claudin-1 and zonula occludens-1. The resulting permeability permits lipopolysaccharide and other microbial products to enter the circulation. Studies summarized in the review link this endotoxemia with higher C-reactive protein and interleukin-6, lower residual kidney function and the low-grade inflammation that characterizes maintenance dialysis. Metabolomic studies also report depleted short-chain fatty acids and altered tryptophan and bile-acid pathways, although most comparisons remain observational.

Immune dysfunction follows two apparently contradictory directions. Pattern-recognition receptors including Toll-like receptor 4, NOD2, NLRP3 and RIG-I respond to microbial products by activating nuclear factor-kappa B and mitogen-activated protein kinase signaling, increasing inflammatory cytokines such as interleukin-6, tumor necrosis factor alpha and interleukin-1 beta. At the same time, uremia impairs neutrophil phagocytosis, monocyte oxidative burst, dendritic-cell antigen presentation and aspects of antibody defense. Altered T-cell and monocyte populations, expansion of myeloid-derived suppressor cells and exhausted T-cell markers have also been described. The result is a state resembling “immunological paralysis”: persistent immune activation alongside reduced capacity to contain infection. The cellular evidence is less extensive than the cytokine evidence and still needs systematic characterization in dialysis-specific cohorts.

Nutrition and muscle are woven into the same feedback system. Protein-energy wasting combines loss of muscle and other body stores with inadequate energy availability. The review reports that muscle mass declines two to three times faster in kidney disease than during normal aging and that malnutrition affects an estimated 18% to 75% of dialysis populations, depending on how it is defined. Inflammation can suppress appetite, activate the ubiquitin-proteasome system and alter myostatin, insulin-like growth factor and JAK–STAT signaling. Restrictive diets may reduce potassium and phosphorus but also limit fiber diversity and the substrates required by saccharolytic bacteria. Evidence connecting dysbiosis to sarcopenia is still mainly cross-sectional or mechanistic: it cannot yet establish whether microbial change causes muscle loss, follows malnutrition and inactivity, or shares inflammation as an upstream driver.

Hemodialysis and peritoneal dialysis place different kinds of stress on this network. Hemodialysis produces rapid fluctuations in toxins and fluid, and rapid ultrafiltration can contribute to intradialytic hypotension and myocardial stunning. Blood repeatedly contacts artificial membranes, activating complement and circulating leukocytes. The review reports more pronounced depletion of butyrate-producing organisms, enrichment of Proteobacteria and Fusobacteria, and generally greater endotoxemia. Peritoneal dialysis is continuous and often preserves residual kidney function and hemodynamic stability for longer, but continuous absorption of glucose adds calories and can worsen insulin resistance, dyslipidemia and adiposity. Glucose degradation products and advanced glycation end-products promote peritoneal inflammation, fibrosis, neoangiogenesis and eventual ultrafiltration failure. Its microbial profile includes amplification of Enterobacteriaceae and depletion of Bifidobacterium and Lactobacillus, though the peritoneal literature is smaller and more vulnerable to confounding by diet, shared environments, medications and other illnesses.

Multi-omics methods offer a way to examine these systems together. Metagenomics and metatranscriptomics characterize microbial genes and active pathways, while metabolomics measures circulating and fecal metabolites, and proteomics, lipidomics and single-cell transcriptomics can characterize immune states. Network analysis and supervised machine learning may detect coordinated signatures that single-domain tests miss. One case-control CKD study cited in the review found that gut microbial functional profiles, including carbohydrate-active enzyme genes, predicted early disease more accurately than serum-metabolite or immune-cell models alone and correlated with circulating amino acids and immune-cell clusters. Such findings demonstrate analytical promise, not a validated patient-classification system. The proposed MIN Index would combine 16S sequencing or metagenomics, multiplex immune assays, biochemical measurements, body composition, functional testing and dietary assessment, with modality-specific weights estimated from outcomes such as infection, hospitalization, cardiovascular events, technique failure and mortality.

