Abstract
Chronic kidney disease (CKD) is a life-threatening condition, and constipation is a progressive risk factor. We evaluated changes in uremic toxins, renal function, and the safety of lubiprostone, a selective chloride channel activator, in patients with CKD. In this phase 2, randomized, double-blind, placebo-controlled trial across nine centers in Japan, 150 patients with stage IIIb–IV CKD received lubiprostone (8 or 16 micrograms) or placebo for 24 weeks. The primary end point was change in indoxyl sulfate levels. Secondary end points included other uremic toxins and renal function markers. Lubiprostone did not alter uremic toxin levels but improved or preserved estimated glomerular filtration rate and its slope in the 16-microgram group. Mild-to-moderate gastrointestinal events occurred in the placebo and 16-microgram groups. Multiomics analysis revealed that lubiprostone modulated the gut microbial agmatine pathway and increased spermidine levels, thereby improving renal mitochondrial function. Lubiprostone is a previously unknown and safe therapeutic option to mitigate renal decline in CKD.
Lubiprostone protects the kidneys by modulating the gut microbiota, increasing spermidine, and enhancing mitochondrial function.
INTRODUCTION
Chronic kidney disease (CKD) is a prevalent health problem affecting a large portion of the global population and is a major risk factor for end-stage renal disease (1, 2). Angiotensin-converting enzyme (ACE) inhibitors (3, 4), angiotensin II receptor blockers (ARBs) (5), sodium-glucose cotransporter 2 (SGLT2) inhibitors (6), mineral corticoid receptor (MR) antagonist (7), and glucagon-like peptide-1 (GLP1) agonist (8) have been widely used to mitigate declining renal function; however, the number of patients requiring dialysis has not substantially decreased. Constipation is highly prevalent in patients with CKD, and its severity is associated with progressive estimated glomerular filtration rate (eGFR) decline and a high risk of end-stage renal disease (9–11). In addition, patients with CKD often suffer from gut microbiota dysbiosis, which leads to impaired barrier function as “leaky gut.” This condition is associated with increased translocation of living bacteria and bacterial components, including breakdown of the intestinal barrier, ultimately affecting host homeostasis (10). This process also contributes to the accumulation of uremic toxins and induces systemic inflammation in patients with CKD (12). Colonic transit is related to gut microbiota–derived metabolism and mucosal turnover (13) and predisposes patients to CKD (9). Recently, new categories of laxatives, such as lubiprostone (a chloride channel activator) (14), linaclotide (a guanylate cyclase C agonist) (15), and elobixibat (a bile acid transporter inhibitor) (16) have been introduced for chronic constipation (17). We conducted animal experiments to investigate whether correcting constipation might improve uremic toxin levels and renal function. We previously reported that lubiprostone (18) and linaclotide (19) reduce circulating gut-derived uremic toxins, such as indoxyl sulfate (IS), p-cresyl sulfate (PCS), trimethylamine N-oxide (TMAO), and phenyl sulfate (20, 21). These drugs also enhance renal function in animal models of renal failure (RF). Recently, Sumida et al. (22) reported an observational study of US veterans that found that lubiprostone use was independently associated with a lower risk of adverse kidney outcomes in patients with CKD and constipation. This is a pivotal observational study linking our basic research on lubiprostone with its clinical implementation. However, owing to the lack of randomized studies, the effects of constipation treatment on patients with CKD remain unclear. Furthermore, the potential risk of dehydration due to drug-induced diarrhea is a major concern in patients with CKD. To address these gaps, we conducted a randomized, double-blind, exploratory phase 2 clinical trial to validate the renoprotective effects of lubiprostone in patients with CKD. We also explored the effective group and determined a safe dosage that did not cause adverse effects.
RESULTS
Effects of lubiprostone on uremic toxins and renal function
The LUBI-CKD TRIAL was a multicenter, randomized, double-blind, placebo-controlled exploratory phase 2 trial designed to evaluate the efficacy and safety of lubiprostone in patients with CKD. Participants were recruited from nine centers in Japan between July 2016 and December 2019. The primary end point was the change in IS values from baseline to 24 weeks after drug administration for each treatment group (8 or 16 μg/day) compared with the placebo group. Cohort randomization details and deviations are shown in the CONSORT diagram (Fig. 1).
Fig. 1. Clinical study flow diagram.
Of the 118 participants, 6 patients (4 from the lubiprostone 16-μg/day group and 2 from the placebo group) withdrew from the trial. One participant each from the lubiprostone 16-μg/day and the placebo groups was not included in the analysis of the primary end point because of their withdrawal before the first measurement of the indoxyl sulfate after the study drug administration. Clinical Trials registry number: UMIN000023850.
A total of 150 patients were screened, and 118 eligible patients were randomly assigned to the placebo group (n = 35), lubiprostone 8–μg/day group (n = 33), or lubiprostone 16–μg/day group (n = 50). Among these, 116 patients were included in the efficacy analysis (full analysis set, FAS), excluding those without primary efficacy measurements, and all 118 treated patients were included in the safety analysis. Two patients (5.7%) in the placebo group and four patients (8%) in the lubiprostone 16-μg group withdrew from the study. Reasons for withdrawal are shown in Fig. 1 and table S1.
The baseline characteristics were generally balanced among the three groups (Table 1). The mean age of the patients was 63 years, and 34.5% were women. All participants were of Asian ethnicity. The number of participants with moderate renal dysfunction (eGFR, 36 to 45 ml/min per 1.73 m2, n = 55) and severe renal dysfunction (eGFR, 25 to 35 ml/min per 1.73 m2, n = 61) were approximately equal. Furthermore, 7.8% of the patients had diabetic kidney disease. We also explored the use of renin-angiotensin system inhibitors (ARBs and ACE inhibitors), SGLT2 inhibitors, MR antagonists, and GLP1 agonists. No notable differences were observed in any clinical characteristics between the placebo and lubiprostone groups.
Table 1. Baseline patient characteristics.
Continuous variables are presented as the means ± SD, and categorical variables are presented as percentages. CKD, chronic kidney disease; DKD, diabetic kidney disease; eGFR, estimated glomerular filtration rate; ACE, angiotensin converting enzyme; ARB, angiotensin II receptor blocker; SGLT2, sodium glucose cotransporter 2; GLP1, glucagon-like peptide 1; MR, mineralcorticoid receptor.
| Characteristic | Mean (SD) | ||
|---|---|---|---|
| Placebo (n = 34) | Lubiprostone | ||
| 8 μg/day (n = 33) | 16 μg/day (n = 49) | ||
| Age, year | 66.1 (12.1) | 62.3 (13.8) | 61.3 (13.0) |
| Female sex, no. (%) | 12 (35.3) | 12 (36.4) | 16 (32.7) |
| Race or ethnic group, no. (%) | |||
| Asian | 34 (100) | 33 (100) | 49 (100) |
| Body height, cm | 161.4 (8.4) | 162.0 (9.9) | 164.3 (10.4) |
| Body weight, kg | 66.0 (14.7) | 66.8 (14.5) | 71.5 (16.0) |
| eGFR, ml/min per 1.73 m2 | 34.3 (5.1) | 35.0 (6.4) | 35.4 (5.0) |
| proteinuria, g/gCr | 0.7 (0.7) | 0.6 (0.9) | 0.7 (0.9) |
| Comorbidities, no. (%) | |||
| Hypertension | 29 (85.3) | 27 (81.8) | 43 (87.8) |
| Dyslipidemia | 19 (55.9) | 21 (63.6) | 27 (55.1) |
| Diabetes mellitus | 3 (8.8) | 5 (15.2) | 8 (16.3) |
| Hyperuricemia | 1 (2.9) | 1 (3.0) | 2 (4.1) |
| Blood pressure, mmHg | |||
| Systolic | 126.7 (18.5) | 126 (15.7) | 127.6 (13.1) |
| Diastolic | 74.0 (11.9) | 70.5 (11.4) | 76.8 (11.2) |
| Alcohol consumption, no. (%) | |||
| Past | 3 (8.8) | 2 (6.1) | 7 (14.3) |
| Current | 18 (52.9) | 19 (57.6) | 31 (63.3) |
| None | 13 (38.2) | 12 (36.4) | 11(22.4) |
| Smoking, no. (%) | |||
| Past | 14 (41.2) | 11 (33.3) | 20 (40.8) |
| Current | 2 (5.9) | 4 (12.1) | 5 (10.2) |
| None | 18 (52.9) | 18 (54.5) | 24 (49.0) |
| Cause of CKD, no. (%) | |||
| DKD | 2 (5.9) | 2 (6.1) | 5 (10.2) |
| Chronic glomerulonephritis | 11 (32.4) | 10 (30.3) | 12 (24.5) |
| Nephrosclerosis | 10 (29.4) | 10 (30.3) | 16 (32.7) |
| Others | 12 (35.3) | 13 (39.4) | 19 (38.8) |
| Previous medication, no. (%) | |||
| ACE inhibitor/ARB | 25 (73.5) | 26 (78.8) | 40 (81.6) |
| SGLT2 inhibitor | 1 (2.9) | 2 (6.1) | 1 (2.0) |
| GLP1 agonist | 0 | 0 | 1 (2.0) |
| MR antagonist | 2 (5.9) | 3 (9.1) | 0 |
| eGFR category, no. (%) | |||
| 36 to 45 ml/min per 1.73 m2 (moderate) | 17 (50.0) | 16 (48.5) | 22 (44.9) |
| 25 to 35 ml/min per 1.73 m2 (severe) | 17 (50.0) | 17 (51.5) | 27 (55.1) |
We set IS reduction as the primary end point and suppression of eGFR decline, blood urea nitrogen (BUN) levels, and urinary protein levels as secondary end points (Figs. 2 and 3 and Table 2). The changes in IS levels between baseline and the 24-week end point did not differ among the three groups (Fig. 2A, left, and Table 2). In the placebo group, the least squares mean (LSM) was 0.130 μg/ml [95% confidence interval (CI): −0.168, 0.429]; in the lubiprostone 8-μg group, the LSM was 0.091 μg/ml (95% CI: −0.211, 0.394); and in the lubiprostone 16-μg group, the LSM was 0.126 μg/ml (95% CI: −0.123, 0.375). Subgroup analysis of patients with moderate renal dysfunction (eGFR: 36 to 45 ml/min per 1.73 m2; Fig. 2A, center, and table S2) and those with severe renal dysfunction (eGFR: 25 to 35 ml/min per 1.73 m2; Fig. 2A, right, and table S3) revealed no changes in IS levels in either group. Changes in the levels of the other three toxins (phenyl sulfate, PCS, and TMAO) did not differ at the end point (Fig. 2, B to D, and Table 2). However, reductions in PCS levels were noted at week 4 in the lubiprostone 16-μg group (Fig. 2C). No changes were observed in either subgroup (tables S2 and S3). These data suggest that lubiprostone does not alter uremic toxin levels in patients in this clinical trial.
