Prevalence of comorbidities in patients with type-2 diabetes mellitus

Association of Sex Differences on Weight Loss and Complications Following Bariatric Surgery

INTRODUCTION

Sex as a biologic variable remains largely understudied, even for the most commonly performed operations. The most effective treatment for obesity and obesity-associated comorbidities is bariatric surgery. There are limited data to describe potential differences in outcomes between male and female patients, particularly with regards to weight loss. Within this context, we examined weight loss and complications up to 1 y following sleeve gastrectomy or gastric bypass within a statewide bariatric quality improvement collaborative.

METHODS

We performed a retrospective cohort study among patients who had bariatric surgery. Using a state-wide bariatric-specific data registry, all patients who underwent gastric bypass or sleeve gastrectomy between June 2006 and June 2022 were identified. The primary outcome was percent excess body weight loss and change in body mass index (BMI) at 1 y. The secondary outcome was 30-d risk-adjusted complications.

RESULTS

Among 107,504 patients, the majority (n = 85,135; 79.2%) were female and most patients (n = 49,731; 58%) underwent sleeve gastrectomy. Compared to female patients, male patients were older (47.6 y versus 44.8 y; P < 0.0001), had higher baseline weight (346.6 lbs versus 279.9 lbs; P < 0.0001), had higher preoperative BMI (49.9 kg/m2versus 47.2 kg/m2; P < 0.0001), and higher prevalence of most comorbid conditions including hypertension, hyperlipidemia, diabetes, and sleep apnea (P < 0.0001). Compared to female patients, male patients experienced greater total body weight loss (105.1 lbs versus 84.9 lbs; P < 0.0001) and higher excess body weight loss (60.0% versus 58.8%; P < 0.0001) but had higher BMI overall (34.0 kg/m2versus 32.8 kg/m2; P < 0.0001) at 1-y follow-up. Males had higher rates of serious complications (2.5% versus 1.9%; P < 0.0001), leak and perforation (0.5% versus 0.4%; P < 0.0001), venous thromboembolism (0.7% versus 0.4%; P < 0.0001), and medical complications (1.5% versus 1%; P < 0.0001).

CONCLUSIONS

In this study we found that both males and females experienced excellent weight loss with a low risk of complications following bariatric surgery. Male sex was associated with slightly greater weight loss and slightly higher incidence of complications. However, although statistically significant, clinically, the differences in weight loss was not. Due to males having higher prevalence of comorbidities, providers should consider referring males earlier for bariatric surgery which may improve outcomes for this population.

Prevalence and co-prevalence of comorbidities among Chinese adult patients with type 2 diabetes mellitus: a cross-sectional, multicenter, retrospective, observational study based on 3B study database

Purpose

This study aimed to investigate the prevalence and co-prevalence of comorbidities among Chinese individuals with type 2 diabetes (T2DM).

Methods

Medical records were retrospectively retrieved from the 3B Study database, which provided a comprehensive assessment of comorbid conditions in Chinese adult outpatients with T2DM. Patient characteristics, laboratory measures, and comorbidities were summarized via descriptive analyses, overall and by subgroups of age (<65, 65-74, 75 years) and gender.

Results

Among 25,454 eligible patients, 53% were female, and the median age was 63 years. The median time of diabetes duration was 6.18 years. A total of 20,309 (79.8%) patients had at least one comorbid condition alongside T2DM. The prevalence of patients with one, two, three, and four or more comorbid conditions was 28.0%, 24.6%, 15.6%, and 11.6%, respectively. Comorbidity burden increased with longer T2DM duration. Older age groups also exhibited higher comorbidity burden. Females with T2DM had a higher overall percentage of comorbidities compared to males (42.7% vs. 37.1%). The most common comorbid conditions in T2DM patients were hypertension (HTN) in 59.9%, overweight/obesity in 58.3%, hyperlipidemia in 42.0%, retinopathy in 16.5%, neuropathy in 15.2%, cardiovascular disease (CVD) in 14.9%, and renal disease in 14.4%. The highest co-prevalence was observed for overweight/obesity and HTN (37.6%), followed by HTN and hyperlipidemia (29.8%), overweight/obesity and hyperlipidemia (27.3%), HTN and CVD (12.6%), HTN and retinopathy (12.1%), and HTN and renal disease (11.3%).

