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Certain people with Type 2 diabetes can now donate a kidney. A Mayo Clinic nephrologist explains – Post Bulletin

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ROCHESTER — A change in federal guidelines for living organ donation opens the door for people with well-controlled Type 2 diabetes to become kidney donors.

“I think this is a very significant shift in the eligibility criteria for living kidney donation in the U.S.,” said Dr. Naim Issa, a transplant nephrologist at Mayo Clinic in Rochester, the county’s second-largest living organ donor center. “In Europe, actually, diabetes was not completely (a) contraindication to donate a kidney.”

Before the Organ Procurement and Transplantation Network updated its policies in 2022, a potential living kidney donor would be disqualified if they were diagnosed with either Type 1 or Type 2 diabetes.

“Diabetes, especially if it’s poorly controlled, can lead to complications affecting our vital organs, especially the kidneys, the eyes, the heart,” Issa said. “And diabetes, in fact, is the leading cause of kidney disease in the U.S.”

But now, people with Type 2 diabetes could become kidney donors if they meet certain critera. (Type 1 diabetes is still excluded.)

Through Mayo Clinic, a potential kidney donor would be eligible if they don’t use insulin, are not overweight, don’t have a family history of kidney disease and go through a health assessment. Right now, those donors would also need to be at least 60 years old, Issa said.

“If you’re young with Type 2 diabetes … (you’ll) have another 20, 30 years to live, and we don’t know what will happen to their kidney function and to their vital organs,” Issa said.

Additionally, potential donors between the ages of 60 and 64 would need to not be on any medications for their diabetes. But, at age 65 and older, they can be taking up to two oral medications and still be eligible, according to the Mayo Clinic guidelines.

While Issa said he only expects a “handful” of living kidney donations per year from donors who meet the Type 2 diabetes critera, he said this type of donation can be helpful in certain situations, such as when a person wants to donate a kidney to their spouse.

naim-issa-14325870.png

Dr. Naim Issa, a transplant nephrologist at Mayo Clinic in Rochester.

Contributed / Mayo Clinic

“If a wife needs a kidney, they don’t have any potential donors, instead of staying on the waiting list for five to seven years,” Issa said, “Let’s say the husband is diabetic, but very well-controlled. (He’s) lean, maybe takes one medication, older than 65 and the diabetes did not affect any of the vital organs, the heart, the kidneys or the eyes.”

The expanded critera for living kidney donation comes at a time when, Issa said, the need for kidney transplants is increasing and the wait time for kidneys from deceased donors can last years for some patients.

“People are getting older, more diabetes and more obesity causing more and more kidney disease in this country — we have more than 90,000 people waiting for a kidney transplant,” Issa said. “This is mainly to address the increasing demand fo rkidneys and provide some people with a better chance for successful transplant and, of course, improve quality of life.”

Deceased donors with diabetes have been able to donate kidneys, Issa said, if their organs weren’t substantially harmed by their diabetes.





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Analysis of Hematological Parameters in Type 1 and Type 2 Diabetes Patients

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The following is a summary of “Changes in selected hematological parameters in patients with type 1 and type 2 diabetes: a systematic review and meta-analysis,” published in the February 2024 issue of Hematology by Bambo et al.


Researchers conducted a retrospective study to uncover pooled mean differences in white and red blood cell parameters among diabetic patients, aiming to shed light on potential hematological imbalances in type 1 and type 2 diabetes mellitus.

Using appropriate entry terms, they extensively searched articles in various bibliographic databases, including PubMed, Cochrane Library, Scopus, Web of Science, PsycINFO, Embase, online archives, and university repositories. Relevant studies were identified based on eligibility criteria. Data, including author details, study characteristics, diabetes type, sample size, and hematological parameter means with SD, were extracted in Excel and analyzed in Stata 11. Pooled standardized mean difference (SMD) was determined with a random effects model, assessing heterogeneity using Higgins’ I2 statistics. Egger’s test and funnel plot analysis evaluated bias. A sensitivity analysis assessed the impact of small studies.

The results showed 39,222 articles following methodology screening, 22 articles with 14,041 participants (6,146 T2DM, 416 T1DM patients, and 7,479 HCs). Pooled SMD in TLC were 0.66, 109 for T2DM and -0.21 for T1DM. Absolute differential WBC counts in T2DM showed differences of 0.84 (neutrophils), -1.59 (eosinophils), 3.20 (basophils), 0.36 (lymphocytes), and 0.26 (monocytes). Relative differential counts in T2DM were neutrophils (1.31%), eosinophils (-0.99%), basophils (0.34%), lymphocytes (-0.19%), and monocytes (-0.64%). In T1DM, SMD of WBC  109 parameters were neutrophils (-0.10), lymphocytes (-0.69), monocytes (0.19), and basophils (-0.32). Pooled SMD in RBC parameters for T2DM were: RBC (-0.57, 106/μL), Hb (-0.73 g/dL), and HCT (-1.22%). In T1DM, RBC, Hb, and HCT were -1.23 (106/μL), -0.80 g/dL, and -0.29%, respectively.