Fig. 2. Time courses for changes in uremic toxins.
(A to D) Time course of change from baseline in levels of IS (A), PS (B), PCS (C), TMAO (D). Left, main analysis (all patients). Middle and right, subgroup analysis (CKD moderate group: eGFR, 36 to 45 ml/min per 1.73 m2; CKD severe group: eGFR, 25 to 35 ml/min per 1.73m2) groups. These were expressed as LSM ± 95% CI. P values were calculated for between-group comparisons as follows: an analysis of covariance (ANCOVA) model was applied at each measurement time point, followed by Dunnett’s post hoc test. No adjustment for multiple comparisons between time points was made. *P < 0.05, lubiprostone 16-μg/day group versus placebo group and †P < 0.05, lubiprostone 8-μg/day group versus placebo group. IS, indoxyl sulphate; PS, phenyl sulphate; PCS, p-cresyl sulphate; TMAO, trimethylamine N-oxide; LOCF, last observation carried forward.
Fig. 3. Time courses for changes in renal functions.
(A, B, D, and F) Time course of change from baseline in levels of BUN (A), Cr (B), eGFRCr (D), and eGFRCys (F). (C, E, and G) The slope of 1/Cr (C), eGFRCr (E), and eGFRCys (G). [(A) to (G)] Left, main analysis (all patients). Middle and right, subgroup analysis (CKD moderate group: eGFR 36 to 45 ml/min per 1.73 m2; CKD severe group: eGFR 25 to 35 ml/min per 1.73 m2) groups. The slope of 1/Cr, eGFRCr, and eGFRCys were expressed as mean ± 95% CI, along with the linear regression line, while others were expressed as LSM ± 95% CI. P values were calculated for between-group comparisons as follows: For the slope of 1/Cr, eGFRCr and eGFRCys, a mixed-effects model was used; for the other end points, an ANCOVA model was applied at each measurement time point, followed by Dunnett’s post hoc test. No adjustment for multiple comparisons between time points was made. *P < 0.05, **P < 0.01, lubiprostone 16-μg/day group versus placebo group and †P < 0.05, ††P < 0.01, lubiprostone 8-μg/day group versus placebo group. BUN, blood urea nitrogen.
Table 2. Primary and secondary end points.
The primary end point was the change in IS from baseline to 24 weeks after drug administration. This table summarizes the LSM and 95% CIs for each end point. P values for intergroup comparisons were calculated as follows: A mixed-effects model was used for the slope of 1/Cr, and an ANCOVA model was applied to other end points, followed by Dunnett’s post hoc test. *P < 0.05. Abbreviations: LSM, least squares mean; CI, confidence interval; IS, indoxyl sulfate; PS, phenyl sulfate; PCS, p-cresyl sulfate; TMAO, trimethylamine N-oxide; eGFRcr, eGFR from creatinine; eGFRcys, eGFR from cystatin C; Cr, creatinine; Cys, cystatin C; BUN, blood urea nitrogen; UPCR, urinary protein-creatinine ratio. SI conversions: To convert IS to μM, multiply by 4.69; PS to μM, multiply by 5.77; PCS to μM, multiply by 5.31; TMAO to μM, multiply by 13.3; eGFRcr, eGFRcys, and Eqaveage to ml/s per m2, multiply by 0.0167; Cr to μM, multiply by 88.4; Cys to μM, multiply by 0.0749; BUN to mM, multiply by 0.357.
| LSM (95% CI) | |||
|---|---|---|---|
| Outcome | Placebo (n = 34) | Lubiprostone | |
| 8 μg/day (n = 33) | 16 μg/day (n = 49) | ||
| Primary end point | |||
| Change in IS | |||
| Change from weeks 0 to 24 μg/ml | 0.130 (−0.168 to 0.429) | 0.091 (−0.211 to 0.394) | 0.126 (−0.123 to 0.375) |
| LSM difference versus placebo, μg/ml | −0.039 (−0.519 to 0.440) | −0.005 (−0.443 to 0.434) | |
| P value | 0.98 | >0.99 | |
| Key secondary end points | |||
| Change in PS | |||
| Change from week. 0 to 24, μg/ml | 0.187 (−0.397 to 0.771) | 0.202 (−0.383 to 0.788) | 0.460 (−0.022 to 0.942) |
| LSM difference versus placebo, μg/ml | 0.015 (−0.923 to 0.953) | 0.273 (−0.586 to 1.133) | |
| P value | >0.99 | 0.69 | |
| Change in PCS | |||
| Change from weeks. 0 to 24, μg/ml | 0.222 (−1.222 to 1.666) | −0.280 (−1.743 to 1.182) | 0.127 (−1.081 to 1.315) |
| LSM difference versus placebo, μg/ml | −0.502 (−2.833 to 1.829) | −0.105 (−2.223 to 2.012) | |
| P value | 0.84 | 0.99 | |
| Change in TMAO | |||
| Change from weeks. 0 to 24, μg/ml | 0.119 (−0.428 to 0.650) | −0.407 (−0.954 to 0.141) | −0.450 (−0.901 to 0.000) |
| LSM difference versus placebo, μg/ml | −0.518 (−1.384 to 0.349) | −0.561 (−1.355 to 0.233) | |
| P value | 0.31 | 0.20 | |
| Change in eGFRcr | |||
| Change from weeks. 0 to 24, ml/min per 1.73 m2 | −1.55 (−2.83 to −0.27) | −0.34 (−1.64 to 0.96) | 0.37 (−0.70 to 1.44) |
| LSM difference versus placebo, ml/min per 1.73 m2 | 1.21 (−0.85 to 3.27) | 1.92 (0.03 to 3.80) | |
| P value | 0.32 | 0.046 | |
| Change in eGFRcys | |||
| Change from weeks. 0 to 24, ml/min per 1.73 m2 | −1.37 (−2.98 to 0.24) | −0.87 (−2.49 to 0.74) | 0.33 (−1.01 to 1.68) |
| LSM difference versus placebo, ml/min per 1.73 m2 | 0.50 (−2.08 to 3.07) | 1.70 (−0.69 to 4.10) | |
| P value | 0.87 | 0.19 | |
| Change in Cr | |||
| Change from weeks. 0 to 24, mg/dl | 0.050 (−0.008 to 0.107) | 0.027 (−0.031 to 0.086) | −0.007 (−0.055 to 0.041) |
| LSM difference versus placebo, mg/dl | −0.022 (−0.115 to 0.070) | −0.056 (−0.141 to 0.028) | |
| P value | 0.81 | 0.23 | |
| Change in Cys | |||
| Change from weeks. 0 to 24, mg/liter | 0.056 (−0.002 to 0.114) | 0.013 (−0.046 to 0.071) | −0.008 (−0.057 to 0.041) |
| LSM difference versus placebo, mg/liter | −0.044 (−0.136 to 0.049) | −0.064 (−0.150 to 0.022 | |
| P value | 0.47 | 0.17 | |
| Change in BUN | |||
| Change from weeks. 0 to 24, mg/dl | 1.71 (0.07 to 3.36) | 0.05 (−1.63 to 1.72) | −1.19 (−2.56 to 0.18) |
| LSM difference versus placebo, mg/dl | −1.67 (−4.31 to 0.98) | −2.90 (−5.32 to −0.49) | |
| P value | 0.27 | 0.02 | |
| Change in UPCR | |||
| Change from wk. 0 to 24, g/gCr | 0.006 (−0.157 to 0.168) | −0.148 (−0.313 to 0.018) | −0.019 (−0.155 to 0.117) |
| LSM difference versus placebo, g/gCr | −0.154 (−0.416 to 0.108) | −0.025 (−0.264to 0.214) | |
| P value | 0.32 | 0.96 | |
| 1/Cr slope | |||
| Slope (95% CI) | −0.0010 (−0.0032 to 0.0011) | −0.0002 (−0.0031 to 0.0026) | 0.0006 (−0.0013 to 0.0026) |
| Intercept (95% CI) | 0.684 (0.653 to 0.716) | 0.695 (0.654 to 0.736) | 0.685 (0.657 to 0.713) |
| P value* | 0.13 | 0.02 | |
| eGFRcr slope | |||
| Slope (95% CI) | −0.060 (−0.100 to −0.020) | −0.011 (−0.052 to 0.030) | 0.010 (−0.024 to 0.045) |
| Intercept (95% CI) | 34.5 (32.5 to 36.5) | 35.3 (33.3 to 37.3) | 35.4 (33.8 to 37.1) |
| P value* | 0.10 | 0.01 | |
| eGFRcys slope | |||
| Slope (95% CI) | −0.033 (−0.083 to 0.018) | 0.009 (−0.042 to 0.060) | 0.001 (−0.042 to 0.044) |
| Intercept (95% CI) | 37.8 (34.1 to 41.5) | 40.4 (36.6 to 44.1) | 42.7 (39.6 to 45.8) |
| P value* | 0.25 | 0.31 | |
*Primary and key secondary outcomes (corresponding analyses using the FAS) evaluated treatment effects, regardless of treatment adherence. Missing values were imputed using the LOCF method. For changes in variables, an analysis of covariance model was used with the value at the start of study drug administration as the covariate and eGFR as the allocation factor (25 to 35 ml/min per 1.73 m2 or 36 to 45 ml/min per 1.73 m2), followed by Dunnett’s post hoc test. For the slope of 1/Cr, a mixed-effects model was used, with participants as a random effect and treatment, week, and treatment × week as fixed effects.