Conclusion

The majority of T2DM patients exhibit multiple comorbidities. Considering the presence of multimorbidity is crucial in clinical decision-making.

Systematic review registration

https://clinicaltrials.gov/, identifier NCT01128205.

Interplay between diabetes mellitus and atopic dermatitis

Inflammatory dermatoses such as atopic dermatitis (AD) have long been linked to the pathogenesis of diabetes mellitus. Indeed, numerous studies show an increased risk of diabetes mellitus in individuals with AD although lower prevalence of diabetes mellitus is also observed in few studies. Though the underlying mechanisms accounting for the reciprocal influence between these two conditions are still unclear, the complex interplay between diabetes mellitus and AD is attributable, in part, to genetic and environmental factors, cytokines, epidermal dysfunction, as well as drugs used for the treatment of AD. Proper management of one condition can mitigate the other condition. In this review, we summarize the evidence of the interaction between diabetes mellitus and AD, and discuss the possible underlying mechanisms by which these two conditions influence each other.

Impact of diabetes on healthcare utilization and expenditure among older adults in India

Background and aims

The present study aims to investigate the disparities in healthcare utilization and healthcare burden among individuals with and without diabetes. The prevalence of diabetes is more pronounced among older adults, which can detrimentally influence their health and quality of life while also restricting their capacity to self-manage and giving rise to competing healthcare demands. Thus, it is crucial to understand the implications of diabetes on healthcare demands and expenditures to mitigate its detrimental consequences.

Methods

Data was used from the initial round of the Longitudinal Aging Study in India (LASI), conducted in 2017-18. The analytical sample included 65,562 individuals aged 45 or above, and 8429 individuals were identified as having diabetes. The primary outcome variable was the out-of-pocket expenditure (OOPE) in the most recent hospitalization. Descriptive statistics and logistic regression are used to find the trend in the prevalence of morbidities both in diabetic and nondiabetic groups. Additionally, quantile regression was used to study the association between the presence of diabetes and the risk of excess healthcare expenditure calculated through out-of-pocket expenditure for hospitalization and expenditure on medicines.

Results

16% of people with diabetes were hospitalized in the past year, compared to 9% without diabetes. The mean hospitalization length for diabetics was 13.6 days, compared to 6.5 for nondiabetics. Diabetes is significantly associated with higher OOPE for hospitalization across all quintiles, and its effect on hospitalization is weakest in the highest quintile. Having diabetes is also found to be significantly associated with the expenditure on medicines across all quintiles.

Conclusion

The study highlights the need for diabetes awareness programs and interventions integrated into national health policies. The quantile regression model provides crucial insights into the association between diabetes and OOPE for hospitalization and medicine The increase of OOPE for hospitalisation and medicine due to the presence of diabetes emphasizes the need to address the financial burden faced by people with diabetes, highlighting the urgency of prioritizing measures to improve access to affordable care.

Supplementary Information

The online version contains supplementary material available at 10.1007/s40200-023-01319-w.

Prevalence and patterns of comorbidities in older people with type 2 diabetes in Australian primary care settings

OBJECTIVE

The aim of this study was to identify the prevalence and patterns of comorbidity in community-dwelling older people with type 2 diabetes mellitus (T2DM) attending general practice settings in Australia.

METHODS

This study involved a cross-sectional analysis using the Bettering the Evaluation and Care of Health (BEACH) sub-study data. In a series of sub-studies, a representative sample of general practitioners was asked to record all diagnosed chronic conditions for patients at 40 consecutive encounters using structured paper-based recording forms. The dataset was analysed with descriptive analyses, and exploratory factor analyses were applied to examine comorbidity patterns.

RESULTS

Of the 14,042 patients aged 65 years or older, 2688 had a diagnosis of T2DM (19%). Of the 2688 patients with T2DM, hypertension was present in 67% (95% CI: 64.6-70.0), followed by arthritis 52% (95% CI: 48.8-54.8), hyperlipidaemia 45% (95% CI: 41.8-47.9), ischemic heart disease, 23% (95% CI: 20.7-24.9), depression 16% (95% CI: 48.8-54.8), atrial fibrillation 10% (95% CI: 8.9-11.6), congestive heart failure 7% (95% CI: 6.0-8.1), stroke/cerebrovascular accident 7% (95% CI: 5.4-8.2) and peripheral vascular disease 5% (95% CI: 4.4-6.2). We identified two comorbidity patterns among older people with T2DM. The first were psychological and musculoskeletal conditions and the second were cardiovascular conditions and chronic renal failure.

