The Gut Microbiome and Serum Metabolites As a Biological Mechanism Underlying Pain in Kidney Transplantation (BIOME-KT)

The Gut Microbiome and Serum Metabolites As a Biological Mechanism Underlying Pain in Kidney Transplantation (Biome-KT)

Study Summary Nearly half (47%) of people with end-stage kidney disease (ESKD) whose kidney function is restored after kidney transplantation experience chronic pain compared to 19% of adults in the US general population. Pain is associated with comorbid fatigue, depression and anxiety, and withdrawal from usual physical and social activities; resulting in an inability to participate in and enjoy life. Severe pain can result in nonadherence to immunosuppression and treatment protocols and result in an increased risk of rejection, graft loss, and mortality. The role of symbiotic microbes (microbiota) in the gastrointestinal tract, and their functional genes (microbiome), is well established in diseases involving pain. Diet and stress play a major role in synthesis of signaling molecules critical to immunologic, metabolic, and endocrine pathways regulating chronic pain. Dietary patterns change dramatically after transplantation, as recipients move from a restricted "renal" diet to a regular diet, often resulting in increased consumption of foods high in sugars and fat. Moreover, psychological stress significantly impairs the function of the microbiome, initiating biological pathways involved in pain, leading to a disproportionate pain burden. Because the microbiome, serum metabolites, and pain are dynamic, our novel investigation will employ a prospective repeated measures design to interrogate the dynamic temporal relationships between the microbiome, metabolites associated with pathways regulating pain, transplantation factors (e.g. immunosuppression, kidney function), changing dietary patterns, and perceived stress, on pain scores before and after kidney transplantation. The investigators posit the gut microbiome, and its byproducts, may partially explain the underlying biological mechanisms of pain Interference in kidney disease. The investigators will address three aims: 1) To determine differential dynamic temporal relationships between microbial composition/functional genes and circulating serum metabolites in KTRs with pain vs no pain, 2) To determine the moderation effects of diet and perceived stress on dynamic temporal relationships between microbiome features, serum metabolites, and pain scores among KTRs, and 3) To use machine learning algorithms to identify host-microbial interactions that are causally linked to pain interference among KTRs. Because kidney function is restored, the kidney transplant model is powerful to study the longitudinal relationships between the microbiome, circulating metabolites and chronic pain in people with ESKD to develop patient-centered interventions to treat pain across the spectrum of CKD.

Study Overview

• Status: Recruiting
• Conditions: Gut Microbiome; Kidney Transplant Symptoms
• Intervention / Treatment: Other: no intervention, observational

Detailed Description

Objectives Aim 1: To determine differential dynamic temporal relationships between microbial composition/ functional genes and circulating serum metabolites in KTRs with pain vs no pain. H1: Specific gut microbiota phenotypes (e.g., low abundance of Akkermansia, differential beta diversity indices) will be identified in those with pain vs no pain, and microbiota taxa will be associated with serum metabolite levels (e.g., decreased SCFAs, serotonin, kynurenic acid, indoles; increased neurotoxic quinolinic acid, endotoxin, urea).

Aim 2: To determine the moderation effects of diet and stress on dynamic temporal relationships between microbiome features, serum metabolites, and pain scores among KTRs. H2: Those with pain will report higher stress and consume a low fiber diet (e.g., fiber grams per 1000 kcal), resulting in a shift to a proinflammation microbiome phenotype (e.g., lower alpha diversity, lower abundances of Akkermansia, higher Enterococcus), lower serum levels of SCFAs, and higher levels of neurotoxic metabolites (e.g., quinolinic acid).

Aim 3 (exploratory): To identify host-microbial interactions that are causally linked to PI among KTRs. H3: Integration of longitudinal data from biomarkers associated with PI into clinical-based dynamic machine learning models (e.g., race, age, income, kidney function, diet, stress) will improve their accuracy by >30% as host and microbial biomarkers can better capture environmental factors associated with PI.

• Study Type: Observational
• Enrollment (Estimated): 133

Contacts and Locations

• Study Contact: Name: Mark Lockwood; Phone Number: 314-604-2050; Email: lockmar@uic.edu
• Study Contact Backup: Name: Gullermo Zamora; Phone Number: 7732208631; Email: gzamor2@uic.edu

Study Locations

• United States
  • Illinois
    • Chicago, Illinois, United States, 60612
      • Recruiting
      • UI Health
      • Contact: Mark Lockwood; Phone Number: 314-604-2050; Email: lockmar@uic.edu

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: Adult; Older Adult
• Accepts Healthy Volunteers: No

• Sampling Method: Non-Probability Sample

Study Population

Kidney Transplant Recipients

Description

Inclusion Criteria:

• Receiving a kidney transplant at the University of Illinois Hospital & Health Sciences System (UI Health) Transplant Center at the University of Illinois Chicago (UIC),
• 18 years of age or older (adult), and
• Understand the study process and provide written informed consent to participate.

Exclusion Criteria:

• Having taken systemic antimicrobials (except prophylactic penicillin) in the preceding 4 weeks.
• Having received a previous solid organ transplant.
• History of colon cancer or of an inflammatory bowel disease.
• Planning to receive a multiorgan transplant (e.g., simultaneous pancreas and kidney transplant).
• Having a history of Clostridium difficile infection in the preceding 8 weeks.

Study Plan

How is the study designed?

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
PROMIS	Patient Reported Outcomes Measurement System	baseline, 3 months, 6 months (T scores range form 0-100, higher scores indicate worse level of symptoms).

Secondary Outcome Measures

Outcome Measure	Measure Description	Time Frame
PSS	Perceived Stress Scale	baseline, 3 months, 6 months

Other Outcome Measures

Outcome Measure	Measure Description	Time Frame
Vioscreen	FFQ	baseline, 3 months, 6 months

Collaborators and Investigators

• Sponsor: University of Illinois at Chicago
• Collaborators: Lisa Tussing-Humphreys; Ardith Doorenbos; Beatriz Peñalver Bernabé; Mario Spaggiari; Chang Park

Study record dates

Study Major Dates

• Study Start (Actual): October 9, 2023
• Primary Completion (Estimated): October 1, 2027
• Study Completion (Estimated): March 1, 2028

Study Registration Dates

• First Submitted: January 4, 2024
• First Submitted That Met QC Criteria: January 4, 2024
• First Posted (Actual): January 16, 2024

Study Record Updates

• Last Update Posted (Actual): March 25, 2025
• Last Update Submitted That Met QC Criteria: February 25, 2025
• Last Verified: February 1, 2025

More Information

Terms related to this study

• Other Study ID Numbers: 2023-0193

Plan for Individual participant data (IPD)

• Plan to Share Individual Participant Data (IPD)?: NO

Drug and device information, study documents

• Studies a U.S. FDA-regulated drug product: No
• Studies a U.S. FDA-regulated device product: No
• product manufactured in and exported from the U.S.: No