Risk Of Mortality In Type Research Articles

Impact of the presence and type of cardiovascular disease on the risk of mortality in type 2 diabetic patients: The DIABET-IC trial

IntroductionAll-cause mortality and cardiovascular mortality (CVM) risk can be very high in adults with type 2 diabetes mellitus (DM2) with previous cardiovascular disease (CVD). Our objective was to determine this risk among the different clinical spectrum of CVD. Material and methodsThe DIABET-IC trial is a multicenter, prospective, observational, and analytical study. Consecutive subjects with DM2 attending our outpatients’ clinics were recruited. Data on clinical features, lab test results, and echocardiographic measures were collected.Patients were categorized depending on the presence and type of CVD: heart failure (HF), coronary artery disease (CAD), cerebrovascular disease (CVD) and peripheral artery disease (PAD).All-cause mortality and CVM were the dependent variables analyzed. Mortality rate was expressed as deaths per 1000 patients-year. Cox proportional hazards regressions models were used to establish the mortality risk associated with every type of CVD. ResultsWe studied a total of 1246 patients (mean age, 6.3 (SD, 9.9) years; 31.6%, female) with an initial prevalence of CVD of 59.3%. A total of 122 deaths (46 due to CVD) occurred at the 2.6-year follow-up. All-cause and MCV rates associated with the presence of PAD (85.6/1000 and 33.6/1000, respectively) and HF (72.9/1000 and 28.7/1000 respectively) were the most elevated of all.In multivariate analysis, HF increased all-cause mortality risk (HR, 1.63; CI 95% 1.03–2.58; P=.037) and the risk of CVM (HR, 3.41; 95% CI, 1.68–6.93; P=.001). ConclusionsMortality among DM2 patients is highly increased in the presence of HF and PAD. This justifies the screening of these conditions to intensify therapeutic strategies.

Assessing mortality risk in Type 2 Diabetes patients with prolonged ASCVD risk factors: the inclusive Poh-Ai predictive scoring system with CAC Score integration

PurposeTo enhance the predictive risk model for all-cause mortality in individuals with Type 2 Diabetes (T2DM) and prolonged Atherosclerotic Cardiovascular Disease (ASCVD) risk factors. Despite the utility of the Coronary Artery Calcium (CAC) score in assessing cardiovascular risk, its capacity to predict all-cause mortality remains limited.MethodsA retrospective cohort study included 1929 asymptomatic T2DM patients with ASCVD risk factors, aged 40–80. Variables encompassed demographic attributes, clinical parameters, CAC scores, comorbidities, and medication usage. Factors predicting all-cause mortality were selected to create a predictive scoring system. By using stepwise selection in a multivariate Cox proportional hazards model, we divided the patients into three risk groups.ResultsIn our analysis of all-cause mortality in T2DM patients with extended ASCVD risk factors over 5 years, we identified significant risk factors, their adjusted hazard ratios (aHR), and scores: e.g., CAC score > 1000 (aHR: 1.57, score: 2), CAC score 401–1000 (aHR: 2.05, score: 2), and more. These factors strongly predict all-cause mortality, with varying risk groups (e.g., very low-risk: 2.0%, very high-risk: 24.0%). Significant differences in 5-year overall survival rates were observed among these groups (log-rank test < 0.001).ConclusionThe Poh-Ai Predictive Scoring System excels in forecasting mortality and cardiovascular events in individuals with Type 2 Diabetes Mellitus and extended ASCVD risk factors.

NT-proBNP Mediates the Association Between FGF23 and All-Cause Mortality in Individuals With Type 2 Diabetes.

FGF23 (fibroblast growth factor 23) is associated with a higher mortality risk in type 2 diabetes, but the mechanism is unclear. We aimed to study whether NT-proBNP (N-terminal pro-brain natriuretic peptide) mediates the association between FGF23 and mortality. We analyzed C-terminal FGF23 and NT-proBNP levels in 399 patients with type 2 diabetes. Cox regression analyses were performed, followed by mediation analyses using Structural Equation Modeling. During follow-up of 9.2 [7.6-11.3] years, 117 individuals died. FGF23 was associated with all-cause mortality, independent of potential confounders (fully adjusted hazard ratio [HR], 2.32 [95% CI, 1.21-4.43], P=0.01). The association was lost upon further adjustment for NT-proBNP (HR, 1.84; 95% CI, 0.91-3.73). NT-proBNP accounted for 26% of the mediation effect between FGF23 and all-cause mortality. These findings suggest that a higher FGF23 level is associated with increased mortality in individuals with type 2 diabetes through an effect on volume homeostasis.