They concluded that T2DM showed elevated white & specific cell types, while T1DM had decreased white & red blood cell parameters, highlighting diabetes’ impact on blood composition.

Source: frontiersin.org/articles/10.3389/fmed.2024.1294290/full



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Neutrophil Lymphocyte Ratio as a Predictor of Stroke Severity in Type 2 Diabetes Mellitus: A Single-Center Study

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Comparative Study of Semaglutide and Dapagliflozin in Type 2 Diabetes Management

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Exploring the Efficacy of Semaglutide and Dapagliflozin in Type 2 Diabetes Treatment: A Leap Towards Precision Medicine


A Comparative Study of Semaglutide and Dapagliflozin

Recent advances in the field of diabetes treatment have led to a trial comparing the efficacy of two antidiabetic drugs: semaglutide and dapagliflozin. The randomized open-label trial, published in Nature, aimed to understand their effects on patients with type 2 diabetes, particularly those with severe insulin-deficient diabetes (SIDD) or severe insulin-resistant diabetes (SIRD). The study discovered that semaglutide induced a larger reduction in glycated haemoglobin (HbA1c) levels than dapagliflozin, especially in those with SIDD.

Identifying Treatment Response through Continuous Pathophysiological Variables

Interestingly, the study found no significant interaction between the drug assignment and the SIDD or SIRD subgroup. Instead, continuous pathophysiological variables such as baseline HbA1c and insulin secretion were more informative in predicting treatment response. These variables, along with body mass index, blood pressure, and insulin resistance measures, were useful in identifying patients likely to benefit most in terms of glycaemic control and cardiovascular risk factors by adding semagliflozin or dapagliflozin.

Combination Therapy: A New Approach in Type 2 Diabetes Management

Further research in Pharmacological Research evaluated the impact of combining dapagliflozin and oral semaglutide in type 2 diabetes patients. This combination therapy outperformed dapagliflozin alone by reducing glycated hemoglobin by 1.2% while improving body mass index, blood pressure, cholesterol, and glucose levels. The combination achieved 55% glycated hemoglobin near-normalization, suggesting it may induce type 2 diabetes pharmacological remission in over 50% of patients.

Implications for Clinical Practices

A related study on the combination therapy of dapagliflozin and semaglutide in PRECARE2 noted its superior efficacy in managing type 2 diabetes. The more significant reduction in HbA1c levels with the combination therapy suggests a promising approach to type 2 diabetes management, potentially changing clinical practices. This therapy offers an effective avenue for managing type 2 diabetes by significantly reducing blood sugar levels and improving other health indicators.

Cardiovascular Disease Prevention and Type 2 Diabetes Treatment

A population-based cohort study in JAMA Network Open investigated the outcomes of SGLT 2i and GLP 1RA therapy among patients with type 2 diabetes, varied by the presence or absence of NAFLD. The study found that both therapies were associated with a reduced risk of major adverse cardiovascular events in patients with type 2 diabetes, regardless of NAFLD status. Specifically, SGLT 2i therapy was associated with a reduced risk of hospitalization for heart failure, supporting current guidelines that recommend GLP 1RA as the first line of therapy for patients with type 2 diabetes and NAFLD.

Stepping Towards Precision Medicine in Diabetes

The findings of these trials highlight the potential for personalized treatment in diabetes, providing valuable insights for future clinical and scientific work in precision medicine. Continuous pathophysiological variables could be more informative in predicting treatment response than stratified subgroups, suggesting a need for a more nuanced approach in diabetes treatment. As our understanding of the disease deepens, we move closer to a future where each patient’s treatment can be tailored according to their unique physiology, bringing us one step closer to the reality of precision medicine in diabetes care.



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Cardiac Autonomic Neuropathy in Newly Diagnosed Patients With Type 2 Diabetes Mellitus

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Night owls have a higher risk of Type 2 diabetes, says study. How can night-shifters control blood sugar? | Health and Wellness News

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If you are at a desk job, move around or take a small walk every two hours during the shift. Don’t go near the vending machine and slot an exercise schedule in your functional cycle, says Dr Anoop Misra, Chairman, Fortis C-DOC Hospital for Diabetes and Allied Sciences

diabetes night owlsNight owls may need to pay more attention to their lifestyle because their chronotype may increase the risk for Type-2 diabetes (Source: Getty Images)

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Are you a night owl? Then you are likely to have a higher risk of developing Type-2 diabetes. A recently published study from the US shows that people with “evening chronotype” or those who prefer to be active in the evening, sleep and wake up late are 19 per cent more likely to develop diabetes, even after the researchers accounted for lifestyle factors. This means that unhealthy habits may explain away a large proportion of the risk but not all of it.

