Research on Gut-kidney Axis Regulation of Diabetic Kidney Disease Based on Multi-omics and Bacterio-drug Interaction Control Mechanism
Conditions: Diabetic Nephropathy Type 2, Type 2 Diabetes Mellitus (T2DM), Chronic Kidney Disease
Sponsor: The First Affiliated Hospital of Hunan University of Traditional Chinese Medicine

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Research  on Gut-kidney Axis Regulation of 
Diabetic Kidney Disease Based on Multi-omics and 
Bacterio-drug Interaction Control Mechanism 
Research program 
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Version Date：2021-02-22 
 
 
NCT number:                      
Title Code: U21A20411              
Research Unit: The First Affiliated Hospital of Hunan          
university of Traditional Chinese Medicine 
Department: Endocrinology, Nephrology 
 
 
 
 
 
 
 
 

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1 Background 
Since the 21st century, the number of diabetes mellitus (DM) cases worldwide has 
increased rapidly. According to the International Diabetes Federation, in 2021, 537 
million adults had diabetes globally, and it is expected that by 2045, this number will 
exceed 780 million. China has the highest number of diabetes-related deaths each year, 
approximately 10,000[1]. Nearly half of type 2 diabetes mellitus type 2 (T2DM) will 
develop into diabetic kidney disease (DKD) [2, 3]. DKD has the characteristics of high 
prevalence, many pathogenesis mechanisms, and lack of effective strategies for 
treatment and management. 
In the past 30 years, DKD has become the most common cause of kidney failure 
and end-stage kidney disease (ESKD), and it is also the main cause of death in 
diabetics[4-6]. Early detection helps to overcome treatment inertia and achieve timely 
medical intervention to maximize the preservation of diabetes. The patient's renal 
function is essential to avoid ESKD and improve clinical outcomes. These measures 
are crucial for preventing patients from progressing to ESKD and improving clinical 
outcomes. 
The gold standard for diagnosing DKD is renal biopsy, which offers the highest 
accuracy. However, due to the invasive nature of renal biopsy, patient acceptance is low, 
limiting its widespread applicability. It is primarily used when it is difficult to 
distinguish between DKD and non-diabetic kidney disease, and it is not the preferred 
diagnostic method for DKD. 
Over the past decade, the advent and application of multi-omics technologies, 
including metabolomics, proteomics, and genomics, has led to a growing recognition 
of the pivotal roles played by intestinal dysbiosis and intestinal-derived metabolites in 
the pathogenesis of DKD[7, 8]. The prevailing view in modern medicine is that intestinal 
flora plays a pivotal role in the digestive process, influencing the synthesis, absorption, 
and metabolism of nutrients. Additionally, it forms a crucial component of the intestinal 
barrier, which serves as a vital line of defense against invading pathogens. This concept 
is also reflected in traditional Chinese medicine, where the spleen is regarded as the 
primary regulator of physiological functions. In light of these findings, the 
identification of DKD gut-derived metabolic markers and the restoration of gut flora 
homeostasis from the perspective of the intestinal flora through the use of multi-omics 
techniques may represent promising strategies for the prevention and management of 
DKD. 
    This study aims to employ microbial metabolomics to gain a deeper understanding 
of the Traditional Chinese Medicine (TCM) pathogenesis of DKD. It seeks to provide 
a scientific foundation for this field and to guide TCM theories in the clinical practice 
of DKD prevention and management. 

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2 Methods   
2.1 Trial overview, ethical consideration, and patient recruitment 
This study is an ongoing, single-center, cross-sectional observational clinical trial. 
The subjects were selected for observation and subsequent analysis for key biomarkers 
such as blood glucose, Urine Albumin-to-Creatinine Ratio(UACR), and renal function. 
Blood, urine, feces, and moss on the tongue samples were collected from the subjects 
and stored for subsequent analysis. The study protocol was approved by the Ethics 
Committee of the First Affiliated Hospital of Hunan University of Traditional Chinese 
Medicine in accordance with the relevant ethical standards. The study is currently being 
conducted at the First Affiliated Hospital of Hunan University of Traditional Chinese 
Medicine and involves four groups of participants: type 2 diabetic kidney disease 
(DKD), type 2 diabetes mellitus (T2DM), chronic kidney disease (CKD), and healthy 
human. Subjects will be recruited from inpatients or outpatients of the Department of 
Endocrinology and the Department of Nephrology of the First Affiliated Hospital of 
Hunan University of Traditional Chinese Medicine. Additionally, healthy human 
subjects will be recruited from the general public through printed advertisements, 
invitations, etc. The total number of individuals proposed to be recruited in the four 
groups is 720. 
  
2.2 Study outcomes  
Primary outcome: By employing a combination of microbiomic and metabolomic 
technologies, we conducted a comprehensive analysis of the intestinal flora and serum 
metabolic profiles associated with DKD. This approach enabled us to identify potential 
enterogenous disease markers of DKD and to investigate the metabolic profiles that 
may be linked to disease progression.  
Secondary outcome: The objective of this study is to analyze the interaction 
between intestinal flora and intestinal-derived metabolites in the disease process of 
DKD. Additionally, the aim is to provide new disease diagnostic strategies and 
progression risk assessment indicators for diabetic nephropathy, which can be applied 
to early identification, precise diagnosis, and prognosis of the disease. 
Exploratory outcomes ：In accordance with the test results, each group will select 
four to six remaining fecal samples as the donor source for allogeneic fecal bacteria 
transplantation. Subsequently, the correlation between intestinal flora and enteric 
metabolites in diabetic nephropathy will be investigated through the method of human-
mouse allogeneic flora transplantation. 
  