Regarding secondary end points, BUN levels were significantly restored at 4, 12, 20, and 24 weeks compared with the placebo group and revealed a significant difference between baseline and 24 weeks in the lubiprostone 16-μg group (Fig. 3A, left, and Table 2). Subgroup analyses of the moderate (Fig. 3A, center, and table S2) and severe renal dysfunction groups (Fig. 3A, right, and table S3) revealed no changes in BUN levels in either group. Creatinine (Cr) levels did not change between baseline and week 24 in the lubiprostone groups (Fig. 3B, left, and Table 2). Subgroup analysis of the moderate (Fig. 3B, center, and table S2) and severe renal dysfunction groups (Fig. 3B, right, and table S3) demonstrated no changes in the Cr levels in either group, although P values of 0.05 (8-μg group) and 0.06 (16-μg group) were observed in the moderate group (table S2). Conversely, the slope of 1/Cr was significantly restored in the lubiprostone 16-μg group (Fig. 3C, left). Subgroup analysis of the moderate (Fig. 3C, center, and table S2) and severe (Fig. 3C, right, and table S3) renal dysfunction groups demonstrated that the slope of 1/Cr significantly improved in both the 8-μg and 16-μg groups in the moderate subgroup (Fig. 3C, center, and table S2). Regarding eGFR, eGFR calculated on the basis of serum Cr (eGFRCr) was significantly maintained in the 16-μg group between baseline and the 24-week end point (Fig. 3D, left, and Table 2). The LSMs and 95% CIs were as follows: for the placebo group, LSM: −1.55 ml/min per 1.73 m2 (95% CI: −2.83 to −0.27); for the lubiprostone 8-μg group, LSM: −0.34 ml/min per 1.73 m2 (95% CI: −1.64 to 0.96); and for the lubiprostone 16-μg group, LSM: 0.37 ml/min per 1.73 m2 (95% CI: −0.70 to 1.44). Statistical significance was as follows: placebo versus lubiprostone 8 μg, P = 0.32; placebo versus lubiprostone 16 μg, P = 0.0457. Significant changes were also observed in eGFRCr levels after 8, 12, 16, and 20 weeks of administration in the 16-μg group (Fig. 3D, left). Subgroup analysis further revealed that, in the moderate group, the 8-μg dose improved eGFRCr at 8, 12, 16, 20, and 24 weeks, whereas the 16-μg dose improved eGFRCr at 8, 12, 16, and 20 weeks (Fig. 3D, center, and table S2). Conversely, no significant difference in eGFRCr levels was observed in the severe group (Fig. 3D, right, and table S3). Furthermore, the slope of the eGFRCr between baseline and the 24-week end point was analyzed. Compared with the control group, the slope of eGFRCr was significantly preserved in the 16-μg group (Fig. 3E, left, and Table 2). Subgroup analysis revealed that, in the moderate group, the slope of eGFRCr was also preserved in both the 8-μg and 16-μg groups (Fig. 3E, center, and table S2). There was no difference in the suppression of the eGFR decline in the severe group (Fig. 3E, right, and table S3). These findings indicate that lubiprostone exerts a renoprotective effect in the 16-μg group, with a similar but less pronounced trend observed in the 8-μg group. Notably, in the moderate group, both the 8-μg and 16-μg doses demonstrated renoprotective effects. Contrarily, no changes were noted in the cystatin C levels (fig. S1A and Table 2), eGFR calculated on the basis of serum cystatin C (eGFRcys; Fig. 3F and Table 2), or the slope of eGFRcys (Fig. 3G and Table 2) even in the moderate and severe subgroups. Furthermore, the urinary protein levels did not change at any time point (fig. S1B and Table 2) or in any group (tables S2 and S3). As no decrease in body weight, uric acid levels, or increased B-type natriuretic peptide (BNP) levels was observed, it was suggested that the increase in eGFR was not due to decreased muscle volume or body fluid retention (fig. S2). These data strongly suggest that lubiprostone suppresses renal function decline, particularly in patients with CKD and moderate renal dysfunction, without affecting uremic toxins. The measured time course for each end point is shown in figs. S3 and S4.
Serious adverse events
As an anticipated adverse event, diarrhea was observed in 12.1% of the 8-μg group and 16% of the 16-μg group. Overall discontinuation due to adverse effects occurred in 8% of the 16-μg group, 5.7% of the placebo group, and none in the 8-μg group; these differences were not statistically significant. Among the patients who discontinued in the 16-μg group, a causal relationship with lubiprostone was suspected in three cases (one case each of abdominal pain, diarrhea, and dizziness) (table S4). In one case of elevated Cr levels in the patients who discontinued in the 16-μg group, the attending physician suspected an allergic reaction to lubiprostone and performed a delayed lymphocyte sensitivity test, which yielded negative results.
SAEs were experienced by 5 of the 118 patients: 3 in the placebo group and 1 each in the lubiprostone 8-μg and 16-μg groups. One patient in the lubiprostone 16-μg group was found to have a causal relation with lubiprostone administration (gastroesophageal reflux disease) (table S5). Dehydration and disturbances in salt and electrolyte levels are potential concerns with osmotic saline laxatives, especially in patients with cardiac dysfunction or CKD (23). Although some cases of diarrhea were reported, no clinically meaningful electrolyte imbalances were observed in this study, indicating that lubiprostone is well tolerated by patients with CKD.
Lubiprostone modulated microbial community and polyamine metabolism
To elucidate the renoprotective mechanism of lubiprostone, we conducted a comprehensive analysis including capillary electrophoresis time-of-flight mass spectrometry (CE-TOFMS)–based target metabolome analysis, 16S ribosomal RNA (rRNA) amplicon sequencing, and shotgun metagenome analysis (Fig. 4A). Metabolites, bacterial species, and microbial enzyme genes exhibiting similar trends in the lubiprostone 8-μg and 16-μg groups were analyzed to identify the dose-dependent effects of lubiprostone. Metabolomic analyses revealed 154 (62 anionic and 92 cationic), 236 (87 anionic and 149 cationic), and 274 (102 anionic and 172 cationic) metabolites in plasma, urine, and feces, respectively (Fig. 4A, fig. S5, and table S6). In plasma, lubiprostone increased 4-acetylbutyrate and decreased α-aminoadipate, trans-aconitate, and lactate. As trans-aconitate is known to accumulate in the RF (24), these changes demonstrate the renoprotective effect of lubiprostone by preserving eGFR. In feces, lubiprostone increased lactate while decreasing 4-pyridoxate, N-ε-acetyllysine, and ornithine. Ornithine, a polyamine precursor, is converted into putrescine (PUT) by ornithine decarboxylase (25). Therefore, the reduction in ornithine levels indicates accelerated polyamine synthesis in feces. Subsequently, we conducted taxonomic analyses to identify the lubiprostone-responsive bacterial species using 16S rRNA sequencing and shotgun metagenomics (Fig. 4A, fig. S6, and table S7). 16S rRNA analysis identified 270 genera, whereas shotgun metagenomics identified 3780 species. Among these, five genera fluctuated in the 16S rRNA analysis, and nine species changed in the shotgun metagenomic analysis. In the 16S rRNA analysis, Marvinbryantia, Roseburia, Coprococcus 3, and Lachnospiraceae UCG-004 were increased, whereas Desulfovibrio was decreased following lubiprostone treatment (Fig. 4A). In the shotgun metagenomic analysis, Murimonas intestini, Senegalimassilia anaerobia, Blautia stercoris (unclassified), Slackia heliotrinireducens, Ruminococcus gauvreauii, Marvinbryantia formatexigens, Streptococcus cristatus, Actinomyces vaccimaxillae, and Pectobacterium cacticida were increased, whereas Bacteroides gallinarum, Clostridium perfringens, and Bacteroides stercoris decreased (Fig. 4A). Blautia, Roseburia (26), and Marvinbryantia (27), which produce short-chain fatty acids and are associated with conditions such as diabetes, have been recognized as beneficial bacteria for human health (28). Blautia also produces S-adenosylmethionine, acetylcholine, and ornithine (29). These findings indicate an alteration in the microbiota and a modulation of metabolites in response to lubiprostone.
Fig. 4. Multiomics analysis.
Analyses included only patients with both pre- and posttreatment fecal samples (placebo: 13; 14 μg: 8; 16 μg: 19). To focus on higher efficacy, placebo and lubiprostone 16-μg/day groups were further divided into responders (placebo: 7; 16 μg: 9) and nonresponders (placebo: 6; 16 μg: 10). (A and B) Heatmaps integrating metabolomics, 16S rRNA, and shotgun metagenomics (phylogenetic and functional) show items meeting criteria detailed in Methods. [(A) Three-group analysis; (B) Responder/nonresponder]. Colors represent group-averaged z scores. Items are ordered by ascending P values; those increased are shown first. For functional analysis, the top 12 (A) or 13 (B) increased items are shown. Key KO, aguA, is bolded. (C) Bar plots show taxa with substantial changes (ALDEx2), ordered by descending effect size. Symbols beside box plots indicate presence of aguA in KEGG-registered strains within each genus: “+” (present), “−” (only absent), “?” (no data). (D) Relative abundance of aguA from shotgun metagenomics (Left, three-group; right, responder analysis). Lines connect the same participants. Wilcoxon signed-rank test was used. (E) Diagram of polyamine biosynthesis pathway involving aguA. (F) Box plots of plasma polyamines (PUT, SPD, and SPM) before versus after treatment. Changes from baseline were compared using ANCOVA, as for the primary end point. *P < 0.05, **P < 0.01. speA, arginine decarboxylase; speB, agmatinase; speC, ornithine decarboxylase; speE, spermidine synthase; aguA, agmatine deiminase; aguB, N-carbamoylputrescine amidase; rocF, arg, arginase; SMS, spermine synthase; PUT, putrescine; SPD, spermidine; SPM, spermine.
