Prospective Cohort Study on the Impact of Gut Microbiota and Serum Metabolites on Glycemic Variability and Clinical Outcomes in Patients With Diabetic Kidney Disease
Conditions: Diabetic Kidney Disease
Sponsor: Eighth Affiliated Hospital, Sun Yat-sen University

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The Effect of Gut Microbiota and Serum
Metabolites on Glycemic Variability and
Prognosis in Patients With Diabetic Neph-
ropathy: a Prospective Cohort Study
Sponsoring institution: The 8th Affiliated Hospital of Sun Yat-sen University (Shenzhen Futian)
Project leader: Shen Yunfeng
Department: Endocrinology
Contact: 19875390249
declaration of secrecy
all information contained in this protocol is the property of the investigators of
the project and is provided only for review by the ethics committee and relevant
institutions.

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Research Plan Confirmation and Signature Page
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Title: A Prospective Cohort Study on the Effects of Gut Microbiota and Serum Metabolites
on Blood Glucose Variability and Prognosis in Patients with Diabetic Nephropathy
I will conscientiously fulfill my duties as a researcher in accordance with the
China GCP regulations, personally participating in or directly guiding this clinical
study. I have carefully read this protocol and agree to fulfill the relevant responsibilit-
ies in accordance with Chinese law, the Declaration of Helsinki, China GCP, and this
study protocol, and will only implement it after obtaining the consent of the ethics
committee, unless measures must be taken to protect the safety, rights, and interests of
the study subjects.
Clinical research unit: The 8th Affiliated Hospital of Sun Yat-sen University (Shenzhen Futian)
Principal Investigator: Shen Yunfeng
date ：
YTD

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I. Summary of the Research Plan
Diabetic kidney disease (DKD), one of the most significant microvascular complications of
diabetes, has become the leading cause of end-stage renal disease. Glycemic variability (GV) is
considered closely associated with disease progression in these patients. Recent studies indicate
that gut microbiota and their serum metabolites play crucial roles in regulating glycemic
variability and disease prognosis. However, the mechanisms by which gut microbiota and their
metabolites influence glycemic variability and subsequently affect disease outcomes in DKD
patients remain unclear. This study aims to address three key questions: ①Characterizing gut
microbiota and serum metabolites in DKD patients with varying disease stages and progression
levels, while precisely evaluating the prognostic value of gut microbial markers for adverse
outcomes. ②Investigating the relationship between gut microbiota, serum metabolites, and
glycemic variability in DKD patients. ③Determining whether gut microbiota and serum
metabolites interfere with glycemic variability to influence disease prognosis. To address these
questions, this observational prospective cohort study will enroll 270 patients with diabetic kidney
disease. By integrating continuous glucose monitoring (CGM), 16S rRNA high-throughput
sequencing, and serum non-targeted liquid chromatography-mass spectrometry (LC-MS), this
study evaluated glycemic variability, analyzed gut microbiota and serum metabolites, and
constructed and validated predictive models for poor prognosis in diabetic kidney disease (DKD)
patients. The findings revealed correlations between gut microbiota, serum metabolites, and
glycemic variability, elucidated their prognostic roles in DKD, and provided early identification
tools for high-risk DKD patients. We aim to offer novel perspectives and strategies for DKD
prognosis management, thereby improving clinical outcomes and quality of life for patients.
2. Preface
2.1 Research Background and Project Justification
2.1.1 Epidemiology and Characteristics of Diabetic Nephropathy
[1]
[2]
[3]
The latest Global Diabetes Atlas, released by the International Diabetes Federation
(IDF) in its 10th edition, reveals that approximately 537 million adults worldwide
suffered from diabetes mellitus(DM) in 2021. Projections indicate this number will reach
643 million by 2030 and rise to 783 million by 2045
. Diabetic Kidney Disease(DKD),
one of the most significant microvascular complications of diabetes, involves chronic
kidney damage caused by hyperglycemia. The condition affects the glomeruli, renal
tubules, interstitial tissue, and blood vessels, clinically manifesting as persistent
albuminuria and/or progressive decline in estimated glomerular filtration rate(eGFR),
which may eventually progress to end-stage renal disease(ESRD)
. Globally, DKD has
become the leading cause of ESRD
. The pathophysiological mechanisms of DKD
remain complex, with current understanding still requiring further investigation.

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The occurrence and development of the disease are related to metabolic disorders, hemodynamic changes and their interaction.
[4]
[5]
[6]
[7]
In recent years, an increasing number of studies have shown that DKD is also an inflammatory
reactive disease, in which inflammatory pathways play an important role and may be a key
factor leading to the progressive damage of organs. In a hyperglycemic environment,
inflammatory cell signaling pathways should be activated, mainly including P38 mitogen-
activated protein kinase (MAPK), c-Jun N-terminal kinase (JNK), etc., which in turn activate
the NF-κB-related signaling pathway. After activation, NF-κB stimulates the transcription of
chemokines, adhesion factors, and pro-inflammatory cytokines, leading to inflammatory cell
infiltration in renal tissue and promoting the occurrence and development of DKD
. In China,
DKD has become the primary cause of end-stage renal disease in middle-aged and elderly
patients
. Therefore, how to prioritize the prevention, identification, and management of
DKD is particularly important. Although renal biopsy is the gold standard for diagnosing
DKD, it has not been widely adopted due to its invasive and traumatic nature. Current
diagnostic criteria for diabetic kidney disease(DKD) primarily rely on clinical manifestations,
including the urine protein-to-creatinine ratio, estimated glomerular filtration rate(eGFR),
serum creatinine levels, renal ultrasound findings, and retinopathy presence
. However, these
indicators demonstrate limited diagnostic sensitivity and specificity for DKD, and fail to
effectively assess the severity of renal impairment
. Identifying novel biomarkers to
identify high-risk populations with progressive DKD for early intervention could improve
patient outcomes, establish dynamic and precise disease progression prediction models,
and ultimately enable personalized treatment strategies.
2.1.2 Variability of Blood Glucose and Diabetic Nephropathy
[8]
[9]
[10]
Glucose variability(GV) typically refers to fluctuations in blood glucose levels or
parameters related to glucose homeostasis measured over specific time intervals, including both
long-term and short-term glucose fluctuations
. Compared to persistent hyperglycemia, high
GV can lead to more severe oxidative stress
. While chronic hyperglycemia has traditionally
been considered the primary risk factor for diabetic complications, studies have also suggested
that frequent or wide-ranging glucose fluctuations may serve as independent triggers for such
complications. In addition to glycated hemoglobin(HbA1c), GV could be another independent
risk factor for diabetic complications
.
[11]
[12]
HbA1c has served as the gold standard for blood glucose control for nearly three decades.
However, both clinical practice and scientific research have gradually revealed certain limitations of
HbA1c. Studies indicate that approximately 20% of diabetic patients' HbA1c values may not
accurately reflect their average blood glucose levels
. Additionally, as a long-term glucose control
indicator, HbA1c fails to capture short-term fluctuations in blood sugar. Since HbA1c is directly
related to red blood cell lifespan, any factors affecting cell renewal can impact the accuracy of HbA1
c measurement, which is unrelated to blood glucose levels
. In diabetic kidney disease (DKD), as

