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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.

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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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8. Nakashima A, Kato K, Ohkido I, Yokoo T. Role and treatment of insulin resistance in patients with chronic kidney disease: a review. Nutrients. 2021;13(12):4349. doi:10.3390/nu13124349

9. Chamroonkiadtikun P, Ananchaisarp T, Wanichanon W. The triglyceride-glucose index, a predictor of type 2 diabetes development: a retrospective cohort study. Prim Care Diabetes. 2020;14(2):161–167. doi:10.1016/j.pcd.2019.08.004

10. Liu L, Xia R, Song X, et al. Association between the triglyceride-glucose index and diabetic nephropathy in patients with type 2 diabetes: a cross-sectional study. J Diabetes Investig. 2021;12(4):557–565. doi:10.1111/jdi.13371

11. Luciano RL, Moeckel GW. Update on the native kidney biopsy: core curriculum 2019. Am J Kidney Dis. 2019;73(3):404–415. doi:10.1053/j.ajkd.2018.10.011

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14. Association AD. 11. Microvascular complications and foot care. Diabetes Care. 2019;42(Suppl 1):S124–S138. doi:10.2337/dc19-S011

15. Tao L-C, J-n X, Wang -T-T, Hua F, J-j L. Triglyceride-glucose index as a marker in cardiovascular diseases: landscape and limitations. Cardiovasc Diabetol. 2022;21(1):68. doi:10.1186/s12933-022-01511-x

16. Tang G, Qi L, Sun Z, et al. Evaluation and analysis of incidence and risk factors of lower extremity venous thrombosis after urologic surgeries: a prospective two-center cohort study using LASSO-logistic regression. Int J Surg. 2021;89:105948. doi:10.1016/j.ijsu.2021.105948

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26. Wang S, Shi J, Peng Y, et al. Stronger association of triglyceride glucose index than the HOMA-IR with arterial stiffness in patients with type 2 diabetes: a real-world single-centre study. Cardiovasc Diabetol. 2021;20(1):82. doi:10.1186/s12933-021-01274-x

27. J-y H, Wang Y, Tong X-M, Yang T. When to consider logistic LASSO regression in multivariate analysis? Eur J Surg Oncol. 2021;47(8):2206. doi:10.1016/j.ejso.2021.04.011

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Ferroptosis and Chinese medicine for type 2 diabetes

Diabetes News


Introduction

With an aging global population and social lifestyle changes, the diabetes mellitus (DM) prevalence rate continues to increase annually. The most recent data released by the International Diabetes Federation (IDF)1 stated that the DM global prevalence in adults aged 20–79 years was expected to be approximately 10.5% (>500 million people) in 2021. Moreover, the largest increase in DM prevalence by 2045 is expected to occur in middle-income countries. In China, the most recent epidemiological survey2–4 revealed that DM prevalence among adults aged ≥18 years increased from 9.7% in 2007–2008 to 11.2% in 2015–2017, among which type 2 DM (T2DM) accounted for >90% of cases. T2DM is a metabolic disease characterized by elevated blood glucose caused by insulin resistance (IR) combined with a relative decrease in insulin secretion. Long-term carbohydrate metabolism disorders and related fat and protein metabolism impairments can cause chronic progressive damage to the kidney, eye, nerves, heart vessels, bone, and other tissues and organs. T2DM and its complications have become an important public health problem that seriously threatens human life and health and represents an important cause of death and disability.5 The etiology and pathogenesis of T2DM are complex, with genetic, environmental, and gut microbiota factors considered the major causes of T2DM, while oxidative stress, inflammation, endothelial cell damage, apoptosis, and autophagy are closely related to its development.

Ferroptosis, a recently identified form of regulated programmed cell death, is characterized by its iron dependence and lipid peroxidation-induced cellular dysfunction. It has been implicated in various diseases, including tumors and neurodegenerative diseases. In the context of T2DM, studies have explored the association between ferroptosis and its occurrence and development.6 Patients with T2DM often exhibit elevated serum iron concentrations and reactive oxygen species (ROS) levels in pancreatic tissues and cells.7,8 Moreover, pancreatic β cells, responsible for insulin secretion, possess weaker antioxidant defenses and are susceptible to ferroptosis compared to other tissues.9–11 Therefore, ferroptosis may contribute to the dysfunction of pancreatic β cells and the development of T2DM. Traditional Chinese medicine (TCM) has been used to treat DM for a long time and was described in ancient Chinese medicinal texts such as Huangdi Neijing (425–221 BC) regarding obesity and overeating. The TCM theory classifies DM into the category of “xiaoke” or “xiaodan”. With the continuous in-depth understanding and practice related to DM among the doctors of previous dynasties, the clinical TCM theory of T2DM has been gradually enriched. TCM is multi-targeted and multi-component, with anti-inflammatory, immunoregulation, antioxidant stress, and intestinal flora regulation effects, which have unique advantages for preventing and treating T2DM. However, its targets and specific mechanisms of action are still not fully elaborated. Increasing evidence suggests that Chinese herbs and their active ingredients may modulate ferroptosis and thereby exert therapeutic effects on T2DM and its complications.12,13 This review paper explores the concept, mechanism, and regulatory pathways of ferroptosis and its involvement in T2DM development of and develops a search strategy with strict inclusion and exclusion criteria, By summarizing and analyzing the mechanisms underlying TCM’s treatment of T2DM and its complications, this study provides a novel theoretical basis and clinical perspective for the utilization of TCM in the management of T2DM.