Functional analysis of the fecal microbiota using shotgun metagenomics revealed 6396 Kyoto Encyclopedia of Genes and Genomes (KEGG) orthologs (KO), of which 53 were significantly altered by lubiprostone. The top 12 KOs are presented (Fig. 4A, fig. S7, and table S8). These include hydrogenase-4 component J (hyfJ), hydrogenase-4 component H (hyfH), hydrogenase-4 component C (hyfC), and hydrogenase-3 maturation protease (hycl). Hyf encodes a putative proton-translocating formate hydrogenlyase system (30). Hydrogen metabolism is widespread (31) and is a commonly used energy source for microbial growth and survival (32), whereas agmatine deiminase (aguA) is an enzyme involved in polyamine biosynthesis. Polyamines exert anti-inflammatory effects through autophagy and mitochondrial maintenance (33). These data indicate that lubiprostone promotes changes in the gut microbial community and modulates polyamines and short-chain fatty acid metabolites in patients with CKD.
Why does lubiprostone demonstrate efficacy in certain cases but not in others?
On the basis of the findings of our clinical trial, we noted that even within the 16-μg lubiprostone treatment group, subgroups of patients responded effectively to the treatment, whereas others did not exhibit substantial therapeutic benefits. To further explore the renoprotective effect of lubiprostone, participants in the 16-μg lubiprostone group were divided into responder (n = 9) and nonresponder (n = 10) groups based on the degree of improvement in eGFR. These groups were compared with the control group using all accompanying datasets, including fecal samples (fig. S8 and table S9). There were no notable biases in age, sex, weight, or baseline eGFR between the groups. Nephrosclerosis was more prevalent in the responder group (placebo, 23.1%; responder, 66.7%; nonresponder, 20%), whereas diabetic kidney disease was more common in the nonresponder group (placebo, 7.7%; responder, 0%; nonresponder, 30%). No differences in the use of ARBs/ACE inhibitors, SGLT2 inhibitors, GLP1 agonists, or MR antagonists were observed between the study groups (table S9).
Metabolomic analysis using CE-TOFMS revealed that diethanolamine levels increased in the plasma (Fig. 4B, fig. S9A, and table S10), whereas α-aminoadipate, Cr, uric acid, lysine, symmetric dimethylarginine, valine, and γ-butyrobetaine levels were decreased (Fig. 4B, fig. S9B, and table S10). The reductions in Cr, uric acid, symmetric dimethylarginine, and γ-butyrobetaine—compounds known to accumulate in RF (24, 34)—confirm that lubiprostone enhanced renal function in the responder group. In the urine, 2,3-pyridinedicarboxylate levels were elevated (Fig. 4B, fig. S9C, table S10). Similar to the initial analysis (Fig. 4A), lactate levels increased in the feces of the responder group (Fig. 4B, fig. S9D, and table S10).
In the 16S rRNA analysis, only Holdemania demonstrated a significant decrease (Fig. 4B, fig. S10A, and table S11). In the shotgun metagenomic analysis, Blautia stercoris (unclassified), Roseburia inulinivorans, Anaerosporobacter mobilis, Bacteroides salyersiae, Streptococcus lactarius, Clostridium vincentii, Akkermansia muciniphila, and Streptococcus parasanguinis increased, whereas B. gallinarum decreased (Fig. 4B, fig. S10B, and table S11). Holdemania is involved in inflammatory responses and associated with neurological disorders (35). Blautia, Roseburia, and Akkermansia, which increased in the responder group, are beneficial bacteria that produce short-chain fatty acids (26, 28, 36). Functional analysis using shotgun metagenomics demonstrated that in the responder group, the abundance of 23 KOs decreased, whereas that of 3 KOs increased (Figs. 4B, fig. S11, and table S12). Similar to the initial analysis shown in Fig. 4A, aguA significantly increased, whereas 2-dehydro-3-deoxyphosphogluconate aldolase (eda) revealed a decrease (Fig. 4B).
On the basis of the finding that lubiprostone administration might be related to the polyamine and aguA pathways, we reanalyzed the composition of the gut microbiota in the responder group (Fig. 4C and table S13). Using ALDEx2 analysis (37) on 16S rRNA, Roseburia, Odoribacter, Tannerellaceae;g__NA, Ruminococcus, Gordonibacter, Actinomyces, Fusicatenibacter, Streptococcus, Lachnospiraceae;g__NA, Adlercreutzia, Blautia, Intestinibacter, and Prevotella_9 were increased, whereas Holdemania, Colidextribacter, Family XIII UCG-001, Agathobacter, Flavonifractor, GCA-900066575, Veillonella, Raoultibacter, and Prevotellaceae;g__NA were decreased. Of the 13 genera increased by lubiprostone, Roseburia, Ruminococcus, Gordonibacter, Streptococcus, Adlercreutzia, and Blautia were found to harbor aguA (Fig. 4C, indicated by +, and table S13). The increase in these bacteria collectively contributed to the elevation of aguA, leading to an increase in polyamines.
Therefore, we measured the fecal aguA expression (Fig. 4D). The fecal expression of the aguA gene increased in the 16-μg group in the initial clinical trial, while the 8-μg group also revealed a trend of increasing aguA in the feces (Fig. 4D, left). In the responder-versus-nonresponder analysis, the fecal expression of the aguA gene was also increased in the 16-μg group, with the 8-μg group again showing a similar trend (Fig. 4D, right). These results strongly suggest that the polyamine pathway plays a significant role in the renoprotective effects of lubiprostone. aguA is a metabolic enzyme within the pathway that synthesizes PUT from arginine via agmatine (Fig. 4E). In humans, polyamines are not only derived from the diet but also synthesized by the gut microbiota, absorbed, and used in the body (38). Recently, we developed a sensitive method to identify the concentrations of PUT, spermidine (SPD), and spermine (SPM) using gas chromatography–tandem MS (GC-MS/MS) (38). Therefore, we measured plasma and fecal polyamine levels (Fig. 4F and fig. S12). Among the polyamines, plasma SPD levels were higher in the responder group than in the placebo group (Fig. 4F and fig. S12A). Conversely, plasma PUT and SPM levels did not change. In the feces, SPD did not change in the responder group (fig. S12B), although the SPM levels increased. Because aguA is an enzyme involved in the polyamine synthesis pathway through arginine and agmatine, these results strongly suggest that lubiprostone may exert a renoprotective effect by increasing aguA-containing bacterial flora in feces and enhancing polyamine production.
SPD improves renal function with mitochondrial recovery
SPD enhances mitochondrial function, demonstrating various organ-protective effects (33), such as reducing cardiovascular and cancer-related mortality in humans (39). To investigate whether lubiprostone enhances renal function by modulating mitochondrial function through increased SPD levels, SPD was orally administered to adenine-induced RF mice, and renal mitochondrial function was subsequently analyzed (Fig. 5A). Compared to the RF group, the RF + SPD group revealed an improvement in Cr levels (Fig. 5B). Histologically, the reduced tubular area observed in the RF group was significantly restored in the SPD group (Fig. 5C). GDF15 is a circulating biomarker not only for mitochondrial diseases (40) but also for diagnosing chronic heart failure (41) and aging (42). In the RF group, the GDF15 level was higher than that in the control group, indicating mitochondrial dysfunction. Under these conditions, the elevated GDF15 levels in the RF group were significantly reduced by SPD (Fig. 5D). These data indicate that administration of SPD to mice with RF improves renal function by ameliorating mitochondrial dysfunction. To further investigate whether SPD preserved mitochondrial morphology in adenine-induced RF, we conducted an imaging analysis of three-dimensional (3D) mitochondrial structures in the proximal tubules using spinning-disk confocal technology (SoRa imaging) (Fig. 5E) (43, 44). We found that total mitochondrial volume (total mitochondrial volume in a field divided by the number of proximal tubule cell nuclei) was reduced in RF mice and attenuated by treatment with SPD. Furthermore, the proportion of the continuous network decreased in RF mice but was preserved in SPD-treated RF mice. Fragmentation, defined by sphericity, was accelerated in the RF mouse kidney; consequently, the intermittent form increased in the RF group. However, mitochondrial damage was rescued by the administration of SPD.
Fig. 5. Oral administration of polyamines to adenine-induced RF mice also showed improvement in RF.
(A) Protocol diagram. SPD (3 mM) was administered via drinking water. (B) Plasma Cr. (C) H&E and MT staining of renal cortex. Tubular area was quantified in the entire cortex stained with MT. Scale bar, 100 μm. (D) Plasma GDF15 concentration. (E) SoRa imaging of COX IV–stained mitochondria. Representative maximum intensity projections and quantification of mitochondrial volume, network, intermediate, and fragmented features. Scale bar, 5 μm. (F to H) RNA-seq analysis of renal tissue. (F) Principal components analysis (PCA) comparing the three groups: Control, RF, and SPD. (G) Venn diagram, heatmap, and enrichment analysis of genes up-regulated in RF and down-regulated by SPD. (H) Venn diagram and enrichment analysis of genes down-regulated in RF and restored by SPD. A heatmap highlights mitochondrial genes within this set. Statistical tests: one-way analysis of variance (ANOVA) with Tukey test for (B) and (D); Kruskal-Wallis with Dunn test for (E). *P < 0.05, **P < 0.01, ***P < 0.001, ****P < 0.0001. [(A) to (E)] Control (Ctrl), n = 6; RF, n = 6; RF + SPD, n = 7. [(F) to (H)] Control (Ctrl), n = 6; RF, n = 5; RF + SPD, n = 5. H&E, hematoxylin and eosin; MT, Masson-Trichrome; RF, renal failure; SPD, spermidine; COX, cytochrome c oxidase; Ctrl, control; Lubi, lubiprostone.
