Prediction of five-year all-cause mortality in Chinese patients with type 2 diabetes mellitus - A population-based retrospective cohort study

Development and validation of a nomogram of all-cause mortality in adult Americans with diabetes

This study aimed to develop and validate a predictive model of all-cause mortality risk in American adults aged ≥ 18 years with diabetes. 7918 participants with diabetes were enrolled from the National Health and Nutrition Examination Survey (NHANES) 1999-2016 and followed for a median of 96 months. The primary study endpoint was the all-cause mortality. Predictors of all-cause mortality included age, Monocytes, Erythrocyte, creatinine, Nutrition Risk Index (NRI), neutrophils/lymphocytes (NLR), smoking habits, alcohol consumption, cardiovascular disease (CVD), urinary albumin excretion rate (UAE), and insulin use. The c-index was 0.790 (95% CI 0.779-0.801, P < 0.001) and 0.792 (95% CI: 0.776-0.808, P < 0.001) for the training and validation sets, respectively. The area under the ROC curve was 0.815, 0.814, 0.827 and 0.812, 0.818 and 0.829 for the training and validation sets at 3, 5, and 10 years of follow-up, respectively. Both calibration plots and DCA curves performed well. The model provides accurate predictions of the risk of death for American persons with diabetes and its scores can effectively determine the risk of death in outpatients, providing guidance for clinical decision-making and predicting prognosis for patients.

Cardio-cerebrovascular Outcomes in MODY, Type 1 Diabetes, and Type 2 Diabetes: A Prospective Cohort Study

CONTEXT

Cardio-cerebrovascular events are severe complications of diabetes.

OBJECTIVE

We aim to compare the incident risk of cardio-cerebrovascular events in maturity onset diabetes of the young (MODY), type 1 diabetes, and type 2 diabetes.

METHODS

Type 1 diabetes, type 2 diabetes, and MODY were diagnosed by whole exome sequencing. The primary endpoint was the occurrence of the first major adverse cardiovascular event (MACE), including acute myocardial infarction, heart failure, stroke, unstable angina pectoris, and cardio-cerebrovascular-related mortality. Cox proportional hazards models were applied and adjusted to calculate hazard ratios (HRs) and 95% CIs for the incident risk of MACE in type 1 diabetes, type 2 diabetes, MODY, and MODY subgroups compared with people without diabetes (control group).

RESULTS

Type 1 diabetes, type 2 diabetes, and MODY accounted for 2.7%, 68.1%, and 11.4% of 26 198 participants with diabetes from UK Biobank. During a median follow-up of 13 years, 1028 MACEs occurred in the control group, contrasting with 70 events in patients with type 1 diabetes (HR 2.15, 95% CI 1.69-2.74, P < .05), 5020 events in patients with type 2 diabetes (HR 7.02, 95% CI 6.56-7.51, P < .05), and 717 events in MODY (HR 5.79, 95% CI 5.26-6.37, P < .05). The hazard of MACE in HNF1B-MODY was highest among MODY subgroups (HR 11.00, 95% CI 5.47-22.00, P = 1.5 × 10-11).

CONCLUSION

MODY diagnosed by genetic analysis represents higher prevalence than the clinical diagnosis in UK Biobank. The risk of incident cardio-cerebrovascular events in MODY ranks between type 1 diabetes and type 2 diabetes.

Development and Validation of the Life Expectancy Estimator for Older Adults with Diabetes (LEAD): the Diabetes and Aging Study

BACKGROUND

Estimated life expectancy for older patients with diabetes informs decisions about treatment goals, cancer screening, long-term and advanced care, and inclusion in clinical trials. Easily implementable, evidence-based, diabetes-specific approaches for identifying patients with limited life expectancy are needed.

OBJECTIVE

Develop and validate an electronic health record (EHR)-based tool to identify older adults with diabetes who have limited life expectancy.

DESIGN

Predictive modeling based on survival analysis using Cox-Gompertz models in a retrospective cohort.

PARTICIPANTS

Adults with diabetes aged ≥ 65 years from Kaiser Permanente Northern California: a 2015 cohort (N = 121,396) with follow-up through 12/31/2019, randomly split into training (N = 97,085) and test (N = 24,311) sets. Validation was conducted in the test set and two temporally distinct cohorts: a 2010 cohort (n = 89,563; 10-year follow-up through 2019) and a 2019 cohort (n = 152,357; 2-year follow-up through 2020).

