Predictors of mortality over 8 years in type 2 diabetic patients: Translating Research Into Action for Diabetes (TRIAD)

Associations of Regional Body Fat With Risk of Cardiovascular Disease and Mortality Among Individuals With Type 2 Diabetes

CONTEXT

It is largely unknown whether regional fat accumulation is associated with risk of cardiovascular disease (CVD) and mortality among individuals with type 2 diabetes (T2D), who often exhibit changes in relative fat distribution and have increased CVD risk.

OBJECTIVE

To prospectively examine the association between regional body fat and risk of CVD in individuals with T2D and to determine whether the associations are independentof traditional measures of obesity.

METHODS

The main analysis included 21 472 participants with T2D from the UK Biobank. Regional body fat was measured by bioelectric impedance assessment. Cox proportional-hazards regression models were used to estimate hazard ratios (HRs) and 95% CIs.

RESULTS

Over a median of 7.7 years of follow-up, 3976 CVD events occurred. After multivariable adjustment, upper and lower body fat were independently and oppositely associated with CVD risk among patients with T2D. Higher arm fat percentage was linearly associated with increased CVD risk (Pnonlinear > .05), while higher trunk fat percentage was nonlinearly associated with increased CVD risk (Pnonlinear < .05). In contrast, higher leg fat percentage was nonlinearly associated with lower CVD risk (Pnonlinear < .05). When comparing extreme quartiles, the multivariable-adjusted HR (95% CI) of CVD was 0.72 (0.58-0.90) for leg fat percentage, 1.63 (1.29-2.05) for arm fat percentage, and 1.27 (1.06-1.52) for trunk fat percentage. Similar patterns of associations were observed for all-cause and CVD mortality. In addition, leg fat percentage, but not other regional fat percentage, was associated with CVD risk independently of traditional measures of obesity.

CONCLUSION

Among people with T2D, arm fat and trunk fat were positively, whereas leg fat was inversely, associated with the risk of CVD and mortality. These findings highlight the importance of considering both the amount and the location of body fat when assessing CVD and mortality risk among individuals with T2D.

Comparison of mortality in people with type 2 diabetes between different ethnic groups: Systematic review and meta-analysis of longitudinal studies

AIMS

Type 2 diabetes (T2D) is more common in certain ethnic groups. This systematic review compares mortality risk between people with T2D from different ethnic groups and includes recent larger studies.

METHODS

We searched nine databases using PRISMA guidelines (PROSPERO CRD42022372542). We included community-based prospective studies among adults with T2D from at least two different ethnicities. Two independent reviewers undertook screening, data extraction and quality assessment using the Newcastle-Ottawa Scale. The primary outcome compared all-cause mortality rates between ethnic groups (hazard ratio (HR) with 95% confidence intervals).

RESULTS

From 30,825 searched records, we included 13 studies (7 meta-analysed), incorporating 573,173 T2D participants; 12 were good quality. Mortality risk was lower amongst people with T2D from South Asian [HR 0.68 (0.65-0.72)], Black [HR 0.82 (0.77-0.87)] and Chinese [HR 0.57 (0.46-0.70)] ethnicity compared to people of White ethnicity. Narrative synthesis corroborated these findings but demonstrated that people of indigenous Māori ethnicity had greater mortality risk compared to European ethnicity.

CONCLUSIONS

People with T2D of South Asian, Black and Chinese ethnicity have lower all-cause mortality risk than White ethnicity, with Māori ethnicity having higher mortality risk. Factors explaining mortality differences require further study, including understanding complication risk by ethnicity, to improve diabetes outcomes.

Predictive models and determinants of mortality among T2DM patients in a tertiary hospital in Ghana, how do machine learning techniques perform?

BACKGROUND

The increasing prevalence of type 2 diabetes mellitus (T2DM) in lower and middle - income countries call for preventive public health interventions. Studies from Africa including those from Ghana, consistently reveal high T2DM-related mortality rates. While previous research in the Ho municipality has primarily examined risk factors, comorbidity, and quality of life of T2DM patients, this study specifically investigated mortality predictors among these patients.

METHOD

The study was retrospective involving medical records of T2DM patients. Data extracted included mortality outcome (dead or alive), sociodemographic characteristics (age, sex, marital status, educational level, occupation and location), family history of diseases (diabetes, cardiovascular disease (CVD), or asthma), lifestyle (smoking and alcohol intake), comorbidities (such as skin infections, sickle cell disease, urinary tract infections, and pneumonia) and complications of diabetes (CVD, nephropathy, neuropathy, foot ulcers, and diabetic ketoacidosis) were analyzed using Stata version 16.0 and Python 3.6.1 programming language. Both descriptive and inferential statistics were done to describe and build predictive models respectively. The performance of machine learning (ML) techniques such as support vector machine (SVM), decision tree, k nearest neighbor (kNN), eXtreme Gradient Boosting (XGBoost) and logistic regression were evaluated using the best-fitting predictive model for T2DM mortality.

RESULTS

Of the 328 participants, 183 (55.79%) were female, and the percentage of mortality was 11.28%. A 100% mortality was recorded among the T2DM patients with sepsis (p-value = 0.012). T2DM in-patients were 3.83 times as likely to die [AOR = 3.83; 95% CI: (1.53-9.61)] if they had nephropathy compared to T2DM in-patients without nephropathy (p-value = 0.004). The full model which included sociodemographic characteristics, family history, lifestyle variables and complications of T2DM had the best prediction of T2DM mortality outcome (ROC = 72.97%). The accuracy for (test and train datasets) were as follows: (90% and 90%), (100% and 100%), (90% and 90%), (90% and 88%) and (88% and 90%) respectively for the various ML classification techniques: logistic regression, Decision tree classifier, kNN classifier, SVM and XGBoost.

CONCLUSION

This study found that all in-patients with sepsis died. Nephropathy was the identified significant predictor of T2DM mortality. Decision tree classifier provided the best classifying potential.

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.

Bariatric surgery provision in response to the COVID-19 pandemic: retrospective cohort study of a national registry

BACKGROUND

When surgery resumed following the outbreak of the COVID-19 pandemic, guidelines recommended the prioritization of patients with greater obesity-related co-morbidities and/or higher body mass index.

OBJECTIVE

The aim of this study was to record the effect of the pandemic on total number, patient demographics, and perioperative outcomes of elective bariatric surgery patients in the United Kingdom.

SETTING AND METHODS

The United Kingdom National Bariatric Surgical Registry was used to identify patients who underwent elective bariatric surgery during the pandemic (1 yr from April 1, 2020). Characteristics of this group were compared with those of a pre-pandemic cohort. Primary outcomes were case volume, case mix, and providers. National Health Service cases were analyzed for baseline health status and perioperative outcomes. Fisher exact, χ2, and Student t tests were used as appropriate.

RESULTS

The total number of cases decreased to one third of pre-pandemic volume (8615 to 2930). The decrease in operating volume varied, with 36 hospitals (45%) experiencing a 75%-100% reduction. Cases performed in the National Health Service fell from 74% to 53% (P < .0001). There was no change in baseline body mass index (45.2 ± 8.3 kg/m2 from 45.5 ± 8.3 kg/m2; P = .23) or prevalence of type 2 diabetes (26% from 26%; P = .99). Length of stay (median 2 d) and surgical complication rate (1.4% from 2.0%; relative risk = .71; 95% CI .45-1.12; P = .13) were unchanged.

CONCLUSIONS

In the context of a dramatic reduction in elective bariatric surgery due to the COVID-19 pandemic, patients with more severe co-morbidities were not prioritized for surgery. These findings should inform preparation for future crises.

