Nutrition Supplements and Serum Albumin in Hemodialysis: A Meta-Analysis

ABSTRACT

Objective: The present meta-analysis was designed to examine the effect of oral nutrition supplements (ONS) and/or intradialytic parenteral nutrition (IDPN) therapy on serum albumin, a marker of malnutrition risk in adults on hemodialysis (HD).

Study Design: Meta-analysis

Methods: The EMBASE, PubMed, Google Scholar, Cochrane Collaboration, ClinicalTrials.gov, and Australia New Zealand Clinical Trials Registry databases were queried using the search terms malnutrition, albumin, hemodialysis, oral nutrition supplement, and intradialytic parenteral nutrition. Studies published or registered from 2019 to 2024 were reviewed and scored by 2 investigators using the Critical Appraisal Skills Programme checklist. Only studies with a mean score of 9 or greater (70% of the total possible score) were included. Data were analyzed using MedCalc software 20.210. The Q and I² tests were calculated; a forest plot was generated, and the pooled standardized mean differences (SMDs) with 95% CIs were calculated using a random-effects model.

Results: Of the originally identified 137 studies, 8 randomized clinical trials were included, involving 382 adult HD patients. The SMD in serum albumin between the active treatment group (ONS or IDPN) and the control group in the random-effects model was 0.372 g/dL (95% CI, 0.103-0.641; P = .007). Findings were robust in the fixed-effects model. Publication bias was not detected.

Conclusion: ONS/IDPN significantly improved serum albumin in this random-effects meta-analysis model, and findings were consistent in the fixed-effects model. It can be concluded that ONS/IDPN improves serum albumin levels, which can serve as a marker of malnutrition risk in patients on maintenance HD.

Takeaway Points

• Oral nutrition supplements (ONS) and/or intradialytic parenteral nutrition (IDPN) may be prescribed to improve nutritional status in patients on hemodialysis (HD); however, the evidence for their efficacy comes primarily from small, underpowered studies.
• The present meta-analysis, which followed PRISMA guidelines, identified randomized clinical trials published between 2019 and 2024 that tested the efficacy of ONS and/or IDPN on nutrition status (measured as serum albumin) in patients on maintenance HD.
• This study identified a significant treatment benefit for HD patients, with improved serum albumin levels. These findings enable the clinical practitioner to recommend ONS and/or IDPN with greater confidence in treatment benefit for patients.

Malnutrition is a condition in which deficient, excess, or imbalanced nutrition contributes to adverse clinical, metabolic, anthropometric, and functional outcomes,¹ and it is associated with increased mortality risk, especially in individuals with chronic disease.² In patients with end-stage kidney disease receiving hemodialysis (HD) as their kidney replacement therapy, malnutrition prevalence estimates exceed 30%,^3-5 depending on the definition used. Variability in malnutrition prevalence estimates among HD patients is partially attributable to the multiple ways in which the condition is defined and measured.⁶ Biochemical measures used to assess malnutrition in HD patients include creatinine, total cholesterol, transferrin, total protein, and albumin.⁷ Hypoalbuminemia has been reported in 41% of HD patients using a serum albumin cutoff of 3.4 g/dL.⁸ The cutoff for hypoalbuminuria used by the International Society of Renal Nutrition and Metabolism is less than 3.8 g/dL, which would increase malnutrition prevalence levels.⁹

The use of hypoalbuminemia as a marker of malnutrition in the general population has been questioned. A position paper of the American Society for Parenteral and Enteral Nutrition recommended that visceral proteins such as albumin and prealbumin not be used in defining malnutrition, stating that these proteins are inversely associated with inflammation due to alterations in hepatic protein synthesis and increased capillary permeability; in other words, visceral proteins are associated with inflammation, and inflammation is associated with malnutrition, but malnutrition is not associated with visceral proteins. Nevertheless, inflammation and malnutrition are both associated with mortality.¹⁰ In patients on HD, however, serum albumin remains an important and useful measure of malnutrition and malnutrition risk.¹¹ Serum albumin is a sensitive method for identifying patients at risk for protein-energy wasting as defined by the 7-point Subjective Global Assessment score.¹² Moreover, serum albumin is strongly correlated with anthropometric measures of malnutrition, including triceps skinfold, midarm circumference, and midarm muscle circumference.¹³ An association among dietary protein, normalized protein catabolic rate (a marker of dietary protein intake), and albumin has been identified, implying that serum albumin responds to consumed protein.¹⁴ The Kidney Disease Outcomes Quality Initiative 2020 update recommended the routine measurement of serum albumin as part of nutrition assessment.¹⁵