Mechanisms of Ferroptosis

Overview of Ferroptosis

In 2012, the Stockwell research team proposed a new form of regulated programmed cell death, termed ferroptosis, which differs from apoptosis, necrosis, and autophagy.14 Under iron-rich and ROS conditions, phospholipids containing polyunsaturated fatty acids (PUFAs) in the cell membranes are prone to peroxidation, resulting in the continuous accumulation of lipid peroxidation products. These products eventually disrupt cell membrane integrity and induce the cell death known as ferroptosis. The main cellular morphological changes associated with ferroptosis are mitochondrial atrophy, which includes the reduction or loss of mitochondrial cristae, outer mitochondrial membrane rupture, and mitochondrial membrane wrinkling. The primary biochemical features include intracellular iron and ROS accumulation, inhibition of the cystine/glutamate antiporter (system Xc), decreased glutathione peroxidase 4 (GPX4) activity, and reduced glutathione (GSH) production.15,16 The production of oxides of phospholipids containing PUFAs (PLOOHs) enforces ferroptosis, and PLOOH accumulation can lead to rapid and irreparable cell membrane damage, causing cellular iron death.

GSH represents the most abundant reducing agent in mammalian cells and is a cofactor of many enzymes (GPX4 and glutathione-S-transferase). System Xc is an important intracellular antioxidant system (a transmembrane protein complex composed of the light chain subunit SLC7A11 and the heavy chain subunit SLC3A2) that regulates GSH synthesis by mediating cystine uptake and glutamate release. GPX4 is a selenoprotein that functions as a key enzyme to catalyze the reduction of PLOOHs to the corresponding alcohols to reduce lipid peroxide production.17–19 From this perspective, ferroptosis is involved in several pathophysiological processes and linked to cellular metabolism through iron, selenium, lipid, and redox reactions. Ferroptosis is associated with disease pathogenesis, including tumors, ischemic organ damage, neurodegenerative lesions, pulmonary fibrosis, and endocrine metabolic diseases. Therefore, targeting ferroptosis potentially represents an effective therapeutic modality for ferroptosis-related diseases by regulating the ferroptosis-related mechanisms.20,21

Regulation of Ferroptosis

Mechanisms Governing Ferroptosis

Essentially, ferroptosis occurs when the cellular antioxidant capacity becomes weakened and catalyzed by ferrous ions, intracellular lipid peroxidation metabolites continuously accumulate, intracellular redox homeostasis is imbalanced, and ferroptosis occurs. These factors cause irreparable cell membrane damage and result in cellular dysfunction.20 Therefore, the core molecular mechanism of ferroptosis is an imbalance of cellular metabolism and redox homeostasis, where the key signals include the accumulation of intracellular iron, ROS, and lipid peroxidation products (Figure 1).16,20,21

Figure 1 Mechanism of ferroptosis occurrence. This figure was created with Figdraw (www.figdraw.com).

Regulation of Ferroptosis-Suppressing Pathways and Suppressors

It is currently believed that three major systems: cyst(e)ine/GSH/GPX4, FSP1/CoQ (ferroptosis suppressor protein 1, ubiquinone), and GCH1/BH4/DHFR (GTP cyclohydrolase 1, tetrahydrobiopterin, dihydrofolate reductase), effectively inhibit lipid peroxidation and thereby counteract the onset of ferroptosis (Figure 2).20–22

Figure 2 Regulation of ferroptosis suppressing pathways and suppressors. This figure was created with Figdraw (www.figdraw.com). Cytoplasmically located GPX4, mitochondrially located GPX4 and DHODH, plasma membrane located FSP1, and GCH1 (“?” indicates that the exact subcellular localization is unknown), together mediating the ferroptosis defense mechanism.

Cyst(e)ine/GSH/GPX4: The classical ferroptosis-suppressing pathway. Located in the cytoplasm and mitochondria, GPX4 converts reduced GSH into oxidized GSH (glutathione disulfide, GSSG), which is converted to GSH by glutathione reductase (GSR) through the action of electrons donated by nicotinamide adenine dinucleotide phosphate (NADPH), thereby enabling GSSG recycling.20,22,23 CoQ10/FSP1: Located primarily in the plasma membrane, FSP1 inhibits ferroptosis by preventing lipid peroxide accumulation by reducing CoQ to ubiquinol (CoQH2) via NADPH and by acting on α-tocopherol (α-TOH).20–22,24,25 GCH1/BH4/DHFR: GCH1 (exact subcellular localization unknown) is a GPX4-independent ferroptosis suppressor gene identified using the CRISPR/dCas9 screening technique.26,27 GCH1 inhibits ferroptosis through its metabolites BH4 and dihydrobiopterin (BH2).21,26,27

Dihydroorotate dehydrogenase (DHODH) is a recently identified ferroptosis suppressor that is primarily located in the mitochondria.28 DHODH inhibits ferroptosis in the mitochondria by reducing CoQ to CoQH2 in concert with mitochondrial GPX4.21,29

The GPX4 in the cytoplasm, GPX4 and DHODH in the mitochondria, and FSP1 in the plasma membrane form a triad within the cell and together mediate the ferroptosis defense mechanism.29