The association between circulatory, local pancreatic PCSK9 and type 2 diabetes mellitus: The effects of antidiabetic drugs on PCSK9

Proprotein convertase subtilisin/kexin type 9 (PCSK9) is a potent modulator of cholesterol metabolism and plays a crucial role in the normal functioning of pancreatic islets and the progression of diabetes. Islet autocrine PCSK9 deficiency can lead to the enrichment of low-density lipoprotein (LDL) receptor (LDLR) and excessive LDL cholesterol (LDL-C) uptake, subsequently impairing the insulin secretion in β-cells. Circulatory PCSK9 levels are primarily attributed to hepatocyte secretion. Notably, anti-PCSK9 strategies proposed for individuals with hypercholesterolemia chiefly target liver-derived PCSK9; however, these anti-PCSK9 strategies have been associated with the risk of new-onset diabetes mellitus (NODM). In the current review, we highlight a new direction in PCSK9 inhibition therapy strategies: screening candidates for anti-PCSK9 from the drugs used in type 2 diabetes mellitus (T2DM) treatment. We explored the association between circulating, local pancreatic PCSK9 and T2DM, as well as the relationship between PCSK9 monoclonal antibodies and NODM. We discussed the emergence of artificial and natural drugs in recent years, exhibiting dual benefits of antidiabetic activity and PCSK9 reduction, confirming that the diverse effects of these drugs may potentially impact the progression of diabetes and associated disorders, thereby introducing novel avenues and methodologies to enhance disease prognosis.

Advanced glycation end products: Key mediator and therapeutic target of cardiovascular complications in diabetes

The incidence of type 2 diabetes mellitus is growing in epidemic proportions and has become one of the most critical public health concerns. Cardiovascular complications associated with diabetes are the leading cause of morbidity and mortality. The cardiovascular diseases that accompany diabetes include angina, myocardial infarction, stroke, peripheral artery disease, and congestive heart failure. Among the various risk factors generated secondary to hyperglycemic situations, advanced glycation end products (AGEs) are one of the important targets for future diagnosis and prevention of diabetes. In the last decade, AGEs have drawn a lot of attention due to their involvement in diabetic patho-physiology. AGEs can be derived exogenously and endogenously through various pathways. These are a non-homogeneous, chemically diverse group of compounds formed non-enzymatically by condensation between carbonyl groups of reducing sugars and free amino groups of protein, lipids, and nucleic acid. AGEs mediate their pathological effects at the cellular and extracellular levels by multiple pathways. At the cellular level, they activate signaling cascades via the receptor for AGEs and initiate a complex series of intracellular signaling resulting in reactive oxygen species generation, inflammation, cellular proliferation, and fibrosis that may possibly exacerbate the damaging effects on cardiac functions in diabetics. AGEs also cause covalent modifications and cross-linking of serum and extracellular matrix proteins; altering their structure, stability, and functions. Early diagnosis of diabetes may prevent its progression to complications and decrease its associated comorbidities. In the present review, we recapitulate the role of AGEs as a crucial mediator of hyperglycemia-mediated detrimental effects in diabetes-associated complications. Furthermore, this review presents an overview of future perspectives for new therapeutic interventions to ameliorate cardiovascular complications in 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.

Current Knowledge on the Pathophysiology of Lean/Normal-Weight Type 2 Diabetes

Since early times, being overweight and obesity have been associated with impaired glucose metabolism and type 2 diabetes (T2D). Similarly, a less frequent adult-onset diabetes in low body mass index (BMI) people has been known for many decades. This form is mainly found in developing countries, whereby the largest increase in diabetes incidence is expected in coming years. The number of non-obese patients with T2D is also on the rise among non-white ethnic minorities living in high-income Western countries due to growing migratory flows. A great deal of energy has been spent on understanding the mechanisms that bind obesity to T2D. Conversely, the pathophysiologic features and factors driving the risk of T2D development in non-obese people are still much debated. To reduce the global burden of diabetes, we need to understand why not all obese people develop T2D and not all those with T2D are obese. Moreover, through both an effective prevention and the implementation of an individualized clinical management in all people with diabetes, it is hoped that this will help to reduce this global burden. The purpose of this review is to take stock of current knowledge about the pathophysiology of diabetes not associated to obesity and to highlight which aspects are worthy of future studies.

Temporal patterns selection for All-Cause Mortality prediction in T2D with ANNs

Mortality prevention in T2D elderly population having Chronic Kidney Disease (CKD) may be possible thorough risk assessment and predictive modeling. In this study we investigate the ability to predict mortality using heterogeneous Electronic Health Records data. Temporal abstraction is employed to transform the heterogeneous multivariate temporal data into a uniform representation of symbolic time intervals, from which then frequent Time Intervals Related Patterns (TIRPs) are discovered. However, in this study a novel representation of the TIRPs is introduced, which enables to incorporate them in Deep Learning Networks. We describe here the use of iTirps and bTirps, in which the TIRPs are represented by a integer and binary vector representing the time respectively. While bTirp represents whether a TIRP's instance was present, iTirp represents whether multiple instances were present. While the framework showed encouraging results, a major challenge is often the large number of TIRPs, which may cause the models to under-perform. We introduce a novel method for TIRPs' selection method, called TIRP Ranking Criteria (TRC), which is consists on the TIRP's metrics, such as the differences in its recurrences, its frequencies, and the average duration difference between the classes. Additionally, we introduce an advanced version, called TRC Redundant TIRP Removal (TRC-RTR), TIRPs that highly correlate are candidates for removal. Then the selected subset of iTirp/bTirps is fed into a Deep Learning architecture like a Recurrent Neural Network or a Convolutional Neural Network. Furthermore, a predictive committee is utilized in which raw data and iTirp data are both used as input. Our results show that iTirps-based models that use a subset of iTirps based on the TRC-RTR method outperform models that use raw data or models that use full set of discovered iTirps.

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/.

Associations of Nutritional, Environmental, and Metabolic Biomarkers with Diabetes-Related Mortality in U.S. Adults: The Third National Health and Nutrition Examination Surveys between 1988-1994 and 2016

Background: Nutritional, environmental, and metabolic status may play a role in affecting the progression and prognosis of type 2 diabetes. However, results in identifying prognostic biomarkers among diabetic patients have been inconsistent and inconclusive. We aimed to evaluate the associations of nutritional, environmental, and metabolic status with disease progression and prognosis among diabetic patients. Methods: In a nationally representative sample in the NHANES III (The Third National Health and Nutrition Examination Survey, 1988−1994), we analyzed available data on 44 biomarkers among 2113 diabetic patients aged 20 to 90 years (mean age: 58.2 years) with mortality data followed up through 2016. A panel of 44 biomarkers from blood and urine specimens available from NHANES III were included in this study and the main outcomes as well as the measures are mortalities from all-causes. We performed weighted logistic regression analyses after controlling potential confounders. To assess incremental prognostic values of promising biomarkers beyond traditional risk factors, we compared c-statistics of the adjusted models with and without biomarkers, separately. Results: In total, 1387 (65.2%) deaths were documented between 1988 and 2016. We observed an increased risk of all-cause mortality associated with higher levels of serum C-reactive protein (p for trend = 0.0004), thyroid stimulating hormone (p for trend = 0.04), lactate dehydrogenase (p for trend = 0.02), gamma glutamyl transferase (p for trend = 0.02), and plasma fibrinogen (p for trend = 0.03), and urine albumin (p for trend < 0.0001). In contrast, higher levels of serum sodium (p for trend = 0.005), alpha carotene (p for trend = 0.006), and albumin (p for trend = 0.005) were associated with a decreased risk of all-cause mortality. In addition, these significant associations were not modified by age, sex, or race. Inclusion of thyroid stimulating hormone (p = 0.03), fibrinogen (p = 0.01), and urine albumin (p < 0.0001), separately, modestly improved the discriminatory ability for predicting all-cause mortality among diabetic patients. Conclusions: Our nationwide study findings provide strong evidence that some nutritional, environmental, and metabolic biomarkers were significant predictors of all-cause mortality among diabetic patients and may have potential clinical value for improving stratification of mortality risk.

Association Between Cardiovascular Disease Risk Factors and Mortality in Adults With Diabetes: A Stratified Analysis by Sex, Race, and Ethnicity

Objectives: To assess sex and racial/ethnic differences in the relationship between multiple cardiovascular disease (CVD) risk factors and mortality among a nationally representative sample of adults with diabetes.

Methods: Data were analyzed from 3,503 adults with diabetes from the National Health and Nutrition Examination Survey 2001–2010 and its linked mortality data through 31 December 2011. The outcome was mortality; the independent variables were sex and race/ethnicity. Covariates included demographics, comorbidity, and lifestyle variables. Cox proportional hazards regression was used to test associations between mortality and CVD risk factors.