Following these in vivo findings in mice, we investigated whether SPD alters mitochondrial bioenergetics in vitro using a cell flux analyzer (45). We evaluated the effect of SPD on mitochondrial bioenergetics using human proximal tubular HK-2 cells (fig. S13). Exposure to SPD led to increases in basal and maximal mitochondrial respiration and spare respiratory capacity (SRC). Increased adenosine 5′-triphosphate (ATP) production has also been observed. In addition, we observed elevated glycolytic activity. This suggests that SPD may also facilitate energy production via glycolysis. Our findings demonstrated that SPD exposure leads to an overall enhancement of mitochondrial respiratory function. Furthermore, these data suggest that increasing SPD levels during RF helps to preserve mitochondrial function in the kidney and contributes to the restoration of renal function.
RNA sequencing
To further confirm the protective effects of SPD against mitochondrial and renal impairment, RNA sequencing (RNA-seq) was performed on the kidneys. Principal components analysis revealed clear differences in the RNA expression profiles between each group, particularly between the RF and RF + SPD groups (Fig. 5F). When focusing on genes that were up-regulated in the RF group and down-regulated in the RF + SPD group, 50 genes were identified [criteria: RF versus control, false discovery rate (FDR) < 0.01, and log fold change (FC) > 1; RF + SPD versus RF, FDR < 0.01, and log FC < −1] (Fig. 5G, left). Enrichment analysis of these 50 genes demonstrated significant expression of pathways related to “cytokines and inflammatory responses” (Fig. 5G, right). When mitochondria are injured, mitochondrial DNA is released into the cytoplasm following a decline in the mitochondrial membrane potential (MMP). Furthermore, mitochondrial DNA can trigger the cGAS-STING (46) or NLRP3 pathways (47, 48). In this context, the reduction in mitochondrial stress by SPD mitigated the cytokine and inflammatory responses in the RF kidneys. By further focusing on the genes that were down-regulated in the RF group and up-regulated in the RF + SPD group, 70 genes were identified (RF versus control, log FC < 0 and FDR < 1 × 10−15; RF + SPD versus RF, log FC > 0 and FDR < 0.005) (Fig. 5H, left). Enrichment analysis of these 70 genes demonstrated a significant expression in the mitochondrial matrix, which was the most significant pathway reduced by RF and ameliorated by SPD (Fig. 5H, right). Further examination of mitochondria-derived RNA showed similar behavior in oxidative phosphorylation (OXPHOS) components (ND2–6, ATP6, ATP8, COX1–3, and Cytb), with down-regulation in the RF group and up-regulation in the RF + SPD group (Fig. 5H, center). These findings suggest that SPD mitigates the mitochondrial damage caused by renal impairment and reduces inflammation, thereby maintaining mitochondrial capacity and improving renal function.
DISCUSSION
To date, most studies have focused on the identification and characterization of gut-derived uremic toxins (IS, PCS, and TMAO) that correlate with the prognosis and incidence of cardiovascular events in CKD (49–51). Oral administration of charcoal AST120 reduces blood uremic toxins, such as IS, by adsorbing tryptophan and indole in the intestine. However, clinical trials in the United States and Korea have failed to meet the end points of improving renal function [EPPIC (52, 53) and K-STAR (54)]. Therefore, we have reconsidered the conventional understanding of the relation between uremic toxins and renal function. Long colonic transit times have been associated with high microbial richness and are accompanied by a shift in colonic metabolism from carbohydrate fermentation to protein catabolism (13), leading to the accumulation of tryptophan and phenol metabolites during constipation. Conversely, shorter colonic transit times are correlated with metabolites that may reflect increased renewal of the colonic mucosa (13). Thus, increasing colonic transit time may reduce the decline renal function. Furthermore, our animal studies demonstrated that lubiprostone (18) and linaclotide (19) lower blood uremic toxin levels and enhance renal function. On the basis of these findings, we hypothesized that lowering uremic toxin levels and/or reducing colonic transit time would preserve renal function in patients with CKD; thus, we conducted a human clinical trial.
This study demonstrates that lubiprostone exerts a renoprotective effect independent of uremic toxins, which may be attributed to the enhancement of mitochondrial function mediated by SPD produced by the gut microbiota in humans (Fig. 6). Polyamines, such as PUT and SPD, are bioactive compounds that promote autophagy and suppress inflammation, thereby exerting cytoprotective and organ-protective effects (55). Furthermore, SPD enhances ATP production, increases MMP, reduces reactive oxygen species, improves mitochondrial respiration in agedinduced pluripotent stem cell (iPSC)−derived neurons (56), and attenuates aortic valve calcification (57). Moreover, given our findings regarding mitochondrial bioenergetic function, the observed augmentation of SRC is of particular clinical and physiological significance. SRC is a particularly robust functional parameter for evaluating the mitochondrial reserve capacity, and its increase may indicate an enhanced ability of cells to respond to metabolic stress (58). It is also reported that SPD protects against acute kidney injury by reducing NLRP3 inflammasome activated by mitochondrial fragmentation (59). These findings indicate that SPD positively modulates this key bioenergetic parameter, thereby potentially enhancing cellular adaptive capacity to metabolic and oxidative stressors and reducing inflammation.
Fig. 6. Polyamine-mediated renal protection via the lubiprostone-gut-kidney axis.
This is a schematic model showing the improvement in renal function caused by the administration of lubiprostone. The administration of lubiprostone alters the microbiota, and agmatine deiminase (aguA) increases. The polyamine concentration in the host blood increases, and this has a protective effect on the mitochondria and suppresses inflammation in the kidneys, preventing the progression of RF. Created in BioRender. Abe, T. (2025) https://BioRender.com/51t2iwg.
In the control group, SPD levels decreased during the study period, whereas in the responder group, no significant reduction in SPD levels was observed after treatment with lubiprostone (fig. S12). These findings suggest that lubiprostone may potentially suppress the decline in SPD level. In addition to the SPD-related mechanism, other possibilities may explain the effects of lubiprostone. First, lubiprostone may act directly on chloride channels in the kidney. The voltage-gated chloride channel CLCN2 is widely expressed not only in the intestine but also in the brain, lung, and kidney (60). Enhancing CLCN2 activity, which is crucial for electrochemical gradients, may therefore contribute to correcting ion imbalances and improving kidney function as reported (61). Second, the renoprotective effect may be partly attributed to its active metabolite, M3, which is the predominant form detected in circulation (62). Recently, we conducted another randomized, placebo-controlled clinical trial (38) and found that Bifidobacterium supplemented with arginine in humans increased serum PUT, SPD, and fecal PUT, thereby enhancing vascular endothelial function (38). SPD enhances eEF5/EIF5A hypusination and promotes the efficient expression of a subset of mitochondrial proteins involved in the Krebs cycle and oxidative phosphorylation (63). Furthermore, SPD protects against sepsis-induced acute kidney injury by down-regulating the inflammasome and interleukin-1β production (59). These findings suggest that lubiprostone preserves renal function through the polyamine-mitochondrial axis, independent of uremic toxins.
We set IS reduction as the primary end point and the suppression of eGFR, BUN, and urinary protein levels as secondary end points. This end point design was based on reports indicating that patients with serum IS levels of ≥0.3 mg/dl experienced significantly faster CKD progression than those with levels <0.3 mg/dl (64). However, in our study, unexpectedly, the change in IS levels ranged from 1.91 to 2.41 mg/ml, which corresponds to a change of approximately 0.2 mg/dl—a value too low to evaluate the effect of lubiprostone effectively. Thus, the effect of lubiprostone on IS levels could not be conclusively evaluated because of the lower-than-expected changes. Conversely, we found that lubiprostone preserved renal parameters, indicating that its renoprotective effects are independent of uremic toxins. To further elucidate the pharmacological effects, we stratified the participants into responder and nonresponder groups for comparative analysis. Baseline comparisons of metabolomic and metagenomic profiles between responders and nonresponders revealed several distinctions (tables S14 to S17). Specifically, plasma urate, fecal Holdemania, and aguA were identified as the features that exhibited responder-specific changes. Both urate and Holdemania levels were higher at baseline in responders and decreased following lubiprostone administration, suggesting that the marked reduction may be attributable to their initially elevated levels. Conversely, aguA was also higher at baseline in responders but further increased after lubiprostone treatment, indicating that responders have a gut microbiota relatively enriched in aguA. This lubiprostone-induced enhancement of aguA may have contributed to the renoprotective effects observed in this group. In addition, surrogate marker to predict the effects of lubiprostone is necessary. In this context, plasma urate, fecal Holdemania, and aguA in feces are possible markers for detecting and evaluating the effectiveness of lubiprostone in patients (tables S14 to S17). Further clinical trials are required to validate these findings.
eGFRCr or eGFRcys
In this clinical study, lubiprostone enhanced eGFRCr but not eGFRcys. The cause of the discrepancy between the results of eGFRCr and eGFRcys needs to be clarified to evaluate the effect of lubiprostone on renal function in patients with CKD. In this trial, no reductions in body weight, uric acid, or BNP levels were observed, indicating that the increase in eGFRCr levels was not a result of decreased muscle volume or dilution (fig. S2). Several studies have reported differences in the eGFR based on Cr and cystatin C levels (65). To compensate for inaccuracies in estimating renal function, the combined use of Cr and cystatin C in equations estimating GFR (CKD–EPICr-cys) has been suggested to improve accuracy (66). However, these equations also account for the effects of race (67). As a resolution, the Japanese coefficient–modified CKD-EPI equation (Eqaverage) has been proposed, demonstrating significantly improved prediction accuracy in the group with eGFR 30 to 59 ml/min per 1.73 m2 compared to CKD-EPICr-cys in the Japanese population (68, 69). However, because the populations enrolled in eGFRCr and Eqaverage differed, there was little significance when comparing the populations using Eqaverage (fig. S14A) or slope of Eqaverage (fig. S14B). On the basis of these findings, we reevaluated the enrolled patients and found that approximately 10% of participants recalculated for eGFR were outside the 25-to-45 range and were excluded (reanalysis placebo: n = 28; reanalysis lubiprostone 8 μg: n = 22; reanalysis lubiprostone 16 μg: n = 42; fig. S14C). Significant changes in Eqaverage were observed in eGFR after 20 weeks of administration in the lubiprostone 8-μg group and after 20 and 24 weeks in the lubiprostone 16-μg group (fig. S14D, right). Subgroup analysis revealed that in the moderate group, Eqaverage was also preserved in the 16-μg group (fig. S14D, middle). However, there was no significant difference in Eqaverage in the severe group (fig. S14D). Furthermore, the slope of Eqaverage was preserved in the 8-μg and 16-μg groups (fig. S14E, left). Subgroup analysis revealed that in the moderate group, the decline in Eqaverage was suppressed after 12 weeks of administration in the 8-μg group and significantly suppressed after 8, 12, 16, 20, and 24 weeks in the 16-μg group (fig. S14E, middle). In the severe group, No difference was observed in the slope of Eqaverage (fig. S14E, right). Therefore, it is important to use these two parameters to understand the features and factors of dissociation when estimating renal function.