Ferroptosis and the Pathogenesis of T2DM and Its Related Complications

Ferroptosis and T2DM Pathogenesis

Pancreatic β cell dysfunction and IR are the two main links in T2DM pathogenesis, T2DM occurs when β cells lose compensation to IR. The etiology of pancreatic β cell injury and the pathogenesis of T2DM are closely related to iron overload and ROS accumulation. Pancreatic β-cells are sensitive to ferroptosis. Iron overload7 and increased ROS8 are often present in the pancreatic tissues and cells of patients with T2DM. Compared with other tissues, the pancreas has the weaker antioxidant defense, pancreatic tissues have lower expression and activity of antioxidant enzymes (superoxide dismutase [SOD], catalase [CAT], GPX), and pancreatic β cells are susceptible to ROS-induced oxidative stress damage.9–11 When human pancreatic islet β cells were treated with the ferroptosis inducer erastin in vitro, the glucose-stimulated insulin secretion (GSIS) capacity was significantly reduced, whereas treatment with the ferroptosis inhibitor ferrostatin-1 (Fer-1) or the iron-chelating agent deferoxamine (DFO) rescued GSIS injury.30 These findings suggested that ferroptosis may be involved in T2DM occurrence and development by affecting the insulin secretion capacity of pancreatic β cells.

Environmental factors, such as long-term arsenic exposure and excessive iron intake, are also important factors in T2DM development.31–34 Wei et al35 constructed pancreatic dysfunction models both in vivo and in vitro using NaAsO2-induced Sprague-Dawley rats and MIN6 cells, respectively. They reported that ferroptosis was present in the pancreatic islet β cell injury models both in vivo and in vitro. The NaAsO2-induced mitochondrial damage produced excess mitochondrial ROS (MtROS), increased intracellular free iron levels and MtROS-dependent autophagy, and resulted in imbalanced iron homeostasis. These changes ultimately led to ferroptosis and insulin secretion dysfunction in pancreatic cells, whereas inhibiting the MtROS–autophagy–ferritin pathway improved the insulin secretion capacity of pancreatic β cells. In another study,36 iron stores were associated with the risk of developing DM. Iron regulatory genes, ferritin heavy chain (FTH1), and ferritin light chain (FTL) were highly expressed in islet tissues derived from diabetic patients and high-glucose-cultured INS-1 cells, heme oxygenase-1 (HO-1) and the inhibitor of differentiation proteins (ID1, ID3) may serve as potential endogenous antioxidants for pancreatic β cells against ROS and iron-overload, thereby protecting pancreatic β cells from oxidative stress and ferroptosis in T2DM patients.36

Ferroptosis and the Pathogenesis of T2DM Microangiopathy

Diabetic microvascular complications can affect various tissues and systems throughout the body and are associated with a variety of factors including microcirculatory disorders, inflammatory damage, and oxidative stress, among which nephropathy and retinopathy are the most common. Diabetic kidney disease (DKD) is a common chronic kidney disease and is thought to represent a major cause of end-stage renal disease (ESRD), which is responsible for approximately 30% to 50% of ESRD worldwide.37 Recent studies demonstrated that iron overload, ROS, and lipid peroxidation products accumulated in both mouse models of DKD and human renal tubular epithelial cells (HK-2) cultured under high glucose. The use of DFO or Fer-1 reduced renal iron accumulation and injury.38,39 Kim et al40 reported that renal biopsy samples derived from DKD patients had lower SLC7A11 and GPX4 mRNA expression compared to that from non-diabetic patients. They used streptozotocin (STZ)-induced DKD mice and transforming growth factor-β-1-stimulated proximal tubular epithelial cells in vivo and in vitro experiments, respectively, and found that the concentration of GSH was reduced, total iron levels and malondialdehyde (MDA, a lipid peroxidation product) were increased, SLC7A11 and GPX4 protein and mRNA expression levels were lower than in controls, and lipid peroxidation was enhanced. Fer-1 treatment alleviated these changes and significantly improved kidney damage and proteinuria caused by DM.40 It is suggested that ferroptosis is associated with the development of DKD and that inhibiting or attenuating ferroptosis may improve renal function in DKD.

Diabetic retinopathy (DR) is another common microvascular complication of T2DM. DR is a major cause of blindness in diabetic patients and is closely related to endothelial dysfunction and increased retinal capillary permeability.41 The current main DR treatment modalities include anti-angiogenic drug therapy and laser or surgical treatment.42 However, the benefits of these treatments for patients are also associated with adverse drug reactions or surgical risks. Zhang et al43 reported that human retinal vascular endothelial cell death induced by high-glucose treatment was associated with ferroptosis. Further investigation revealed that the high-glucose treatment upregulated TRIM46 (a member of the E3 ubiquitin ligase family TRIM), facilitated GPX4 ubiquitination, and induced ferroptosis in the cells. It was suggested that inhibiting ferroptosis by targeting TRIM46 and GPX4 represents a potential mechanism for effective DR treatment.

Ferroptosis and the Pathogenesis of T2DM Cardiovascular Complications

Patients with T2DM often have risk factors, such as obesity, abnormal lipid metabolism, and hypertension. Compared with the non-diabetic population, diabetic patients have a substantially increased risk of atherosclerotic vascular disease, which is one of the main causes of death in patients with T2DM.44–46 Diabetic cardiovascular complications can affect the heart, large blood vessels, and myocardial tissue, causing coronary atherosclerotic heart disease, diabetic cardiomyopathy, and other cardiac lesions.

