Estimation of Mortality Risk in Type 2 Diabetic Patients (ENFORCE): An Inexpensive and Parsimonious Prediction Model

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.

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.

Precision prognostics for the development of complications in diabetes

Individuals with diabetes face higher risks for macro- and microvascular complications than their non-diabetic counterparts. The concept of precision medicine in diabetes aims to optimise treatment decisions for individual patients to reduce the risk of major diabetic complications, including cardiovascular outcomes, retinopathy, nephropathy, neuropathy and overall mortality. In this context, prognostic models can be used to estimate an individual's risk for relevant complications based on individual risk profiles. This review aims to place the concept of prediction modelling into the context of precision prognostics. As opposed to identification of diabetes subsets, the development of prediction models, including the selection of predictors based on their longitudinal association with the outcome of interest and their discriminatory ability, allows estimation of an individual's absolute risk of complications. As a consequence, such models provide information about potential patient subgroups and their treatment needs. This review provides insight into the methodological issues specifically related to the development and validation of prediction models for diabetes complications. We summarise existing prediction models for macro- and microvascular complications, commonly included predictors, and examples of available validation studies. The review also discusses the potential of non-classical risk markers and omics-based predictors. Finally, it gives insight into the requirements and challenges related to the clinical applications and implementation of developed predictions models to optimise medical decision making.

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/.

Circulating Metabolites Associate With and Improve the Prediction of All-Cause Mortality in Type 2 Diabetes

Death rate is increased in type 2 diabetes. Unraveling biomarkers of novel pathogenic pathways capable to identify high-risk patients is instrumental to tackle this burden. We investigated the association between serum metabolites and all-cause mortality in type 2 diabetes and then whether the associated metabolites mediate the effect of inflammation on mortality risk and improve ENFORCE (EstimatioN oF mORtality risk in type2 diabetic patiEnts) and RECODe (Risk Equation for Complications Of type 2 Diabetes), two well-established all-cause mortality prediction models in diabetes. Two cohorts comprising 856 individuals (279 all-cause deaths) were analyzed. Serum metabolites (n = 188) and pro- and anti-inflammatory cytokines (n = 7) were measured. In the pooled analysis, hexanoylcarnitine, kynurenine, and tryptophan were significantly and independently associated with mortality (hazard ratio [HR] 1.60 [95% CI 1.43-1.80]; 1.53 [1.37-1.71]; and 0.71 [0.62-0.80] per 1 SD). The kynurenine-to-tryptophan ratio (KTR), a proxy of indoleamine-2,3-dioxygenase, which degrades tryptophan to kynurenine and contributes to a proinflammatory status, mediated 42% of the significant association between the antiatherogenic interleukin (IL) 13 and mortality. Adding the three metabolites improved discrimination and reclassification (all P < 0.01) of both mortality prediction models. In type 2 diabetes, hexanoylcarnitine, tryptophan, and kynurenine are associated to and improve the prediction of all-cause mortality. Further studies are needed to investigate whether interventions aimed at reducing KTR also reduce the risk of death, especially in patients with low IL-13.

A Serum Resistin and Multicytokine Inflammatory Pathway Is Linked With and Helps Predict All-cause Death in Diabetes

CONTEXT

Type 2 diabetes (T2D) shows a high mortality rate, partly mediated by atherosclerotic plaque instability. Discovering novel biomarkers may help identify high-risk patients who would benefit from more aggressive and specific managements. We recently described a serum resistin and multicytokine inflammatory pathway (REMAP), including resistin, interleukin (IL)-1β, IL-6, IL-8, and TNF-α, that is associated with cardiovascular disease.

OBJECTIVE

We investigated whether REMAP is associated with and improves the prediction of mortality in T2D.

METHODS

A REMAP score was investigated in 3 cohorts comprising 1528 patients with T2D (409 incident deaths) and in 59 patients who underwent carotid endarterectomy (CEA; 24 deaths). Plaques were classified as unstable/stable according to the modified American Heart Association atherosclerosis classification.

RESULTS

REMAP was associated with all-cause mortality in each cohort and in all 1528 individuals (fully adjusted hazard ratio [HR] for 1 SD increase = 1.34, P < .001). In CEA patients, REMAP was associated with mortality (HR = 1.64, P = .04) and a modest change was observed when plaque stability was taken into account (HR = 1.58; P = .07). REMAP improved discrimination and reclassification measures of both Estimation of Mortality Risk in Type 2 Diabetic Patients and Risk Equations for Complications of Type 2 Diabetes, well-established prediction models of mortality in T2D (P < .05-< .001).