MAIN MEASURES

Demographics, diagnoses, utilization and procedures, medications, behaviors and vital signs; mortality.

KEY RESULTS

In the training set (mean age 75 years; 49% women; 48% racial and ethnic minorities), 23% died during 5 years follow-up. A mortality prediction model was developed using 94 candidate variables, distilled into a life expectancy model with 11 input variables, and transformed into a risk-scoring tool, the Life Expectancy Estimator for Older Adults with Diabetes (LEAD). LEAD discriminated well in the test set (C-statistic = 0.78), 2010 cohort (C-statistic = 0.74), and 2019 cohort (C-statistic = 0.81); comparisons of observed and predicted survival curves indicated good calibration.

CONCLUSIONS

LEAD estimates life expectancy in older adults with diabetes based on only 11 patient characteristics widely available in most EHRs and claims data. LEAD is simple and has potential application for shared decision-making, clinical trial inclusion, and resource allocation.

Influence of Diabetes Mellitus and Smoking on Pro- and Anti-Inflammatory Cytokine Profiles in Gingival Crevicular Fluid

Smoking and diabetes mellitus have been recognized as significant modifying factors of the evolution of periodontitis, being considered at the moment as descriptive factors in the periodontitis grading system. The purpose of this study was to assess the consequence of smoking, type 2 diabetes, and the combination of these two factors on clinical periodontal parameters, on the levels of gingival crevicular fluid (GCF), and also on ratios of pro-inflammatory and anti-inflammatory cytokines by using a commercially available kit-based multiplex fluorescent immunoassay. The study was carried out on 124 volunteers (control (C) group = 29, diabetes mellitus (DM) group = 32, smoking (S) group = 31, and S + DM group = 32). Total mean bleeding on probing was significantly lower in the S and S + DM groups, compared to that of the other groups (p < 0.05). Total amounts of TGF-β, MIP-1α, IL-6, IL-2, and IL-17 were significantly increased in the periodontally healthy sites of diabetes patients (p < 0.05), compared to those of the controls. Systemically healthy smoking patients had higher values of GM-CSF, TGF-β, IL-4, TNF-α, IL-5, and IL-7, while diabetic smoking patients showed higher values of IL-4, TGF-β, and MIP-1α. In smoking and systemically healthy patients, IL-23, IL-7, and IL-12 showed increased concentrations, while concentrations of TGF-β, MIP-1α, IL-2, IL-7, IL-12, IL-17, IL-21, and IL-23 were higher in smoking DM patients. In conclusion, in our study, diabetes mellitus induced a general pro-inflammatory state, while smoking mainly stimulated immunosuppression in the periodontal tissues of periodontitis subjects.

Diagnostic efficacy of cystatin-c in association with different ACE genes predicting renal insufficiency in T2DM

Diabetic nephropathy (DN) seems to be the major cause of chronic kidney disease that may finally lead to End Stage Renal Disease. So, renal function assessment in type 2 diabetes mellitus (T2DM) individuals is very important. Clearly, DN pathogenesis is multifactorial and different proteins, genes and environmental factors can contribute to the onset of the disease. We assessed sensitive and specific biomarkers (in blood and urine) which can predict kidney disease susceptibility among T2DM patients. Serum cystatin-c (cyst-c) in blood and urinary hemeoxygenase (HO-1) in addition to ACE I/D polymorphism and ACE G2350A genotypes. Hundred and eight T2DM patients and 85 controls were enrolled. Serum cystatin-c and urinary (HO-1) were tested by ELISA. Genetic determination of both ACE I/D polymorphism and ACE G2350A genotypes was performed by PCR for all participants. Significant rise in serum cystatin-c and urinary HO-1 levels were shown in diabetic groups compared with control group. Moreover, GG genotype of ACE G2350A gene in diabetic group was associated with rise in serum cystatin-c and urinary HO-1 compared with control group. Mutant AA genotype demonstrated increase in urinary HO-1. DD polymorphism was associated with rise in serum creatinine and cyst-c in diabetic group. Positive correlation was seen between duration of diabetes and serum cyst-c and between serum glucose and urinary (HO-1) in diabetic group. The results from this study indicated an association of serum cystatin-c with GG genotype of ACE G2350A in conjugation with DD polymorphism of ACE I/D which could be an early predictor of tubular injury in T2DM diabetic patients.