Associations between handgrip strength and skeletal muscle mass with all-cause mortality and cardiovascular mortality in people with type 2 diabetes: A prospective cohort study of the UK Biobank.

To explore the associations between handgrip strength (HGS) and skeletal muscle mass (SMM) with all-cause and cardiovascular disease (CVD) mortality risk in type 2 diabetes (T2DM) patients. Data were obtained from the UK Biobank. Baseline survey was conducted between 2006 and 2010, and followed up for a median of 12.52 years. HGS was measured using dynamometer, and SMM was measured using bioelectrical impedance method. Mortality was available via links to the National Health Service Information Centre. Sex-specific analyses were conducted. A total of 13 392 T2DM participants were included, with a mean age of 60.39 years and 52.35% men. During the follow-up, there were 3006 (22.45%) deaths, including 746 (5.57%) CVD deaths. The risk for all-cause mortality and CVD mortality among both men and women increased progressively with decreasing HGS quartiles (p trend <.05). A 1 SD decrease in HGS was found to both increase the all-cause risk (HR: 1.31 [95% CI: 1.24-1.38]) and CVD mortality risk (HR: 1.35 [95% CI: 1.22-1.50]) for men, and all-cause risk (HR: 1.26 [95% CI: 1.11-1.42]) and CVD mortality risk (HR: 1.43 [95% CI: 1.09-1.89]) for women. There was no statistically significant trend association between SMM/height2 and mortality risk, and the restricted cubic regression splines indicated that SMM/height2 showed a U-shaped nonlinear relationship (pnonlinear <.05). Grip strength displayed a linear downward trend with mortality risk among T2DM patients, whereas muscle mass showed a U-shaped relationship. Low grip strength seemed to be a better predictor for mortality compared to low muscle mass.

The Impact of Cardiac Comorbidity Sequence at Baseline and Mortality Risk in Type 2 Diabetes Mellitus: A Retrospective Population-Based Cohort Study.

The presence of multiple comorbidities increases the risk of all-cause mortality, but the effects of the comorbidity sequence before the baseline date on mortality remain unexplored. This study investigated the relationship between coronary heart disease (CHD), atrial fibrillation (AF) and heart failure (HF) through their sequence of development and the effect on all-cause mortality risk in type 2 diabetes mellitus. This study included patients with type 2 diabetes mellitus prescribed antidiabetic/cardiovascular medications in public hospitals of Hong Kong between 1 January 2009 and 31 December 2009, with follow-up until death or 31 December 2019. The Cox regression was used to identify comorbidity sequences predicting all-cause mortality in patients with different medication subgroups. A total of 249,291 patients (age: 66.0 ± 12.4 years, 47.4% male) were included. At baseline, 7564, 10,900 and 25,589 patients had AF, HF and CHD, respectively. Over follow-up (3524 ± 1218 days), 85,870 patients died (mortality rate: 35.7 per 1000 person-years). Sulphonylurea users with CHD developing later and insulin users with CHD developing earlier in the disease course had lower mortality risks. Amongst insulin users with two of the three comorbidities, those with CHD with preceding AF (hazard ratio (HR): 3.06, 95% CI: [2.60-3.61], p &lt; 0.001) or HF (HR: 3.84 [3.47-4.24], p &lt; 0.001) had a higher mortality. In users of lipid-lowering agents with all three comorbidities, those with preceding AF had a higher risk of mortality (AF-CHD-HF: HR: 3.22, [2.24-4.61], p &lt; 0.001; AF-HF-CHD: HR: 3.71, [2.66-5.16], p &lt; 0.001). The sequence of comorbidity development affects the risk of all-cause mortality to varying degrees in diabetic patients on different antidiabetic/cardiovascular medications.

Could Duodenal Molecular Mechanisms be Involved in the Hypocholesterolemic Effect of Silicon Used as Functional Ingredient in Late-Stage Type 2 Diabetes Mellitus?