The disorders of lipid and glucose metabolism are closely related to atherosclerosis development.47,48 The pathogenesis of diabetic atherosclerotic vasculopathy may be related to iron accumulation and lipid peroxidation.49–51 Using gene microarray technology (mRNA expression profiling) and bioinformatics analysis, Meng et al identified ferroptosis and HO-1 as important factors in diabetic atherosclerotic vascular disease.52 In vitro and in vivo diabetic atherosclerosis models were constructed using ApoE knockout mice and human umbilical vein endothelial cells (HUVECs), respectively. The results confirmed that Fer-1 reduced ROS production, attenuated high-glucose- and high-fat-induced lipid peroxidation, and reduced diabetic atherosclerosis formation. Similarly, knockout of the HO-1 gene reduced iron content, ROS production, lipid peroxidation, and ferroptosis in the HUVECs under a high-glucose environment. These findings suggested that ferroptosis is involved in diabetic atherosclerosis formation and that HO-1 may be a potential target for the treatment or drug development of diabetic atherosclerotic vascular disease.

Recently, several studies confirmed that diabetic cardiomyopathy development is associated with ferroptosis.53–55 Both DM myocardial ischemia-reperfusion injury model rats and a high-glucose hypoxia-reoxygenation cardiomyocyte model exhibited increased levels of iron ion concentration, ROS, SOD, MDA, and myocardial injury markers (serum creatine kinase MB and lactate dehydrogenase), ferroptosis, endoplasmic reticulum stress (ERS), and myocardial functional impairment. The ferroptosis inducer erastin or the inhibitor Fer-1 aggravated or reduced myocardial cell injury, respectively. Moreover, inhibiting endothelial network stress reduced ferroptosis and cell injury.54 Therefore, these findings indicated that ferroptosis is involved in DM myocardial ischemia–reperfusion-induced cardiomyocyte injury and is associated with ERS.

Endothelial cell injury is another important pathological mechanism in DM and diabetic cardiovascular disease.56 Luo et al57 reported that in HUVECs treated with high glucose and interleukin 1β, cell viability decreased, lipid ROS increased, and GSH and GPX4 concentrations decreased, and after treatment with ferroptosis inhibitors DFO and Fer-1, ROS levels in HUVECs decreased significantly and cell viability and GPX4 concentrations increased compared to pre-treatment. Further, transient transfection of HUVECs using p53 small interfering ribonucleic acid revealed that p53 small interfering ribonucleic acid attenuated the decrease in xCT (the light chain subunit of system Xc, also known as SLC7A11) and GSH and the increase in ROS induced by HG and IL-1β. In addition, in the aortic endothelium of db/db mice, p53 mRNA was up-regulated, xCT mRNA was down-regulated, and de-endothelialization areas were also observed. These findings suggested that ferroptosis may be involved in the pathogenesis of diabetic vascular endothelial cell dysfunction through the p53-xCT-GSH axis.57

Together, the aforementioned studies suggested that ferroptosis is involved in the pathogenesis of diabetic cardiovascular complications and that inhibiting the ferroptosis-related mechanisms represents a potential therapeutic target for diabetic cardiovascular disease.58

Ferroptosis and the Pathogenesis of Abnormal Bone Metabolism in T2DM

In patients with T2DM, chronic hyperglycemia leads to the accumulation of advanced glycation end-products (AGEs) in the bone matrix, triggering non-enzymatic glycosylation reactions that result in decreased bone quality, increased bone fragility, and a heightened risk of osteoporosis (OP) and fractures.59,60 Recently, Ge et al investigated the role of AGEs in diabetes-related OP and reported that the serum AGEs levels and bone mineral density in patients with OP were positively and negatively correlated with fasting glucose, respectively, and that AGEs and serum from patients with OP and T2DM could promote the development of ferroptosis in hFOB1.19 osteoblast, which was reversed by the ferroptosis inhibitor DFO. The results suggest that AGEs may promote OP by disrupting osteoblast function.61 The loss of osteocyte viability is another important factor in the development of diabetic osteoporosis. Another recent study62 reported that osteocytes cultured in a diabetic microenvironment had increased lipid peroxidation, iron overload, ferroptosis pathway activation, and significant upregulation of HO-1 expression. Moreover, targeting ferroptosis or HO-1 rescued osteocyte death and improved bone structural degeneration in the diabetic OP. These studies suggested that ferroptosis is involved in the development of diabetic OP and that targeting ferroptosis may represent an effective mechanism-based strategy for OP treatment.

Application of Ferroptosis in TCM Treatment of T2DM and Its Related Complications

Search Strategy

We searched PubMed, Web of Science, the Cochrane Library, the Chinese National Knowledge Infrastructure database (CNKI), the Chinese Biomedical Literature database (CBM), the Chinese Scientific Journal database (VIP), and the Wan Fang database for articles published from January 1, 2012, to March 27, 2022. No language restrictions were imposed. The medical subject headings and main keywords used for the search were (“Diabetes Mellitus” OR diabet* OR glucose) AND (“Ferroptosis” OR ferropto* OR “iron death” OR (iron AND “cell death”)). The full search strategy used is shown in the Supplemental Appendix. The supplemental literature was searched manually.