Developing a prediction model for all-cause mortality risk among patients with type 2 diabetes mellitus in Shanghai, China

BACKGROUND

All-cause mortality risk prediction models for patients with type 2 diabetes mellitus (T2DM) in mainland China have not been established. This study aimed to fill this gap.

METHODS

Based on the Shanghai Link Healthcare Database, patients diagnosed with T2DM and aged 40-99 years were identified between January 1, 2013 and December 31, 2016 and followed until December 31, 2021. All the patients were randomly allocated into training and validation sets at a 2:1 ratio. Cox proportional hazards models were used to develop the all-cause mortality risk prediction model. The model performance was evaluated by discrimination (Harrell C-index) and calibration (calibration plots).

RESULTS

A total of 399 784 patients with T2DM were eventually enrolled, with 68 318 deaths over a median follow-up of 6.93 years. The final prediction model included age, sex, heart failure, cerebrovascular disease, moderate or severe kidney disease, moderate or severe liver disease, cancer, insulin use, glycosylated hemoglobin, and high-density lipoprotein cholesterol. The model showed good discrimination and calibration in the validation sets: the mean C-index value was 0.8113 (range 0.8110-0.8115) and the predicted risks closely matched the observed risks in the calibration plots.

CONCLUSIONS

This study constructed the first 5-year all-cause mortality risk prediction model for patients with T2DM in south China, with good predictive performance.

Prediction models of diabetes complications: a scoping review

BACKGROUND

Diabetes often places a large burden on people with diabetes (hereafter 'patients') and the society, that is, in part attributable to its complications. However, evidence from models predicting diabetes complications in patients remains unclear. With the collaboration of patient partners, we aimed to describe existing prediction models of physical and mental health complications of diabetes.

METHODS

Building on existing frameworks, we systematically searched for studies in Ovid-Medline and Embase. We included studies describing prognostic prediction models that used data from patients with pre-diabetes or any type of diabetes, published between 2000 and 2020. Independent reviewers screened articles, extracted data and narratively synthesised findings using established reporting standards.

RESULTS

Overall, 78 studies reported 260 risk prediction models of cardiovascular complications (n=42 studies), mortality (n=16), kidney complications (n=14), eye complications (n=10), hypoglycaemia (n=8), nerve complications (n=3), cancer (n=2), fracture (n=2) and dementia (n=1). Prevalent complications deemed important by patients such as amputation and mental health were poorly or not at all represented. Studies primarily analysed data from older people with type 2 diabetes (n=54), with little focus on pre-diabetes (n=0), type 1 diabetes (n=8), younger (n=1) and racialised people (n=10). Per complication, predictors vary substantially between models. Studies with details of calibration and discrimination mostly exhibited good model performance.

CONCLUSION

This rigorous knowledge synthesis provides evidence of gaps in the landscape of diabetes complication prediction models. Future studies should address unmet needs for analyses of complications n> and among patient groups currently under-represented in the literature and should consistently report relevant statistics. SCOPING REVIEW REGISTRATION: https://osf.io/fjubt/.

Developing a Prediction Model for 7-Year and 10-Year All-Cause Mortality Risk in Type 2 Diabetes Using a Hospital-Based Prospective Cohort Study