Results: In adjusted analyses, the association between diastolic blood pressure and mortality was significantly different by sex and race/ethnicity (unadjusted p = 0.009; adjusted p = 0.042). Kaplan-Meier survival curves showed Hispanic women had the highest survival compared to Hispanic men and Non-Hispanic Black (NHB) and Non-Hispanic White (NHW) men and women; NHW men had the lowest survival probability.

Conclusion: In this nationally representative sample, stratified analyses showed women had higher survival rates compared to men within each race/ethnicity group, and Hispanic women had the highest survival compared to all other groups.

Body mass index and all-cause mortality in patients with newly diagnosed type 2 diabetes mellitus in South Korea: a retrospective cohort study

OBJECTIVES

The lower risk of death in overweight or obese patients, compared with normal-weight individuals, has caused confusion for patients with diabetes and healthcare providers. This study investigated the relationship between body mass index (BMI) and mortality in patients with type 2 diabetes.

DESIGN

A retrospective cohort study.

SETTING

We established a national population database by merging the Korea National Health Insurance (KNHI) claims database, the National Health Check-ups Database and the KNHI Qualification Database of South Korea.

PARTICIPANTS

A total of 53 988 patients who were newly diagnosed with type 2 diabetes (E11 in International Classification of Diseases, 10th Edition) in 2007, had available BMI data, lacked a history of any serious comorbidity, received diabetes medication and did not die during the first 2 years were followed up for a median of 8.6 years.

PRIMARY OUTCOME MEASURES

All-cause mortality.

RESULTS

The mean BMI was 25.2 (SD 3.24) kg/m², and the largest proportion of patients (29.4%) had a BMI of 25-27.4 kg/m². Compared with a BMI of 27.5-29.9 kg/m² (the reference), mortality risk continuously increased as BMI decreased while the BMI score was under 25 (BMI <18.5 kg/m²: adjusted HR (aHR) 2.71, 95% CI 2.24 to 3.27; BMI 18.5-20.9 kg/m²: aHR 1.94, 95% CI 1.70 to 2.22; BMI 21-22.9 kg/m²: aHR 1.51, 95% CI 1.34 to 1.70; and BMI 23-24.9 kg/m²: aHR 1.14, 95% CI 1.01 to 1.28). For patients aged ≥65 years, the inverse association was connected up to a BMI ≥30 kg/m² group (aHR 0.76, 95% CI 0.59 to 0.98). However, the associations for men, patients aged <65 years and ever smokers resembled a reverse J curve, with a significantly greater risk of death in patients with a BMI ≥30 kg/m².

CONCLUSIONS

This study suggests that, for patients with type 2 diabetes at a normal weight, distinct approaches are needed in terms of promoting muscle mass improvement or cardiorespiratory fitness, rather than maintaining weight status. Improved early diagnosis considering the inverse association between BMI and mortality is also needed.

Cardiovascular risk in patients with type 2 diabetes: A systematic review of prediction models

AIMS

To identify all cardiovascular disease risk prediction models developed in patients with type 2 diabetes or in the general population with diabetes as a covariate updating previous studies, describing model performance and analysing both their risk of bias and their applicability METHODS: A systematic search for predictive models of cardiovascular risk was performed in PubMed. The CHARMS and PROBAST guidelines for data extraction and for the assessment of risk of bias and applicability were followed. Google Scholar citations of the selected articles were reviewed to identify studies that conducted external validations.

RESULTS

The titles of 10,556 references were extracted to ultimately identify 19 studies with models developed in a population with diabetes and 46 studies in the general population. Within models developed in a population with diabetes, only six were classified as having a low risk of bias, 17 had a favourable assessment of applicability, 11 reported complete model information, and also 11 were externally validated.

CONCLUSIONS

There exists an overabundance of cardiovascular risk prediction models applicable to patients with diabetes, but many have a high risk of bias due to methodological shortcomings and independent validations are scarce. We recommend following the existing guidelines to facilitate their applicability.

Revisiting the Obesity Paradox in Health Care Expenditures Among Adults With Diabetes

Recent studies of diabetes suggest an obesity paradox: mortality risk increases with weight in people without diabetes but decreases with weight in people with diabetes. A recent study also reports the paradox more generally with health care utilization. Whether this paradox in health care utilization and spending is causal or instead the result of empirical biases and confounding factors has yet to be examined in detail. This study set out to examine changes in the relationship between BMI and health care expenditures in populations with versus without diabetes, controlling for confounding risk factors. It found that the obesity paradox does not exist and is the result of statistical biases such as confounding and reverse causation. Obesity is not cost-saving for people with diabetes. Thus, insurers and physicians should renew efforts to prevent obesity in people with diabetes.

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.

Predictors of mortality among patients with type 2 diabetes in Jordan

BACKGROUND

Diabetes Mellitus (DM) is a common metabolic disease associated with increased risk of mortality.

OBJECTIVE

The aim of this study was to examine predictors of mortality among patients with type 2 diabetes in the north of Jordan.

METHODS

Electronic data files for diabetes patients admitted between the period of 2014-2018 at a tertiary center in the north of Jordan were reviewed. Patient's characteristics, clinical and laboratory data, use of medications and mortality rate were collected.

RESULTS

Mean age of patients (n = 957) was 60.99 ± 0.37 (mean ± sem). Most of patients had multiple risk factors and underlying cardiovascular diseases (CVDs). Mortality rate was 10.1%. Univariate predictors of mortality included age, chronic kidney disease (CKD), acute kidney injury, hypertension, heart failure (HF), coronary artery disease, venous thromboembolism (VTE), stroke, atrial fibrillation (AF), and chronic obstructive pulmonary disease (COPD). As the number of CVDs increases, mortality rate also increases (Odd ratio 2.0, p < 0.0001). Use of insulin, aspirin, ACEi/ARBS, beta blockers, and diuretics were also associated with mortality. Fasting glucose and percentage of glycated hemoglobin were not associated with mortality. By multivariable logistic regression analysis adjusting for confounders and collinearity; age, HF, AF, COPD, VTE, and CKD were associated with mortality.

CONCLUSION

Key risk factors of mortality are CVDs and CKD indicating that the primary step of management should focus on optimizing risk factors to prevent diabetes complications and death.

A large remaining potential in lipid-lowering drug treatment in the type 2 diabetes population: A Danish nationwide cohort study

AIM

To assess lipid-lowering drug (LLD) use patterns during 1996-2017 and examine lipid levels in relation to the use of LLDs and prevalent atherosclerotic cardiovascular disease (ASCVD).

METHODS

Using a nationwide diabetes register, 404 389 individuals with type 2 diabetes living in Denmark during 1996-2017 were identified. Individuals were followed from 1 January 1996 or date of type 2 diabetes diagnosis until date of emigration, death or 1 January 2017. Redemptions of prescribed LLDs were ascertained from the nationwide Register of Medicinal Products Statistics. Data on lipid levels were sourced from the National Laboratory Database since 2010. LLD coverage was calculated at any given time based on the redeemed amount and dose. Trends in lipid levels were estimated using an additive mixed-effect model. Low-density lipoprotein cholesterol (LDL-C) goal attainment was assessed based on recommended targets by the 2011, 2016 and 2019 guidelines for management of dyslipidaemias.

RESULTS

LLD use has decreased since 2012 and only 55% of those with type 2 diabetes were LLD users in 2017. A decline in levels of total cholesterol and LDL-C, and an increase in triglycerides, was observed during 2010-2017. Annual mean levels of LDL-C were lower among LLD users compared with non-users (in 2017: 1.84 vs. 2.57 mmol/L). A greater fraction of LLD users achieved the LDL-C goal of less than 1.8 mmol/L compared with non-users (in 2017: 51.7% and 19%, respectively). Among LLD users with prevalent ASCVD, 26.9% and 55% had, as recommended by current 2019 European guidelines, an LDL-C level of less than 1.4 mmol/L and less than 1.8 mmol/L, respectively, in 2017.

CONCLUSIONS

LLD use and LDL-C levels are far from optimal in the Danish type 2 diabetes population and improvement in LLD use could reduce ASCVD events.