Selection Criteria

Inclusion criteria: (1) study type: clinical trials or basic experimental studies; (2) study object: patients with T2DM or its related complications, animal or cell models; (3) interventions: active ingredients, monomers, or compound preparation of TCM; (4) mechanism of action: ferroptosis. Correspondence, comments, editorials, reviews, meta-analyses, and conference abstracts were excluded.

Data Extraction

Two investigators independently reviewed the full text of the studies that met the selection criteria and extracted the following data: first author’s name, year of publication, disease type, study object, ferroptosis regulation mechanism, and characteristics of action (Table 1). When there was disagreement between the two investigators, a third researcher was consulted to make the final decision.

Table 1 The Role of Ferroptosis in the Therapeutic Use of TCM for T2DM and Its Related Complications

Results

Figure 3 illustrates the inclusion screening process employed in this study. A comprehensive database search yielded a total of 726 potentially relevant studies. Additionally, two articles were manually searched, bringing the cumulative number of potentially relevant studies to 728. Subsequently, eliminating 205 duplicate articles and 502 articles that were deemed not relevant after reading the titles and abstracts, 21 articles were entered in the full-text review. After a critical evaluation of the complete texts according to the predetermined inclusion and exclusion criteria, eight articles were excluded. Ultimately, a total of 13 eligible studies were included in the final analysis.

Figure 3 Flow chart of the literature retrieval and screening process.

These 13 studies primarily consisted of basic experimental investigations conducted on animal or cell models. Among them, five studies were relevant to T2DM, four to DKD, three to diabetic cardiomyopathy, and one addressed DR. Notably, no study exploring the treatment of diabetes-related OP through TCM via the modulation of ferroptosis was identified. Table 1 provides an overview of the main characteristics of the studies included in this review.

Analysis

Application of Ferroptosis in TCM Treatment of T2DM

Diabetic patients have elevated ROS levels8 and dietary iron intake is associated with the risk of developing T2DM.76,77 Iron overload tends to lead to cellular oxidative damage, promoting the occurrence of ferroptosis, causing pancreatic β cell dysfunction, and thereby participating in T2DM occurrence and development.78 Recent studies64,79–81 indicate that natural polyphenolic compounds possess iron-chelating properties in addition to their well-known antioxidant, anti-inflammatory, and anti-tumor effects, enabling them to regulate ferroptosis and reduce blood glucose levels.

Curcumin, derived from the rhizomes of turmeric (Curcuma longa L.) and other ginger family plants, (-)-Epigallocatechin-3-gallate (EGCG) found in tea, especially green tea, and grapeseed procyanidin extract (GSPE) abundant in various plants, particularly grape seeds, all containing polyphenols, have been studied. Treatment with these polyphenolic compounds, such as curcumin, EGCG, and GSPE, has shown an increase in cell viability and a decrease in iron accumulation, depletion of GSH, inactivation of GPX4, levels of acyl-CoA synthetase long-chain family member 4 (ACSL4) and lipid peroxidation in mouse pancreatic MIN6 cells when compared to control cells exposed to erastin alone.63,64 Consistent with these findings, diabetic rats exhibited decreased iron content, increased GSH activity in pancreatic tissue, alleviation of ferroptosis and pancreatic damage, increased insulin levels, and reduced blood glucose levels.65 These effects may be associated with the activation of Nrf2-related signaling pathways.63,64

Quercetin, a flavonoid found widely in various plants,82–84 has been found to potentially regulate ferroptosis.66 Compared to the control group, quercetin reduced the iron content in the T2DM mice pancreas, increased the expression of GSH and GPX4, and reduced oxidative stress in pancreatic tissues. Furthermore, quercetin demonstrated the viability to restore the viability of pancreatic β cells under high-glucose stimulation, suggesting its potential beneficial effects on T2DM by inhibiting pancreatic iron accumulation and ferroptosis in pancreatic β cells.

Additionally, studies focused on mulberry (Morus alba L.) leaf extract, a Chinese herbal medicine, revealed its potential mechanism to regulate abnormalities in glycolipid metabolism.85–87 Cryptochlorogenic acid,67 the primary active substance in mulberry leaves, ameliorated islet damage in diabetic rats by inhibiting ferroptosis, reducing iron overload and accumulation of lipid peroxides, and lowering blood glucose levels. The mechanism underlying these effects involves the activation of the cystine/Xc/GPX4/Nrf2 pathway and the inhibition of nuclear receptor coactivator 4 (NCOA4).67

Taken together, these findings underscore the potential of TCM Taken together, these findings underscore the potential of Chinese herbal medicine to elevate GSH and GPX4 levels in mice with T2DM by modulating ferroptosis in pancreatic tissues or cells, possibly involving the cystine/ Xc/GPX4/Nrf2 pathway.