Once dialysis is initiated, the treatment itself leads to nutrient loss and inflammation, both of which contribute to malnutrition, morbidity, and mortality.¹⁶ Other contributors to malnutrition in HD patients include metabolic acidosis, which hastens protein loss by increasing protein catabolism and inhibiting protein synthesis¹⁷; poor dietary intake, aggravated by uremia-induced suppression of appetite¹⁸; dysgeusia¹⁹; dialysis dose, frequency, and duration²⁰; insulin resistance²¹; depression²²; and medications.²³

Oral nutrition supplements (ONS) and intradialytic parenteral nutrition (IDPN) are strategies for mitigating malnutrition in HD patients. ONS is provided to increase energy and protein intake in individuals who are failing to meet nutritional requirements while consuming their usual diet.²⁴ However, access to ONS may be limited by cost and local availability.²⁵ IDPN is typically used when other methods have failed; however, IDPN implementation may be limited by a lack of guidelines and the need for greater staff training.²⁶ Furthermore, little is known about ONS or IDPN prescribing practices, but underprescribing has been observed among HD patients with malnutrition or elevated malnutrition risk.⁵

The extent to which ONS or IDPN improves nutritional status, as indicated by serum albumin levels, is not well established. Knowledge of ONS or IDPN efficacy can help guide clinicians in formulating and implementing treatment plans.

METHODS

Protocol and Registration

The present meta-analysis followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyis (PRISMA) guidelines.²⁷ The protocol was registered on the National Institute for Health and Care Research PROSPERO site, registration number CRD42025645765.²⁸

Inclusion Criteria

Studies were eligible for inclusion in the present meta-analysis if they were randomized clinical trials (RCTs) in which participants were randomly assigned to treatment with ONS of any kind (not limited to ONS designed for dialysis patients) and/or IDPN, and the control group received treatment with usual care (nutrition counseling) or placebo. The study populations in the included studies comprised adults 18 years or older with end-stage kidney disease receiving HD as their kidney replacement therapy. All included articles were published from 2019 to 2024. Acceptable were publications (published or in press) in English in peer-reviewed, scientific journals. Additionally, studies registered with ClinicalTrials.gov or the Australia New Zealand Clinical Trials Registry (ANZCTR) but not published in journals were eligible.

For all included studies, ONS and/or IDPN was the stated exposure, and end-of-treatment serum albumin was a stated (but not necessarily primary) outcome.

Exclusion Criteria

In the present meta-analysis, studies were excluded if the information necessary for full data extraction was missing or unavailable or if they were a study other than an RCT in which commercially available ONS and/or IDPN were the stated exposure.

Databases

The following databases were queried: MEDLINE (via PubMed), EMBASE, Google Scholar, and Cochrane Collaboration. The ClinicalTrials.gov and the ANZCTR registries were also searched.

Search Words

Search words used in each database were malnutrition, albumin, oral nutrition supplement, intradialytic parenteral nutrition, randomized clinical trial, and hemodialysis. The search was filtered to include only studies published in English between 2019 and 2024. Studies were compared among databases, and duplicates were removed. Abstracts were obtained and reviewed for the remaining studies, and the studies were compared against inclusion/exclusion criteria. Full text manuscripts were obtained for each study meeting these criteria and scored using the Critical Appraisal Skills Programme (CASP) checklist.²⁹

Study Quality

Before inclusion, studies were reviewed using the CASP checklist for RCTs. One point was awarded for each criterion met, yielding a score of 0 to 13. Two investigators (AA, MB) scored each study independently, and then scores were compared between the investigators. Finally, each paper received the mean score assigned by the 2 investigators. A mean score of 9 (70% of the total possible score) was the lowest mean score that conferred inclusion.