Developing that framework would require evidence rather than conceptual polish. The authors suggest a derivation cohort of at least 300 to 500 hemodialysis and peritoneal dialysis patients followed for 12 to 24 months, followed by external validation in a separate population. Sampling would need to cover diet, diabetes, residual kidney function, antibiotics, iron, phosphate binders, proton-pump inhibitors and other medications that can alter microbial communities. Because high-dimensional data invite overfitting, weights derived in one cohort would have to be tested without refitting in another. The index would also have to demonstrate added value over cheaper measures such as the Malnutrition–Inflammation Score, albumin or C-reactive protein, using outcomes that patients experience rather than biomarker changes alone. Its inputs would need to be collected within a defined window, and the test–retest stability of repeated microbiome measurements would need to be established because diet, infection and antibiotics can alter gut composition within days.

Therapeutic ideas remain largely ahead of clinical proof. Fiber-rich and plant-based diets may support short-chain-fatty-acid production, but potassium and phosphorus restrictions complicate implementation, especially in regions where vegetarian diets are already common. Probiotic and prebiotic trials have reported modest improvements in toxins, bowel function or inflammatory markers, generally in small cohorts without demonstration of fewer infections, hospitalizations or deaths. Fecal microbiota transplantation improved kidney and inflammatory measures in a rat model and has early human evidence outside dialysis, but transmission risks, donor screening, uncertain durability and the uremic environment complicate use in frail patients. Omega-3 fatty acids and polyphenols may influence inflammatory pathways, but clinical results are inconsistent. Advanced high-volume hemodiafiltration improves middle-molecule clearance and has produced favorable mortality results in one trial, yet its direct effects on the gut axis remain unmeasured. The review consequently supports carefully monitored research rather than routine use of an unvalidated index or unproven microbiome-based treatment.

Closing the gap will depend on larger, more diverse and longer studies. Most available evidence comes from small, single-center or cross-sectional investigations, often in Western populations, while healthcare access, diet, genetics, environmental exposure and medication use differ markedly across regions. Standardized stool collection, sequencing, bioinformatics and metadata reporting are needed so results can be pooled; common repositories and international consortia could make that possible. Longitudinal cohorts beginning before dialysis could establish whether dysbiosis precedes or follows uremia, infection and malnutrition, while randomized trials of combination interventions could test whether altering several linked domains is more effective than targeting one. The central proposal is therefore not that a single score can immediately guide dialysis care, but that dialysis outcomes may become more predictable if researchers model the gut, immune system, nutrition and residual kidney function as the interacting system they are. The MIN Index offers a structured hypothesis for that work, with its clinical value still to be demonstrated.

Subject of Research: Kidney–gut–immune–nutrition interactions and risk stratification in hemodialysis and peritoneal dialysis

Article Title: Beyond solute clearance: the kidney–gut–immune–nutrition axis in hemodialysis and peritoneal dialysis and a proposed framework for integrated risk stratification

Article References: Rawat, A., Prasad, N., Rawat, P., Ahmed, S., Kumar, B., Singh, A., Nayak, T., & Kushwaha, R. S. (2026). Beyond solute clearance: the kidney–gut–immune–nutrition axis in hemodialysis and peritoneal dialysis and a proposed framework for integrated risk stratification. Frontiers in Nutrition, 13, Article 1931760. https://doi.org/10.3389/fnut.2026.1931760

Image Credits: AI Generated

DOI: 10.3389/fnut.2026.1931760

Keywords: dialysis, gut microbiome, uremic toxins, immune dysregulation, protein-energy wasting, multi-omics, hemodialysis, peritoneal dialysis, risk stratification, clinical nutrition, Beyond, solute

The study analyzed clinical records from three dialysis centers in Chongqing, China: Daping Hospital, Kaizhou District People’s Hospital and Yongchuan Hospital of Chongqing Medical University. Eligible participants were at least 16 years old, had chronic renal failure and had received regular peritoneal dialysis for more than one month. The source cohort contained 824 records for people who began peritoneal dialysis for the first time between August 2007 and September 2025. After excluding patients with acute kidney injury, emergency dialysis, subsequent renal recovery or kidney transplantation, as well as those lost to follow-up, 737 records remained. Detection of 37 statistical outliers produced a final analytic cohort of 700 patients, who were randomly divided into a training set of 491 and a test set of 209.