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[13]
[14]
[15]
[16]
[17]
The decline in eGFR, shortened red blood cell lifespan, and potential decrease in glycated
hemoglobin(HbA1c) levels occur independently of blood glucose fluctuations
. Ford et al.
demonstrated through Deming regression analysis that the correlation between HbA1c and fasting
glucose intensifies with renal dysfunction. Hypoglycemia is prevalent among dialysis patients
with diabetic kidney disease(DKD)
, affecting 23.8% to 47.6% of cases, primarily due to
reduced gluconeogenesis in residual renal tissue, impaired insulin clearance, and glucose diffusion
into red blood cells during hemodialysis
. Conversely, hemodialysis and its complications
induce glucose metabolism disorders, alter insulin secretion and degradation patterns, and modify
drug metabolism, collectively contributing to dialysis-associated hyperglycemia
.
[18]
[19]
[20]
Reducing the incidence of hypoglycemia and minimizing blood glucose fluctuations are
crucial for improving the prognosis of diabetic kidney disease (DKD) patients. Timely and
accurate blood glucose monitoring is essential for maintaining good glycemic control.
However, self-monitoring of blood glucose (SMBG) faces limitations such as poor adherence
and constraints from environmental and time factors. Continuous glucose monitoring systems
(CGMS) provide diabetic patients with multiple reliable glucose assessment metrics. These
include time in range (TIR) and its derivatives time above range (TAR) and time below range
(TBR) for evaluating target-range compliance, daily glucose fluctuations measured by standard
deviation (SD), coefficient of variability (CV), and mean amplitude of glycemic excursions
(MAGE), as well as daily mean absolute differences (MODD) to monitor intra-day variability.
Additionally, the system assesses hyperglycemic index (HBGI) and hypoglycemic index
(LBGI) to identify risks of hyperglycemia and hypoglycemia
. In 2019, the Advanced
Technology and Therapy for Diabetes Committee (ATTD) emphasized in the "International
Expert Consensus on Clinical Application of Continuous Glucose Monitoring" that the core
monitoring indicator TIR combined with different blood glucose monitoring indicators can
serve as a baseline for evaluating glycemic control. In the 2020 China guidelines for the
prevention and treatment of type 2 diabetes, TIR was also included in the control targets
.
Beck et al.
used SMBG data from patients in the Diabetes Control and Complications Trial
(DCCT) to calculate TIR and found that TIR was independently associated with the
progression of diabetic retinopathy (DR) and proteinuria, suggesting that TIR could serve as an
effective end-point indicator in clinical trials. Lu et al. evaluated the relationship between HbA
1C estimated from CGM mean glucose and TIR in 2,893 patients with type 2 diabetes,
observing a good linear relationship between the two(r=-0.91, P<0.001). This parameter is
more reliable than HbA1c data and can be applied to patients with advanced chronic kidney
disease (CKD), including those undergoing dialysis. Currently, few studies have analyzed
the relationship between CGM-derived indicators and the risk of adverse renal outcomes
in patients with diabetic kidney disease (DKD).
2.1.3 Metabolism of Intestinal Bacteria and Diabetic Nephropathy