Search Strategy and Data Extraction

The study examined all published, in-publication, and registered protocols from 2019 to 2024. Only English-language publications were included. Data extracted from each included article were author names, date of publication, country of publication, intervention and control group sample size, and baseline and end point serum albumin and C-reactive protein (mean and SD).

Data Analysis

Data were uploaded from the Excel spreadsheet to MedCalc 20.210 (MedCalc Software Ltd) for analysis. The Q and I² tests were calculated to examine the degree of heterogeneity across studies. Publication bias was assessed using Egger and Begg tests. The meta-analysis examined the standardized mean difference (SMD) in end point values between active treatment and control groups with 95% CIs, using the random-effects model. Additionally, a forest plot was generated. The fixed-effects model was also calculated. No baseline adjustment for serum albumin was performed in any of the included studies because groups were balanced at study onset.

RESULTS

Selection of Studies

The flow of study selection is presented in Figure 1. As shown, 137 studies were originally identified: 136 from databases and 1 from a registry. Of these, 51 studies were omitted from the databases because they were duplicates, appearing in more than 1 database. The 1 protocol identified from the registry was also omitted when the authors did not respond to multiple attempts to contact them. Thus, a total of 85 abstracts were reviewed.

From these, 67 studies were eliminated due to the following reasons: inappropriate study design (any design other than RCT; n = 34), wrong study population (children, individuals with specific comorbidities such as cancer or autoimmune disease; n = 10), intervention with a single micronutrient or functional food (n = 8), exposure other than ONS/IDPN (n = 6), outcome did not include serum albumin (n = 5), publication not in English (n = 2), and full text not available (n = 2).

Using the CASP checklist for RCTs,²⁹ 18 studies underwent a full-text review by 2 investigators.^30-47 A short description of these studies and the scores assigned by each investigator are shown in eAppendix 1 (eAppendices available at ajmc.com). Finally, 8 studies were included in the present meta-analysis, including 382 adult HD patients.^{33-35,41,42,44,45,47}

Description of Included Studies

All included studies were RCTs that enrolled 30 to 80 participants. The follow-up duration in the included studies ranged from 30 days to 6 months. Most of the studies were from Asia: 2 from Thailand, 1 from Taiwan, 1 from China, and 1 from Japan. Two were from Europe: 1 from Italy and 1 from Spain. One study was from Egypt. Publication dates ranged from 2021 to 2024. All of the studies measured serum albumin as an indicator of nutritional status.

Study participants were generally older adults. Female participation was 47% overall; women were somewhat overrepresented in the Gharib et al³⁴ and Hung et al³³ studies but underrepresented in the Hevilla et al⁴⁴ study. Dialysis vintage was not reported in the Hung et al,³³ Hevilla et al,⁴⁴ or Kabasawa et al⁴⁷ studies, but in the 5 studies in which this information was reported, the mean duration was greater than 1 year. Detailed information about the included studies can be found in eAppendix 2 and eAppendix 3.

Table 1 presents the influence of the study intervention on serum albumin. End point serum albumin declined in the active treatment group compared with the control group in the Hevilla et al⁴⁴ study. In the Kabasawa et al⁴⁷ and Murtas et al⁴² studies, there was no difference in end point serum albumin between the active treatment and control groups. In the other 5 of the 8 included studies, serum albumin increased at end point in the active treatment group relative to the control group. In light of the association between albumin and C-reactive protein as measures of malnutrition in addition to inflammation, Table 1 also presents C-reactive protein by intervention group at baseline and at end point. Studies by Limwannata et al,⁴¹ Qin et al,⁴⁵ and Kabasawa et al⁴⁷ did not report this end point. Hung et al³³ presented C-reactive protein for the active intervention group but not for the control group, although they stated that the by-treatment-group difference was not significant at baseline or end point. In the Gharib et al³⁴ study, baseline C-reactive protein did not differ by treatment group, but it was significantly lower in the ONS group vs the control group at end point (P < .001). None of the other studies reporting C-reactive protein reported by-group differences at baseline or end point despite changes in serum albumin. Thus, a total of 4 studies reported pre- and postintervention C-reactive protein values for each treatment group. Although this was not the objective of the present meta-analysis, an exploratory analysis was performed, and no between-group difference in this outcome was detected. In the random-effects model, the SMD was –0.645 (95% CI, –2.478 to 1.189) and the SE was 0.93 (P = .489). In the fixed-effects model, the SMD was 0.262 (95% CI, –0.569 to 0.046) and the SE was 0.156 (P = .1).