CONCLUSIONS

The prevalence of cardiovascular and non-cardiovascular comorbidities in community-dwelling older people with T2DM was high. Adequate primary care strategies should be in place to support the long-term care for this population.

Cost-Savings Associated with Multi-Disciplinary Team Approach for Reducing Macrovascular and Microvascular Complications in Patients with Type 2 Diabetes: A Predictive Model

Purpose

This study aims to predict the expected cost savings associated with implementing a multidisciplinary team (MDT) approach to reduce macrovascular and microvascular complications among patients with type 2 diabetes mellitus (T2DM).

Methods

This economic evaluation study was conducted in Riyadh First Health Cluster, Saudi Arabia as a predictive model conceptualized by the authors based on models used in previous studies, particularly the CORE Diabetes Model. Our model was designed based on 1) the level of glycemic control among 24,755 T2DM patients served by MDTs; 2) the expected incidence of diabetes-related complications without intervention; 3) the predicted risk reduction of developing diabetes-related complications with MDTs. Costs of complications and cost savings were then calculated and expressed as mean incremental annual cost savings adjusted for a 1% reduction in HbA1c, and a 10 mmHg reduction in systolic blood pressure (SBP).

Results

Along with the expected reduction in all diabetes-related complications, the average incremental cost savings per diabetic patient is predicted to be ($38,878) with approximately ($11,108) in the year of complication onset and ($27,770) over the subsequent post-index 10-years. On adjustment of cost savings, the average incremental cost savings are predicted to be ($22,869) for each 1% reduction in HbA1c per diabetic patient and ($27,770) for every 10 mmHg reduction in SBP per diabetic patient.

Conclusion

MDT as a model of care is effective in glycemic control among T2DM patients with a predicted significant reduction of all diabetes-related complications and in turn, a predicted significant cost savings.

Glycemic control and associated factors among type 2 diabetes mellitus patients: a cross-sectional study of Azar cohort population

BACKGROUND

Despite the growing prevalence of diabetes and its complications, there is a dearth of data regarding factors associated with glycemic control. Therefore, in this cross-sectional study, we aimed to identify factors influencing glycemic control in patients with type 2 diabetes mellitus (T2DM) in the Iranian population.

METHODS

This cross-sectional study was conducted among the Azar cohort population and the glycemic control status of patients with T2DM was investigated. Possible risk factors including age, sex, marital status, educational level, smoking status, sleep duration, family history of diabetes and hypertension, socioeconomic status, physical activity level, and co-existence of other chronic diseases and their relationship with glycemic control status were also assessed. Multivariate logistic regression analysis was used to identify determinants of glycemic control.

RESULTS

Among 1,710 T2DM patients (60.2% female), the overall prevalence of poor glycemic control was 56.8%. In the unadjusted logistic regression analysis model, a low wealth score index significantly increased the risk of poor glycemic control (OR: 1.49;1.10-2.02). Variables significantly associated with poor glycemic control even after adjusting for confounding factors were first-degree family history of diabetes (OR: 1.34; 1.08-1.65), and sleep duration (OR: 1.29 ;1.02-1.62 for 6.6-8 h/d; OR:1.42;1.10-1.88 for > 8 h/d). Interestingly, we found that the co-existence of ≥ 3 chronic diseases with diabetes decreased the risk of poor glycemic control.

CONCLUSIONS

In the current study, most of the patients with T2DM had uncontrolled glycemic control. Due to the individual and social costs of diabetes complications, it is necessary to suggest tailored and effective interventions for controlling blood glucose levels in people with diabetes.

Antihyperglycemic activity of 14-deoxy, 11, 12-didehydro andrographolide on streptozotocin-nicotinamide induced type 2 diabetic rats

BACKGROUND

Diabetic Mellitus is characterized by a lack or failure of insulin to bind to its target receptor or failure of the pancreas to yield insulin. This study evaluated the antihyperglycemic activity of 14-deoxy, 11, 12-didehydro andrographolide on streptozotocin-nicotinamide-induced type 2 diabetic rats. Diabetic conditions were induced by administering streptozotocin at a dosage of 45 mg/kg body weight and nicotinamide at a dosage of 110 mg/kg body weight through intraperitoneal injection.