Limitations
Regarding the period of intervention and number of patients, the clinical study period was relatively short, and the number of patients was relatively small. Conversely, Levey et al. (70) reported that an eGFR slope reduction of 0.5 to 1.0 ml/min per 1.73 m2 per year or a urine albumin creatinine ratio (UACR) reduction of 30% would fulfill the criteria for surrogate endpoints in clinical trials assessing CKD progression. In our study, the 24-week change in eGFR was equivalent to −3.36 ml/min per 1.73 m2 per year for the placebo group and 0.8 ml/min per 1.73 m2 per year for the 16 μg lubiprostone group, resulting in a difference of approximately 4 ml/min per 1.73 m2 per year, which satisfies their criteria. The criteria and patient numbers were approved by the Pharmaceuticals and Medical Devices Agency of Japan for an exploratory clinical trial. In this study, the renoprotective effects observed were secondary and exploratory end points; therefore, the results should be interpreted with caution. Multiple end points can also be used in a phase 2 study for a comprehensive evaluation. According to the FDA guidelines (https://fda.gov/regulatory-information/search-fda-guidance-documents/multiple-endpoints-clinical-trials-guidance-industry), incorporating multiple end points, such as IS and eGFR, should be considered. Coresh et al. (71) reported that a > 30% decline in eGFR is a clinically meaningful indicator of CKD progression and is associated with an increased risk of end-stage renal disease and mortality. In future clinical trials, these criteria should be incorporated into the design to enable a more comprehensive evaluation of the renoprotective effects of lubiprostone. We conducted multiomics analyses to clarify the mechanism of action of lubiprostone in patients with CKD. The reliability of information on omics often depends on the effect size, sample size tested, and magnitude of the effect of confounding factors (72). Batch effects and technical variability also affected the results. In addition, omics and multiomics analyses often involve thousands of statistical tests: therefore, there is a severe risk of detecting false positives due to noise or random chance (73). This should be interpreted carefully, and a validation cohort study is required. Furthermore, we mentioned that all enrolled patients was Japanese (Asian), which limits the generalizability to other ethnic groups or broader CKD populations. An additional responder analysis showed increased plasma SPD; however, its clinical significance is preliminary and unclear, and the small sample size undermines confidence in this result. Therefore, further clinical studies with a longer duration, a larger number of participants, and inclusion of different ethnic populations are necessary to validate the efficacy of lubiprostone in the treatment of CKD.
Lubiprostone is a previously unknown therapeutic agent for CKD that improves renal function by modulating mitochondrial function. The primary objective of CKD treatment is to improve renal function rather than target surrogate markers, such as uremic toxins or proteinuria/albuminuria. Therefore, future trials on lubiprostone that focus on improving renal function are imperative.
METHODS
Study design
This was a multicenter, randomized, double-blind, and placebo-controlled trial. The methods, conduct, and analysis are described in the Supplementary Protocol and statistical analysis. The trial was designed and conducted by the investigators and was supported by the Tohoku University Hospital Clinical Research Center. The funding agency, the Japan Agency for Medical Research and Development, had no role in the design or conduct of this trial. All authors attest to the completeness and accuracy of the data and the fidelity of the trial to the protocol. This study was registered with the Clinical Trials Registry (UMIN000023850), and the protocol received ethical approval from the Tohoku University Ethics Committee (reference number: 2013-2-196-1, 2018-2-239). Written informed consent was obtained from all participants before their enrollment in the study, following a thorough explanation of the study’s purpose, procedures, potential risks and benefits, and their right to withdraw at any time.
Trial sites and participants
The participants were recruited between July 2016 and December 2019 from nine centers in Japan: Tohoku University, Jikei University, JCHO Sendai Hospital, Toranomon Hospital, Fukushima Prefecture Medical School, Akita University, Chiba University, Juntendo University, and Yamagata University. Patients with CKD aged ≥20 years with persistent eGFR of 25 to 45 ml/min per 1.73 m2 for ≥3 months during screening were included. The patient eligibility criteria are summarized in table S18. Written informed consent to participate in the trial was obtained from all participants or their relatives, if they were incapacitated. Patients on immunosuppressants, oral adsorbents, laxatives, and intestinal regulators, and those with advanced proteinuria, acute RF, drug-induced kidney injury, and previous kidney transplantation were excluded (table S19).
Randomization and trial interventions
On the basis of the baseline eGFR values, patients were stratified in a 2:2:3 ratio into the placebo, lubiprostone 8-μg/day, and lubiprostone 16-μg/day groups to equalize the impact of eGFR on the assessment between groups. Random allocation sequences for patients and the study drug were created and controlled by an unblinded study drug allocation manager. Patients received either lubiprostone 8 μg once or twice daily or placebo with identical packaging to maintain masking for treatment allocation. This regimen was administered orally for 24 weeks (the 8-μg/day group received lubiprostone in the morning and placebo in the evening). Blood and urine tests were performed at the start of drug administration and subsequently at 4- and 8-week intervals. Treatment was continued for 24 weeks unless serious AEs occurred, consent was withdrawn, or the investigator decided to discontinue treatment.
End points
The primary end point was the change in the IS values from baseline to 24 weeks after drug administration. Secondary end points included alterations in amount and percentage from baseline to each measurement point of the concentrations of plasma uremic toxins generated by gut microbiota (IS, PS, PCS, and TMAO), renal function (BUN, Cr, cystatin C, eGFR estimated with Cr or with cystatin C, urine protein/Cr ratio, the slope of the reciprocal of Cr from the start of drug administration), defecation frequency, and changes in the Bristol scale and breath gas factors (74). Safety assessment included monitoring of AEs from the initiation of study drug administration until after the final dose, and the severity of AEs was assessed according to the National Centre Institute Common Terminology Criteria for AEs v.5.0.
Assessment and interpretations
A pilot clinical study was conducted to examine the effect of lubiprostone on uremic toxins in a small number of patients with CKD for 3 months (Tohoku University Ethical Committee #2014-2-92). Because 24-μg/day dose of lubiprostone caused an average change of −0.24 mg/dl in the IS concentration, we assumed a change of −0.08 and −0.16 mg/dl for doses of 8 and 16 μg/day, respectively. The SD of the IS concentration in blood in CKD stage III was 0.19 mg/dl, and the SD of the amount of change was assumed to be larger than this, and 0.33 mg/dl, which corresponds to approximately 1.7 times the SD of the amount of change, was assumed to be the SD of the amount of change. On the basis of these assumptions, the Bonferroni method was used to avoid multiplicity in primary end point analysis. The significance level for testing the placebo group and each dose group was set at 2.5% for two-tailed testing with a power of 80%, resulting in 32 patients per group. The sample size was set at 40 for each group, considering discontinuations and dropouts, and an additional 20 patients were added to the 16-μg/day dose (n = 60), to avoid further dropouts due to AEs, particularly diarrhea. In addition, we determined the period during which a decline in eGFR could be detected in Japan (6 months) based on our Gonryo CKD study comprising 2692 patients (75, 76). One person in the placebo group discontinued treatment at week 22, and data from week 24 (the predefined final assessment point) were considered valid and counted as “complete treatment” in the efficacy analysis.
Statistical analysis for clinical trial
Summary statistics and mean 95% CIs were calculated for each treatment group for the end points. The change from baseline was compared between the lubiprostone dose group and the placebo group by calculating the LSM and 95% CIs by analysis of covariance (ANCOVA) with the value at the start of study drug administration as the covariate and eGFR as the allocation factor (25 to 35 ml/min per 1.73 m2 or 36 to 45 ml/min per 1.73 m2). For comparisons, Dunnett’s method was applied to account for multiplicity. Missing data were supplemented by last observation carried forward. The analysis population was the FAS, which is the population of participants who had at least one primary end point measured at least once. As an additional test, the change from baseline for each end point was compared between the placebo and lubiprostone groups at each measurement. The analysis population was defined as per protocol set. Because these analyses were exploratory, Dunnett’s method was applied for between-group comparisons, but multiplicity of measures was not considered. The reciprocal of serum Cr after the start of treatment with the study drug was analyzed using a mixed-effects model with subjects as a variable effect, treatment group, each assessment time point, and treatment group × each assessment time point as fixed effects, and the slopes were compared for each of the three groups. The summary statistics and 95% CIs of the mean values were calculated for the actual values at each evaluation time point, the amount of change from the start of treatment with the study drug, and the rate of change. Safety analyses were performed on a population of subjects who received at least one dose of study drug (SAS). Adverse event names were coded using the MedDRA (Medicines Regulation Glossary), and the occurrence of adverse events, SAEs, adverse events that resulted in discontinuation of study treatment, adverse reactions, serious adverse reactions, and adverse reactions that resulted in discontinuation of study treatment were tabulated by dose group. The significance level of the test was set at P < 0.05. Statistical analyses were performed using SAS, R v4.2.1 (https://r-project.org/), and Graph Pad Prism v10.0.3 (GraphPad Software).