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[22]
[23]
[24]
[25]
[26]
[27]
[28]
In recent years, there has been growing attention to the role of gut microbiota composition
and function in regulating human metabolic processes. The intestinal microbial community
participates in key metabolic processes such as food digestion, energy absorption, and glucose
metabolism. Disruption of gut microbiota is associated with the occurrence and progression of
various common metabolic diseases, including type 1 diabetes mellitus (T1DM)
, type 2
diabetes mellitus (T2DM)
, diabetic kidney disease (DKD)
, and chronic kidney disease
(CKD)/end-stage renal disease (ESKD)
. In patients with DKD, the gut microbiota typically
shows increased pathogenic bacteria and reduced beneficial bacteria
. Compared with healthy
individuals and diabetic patients without kidney disease (DM), patients with diabetic kidney
disease (DKD) exhibit reduced Firmicutes and Prevotella_9, along with increased Proteobacteria
and Escherichia-Shigella. Notably, Prevotella_9 and Escherichia-Shigella serve as effective
biomarkers to distinguish diabetic nephropathy (DN) from DM
. A meta-analysis incorporating
15 studies involving 1,640 participants from DKD, DM, non-DKD, and healthy populations
revealed characteristic gut microbiota alterations in DKD patients, including increased Hungatella
and Escherichia species alongside decreased butyrate-producing bacteria
. These findings
demonstrate that DKD induces distinctive changes in the gut microbiota profile.
[29]
[30]
[31]
[32]
The imbalance of gut microbiota in diabetic kidney disease (DKD) is significantly associated
with clinical indicators such as lipid metabolism, glucose metabolism, and renal function. Gut
microbiota may serve as a predictive factor for the onset and progression of DKD, potentially
playing a crucial role in its pathogenesis. This includes abnormal gut microbiota metabolites,
damage to the intestinal mucosal barrier, increased inflammation, activation of the immune
system, and activation of the renin-angiotensin system (RAS)
. Gut microbiota-derived uremic
toxins can induce the secretion of cytokines and chemokines, increase white blood cell
recruitment, promote oxidative stress and inflammation, leading to glomerular and tubular
damage and fibrosis, thereby accelerating the development and progression of DKD
. Short-
chain fatty acids (SCFAs), the final metabolites produced by gut microbiota fermentation of
dietary polysaccharides, also play significant roles in multiple aspects of renal physiology. These
include regulating hormone secretion and metabolism, lowering blood pressure, participating in
immune modulation, reducing inflammation and fibrosis, protecting the intestinal epithelial
barrier, and inhibiting endotoxemia
. Bile acids are synthesized in the liver and metabolized by
gut microbiota into secondary bile acids. These secondary bile acids antagonize Farnesoid X
receptors (FXR) and Takeda G protein-coupled receptors 5 (TGR5) in the gut, which are involved
in diabetic kidney disease (DKD)
. Regarding the intestinal mucosal barrier and inflammation,
dysbiosis of gut microbiota leads to an increase in Gram-negative (G-) bacteria. Lipopolysaccha-
ride (LPS), the primary structural component of G-bacteria's outer membrane, can disrupt the
intestinal mucosal barrier and leak into the bloodstream. This results in elevated serum LPS
levels, promotes secretion of inflammatory cytokines, and causes persistent low-level chronic
inflammation along with metabolic endotoxemia. Inflammation plays a critical role in the
progression of DKD. Epidemiological and

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Mechanistic studies have provided strong evidence linking gut microbiota alterations to the
development of diabetic kidney disease (DKD). However, the specific gut microbiota and
metabolite profiles in patients with DKD at different stages and disease progression levels
remain to be elucidated. Notably, the gut microbiota and its metabolites hold significant
diagnostic potential for DKD, offering novel perspectives for early disease identification
through biomarker applications. Therefore, utilizing these microbial components as
biomarkers not only aids in early disease detection but also provides a theoretical
foundation for developing innovative therapeutic strategies and intervention approaches.
2.1.4 Hypothesis and Significance of the Research
To date, no studies have explored the impact of gut microbiota on the prognosis of diabetic
kidney disease (DKD) patients with varying degrees of glycemic variability. Based on existing
literature, we propose the following hypotheses: 1) Glycemic variability and specific gut
microbiota serum metabolites serve as predictors of adverse cardiovascular events in DKD
patients; 2) The gut microbiota and serum metabolites of DKD patients are associated with
glycemic variability (GV), which may influence patient outcomes through gut microbiota
metabolism. Our proposed methodology includes: 1) Assessing glycemic variability in DKD
patients via continuous glucose monitoring (CGM) to investigate its relationship with prognosis
and determine optimal glycemic variability control targets. 2) Utilizing 16S rRNA high-
throughput sequencing and serum non-targeted liquid chromatography-mass spectrometry (LC-
MS) to analyze the relationship between gut microbiota, metabolites, inflammatory factors, and
DKD patient prognosis. This study aims to optimize clinical management strategies, establish risk
prediction models, promote interdisciplinary research, and improve patient quality of life. The
findings will provide new scientific evidence and tools for precision treatment and prognosis
management in DKD patients, demonstrating significant scientific and clinical value.
2.2 Benefit / Risk Assessment of the Study
2.2.1. Benefits received
①Optimization of glycemic management: By evaluating glycemic variability with
continuous glucose monitoring (CGM), subjects can obtain more accurate glycemic control data,
which helps optimize glycemic management, reduce glycemic fluctuations, and reduce the risk of
diabetes complications.
②Personalized treatment: The findings may reveal the relationship between specific gut bacterial
metabolites and prognosis, and provide personalized treatment advice for subjects, such as diet adjustment,
probiotics or prebiotics.
③Early warning: If studies find that specific gut bacterial metabolic biomarkers are associated with
poor prognosis, subjects could be intervened earlier to prevent cardiovascular events and progression of
nephropathy.
2.2.2 Risk profile
2.2.2.1 Known Adverse Effects:

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①Wear continuous glucose monitoring (CGM): Wearing a CGM device
may cause mild stinging or itching of the skin.
②Blood sample collection: There may be mild pain during blood
collection, and there may be mild bruising or swelling at the site of blood collection.
2.2.2.2 Potential risks:
①Infection risk: Any invasive procedure (CGM wearing and blood collection) has a minimal
risk of infection. The research team will strictly follow aseptic procedures to minimize this risk.
②Privacy risk: The study requires the provision of personal health information, and there is a risk of
privacy leakage. The research team will take strict confidentiality measures to ensure the security of the personal
information of the subjects.
2.2.3
comprehensive eva-
luation
The benefits outweigh the risks: While there may be some minor discomfort and potential risks involved,
participants will receive more precise blood sugar management advice and personalized treatment plans
through their research participation, while contributing to public health. The research team will take all
necessary measures to ensure participant safety and privacy, guaranteeing that the benefits outweigh the risks.
III. RESEARCH OBJECTIVES
3.1 Main objective: To explore the relationship between glycemic variability, gut microbiota metabolites and prognosis of patients with DKD.
（1）Study subjects: patients with diabetic nephropathy.
（2 ）Exposed factors: glycemic variability (assessed by continuous glucose monitoring
CGM); gut microbiota, serum metabolites (assessed by 16S rRNA high-throughput sequencing
technology and serum non-targeted liquid chromatography-mass spectrometry LC-MS) and
inflammatory factors.
（3）Study endpoint:
①Primary end point: progression of DKD. Progression of DKD was defined as renal failure
(requiring transplantation or dialysis, or persistent eGFR <15 ml/min/1.73 m2) or a persistent decline of 30%
in eGFR from baseline.
②Secondary End Points: Ⅰ. Cardiogenic Death: Death caused by cardiac disease or
related complications. Ⅱ. Major Adverse Cardiovascular Events (MACE): Including myocar-
dial infarction, stroke, unstable angina pectoris, and other significant cardiovascular events. Ⅲ.
Kidney Transplantation: Successful cases of kidney transplantation surgery. Ⅳ. Hypoglycemic
Events: Documentation of the frequency and severity of hypoglycemia occurrences.
（4 ）Specific objectives: I. Evaluate the relationship between GV and prognosis in patients with DKD
and determine the optimal GV control target. II. Screen for biomarkers of gut microbiota, metabolites, and
inflammatory factors associated with DKD prognosis.
3.2 Secondary Purpose