Leveraging easily accessible data from hospitals to identify high-risk mortality rates for clinical diabetes care adjustment is a convenient method for the future of precision healthcare. We aimed to develop risk prediction models for all-cause mortality based on 7-year and 10-year follow-ups for type 2 diabetes. A total of Taiwanese subjects aged ≥18 with outpatient data were ascertained during 2007-2013 and followed up to the end of 2016 using a hospital-based prospective cohort. Both traditional model selection with stepwise approach and LASSO method were conducted for parsimonious models' selection and comparison. Multivariable Cox regression was performed for selected variables, and a time-dependent ROC curve with an integrated AUC and cumulative mortality by risk score levels was employed to evaluate the time-related predictive performance. The prediction model, which was composed of eight influential variables (age, sex, history of cancers, history of hypertension, antihyperlipidemic drug use, HbA1c level, creatinine level, and the LDL /HDL ratio), was the same for the 7-year and 10-year models. Harrell's C-statistic was 0.7955 and 0.7775, and the integrated AUCs were 0.8136 and 0.8045 for the 7-year and 10-year models, respectively. The predictive performance of the AUCs was consistent with time. Our study developed and validated all-cause mortality prediction models with 7-year and 10-year follow-ups that were composed of the same contributing factors, though the model with 10-year follow-up had slightly greater risk coefficients. Both prediction models were consistent with time.

Three-year trajectories of metabolic risk factors predict subsequent long-term mortality in patients with type 2 diabetes

AIM

This study aims to evaluate the associations between 3-year trajectories of metabolic risk factors and subsequent mortality in patients with type 2 diabetes.

METHODS

A total of 6400 persons aged ≥ 30 years with type 2 diabetes and ≥ 3 years of follow-up period were included. The cluster analysis determined the patterns of 3-year trajectories, and Cox proportional hazards models evaluated the associations between patterns and mortality.

RESULTS

Three trajectory subgroups of metabolic risk factors, namely, cluster 1, normal; cluster 2, high-stable or reducing with high level at baseline; and cluster 3, fluctuation: elevated and decreasing, were generated. The clusters 2 and 3 of body mass index (BMI), fasting plasma glucose (FPG), HbA1c, and triglyceride (TG) trajectories were associated with increased risks of all-cause mortality compared with cluster 1 (hazard ratio = 1.27, 95% confidence interval = 1.06-1.51 and 1.45, 1.19-1.78 for BMI; 1.41, 1.22-1.62 and 1.81, 1.38-2.38 for FPG; 1.42, 1.23-1.64 and 1.47, 1.23-1.75 for HbA1c; 1.34, 1.10-1.63 and 2.40, 1.30-4.37 for TG, respectively). For the systolic blood pressure trajectory, only cluster 3 was associated with an increased mortality risk relative to cluster 1 (1.76, 1.13-2.77).

CONCLUSIONS

Long-term metabolic risk factor trajectories may be associated with subsequent mortality.

Identification of Novel Biomarker for Early Detection of Diabetic Nephropathy

Diabetic nephropathy (DN) is one of the most common complications of diabetes mellitus. After development of DN, patients will progress to end-stage renal disease, which is associated with high morbidity and mortality. Here, we developed early-stage diagnostic biomarkers to detect DN as a strategy for DN intervention. For the DN model, Zucker diabetic fatty rats were used for DN phenotyping. The results revealed that DN rats showed significantly increased blood glucose, blood urea nitrogen (BUN), and serum creatinine levels, accompanied by severe kidney injury, fibrosis and microstructural changes. In addition, DN rats showed significantly increased urinary excretion of kidney injury molecule-1 (KIM-1) and neutrophil gelatinase-associated lipocalin (NGAL). Transcriptome analysis revealed that new DN biomarkers, such as complementary component 4b (C4b), complementary factor D (CFD), C-X-C motif chemokine receptor 6 (CXCR6), and leukemia inhibitory factor (LIF) were identified. Furthermore, they were found in the urine of patients with DN. Since these biomarkers were detected in the urine and kidney of DN rats and urine of diabetic patients, the selected markers could be used as early diagnosis biomarkers for chronic diabetic nephropathy.

Building clinical risk score systems for predicting the all-cause and expanded cardiovascular-specific mortality of patients with type 2 diabetes

AIM

To develop and validate risk score systems by examining the effects of glycaemic and blood pressure variabilities on the all-cause and expanded cardiovascular-specific mortality of people with type 2 diabetes.

MATERIALS AND METHODS

This retrospective cohort study consisted of 9692 patients aged 30-85 years, diagnosed with type 2 diabetes and enrolled in a managed care programme of a medical centre from 2002 to 2016. All the patients were randomly allocated into two groups, namely, training and validation sets (2:1 ratio), and followed up until death or August 2019. Cox's proportional hazard regression was performed to develop all-cause and expanded cardiovascular-specific mortality prediction models. The performance of the prediction model was assessed by using the area under the receiver operating characteristic curve (AUROC).