The Impact of Morbid Obesity on the Health Outcomes of Hospital Inpatients: An Observational Study

Morbid obesity poses a significant burden on the health-care system. This study determined whether morbid obesity leads to worse health-outcomes in hospitalised patients. This retrospective-study examined nutritional data of all inpatients aged 18-79 years, with a body-mass-index (BMI) ≥ 18.5 kg/m² admitted over a period of 4 years at two major hospitals in Australia. Patients were divided into 3 groups for comparison: normal/overweight (BMI 18.5-29.9 kg/m²), obese (BMI 30-39.9 kg/m²) and morbidly-obese (BMI ≥ 40 kg/m²). Outcome measures included length-of-hospital-stay (LOS), in-hospital mortality, and 30-day readmissions. Multilevel-mixed-effects regression was used to compare clinical outcomes between the groups after adjustment for potential confounders. Of 16,579 patients, 1004 (6.1%) were classified as morbidly-obese. Morbidly-obese patients had a significantly longer median (IQR) LOS than normal/overweight patients (5 (2, 12) vs. 5 (2, 11) days, p value = 0.012) and obese-patients (5 (2, 12) vs. 5 (2, 10) days, p value = 0.036). After adjusted-analysis, morbidly-obese patients had a higher incidence of a longer LOS than normal/overweight patients (IRR 1.04; 95% CI 1.02-1.07; p value < 0.001) and obese-patients (IRR 1.13; 95% CI 1.11-1.16; p value < 0.001). Other clinical outcomes were similar between the different groups. Morbid obesity leads to a longer LOS in hospitalised patients but does not adversely affect other clinical outcomes.

Explaining the obesity paradox in healthcare utilization among people with type 2 diabetes

Background

Several studies of diabetes suggest an obesity paradox: persons without diabetes have an increased risk of death due to obesity, whereas obesity decreases the risk of death for people with diabetes. A recent study finds the same obesity paradox with the number of healthcare visits. Whether empirical biases and confounding lead to this paradox is yet to be determined.

Objective

To examine changes in the relationship between BMI and number of visits in diabetic vs nondiabetic populations, controlling for confounding risk factors.

Methods

Using adults in the nationally representative Medical Expenditure Panel Survey (MEPS) from 2008 to 2016, N = 210,317, we examine the proposed relationship using six measures of healthcare visits with zero-inflated negative binomial regressions controlling for age, gender, race/ethnicity, income, education, region, health insurance, chronic conditions, and smoking. We excluded persons with type 1 diabetes and gestational diabetes.

Results

We find an obesity paradox among people with diabetes for three measures. That is, relative to people without diabetes, normal weight people with diabetes have more emergency room visits, inpatient, and office-based physician visits than do the obese with diabetes. However, we do not find an obesity paradox in any of the six measures once we exclude smokers and persons ever diagnosed with cancer or cardiovascular disease.

Conclusion

The obesity paradox does not exist at the utilization level and is due to the presence of statistical biases such as confounding and reverse causation. Physicians should continue to focus on efforts to prevent obesity in patients with diabetes.

Transporting experimental results with entropy balancing

We show how entropy balancing can be used for transporting experimental treatment effects from a trial population onto a target population. This method is doubly robust in the sense that if either the outcome model or the probability of trial participation is correctly specified, then the estimate of the target population average treatment effect is consistent. Furthermore, we only require the sample moments of the effect modifiers drawn from the target population to consistently estimate the target population average treatment effect. We compared the finite-sample performance of entropy balancing with several alternative methods for transporting treatment effects between populations. Entropy balancing techniques are efficient and robust to violations of model misspecification. We also examine the results of our proposed method in an applied analysis of the Action to Control Cardiovascular Risk in Diabetes Blood Pressure trial transported to a sample of US adults with diabetes taken from the National Health and Nutrition Examination Survey cohort.

Insulin-induced vascular redox dysregulation in human atherosclerosis is ameliorated by dipeptidyl peptidase 4 inhibition

Recent clinical trials have revealed that aggressive insulin treatment has a neutral effect on cardiovascular risk in patients with diabetes despite improved glycemic control, which may suggest confounding direct effects of insulin on the human vasculature. We studied 580 patients with coronary atherosclerosis undergoing coronary artery bypass surgery (CABG), finding that high endogenous insulin was associated with reduced nitric oxide (NO) bioavailability ex vivo in vessels obtained during surgery. Ex vivo experiments with human internal mammary arteries and saphenous veins obtained from 94 patients undergoing CABG revealed that both long-acting insulin analogs and human insulin triggered abnormal responses of post-insulin receptor substrate 1 downstream signaling ex vivo, independently of systemic insulin resistance status. These abnormal responses led to reduced NO bioavailability, activation of NADPH oxidases, and uncoupling of endothelial NO synthase. Treatment with an oral dipeptidyl peptidase 4 inhibitor (DPP4i) in vivo or DPP4i administered to vessels ex vivo restored physiological insulin signaling, reversed vascular insulin responses, reduced vascular oxidative stress, and improved endothelial function in humans. The detrimental effects of insulin on vascular redox state and endothelial function as well as the insulin-sensitizing effect of DPP4i were also validated in high-fat diet-fed ApoE^-/- mice treated with DPP4i. High plasma DPP4 activity and high insulin were additively related with higher cardiac mortality in patients with coronary atherosclerosis undergoing CABG. These findings may explain the inability of aggressive insulin treatment to improve cardiovascular outcomes, raising the question whether vascular insulin sensitization with DPP4i should precede initiation of insulin treatment and continue as part of a long-term combination therapy.

Association between body mass index and mortality in atrial fibrillation patients with and without diabetes mellitus: Insights from a multicenter registry study in China

BACKGROUND AND AIMS

The aim of this study was to evaluate the association between body mass index (BMI) and mortality in atrial fibrillation (AF) patients with and without diabetes mellitus (DM).

METHODS AND RESULTS

A total of 1991 AF patients were enrolled and divided into two groups according to whether they have DM at recruitment. Baseline information was collected and a mean follow-up of 1 year was carried out. The primary outcome was defined as all-cause mortality with the secondary outcomes including cardiovascular mortality, stroke and major adverse events (MAEs). Univariable and multivariable Cox regression were performed to estimate the association between BMI and 1-year outcomes in AF patients with and without DM. 309 patients with AF (15.5%) had comorbid DM at baseline. Patients with DM were more likely to have cardiovascular comorbidities, receive relevant medications but carry worse 1-year outcomes. Multivariable Cox regressions indicated that elevated BMI was related with reduced risk of all-cause mortality, cardiovascular mortality and major adverse events. Compared to normal weight, overweight [HR (95% CI): 0.548 (0.405-0.741), p < 0.001] and obesity [HR (95% CI): 0.541 (0.326-0.898), p = 0.018] were significantly related with decreased all-cause mortality for the entire cohort. Remarkably reduced all-cause mortality in the overweight [HR (95% CI): 0.497 (0.347-0.711), p < 0.001] and obesity groups [HR (95% CI): 0.405 (0.205-0.800), p = 0.009] could also be detected in AF patients without DM, but not in those with DM.

CONCLUSION

Elevated BMI was associated with reduced mortality in patients with AF. This association was modified by DM. The obesity paradox confined to AF patients without DM, but could not be generalized to those with DM.

Bariatric and metabolic surgery during and after the COVID-19 pandemic: DSS recommendations for management of surgical candidates and postoperative patients and prioritisation of access to surgery

The coronavirus disease 2019 pandemic is wreaking havoc on society, especially health-care systems, including disrupting bariatric and metabolic surgery. The current limitations on accessibility to non-urgent care undermine postoperative monitoring of patients who have undergone such operations. Furthermore, like most elective surgery, new bariatric and metabolic procedures are being postponed worldwide during the pandemic. When the outbreak abates, a backlog of people seeking these operations will exist. Hence, surgical candidates face prolonged delays of beneficial treatment. Because of the progressive nature of obesity and diabetes, delaying surgery increases risks for morbidity and mortality, thus requiring strategies to mitigate harm. The risk of harm, however, varies among patients, depending on the type and severity of their comorbidities. A triaging strategy is therefore needed. The traditional weight-centric patient-selection criteria do not favour cases based on actual clinical needs. In this Personal View, experts from the Diabetes Surgery Summit consensus conference series provide guidance for the management of patients while surgery is delayed and for postoperative surveillance. We also offer a strategy to prioritise bariatric and metabolic surgery candidates on the basis of the diseases that are most likely to be ameliorated postoperatively. Although our system will be particularly germane in the immediate future, it also provides a framework for long-term clinically meaningful prioritisation.