Application of Ferroptosis in TCM Treatment of T2DM Microangiopathy

TCM has demonstrated significant efficacy in ameliorating kidney damage and preserving kidney function and is widely used in DKD treatment in some countries, including China. Numerous studies have elucidated that the therapeutic protective effects of TCM on DKD are closely intertwined with its regulation of glucose/lipid metabolism, antioxidant activity, anti-inflammatory response, anti-fibrotic properties, and protection of podocytes.88 Recent reports have highlighted the pivotal role of ferroptosis in the TCM treatment of DKD. Notably, TCM rhubarb (Rheum palmatum L.) has shown remarkable efficacy in improving lipid metabolism in DKD patients.89 Ding et al68 reported that sennoside A, an active compound found in rhubarb, reduced MDA levels, downregulated the expression of HO-1 and PTGS2, and increased GSH concentration to inhibit ferroptosis in DKD mice, thereby improving oxidative stress and renal injury in DKD. Another active compound, berberine, extracted from the rhizome of Coptis chinensis Franch (known as Huanglian in Chinese), has been found to effectively reduce ROS, PTGS2, and ACSL4 levels while upregulating the expression of Nrf2, HO-1, and GPX4 to improve ferroptosis in high-glucose-induced podocytes.69 These effects were achieved through the regulation of the Nrf2/HO-1/GPX4 pathway, thus providing a new theoretical foundation for the application of berberine in DKD treatment. Additionally, a study70 investigating the mechanism of action of umbelliferone, a coumarin derivative found in traditional herbal components such as Cnidium monnieri (L.) Cuss, Angelica dahurica (Fisch. ex Hoffm) Benth. et Hook. f., and Peucedanum praeruptorum Dunn, revealed that it protected against DKD. Umbelliferone treatment decreased ROS accumulation, downregulated ACSL4, and upregulated GPX4, Nrf2, and HO-1 expression, resulting in the alleviation of ferroptosis and renal pathological damage in db/db DKD mice.70 Knockdown of Nrf2 blocked the inhibitory effect of umbelliferone on ferroptosis in DKD model cells. Moreover, platycodin D, a triterpene saponin derived from the dried root of Platycodon grandiflorum (Jacq.) A. DC., exhibited various pharmacological effects, including anti-tumor, anti-inflammatory, and neuroprotective properties.90–92 In a recent study utilizing high-glucose-induced HK-2 cells as an in vitro DKD model, platycodin D treatment inhibited high-glucose-induced ferroptosis, upregulated GPX4, FTH-1, and SLC7A11 expression, and downregulated ACSL4 and TFR1 expression. These effects led to increased cell viability and reduced cellular damage.71 Collectively, these studies indicate that TCM may exert therapeutic effects on DKD by inhibiting ferroptosis through the regulation of Nrf2/HO-1-related pathways.

Astragaloside IV, an active ingredient of the TCM Astragalus membranaceus (Fisch.) Bge., exhibits anti-inflammatory, antioxidative stress, and immunomodulatory effects. Astragaloside IV has been used in the treatment of various diseases, including tumors, DM, and autoimmune diseases.93–95 Recent findings have also demonstrated its effective inhibition of retinal endothelial cell death and amelioration of pathological damage associated with DR.96,97 In an in vitro model of DR utilizing a high-glucose culture of ARPE-19 cells, Tang et al reported that astragaloside IV attenuated the decrease of Sirt1 and Nrf2 levels induced by high glucose in retinal pigment epithelial cells. It increased the levels of GPX4, glutamate cysteine ligase (GCLM), and glutamate cysteine ligase catalytic subunit (GCLC), leading to the reduction of ferroptosis, increased cell viability, and enhanced antioxidant capacity. These effects may be associated with the inhibition of miR-138-5p expression and activation of the Sirt1/Nrf2 pathway.75

In conclusion, TCM demonstrates a beneficial role in the management of T2DM microangiopathy, including DKD and DR, through the regulation of ferroptosis. The underlying mechanism is likely associated with the modulation of Nrf2-related pathways.

Application of Ferroptosis in TCM Treatment of T2DM Cardiovascular Complications

Resveratrol, a natural polyphenolic compound found in various Chinese herbal medicine plants such as Veratrum nigrum L. and Polygonum cuspidatum Sieb. et Zucc., exhibits varied pharmacological effects including anti-inflammatory and antioxidative stress properties. It is commonly used in the prevention and treatment of tumors, cardiovascular diseases, and DM.98–100 In an in vitro model of diabetic myocardial injury utilizing H9c2 cells cultured in a high-glucose environment, resveratrol demonstrated significant effects. It notably increased cell viability, SOD activity, and protein levels of HSF1, GPX4, and SLC7A11, while decreasing MDA levels and iron ion content in H9c2 cells. These findings indicate that resveratrol may improve high-glucose-induced cardiomyocyte injury by inhibiting ferroptosis through the upregulation of HSF1 expression.72

Previous studies have confirmed the positive effects of puerarin, the main active flavonoid in Pueraria lobata (Willd.) Ohwi, on improving cardiac function in rats with heart failure by reducing lipid peroxidation and ferroptosis.101 Similarly, baicalein, the active ingredient of Scutellaria baicalensis Georgi, has shown a neuroprotective role as a natural ferroptosis inhibitor.102 Building upon this knowledge, Yu et al observed the effects of Gegen Qinlian decoction, a Chinese herbal compound preparation composed mainly of P. lobata (Willd.) Ohwi and S. baicalensis Georgi, on the cardiac diastolic function of diabetic mice with the damp-heat syndrome.73 The study revealed that Gegen Qinlian decoction upregulated GPX4 and SLC7A11 levels, while downregulating ACSL4 and PTGS2 levels. It also reduced MDA content and alleviated damp-heat symptoms, such as elevated blood glucose, reduced diet, and urination. Furthermore, Gegen Qinlian decoction mitigated lipid peroxidation in myocardial tissue, improved cardiac diastolic function, and reversed myocardial remodeling in the mice. These findings demonstrated that Gegen Qinlian decoction’s beneficial effects on cardiac remodeling and diastolic function in diabetic mice with the damp-heat syndrome may be associated with the inhibition of ferroptosis in cardiomyocytes.