CONCLUSION

REMAP is independently associated with and improves predict all-cause mortality in T2D; it can therefore be used to identify high-risk individuals to be targeted with more aggressive management. Whether REMAP can also identify patients who are more responsive to IL-6 and IL-1β monoclonal antibodies that reduce cardiovascular burden and total mortality is an intriguing possibility to be tested.

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.

All-cause mortality prediction models in type 2 diabetes: applicability in the early stage of disease

AIMS

The rate of all-cause mortality is twofold higher in type 2 diabetes than in the general population. Being able to identify patients with the highest risk from the very beginning of the disease would help tackle this burden.

METHODS

We tested whether ENFORCE, an established prediction model of all-cause mortality in type 2 diabetes, performs well also in two independent samples of patients with early-stage disease prospectively followed up.

RESULTS

ENFORCE's survival C-statistic was 0.81 (95%CI: 0.72-0.89) and 0.78 (95%CI: 0.68-0.87) in both samples. Calibration was also good. Very similar results were obtained with RECODe, an alternative prediction model of all-cause mortality in type 2 diabetes.

CONCLUSIONS

In conclusion, our data show that two well-established prediction models of all-cause mortality in type 2 diabetes can also be successfully applied in the early stage of the disease, thus becoming powerful tools for educated and timely prevention strategies for high-risk patients.

Development and validation of the CHIME simulation model to assess lifetime health outcomes of prediabetes and type 2 diabetes in Chinese populations: A modeling study

BACKGROUND

Existing predictive outcomes models for type 2 diabetes developed and validated in historical European populations may not be applicable for East Asian populations due to differences in the epidemiology and complications. Despite the continuum of risk across the spectrum of risk factor values, existing models are typically limited to diabetes alone and ignore the progression from prediabetes to diabetes. The objective of this study is to develop and externally validate a patient-level simulation model for prediabetes and type 2 diabetes in the East Asian population for predicting lifetime health outcomes.

METHODS AND FINDINGS

We developed a health outcomes model from a population-based cohort of individuals with prediabetes or type 2 diabetes: Hong Kong Clinical Management System (CMS, 97,628 participants) from 2006 to 2017. The Chinese Hong Kong Integrated Modeling and Evaluation (CHIME) simulation model comprises of 13 risk equations to predict mortality, micro- and macrovascular complications, and development of diabetes. Risk equations were derived using parametric proportional hazard models. External validation of the CHIME model was assessed in the China Health and Retirement Longitudinal Study (CHARLS, 4,567 participants) from 2011 to 2018 for mortality, ischemic heart disease, cerebrovascular disease, renal failure, cataract, and development of diabetes; and against 80 observed endpoints from 9 published trials using 100,000 simulated individuals per trial. The CHIME model was compared to United Kingdom Prospective Diabetes Study Outcomes Model 2 (UKPDS-OM2) and Risk Equations for Complications Of type 2 Diabetes (RECODe) by assessing model discrimination (C-statistics), calibration slope/intercept, root mean square percentage error (RMSPE), and R2. CHIME risk equations had C-statistics for discrimination from 0.636 to 0.813 internally and 0.702 to 0.770 externally for diabetes participants. Calibration slopes between deciles of expected and observed risk in CMS ranged from 0.680 to 1.333 for mortality, myocardial infarction, ischemic heart disease, retinopathy, neuropathy, ulcer of the skin, cataract, renal failure, and heart failure; 0.591 for peripheral vascular disease; 1.599 for cerebrovascular disease; and 2.247 for amputation; and in CHARLS outcomes from 0.709 to 1.035. CHIME had better discrimination and calibration than UKPDS-OM2 in CMS (C-statistics 0.548 to 0.772, slopes 0.130 to 3.846) and CHARLS (C-statistics 0.514 to 0.750, slopes -0.589 to 11.411); and small improvements in discrimination and better calibration than RECODe in CMS (C-statistics 0.615 to 0.793, slopes 0.138 to 1.514). Predictive error was smaller for CHIME in CMS (RSMPE 3.53% versus 10.82% for UKPDS-OM2 and 11.16% for RECODe) and CHARLS (RSMPE 4.49% versus 14.80% for UKPDS-OM2). Calibration performance of CHIME was generally better for trials with Asian participants (RMSPE 0.48% to 3.66%) than for non-Asian trials (RMPSE 0.81% to 8.50%). Main limitations include the limited number of outcomes recorded in the CHARLS cohort, and the generalizability of simulated cohorts derived from trial participants.