During follow-up, 320 patients transitioned to hemodialysis, 178 died and 202 remained on peritoneal dialysis without either event by the end of observation. The investigators defined a hemodialysis transition as a move to hemodialysis lasting more than 90 days without a return to peritoneal dialysis, following a 2026 position statement from the International Society for Peritoneal Dialysis. Patients who died within that 90-day observation window were classified as having died rather than having made a sustained transition. Patients with unknown outcomes at censoring were treated as censored observations, not as competing deaths. Kidney transplantation and renal recovery were excluded from the primary analysis because those categories had few events, which the researchers said could make competing-risk estimates unstable.

Because the investigators expected death to compete with the planned hemodialysis transition, they compared a cause-specific proportional hazards model, two Fine–Gray model-development strategies and a conventional LASSO-Cox model that censored death. The cause-specific model used LASSO feature selection with tenfold cross-validation, while the Fine–Gray models were built using three penalized regression methods—LASSO, smoothly clipped absolute deviation and minimax concave penalty—as well as a boosting procedure. The researchers retained variables identified in univariate screening but also allowed the feature-selection procedures to consider other potentially relevant clinical characteristics. Predictive performance was assessed with time-dependent area under the curve and concordance-index curves, while calibration and discrimination were examined at three, six and nine years.

In the training set, the penalized Fine–Gray model had a mean time-dependent area under the curve of 0.716, compared with 0.696 for the cause-specific model and 0.718 for the boosted Fine–Gray model. Its training-set concordance index was 0.662. The conventional LASSO-Cox model had the highest mean training area under the curve, 0.762, but its performance was weaker in the held-out data: its test-set concordance index was 0.573, compared with 0.646 for the penalized Fine–Gray model. The penalized model also had the highest mean test-set area under the curve at 0.689. The investigators therefore selected it as the best overall model, citing its held-out performance and its smaller predictor set relative to the boosted Fine–Gray model. This was a predictive comparison within one retrospective dataset, not evidence that changing care according to the model would improve patient outcomes.

The final penalized model associated a higher body mass index with a greater subdistribution hazard of hemodialysis transition, with a subdistribution hazard ratio of 1.05 and a 95% confidence interval of 1.00 to 1.09 per BMI unit. A history of hospitalization for peritonitis was associated with more than twice the subdistribution hazard, with a ratio of 2.15 and a confidence interval of 1.64 to 2.84. Better nutrition-related parameters were associated with lower transition hazard, with a ratio of 0.27 and a confidence interval of 0.08 to 0.90. Other significant associations in the penalized model included older age, previous urological surgery and previous hemodialysis. The investigators discussed several plausible mechanisms without proving them in this study: peritonitis may make peritoneal dialysis technically unsustainable or damage the peritoneum; malnutrition may impair immune function and tissue repair; and higher BMI has previously been associated with peritonitis, loss of residual kidney function and hospitalization.

Researchers converted the penalized model into a nomogram that assigns points to clinical variables and maps a patient’s total score to estimated cumulative incidence of hemodialysis transition at three, six and nine years. In a worked example, a 60-year-old man with a BMI of 20.4 kg/m², reduced urine output and previous hospitalization for both peritonitis and cardiovascular disease received approximately 398 points. The nomogram estimated his cumulative transition risk as 39% at three years, 60% at six years and 69% at nine years. Patients in the test cohort were divided into low-, middle- and high-risk groups using score thresholds derived from the training set. Their cumulative incidence curves differed significantly for both hemodialysis transition and death, and the high-risk group had the highest incidence of transition, although a minor early crossing occurred between the low- and middle-risk groups.

Diagnostics also exposed important limitations in the model’s statistical assumptions. The proportional subdistribution hazards assumption was violated for normal 24-hour urine output, hospitalization for peritonitis and the nutrition-related parameters. Stratifying by peritonitis hospitalization did not resolve the violation, while splitting patients at a nutrition-derived threshold produced subgroups that satisfied the assumption. However, the well-nourished subgroup showed substantial differences between training and test performance: its mean area under the curve fell from 0.763 in training to 0.609 in testing, and its concordance index fell from 0.686 to 0.579. The malnutrition subgroup performed more closely between training and testing, with area-under-the-curve values of 0.714 and 0.691 and concordance indices of 0.661 and 0.640. The authors consequently treated the subgroup models as exploratory and retained nutrition as a continuous variable in the main analysis.