Hypercholesterolemia increases the risk of mortality in type 2 diabetes mellitus (T2DM), especially in the late-stage. Consumption of bioactive compounds as functional ingredients would help achieve therapeutic goals for cholesterolemia. Silicon has demonstrated a hypocholesterolemic effect and the ability to reduce fat digestion. However, it is unclear whether silicon exerts such effect in late-stage T2DM (LD) and the intestinal mechanisms involved. Three groups of eight rats were included: early-stage T2DM control (ED), LD, and the LD group treated with silicon (LD-Si) once the rats were diabetic. Morphological alterations of the duodenal mucosa, and levels of markers involve in cholesterol absorption and excretion, beside cholesterolemia, and fecal excretion were assayed. Silicon included as a functional ingredient significantly reduces cholesterolemia in part due to: 1) reducing cholesterol intestinal absorption by decreasing the absorptive area and Acetyl-Coenzyme A acetyltransferase-2 (ACAT2) levels; and 2) increasing cholesterol excretion to the lumen by induction of the liver X receptor (LXR) and consequent increase of adenosine triphosphate-binding cassette transporter (ABCG5/8). These results provide insight into the intestinal molecular mechanisms by which silicon reduces cholesterolemia and highlights the efficacy of the consumption of silicon-enriched functional foods in late-stage T2DM.

Leisure-time physical activity and mortality risk in type 2 diabetes: A nationwide cohort study

AimPhysical activity improves insulin resistance, inhibits inflammation, and decreases the incidence of cardiovascular disease. These are major causes of death in patients with diabetes. MethodsThe Taiwan National Health Interview Survey collected baseline characteristics of socioeconomic level, education, marriage, and health behaviour, including leisure time physical activity in 2001, 2005, 2009, and 2013. The National Health Insurance research dataset 2000–2016 contained detailed information on medical conditions, including all comorbidities. All-cause and cardiovascular deaths were confirmed by the National Death Registry. ResultsA total of 4859 adults with type 2 diabetes were included in the analysis; 2389 (49 %) were men and the mean±SD age was 60±13 years. Kaplan-Meier curve of all-cause (log-rank P<0.001) and cardiovascular death (log-rank P=0.038) categorized by leisure-time physical activity showed a significant difference. The multivariable Cox regression model showed that those who had more leisure time physical activity had a significantly lower risk of all-cause death than those with no physical activity (physical activity of 1-800 MET-min/week HR = 0.66, 95% CI: 0.54–0.81, physical activity of >800 MET-min/week HR = 0.67, 95% CI: 0.56-0.81). A significant trend was also observed (P <0.001). Similar results were also observed for cardiovascular mortality (physical activity of 1-800 MET-min/week HR = 0.54, 95% CI: 0.36–0.84, physical activity of >800 MET-min/week HR = 0.78, 95% CI: 0.55–1.13). ConclusionFor those with diabetes, increased leisure-time physical activity significantly reduced risk of all-cause and cardiovascular death. Further research is warranted to determine the proper prescription for physical activity to prolong healthy life.

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.

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

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

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

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

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

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

The influence of diabetes and hypertension on outcome in COVID-19 patients: Do we mix apples and oranges?

The influence of diabetes and hypertension on outcome in COVID-19 patients: Do we mix apples and oranges?