MATERIALS AND METHODS

Diabetic-induced rats were treated with 14-deoxy, 11, 12-didehydro andrographolide concentrations between 10 and 500 mg/kg body weight. The blood glucose level and body weight of the rats were periodically examined. The pancreas was isolated and the histopathological staining was performed after making fine sections of the pancreas using a microtome. The influence of 14-deoxy, 11, 12-didehydro andrographolide on the expression level of various insulin signaling cascades was determined with q-PCR and western blotting.

RESULTS

The blood glucose level of the diabetic-induced rats was significantly (p < 0.05) higher when compared with the control group and resulted in a drop in the blood glucose level of the diabetic rats. Oral glucose level was also reduced in the treatment group and no significant reduction was noted in the untreated. The lipid profiling revealed that the atherogenic index and cholesterol ratio was increased in the diabetic group over the control group. Upregulation of the insulin cascades like IRTK and GLUT4 was observed by the q-PCR and upregulation of GLUT4 and IR-β was observed by the western blot analysis.

CONCLUSION

Overall, the finding indicates that 14-deoxy, 11, 12-didehydro andrographolide exhibited antihyperglycemic activity by modulating the expression of insulin cascades.

LASSO-based machine learning algorithm to predict the incidence of diabetes in different stages

BACKGROUND

Formal risk assessment is crucial for diabetes prevention. We aimed to establish a practical nomogram for predicting the risk incidence of prediabetes and prediabetes conversion to diabetes.

METHODS

A cohort of 1428 subjects was collected to develop prediction models. The LASSO was used to screen for important risk factors in prediabetes and diabetes and was compared with other algorithms (LR, RF, SVM, LDA, NB, and Treebag). Multivariate logistic regression analysis was used to construct the prediction model of prediabetes and diabetes, and drawn the predictive nomogram. The performance of the nomograms was evaluated by receiver-operating characteristic curve and calibration.

RESULTS

These findings revealed that the other six algorithms were not as good as LASSO in terms of diabetes risk prediction. The nomogram for individualized prediction of prediabetes included "Age," "FH," "Insulin_F," "hypertension," "Tgab," "HDL-C," "Proinsulin_F," and "TG" and the nomogram of prediabetes to diabetes included "Age," "FH," "Proinsulin_E," and "HDL-C". The results showed that the two models had certain discrimination, with the AUC of 0.78 and 0.70, respectively. The calibration curve of the two models also indicated good consistency.

CONCLUSIONS

We established early warning models for prediabetes and diabetes, which can help identify prediabetes and diabetes high-risk populations in advance.

Incidence and Factors Associated with Hyperglycemia in Patients with First Hospitalization for Major Depression Disorder: A Large Cross-Sectional Sample

Purpose

Major depressive disorder (MDD) is a mood disorder characterized by persistent spontaneous depression and has a high rate of disability and mortality. There is a complex relationship between MDD and disorders of glucose metabolism, and our study aimed to investigate the prevalence and risk factors for hyperglycemia in patients with MDD who were hospitalized for the first times.

Patients and Methods

A total of 981 first-time inpatients with MDD were recruited, socio-demographic information, anthropometric data, and biochemical parameters were collected for each participant. The 17-item Hamilton Assessment Scale for Depression (HAMD-17), the 14-item Hamilton Anxiety Scale (HAMA-14), the Positive Syndrome Scale (PSS), and Clinical General Impressions Inventory-Severity of Illness (CGI-SI) scores were used to assess patients' clinical symptoms.

Results

The prevalence of hyperglycemia was 9.28% among patients with MDD who were hospitalized for the first time. Compared to the non-hyperglycemic subgroup, patients in the hyperglycemic subgroup were found to have more extensive and significant demographic and clinical characteristics, higher levels of metabolism-related parameters, and more severe psychological and psycho-pathological symptoms. Age, thyroid stimulating hormone (TSH), triglycerides (TG) were risk factors for hyperglycemia in MDD patients, while course of disease was a protective factor.

Conclusion

The study findings suggest that the prevalence of hyperglycemia is not high in patients with MDD who are hospitalized for the first time. The risk variables for predicting hyperglycemia include age, TSH and TG. The above three factors and course of disease have good combined diagnostic ability for hyperglycemia.