RESULTS

Overall, 2036 deaths were identified after a mean of 8.6 years of follow-up. The AUROC-measured prediction accuracies of 3-, 5-, 10- and 15-year all-cause mortalities based on a model containing the identified traditional risk factors, biomarkers and variabilities in fasting plasma glucose, HbA1c and blood pressure in the validation set were 0.79 (0.76-0.83), 0.78 (0.76-0.81), 0.80 (0.78-0.82) and 0.80 (0.78-0.82), respectively. The corresponding values of the expanded cardiovascular-specific mortalities were 0.85 (0.80-0.90), 0.83 (0.79-0.86), 0.80 (0.77-0.83) and 0.79 (0.77-0.82), respectively.

CONCLUSIONS

Our prediction models considering glycaemic and blood pressure variabilities had good prediction accuracy for the expanded cardiovascular-specific and all-cause mortalities of patients with type 2 diabetes.

Smoking and diabetes: dangerous liaisons and confusing relationships

The combined harmful effects of cigarette smoking and hyperglycemia can accelerate vascular damage in patients with diabetes who smoke, as is well known. Can smoking cause diabetes? What are the effects of smoking on macro and microvascular complications? Now growing evidence indicates that regular smokers are at risk of developing incident diabetes. Since the prevalence rates of smoking in patients with diabetes are relatively similar to those of the general population, it is essential to address the main modifiable risk factor of smoking to prevent the onset of diabetes and delay the development of its complications. Quitting smoking shows clear benefits in terms of reducing or slowing the risk of cardiovascular morbidity and mortality in people with diabetes. Does quitting smoking decrease the incidence of diabetes and its progression? What are the effects of quitting smoking on complications? The current evidence does not seem to unequivocally suggest a positive role for quitting in patients with diabetes. Quitting smoking has also been shown to have a negative impact on body weight, glycemic control and subsequent increased risk of new-onset diabetes. Moreover, its role on microvascular complications of the disease is unclear. What are the current smoking cessation treatments, and which ones are better for patients with diabetes? Stopping smoking may be of value for diabetes prevention and management of the disease and its macrovascular and microvascular complications. Unfortunately, achieving long-lasting abstinence is not easy and novel approaches for managing these patients are needed. This narrative review examines the evidence on the impact of smoking and smoking cessation in patients with diabetes and particularly in type 2 diabetes mellitus and its complications. In addition, management options and potential future directions will be discussed.

Ten-year risk prediction models of complications and mortality of Chinese patients with diabetes mellitus in primary care in Hong Kong: a study protocol

INTRODUCTION

Diabetes mellitus (DM) is a major disease burden worldwide because it is associated with disabling and lethal complications. DM complication risk assessment and stratification is key to cost-effective management and tertiary prevention for patients with diabetes in primary care. Existing risk prediction functions were found to be inaccurate in Chinese patients with diabetes in primary care. This study aims to develop 10-year risk prediction models for total cardiovascular diseases (CVD) and all-cause mortality among Chinese patients with DM in primary care.

METHODS AND ANALYSIS

A 10-year cohort study on a population-based primary care cohort of Chinese patients with diabetes, who were receiving care in the Hospital Authority General Outpatient Clinic on or before 1 January 2008, were identified from the clinical management system database of the Hospital Authority. All patients with complete baseline risk factors will be included and followed from 1 January 2008 to 31 December 2017 for the development and validation of prediction models. The analyses will be carried out separately for men and women. Two-thirds of subjects will be randomly selected as the training sample for model development. Cox regressions will be used to develop 10-year risk prediction models of total CVD and all-cause mortality. The validity of models will be tested on the remaining one-third of subjects by Harrell's C-statistics and calibration plot. Risk prediction models for diabetic complications specific to Chinese patients in primary care will enable accurate risk stratification, prioritisation of resources and more cost-effective interventions for patients with DM in primary care.

ETHICS AND DISSEMINATION

The study was approved by the Institutional Review Board of the University of Hong Kong-the Hospital Authority Hong Kong West Cluster (reference number: UW 15-258).