CONCLUSIONS

Our study shows that the CHIME model is a new validated tool for predicting progression of diabetes and its outcomes, particularly among Chinese and East Asian populations that has been lacking thus far. The CHIME model can be used by health service planners and policy makers to develop population-level strategies, for example, setting HbA1c and lipid targets, to optimize health outcomes.

Predictive models for cardiovascular and kidney outcomes in patients with type 2 diabetes: systematic review and meta-analyses

OBJECTIVE

To inform a clinical practice guideline (BMJ Rapid Recommendations) considering sodium glucose cotransporter-2 inhibitors and glucagon-like peptide-1 receptor agonists for treatment of adults with type 2 diabetes, we summarised the available evidence regarding the performance of validated risk models on cardiovascular and kidney outcomes in these patients.

METHODS

We systematically searched bibliographic databases in January 2020 to identify observational studies evaluating risk models for all-cause and cardiovascular mortality, heart failure (HF) hospitalisations, end-stage kidney disease (ESKD), myocardial infarction (MI) and ischaemic stroke in ambulatory adults with type 2 diabetes. Using a random effects model, we pooled discrimination measures for each model and outcome, separately, and descriptively summarised calibration plots, when available. We used the Prediction Model Risk of Bias Assessment Tool to assess risk of bias of each included study and the Grading of Recommendations, Assessment, Development, and Evaluation approach to evaluate our certainty in the evidence.

RESULTS

Of 22 589 publications identified, 15 observational studies reporting on seven risk models proved eligible. Among the seven models with >1 validation cohort, the Risk Equations for Complications of Type 2 Diabetes (RECODe) had the best calibration in primary studies and the highest pooled discrimination measures for the following outcomes: all-cause mortality (C-statistics 0.75, 95% CI 0.70 to 0.80; high certainty), cardiovascular mortality (0.79, 95% CI 0.75 to 0.84; low certainty), ESKD (0.73, 95% CI 0.52 to 0.94; low certainty), MI (0.72, 95% CI 0.69 to 0.74; moderate certainty) and stroke (0.71, 95% CI 0.68 to 0.74; moderate certainty). This model does not, however, predict risk of HF hospitalisations.

CONCLUSION

Of available risk models, RECODe proved to have satisfactory calibration in primary validation studies and acceptable discrimination superior to other models, though with high risk of bias in most primary studies.

TRIAL REGISTRATION NUMBER

CRD42020168351.

The Synergic Association of hs-CRP and Serum Amyloid P Component in Predicting All-Cause Mortality in Patients With Type 2 Diabetes

OBJECTIVE

Type 2 diabetes is characterized by increased death rate. In order to tackle this dramatic event, it becomes essential to discover novel biomarkers capable of identifying high-risk patients to be exposed to more aggressive preventive and treatment strategies. hs-CRP and serum amyloid P component (SAP) are two acute-phase inflammation proteins, which interact physically and share structural and functional features. We investigated their combined role in associating with and improving prediction of mortality in type 2 diabetes.

RESEARCH DESIGN AND METHODS

Four cohorts comprising 2,499 patients with diabetes (643 all-cause deaths) were analyzed. The improvement of mortality prediction was addressed using two well-established prediction models, namely, EstimatioN oF mORtality risk in type 2 diabetiC patiEnts (ENFORCE) and Risk Equations for Complications of Type 2 Diabetes (RECODe).

RESULTS

Both hs-CRP and SAP were independently associated with all-cause mortality (hazard ratios [HRs] [95% CIs]: 1.46 [1.34-1.58] [P < 0.001] and 0.82 [0.76-0.89] [P < 0.001], respectively). Patients with SAP ≤33 mg/L were at increased risk of death versus those with SAP >33 mg/L only if hs-CRP was relatively high (>2 mg/L) (HR 1.96 [95% CI 1.52-2.54] [P < 0.001] and 1.20 [0.91-1.57] [P = 0.20] in hs-CRP >2 and ≤2 mg/L subgroups, respectively; hs-CRP-by-SAP strata interaction P < 0.001). The addition of hs-CRP and SAP significantly (all P < 0.05) improved several discrimination and reclassification measures of both ENFORCE and RECODe all-cause mortality prediction models.