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3.2.1 Construct a Predictive Model to Predict the Risk of Adverse Prognosis in Patients With DKD.
（1）Study subjects: patients with diabetic nephropathy.
（2 ）Exposure factors: glycemic variability index, clinical indicators,
intestinal flora, serum metabolites, inflammatory factors.
（3）study OUTCOMES:
①Primary end point: death from any cause, including death from all causes.
②Secondary end point: Unintentional major cardiovascular events (MACE): major
cardiovascular events including myocardial infarction, stroke, and unstable angina pectoris.
（4 ）Specific objectives: Construct a prediction model by incorporating
the above indicators to predict the prognosis of DKD patients.
3.2.2
Characteristics of Gut Microbiota and Serum Metabolites in
Different GV Groups and Their Correlation With Prognosis
（1）Study subjects: patients with diabetic nephropathy
（2）Exposure factors: different glycemic variability (GV) groups.
（3）Study outcome: characteristics of intestinal microbiota and serum metabolites.
（4）Specific objectives: Through 16S rRNA high-throughput sequencing technology and serum non-target liquid chromatography
-Mass Spectrometry (LC-MS) was used to investigate the characteristics of intestinal microflora
and serum metabolites in patients with different glycemic variability (GV) groups. The relationship
between the bacterial co-abundance groups (CAG) and the functional modules of serum metabolites
was further established to explore their correlations with glycemic variability and poor prognosis.
4. Research Content and Innovation
4.1. Research Content
This prospective observational cohort study was designed to investigate the
relationship between gut microbiota, serum metabolites, and glycemic variability
(GV) and their effect on the prognosis of patients with diabetic nephropathy (DKD).
4.1.1 Assessing the impact of glycemic variability on prognosis in patients with diabetic kidney disease
（1）in prospective cohort studies, patients with DKD were enrolled, and their blood glucose was
monitored with a continuous glucose monitor for 14 days, followed by longitudinal follow @-@ up to record
adverse prognostic events.
（2）The relationship between glycemic variability and prognosis was
analyzed, and the optimal TIR control target for DKD patients was discussed.

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4.1.2 Characteristics of gut microbiota and serum metabolites in patients with
DKD and their correlation with prognosis
（1）The fecal and blood samples were collected and analyzed by 16S rRNA
sequencing, non-targeted metabolites and inflammatory factors.
（2）The biomarkers associated with poor prognosis were screened.
4.1. 3. Gut Microbiota and Serum Metabolites in DKD Patients with Different
Blood Glucose Variability Groups and Their Correlation with Prognosis
（1）Patients with DKD were divided into high GV group and low GV group according to GV status,
and the intestinal microbiota characteristics and serum metabolites characteristics of patients in different GV
groups were analyzed.
（2）Based on the association between gut microbiota, serum metabolic biomarkers and
prognosis, the relationship between the bacterial co-abundance group (CAG) and the functional
modules of serum metabolites was further established to explore their correlation with glycemic
variability and poor prognosis.
4.1.4 Construction and validation of adverse event prediction model for DKD patients
（1）Based on the screening of the predictive factors related to adverse events in DKD
prognosis, including glycemic variability index, related clinical indicators, intestinal flora,
serum metabolites, inflammatory factors, etc., the predictive model was established by using
machine learning algorithm, and the performance and stability of the model were evaluated.
（2）independent cohorts of patients with DKD were added as validation
cohorts to evaluate the performance and stability of the predictive model.
4.2 Innovativeness
（1）Multidimensional assessment: On one hand, continuous glucose monitoring (CGM)
technology is employed to dynamically track glycemic variability in diabetic kidney disease (DKD)
patients, capturing detailed fluctuations in blood glucose levels. On the other hand, integrated analysis
combines 16S rRNA high-throughput sequencing with serum non-targeted LC-MS to comprehensively
evaluate gut microbiota composition and serum metabolite profiles, thereby elucidating their correlations
with glycemic variability and disease prognosis.
（2）DESIGN OF PROSPECTIVE COHORT STUDY: A prospective cohort study was
designed to enroll patients with different clinical stages of DKD and follow up for 3 years. Adverse
prognostic events during follow-up were recorded in detail to ensure the continuity and integrity of the
collected data, which provided a strong guarantee for the accuracy and reliability of the study results.
（3）multilevel data integration: the multilevel data, including glycemic variability, clinical indicators,
gut microbiota, serum metabolites and inflammatory factors, were integrated to construct a predictive model for
DKD prediction.