Mortality risk assessment in type 2 cardiorenal syndrome: comparative study

Abstract Background Type 2 cardiorenal syndrome is a serious, life threatening clinical condition, associated with adverse clinical outcomes. Although several prognostic biomarkers have been reported, early and accurate prognosis still remains a challenge. Aims This study was aimed to identify the best prognostic renal markers, to develop and validate an individualized predictive formula for the mortality risk in type 2 cardiorenal patients. Methods A total of 170 hospitalized patients (between 2014 and 2018) were included in this study. Renal function and glomerular filtration rate (GFR) was assessed using the most popular formulas for GFR estimation: the Cockcroft-Gault (CG), the four-variable Simplified Modification of Diet in Renal Disease (sMDRD), CKD-Epidemiology Collaboration (CKD-EPI) based on serum cystatin-C, creatinine and their combination, and the simple cystatin-C formula. All data were used to screen the predictors via univariate and multivariate analyses. A model was developed based on these predictors and validated by internal validation. The model validation comprised discriminative ability determined by the area under the curve (AUC) of receiver operating characteristic (ROC) curve and the predictive accuracy by calibration plots. Results During a mean follow-up of 6 months, 29 (16.2%) deaths were recorded. In an adjusted model, renal biomarkers and estimated glomerular filtration rate showed different prognostic value according to the area under the curve. Area under the ROC curve was 0.58 (95% CI:0.47–0.69, P=0.05) for serum creatinine, 0.67 (95% CI: 0.56–0.79, P&amp;lt;0.05) for serum cystatin-C; 0.76±0.05 (95% CI: 0.67–0.82, P&amp;lt;0.01) for CKD-EPI formula based on serum cystatin-C; 0.73±0.06 (95% CI: 0.65–0.79, P&amp;lt;0.01) for simple formula based on serum cystatin-C; 0.72±0.05 (95% CI: 0.64–0.79, P&amp;lt;0.01) for CKD-EPI formula based on serum cystatin-C and serum creatinine; 0.617±0.06 (95% CI: 0.53–0.69, P&amp;lt;0.01) for CKD-EPI formula based on serum creatinine; 0.615±0.06 (95% CI: 0.53–0.69, P&amp;lt;0.01) for sMDRD formula and 0.58±0.07 (95% CI: 0.53–0.69, P&amp;lt;0.01) for CG formula. Multivariate logistic regression revealed that cardiovascular disease length, psycho-emotional stress, NYHA class, hemoglobin, serum cystatin-C and GFR using the simple cystatin-C formula were independently associated with mortality in type 2 cardorenal syndrome. A model developed based on the seven variables had a specificity of 79.31%, and sensitivity 78.72% (P&amp;lt;0.01), while AUC was 0.792 (P&amp;lt;0.01), in internal validation. Conclusion GFR is an independent predictor for short term mortality of type 2 cardiorenal syndrome. Cystatin-C based formulas seems to offer improved prognostication in this population, while CG formula and serum creatinine fail to predict short term mortality The proposed model could predict the individualized mortality risk with good accuracy, high discrimination, and potential clinical applicability in cardiorenal patients. Funding Acknowledgement Type of funding source: None

Skin intrinsic fluorescence scores are a predictor of all-cause mortality risk in type 1 diabetes: The Epidemiology of Diabetes Complications study.

We assessed the association of skin intrinsic fluorescence (SIF) scores, as a measure of advanced glycation end-products (AGE), with all-cause mortality in type 1 diabetes (T1D). This is an observational retrospective study of a convenience sample from the Epidemiology of Diabetes Complications (EDC) study. AGEs were measured with a SIF score between 2007 and 2014; vital status was assessed in 2020. Among 245 participants, mean age was 48.6 ± 7.4 years, median diabetes duration was 39.5 years (IQR: 34.2, 44.9), and 53.5% were female. Compared to survivors, the deceased (n = 20) were older, with higher SIF scores, longer diabetes duration, lower body mass index (BMI), and an adverse risk factor profile (all p≤0.05). Univariate Cox regression showed a marginal association between SIF score and mortality (HR: 1.1, 95% CI 0.9-1.2, p = 0.06), which persisted after adjustment for multiple daily insulin shots/pump (MDI) use (HR: 1.1, 95% CI 1.0-1.2, p = 0.04). This association was attenuated after adjustment for T1D duration, A1c months, or estimated glomerular filtration rate (eGFR). In individuals with long duration T1D, SIF scores adjusted for MDI predicted all-cause mortality, although this association was attenuated after adjustments. Given the nature of sampling and small number of events, our findings require replication.

Thresholds for postprandial hyperglycemia and hypertriglyceridemia associated with increased mortality risk in type2 diabetes patients: A real-world longitudinal study.