“Night owls may need to pay more attention to their lifestyle because their chronotype may increase the risk for Type-2 diabetes,” say the study’s corresponding author and associate epidemiologist from Brigham and Women’s Hospital, Boston, Tianyi Huang. Chronotype refers to a person’s preferred timing of sleeping and waking up, which is partly determined by genetics, and cannot be changed easily. The study, based on data from over 63,000 women nurses, found that those who prefer to stay up and work at night are more likely to consume alcohol in higher quantities, have low-quality diet, smoke and have less physical activity.

Why does the evening chronotype increase the risk of diabetes?

First, the evening chronotype is likely to develop more unhealthy lifestyle habits. “Those who go to bed late at night are much more likely to snack after dinner. When they wake up, they are unlikely to have time for exercise before heading out for work or through afternoons and evenings when they are at work,” says Dr Anoop Misra, Chairman, Fortis C-DOC Hospital for Diabetes and Allied Sciences. Those with evening chronotype are also much more likely to have an irregular sleep pattern that leads to increased glucose intolerance.

The chronotype can also impact hormones. As Dr Misra explains, “The secretion of melatonin depends on the light. Exposure to a lot of light during the night is likely to reduce melatonin secretion, which is known to regulate insulin secretion. The sleep-wake cycle also affects the cortisol levels in the body, which in turn leads to insulin resistance and obesity.”

How can I protect myself?

Dr Misra says even those with evening chronotype can reduce their risk of diabetes by following a good routine and remaining disciplined. “If a person makes time for a workout in the evening, if they do not snack at night and follow a proper routine within their functional cycle, they can have a reduced risk of diabetes.” Those with evening chronotype may also try to retrain themselves by waking up and going to bed 15 to 30 minutes earlier each day, experts say.

The US researchers found the increased risk associated with evening chronotype in nurses who worked day shifts, not those who worked overnight shifts. “When chronotype was not matched with work hours, we saw an increase in Type 2 diabetes risk. That was another very interesting finding, suggesting that more personalised work scheduling could be beneficial,” say researchers.

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What can I do if I work on a night shift?

If you cannot avoid a night shift, Dr Misra suggests some practical ways you can remain healthy. If you are at a desk job, you should move around or take a small walk every two hours during the shift. He advises that people consume healthy snacks at night and avoid going to vending machines. People must avoid colas that may be more readily available at night than healthier beverages. Most importantly, those working night shifts should also make time for regular exercise in their routine.

“The good thing about the current study is that it has a big sample size. However, the study is based on a single questionnaire. Multiple questionnaires over a period of time would have given more details on whether the participants stuck to a particular lifestyle,” says Dr Misra.

© IE Online Media Services Pvt Ltd

First published on: 14-09-2023 at 12:42 IST



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Depression a direct cause of type 2 diabetes. How can it be managed?

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A recent genetic study from UK scientists suggests that depression may be a direct cause of type 2 diabetes, which could support attempts to prevent the disease.

Over 500 million individuals worldwide have type 2 diabetes, which has been linked to depression through shared genetics and a causal association, according to The Guardian.

Following the study, suggestions have been made for depression to be added to the list of type 2 diabetes risk factors, along with other variables including obesity, inactivity, and a family history of the disease.

Both diseases have historically been associated with one another as people having type 2 diabetes are twice as likely to be diagnosed with depression than the general population. According to Dr Shaunak Ajinkya, Consultant, Psychiatrist, Kokilaben Dhirubhai Ambani Hospital, Mumbai, depression and type 2 diabetes have a bidirectional relationship, meaning they can influence each other and increase the risk of the other condition.

Although it was never made clear whether type 2 diabetes led to depression or vice versa, or if other factors, including obesity, were also at play. This research suggests that depression causes diabetes, rather than the other way around.

What is the relationship between depression and type 2 diabetes?

Depression can be a risk factor for type 2 diabetes because it can affect various aspects of a person’s life that are closely related to diabetes development.

Chronic stress and depression can lead to unhealthy behaviors like overeating, physical inactivity, and poor sleep, which can contribute to obesity and insulin resistance, both of which are a major risk factor for type 2 diabetes, explained Dr Sanjay Singh, General Physician, Cygnus Laxmi Hospital.

A person with a history of depression or a family history of type 2 diabetes are at higher risk. (Source: Freepik)

Additionally, depression can impact the body’s stress response system and lead to dysregulation of hormones involved in glucose metabolism, such as cortisol, insulin, and glucagon, as per Dr Ajinkya, who added that this dysregulation also contributes to the development of insulin resistance.

On the other hand, type 2 diabetes can also be a risk factor for developing depression. “The burden of managing a chronic illness, the stress associated with it, and the potential for complications can significantly affect a person’s mental health and increase the risk of depression,” Dr Ajinkya said.