Body mass index and mortality among middle-aged Japanese individuals with diagnosed diabetes: The Japan Public Health Center-based prospective study (JPHC study)

AIM

To examine the association between body mass index (BMI) and mortality among middle-aged people with diabetes in Japan.

METHODS

A total of 3032 men and 1615 women, aged 40-69 years, with diabetes were analyzed. Cox proportional hazards models, adjusted for potential confounding factors, were used to estimate mortality hazard ratios (HRs) across BMI categories at the baseline.

RESULTS

There were 1761 deaths during a mean follow-up period of 18.5 years. Increased all-cause mortality was observed at both ends of the BMI distribution; compared with the reference BMI category (23.0-24.9 kg/m²), the HRs were 1.25 (95% confidence interval [CI], 0.9997-1.56) in the lowest (14.0-18.9 kg/m²) and 1.36 (95% CI, 1.06-1.74) in the highest (30.0-39.9 kg/m²) categories (P = 0.001). Similar all-cause mortality trends were observed after excluding deaths within 3 years of follow-up, as well as for men and men who had ever smoked. While a similar non-linear pattern was observed for cancer-specific mortality, heart disease-specific mortality was only increased in the highest BMI category (HR, 1.86; 95% CI, 1.06-3.25).

CONCLUSION

This population-based prospective study demonstrated increased all-cause mortality at both ends of the BMI distribution among Japanese people with diabetes.

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.

A comparison of obesity indices in relation to mortality in type 2 diabetes: the Fremantle Diabetes Study

AIMS/HYPOTHESIS

This prospective association study aimed to compare the relationship between each of four obesity indices and mortality risk in people with type 2 diabetes.

METHODS

The associations of BMI, waist circumference, WHR and A Body Shape Index (ABSI) with all-cause mortality were analysed in 1282 participants of the Fremantle Diabetes Study, followed for up to 20 years after baseline assessment. Models were adjusted for age and other confounders; assessments as continuous measures and by quintile were carried out for men and women separately. Sensitivity analyses were conducted to minimise reverse causality.

RESULTS

When indices were assessed as continuous variables, there were significant bivariate associations with mortality for: ABSI, which was greater in both men and women who died (p < 0.001); WHR, which was greater in women only (p = 0.033); and BMI, which was lower in women only (p < 0.001). When assessed by quintile, there were significant bivariate associations with mortality for ABSI in men and women (p < 0.001) and BMI in women only (p = 0.002). In Cox models of time to death, adjusted for age, diabetes duration, ethnicity and smoking, ABSI quintiles showed a linear trend for both men (p = 0.003) and women (p = 0.035). Men in the fifth ABSI quintile had an increased mortality risk compared with those in the first quintile (HR [95% CI]: 1.74 [1.24, 2.44]) and women in the fifth ABSI quintile had an increased mortality risk that approached statistical significance (1.42 [0.97, 2.08], p = 0.08). Men in the fifth WHR quintile had an increased mortality risk (1.47 [1.05, 2.06]). There was no association between mortality and BMI or waist circumference in either sex.

CONCLUSIONS/INTERPRETATION

ABSI was the obesity index most strongly associated with all-cause mortality in Australians with type 2 diabetes. There was no evidence for an obesity paradox with any of the assessed indices. ABSI may be a better index of central obesity than waist circumference, BMI or WHR when assessing mortality risk in type 2 diabetes.

Risk of Cardiovascular Events in Patients With Type 2 Diabetes and Metabolic Dyslipidemia Without Prevalent Atherosclerotic Cardiovascular Disease

BACKGROUND

The relationship between achieved low-density lipoprotein cholesterol (LDL-C) levels and risk of incident atherosclerotic cardiovascular disease events among patients with diabetes and metabolic dyslipidemia has not been well described.

METHODS

We conducted an observational cohort study of statin-treated adults (ages 21-90 years) with type 2 diabetes without established atherosclerotic cardiovascular disease (as of January 1, 2006) who had metabolic dyslipidemia (elevated triglycerides ≥150 mg/dL and low high-density lipoprotein cholesterol, <50 mg/dL [women] and <40 mg/dL [men]). All subjects were members of Kaiser Permanente Northern California, an integrated health care delivery system. Adjusted multivariable Cox models were specified to estimate hazard ratios (HRs) for incident atherosclerotic cardiovascular disease events by achieved LDL-C levels (<50, 50-<70, 70-<100, and ≥100 mg/dL). Incident atherosclerotic cardiovascular disease events were defined as a composite of nonfatal myocardial infarction, ischemic stroke, or coronary heart disease death through December 31, 2013.

RESULTS

A total of 19,095 individuals met the selection criteria. Mean age was 63.4 years, 53.5% were women, and the mean follow-up was 5.9 years. Unadjusted rates of atherosclerotic cardiovascular disease events were not significantly different across specified LDL-C categories. In models adjusted for demographics and clinical characteristics, the risk was significantly lower with decreasing achieved LDL-C levels (P <0.0001 for trend). Relative to achieved LDL-C ≥100 mg/dL, LDL-C <50 mg/dL had an hazard ratio of 0.66 (95% confidence interval [CI] 0.52-0.82).

CONCLUSION

In a large, contemporary cohort of statin-treated patients with type 2 diabetes and metabolic dyslipidemia without established atherosclerotic cardiovascular disease, lower achieved LDL-C levels were associated with a monotonically lower risk of incident atherosclerotic cardiovascular disease events. The benefits of achieving very-low LDL-C (<50 mg/dL) in this population requires further evaluation in prospective interventional studies.

Association between Body Mass Index and Stroke Risk Among Patients with Type 2 Diabetes

CONTEXT

Very few studies focused on the association between body mass index (BMI) and stroke risk among patients with diabetes.

OBJECTIVE

We aimed to investigate the association between BMI and stroke risk in patients with type 2 diabetes.

DESIGN

Demographic, anthropometric, laboratory, and medication information were extracted from the National Patient-Centered Clinical Research Network common data model.

PARTICIPANTS

We performed a retrospective cohort study of 67 086 patients with type 2 diabetes.

MAIN OUTCOME MEASURES

Incident stroke including both ischemic and hemorrhagic stroke were defined.

RESULTS

During a mean follow up of 3.74 years. 8918 incident stroke events occurred. Multivariable-adjusted hazard ratios across different categories of BMI at baseline (18.5-24.9 [reference group], 25.0-29.9, 30.0-34.9, 35.0-39.9, and ≥40 kg/m2) were 1.00, 0.92, 0.85, 0.74, and 0.63 (Ptrend <0.001) for total stroke; 1.00, 0.93, 0.88, 0.77, and 0.65 (Ptrend <0.001) for ischemic stroke; and 1.00, 0.79, 0.50, 0.50, and 0.41 (Ptrend <0.001) for hemorrhagic stroke, respectively. When we used an updated mean value of BMI, the graded inverse association of body mass index with stroke risk did not change. This linear association was consistent among patients of different subgroups. Further sensitivity analysis excluding patients who were diagnosed stroke within 6 months after first diagnosis of type 2 diabetes or including non-smokers only also confirmed our findings.

CONCLUSION

The present study found an inverse association between BMI and the risk of total, ischemic, and hemorrhagic stroke among patients with type 2 diabetes. More clinical and molecular insights are still needed in explaining these findings.

Soy Products Ameliorate Obesity-Related Anthropometric Indicators in Overweight or Obese Asian and Non-Menopausal Women: A Meta-Analysis of Randomized Controlled Trials

Background: The effect of soy products on the weight of overweight or obese people is controversial, so we aimed to conduct a systematic review and a meta-analysis of published randomized controlled trials to analyze whether supplementation with soy products can help them to lose weight. Methods: The relevant data before January 2019 in PubMed, Embase and Cochrane Library were searched. A random-effect model was adopted to calculate the weighted average difference of net changes of body weight, body mass index (BMI), body fat percentage, fat mass, waist circumference, etc. Results: A total of 22 trials (870 overweight or obese participants) were reflected in the present meta-analysis. Analysis showed that soy products significantly reduced body weight, BMI, body fat percent and waist circumference in overweight or obese Asian populations (−0.37 kg, P = 0.010; −0.27 kg/m², P = 0.042; −0.36%, P = 0.032; −0.35 cm, P = 0.049) and more significant effects were observed in non-menopausal women reduced body weight (−0.59 kg, P = 0.041), BMI (−0.59, P = 0.041) and waist circumference (−0.59 cm, P = 0.041) in overweight or obese populations. Conclusion: This meta-analysis showed that soy products have weight loss effects, mainly due to soy protein, isoflavone and soy fiber.