Metabolomics data analysis
A quantitative analysis of charged metabolites by CE-TOFMS was performed as previously described (20, 77). Plasma, urinary, and fecal metabolites were extracted by vigorous shaking with methanol containing 20 μM each of methionine sulfone and D-camphol 10-sulfonic acid as the internal standards. All CE-TOFMS experiments were performed using the Agilent CE capillary electrophoresis system (Agilent Technologies, Palo Alto, CA), Agilent G3250AA LC/MSD TOF system (Agilent Technologies), Agilent 1100 series binary high-performance liquid chromatography pump, G1603A Agilent CE-MS adapter, and G1607A Agilent CE-ESI-MS sprayer kit. Principal components analysis of CE-TOFMS data was performed using SIMCA 13.0 (Umetrics, Umea, Sweden). For the analysis, concentrations below the detection limit were substituted with zero, and metabolites whose levels were below the detection limit in all the samples were excluded.
We further evaluated the dose-dependent response to lubiprostone by extracting substances that met the following criteria for each analysis:
1) The P value for the change in substance before and after treatment (pre = 0 weeks, post = 24 weeks) was calculated using the Wilcoxon signed-rank test.
2) Lubiprostone 16-μg group: P < 0.05 (shotgun analysis), P < 0.1 (metabolomic and 16S rRNA sequencing analyses).
3) Lubiprostone 8 μg group: P < 0.3 with a change in the same direction (either positive or negative) as the 16-μg group.
4) Placebo group: P ≥ 0.3 or a change in the opposite direction (positive or negative) compared to the 16-μg group.
16S rRNA sequencing analysis
Fecal DNA extraction
Fecal DNA isolation was performed as described previously, with some modifications. Each freeze-dried fecal sample was combined with four 3.0-mm zirconia beads, approximately 100 mg of 0.1-mm zirconia/silica beads, 400 μl of DNA extraction buffer [TE containing 1% (w/v) SDS], and 400 μl of phenol/chloroform/isoamyl alcohol (25:24:1) and vigorous shakes (1500 rpm for 15 min) using a Shake Master (Biomedical Science, Shinjuku, Tokyo, Japan). The resulting emulsion was centrifuged at 17,800g for 10 min at room temperature, and bacterial genomic DNA was purified from the aqueous phase by a standard phenol/chloroform/isoamyl alcohol protocol (78), and RNA was digested in the sample by ribonuclease A treatment; the resulting DNA sample then was purified again by another round of phenol/chloroform/isoamyl alcohol treatment.
16S rRNA gene sequencing
16S rRNA genes in the fecal microbiota DNA samples were analyzed using a Miseq sequencer (Illumina, San Diego, California). The V1–V2 region of the 16S rRNA genes was amplified from the DNA (approximately 10 ng per reaction) using a universal bacterial primer set consisting of primers 27Fmod with an overhang adapter (5′-AGRGTTTGATYMTGGCTCAG-3′) and 338R with an overhang adapter (5′-TGCTGCCTCCCGTAGGAGT-3′) (79). Polymerase chain reaction (PCR) was performed with Tks Gflex DNA Polymerase (Takara Bio Inc., Kusatsu, Shiga, Japan), and amplification via the following programme: 1 cycle denaturation at 98°C for 1 min; 20 cycles of amplification at 98°C for 10 s, 55°C for 15 s, and 68°C for 30 s, with final extension at 68°C for 3 min. The amplified products were purified using Agencourt AMPure XP kits (Beckman Coulter, Atlanta, GA). The purified products were then further amplified using a primer pair as follows: a forward primer (5′-AATGATACGGCGACCA CCGAGATCTACAC-NNNNNNNN-TATGGTAATTGTAGRGTTTGATYMTGGCTCAG-3) containing the p5 sequence, a unique 8–base pair (bp) barcode sequence for each sample (indicated by the string of Ns), and an overhang adapter, as well as a reverse primer (5′-CAAGCAGAAGACGGCATACGAGAT-NNNNNNNN-AGTCAGTCAGCCTGCTGCCTCCCGTAGGAGT-3) containing the P7 sequence, a unique 8-bp barcode sequence for each sample (indicated by the string of Ns), and an overhang adapter. After purification using Agencourt AMPure XP kits, the purified products were mixed in approximately equal molar concentrations to generate a 4 nM library pool, after which the final library pool was diluted to 6 pM, including a 10% Phix Control v3 (Illumina, San Diego, California) spike-in for sequencing. Last, Miseq sequencing was performed according to the manufacturer’s instructions. In this study, 2 × 300-bp paired-end sequencing was used. The microbiome analysis data have been deposited into the DNA Data Bank of Japan (DDBJ) under the accession number PRJDB20714.
Analysis of 16S rRNA gene sequences using QIIME 2
Analysis of 16S rRNA gene sequences was performed as described with some modifications (80). In brief, filter-passed reads were processed using Quantitative Insights into Microbial Ecology (QIIME) 2 (2019.10.0) (81). Denoising and trimming of sequences were carried out using DADA2. The first 20 and 19 bp were trimmed from the 5′ end of both forward and reverse reads, respectively, to remove primer sequences. The resulting 135- and 220-bp reads from the respective 5′ ends were used for subsequent steps. Taxonomy was assigned using the SILVA132 database using the Naive Bayesian Classifier algorithm (82, 83). Alpha diversity of gut microbiota was analyzed using observed species, Chao 1, and Shannon indices. PCoA based on UniFrac distances and ANOSIM was carried out using QIIME 2.
Shotgun metagenomics data analysis
DNA extraction and sequencing
DNA extracton and sequencing were performed by Macrogen Japan Corp. (Tokyo, Japan). Briefly, for library construction, DNA/RNA is extracted from a sample. After performing quality control (QC), qualified samples proceed to library construction.
The TruSeq DNA PCR-Free kit (Illumina Inc. San Diego, CA) was used as the Library Kit. The Illumina platform (Illumina Inc. San Diego, CA) was used for sequencing.
Sequence analysis
Raw sequences were preprocessed similarly to a previous study (84) with slight modifications. In this study, adapter trimming was done with fastp v0.20.0 (85) (default parameters) instead of cutadapt. Other steps were done as is. Taxonomic profiling was done by mapping the high-quality reads against VITCOMIC2 (86) to extract 16S rRNA reads, which were then mapped against the SILVA LTP v123 (87) database. Read mapping was done with BLAST (88). The microbiome analysis data have been deposited into the Sequence Read Archive (SRA) under the accession number PRJNA1221235
Functional analysis
High-quality reads were assembled per sample with IDBA-UD. Open reading frame of the resulting scaffolds were predicted with MetaGeneMark (89). Genes with more than 50 amino acids were filtered and mapped against a KEGG GENES database (90) with DIAMOND (91). Gene abundances were estimated by mapping the high-quality reads to the scaffolds using bowtie2 (92), which was then corrected for gene length.
Extraction of key items for renoprotection across analyses
To assess the dose-dependent response to lubiprostone administration, we extracted substances meeting the following criteria across metabolome analysis, microbiome analysis, and functional analysis:
1) Lubiprostone 16-μg group: P < 0.05 (shotgun), P < 0.1 (metabolome and 16S).
2) Lubiprostone 8-μg group: P < 0.3 and the same direction of positive or negative change as in the 16-μg group.
3) Placebo group: P ≥ 0.3 or a different direction of positive or negative change compared to the 16-μg group.
In addition, to capture marked changes particularly in lubiprostone-treated responders who showed improvements in kidney function, we divided the 16-μg lubiprostone group into two subgroups based on whether kidney function improved from baseline (Responder: eGFR change ≥0) or not (Nonresponder: eGFR change <0), and extracted substances that met the following criteria:
1) No significant difference before and after treatment in the placebo group (P ≥ 0.05).
2) Significant difference before and after treatment in the lubiprostone 16-μg responder group (P < 0.05).
3) No significant difference before and after treatment in the lubiprostone 16-μg nonresponder group (P ≥ 0.05).
Responder
To assess the nonresponder and responder, the following criteria were used to extract data:
1) Obtain the P value for the change in the substance levels before and after treatment in the placebo group, the responder group, and the nonresponder group, respectively (pretreatment = 0 weeks, posttreatment = 24 weeks), using the Wilcoxon signed-rank test;
2) Responder group: P < 0.05 (indicating significant changes);
3) Nonresponder group: P ≥ 0.05 (no significant changes);
4) Placebo group: P ≥ 0.05 (no significant changes).
To further evaluate the baseline differences between responders and nonresponders, we compared the results of baseline metabolomic and metagenomic analyses between these two groups. The metabolites, microbial taxa, and KOs from each analysis were then listed in ascending order of their P values.
Measurement of polyamine concentrations
The fecal polyamine concentration was measured using 6-aminoquinolyl-N-hydroxysuccinimidyl carbamate as a derivatization reagent, as described previously (93). An ACQUITY ultra-performance liquid chromatography system with a fluorescence detector (Waters, Milford, MA) was used for the analysis. Plasma polyamine was derivatized with ethyl chloroformate and trifluoroacetic anhydride and quantified by GC-MS, as described previously (38). A GC-MS QP2010 (Shimadzu, Kyoto, Japan) was used for the analysis.
Animal experiments
This study was conducted in accordance with the Guide for the Care and Use of Laboratory. The animal protocols were approved by the Tohoku University Institutional Animal Care and Use Committee (2020-001-18). C57BL6 mice were obtained from CLEA Japan (Tokyo, Japan). Lubiprostone and SPD were purchased from Viatris (Canonsburg, PA) and Wako (Osaka, Japan), respectively. For the RF model, 6-week-old C57BL/6j mice were fed a sterilized CE-2 diet containing 0.5% adenine (Wako, Osaka, Japan) for 1 week to induce tubular injury (18, 94); each group was further divided, and SPD (3 mM) was administered orally by free drinking of water in which it was dissolved for 3 days before the adenine diet began. At the end of the study, the mice were killed under isoflurane-induced anesthesia, and blood, urine, fresh feces, and kidney were collected. In experiments with lubiprostone administration to RF, 6-week-old C57BL/6j mice were fed a sterilized CE-2 diet containing 0.2% adenine for 6 weeks. Following that, they were fed CE-2 diet not containing adenine for 2 weeks and were gavaged with lubiprostone (500 μg/kg) daily (18). At the end of the studies, the mice were killed under isoflurane-induced anesthesia, and blood, urine, fresh feces, and kidney were collected. All animal experiments were approved by the Animal Committee of Tohoku University, School of Medicine.