What are the risk factors of developing type 2 diabetes in individuals with depression?

Assessing the risk can be done through a combination of factors, according to the experts, including:

Medical history

A person with a history of depression or a family history of type 2 diabetes are at higher risk.

Lifestyle factors

Assessing the person’s lifestyle habits, including diet, physical activity levels, and substance abuse, can help determine their risk.

Physical examination

Checking for signs of obesity or other metabolic abnormalities can provide an indication of diabetes risk.

Blood tests

Assessing fasting blood glucose levels and HbA1c (average blood glucose levels over the past few months) can help determine if a person has diabetes or is at risk of developing it.

Managing depression to reduce the risk of developing diabetes involves a multi-faceted approach. (Source: Getty Images/ Thinkstock)

If someone with depression is identified to be at risk of developing type 2 diabetes, interventions such as lifestyle modifications, regular physical activity, and mental health assessment and support may be recommended to reduce the risk and improve overall well-being.

Apart from depression, these experts say that several other risk factors can increase the likelihood of developing type 2 diabetes:

  • Family history of diabetes
  • Obesity or excess body weight, especially around the abdomen
  • Sedentary lifestyle
  • Poor diet high in sugar and unhealthy fats
  • High blood pressure
  • Age (risk increases with age)
  • Ethnicity (certain ethnic groups are at higher risk)

How to manage depression so that it doesn’t end up in the development of diabetes?

Managing depression to reduce the risk of developing diabetes involves a multi-faceted approach, as per the experts, such as:

Seek professional help and build a support system

Consult with a mental health expert or therapist to address and manage your depression. This may include therapy, such as cognitive-behavioral therapy (CBT), if necessary.

Seek support from friends, family, or support groups. Sharing your feelings and experiences with others who understand can help alleviate symptoms of depression.

Adopt a healthy lifestyle

Focus on regular exercise, a balanced diet, and weight management to improve insulin sensitivity and overall health. Alcohol and certain substances worsen symptoms of depression and increase the risk of developing diabetes, so it’s best to avoid their use.

Depression can be a risk factor for type 2 diabetes because it can affect various aspects of a person’s life that are closely related to diabetes development. (Source: Pixabay)

Get enough sleep

Prioritise a regular sleep schedule and aim for 7 to 9 hours of quality sleep every night. Poor sleep can worsen symptoms of depression and increase the risk of developing diabetes.

Stress management

Practice relaxation techniques such as mindfulness, yoga, or meditation to reduce stress levels. Find healthy ways to cope with stress like engaging in hobbies or activities which you enjoy.

Medication, if needed

In some cases, medication may be necessary to treat depression, and it’s essential to follow a healthcare provider’s guidance. If you are taking medication for depression, it’s important to take it as prescribed and attend regular follow-up appointments with your healthcare provider.

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Regular monitoring

If you’re at risk, maintain regular check-ups with your healthcare provider to monitor your blood sugar levels and overall health.

Remember that a collaborative approach involving mental health professionals, primary care physicians, and lifestyle modifications is crucial to manage both depression and reduce the risk of type 2 diabetes effectively.

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Continuous Glucose Monitoring vs. Capillary Blood Glucose in Hospitalized Type 2 Diabetes Patients

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Oral Manifestations of Type II Diabetes Mellitus and Comparison of Blood and Salivary Glucose Levels

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Prediction of Diabetic Kidney Disease in Newly Diagnosed Type 2 Diabet

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Introduction

The latest global diabetes mellitus (DM) map demonstrated by the International Diabetes Federation (IDF) showed that the incidence of DM patients (aged 20–79) would rise to 12.2% (783.2 million people) in 2045.1 China has the most DM currently, and the number is expected to exceed 174 million by 2045.1 Diabetic kidney disease (DKD), characterized by persistent microalbuminuria or decreased estimated glomerular filtration rate (eGFR), is one of the most significant microvascular complications of diabetes. DKD is now more common than primary glomerulonephritis and is the major cause of chronic kidney disease (CKD) in China, where it affects 40% of those with type 2 diabetes mellitus (T2DM) who are hospitalized.2–4

DKD is always asymptomatic until it develops to the advanced stage. Currently, the main methods for early diagnosis of DKD are urinary albumin creatinine ratio (UACR) and eGFR. However, on the one hand, these diagnostic methods have their limitations. UACR does not increase in the early stage (stages I and II) of DKD,5 meanwhile eGFR increases in the early stage.6 On the other hand, the increasing incidence of DKD is placing a burden on the healthcare system, making it essential to find an approach to screen DKD in the medical environment. Hence, early detection and treatment of DKD are critical.