CONCLUSIONS

In type 2 diabetes, hs-CRP and SAP show opposite and synergic associations with all-cause mortality. The use of both markers, possibly in combination with others yet to be unraveled, might improve the ability to predict the risk of death in the real-life setting.

Predicting 10-Year Risk of End-Organ Complications of Type 2 Diabetes With and Without Metabolic Surgery: A Machine Learning Approach

OBJECTIVE

To construct and internally validate prediction models to estimate the risk of long-term end-organ complications and mortality in patients with type 2 diabetes and obesity that can be used to inform treatment decisions for patients and practitioners who are considering metabolic surgery.

RESEARCH DESIGN AND METHODS

A total of 2,287 patients with type 2 diabetes who underwent metabolic surgery between 1998 and 2017 in the Cleveland Clinic Health System were propensity-matched 1:5 to 11,435 nonsurgical patients with BMI ≥30 kg/m2 and type 2 diabetes who received usual care with follow-up through December 2018. Multivariable time-to-event regression and random forest machine learning models were built and internally validated using fivefold cross-validation to predict the 10-year risk for four outcomes of interest. The prediction models were programmed to construct user-friendly web-based and smartphone applications of Individualized Diabetes Complications (IDC) Risk Scores for clinical use.

RESULTS

The prediction tools demonstrated the following discrimination ability based on the area under the receiver operating characteristic curve (1 = perfect discrimination and 0.5 = chance) at 10 years in the surgical and nonsurgical groups, respectively: all-cause mortality (0.79 and 0.81), coronary artery events (0.66 and 0.67), heart failure (0.73 and 0.75), and nephropathy (0.73 and 0.76). When a patient's data are entered into the IDC application, it estimates the individualized 10-year morbidity and mortality risks with and without undergoing metabolic surgery.

CONCLUSIONS

The IDC Risk Scores can provide personalized evidence-based risk information for patients with type 2 diabetes and obesity about future cardiovascular outcomes and mortality with and without metabolic surgery based on their current status of obesity, diabetes, and related cardiometabolic conditions.

Estimation of Mortality Risk in Type 2 Diabetic Patients (ENFORCE): An Inexpensive and Parsimonious Prediction Model

CONTEXT

We previously developed and validated an inexpensive and parsimonious prediction model of 2-year all-cause mortality in real-life patients with type 2 diabetes.

OBJECTIVE

This model, now named ENFORCE (EstimatioN oF mORtality risk in type 2 diabetiC patiEnts), was investigated in terms of (i) prediction performance at 6 years, a more clinically useful time-horizon; (ii) further validation in an independent sample; and (iii) performance comparison in a real-life vs a clinical trial setting.

DESIGN

Observational prospective randomized clinical trial.

SETTING

White patients with type 2 diabetes.

PATIENTS

Gargano Mortality Study (GMS; n = 1019), Foggia Mortality Study (FMS; n = 1045), and Pisa Mortality Study (PMS; n = 972) as real-life samples and the standard glycemic arm of the ACCORD (Action to Control Cardiovascular Risk in Diabetes) clinical trial (n = 3150).

MAIN OUTCOME MEASURE

The endpoint was all-cause mortality. Prediction accuracy and calibration were estimated to assess the model's performances.

RESULTS

ENFORCE yielded 6-year mortality C-statistics of 0.79, 0.78, and 0.75 in GMS, FMS, and PMS, respectively (P heterogeneity = 0.71). Pooling the three cohorts showed a 6-year mortality C-statistic of 0.80. In the ACCORD trial, ENFORCE achieved a C-statistic of 0.68, a value significantly lower than that obtained in the pooled real-life samples (P < 0.0001). This difference resembles that observed with other models comparing real-life vs clinical trial settings, thus suggesting it is a true, replicable phenomenon.

CONCLUSIONS

The time horizon of ENFORCE has been extended to 6 years and validated in three independent samples. ENFORCE is a free and user-friendly risk calculator of all-cause mortality in white patients with type 2 diabetes from a real-life setting.