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The risk of adverse prognostic events provides a more comprehensive and accurate tool for clinical risk assessment.
5. Research Design and Technical Route
5.1 General Research Design
this study was a single @-@ center, observational study of patients with type 2 diabetes mellitus and chronic kidney disease, aged 18 to 56 years.
sex, prospective cohort study.
5.2 Technical Roadmap
Note: DKD: Diabetic Kidney Disease; CGM: Continuous Glucose Monitoring; TIR: Time in
Range; GV: Glucose Variability.
5.3 Study Site and Study Population
5.3.1 Study site: From July 1, 2025 to December 31,2025, the subjects were recruited in the inpatient
department of endocrinology department of the Eighth Affiliated Hospital of Sun Yat-sen University
according to the inclusion and exclusion criteria.
5.3.2 Study population: patients with type 2 diabetes and chronic kidney disease (DKD) (n=270)
1) Inclusion Criteria:

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①Age 18-65 years;
②The diagnosis of DKD meets the relevant criteria in the "China Diabetes
Nephropathy Prevention and Treatment Guidelines (2021 Edition)";
③Type 2 diabetes mellitus, treated with insulin analogues or oral
antidiabetic drugs, diagnosed according to the 1999 WHO criteria;
④no changes in the antidiabetic regimen were made for at least 3 months before enrollment.
⑤informed consent was obtained voluntarily.
2) Exclusion criteria:
①The patients with diabetes mellitus complicated with ketoacidosis, primary glomerul-
onephritis, nephrotic syndrome, lupus nephritis and other primary or secondary kidney diseases.
②The body was in a state of stress such as infection and trauma.
③women who are pregnant, breastfeeding, or planning to become pregnant next month;
④severe skin lesions, such as extensive eczema, extensive scarring,
extensive tattoos, papular dermatitis, severe edema and psoriasis;
⑤Sensory surface of the sensor is scarred due to severe skin burns, scalds,
sunburns, trauma, ulcers, surgery, etc.
⑥coagulation dysfunction, anemia or abnormal hematocrit;
⑦Patients with chronic gastrointestinal diseases or a history of biliary or gastrointestinal surgery were excluded from the study.
⑧consumed probiotics, prebiotics, or received antibiotic treatment for more than 3 days
in the past 3 months; consumed yogurt or yogurt-containing foods or beverages in the past week.
3) Exit criteria:
researcher decided withdrawal
①those who had adverse reactions or serious adverse events or other complications and special
physiological changes, and who should be stopped from the study and taken emergency treatment measures
such as medication, hospitalization or surgery according to the judgment of the investigator;
②Women became pregnant during the study;
③Serious violation of the study protocol, such as enrollment in the study despite
not meeting the inclusion criteria. Participants may withdraw voluntarily.
①provisional withdrawal of informed consent;
②Participants cannot tolerate certain tests;

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③unforeseen events occurred during the study that made it impossible for the participant to continue the study.
5.4 Methods for Determining the Required Sample Size for Research
This exploratory study investigates the impact of gut microbiota and related metabolites on
prognosis in diabetic kidney disease (DKD) patients. To achieve this, patients will be divided
into two groups based on adverse prognosis outcomes, with subsequent comparisons of gut
microbiota and metabolite differences between groups. To minimize microbial variations
caused by individual differences, each group requires at least 30 participants, meaning there
must be at least 30 patients with adverse prognosis events. Drawing from existing research, a
2023 study published in *Diabetes Care* [Diabetes Care.2023;46(4):868-873.] included 975
DKD patients with an average follow-up of 4.7 years, reporting 156 major outcomes of DKD
progression. Consequently, the total sample size was calculated as 975/156*30=187.5.
Considering potential dropouts and incomplete responses, we typically increase the sample size
by 10%, ultimately enrolling 210 patients as the modeling cohort. Additionally, 60 DKD
patients were included as a validation cohort. During actual recruitment, sample size
adjustments may be made based on factors such as recruitment speed and data quality. Regular
assessments of sample adequacy should also be conducted to ensure statistical power.
5.5 Investigation Content (CRF Form)
（1）Collect Baseline Information
1）The data collected included demographic information such as age, sex, and
education level; personal history such as smoking and drinking; medical history such as the
presence of other diseases such as hypertension; medication history such as antidiabetic and
antihypertensive drugs; and gastrointestinal status and dietary habits.
2）Physical examination: General condition, height, weight, blood pressure (BP) including systolic
(SBP) and diastolic (DBP), waist circumference (WC), hip circumference (HC), waist-to-hip ratio (WHR),
etc.
3）The test items included complete blood count, liver and kidney function,
HbA1c, TC, TG, HDL-c, LDL-c, FPG, fasting insulin (FIns), and serum uric acid
(SUA).
（2）Wear a Continuous Glucose Monitor and Collect Samples
All enrolled subjects were required to wear a continuous glucose monitor (CGM) for 14 days during
their first 24-hour hospital stay. Endocrinologists provided personalized dietary guidance based on blood
glucose levels, with standardized glucose-lowering regimens. Significant deviations from baseline glucose
levels warranted necessary dosage adjustments. Blood glucose variability metrics were collected after 14
days. Additionally, stool and blood samples from all participants were collected and preserved for
subsequent analysis.
（3）Follow-up in the Queue:

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1）follow-up time: 3 years after discharge.
2）Follow-up methods: outpatient follow-up, telephone follow-up and review of relevant medical records.
3）Outcome measures:
①Primary end point: progression of DKD. Progression of DKD was defined as renal failure (requiring
transplantation or dialysis, or persistent eGFR <15 ml/min/1.73 m2) or a persistent decline of 30% in eGFR from
baseline.
②Secondary End Points: I. Cardiogenic Death: Death caused by cardiac disease or
related complications. II. Major Adverse Cardiovascular Events (MACE): Including major
cardiovascular events such as myocardial infarction, stroke, and unstable angina. III.
Kidney Transplantation: Successful cases of kidney transplantation surgery. IV. Hypogl-
ycemic Events: Record the frequency and severity of hypoglycemia occurrences.
（4）16S RNA Sequencing Analysis and Non-Target Metabolic Analysis
We performed 16S rRNA sequencing, non-target metabolic analysis, and inflammatory factor
detection on collected fecal and blood samples to compare differences in gut microbiota,
metabolites, and inflammatory factors across different prognostic groups, and to identify prognostic
markers. We also compared the differences in gut microbiota, metabolites, and inflammatory
factors among patients with high glycemic variability, intermediate glycemic variability, and low
glycemic variability. Furthermore, we identified gut microbiota markers and serum metabolic
markers associated with high glycemic variability.
（5）Construction and Evaluation of Forecasting Model
First, feature screening was performed using LASSO regression to select variables from five
aspects: demographic characteristics, baseline scales, gut microbiota and non-target metabolites,
and peripheral blood inflammatory biomarkers. Nine machine learning algorithms were
employed to construct classification models, with performance evaluated through resampling
training/verification mechanisms. The optimal model was selected via 10-fold cross-validation
and performance metrics. Model performance and applicability were assessed using AUROC
curves, calibration curves, Brier scores, and decision analysis plots. During training, validation,
and testing phases of the optimal model, five-fold cross-validation and learning curves were used
to evaluate model fit and stability. Finally, SHAP package was utilized to generate SHAP
explanation plots to determine the importance and contribution of individual features.
5.6 Experimental Scheme
Step
Period
Filter period
Period of wearing CGM
Follow-up period
Study follow-up
Visit 0
Visit 1
Visit 2
Visit 3
Visit 4
Visit 5
Visit 6
Study date (days)
1 day before
enrollment
1 day after
enrollment
7 days after
enrollment
14
days
after
en-
rollment
Within 1
year after
enrollment
Within 2
years after
enrollment
Within 3
years after
enrollment