Evaluation of the Prevalence and Risk Factors of Drug-Related Problems in Hypertension and Type 2 Diabetes Mellitus Patients at a Tertiary Care Hospital: A Cross-Sectional Study

Background Drug-related problems (DRPs) potentially interfere with the desired treatment goals which may lead to increased healthcare costs, morbidity, and mortality. Despite the negative consequences of DRPs, there is a lack of comprehensive research on their prevalence and risk factors, particularly in chronic diseases such as hypertension and type 2 diabetes mellitus (DM). This study aims to evaluate the prevalence and contributing factors of DRPs among hypertension, type 2 DM, and hypertension with type 2 DM in the outpatient general medicine department. Methodology A hospital-based, prospective, observational study was conducted over three months. DRPs were classified using the Helper-Strand classification. The potential risk factors contributing to DRPs were assessed using binary and multinomial logistic regression methods. A p-value <0.05 was considered statistically significant. Results Among the 236 study participants, DRPs were more prevalent in males, and the mean age of the participants was 51.73 ± 9.47 years. DRPs were found in 76% of the study participants, and the mean number of DRPs per patient was 1.16 ± 0.45. Among the identified DRPs, suboptimal therapeutic goals (33%) were the most frequently observed, followed by ineffective drugs (32%), medication non-adherence (23%), and drug-drug interaction (5%). Therapeutic duplication and overdose were less commonly encountered as DRPs. The presence of comorbidity (adjusted odds ratio (AOR) = 5.77), and smoking (AOR = 21.07) were found to be significant risk factors (p < 0.05) contributing to DRPs. Conclusions DRPs are more prevalent in hypertension, type 2 DM, and hypertension with type 2 DM. Age range (40-60 years), comorbidity, and smoking were found to be associated with a higher incidence of DRPs. The implementation of a multidisciplinary team approach involving clinical pharmacists and physicians can effectively identify the prevalence and determine the associated risk factors of DRPs and subsequently may help employ targeted interventions to mitigate the development of DRPs.

Urine metabolomics reveals biomarkers and the underlying pathogenesis of diabetic kidney disease

PURPOSE

Diabetic kidney disease (DKD) is the most common complication of type 2 diabetes mellitus (T2DM), and its pathogenesis is not yet fully understood and lacks noninvasive and effective diagnostic biomarkers. In this study, we performed urine metabolomics to identify biomarkers for DKD and to clarify the potential mechanisms associated with disease progression.

METHODS

We applied a liquid chromatography-mass spectrometry-based metabolomics method combined with bioinformatics analysis to investigate the urine metabolism characteristics of 79 participants, including healthy subjects (n = 20), T2DM patients (n = 20), 39 DKD patients that included 19 DKD with microalbuminuria (DKD + micro) and 20 DKD with macroalbuminuria (DKD + macro).

RESULTS

Seventeen metabolites were identified between T2DM and DKD that were involved in amino acid, purine, nucleotide and primarily bile acid metabolism. Ultimately, a combined model consisting of 2 metabolites (tyramine and phenylalanylproline) was established, which had optimal diagnostic performance (area under the curve (AUC) = 0.94). We also identified 19 metabolites that were co-expressed within the DKD groups and 41 metabolites specifically expressed in the DKD + macro group. Ingenuity pathway analysis revealed three interaction networks of these 60 metabolites, involving the sirtuin signaling pathway and ferroptosis signaling pathway, as well as the downregulation of organic anion transporter 1, which may be important mechanisms that mediate the progression of DKD.

CONCLUSIONS

This work reveals the metabolic alterations in T2DM and DKD, constructs a combined model to distinguish them and delivers a novel strategy for studying the underlying mechanism and treatment of DKD.

Metabolic characteristics, prevalence of anxiety and its influencing factors in first-episode and drug-naïve major depressive disorder patients with impaired fasting glucose

BACKGROUND

Both major depressive disorder (MDD) and impaired fasting glucose (IFG) are associated with metabolic abnormalities and anxiety, but few studies have investigated the relationship between abnormal metabolism and anxiety in first-episode and drug-naïve (FEDN) MDD patients with IFG. This study investigated the psychological status, metabolic properties, the prevalence and influencing factors of anxiety symptoms in the FEDN MDD patients with IFG.

METHODS

A total of 1718 FEDN MDD outpatients were recruited. Sociodemographic and suicide data were collected for each participant. The Positive and Negative Syndrome Scale (PANSS), Hamilton Depression Rating Scale (HAMD), and Hamilton Anxiety Rating Scale (HAMA) were used to assess patients' clinical symptoms. Fasting blood glucose, lipids, body mass index (BMI), and thyroid function-related indicators were also measured.