The retrospective design, missing laboratory data and lack of time-varying clinical measurements constrain how broadly the results can be applied. Some clinical indicators had missing rates exceeding 30%, requiring mean, nearest-neighbor or multiple-linear-regression imputation that may have introduced bias. The dataset was drawn from three centers in one Chinese city, and the paper’s limitations section describes the work as single-center despite the multicenter data sources, an internal inconsistency that further emphasizes the need for independent validation. The model also used baseline information rather than changes in nutrition, infection or laboratory status during follow-up, and it did not establish that risk prediction led to better clinical decisions or outcomes. The authors proposed prospective multicenter validation and models capable of incorporating dynamic covariates. Until those steps are completed, the penalized Fine–Gray model and nomogram are best regarded as retrospective risk-estimation tools whose discrimination is moderate, with meaningful uncertainty around their individual predictions.

Subject of Research: Competing-risk prediction of peritoneal dialysis-to-hemodialysis transition in adults

Article Title: Optimal prediction of peritoneal dialysis to hemodialysis transition: a penalized fine-gray model

Article References: Wan, J., Wei, Q., Wu, D., Hou, S., Yv, K., Tang, R., He, J., Chen, X., Li, Y., Hu, Y., Yang, J., Li, T., & Chen, K. (2026). Optimal prediction of peritoneal dialysis to hemodialysis transition: a penalized fine-gray model. Frontiers in Medicine, 13, Article 1843381. https://doi.org/10.3389/fmed.2026.1843381

Image Credits: AI Generated

DOI: 10.3389/fmed.2026.1843381

Keywords: peritoneal dialysis, hemodialysis transition, competing risks, Fine–Gray model, clinical prediction, nomogram, peritonitis, nutritional status, retrospective cohort, model validation, Optimal, prediction

The new study, published in Frontiers in Nephrology, drew on ten years of data from ten Kaiser Permanente Southern California hospitals between 2014 and 2023. Researchers led by Max T. Hukill of the Kaiser Permanente Bernard J. Tyson School of Medicine, together with Andrea M. Yeung and Talha H. Imam of the Southern California Permanente Medical Group, mined the Renal Registry of the Southern California Permanente Medical Group for quarterly records of exit-site infection episodes and program sizes. Their central finding: each additional calendar year was associated with a 6.4 percent relative decrease in infection rate, a statistically significant trend that compounded into an estimated 45.1 percent decline across the study period.

What makes the result remarkable is not just its magnitude but its setting. Unlike the small, tightly controlled trials that dominate the literature on dialysis complications, this analysis captured the messy reality of routine clinical practice across a large integrated health care system. The overall mean exit-site infection rate across the decade was 0.86 episodes per 100 patient-months, with observed yearly means falling from 1.26 in 2014 to 0.72 in 2023. Nine hospitals reported data throughout the full study period; a tenth joined in 2018. After excluding seven hospital-quarters with missing records, the final analytic sample comprised 375 hospital-quarters of data.

The stakes of these infections are well documented. Peritonitis, the most frequent reason for peritoneal dialysis catheter removal and patient dropout, has long been known to follow exit-site infections. Researchers first noted the connection in the 1980s, when patients with an infected exit site were found to be more likely to develop peritonitis than those without. A strong association between exit-site infections and subsequent peritonitis persists up to 60 days after the initial infection diagnosis. The International Society of Peritoneal Dialysis now recommends that overall peritonitis rates should not exceed 0.4 episodes per patient-year at risk, a benchmark that reflects decades of improvement in prevention strategies.