Prolonged elevated blood glucose levels in T2DM contribute to significant production and accumulation of AGEs in the body, particularly in the extracellular matrix of the heart., AGEs are an important feature of diabetic cardiomyopathy (DCM) pathogenesis. Sulforaphane, an isothiocyanate widely found in plants like broccoli, possesses anti-tumor and antioxidant effects. It has been shown to alleviate diabetes-induced oxidative stress and cardiac functional impairment.103,104 Further studies74 have revealed that sulforaphane alleviated ferroptosis and lipid peroxidation through AMPK-mediated activation of Nrf2, leading to amelioration of cardiac injury in mice with AGE-induced DCM and enhancing the cardioprotective effect.

Overall, these findings suggest that TCM may improve AGE accumulation and reduce lipid peroxidation in T2DM cardiovascular complications by regulating ferroptosis, thereby enhancing the cardioprotective effect on the heart.

Summary

Through the above-detailed analysis of the therapeutic effects of TCM and its active ingredients on T2DM,63–67 as well as related complications such as DKD,68–71 DR75 and DCM,72–74 we found that Chinese herbs and their main active ingredients exerted therapeutic or protective effects by inhibiting ferroptosis, and the specific mechanisms are summarized in Figure 4.

Figure 4 Mechanism in the treatment of T2DM and its complications with TCM by inhibiting ferroptosis. This figure was created with Figdraw (www.figdraw.com).

Discussion

Ferroptosis is a recently proposed new cell death model and is closely related to the occurrence and development of various diseases (tumors, ischemia-reperfusion injury, neurological diseases, and metabolic diseases).6,20 Recent studies have confirmed that, in addition to oxidative stress, the inflammatory response, endothelial cell damage, apoptosis, and autophagy, iron-overload, ROS, and lipid peroxide accumulation are also important pathogenic mechanisms of T2DM and the related complications, and blocking the iron-dependent death pathways with ferroptosis inhibitors or iron-chelating agents can treat or delay the progression of T2DM and its related complications.105

TCM has a long history of use for treating T2DM, being multi-component, multi-target, systematic, and basing treatment on syndrome differentiation (different conditions of each patient), and is highly effective in the clinical treatment and early prevention of disease progression of T2DM. With progressive research on the relationship between the mechanism of TCM and ferroptosis, several studies have confirmed that TCM exerts therapeutic effects on T2DM and its complications by regulating the ferroptosis-related pathways.102,106–108 In this paper, the concept, mechanism of occurrence, regulatory pathways of ferroptosis, and its correlation with T2DM and its related complications were described. The application of ferroptosis to related studies of TCM for treating T2DM and its complications was summarized and analyzed for the first time. In this review, we identified the existence of ferroptosis in T2DM and its related complications, Chinese herbs or their active ingredients (quercetin, curcumin, cryptochlorogenic acid, resveratrol, platycodin D, astragaloside IV) exert beneficial effects on T2DM and its complications by inhibiting ferroptosis. These TCMs all exert their therapeutic effects by inhibiting ferroptosis, with different regulatory mechanisms. However, the number of related studies is relatively small, and all of them are basic experiments. Therefore, future studies should continue to target ferroptosis, explore the mechanism of T2DM occurrence and progression, further clarify the exact mechanism by which different TCMs and their active ingredients mediate their therapeutic effects, and actively explore the relevant regulatory signaling pathways and specific molecular markers. The elucidation of these mechanisms will provide a new theoretical basis for TCM treatment of T2DM and will be crucial in leveraging the knowledge of ferroptosis for the clinical therapeutic benefit.21

Despite the comprehensive and systematic literature search, our review has several limitations. First, the studies included in this review were all basic experimental studies and no published clinical trials were retrieved. Moreover, given the wide variation in animal and in vitro conditions, the generalization of the experimental results to humans requires careful evaluation in rigorous clinical trials. Second, as all experiments included studies that were conducted in China, there may be geographical bias. Third, most of the Chinese herbal medicines used in the included studies were active ingredients of TCM or herbal monomers, and there is a need to increase the study of ferroptosis-related mechanisms of single herbs or TCM compound preparations. Finally, there were only a small number of studies on ferroptosis for TCM treatment of T2DM and its related complications, and TCM treatments for the common complications of T2DM (diabetic neuropathy and abnormal bone metabolism) were not retrieved.

Conclusion and Prospects

TCM treatment of T2DM and its related complications is an effective treatment modality. T2DM is closely associated with ferroptosis and lipid peroxidation, and TCM interventions may play a therapeutic or beneficial role by inhibiting ferroptosis, and the specific mechanism may be relevant to Nrf2-related pathways. Currently, research efforts focusing on the mechanism of ferroptosis in TCM treatment of T2DM primarily concentrate on TCM extracts. However, future studies should delve into the mechanism of ferroptosis in the treatment of T2DM using single herbs and Chinese herbal compounds. This will contribute to the development of new theoretical foundations and potential therapeutic strategies for T2DM and its complications using TCM.