Effect of Once-Weekly Exenatide on Clinical Outcomes According to Baseline Risk in Patients With Type 2 Diabetes Mellitus: Insights From the EXSCEL Trial

Background

In the EXSCEL (Exenatide Study of Cardiovascular Event Lowering), exenatide once‐weekly resulted in a nonsignificant reduction in major adverse cardiovascular events (MACEs) and a nominal 14% reduction in all‐cause mortality in 14 752 patients with type 2 diabetes mellitus (T2DM) with and without cardiovascular disease. Whether patients at increased risk for events experienced a comparatively greater treatment benefit with exenatide is unknown.

Methods and Results

In the EXSCEL population, we created risk scores for MACEs and all‐cause mortality using step‐wise selection of baseline characteristics. A risk score was calculated for each patient, and a time‐to‐event model for each end point was developed including the risk score, treatment assignment, and risk‐treatment interaction. Interaction P values evaluating for a differential treatment effect by baseline risk were reported. Over a median follow‐up of 3.2 years (interquartile range, 2.2, 4.4), 1091 (7.4%) patients died and 1744 (11.8%) experienced a MACE. Independent predictors of MACEs and all‐cause mortality included age, sex, comorbidities (eg, previous cardiovascular event), body mass index, blood pressure, hemoglobin A1c, and estimated glomerular filtration rate. The all‐cause mortality and MACE risk models had modest discrimination with optimism‐corrected c‐indices of 0.73 and 0.71, respectively. No interaction was observed between treatment effect and risk profile for either end point (both interactions, P>0.1).

Conclusions

Baseline characteristics (eg, age, previous cardiovascular events) and routine laboratory values (eg, hemoglobin A1c, estimated glomerular filtration rate) provided modest prognostic value for mortality and MACEs in a broad population of patients with type 2 diabetes mellitus. Exenatide's effects on mortality and MACEs were consistent across the spectrum of baseline risk.

Clinical Trial Registration

URL: https://www.clinicaltrials.gov. Unique identifier: NCT01144338.

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.

A Multicenter Large-Scale Retrospective Analysis of the Correlation between Body Mass Index and All-Cause Mortality in Patients with Type 2 diabetes Mellitus: A Seven-Year Real-World Study

Aims: To investigate the association between body mass index (BMI) and all-cause mortality in patients with type 2 diabetes mellitus (T2DM) and to determine any sex-specific differences in this association. Methods: We retrospectively enrolled patients with T2DM and investigated the annual death data for seven years starting from 2010. All-cause mortality was calculated using Life Tables analysis. Multivariate Cox proportional hazards analysis was performed to identify the association between BMI and mortality. Results: During a mean survey period of 7.33 ± 1.42 years (X± SD), 996 of the 17259 patients enrolled died, resulting in an all-cause mortality rate of 5.77%, with no significant difference between women and men (6.04% vs. 5.56%; x² = 1.766, P = 0.184). The top three causes of death were ischemic heart disease, cerebrovascular disease, and chronic kidney failure. A total of 87, 266, 332, and 311 patients with a BMI of <18.5, 18.5-23.99, 24.0-27.99, and ≥28.0 kg/m², respectively, died, with the corresponding mortality rate calculated at 15.45%, 3.30%, 5.80%, and 10.70%, respectively. The BMI value associated with the highest all-cause mortality was <18.5 kg/m², but this association was only significant in women aged <50 years (HR: 3.12; 95% CI, 1.62-4.34; P < 0.001). Conclusions: In patients with T2DM, a low BMI in women aged <50 years predicted high all-cause mortality.

Erythropoietin (EPO) haplotype associated with all-cause mortality in a cohort of Italian patients with Type-2 Diabetes

Type-2 Diabetes (T2D), diabetic complications, and their clinical risk factors harbor a substantial genetic component but the genetic factors contributing to overall diabetes mortality remain unknown. Here, we examined the association between genetic variants at 21 T2D-susceptibility loci and all-cause mortality in an elderly cohort of 542 Italian diabetic patients who were followed for an average of 12.08 years. Univariate Cox regression analyses detected age, waist-to-hip ratio (WHR), glycosylated haemoglobin (HbA1c), diabetes duration, retinopathy, nephropathy, chronic kidney disease (CKD), and anaemia as predictors of all-cause mortality. When Cox proportional hazards multivariate models adjusted for these factors were run, three erythropoietin (EPO) genetic variants in linkage disequilibrium (LD) with each other (rs1617640-T/G, rs507392-T/C and rs551238-A/C) were significantly (False Discovery Rate < 0.1) associated with mortality. Haplotype multivariate analysis revealed that patients carrying the G-C-C haplotype have an increased probability of survival, while an opposite effect was observed among subjects carrying the T-T-A haplotype. Our findings provide evidence that the EPO gene is an independent predictor of mortality in patients with T2D. Thus, understanding the mechanisms by which the genetic variability of EPO affects the mortality of T2D patients may provide potential targets for therapeutic interventions to improve the survival of these patients.

The interaction of nonalcoholic fatty liver disease and smoking on mortality among adults in the United States

BACKGROUND & AIMS

Nonalcoholic fatty liver disease (NAFLD) has become the most common liver disease in Western countries. Smoking and diabetes mellitus (DM) have been shown to increase mortality; however, whether NAFLD adds to the detrimental effect of smoking in DM and non-DM patients is unknown. We evaluated the possible interactive effect of NAFLD and smoking on mortality risk in a US population-based sample.

METHODS

Cross-sectional data from 11 205 participants in the third National Health and Nutrition Examination Survey were analysed. NAFLD was defined as ultrasonographic hepatic steatosis without evidence of other liver diseases. Proportional hazards regression modelling was used to test for the multiplicative interaction of NAFLD and smoking on overall mortality, controlling for DM.

RESULTS

36.5% of the participants had NAFLD of whom 21.1% were current smokers, while among non-NAFLD subjects, 26.2% reported current smoking. Smoking was associated with a hazard ratio (HR) of 2.23 (95% confidence interval (CI): 1.87-2.65) among non-NAFLD subjects, and 2.31 (95% CI: 1.33-2.92, P < 0.01) among NAFLD patients. In contrast, the HR for NAFLD was 1.01 (95% CI: 0.78-1.31, P = 0.96) among smokers and 0.98 (95% CI: 0.87-1.10, P = 0.73) among non-smokers. There was no evidence of interaction between NAFLD and smoking (HR = 1.01, 95% CI: 0.74-1.38, P = 0.94) in the combined model.

CONCLUSION

We found that smoking increased mortality by two-fold among the US population. Although the magnitude of the increase in mortality did not differ from that in non-NAFLD subjects, smoking represents a modifiable determinant of long-term outcomes in NAFLD patients.

Pre-diagnostic plasma enterolactone concentrations are associated with lower mortality among individuals with type 2 diabetes: a case-cohort study in the Danish Diet, Cancer and Health cohort

AIMS/HYPOTHESIS

The phytoestrogen enterolactone is a gut microbiota-derived metabolite of plant lignans with suggested beneficial properties for health. In the current study, we investigated the association between pre-diagnostic plasma enterolactone concentrations and mortality among individuals diagnosed with type 2 diabetes.

METHODS

In a population of people diagnosed with diabetes, nested within the Danish Diet, Cancer and Health cohort, we conducted a case-cohort study including a random sample of n = 450 cases (deceased) and a randomly selected subcohort of n = 850 (in total n = 617 deaths). Information on diagnosis, vital status and cause of death was obtained from Danish registers. Cox proportional hazard models with special weighting were applied to assess all-cause and cause-specific mortality.