Yet while peritonitis rates have been extensively tracked by major registries, exit-site infections have remained a blind spot. Large databases such as the Australia and New Zealand Dialysis and Transplant Registry and the US Renal Data System generally do not record or report exit-site infection rates. The limited studies that do exist report rates ranging from 0.06 to 0.42 episodes per year, but most were small-scale trials not representative of real-world conditions. One larger study involving 1,133 patients over one year found an exit-site infection rate of 0.13 episodes per year, while a Portuguese study reported 0.41 episodes per year with an average of 726 patients over two years. The new analysis is the largest retrospective examination of exit-site infection rates published to date.

To handle the unusual statistical character of the data, the researchers employed a Tweedie generalized linear mixed model, a technique suited to semicontinuous, right-skewed distributions with an exact point mass at zero. Nearly 18 percent of hospital-quarters recorded no infections at all, creating a spike at zero that conventional models struggle with. The model included fixed effects for calendar year and program size, along with a random intercept and slope for calendar year by hospital, allowing each center’s rate of change over time to vary. Confidence intervals for pooled rates were generated by a location-cluster bootstrap with 5,000 replicates, resampling centers with replacement.

Crucially, the decline was not explained by growth in program size. After adjustment for calendar year, program size showed no significant association with infection rate, with a rate ratio of 0.76 per 100-patient increase and a confidence interval spanning 0.52 to 1.11. Variance inflation factors for both predictors were low at 1.39, indicating that the null finding was not an artifact of collinearity between the two variables. The cohort itself grew substantially over the decade, from roughly 631 patients per quarter across nine hospitals in 2014 to 933 patients per quarter across ten hospitals in 2023, yet infection rates fell regardless.

The authors suggest that evolving infection prevention practices, staff training, and care coordination may have driven the improvement, though their data do not permit direct testing of these mechanisms. The care protocols in place across the system are extensive. All catheters were placed with advanced laparoscopic techniques with pre-insertion antibiotics. Exit sites were created with minimal trauma and optimized positioning, avoiding locations under abdominal folds, along belt lines, or facing downward. Daily exit-site care involved washing, topical antibiotics such as mupirocin or gentamicin, sometimes with additional sodium hypochlorite spray, and sterile dressings. Nurses provided continuous support and regular follow-up, monitoring patient practices and adherence with retraining as needed. A surveillance system tracking infections helped identify potential problems before they progressed.

The analysis also revealed significant heterogeneity between hospitals in their rates of decline, with a random slope variance that a likelihood ratio test confirmed was superior to a simpler model. While most centers showed declining trends broadly consistent with the population-level estimate, one center showed a trend in the opposite direction. Given the limited number of hospitals, center-specific estimates carry substantial uncertainty and should be interpreted as hypothesis-generating rather than confirmed hospital-level differences. Still, the heterogeneity indicates that a single shared temporal trend does not adequately describe hospital-level variation, possibly reflecting differences in local infection prevention practices, staffing, or patient case-mix not captured in the analysis.

The study has limitations that temper its conclusions. The dataset lacked baseline demographics of the peritoneal dialysis cohort, preventing analysis of differences in age, sex, ethnicity, and dialysis modality over the study period. As a representative approximation, the current population of 1,481 patients across the system is 61 percent male with a median age of 61 years, and 67.2 percent use continuous cyclic peritoneal dialysis. Program size, patient-months at risk, and discrete infection episode counts were not available as independent quantities, so the researchers modeled infection rate directly rather than episode counts with an offset for patient-time at risk. Missing data clustered by year rather than hospital, concentrated in the first study year, consistent with reporting ramp-up rather than informative missingness. A sensitivity analysis excluding the center with delayed reporting produced materially consistent estimates. As an observational analysis without patient-level data, the findings establish a temporal association rather than a causal mechanism, and generalizability beyond this single integrated health care system should be hypothesized cautiously. Future work will examine whether the reduction in exit-site infections parallels broader declines in peritonitis rates, and mechanism-specific studies may finally pinpoint which factors are driving these improvements.