Previous studies have demonstrated multiple clinically related factors of DKD, such as age, UACR, haemoglobin A1c (HbA1c) and high-density lipoprotein (HDL).7,8 Furthermore, related studies have shown that the development of DKD is strongly associated with insulin resistance (IR), which may accelerate the progression of DKD.9–11 Hyperinsulinemic euglycemic glucose clamp (HEGC), a significant method for evaluating IR, is expensive and difficult to operate.12 Additionally, the homeostasis model for insulin resistance (HOMA-IR) index is also a common tool to assess IR in clinical practice.13 However, serum insulin or C-peptide tests are not appropriate for all inpatients or outpatients owing to the impact of insulin treatment. Triglyceride-glucose index (TyG index), as well as triglyceride and fasting glucose products, demonstrates greater potential in evaluating IR.14,15 Previous clinical studies have demonstrated the performance of the TyG index in evaluating IR, and it was superior to the HOMA-IR index.10 In conclusion, these easily available clinical risk factors may be used to build an accurate prediction model of DKD development in individuals with newly diagnosed T2DM, facilitating DKD screening. In addition, we intended to use the least absolute shrinkage and selection operator (LASSO) regression to screen variables and construct a predictive model. LASSO regression, a compressive estimation method for selecting relevant features for various classes of data, helps deal with biased estimates with complex collinearity data.16 It can perform variable screening and complexity adjustment while fitting generalized linear models, and is widely applied in variable screening of various prediction models, including commonly used clinical variables and related omics data.17,18 LASSO regression has contributed to constructing some prediction models, such as tumors, metabolic diseases, kidney diseases, etc.19,20

In this study, we designed a long-term cohort trial in patients with newly diagnosed T2DM for more than 10 years, aiming to explore the risk factors related to DKD, building and validating the prediction model.

Materials and Methods

Study Population

This was a retrospective analysis on prospectively collected T2DM patients’ data from the Third Affiliated Hospital of Soochow University. We consecutively incorporated 863 newly diagnosed T2DM patients in our hospital from December 2010 to January 2014 for long-term follow-up.

Inclusion criteria: (1) meet the American Diabetes Association (ADA) classification criteria for T2DM and were newly diagnosed patients;14 (2) age > 18 years old.

Exclusion criteria: (1) a history of DKD or other kidney diseases; (2) UACR ≥ 30mg/g, eGFR < 60 mL/min/1.73m2 (CKD-EPI),21 or urine dipstick test ≥ 1 +; (3) presence of an acute infection; (4) presence of a malignancy or pregnancy; (5) incomplete clinical records.

The endpoint event was the occurrence of DKD,22 defined as (1) persistent albuminuria (UACR ≥ 30mg/g) over 3 months (2) eGFR < 60 mL/min/1.73 m2; (3) excluded other kidney diseases.

Participants were followed up until DKD was diagnosed for the first time. Otherwise, patients without DKD events were followed up until June 2022. Figure 1 shows the procession of subject inclusion.

Figure 1 Flow chart of participants.

Abbreviations: T2DM, type 2 diabetes mellitus; DKD, diabetic kidney disease.

Note: The flow chart shows the entire research process.

Data Collection

Baseline demographic data (sex and age) and related clinical data were from the electronic medical record system. The risk prediction model was trained using variables selected from medical record system and published literature,23,24 including age, sex, body mass index (BMI), history of smoking and hypertension, systolic blood pressure (SBP), fasting blood glucose (FBG), postprandial blood glucose (PBG), haemoglobin A1c (Hb1Ac), fasting C-peptide (FCP), white blood cells (WBC), hemoglobin (Hb), neutrophil to lymphocyte ratio (NLR), fibrinogen (FIB), D-dimer, alanine aminotransferase (ALT), aspartate aminotransferase (AST), serum albumin (ALB), bile cid (BA), bicarbonate, blood urea nitrogen (BUN), uric acid (UA), eGFR, total cholesterol (TC), triglyceride (TG), HDL, low density lipoprotein (LDL), apolipoprotein a1 (Apoa1), apolipoprotein b (Apob), UACR, TyG index, HOMA-IR, thyroid stimulating hormone (TSH), free triiodothyronine (FT3), free tetraiodothyronine (FT4), diagnosis of diabetic retinopathy (DR) and diabetic peripheral neuropathy (DPN), as well as antidiabetic and antihypertensive treatment. The superior indicator of the HOMA-IR and TyG index for predicting DKD was also determined by comparing the area under the curve (AUC).