RESULTS

The median enterolactone concentration of the current population was low, 10.9 nmol/l (5th percentile to 95th percentile: 1.3-59.6), compared with previously reported concentrations from the Diet, Cancer and Health cohort. Pre-diagnostic enterolactone concentrations were associated with lower all-cause mortality when assessed linearly per doubling in concentration (log₂) (HR 0.91 [95% CI 0.85, 0.96]) and according to quartiles (HR 0.63 [95% CI 0.48, 0.84]) for the highest quartile of enterolactone compared with the lowest quartile. For cause-specific mortality, only death from diabetes (registered as underlying cause of death) reached statistical significance.

CONCLUSIONS/INTERPRETATION

Based on this large cohort of people with diabetes with detailed and complete baseline and follow-up information, pre-diagnostic enterolactone concentrations were inversely associated with mortality. To our knowledge, this is the first study on enterolactone and type 2 diabetes mortality. Our findings call for further exploration of enterolactone in type 2 diabetes management.

Why primary obesity is a disease?

Obesity must be considered a real pathology. In the world wide, obesity represent one of the major public health issue associated with increased morbidity and mortality. Overweight or obesity, in fact, significantly increases the risk of contracting diseases, such as: arterial hypertension, dyslipidemia, type 2 diabetes mellitus, coronary heart disease, cerebral vasculopathy, gallbladder lithiasis, arthropathy, ovarian polycytosis, sleep apnea syndrome, and some neoplasms. Despite numerous informative campaigns, unfortunately, the fight against obesity does not seem to work: in the last years, the prevalence continued to increase. The progressive and rapid increase in the incidence of obesity, which has characterized most of the economically advanced countries in the last decade, has been the main stimulus for the research of the mechanisms underlying this pathology and the related disorders. The aims of this review is to provide a revision of the literature in order to define obesity as diseases, secondly to highlight the limits and the inaccuracy of common tools used for the diagnosis of obesity, and as a third thing to strengthen the concept of the complexity of obesity as a disease among political health care providers. Obesity may be viewed as a multifactorial pathology and chronic low-grade inflammatory disease. In fact, people affected by obesity have greater risk of developing comorbility and morbility, respect to healthy. Hence, the absolute therapeutic benefit is directly proportional to the basic risk. So, internationally interest on early diagnosis of obesity is growing to avoid under- and overdiagnosis consequences. Therefore, the consequences are an aggravation of the disease and an increase in obesity related pathology like diabetes, cardiovascular disease, and cancer. The most widely used parameter for diagnosis, body mass index (BMI) is not suitable for assessing the body fat. In fact, several studies demonstrate that BMI alone cannot define obesity, which consists not so much in weight gain as in excess fat mass. The use of suitable tools for the assessment of fat mass percentage combined with clinical and genetic analysis allowed to identify different phenotypes of obesity, which explain the various paradoxes of obesity. It is essential to adopt all possible strategies to be able to combat obesity, ameliorate the suffering of patients, and reduce the social and treatment costs of obesity.

Mortality and Cardiovascular Disease in Type 1 and Type 2 Diabetes

PURPOSE OF REVIEW

The aims of this review are to summarize recent data on mortality and cardiovascular disease (CVD) in type 1 and type 2 diabetes and to determine the interventions that could have contributed to a reduction in mortality.

RECENT FINDINGS

Recent studies found a downward trend in mortality and CVD among both diabetics and non-diabetics worldwide over the last few decades. The decline among diabetics is steeper than that among non-diabetics. Despite a parallel trend of decline, an approximately twofold difference in mortality and CVD between the two populations remains. A greater emphasis on glycemic control, management of cardiovascular risk factors, quality improvement programs, and advances in treatment of conditions associated diabetes are the factors that potentially contributed to the improvement. Although the trend is encouraging, a rising prevalence of diabetes will continue the absolute disease burden to the society. Future interventions should focus on prevention of diabetes.

Predictors of 15-year survival among Australian women with diabetes from age 76-81

AIMS

To assess the impact of diabetes on the survival of older women, adjusted for other all-cause mortality predictors.

METHODS

Data were used from the 1921-26 cohort of the Australian Longitudinal Study on Women's Health, when the women were aged 76-81 years at baseline, with linkage to the National Death Index. Survival curves were plotted to compare the survival of women with no diabetes, incident diabetes and prevalent diabetes over 15 years. Cox proportional hazards models were used to examine the association between diabetes and all-cause mortality risks.

RESULTS

A total of 972 (11.7%) of 8296 eligible women reported either incident, 522 (6.3%) or prevalent, 450 (5.4%) diabetes. The median survival times were 10.1, 11.4 and 12.7 years among women with prevalent, incident and no diabetes, respectively. The risks of death were 30% [HR: 1.30 (95% CI: 1.16-1.45)] and 73% [HR: 1.73 (CI: 1.57-1.92)] higher for women with incident and prevalent diabetes compared to women without diabetes. These associations were sustained after controlling for demographics, body mass index, smoking status, comorbidities and health care use.

CONCLUSIONS

This study revealed that diabetes is associated with reduced survival probabilities for older women with minimal moderation after adjustment for other predictors. Our findings suggest that diabetes management guidelines for older women need to integrate factors such as comorbidities, smoking and being underweight to reduce the risk of mortality.

Exercise training for patients with type 2 diabetes and cardiovascular disease: What to pursue and how to do it. A Position Paper of the European Association of Preventive Cardiology (EAPC)

Patients with type 2 diabetes mellitus suffer from dysregulation of a plethora of cardiovascular and metabolic functions, including dysglycaemia, dyslipidaemia, arterial hypertension, obesity and a reduced cardiorespiratory fitness. Exercise training has the potential to improve many of these functions, such as insulin sensitivity, lipid profile, vascular reactivity and cardiorespiratory fitness, particularly in type 2 diabetes mellitus patients with cardiovascular comorbidities, such as patients that suffered from an acute myocardial infarction, or after a coronary intervention such as percutaneous coronary intervention or coronary artery bypass grafting. The present position paper aims to provide recommendations for prescription of exercise training in patients with both type 2 diabetes mellitus and cardiovascular disease. The first part discusses the relevance and practical applicability of treatment targets that may be pursued, and failure to respond to these targets. The second part provides recommendations on the contents and methods to prescribe exercise training tailored to these treatment targets as well as to an optimal preparation and dealing with barriers and risks specific to type 2 diabetes mellitus and cardiac comorbidity.

Associations between multimorbidity, all-cause mortality and glycaemia in people with type 2 diabetes: A systematic review

Introduction

Type 2 diabetes (T2D) is a major health priority worldwide and the majority of people with diabetes live with multimorbidity (MM) (the co-occurrence of ≥2 chronic conditions). The aim of this systematic review was to explore the association between MM and all-cause mortality and glycaemic outcomes in people with T2D.

Methods

The search strategy centred on: T2D, MM, comorbidity, mortality and glycaemia. Databases searched: MEDLINE, EMBASE, CINAHL Complete, The Cochrane Library, and SCOPUS. Restrictions included: English language, quantitative empirical studies. Two reviewers independently carried out: abstract and full text screening, data extraction, and quality appraisal. Disagreements adjudicated by a third reviewer.

Results

Of the 4882 papers identified; 41 met inclusion criteria. The outcome was all-cause mortality in 16 studies, glycaemia in 24 studies and both outcomes in one study. There were 28 longitudinal cohort studies and 13 cross-sectional studies, with the number of participants ranging from 96–892,223. Included studies were conducted in high or upper-middle-income countries. Fifteen of 17 studies showed a statistically significant association between increasing MM and higher mortality. Ten of 14 studies showed no significant associations between MM and HbA1c. Four of 14 studies found higher levels of MM associated with higher HbA1c. Increasing MM was significantly associated with hypoglycaemia in 9/10 studies. There was no significant association between MM and fasting glucose (one study). No studies explored effects on glycaemic variability.

Conclusions

This review demonstrates that MM in T2D is associated with higher mortality and hypoglycaemia, whilst evidence regarding the association with other measures of glycaemic control is mixed. The current single disease focused approach to management of T2D seems inappropriate. Our findings highlight the need for clinical guidelines to support a holistic approach to the complex care needs of those with T2D and MM, accounting for the various conditions that people with T2D may be living with.