Aims/IntroductionTo identify thresholds for postprandial hyperglycemia and hypertriglyceridemia predictive of all‐cause mortality in patients with type 2 diabetes.Materials and MethodsA total of 1,928 patients with type 2 diabetes visited our clinic for the first time from 1995 to 1999 and were followed up for ≥1 year. During the first year, 2‐h post‐breakfast blood glucose (2h‐BG) levels were measured in 1,122 patients (BG cohort) and postprandial serum triglyceride (ppTG) levels were measured in 1,826 patients (TG cohort). Patients were retrospectively followed until 2017 and administered questionnaires. Associations between 2h‐BG and ppTG levels and mortality risk were assessed by the multivariate Cox regression analysis.ResultsOver of 17,429 person‐years, 162 deaths occurred in the BG cohort, and over 28,026 person‐years, 253 deaths occurred in the TG cohort. Hazard ratios (HRs) with 95% confidence intervals for all‐cause mortality per 1‐standard deviation increases in 2h‐BG and ppTG were 1.34 (1.08–1.67) and 1.24 (1.06–1.45), respectively. HRs showed increasing trends across quintiles of 2h‐BG (P = 0.034) and ppTG (P = 0.007). The HR was significantly elevated (2.37, 1.26–4.47) in the fifth quintile of 2h‐BG (≥13.8 mmol/L) compared with the first quintile (<7.0 mmol/L; P = 0.008). The HR was also significantly elevated (1.63, 1.03–2.60) in the fifth quintile of ppTG (≥2.30 mmol/L) compared with the first quintile (<0.91 mmol/L; P = 0.038).ConclusionsPostprandial hyperglycemia and hypertriglyceridemia were associated with all‐cause mortality in patients with type 2 diabetes. We propose thresholds of 13.8 mmol/L 2h‐BG and 2.30 mmol/L ppTG to identify patients at increased risk of mortality.

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

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. 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). 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. 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.

Cancer has overtaken cardiovascular disease as the commonest cause of death in Scottish type2 diabetes patients: A population-based study (The Ayrshire Diabetes Follow-up Cohort study).

Aims/IntroductionThe increased mortality risk associated with diabetes is well established. The aim of the present study was to determine the causes of death of people with type 2 diabetes in Ayrshire and Arran, Scotland, between 2009 and 2014, and compare them with the national mortality rates.Materials and MethodsThe primary causes of death were collated. The causes of death were clustered into nine categories: heart disease, stroke, infection, renal failure, respiratory disorders, cancer, mental health, decompensated diabetes and other. The total rates were compared with national rates using the standardized mortality ratio (SMR), and then individually with heart disease, cerebrovascular disease and cancer.ResultsThere were 2116 deaths with the SMR, and 145 of those were caused by type 2 diabetes (n = 16,643; 95% confidence interval 139–152; P < 0.01). The SMR was >100 in all age bands, particularly in the younger age bands (P < 0.01). The SMR was consistently higher for women (P < 0.01). The SMR for heart disease was significantly >100 for both sexes in all age bands <65 years (P < 0.05). There was no difference in mortality causes related to the duration of diabetes. The most common cause of death was cancer (27.8%), followed by heart disease (24.1%). The SMR for cancer deaths was significantly elevated in women (120, 95% CI 104–137; P < 0.05).ConclusionsThis study confirmed increased mortality risk in type 2 diabetes patients, and suggests that where cardiovascular risk factors are being treated aggressively, cancer takes on a greater importance in the cause of death. Should greater consideration now be given for cancer as a complication of diabetes?

2086-P: The Interaction of Fitness, Body Mass Index, and Mortality Risk in Type 2 Diabetes