Subject of Research: Health

Article Title: Improvement in peritoneal dialysis exit-site infection rates in an integrated health care system in the United States

Article References: Hukill, M. T., Yeung, A. M., & Imam, T. H. (2026). Improvement in peritoneal dialysis exit-site infection rates in an integrated health care system in the United States. Frontiers in Nephrology, 6, Article 1825353. https://doi.org/10.3389/fneph.2026.1825353

Image Credits: AI Generated

DOI: 10.3389/fneph.2026.1825353

Keywords: peritoneal dialysis, exit-site infection, kidney disease, infection prevention, nephrology, Improvement, peritoneal, dialysis, exit-site, infection, rates, integrated

A nationwide retrospective cohort study from South Korea, published in the journal Heliyon, followed essentially the entire population of dialysis patients in the country during the first year vaccinations became available. The researchers, led by Jae Young Kim and Tae Ik Chang of the National Health Insurance Service Ilsan Hospital, drew on a linked database combining the Korea Disease Control and Prevention Agency’s COVID-19 records with the National Health Insurance Service, the single compulsory insurer covering every Korean citizen. Because the insurance system assigns permanent special codes to patients receiving maintenance dialysis for at least three months, the team could identify the full national population of people on hemodialysis or peritoneal dialysis with remarkable reliability.

The starting cohort included 105,323 adults on maintenance dialysis as of February 26, 2021, the day COVID-19 vaccinations began in South Korea. After excluding patients already infected with SARS-CoV-2 or lacking complete baseline data, 103,130 individuals remained under observation through December 31, 2021. Strikingly, 82.3 percent of them — 84,895 people — completed a full vaccine series during that window, a testament to the country’s policy of prioritizing dialysis patients for immunization. The vaccines used included the Pfizer-BioNTech mRNA vaccine BNT162b2, the AstraZeneca vector vaccine ChAdOx1 nCov-19, the Moderna mRNA vaccine mRNA-1273, and a tiny fraction of the single-dose Janssen vaccine Ad26.COV2.S; 42.5 percent of second doses were heterologous, meaning they mixed vaccine platforms.

To make a fair comparison, the investigators used propensity score matching, a statistical technique that pairs vaccinated and unvaccinated patients with nearly identical profiles of age, sex, income, residence, dialysis modality, dialysis duration, and a long list of comorbidities including hypertension, diabetes, heart failure, cerebrovascular disease, dementia, and cancer. Before matching, the unvaccinated group was older and sicker, reflecting hesitancy and access problems among the frailest patients. After nearest-neighbor matching with a caliper of 0.10, the final analytical cohort comprised 14,498 fully vaccinated and 14,498 unvaccinated patients with standardized differences below 0.1 on every measured characteristic — a near-perfectly balanced comparison. The average age was 63.2 years, just over half were men, roughly 89 percent were on hemodialysis, and about half had been on dialysis for five years or more.

The results were unambiguous. Over a median follow-up of 150 days, 375 participants — 1.3 percent of the matched cohort — experienced a first laboratory-confirmed SARS-CoV-2 infection, verified by positive reverse transcription polymerase chain reaction testing. Among vaccinated patients, 0.88 percent were infected, compared with 1.71 percent of unvaccinated patients. In Cox proportional hazards regression, vaccination was associated with a 53 percent lower risk of infection, corresponding to a hazard ratio of 0.47 with a 95 percent confidence interval of 0.38 to 0.58. Kaplan-Meier curves showed the cumulative incidence of infection diverging steadily between the groups throughout the observation period.

The protection against severe disease was even more pronounced. The study defined serious COVID-19 as a composite of severe infection — hospitalization requiring any form of respiratory support such as noninvasive ventilation, high-flow oxygen, or invasive mechanical ventilation, or treatment with extracorporeal membrane oxygenation or continuous renal replacement therapy — together with COVID-19-related death, meaning death during hospitalization or within 28 days of a positive test. Serious outcomes occurred in 0.26 percent of vaccinated patients versus 0.74 percent of unvaccinated patients, a hazard ratio of 0.32. Analyzed separately, severe COVID-19 was reduced by 70 percent (hazard ratio 0.30) and COVID-19-related death was likewise reduced by 70 percent (hazard ratio 0.30). In other words, even when breakthrough infections occurred after two doses, they were far less likely to turn deadly in vaccinated dialysis patients.