Table 2 shows the results of the meta-analysis of the primary end point, serum albumin. The 8 RCTs included a total of 382 participants. The Q statistic was 711.6058 (P = .1143), indicating that the true treatment effect is the same across studies and that the variability is attributable to chance. This is reinforced by the I² statistic, which was 39.69% (95% CI, 0.00%-73.37%), suggesting that very little variability across studies could be assigned to true differences rather than chance. Despite low heterogeneity, the random-effects model was selected because of differences in study populations, intervention type (ONS vs IDPN), age, sex, and follow-up duration across the included studies. The fixed-effects model is nevertheless presented, and the findings are robust.

In the random-effects model, the SMD for between-group (active treatment vs control) end-of-follow-up serum albumin was 0.372 (95% CI, 0.103-0.641; P = .007). The between-group end-of-follow-up serum albumin levels ranged from –0.231 (95% CI, –0.981 to 0.519) g/dL in the Hevilla et al⁴⁴ study to 1.140 (95% CI, 0.442-1.837) g/dL in the Kittiskulnam et al³⁵ study.

The forest plot of the association between ONS/IDPN treatment and end-of-follow-up serum albumin is shown in Figure 2. The forest plot indicates that the random- and fixed-effects models are consistent with one another and show a significant treatment benefit for ONS/IDPN on serum albumin in HD patients. Specifically, the Hung et al,³³ Gharib et al,³⁴ and Kittiskulnam et al³⁵ studies all showed a significantly greater SMD favoring the ONS/IDPN group, indicating improvement in serum albumin with active treatment. The Limwannata et al,⁴¹ Murtas et al,⁴² and Qin et al⁴⁵ studies also had SMD values consistent with improved serum albumin in the ONS/IDPN treatment group, albeit with wide 95% CIs. The Kabasawa et al⁴⁷ study shows no by-treatment difference, whereas the Hevilla et al⁴⁴ study suggests a decreased serum albumin in the ONS/IDPN relative to the control group.

The Egger test indicated that publication bias was not present: intercept = –1.3190 (95% CI, –8.5752 to 5.9372; P = .67). This was confirmed by the Begg test: Kendall τ = –0.14 (P = .62). The funnel plot is presented in Figure 3.

Because the studies by Hung et al,³³ Limwannata et al,⁴¹ and Qin et al⁴⁵ had follow-up durations of less than 3 months, and Hevilla et al⁴⁴ had a follow-up greater than 3 months, a validation analysis was performed that included only the 4 studies with 3-month follow-up. The findings were robust insofar as both the random- and fixed-effects models remained significant. This analysis is presented in eAppendix 4.

DISCUSSION

The purpose of the present study was to estimate the effect of ONS/IDPN on serum albumin as a marker of nutritional status in adults with end-stage kidney failure receiving HD as their kidney replacement therapy. Both the random- and fixed-effects models indicate that treatment with ONS/IDPN improves serum albumin levels compared with a parallel control (either “usual care” or placebo). Thus, the study hypothesis was supported by the meta-analysis. The present analysis indicates that serum albumin at the end of follow-up is 0.38 g/dL (95% CI, 0.175-0.586) greater in patients treated with ONS/IDPN compared with controls (P = .007).The findings remained robust when only studies with 3-month follow-up were included in the analysis, further strengthening them.