Systematic review registration

International Prospective Register of Systematic Reviews CRD42017079500

Risk of Incident Atherosclerotic Cardiovascular DiseaseEvents by Achieved Atherogenic Lipid Levels Among62,428 Statin-Treated Individuals With Diabetes Mellitus

The relevance of low-density lipoprotein cholesterol (LDL-C) or non-high-density lipoprotein cholesterol (non-HDL-C) goals for primary prevention of atherosclerotic cardiovascular disease (ASCVD) among patients with diabetes was assessed. This retrospective cohort study included patients with type 2 diabetes, age 21 to 90years, taking statins, with no history of ASCVD as of January 1, 2006, in Kaiser Permanente Northern California, an integrated healthcare delivery system. Multivariate cox models were utilized to estimate hazard ratios (HRs) for incident ASCVD events by achieved LDL-C and non-HDL-C levels with adjustment for potential confounders. Incident ASCVD events were defined as a composite of myocardial infarction, ischemic stroke, or coronary heart disease death. A cohort of 62,428 patients, with mean age of 64.1years, 46.9% women, and mean follow-up of 6.0 years, was identified. After adjustment, the risk of incident ASCVD for these statin-treated patients was monotonically lower with decreasing achieved LDL-C levels (p<0.0001 for trend) and non-HDL-C levels (p <0.0001 for trend). Relative to achieved LDL-C ≥130 mg/dl, LDL-C <50 mg/dl had HR = 0.58 (95% confidence interval 0.49 to 0.69). Relative to achieved non-HDL-C ≥160mg/dl, non-HDL-C <80 mg/dl had HR = 0.59 (95% confidence interval 0.51 to 0.68). In a large cohort of statin-treated diabetic patients without ASCVD, a monotonically lower risk of incident ASCVD events was associated with lower achieved lipid levels. These findings support the use of LDL-C ornon-HDL-C treatment goals for ASCVD primary prevention in diabetic patients.

Novel Risk Engine for Diabetes Progression and Mortality in USA: Building, Relating, Assessing, and Validating Outcomes (BRAVO)

BACKGROUND

There is an urgent need to update diabetes prediction, which has relied on the United Kingdom Prospective Diabetes Study (UKPDS) that dates back to 1970 s' European populations.

OBJECTIVE

The objective of this study was to develop a risk engine with multiple risk equations using a recent patient cohort with type 2 diabetes mellitus reflective of the US population.

METHODS

A total of 17 risk equations for predicting diabetes-related microvascular and macrovascular events, hypoglycemia, mortality, and progression of diabetes risk factors were estimated using the data from the Action to Control Cardiovascular Risk in Diabetes (ACCORD) trial (n = 10,251). Internal and external validation processes were used to assess performance of the Building, Relating, Assessing, and Validating Outcomes (BRAVO) risk engine. One-way sensitivity analysis was conducted to examine the impact of risk factors on mortality at the population level.

RESULTS

The BRAVO risk engine added several risk factors including severe hypoglycemia and common US racial/ethnicity categories compared with the UKPDS risk engine. The BRAVO risk engine also modeled mortality escalation associated with intensive glycemic control (i.e., glycosylated hemoglobin < 6.5%). External validation showed a good prediction power on 28 endpoints observed from other clinical trials (slope = 1.071, R² = 0.86).

CONCLUSION

The BRAVO risk engine for the US diabetes cohort provides an alternative to the UKPDS risk engine. It can be applied to assist clinical and policy decision making such as cost-effective resource allocation in USA.

Body mass index and risk of all-cause mortality with normoglycemia, impaired fasting glucose and prevalent diabetes: results from the Rural Chinese Cohort Study

BACKGROUND

Previous evidence of an association between body mass index (BMI) and mortality in patients with diabetes was inconsistent. The BMI-mortality association with normal fasting glucose (NFG), impaired fasting glucose (IFG) and prevalent diabetes is still unclear in the Chinese population.

METHODS

We analysed data for 17 252 adults from the Rural Chinese Cohort Study during 2007-2008 and followed for mortality during 2013-2014. Participants were classified with NFG, IFG and diabetes according to baseline measurement values of fasting glucose and self-reported diabetes. Multivariable Cox proportional hazard models were used to calculate HRs and 95% CIs across BMI categories by glycemic status.

RESULTS

During the 6-year follow-up, 1109 participants died (563/10 181 with NFG, 349/5572 with IFG and 197/1499 with diabetes). The BMI-mortality association was curvilinear, with low BMI (even in normal range) associated with increased mortality regardless of glycemic status. In adjusted Cox models, risk of mortality showed a decreasing trend with BMI≤18 kg/m², 18<BMI≤20 kg/m² and 20<BMI≤22 kg/m² vs 22<BMI≤24 kg/m²: HR 2.83 (95% CI 1.78 to 4.51), 2.05 (1.46 to 2.87) and 1.45 (1.10 to 1.90), respectively, for NFG; 2.53 (1.25 to 5.14), 1.36 (0.86 to 2.14) and 1.09 (0.76 to 1.57), respectively, for IFG; and 4.03 (1.42 to 11.50), 2.00 (1.05 to 3.80) and 1.52 (0.88 to 2.60), respectively, for diabetes. The risk of mortality was lower for patients with diabetes who were overweight or obese versus normal weight.

CONCLUSIONS

Low BMI was associated with increased mortality regardless of glycemic status. Future studies are needed to explain the 'obesity paradox' in patients with diabetes.

The All-Cause Mortality and a Screening Tool to Determine High-Risk Patients among Prevalent Type 2 Diabetes Mellitus Patients

AIMS

This study aims to determine the all-cause mortality and the associated risk factors for all-cause mortality among the prevalent type 2 diabetes mellitus (T2DM) patients within five years' period and to develop a screening tool to determine high-risk patients.

METHODS

This is a cohort study of T2DM patients in the national diabetes registry, Malaysia. Patients' particulars were derived from the database between 1st January 2009 and 31st December 2009. Their records were matched with the national death record at the end of year 2013 to determine the status after five years. The factors associated with mortality were investigated, and a prognostic model was developed based on logistic regression model.

RESULTS

There were 69,555 records analyzed. The mortality rate was 1.4 persons per 100 person-years. The major cause of death were diseases of the circulatory system (28.4%), infectious and parasitic diseases (19.7%), and respiratory system (16.0%). The risk factors of mortality within five years were age group (p < 0.001), body mass index category (p < 0.001), duration of diabetes (p < 0.001), retinopathy (p = 0.001), ischaemic heart disease (p < 0.001), cerebrovascular (p = 0.007), nephropathy (p = 0.001), and foot problem (p = 0.001). The sensitivity and specificity of the proposed model was fairly strong with 70.2% and 61.3%, respectively.

CONCLUSIONS

The elderly and underweight T2DM patients with complications have higher risk for mortality within five years. The model has moderate accuracy; the prognostic model can be used as a screening tool to classify T2DM patients who are at higher risk for mortality within five years.

Body mass index and mortality in patients with and without diabetes mellitus

OBJECTIVE

Investigate the association between body mass index (BMI), length of stay (LOS), and mortality in hospitalized patients with and without diabetes mellitus (DM).

METHODS

Historical prospectively collected data of adult patients hospitalized between 2011 and 2013. Body mass index was calculated according to measurement or self-report on admission and classified as follows: underweight (<18.5), normal weight (18.5-24.9), overweight (25-29.9), obese (30-34.9), and severely obese (≥35). The main outcomes were LOS, in-hospital, and end-of-follow-up mortality.

RESULTS

Cohort included 24 233 patients (53% male; mean age ± SD, 65 ± 18), including 7397 patients with DM (31%). Among patients with normal BMI, LOS was shorter compared with underweight patients, but it was longer compared with overweight and obese patients. Following multivariate adjustment, this difference remained significant only for patients with DM. There was a significant interaction between DM status and BMI group, in the models for in-hospital and end-of-follow-up mortality. Compared with normal BMI, in-hospital mortality risk was increased by 80% and 100% for the underweight with and without DM, respectively. For patients with and without DM, in-hospital mortality risk was 30% to 40% lower among overweight and obese patients, and there was no difference between severely obese and normal weight patients. At the end-of-follow-up, mortality risk was 1.6-fold and 1.7-fold higher among underweight patients with and without DM, respectively. For overweight, obese, and severely obese patients, mortality risk was decreased by 30% to 40% in those with DM and by 20% to 30% in those without DM.

CONCLUSIONS

In hospitalized patients with and without DM, there was an inverse association between BMI and mortality.