Introduction: Obesity and poor cardiorespiratory fitness (CRF) commonly coexists in type 2 diabetes (T2DM). Moreover, CRF has been associated with lower mortality risk in T2DM patients. In this study, we assessed the modulatory impact of fitness on mortality risk in T2DM patients across the obesity spectrum. Methods: We identified 6,888 T2DM patients (age 58.3+10.1 years) with no evidence of ischemia as indicated by a maximal standardized exercise treadmill test. We established two CRF categories based on age-adjusted median peak metabolic equivalents (METs) achieved: Unfit (5.4±1.3 METs; n=3,474) and Fit (9.0±2.3 METs; n=3414). We also defined the cohort based on body mass index (BMI) normal-weight (BMI 18.5-24.9 kg/m2); over-weight (BMI 25.0-29.9 kg/m2); and obese (BMI ≥30 kg/m2). We then combined the BMI and CRF categories to form a total of six groups CRF-BMI categories. Cox proportional hazards models, adjusted for known risk factors were constructed to assess the association between CRF-BMI interaction and mortality risk. Results: During a follow-up of 14.2+6.9 years, 2,495 patients died, accounting for 25.5 events/1,000 person-years of observation. When CRF-BMI groups were considered, the normal-weight-Fit category was used as the referent. Mortality risk in the normal-weight but Unfit patients was 64% higher compared to their Fit counterparts (HR 1.64; 95% CI: 1.35-2.00). In the overweight category, mortality risk was 20% lower in the Fit (HR 0.80; 95% CI: 0.67-0.94) and 28% higher (HR 1.28; 95% CI: 1.09-1.51) in Unfit patients. In obese category the risk was 29% lower for the Fit (HR 0.71; 95% CI: 0.60-0.86) and 23% higher for the Unfit patients (HR 1.23; 95% CI: 1.05-1.45). Conclusion: Increased CRF favorably modulates mortality risk across the obesity spectrum in T2DM patients. Poor fitness had a substantially greater and negative impact on Normal than over-weight and obese individuals. Disclosure E. Nylen: None. P. Kokkinos: None. C. Faselis: None. P. Narayan: Research Support; Self; AstraZeneca, Eli Lilly and Company. P. Karasik: None. J.N. Myers: None.

1610-P: On the Combined Effect of C-Reactive Protein (CRP) and Serum Amyloid Component P (SAP) on Mortality Risk in Type 2 Diabetes

CRP and SAP are the two short, strongly homologous pentraxins produced in response to inflammation. Though their common contribution to innate immunity, they seem to differently affect atherosclerosis and related clinical outcomes. In the specific context of mortality rate i) CRP is a recognized risk biomarker; ii) data on SAP are sparse and conflicting; iii) no data are available on their possibly combined effect. Accordingly, the combined effect of CRP and SAP on mortality risk was investigated in the high-risk subset of patients with type 2 diabetes (T2D). Four cohorts of patients with T2D [i.e., Gargano Heart Study (GHS; n=358; follow-up (f-u)=5±3 years; 81 events); Gargano Mortality Study (n=650; f-u=11±4 years; 190 events); Foggia Mortality Study (n=530; f-u=7±3 years; 146 events) and Pisa Mortality Study (n=986; f-u=13±3 years; 230 events)] were analyzed by Cox proportional hazard models. In the pooled sample, with a total of 2,524 individuals experiencing 647 events, hs-CRP was positively [HR=1.53 (95% CI 1.41-1.66)] whereas SAP was negatively [HR=0.80 (95% CI 0.74-0.86)] associated with all-cause mortality. Similar results were observed on cardiovascular death in GHS: HR (95% CI) being 1.90 (1.48-2.44) and 0.78 (0.61-0.98) for hs-CRP and SAP, respectively. The whole sample was then stratified according to relatively low and high hs-CRP and SAP (&amp;lt; or &amp;gt; the median value), so to obtain four groups: low/high (1), low/low (2), high/high (3), and high/low (4) hs-CRP/SAP levels. As compared to group 1, group 2, 3 and 4 had HRs (95% CIs) of 1.25 (0.96-1.64), 1.80 (1.39-2.33) and 2.40 (1.81-3.17) for all-cause mortality (p for trend=1.1x10-11 or, in a model comprising study sample, age, sex, smoking habit, BMI, HbA1c, diabetes duration and all ongoing treatments, p=4.1x10-8). In conclusion, in patients with T2D, hs-CRP and SAP show a combined effect on all-cause death. Whether their simultaneous use may prove to be useful in predicting mortality in T2D needs further investigation. Disclosure M. Scarale: None. M. Copetti: None. M. Garofolo: None. L. Salvemini: None. S. De Csomo: None. O. Lamacchia: None. G. Penno: None. V. Trischitta: None. C. Menzaghi: None. Funding Italian Ministry of Health (RF-2013-02356459)

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

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. 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. Observational prospective randomized clinical trial. White patients with type 2 diabetes. 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). The endpoint was all-cause mortality. Prediction accuracy and calibration were estimated to assess the model's performances. 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. 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.