RESULTS

FEDN MDD patients with IFG (IFG group) had higher psychotic symptoms, suicide attempts, HAMD score, and HAMA score than FEDN MDD patients without IFG (NIFG group). There were also significant differences in blood lipids, BMI, and thyroid function indicators between the two groups. The prevalence of anxiety symptoms in the IFG group was 20.9 %, which was significantly higher than that in the NIFG group (10.4 %). Furthermore, anxiety symptoms were significantly associated with female, marital status, psychotic symptoms, suicide attempts, and low high-density lipoprotein (HDL-C).

CONCLUSION

FEDN MDD patients with anxiety who have IFG are more likely to have problems with thyroid function, lipid metabolism, psychotic symptoms and suicide attempts, especially in female patients. Prevention of these problems should be enhanced when treating such patients.

Statin use and the risk of CVD events, stroke, and all-cause mortality in patients with diabetes: A systematic review and meta-analysis

AIMS

Considering the lack of evidence on statin use and the risk of cardiovascular disease (CVD) in patients with diabetes in primary and secondary prevention, this study aimed to evaluate the effect of statin use in individuals with diabetes for primary and secondary prevention.

DATA SYNTHESIS

The MEDLINE, Web of Science, Embase, ClinicalTrials.gov, and Cochrane Central Register for Controlled Trials databases were searched. We included studies that assessed the effect of statin use in individuals with diabetes for at least 1 year. The outcomes included CVD, all-cause mortality, and stroke. A total of 24 studies including 2,152,137 patients with diabetes were included in the meta-analysis. Compared with statin non-users, patients who received statins showed a lower risk of CVD events (primary prevention: risk ratio [RR] = 0.80, 95% confidence interval [CI] 0.69-0.94, P = 0.006; secondary prevention: RR = 0.75, 95% CI 0.65-0.87, P < 0.0001). No association was observed between statin and non-statin users and the risk of all-cause mortality. The pooled results also revealed that statin use reduced the risk of ischemic stroke in patients with diabetes (primary prevention: RR = 0.83, 95% CI 0.70-0.97, P = 0.020; secondary prevention: RR = 0.74, 95% CI 0.63-0.85, P < 0.0001).

CONCLUSIONS

Statin use significantly reduced the risk of CVD events and stroke, but not all-cause mortality, in individuals with diabetes undergoing both primary and secondary prevention. More data are required to verify the effects of statins in patients with diabetes.

SYSTEMATIC REVIEW REGISTRATION

PROSPERO CRD42021281132.

Outcome of Renal Transplantation in Patients With Diabetes Mellitus: A Single-Center Experience

BACKGROUND

An increasing proportion of kidney recipients have diabetes mellitus (DM). Some concerns have been raised about the kidney transplantation results in diabetic patients. Therefore, we assessed the effect of DM on morbidity and mortality of diabetic patients with renal transplantation.

METHODS

We retrospectively studied adult patients with and without DM who underwent living donor transplantation between 2007 and 2016. Information concerning demographic and clinical data were retrospectively analyzed by reviewing the patient files.

RESULTS

Of the 1536 transplant recipients, 126 (8%) had diabetes mellitus (mean age 49.4 ± 11.8) and 525 patients were evaluated in the non-diabetic control group (mean age 36.2 ± 15.9). The diabetic and non-diabetic patient groups had a mean follow-up after kidney transplantation 42.5 months (0.27-101.7 months) and 58.8 ± 10.6 months, respectively. In the diabetic patient group, only 3 patients had lost graft and 13 patients were exitus. Three patients had lost graft and 5 patients were exitus in non-diabetic patient group. Cardiac death (54.5%) was the most common cause of mortality in diabetic group. The 6-year patient and graft survival rates are 84.9% and 95.3%; 97.5% and 97.2% in the diabetic and non-diabetic patient groups, respectively.

CONCLUSIONS

Both infection and cardiovascular diseases increase morbidity and mortality in renal transplant patients with diabetes mellitus. The mortality risk of diabetic patients after renal transplantation is higher than the non-diabetic kidney recipients. Therefore, diabetic patients need meticulous cardiac evaluation before renal transplantation and a close follow-up, in terms of infection, after transplantation.