Statistical Analysis

Statistical analyses were performed using SPSS software version 25.0, R software version 4.2.2 and GraphPad Prism software version 9.0.0. The median (interquartile range) [M (P25, P75)] was used to represent data with a non-normal distribution, whereas the mean ± standard deviation (SD) was used to express data with a quantitative normal distribution. The qualitative variable was selected as percentages (%). A random number table generated using R software was assigned to the training and validation set in a ratio of 7: 3. There was no statistical difference between the training and validation set for all variables, so the validation set can fully validate the prediction model based on the training set. We screened the variables and built the model based on the training set, and presented the model in a nomogram visualization. In addition, we utilized the validation set to internally validate the performance and clinical utility of the model constructed based on the training set. Spearman correlation was employed for correlation analysis. For further analysis, a nomogram was developed based on multivariate analysis. The logistic regression results were presented as odds ratio (OR), 95% confidence intervals (95% CI), and P values. To obtain the perfect prediction model, we used “glmnet” package for LASSO regression screening to to further filter the predictor variables derived from the traditional univariate logistic regression. The nomogram was drawn using the R package “regplot”, and receiver operating characteristic (ROC) analysis was performed to assess how well the nomogram predicted the risk of developing DKD. Use 1000 bootstrap replications for validation. Calibration curves were conducted using the “calibrate” R package. Draw a decision curve using the “rmda” R package. P < 0.05 was considered statistically significant.

Results

Baseline Characteristics

The final queue size was 521 persons after follow-up (Figure 1). The average follow-up time of the T2DM participants was 7.8 ± 1.8 years. There were 127 DKD events (24.4%) among the 521 individuals identified in our long-time follow-up cohort.

Table 1 presents the baseline demographic and medication of 521 T2DM patients, meanwhile, Table 2 presents the laboratory measurement indicators. The R package randomly divided the whole individuals into the training set and validation set in a ratio of 7: 3. 365 participants (70%) were randomly assigned to the training set and 156 participants (30%) to the validation set. In the training set, 90 patients (24.7%) progressed to DKD, while 37 patients (23.7%) progressed to DKD in the validation set. There was no significant difference in demographic and clinical characteristics between the training and the validation set (P > 0.05). However, variables of significance include DKD participants being on average older in age, having higher FCP, higher BMI, higher BA, higher TG, higher Apob, higher UACR, higher HOMA-IR, and TyG index, and having greater proportion with a history of smoking and hypertension, a higher incidence of DPN, and a higher utilization rate of some antidiabetic and antihypertensive drugs while having lower eGFR and HDL (Table 1 and Table 2). In addition, we also develop logistic and LASSO regression, aiming to identify the related factors with DKD.

Table 1 Demographic and Medication of Newly Diagnosed T2DM Patients

Table 2 Laboratory Measurement Indicators of Newly Diagnosed T2DM Patients

Comparison of TyG Index and HOMA-IR

To evaluate the performance of the HOMA-IR and TyG index in predicting DKD, we plotted AUC. HOMA-IR and TyG index are both tools to assess IR. We determined the AUCs for the TyG index and HOMA-IR in the training set, were 0.694 (95% CI 0.630 to 0.758) and 0.640 (95% CI 0.575 to 0.705), respectively (Figure 2). The findings were following those reported in other studies, which found that TyG index was more accurate than HOMA-IR at predicting the risk of IR and DM-related complications.25,26 In addition, multivariate analysis revealed that the HOMA-IR was no statistical significance after adjusting confounding variables (Table 3).

Table 3 Logistic Regression Analysis of DKD-Related Risk Factor

Figure 2 ROC curve of TyG index and HOMA-IR.

Abbreviations: ROC, receiver operating characteristic; AUC, area under the curve; TyG index, triglyceride-glucose index; HOMA-IR, homeostasis model assessment for insulin resistance.

Notes: ROC curve of TyG index and HOMA-IR drawn according to the training set. We calculated the AUC of the TyG index and HOMA-IR, which were 0.694 (95% CI 0.630 to 0.758) and 0.640 (95% CI 0.575 to 0.705), respectively.

LASSO Regression Analysis

To identify the independent significant factors, we used logistic regression and LASSO regression for analysis. We conducted a correlation analysis and univariate logistic regression (Figure 3 and Table 3). Correlation analysis found a strong collinearity correlation among some variables. LASSO regression, a greedy algorithm, can be used to select the most significant variables to avoid overfitting, then establish a more accurate linear regression model.27 According to previous reports and univariate analysis, we selected 37 variables related to DKD (age, gender, BMI, smoking, hypertension, SBP, HbA1c, FBG, PBG, ALB, BUN, UA, TC, TG, HDL, LDL, Apoa1, Apob, UACR, eGFR, FCP, FT4, TyG index, HOMA-IR, DR, DPN, antihypertensive and antidiabetic treatment) for LASSO analysis. Figure 4A shows the cross-validation curve result, and the coefficient distribution plot is shown in Figure 4B. Through 10-fold cross-validation, we chose the minimum value within 1 standard error and obtained 5 independent variables that were finally screened out (Table 4).

Table 4 LASSO Regression Analysis of DKD-Related Risk Factor

Figure 3 Clinical indicators correlation analysis.