TRIAL REGISTRATION NUMBER

NCT03299010; Pre-results.

Total/high density lipoprotein cholesterol and cardiovascular disease (re)hospitalization nadir in type 2 diabetes

Total cholesterol to HDL cholesterol ratio (TC/HDL) is an important prognostic factor for CVD. This study used restricted cubic spline modeling to investigate the dose-response associations between TC/HDL and both CVD hospitalization and CVD rehospitalization in two independent prospective cohorts. The East Cambridgeshire and Fenland cohort includes 4,704 patients with T2D from 18 general practices in Cambridgeshire. The Randomized controlled trial of Peer Support In type 2 Diabetes cohort comprises 1,121 patients with T2D with posttrial follow-up data. TC/HDL and other demographic and clinical measurements were measured at baseline. Outcomes were CVD hospitalization over 2 years and CVD rehospitalization after 90 days of the prior CVD hospitalization. Modeling showed nonlinear relationships between TC/HDL and risks of CVD hospitalization and rehospitalization consistently in both cohorts (all P < 0.001 for linear tests). The lowest risks of CVD hospitalization and rehospitalization were consistently found for TC/HDL at 2.8 (95% CI: 2.6-3.0) in both cohorts and both overall and by gender. This is lower than the current lipid control target, 4.0 of TC/HDL. Reducing the TC/HDL target to 2.8 would include a further 33-44% patients with TC/HDL in the 2.8-4.0 range. Studies are required to assess the effectiveness and cost-effectiveness of the earlier introduction of, and more intensive, lipid-lowering treatment needed to achieve this new lower TC/HDL target.

Association of estimated glomerular filtration rate and proteinuria with all-cause mortality in community-based population in China: A Result from Kailuan Study

This study was based on 95391 participants (18-98 years old) from the Kailuan study, which assessed all-cause mortality in a community-based population in northern China according to estimated glomerular filtration rate (eGFR) by the Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) formula and proteinuria estimated from urine dipstick results. Data were analysed based on Cox proportional hazards models with adjustment for relevant confounders, and the results were expressed as hazard ratios (HRs) with 95% confidence intervals (CIs). During eight years of follow-up, a total of 6024 participants died. The two indicators, eGFR < 45 ml/min/1.73 m² and the presence of proteinuria, were independently associated with all-cause mortality. Compared with eGFR ≥45 ml/min/1.73 m² with negative proteinuria, HRs of all-cause mortality were 1.26 (95% CI 1.10-1.44) for eGFR < 45 ml/min/1.73 m² without proteinuria, 1.95 (1.78-2.14) for eGFR ≥45 ml/min/1.73 m² with proteinuria, and 2.63 (2.14-3.23) for eGFR < 45 ml/min/1.73 m² with proteinuria. The all-cause mortality risk of eGFR and/or proteinuria was much higher in females than in males (P for interaction < 0.01). In conclusion, both severely decreased eGFR and proteinuria are independent predictors of all-cause mortality in the general northern Chinese population. A combination of severely decreased eGFR and proteinuria increases the risk of all-cause mortality, which is even over 5-fold higher in females.

Cigarette Smoking, Diabetes, and Diabetes Complications: Call for Urgent Action

PURPOSE OF REVIEW

This review aimed to examine the latest evidence linking cigarette smoking and cessation to risk of incident diabetes and its complications.

RECENT FINDINGS

Abundant evidence has demonstrated that smoking is associated with increased risk of type 2 diabetes and cardiovascular disease among diabetic patients, while its relationship with microvascular complications is more limited to diabetic nephropathy and neuropathy in type 1 diabetes. In addition, diabetes risk remains high in the short term after smoking cessation, while it reduces gradually in the long term. Risk of cardiovascular complications also substantially decreases after quitting smoking, but results for microvascular complications are not consistent. Smoking is associated with increased risks of incident diabetes in the general population and cardiovascular complications among diabetic patients. Although the short-term post-cessation diabetes risk needs to be acknowledged, this review calls for urgent action to implement population-wide policies and individual pharmaceutical and lifestyle interventions (if evidence accumulated in future) to aid smoking cessation and prevent diabetes and its complications.