Clinical Trial Details
— Status: Recruiting
Administrative data
| NCT number |
NCT06206486 |
| Other study ID # |
2023-0193 |
| Secondary ID |
|
| Status |
Recruiting |
| Phase |
|
| First received |
|
| Last updated |
|
| Start date |
October 9, 2023 |
| Est. completion date |
March 2028 |
Study information
| Verified date |
September 2023 |
| Source |
University of Illinois at Chicago |
| Contact |
Mark Lockwood |
| Phone |
314-604-2050 |
| Email |
lockmar[@]uic.edu |
| Is FDA regulated |
No |
| Health authority |
|
| Study type |
Observational
|
Clinical Trial Summary
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. We posit the
gut microbiome, and its byproducts, may partially explain the underlying biological
mechanisms of pain Interference in kidney disease. We 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.
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.