Histological examination
Kidney was fixed in 10% neutral-buffered formalin and embedded in paraffin. Kidney sections were stained with hematoxylin and eosin, Masson’s trichrome (MT). Preserved tubule area was quantified by measuring the area of the entire renal cortex of MT-stained sections at 20× magnification using ImageJ software (NIH, Bethesda, MD).
Spinning-disk confocal technology
SoRa imaging and mitochondria volume measurements were performed as previously reported (44). Kidney tissues were imaged using the Nikon Ti2 microscope partnered with the Yokogawa CSU-W1 SoRa system using Apo total internal reflection fluorescence ×100 oil DICN2 as an objective and taken as a z-stack at 0.12 μm per z step. To automate image capture, multiple tubules were chosen at random and imaged using NIS Elements multidimensional image acquisition. A total of five to nine tubules per sample were imaged. SoRa imaging and image processing were performed through the Nikon imaging center in Osaka University.
Mitochondrial volume, surface area, and morphology
Mitochondrial networks were quantified by analyzing the network volume using Imaris software (Bitplane, Concord, MA). SoRA images of tubule cross sections were taken as a z-stack and 3D reconstructed in Imaris. The resulting images were converted to surface renderings. Imaris identifies individual mitochondria and interconnected networks as surfaces. To automate image analysis, parameters for surface rendering were set at the beginning of the analysis for each dataset and the remaining samples were rendered using batch processing. Once the surfaces are rendered, the volume, area, intensity, and other parameters can be generated for quantification. Mitochondria were analyzed using the following criteria: maximum mitochondrial size (cubic micrometer, the largest mitochondrial network in a given field), total volume per proximal tubular cell (PTC) (cubic micrometer, total mitochondrial volume in a field/the number of PTC nuclei), and number of mitochondria per PTC (n, number of mitochondria/PTC nuclei). To investigate the proportion of fragmented mitochondria after injury, PTCs’ mitochondria were divided into three groups by their sphericity as follows: fragmented, sphericity >0.451; intermediate, sphericity between 0.450 and 0.311; and filamentous, sphericity <0.310. Proportion (%) of each group was defined by each group’s mitochondrial volume divided by the total mitochondrial volume. For each sample, five to nine tubules were analyzed and averaged.
Mitochondrial function measurement
The human proximal tubular cell line HK-2 was cultured in Dulbecco’s modified Eagle’s medium (DMEM) supplemented with 10% fetal bovine serum (FBS) and plated at a density of 1.0 × 104 cells per well in 0.1 ml of FBS-free DMEM in 96-well plates. Following the application of SPD at the indicated final concentrations, the HK-2 cells were cultured for 48 hours. The specific exposure concentrations and duration were determined on the basis of previously published reports (95). Bioenergetic analysis of the HK-2 cells was performed as previously described (21, 45, 96). Briefly, the oxygen consumption rate (OCR) was measured using a Seahorse XF e96 analyzer (Agilent, CA). Before the assay, the cells were incubated in assay medium (DMEM supplemented with 10 mM glucose, 1 mM pyruvate, and 2 mM l-glutamine) without CO2 for 60 min to allow for equilibration. OCR measurements were then conducted under basal conditions, followed by sequential injections of 2 μM oligomycin, 1.5 μM carbonyl cyanide p-trifluoromethoxyphenylhydrazone, and 0.5 μM rotenone/antimycin A targeting the oxidative phosphorylation pathway.
RNA sequencing
RNA sequence library preparation was performed using QIAseq Stranded RNA Library Kits (catalog no. 180451, QIAGEN) according to the manufacturer’s instructions. The Illumina libraries were converted into the circular single-stranded DNA libraries. RNA sequencing was performed by DNAFORM (Yokohama, Kanagawa, Japan). Sequence reads in Fastq format were assessed for quality using FastQC. Raw reads were first subjected to filtering to remove low-quality reads using Trimmomatic-0.39. The obtained reads were mapped to the mouse GRCm39 genome using STAR (version 2.7.10a). Reads on annotated genes were counted using StringTie (v2.2.0). Gene expression levels were measured with the Bioconductor package edgeR (version 3.15). The data of RNA-seq data have been deposited into the SRA under the accession number PRJNA1256056.
Statistical analysis for multiomics analysis and animal experiments
Responder group and nonresponder group were divided by the following criteria:
1) Responder group - eGFR change from baseline ≧ 0 ml/min/1.73 m2 (n = 9)
2) Nonresponder group - eGFR change from baseline <0 ml/min/1.73 m2 (n = 10)
Placebo group (n = 13) was added to them and analyzed in three groups. (fig. S8). For the metabolome and 16S rRNA and shotgun metagenomics analyses, Wilcoxon tests were performed before and after lubiprostone administration for each group. Bacteria and metabolites that changed significantly in the responder group and not in the other two groups were identified. In the 16S rRNA analysis, further analysis was conducted using ALDEx2 (37) to determine the effect size of each group. The criteria were that the effect size was less than −0.3 or greater than 0.3 in the responder group, and that the effect size was greater than −0.3 or less than 0.3 in the placebo group. For polyamines, the Wilcoxon test was used for comparisons before and after lubiprostone administration in each group, and the Mann-Whitney U test was used for comparisons between groups. For comparisons between groups of change from baseline, ANCOVA was used on the basis of the method used to evaluate clinical trials. One-way analysis of variance (ANOVA) and Tukey’s test or Mann-Whitney U test and Holm method were used to analyze the mouse model. In quantitative analysis of mitochondrial morphology, tests for significance among groups were conducted via the nonparametric Kruskal-Wallis test and post hoc pairwise tests (Dunn’s correction for multiple tests). Since the multiomics analysis was conducted as an exploratory analysis, multiple testing corrections were not considered. Therefore, the results should not be interpreted as hypothesis testing but rather used as an exploratory indicator. Statistical analyses in multiomics analysis were performed using R v4.2.1 (https://r-project.org/), and Graph Pad Prism v10.0.3 (GraphPad Software).
Acknowledgments
We thank M. Kato and M. Naka (Tohoku University Graduate School of Medicine); F. Date, M. Yoshizawa, N. Shibata, and C. Tazawa (Histological Platform, Tohoku University School of Medicine); and K. Ikuta and W. Tanaka (Kyodo Milk Industry Co. Ltd.) for technical and histological assistance.
Funding: This work was supported by the following: the Japan Agency for Medical Research and Development (AMED) Translational Research Network Program (C35) (JP24zf0127001) (to T.A.); AMED Research on Development of New Drugs (JP22ek0210133) (to T.A.); the National Grant-in-Aid for Scientific Research from the Ministry of Education, Culture, Sports, Science, and Technology of Japan (JP18H02822) (to T.A.); and the Japan Society for the Promotion of Science KAKENHI grant number JP21H02932 (to T.A.). Sucampo Pharma LLC. (Kobe, Japan) provided the lubiprostone for this trial.
Author contributions: Conceptualization: E.M., T.To., and T.A. Data curation: S.W. Formal analysis: S.W., T.Ya., S.M., H.T., Y.Ton., and T.Ka. Investigation: S.W., S.F., T.So., M.M., K.T., H.K., K.K., C.S., M.U., R.K., T.Ka., and LUBI-CKD TRIAL Investigators. Methodology: S.F., T.So.,T.Ya., S.M., M.M., Y.N., K.T., K.F., Y.M., Y.A., Y.Tom., S.I., C.K., Y.O., and T.Sa. Project administration: T.To. and T.A. Resources: M.N., T.Y., S.S., Y.U., A.K., K.A., Y.S., T.Ko., J.J.K., T.Su., and LUBI-CKD TRIAL Investigators. Supervision: T.Ta. Visualization: S.W., H.T., K.T., Y.Ton., and T.Ka. Writing—original draft: S.W., T.To., and T.A. Writing—review and editing: S.W., T.To., and T.A. Funding acquisition: T.A. All authors have read and approved the final manuscript.
Competing interests: T.A. has a patent of lubiprosote for CKD in Japan (JP6090723B2). The authors declare that they have no other competing interests.
Data and materials availability: All data needed to evaluate the conclusions in the paper are present in the paper and/or the Supplementary Materials. However, because of restrictions imposed by the Institutional Review Board for privacy protection, neither individual participant–level data nor biological samples can be shared. Sequencing data from additional analyses, including 16S rRNA amplicon, shotgun metagenomics, and RNA-seq, have been deposited in public repositories, specifically the DDBJ Trace/Read Archive (DRA) and NCBI Sequence Read Archive (SRA). The corresponding BioProject accession numbers and links are as follows: PRJDB20714 (DRA): https://ddbj.nig.ac.jp/search/entry/bioproject/PRJDB20714, PRJNA1221235 (SRA): https://ncbi.nlm.nih.gov/bioproject/PRJNA1221235, and PRJNA1256056 (SRA): https://ncbi.nlm.nih.gov/bioproject/PRJNA1256056. The raw sequencing data can be accessed via these BioProject links by navigating to the associated SRA/DRA entries. The datasets generated and/or analyzed during the current study—including metabolomics data, gut microbiota taxonomic profiles, and KEGG Orthology profiles of the microbiome—are available in Supplementary raw dataset. For clinical data and biological samples, researchers who wish to inquire about access for ethically approved studies may contact the corresponding author (T.A.: Email: takaaki.abe.d1@tohoku.ac.jp; affiliation: Tohoku University School of Medicine) or the data access coordinator (nonauthor) (S. Goto: Email: sawako.goto.d8@tohoku.ac.jp; affiliation: Department of Clinical Biology and Hormonal Regulation, Tohoku University Graduate School of Medicine).
Supplementary Materials
The PDF file includes:
Figs. S1 to S14
Tables S1 to S19
LUBI-CKD TRIAL Investigators
Legend for data S1
Other Supplementary Material for this manuscript includes the following:
Data S1
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Figs. S1 to S14
Tables S1 to S19
LUBI-CKD TRIAL Investigators
Legend for data S1
Data S1