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1
Selection and exclusion crit-
eria
✔
2
Sign the informed consent form.
✔
3
Adorn CGM
✔
4
cl-
inical
data
Basic information of the subjects
✔
Clinical diagnosis
✔
Personal history
✔
History of past
illness
✔
Check-up
✔
5 clinical
examina-
tion
Inspe-
ction
Routine blood
test
✔
Liver
func-
tion
✔
Renal func-
tion
✔
HbA1c
✔
Blood fat
✔
Fasting insulin (FIns)
✔
Blood
su-
gar
✔
Thyroid function
✔
Coagulation func-
tion
✔
6
Medication during
CGM monitoring
✔
7
Fecal collection
✔
8
Blood collection
✔
9
Medications taken after discharge
✔

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10
Download the CGM report
✔
11
Fecal microbiota
sequencing
✔
12
Metabolite assay in serum
✔
13
Progression
events
of
DKD during follow-up
✔
✔
✔
14
Assessment of MACE
events during follow-up
✔
✔
✔
15
Death events dur-
ing follow-up
✔
✔
✔
✔
✔
✔
16
Adverse event monitoring
✔
✔
✔
17
Deviation from plan record
✔
✔
✔
✔
✔
✔
18
Fill in CRF
✔
✔
✔
✔
✔
✔
5.7 Collection, Storage, Use, and Destruction of Research Data and / or Biological Samples
5.7.1 STUDY DATA
Data Collection: This study will collect participants' age, gender, education level, medical
history, medication history, and physical measurements including height, weight, blood pressure
(BP) (systolic [SBP] and diastolic [DBP]), waist circumference (WC), hip circumference (HC), and
waist-to-hip ratio (WHR) through face-to-face interviews, physical examinations, and continuous
glucose monitoring (CGM) tests. The CGM will also collect real-time blood glucose data.
Storage: The above information is stored in the medical information platform of our hospital.
Use: All data collected in this study are used for clinical research data analysis
and will not be used for commercial purposes.
Destruction: The study will be destroyed in accordance with the regulations 5 years after the study is completed.
5.7.2 Biological Specimen
DATA COLLECTION: Blood and stool samples were collected from the participants
and analyzed for complete blood count, liver function, kidney function, glycated hemoglob-
in, blood lipids, fasting insulin, thyroid function, coagulation function, and gut microbiota.
Storage: All the specimens were stored in the biological sample bank of our hospital.
Use: All data collected in this study are used for clinical research data analysis and will not be used for commercial purposes

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road 。
Destruction: The study will be destroyed in accordance with the regulations 5 years after the study is completed.
5.8 Data Management and Statistical Analysis Plan:
5.8.1 Data Management
5.8.1.1 Data Collection Methods and Procedures:
（1）Data collection tools: Electronic Case Report Form (CRF)
（2）The baseline data were collected during the hospitalization of the
patients, and the follow-up data were collected at 1, 2 and 3 years.
（3）Collection: Researcher
（4）Data quality control: Training before data collection, focusing on
instructions and quality monitoring
5.8.1.2 Data Reception Method and Process:
（1）Data reception method: paper or spreadsheet
（2）Reception time and frequency: Send in real time to the general manager for summary
（3）Recipient: Researchers
（4）Data review: Each CRF must be reviewed and approved by the responsible person before consolidation
5.8.1.3 Data Entry Method and Process:
（1）Input tool: Professional data entry software
（2）Entry person: Researcher
（3）Entry time and frequency: Enter immediately after completing each CRF
（4）Data quality check: Quality control is performed after data entry to ensure accuracy and reasonableness
5.8.1.4 Data Management Process:
（1）data collection: researchers collected clinical data, stool, and serum samples according to standard protocols
（2）Data reception: After each CRF form is reviewed and approved, it is stored in the main file repository.
（3）Data entry: Researchers enter data according to the standard
（4）Data cleaning: After data entry, perform quality control and review to
correct and remove non-compliant or duplicate data
（5 ）Data storage: Data is stored in password-protected electronic storage
devices and managed with specialized database software
（6）Data backup: Back up data regularly