Abbreviations: BMI, body mass index; SBP, systolic blood pressure; FBG, fasting blood glucose; PBG, postprandial blood glucose; HbA1c, haemoglobin A1c; FCP, fasting c-peptide; WBC, white blood cell; Hb, haemoglobin; NLR, neutrophil to lymphocyte ratio; FIB, fibrinogen; ALT, alanine aminotransferase; AST, aspartate aminotransferase; ALB, albumin; BA, bile acid; BUN, blood urea nitrogen; UA, uric acid; eGFR, estimated glomerular filtration rate; TC, total cholesterol; TG, triglyceride; HDL, high-density lipoprotein; LDL, low-density lipoprotein; Apoa1, apolipoprotein a1; Apob, apolipoprotein b; UACR, urinary albumin creatinine ratio; TyG index, triglyceride-glucose index; TSH, thyroid stimulating hormone; FT3, free triiodothyronine; FT4, free thyroxine; DR, diabetic retinopathy; DPN, diabetic peripheral neuropathy; CCB, calcium channel blocker; ACEI, angiotensin-converting enzyme inhibitor; ARB, angiotensin receptor blocker.

Notes: *P value < 0.05, **P value < 0.001, significant correlation between variables.

Figure 4 LASSO regression analysis.

Abbreviation: LASSO, less absolute shrinkage and selection operator.

Notes: (A) Cross-validation curve. (B) Coefficient distribution plot of 37 variables. The cross-validated error within one standard error of the minimum is shown on the right vertical line, while the minimum error is shown on the left vertical line.

Development of an Individualized Prediction Model

To display the prediction model vividly, we applied an individualized dynamic nomogram to visualize it (Figure 5). Then, we applied ROC to analyze the performance of the prediction model. Figure 6 shows the AUC of the prediction model. For the training set, the predicted AUC of the nomogram was 0.826 (95% CI 0.775 to 0.876), meanwhile, the AUC of the validation set was 0.803 (95% CI 0.719 to 0.887).

Figure 5 Dynamic nomogram developed for predicting DKD in patients with newly diagnosed T2DM.

Abbreviations: TG, triglyceride; eGFR, estimated glomerular filtration rate; UACR, urinary albumin creatinine ratio; TyG index, triglyceride-glucose index; DKD, diabetic kidney disease; T2DM, type 2 diabetes mellitus.

Notes: Dynamic nomogram can reflect the score of each variable of patients in actual situation, so as to obtain the corresponding DKD risk probability. The risk probability of DKD calculated for the first patient in the cohort by each variable score is shown in the figure above.

Figure 6 ROC curves of the predictive model.

Abbreviations: ROC, Receiver operating characteristic; AUC, area under the curve; V set, validation set; T set, training set.

Notes: For the training set, the predicted AUC of the nomogram was 0.826 (95% CI 0.775 to 0.876), meanwhile, the AUC of the validation set was 0.803 (95% CI 0.719 to 0.887).

Calibration of the Model

To validate the nomogram, we used 1000 bootstrap analysis and draw a calibration curve. The calibration curves of the model revealed a strong correlation between the predicted probability and the actual probability, indicating that our model had been properly calibrated (Figure 7).

Figure 7 Calibration curves of the nomogram.

Abbreviation: DKD, diabetic kidney disease.

Notes: (A) Calibration curve of the model in the training group. (B) Calibration curve of the model in the validation group. The calibration curve reflects the consistency between the predicted probability and the actual probability. Y-axis = actual probability of DKD. X-axis = predicted probability of DKD. The shadow line represents a perfect prediction by an ideal model. The red line represents the performance of our model, which coincides well with the shadow line.

Clinical Use of the Model

To prove the clinical practicability of the nomogram, we carried out a decision curve analysis (DCA). Figure 8 presents the DCA for the DKD nomogram. Between 20% and 40% of T2DM patients progressed to DKD,28 and within this cutoff, both decision curves were above the None and All lines. This demonstrated that predicting DKD in newly diagnosed T2DM using the nomogram model provides more clinical benefits than either treatment or no treatment for all patients.

Figure 8 DCA for the nomogram.

Abbreviations: DCA, decision curve analysis; DKD, diabetic kidney disease.

Notes: (A) Decision curve of the model in the training group. (B) Decision curve of the model in the validation group. The y-axis measures the net benefit. The solid red line represents the nomogram. The solid gray line represents the assumption that all patients have DKD. The solid black line represents the assumption that no patients have DKD.

Discussion

Our study was a single-center analysis of prospectively collected data from 521 newly diagnosed T2DM patients, 127 of whom had a DKD event. We screened variables through LASSO regression and incorporated 5 variables to construct a prediction model. Furthermore, we carried out internal validation, discrimination ability, calibration ability, and clinical practicability of the model. These results revealed that the prediction model performed well and had clinical applicability.