On the other hand, it cannot be overlooked that serum albumin improved in 5 of the 8 included studies. Patient age was more than 6 years older in the 3 studies in which albumin did not improve compared with the studies in which ONS/IDPN was associated with improvement. It is possible that this age difference made the improvement of serum albumin via supplementation more difficult.

Although it has been suggested that albumin level may reflect metabolic processes other than malnutrition risk,⁸ it is still an important and recommended measure of nutrition status in HD patients.⁶ All studies included in the present analysis were RCTs, which isolated the effects of the independent variable (ONS/IDPN) on the outcome (serum albumin). It would seem highly unlikely that ONS or IDPN influenced albumin by reducing inflammation. A meta-analysis of RCTs of ONS in patients with cancer undergoing chemotherapy identified that ONS increased serum albumin but did not alter C-reactive protein, a marker of inflammation.⁴⁸ Consistent with this, an RCT in nursing home residents found that patients randomly assigned to ONS had significantly greater serum albumin but no difference in C-reactive protein compared with patients randomly assigned to standard care (nutrition advice and prepared meals), suggesting that the mode of action was indeed improved nutrition status rather than reduced inflammation.⁴⁹

Present findings are consistent with those of Mah et al,⁵⁰ who performed a meta-analysis of 22 studies published before 2019 and included 1278 participants who had end-stage kidney disease and received either HD or peritoneal dialysis. The meta-analysis examined the impact of protein-based ONS on nutrition status measures, including serum albumin. Compared with no protein-based ONS or placebo, protein-based ONS was associated with a greater increase in serum albumin, especially in the 10 studies involving 526 total participants on HD (SMD, 0.28 g/dL; 95% CI, 0.11-0.46). The authors concluded that treatment with a protein-based ONS was superior to no ONS or placebo in improving serum albumin levels. Strikingly similar to the findings described by Mah et al,⁵⁰ the present study detected an increase in serum albumin of 0.38 g/dL (95% CI, 0.175-0.586) in patients treated with ONS/IDPN compared with controls (P = .007). The present study did not differentiate between ONS and IDPN composition types due to the small number of included studies; this is exactly why the random-effects model was selected.

Study Limitations

The present meta-analysis has a low level of heterogeneity, but the included studies were small and, as indicated by CASP scoring, had a mean score of 10 of 13 possible points, which is approximately 77% of the total possible score, suggesting that the quality of methodology was somewhat better than the older studies included in prior meta-analyses. The small number of studies and participants (n = 382) and the studies’ very short duration (maximum 6 months) make generalization difficult and may impair external validity.

Implications

Findings of the present meta-analysis indicate that nutrition supplementation with ONS or IDPN can improve serum albumin in adult patients receiving HD as their kidney replacement therapy; however, it is not clear that this encouraging finding translates into meaningful clinical end points.

CONCLUSIONS

Hypoalbuminemia is a frequent finding in adults with end-stage kidney disease on HD, and ONS/IDPN is associated with improved serum albumin levels. Further studies in larger populations followed for a longer duration may permit examination of efficacy on clinically meaningful end points. Nevertheless, the findings described herein enable the clinical practitioner to recommend ONS and/or IDPN with greater confidence in treatment benefit for patients.

Author Affiliations: Department of Nutrition Sciences, Ariel University (AA, VK-S, MB), Ariel, Israel.

Source of Funding: None.

Author Disclosures: The authors report no relationship or financial interest with any entity that would pose a conflict of interest with the subject matter of this article.

Authorship Information: Concept and design (MB); acquisition of data (AA, MB); analysis and interpretation of data (VK-S, MB); drafting of the manuscript (MB); critical revision of the manuscript for important intellectual content (AA, VK-S); statistical analysis (MB); provision of patients or study materials (AA); administrative, technical, or logistic support (AA, VK-S); and supervision (MB).

Address Correspondence to: Mona Boaz, PhD, RD, Department of Nutrition Sciences, Ariel University, Kiryat Hamada 3, Ramat Hagolan St 36, Ariel, Israel 40700. Email: monabo@ariel.ac.il.

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