Urine Metabolomics Analysis in Patients With Normoalbuminuric Diabetic Kidney Disease

Objective

Diabetic kidney disease (DKD) leads to low high albuminuria and gradually progresses to very high albuminuria with kidney insufficiency. However, about 20–40% of DKD is normoalbuminuric DKD (NADKD), which has impaired kidney function but normal urine albumin. This study is to investigate the urine metabolomic profiles of patients with NADKD and albuminuria DKD (ADKD).

Methods

In total, 95 patients were divided into a simple diabetes mellitus group (SDM group), an ADKD group, and a NADKD group. All subjects were analyzed for urine metabolites using non-targeted metabolomics based on ultra-performance liquid chromatography – tandem mass spectrometry.

Results

The urine metabolomic profiles of the SDM group, NADKD group, and ADKD group were significantly different, and 65 different metabolites were identified among the three groups. Metabolic pathway analysis of these differential metabolites found that the top three significantly changed metabolic pathways were linoleic acid metabolism, citrate cycle, and, arginine and proline metabolism. There are 12 metabolites enriched in these three metabolic pathways. In detail, compared with those in the SDM group, the levels of γ-linolenic acid in the ADKD group were increased significantly, while the levels of succinic acid, cis-aconitic acid, citric acid, L-proline, L-erythro-4-hydroxyglutamate, N-methylhydantoin, N-carbamoylputrescine, spermidine, and 5-aminopentanoic acid were reduced significantly; compared with those in the NADKD group, the levels of linoleic acid, γ-linolenic acid, and L-malic acid in the ADKD group were increased significantly (P < 0.05), while the levels of L-proline, L-erythro-4-hydroxyglutamate, N-carbamoylputrescine, and spermidine were significantly reduced (P < 0.05). However, there were no significant difference between the SDM group and NADKD group (P > 0.05).

Conclusion

The urine metabolomic profiles between the NADKD group and the ADKD group are significantly different. Specifically, these two groups have distinct levels of linoleic acid, γ-linolenic acid, L-malic acid, L-proline, L-erythro-4-hydroxyglutamate, N-carbamoylputrescine, and spermidine.

Establishment of Clinical Prediction Model Based on the Study of Risk Factors of Stroke in Patients With Type 2 Diabetes Mellitus

Purpose: Stroke has sparked global concern as it seriously threatens people's life, bringing about dramatic health burdens on patients, especially for type 2 diabetes mellitus (T2DM) patients. Therefore, a risk scoring model is urgently valuable for T2DM patients to predict the risk of stroke incidence and for positive health intervention. Methods: We randomly divided 4,335 T2DM patients into two groups, training set (n = 3,252) and validation set (n = 1,083), at the ratio of 3:1. Characteristic variables were then selected based on the data of training set through least absolute shrinkage and selection operator regression. Three models were established to verify predictive ability. Foundation model was composed of basic information and physical indicators. Biochemical model consisted of biochemical indexes. Integrated model combined the above two models. Data of three models were then put into logistic regression analysis to form nomogram prediction models. Tools including C index, calibration plot, and curve analysis were implemented to test discrimination, calibration, and clinical use. To select the best predicting model, net reclassification improvement (NRI) and integrated discrimination improvement (IDI) were put into effect. Results: Eleven risk factors were determined, including age, duration of T2DM, estimated glomerular filtration rate, systolic blood pressure, diastolic blood pressure, low-density lipoprotein, high-density lipoprotein, triglyceride, body mass index, uric acid, and glycosylated hemoglobin A_1c, all with significant P-values through logistic regression analysis. In the training set, areas under the curve of three models were 0.810, 0.819, and 0.884, whereas in the validation set, they were 0.836, 0.832, and 0.909. Through calibration plot, the S:P values in the training set were 0.836, 0.754, and 0.621 and were 0.918, 0.682, and 0.666 separately in the validation set. In terms of the decision curve analysis, the risk thresholds were, respectively, 8-73%, 8-98%, and 8%~ in the training set and 8-70%, 8-90%, and 8-95% in the validation set. With the aid of NRI and IDI, integrated model is proved to be the best model in training set and validation set. Besides, internal validation was conducted on all the subjects in this study, and the C index was 0.890 (0.873-0.907). Conclusion: This study established a model predicting risk of stroke for T2DM patients through a community-based survey.