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（7）Data access: Set permission levels to control data access and ensure security
（8）Security: Storing data in public areas is prohibited to ensure data confidentiality
5.8.2 Statistical Analysis
Descriptive statistical analysis was performed on the collected data. Continuous variables
were presented as mean ± standard deviation (or median and interquartile range), while
categorical variables were expressed as frequency and percentage. For intergroup comparisons
of continuous variables, independent samples t-test or Mann-Whitney U-test was selected
based on data normality. In cases involving multiple group comparisons, one-way ANOVA or
Kruskal-Wallis test was employed. Categorical variable comparisons utilized chi-square test or
Fisher's exact test to assess significant differences between groups. To evaluate relationships
among multiple variables, linear regression was applied to continuous variables, logistic
regression to categorical variables, and Cox proportional hazards regression to time-to-event
data. These analyses adjusted for potential confounders such as age, sex, disease duration, and
baseline glucose levels to ensure result accuracy and reliability. Confounding factors were
included in regression analyses to minimize their impact. Additionally, propensity score
matching was used to balance baseline characteristics between groups, ensuring comparability
and enhancing the internal validity of the study results.
After adjusting for various covariates, we examined the association between adverse prognosis,
hyperglycemic variability, and the gut microbiota α-diversity index. The relationship between
hyperglycemic variability, adverse prognosis, and β-diversity differences was tested using genus-
level Bray-Curtis distance-based permutation ANOVA (PERMANOVA). Using MaAsLin, we
identified gut microbiota communities potentially associated with adverse prognosis and hyperglyce-
mic variability by comparing hyperglycemic variability groups with hypoglycemic variability groups,
and prognostic favorable groups with unfavorable groups. OPLS-DA was employed to identify target
metabolites potentially related to hyperglycemic variability and adverse prognosis, with adjustments
made through linear regression. Mediation analysis was conducted to evaluate whether gut microbiota
metabolism might mediate the association between hyperglycemic variability and adverse prognosis.
Sensitivity analysis was performed to test the robustness of the mediation effect.
Based on baseline data, gut microbiota, serum metabolites, and inflammatory factors, predictive
models will be constructed using machine learning algorithms (e.g., random forests, support vector
machines, neural networks). Feature selection methods (e.g., LASSO regression, recursive feature
elimination) will be employed to identify the most prognostic features, thereby enhancing model
accuracy and interpretability. Internal stability of the models will be assessed through 10-fold cross-
validation, while their generalization ability will be validated using independent validation cohorts.
VI. Ethical Review and Informed Consent
6.1 Ethical Committee Review
This protocol, the written informed consent form, and all materials directly pertaining to the participants must be submitted to the ethics committee.

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The study may commence only after obtaining written approval from the ethics committee.
Investigators must submit an annual research report to the ethics committee at least once per
year. When the study is suspended or completed, investigators must notify the ethics
committee in writing. All changes during the study (including revisions to the protocol and/or
informed consent forms) must be promptly reported to the ethics committee. No such changes
may be implemented without prior ethics committee approval, except for those aimed at
eliminating obvious and direct risks to participants. In such cases, the ethics committee will be
notified.
6.2 Patient Information and Informed Consent
Researchers must provide participants or their legal representatives with an easily understan-
dable, ethics committee-approved informed consent form, and allow sufficient time for participants
or their legal representatives to consider the study. Participants shall not be enrolled until they have
obtained a signed written informed consent. Throughout the study period, all updated versions of
the informed consent form and written information will be provided to participants. The informed
consent form must be retained as an important document for clinical research review.
7. Quality Management Plan
7.1 Measures to Improve the Consistency of Observations
（1）The participating clinical trial institutions are accredited medical device
clinical trial facilities, with facilities and conditions meeting the requirements for safe
and effective clinical trials. The research department's staffing and equipment meet the
trial requirements, and the participating researchers possess the professional
knowledge and experience required for the trial medical devices.
（2）The variable factors should be strictly controlled in outpatient cases.
（3）Through the training before clinical trial, the researchers have a full understanding
of the clinical trial program and the specific connotation of each index, so as to improve the
internal observation consistency and observer consistency of the clinical research data collector,
and ensure the reliability of the clinical research conclusion.
（4）The description of subjective symptoms should be objective and not
suggestive or inducing; the objective indicators should be checked at the specified
time and by the specified method.
（5）Data that deviate significantly from the acceptable range must be
verified and the researcher must provide a necessary explanation.
（6）The evaluation of the questionnaire should be made by at least one attending physician and one
physician independently. The person who filled in the clinical case report form should have the title of physician or
above.

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（7）Each clinical research unit should designate a person to check the
progress of the clinical trial regularly and verify the data and records carefully.
7.2 Measures to ensure subject compliance should strictly control the variable
factors of the subject from the following aspects:
（1）Doctors participating in clinical trials are fixed;
（2）The researcher should explain the process of the study and the obligations of the
participants to the participants patiently, so that the participants can cooperate with the study.
（3）The reasons for not meeting the diagnostic criteria should be recorded in detail.
7.3 Researchers Should Follow the Requirements for Case Report Form Completion
All observations and findings in the clinical trial should be verified to ensure the
reliability of the data and that the conclusions are derived from the original data.
Appropriate data management measures should be implemented during both the
clinical trial and data processing phases.
8. Confidentiality Measures
We ensure the confidentiality of participants 'identities and have established strict
information security protocols to protect their privacy. (1) During data collection,
personal identification information will be processed to safeguard participant privacy:
Each participant's name will be assigned a unique identification number. Personal data
and case records will be entered and stored by designated sample management
personnel. Users will only see the identification number, with no access to personal
names or other identifying details. (2) Participant information will be strictly
confidential unless required by law. Sample data will be electronically stored in secure
systems with password protection, accessible only to authorized personnel. Medical
records will be kept at the hospital for researchers' review only. Government agencies,
hospital ethics committees, and other relevant researchers may access participant data as
required. (3) Research outcomes: Results may be published in medical journals in
statistical analysis format, excluding any identifiable participant information and
without commercial implications.
9. Expected Progress and Completion Date of the Research Project
Total duration of the pro-
ject implementation
4 years