Subgroup analyses reinforced the message across clinically meaningful strata. The association between vaccination and lower risks of both infection and serious outcomes held consistently for patients younger and older than 65, for men and women, for those with and without diabetes or ischemic heart disease, and for those on dialysis for fewer or more than five years. This robustness matters because dialysis patients are extraordinarily heterogeneous: many carry a heavy burden of cardiovascular and metabolic disease, and longer dialysis vintage, anemia, and immunosuppressive therapy have all been linked to poorer antibody responses after mRNA vaccination.

The findings address a persistent scientific worry. Kidney failure induces immune alterations and chronic low-grade systemic inflammation, dialysis itself has immunomodulatory effects, and many patients take immunosuppressive drugs — all of which can interfere with the generation of protective immunity. Previous studies reported post-vaccination antibody levels in dialysis patients ranging from as low as 7 percent up to 66 percent of those seen in healthy controls, and a meta-analysis of 32 studies found an overall immunogenicity rate in dialysis patients of 86 percent relative to non-dialysis individuals. Yet the Korean study, which did not have antibody measurements available, demonstrates that this blunted immunogenicity does not necessarily translate into weaker clinical protection. Its results align with real-world effectiveness studies from the United States and Canada, suggesting that cellular immunity and other mechanisms may compensate for lower antibody titers. The protection observed was nonetheless more modest than the 94 percent efficacy against symptomatic infection reported in randomized trials of the general population, which had excluded nearly all patients with moderate to advanced chronic kidney disease — only 0.7 percent of trial participants had such disease.

The study’s strengths are considerable. By covering the entire national ESKD dialysis population, including home-based peritoneal dialysis patients often omitted from clinic-based research, it avoided the selection biases of single-network studies. Its time-to-event design started follow-up only after completion of the vaccine series plus a 14-day immune-response window, so the estimated risks reflect the period when vaccination would be expected to confer protection. Defining the primary outcome as laboratory-confirmed infection rather than symptomatic illness also sidestepped a practical problem: some dialysis patients cannot reliably complete symptom questionnaires because of language or cognitive limitations. Frequent RT-PCR screening, mandatory for in-center hemodialysis patients before entering their units, made undetected infections rare.

Limitations remain, and the authors are candid about them. As an observational study, residual confounding cannot be excluded; data on obesity, immunosuppressive use, dialysis adequacy, and residual kidney function were unavailable, and unmeasured risky health behaviors among the unvaccinated could have influenced censoring. The follow-up was too short to assess how long protection lasts, viral variant data were absent, and the entire study period preceded both the Omicron wave and the rollout of third booster doses, so the findings speak to the pre-Omicron era. The cohort was also entirely Korean, warranting caution when generalizing to other populations. Still, the core conclusion stands: complete COVID-19 vaccination substantially reduced the risk of SARS-CoV-2 infection, severe disease, and death in one of the pandemic’s most susceptible populations. The authors argue the evidence supports health authorities’ policies to promote and prioritize vaccination for dialysis patients, and to combat the vaccine hesitancy that has kept uptake low in some countries — a population with much to gain from a vaccine, regardless of type.

Subject of Research: Effectiveness of COVID-19 vaccination in preventing SARS-CoV-2 infection and severe outcomes in patients with end-stage kidney disease on maintenance dialysis

Article Title: Impact of COVID-19 vaccination on infection risk and clinical outcomes in patients with end-stage kidney disease undergoing dialysis: A nationwide retrospective study in South Korea

Article References: Kim, J. Y., Son, K. J., Park, P. G., & Chang, T. I. (2026). Impact of COVID-19 vaccination on infection risk and clinical outcomes in patients with end-stage kidney disease undergoing dialysis: A nationwide retrospective study in South Korea. Heliyon, 12(15), Article e45541. https://doi.org/10.1016/j.heliyon.2026.e45541

Image Credits: AI Generated

DOI: 10.1016/j.heliyon.2026.e45541

Keywords: COVID-19 vaccination, end-stage kidney disease, dialysis, SARS-CoV-2, vaccine effectiveness, South Korea, nationwide cohort study, hemodialysis, peritoneal dialysis, propensity score matching, severe COVID-19, immunocompromised patients