Abbreviations

ACSL4, acyl-CoA synthetase long-chain family member 4; AMPK, AMP-activated protein kinase; BH2, dihydrobiopterin; BH4, tetrahydrobiopterin; CoQ10, Coenzyme Q10; CoQ10H2, ubiquinol; DCM, diabetic cardiomyopathy; DHFR, dihydrofolate reductase; DHODH, dihydroorotate dehydrogenase; DKD, diabetic kidney disease; DR, diabetic retinopathy; FSP1, ferroptosis suppressor protein 1; FTH-1, ferritin heavy chain 1; GCH1, GTP cyclohydrolase 1; GCLC, glutamate cysteine ligase catalytic subunit; GCLM, glutamate cysteine ligase; GPX4, glutathione peroxidase 4; GSH, glutathione; HO-1, heme oxygenase-1; HSF1, heat shock factor 1; MDA, malondialdehyde; NADPH, nicotinamide adenine dinucleotide phosphate; NCOA4, nuclear receptor coactivator 4; Nrf2, nuclear factor erythroid 2-related factor 2; PLOOHs, phospholipid hydroperoxides; PTGS2, prostaglandin-endoperoxide synthase 2; PUFA, polyunsaturated fatty acid; PUFA-PL, phospholipid containing PUFA chain; ROS, reactive oxygen species; SLC7A11, solute carrier family 7 member 11; TCM, traditional Chinese medicine; T2DM, type 2 diabetes mellitus; TFR1, transferrin receptor 1.

Acknowledgments

This work was supported by grants from the National Natural Science Foundation of China [grant numbers 82174334 and 81870622], the Changsha Municipal Natural Science Foundation [grant number kq2014251], Hunan Provincial Innovation Foundation for Postgraduate [grant number CX20210372], Scientific Research Project of Hunan Provincial Health Commission [grant number 202112070631], and the Research Projects in the Health Industry of Hainan Province [grant number 22A200053].

Disclosure

All authors declare that they have no conflicts of interest in this work.

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Analysis of remission and relapse rate of type 2 diabetes mellitus in Japan

Diabetes News


The phenomenon of improvement of glucose to levels in a normal range and cessation of the need for medication can occur in some patients diagnosed with type 2 diabetes who are provided with lifestyle therapy, temporary pharmacotherapy, bariatric surgery, or combinations of these treatments.

However, this phenomenon is not yet fully understood in routine care settings, and many factors remain to be clarified. Moreover, since there are differences in insulin secretion and resistance between East Asian and Western populations, the natural history of diabetes seems to differ widely between Western populations and East Asians.

Therefore, these concerns lead Dr. Kazuya Fujihara and colleagues to investigate the incidence/one year relapse from remission and associated factors in patients with type 2 diabetes. In the Diabetes, Obesity and Metabolism paper, the authors addressed these research questions using a database of specialists’ clinics.

They analyzed the data of Japan Diabetes Clinical Data Management Study Group (JDDM) which is one of the largest cohorts of Japanese people with type 2 diabetes. They tracked the information on 48,320 people with diabetes in Japan. In one arm of the study, they calculated remission rates per 1000 person-years. The authors reported that the median follow-up was 5.3 years. During the study period, 3,677 remissions occurred.

The overall incidence of remissions per 1,000 person-years was 10.5 that was similar to 9.7 in the United Kingdom. In addition, those with HbA1c levels of 48 to 53 mmol/mol (6.5% to 6.9%), those taking no glucose-lowering drugs at baseline, and those with a ≥10% body mass index (BMI) reduction in 1 year, it was 27.8, 21.7 and 48.2, respectively.

Male sex, shorter duration, lower baseline HbA1c, higher baseline BMI, higher BMI reduction at 1 year, and no glucose-lowering drugs at baseline were significantly associated with remission. Similar results were obtained with maintenance of remission over 1 year as an outcome. In another arm of the study, the investigators revealed the factors that predicted relapse from remission in 1 year.

Among 3,677 individuals who entered remission, two-thirds (2,490) relapsed from remission within 1 year. Longer duration of diabetes, lower BMI at baseline, and lower BMI reduction at 1 year were significantly associated with relapse. Commenting on the significance of their findings.

Compared to Westerners, Asians have higher insulin sensitivity and a lower acute insulin response. In addition, Asians have a much lower obesity level than Westerners, and the pathogenesis of diabetes mellitus is very different between the two. Therefore, the relationships of baseline BMI and BMI reduction with remission and relapse may be greater in East Asian than in Western populations, implying ethnic differences in returning from overt hyperglycemia to nearly normal glucose levels.”


Hirohito Sone, Niigata University

While the findings of these analytical study are impressive and provide new insight on remission in patients with type 2 diabetes should be, the authors noted that “present study is an observational study and does not show a cause and effect relationship, and that future intervention studies with lifestyle and/or medication will be needed to confirm how many people actually achieve remission and how long the state of remission lasts in real world setting”.

Source:

Journal reference:

Fujihara, K., et al. (2023) Incidence and predictors of remission and relapse of type 2 diabetes mellitus in Japan: Analysis of a nationwide patient registry (JDDM73). Diabetes, Obesity and Metabolism. doi.org/10.1111/dom.15100.



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Prevalence of Vitamin D Deficiency in Type 2 Diabetes Mellitus Patients: A Cross-Sectional Study

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