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Annual Plan Arran-
gements
Schedule
Key contents and outcomes of the stage
objectives
July 2025-May 2026
Main content of the stage goal:
1
Patient recruitment and enrollment
(2025.7-2025.12): Eligible subjects were recruited accord-
ing to the inclusion and exclusion criteria in the protocol
and enrolled.
2
Glucose monitoring and sample collection
(2025.7-2025.12): CGM monitoring was performed for 14
days in enrolled patients, and stool and blood samples were
collected. Ensure that the collection, preservation, and trans-
portation of samples met relevant standards and requirements
to provide high-quality samples for subsequent analysis.
3
Preliminary data analysis (2026.1-2026.5): The
collected CGM data were preliminarily analyzed, patients were
divided into different groups according to blood glucose
variability, and preliminary data analysis report was completed.
4
Follow-up Initiation (2026.1-2026.5): A follow-up
protocol and team were established to conduct a 3-year observat-
ion period for participants. Regular collection of clinical data—
including diabetic kidney disease (DKD) progression, major
adverse cardiac events (MACE), quality of life assessments, and
complication occurrences—provided essential follow-up data for
subsequent predictive model development. The follow-up program
was successfully initiated and implemented.
5
Additional patient recruitment
(2026.1-2026.5): Start additional recruitment of patients
in a validation cohort for validation of the prediction
model.
Achievement
1
The recruitment of patients was completed, and the enrollment rate reached the target.
2
The collection rate of samples was 100%.
3
The preliminary data analysis report was completed.
4
The follow-up program was initiated and proceeded smoothly.
5
additional patients were recruited and the
enrollment rate was as planned.
June 2026-December
2027
Main content of the stage goal:
1
Sequencing and Metabolic Analysis: 16S rRNA high-
throughput sequencing was performed on collected fecal samples,
while serum non-targeted LC-MS analysis was conducted on
blood samples to obtain data on gut microbiota and serum
metabolites. Bioinformatics methods were employed to analyze
sequencing data, identifying the composition and functional
characteristics of gut microbiota. Statistical analysis of metabolite
data was performed to screen differential metabolites associated
with glycemic variability. The final reports were compiled based
on both sequencing and metabolic analysis results.
2
Follow-up data collection: Continue to conduct
patient follow-up to ensure the completeness and accuracy
of the data. Organize and clean the collected follow-up data
to establish a database, providing high-quality data support
for subsequent predictive model construction.

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The data integrity rate was over 95%.
3
Preliminary data analysis: The collected CGM
data were preliminarily analyzed, patients were divided into
different groups according to blood glucose variability, and
preliminary data analysis report was completed.
4
Additional patient follow-up: The additional recru-
ited patients were followed up for 3 years, and the clinical
data of patients, including disease progression, occurrence of
MACE, quality of life assessment, and occurrence of
complications, were collected to provide data support for the
validation of the prediction model. The complete rate of
follow-up data was more than 95%.
Achievement
1
The 16S rRNA sequencing and non-target
metabolic analysis were completed.
2
The complete rate of follow-up data was over 95%.
3
The preliminary data analysis report was completed.
4
The complete rate of follow-up data in the verification queue was over
95%.
January 2028-December
2028
Main content of the stage goal:
1
Follow-up Data Organization and Analysis:
Conduct comprehensive final organization and analysis of
collected follow-up data to ensure data integrity and
accuracy. Perform statistical analysis to evaluate patient
outcomes, including disease progression, major adverse
cardiac events (MACE), quality of life assessment, and
complication occurrence. Complete the follow-up data
analysis report.
2
Association Analysis: By integrating glycemic variability
grouping results, this study investigates the relationship between gut
microbiota characteristics and serum metabolite profiles with clinical
outcomes, including disease progression, major adverse cardiovasc-
ular events (MACE), quality of life, short-term complications, and
overall prognosis. Through correlation analysis and multiple regres-
sion analysis, we aim to uncover underlying biological mechanisms
and predictive biomarkers, ultimately producing a comprehensive
association analysis report.
3
Prediction Model Development: By integrating multi-
dimensional data including baseline information, gut microbiota
profiles, serum metabolite profiles, and inflammatory factors, we
constructed predictive models using machine learning algorithms
such as random forests, support vector machines, and neural
networks. Through cross-validation and other optimization met-
hods, we enhanced the model's predictive performance and
stability, completing the initial development of the prediction
model.
4
Model evaluation and validation: Appropriate evalu-
ation metrics were used to assess the constructed prediction
model, verifying its generalization capability and predictive
performance across different datasets. Based on the evaluation
results, the model was further optimized and adjusted to ensure
reliability and practicality, and the model evaluation report was
completed.
Achievement ：
1
The follow @-@ up data analysis report was complet-
ed.
2
Complete the association analysis report.
3
The preliminary construction of the prediction model was completed.
4
The model evaluation report was completed.

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10. References:
[1] Magliano DJ, Boyko EJ; IDF Diabetes Atlas 10th edition scientific committee . IDF
DIABETES ATLAS. 10th ed. Brussels: International Diabetes Federation; 2021.
[2] Tao S, Li L, Li L, Tao S, Li L, Li L, et al. Understanding the gut-kidney axis among biopsy-
proven diabetic nephropathy, type 2 diabetes mellitus and healthy controls: an analysis of the gut
microbiota composition. Acta Diabetol. 2019;56(5):581-592.
[3] Ali MK, Pearson-Stuttard J, Selvin E, Gregg EW. Interpreting global trends in type 2 diabetes
complications and mortality. Diabetologia. 2022;65(1):3-13.
[4] Dai T, Natarajan R, Nast CC, Dai T, Natarajan R, Nast CC, et al. Glucose and diabetes: effects on podocyte
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Nephropathy: Diagnosis, Prevention and Treatment". J Clin Med. 2020;9(3):813. Published 2020
January 2029-July 2029
Main content of the stage goal:
1
Clinical Validation of the Model: Validate the predictive
model using additional recruited patients to evaluate its practical
value and feasibility in real-world clinical settings. Based on
validation results, make necessary adjustments and improvements to
the model. This will facilitate its clinical application and promote its
adoption, providing scientific evidence for prognostic assessment
and personalized treatment of diabetic kidney disease (DKD)
patients. The process will culminate in the completion of a clinical
validation report.
2
Complete the project: write the paper
and complete the project report. Results:
1
The clinical validation report was completed.
2
The final report was completed, and the
relevant academic papers were written and published.

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