2.3 Study population  
This study primarily recruited Type 2 diabetic kidney disease (DKD),Type 2 
diabetes mellitus (T2DM), Chronic kidney disease (CKD), and healthy human. 
2.3.1 Patient inclusion criteria： 
(1) Age ≥18 years old and ≤75 years old, gender is not limited. 
(2) Complete demographic data. 

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(3) Meet the diagnostic criteria of the Chinese Guidelines for the Prevention and 
Treatment of Diabetic Kidney Disease (2021 edition), the Chinese Guidelines for the 
Prevention and Treatment of Type 2 Diabetes Mellitus (2020 edition), and the 
Guidelines for the Screening, Diagnosis, Prevention and Treatment of Chronic Kidney 
Disease (2017 edition). 
(4) The patient is informed of the study as approved by the ethics committee. 
2.3.2 Healthy population inclusion criteria: 
(1) The patient has no known history of infection or risk factors including HIV 
hepatitis B or C virus syphilis etc. and no current systemic infection;  
(2) No obesity (body mass index >30) and/or diabetes no history of chronic kidney 
disease; 
(3) Approved by the ethics committee patients were informed about the study.  
2.3.3 Exclusion criteria： 
(1) A history of infection or other acute disease within one month prior to 
enrollment.  
(2) People who are in a state of stress.  
(3) Other unstable chronic diseases; 
(4)A history of acute and chronic gastrointestinal diseases, including acute 
gastroenteritis, functional gastrointestinal diseases, inflammatory bowel disease, celiac 
disease, and other chronic gastrointestinal diseases.  
(5) Individuals who have received systemic antibiotics, immunosuppressants, 
chemotherapy, or chronic treatment with proton pump inhibitors within the past three 
months will be excluded from participation.  
(6) Pregnant or lactating women.  
(7) Individuals who have participated in other drug clinical trials within the past 
three months.  
(8) Individuals who suffer from mental illness, intellectual disability, confusion, 
or other conditions that may interfere with their ability to cooperate with the completion 
of relevant information collection will also be excluded.  
In order to ensure the rigor and reliability of the study, subjects who met one or 
more of the aforementioned criteria were excluded.  
2.4 Study procedures  
2.4.1 Questionnaire to collect information 
(1) General items: name, gender, age, BMI, education level, labor intensity, dietary 
preferences, etc; 
(2) TCM symptoms, tongue and pulse; 
(3) Clinical information: disease duration, other complications (e.g., diabetic 
peripheral neuropathy, diabetic retinopathy, diabetic cardiomyopathy, etc.), 
concomitant diseases (hypertension, coronary artery disease, hyperlipidemia, 
cerebrovascular disease, and hyperuricemia, etc.), past medical history, history of 
smoking, alcohol, family history, and history of drug use. 
2.4.2 Outcome Measures 
Indicators of safety: Physical examination (including general condition, blood 
pressure, heart rate, heart rhythm, body temperature, respiratory rate, etc.) was 

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performed before taking the samples to assess the basic condition of the subjects. 
Primary Outcome Measures : Urine Albumin-to-Creatinine Ratio(UACR), 
Estimated Glomerular Filtration Rate(eGFR),blood urea nitrogen(BUN), serum 
creatinine(Scr), fasting blood glucose(FBG), Glycosylated Hemoglobin, Type 
A1C(HbA1c), glucosylated serum protein(GSP) , etc. Test data were obtained from test 
results conducted within one month of enrollment for the purpose of evaluation.  
Secondary Outcome Measures: 16S ribosomal DNA(16S rDNA) identification, 
metagenomic Next Generation Sequencing, Non-targeted and targeted Metabolomics 
technology detection , etc. 
2.5 Data analysis 
SPSS 26.0 will be applied for statistical analysis of clinical indicators. Data that 
exhibited a normal distribution were expressed as the mean ± standard deviation(`x±s), 
and an independent samples t-test was employed for comparison between two groups. 
Data that did not conform to a normal distribution were expressed as the median and 
interquartile spacing [M(IQP)], and a Mann-Whitney rank-sum test (U test) was used 
for comparison between two groups. In the case of count data, these were expressed as 
frequencies or percentages. In the event of unordered categorical count data, 
comparisons between two groups were performed using either the four-cell table or the 
column table c2 test. The Pearson c2 test or Fisher's exact probability method was 
employed in accordance with the sample size and the size of the theoretical frequency. 
In the case of ordered categorical count data, comparisons between two groups were 
performed using the U test. A two-sided test was employed for the normality test and 
variance chi-square test, with an alpha level of 0.1. This resulted in the rejection of the 
original hypothesis, indicating that the data are not normally distributed or that there is 
no significant variance. For other significance tests, an alpha level of 0.05 was used, 
leading to the rejection of the original hypothesis and the conclusion that the difference 
is statistically significant.  
An integrated correlation analysis will be performed using a variety of analytical 
techniques, including intestinal flora species diversity analysis, Spearman's correlation 
analysis, network analysis, Procrustes analysis, MaAsLin analysis, ERMANOVA 
analysis, random forest analysis of differential intestinal flora with differential 
metabolites between groups, and efficacy evaluation. 
3 Discussion  
DKD is a common complication in the late stage of diabetes. What's more, it's a 
common cause of renal failure.By introducing the factors of gut microbiota and 
metabolites through multi-omics techniques, we conduct an analysis on the 
correlation of the onset of diabetic kidney disease to look for marker microbiota or 
metabolites and find relevant risk factors.This not only helps to explore the 
pathogenesis of DKD, but also provides a scientific basis for the early diagnosis, 
prevention and treatment of DKD. Even it is beneficial for delaying the progression of 
the disease, for drug research and development, and for changing potential treatment 

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strategies in the follow-up. 
 
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