Independent prediction indicators were screened by logistic and LASSO regression, and finally, a DKD prediction model for predicting newly diagnosed T2DM was developed. Nomogram revealed that age, renal excretion, lipid metabolism, and IR may be potential good predictors of DKD. Age is recognized as an independent risk factor for T2DM and DKD.29 Persistent microalbuminuria (UACR > 30mg/g) can appear in the early stage of the DKD hyperfiltration state, and metabolic substances such as serum creatinine will accumulate with the decrease of renal filtration level in the later period.30 At present, some scholars considered that mitochondrial dysfunction played an indispensable role in DN, which will cause fatty acid oxidation disorder, resulting in abnormal lipid deposition in the kidney.31 Furthermore, many lipid metabolites have been confirmed to be related to the complications of DM. As an index of IR, the TyG index performs excellently in predicting complications of DM.32,33 TyG index can accurately evaluate whether IR exists only by combining two simple indexes: serum lipid and glucose. Moreover, an Australian study investigated the relationship between the TyG index and end-stage renal disease (ESRD). The results revealed a positive correlation between the TyG index and ESRD risk.9 Therefore, researchers should not ignore the relationship between IR and DKD in the future.

The onset of DKD is hidden, and it may be difficult for patients newly diagnosed with T2DM to realize the risk of developing DKD. However, if DKD occurs, we can only take symptomatic treatment (control albuminuria and serum glucose), then wait for ESRD to evolve slowly. Therefore, incorporating the clinical variables described above into the model and developing a nomogram as a screening tool can well identify newly diagnosed T2DM patients with DKD risk. For the identification of our model, the AUC of the training set and the validation set were 0.826 and 0.803, respectively. These results revealed that the model had an excellent predictive ability in identifying DKD and non-DKD in T2DM patients. The model’s calibration curve revealed a strong correlation between the predicted probability and the actual probability, indicating that our model had been properly calibrated. DCA results showed that using this model to predict DKD had clinical practicability and benefits patients.

In comparison to previous studies on developing DKD prediction models, our study had some strengths. First, our study was a retrospective cohort study with a long-time follow-up. Some patients with DKD have a DM duration of 10 years or more, so it may be difficult to collect enough participants with positive outcomes in short-term follow-up and underestimate the prevalence rate, which ultimately affects the predictive performance. Secondly, the participants in our study were newly diagnosed T2DM patients compared with other research focus individuals, who frequently overlooked the danger of developing DKD. Newly diagnosed T2DM patients are a concern in this study, and the prediction model of DKD is constructed to find all DKD events in DM patients earlier. Finally, to incorporate all related variables, we initially investigated the pathological process and risk factors for T2DM and its complications. IR is a major pathogenic factor and characteristic of T2DM, causing concomitant injury to various target organs, and DKD is no exception.34,35 It is crucial to find an index for assessing IR since it is the chief culprit of glucose metabolism disorders. The performance of the TyG index and the HOMA-IR as IR evaluation indexes varies throughout other studies. Hence, we used AUC to distinguish the performance of the TyG index from the HOMA-IR and finally revealed that the TyG index performs better. Therefore, we incorporated significant correlation factors including the TyG index, constructing a more accurate prediction model.

There were still some limitations in this trial. First, the lack of external validation was one of the important limitations of our study. To duplicate and externally verify the results of this study, more investigation is required. Secondly, this was a cohort study with limited sample size, so the endpoint event was defined as the occurrence of DKD, and no subgroup analysis was carried out. If DKD was divided into subgroups with different renal function levels according to eGFR stages, the research results would be more abundant. In addition, the family history of DM was missing from baseline data, and it is undeniable that it is also a significant factor in the development of DKD. Finally, since this was a retrospective cohort study, it may suffer from recollection bias and loss of follow-up. In this cohort study, there were 82 participants with loss to follow-up, which was controlled within 10% and had no significant impact on the outcome.

Conclusion

In conclusion, the new DKD progression model for T2DM patients constructed in this study had excellent identification and calibration ability, which was helpful for clinical practice. In the future, we need to integrate large-scale T2DM cohort studies to further verify this nomogram prediction model.

Data Sharing Statement

The data underlying this article will be shared on reasonable request to the corresponding author.

Ethics Statement

This research study was complied with the Declaration of Helsinki, and approved by the Ethics Committee of the Third Affiliated Hospital of Soochow University (2013#27). All individuals provided written informed consent for this research study.

Author Contributions

All authors made a significant contribution to the work reported, whether that is in the conception, study design, execution, acquisition of data, analysis and interpretation, or in all these areas; took part in drafting, revising or critically reviewing the article; gave final approval of the version to be published; have agreed on the journal to which the article has been submitted; and agree to be accountable for all aspects of the work.

Funding

This work was supported by grants from the National Natural Science Foundation of China (82000684) and Changzhou Sci & Tech Program (CE20215024) and Top Talent of Changzhou “The 14th Five-Year Plan” High-Level Health Talents Training Project (2022260).

Disclosure

The authors report no conflicts of interest in this work.

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