A long-term quality-of-care score for predicting the occurrence of macrovascular diseases in patients with type 2 diabetes mellitus

Precision prognostics for cardiovascular disease in Type 2 diabetes: a systematic review and meta-analysis

BACKGROUND

Precision medicine has the potential to improve cardiovascular disease (CVD) risk prediction in individuals with Type 2 diabetes (T2D).

METHODS

We conducted a systematic review and meta-analysis of longitudinal studies to identify potentially novel prognostic factors that may improve CVD risk prediction in T2D. Out of 9380 studies identified, 416 studies met inclusion criteria. Outcomes were reported for 321 biomarker studies, 48 genetic marker studies, and 47 risk score/model studies.

RESULTS

Out of all evaluated biomarkers, only 13 showed improvement in prediction performance. Results of pooled meta-analyses, non-pooled analyses, and assessments of improvement in prediction performance and risk of bias, yielded the highest predictive utility for N-terminal pro b-type natriuretic peptide (NT-proBNP) (high-evidence), troponin-T (TnT) (moderate-evidence), triglyceride-glucose (TyG) index (moderate-evidence), Genetic Risk Score for Coronary Heart Disease (GRS-CHD) (moderate-evidence); moderate predictive utility for coronary computed tomography angiography (low-evidence), single-photon emission computed tomography (low-evidence), pulse wave velocity (moderate-evidence); and low predictive utility for C-reactive protein (moderate-evidence), coronary artery calcium score (low-evidence), galectin-3 (low-evidence), troponin-I (low-evidence), carotid plaque (low-evidence), and growth differentiation factor-15 (low-evidence). Risk scores showed modest discrimination, with lower performance in populations different from the original development cohort.

CONCLUSIONS

Despite high interest in this topic, very few studies conducted rigorous analyses to demonstrate incremental predictive utility beyond established CVD risk factors for T2D. The most promising markers identified were NT-proBNP, TnT, TyG and GRS-CHD, with the highest strength of evidence for NT-proBNP. Further research is needed to determine their clinical utility in risk stratification and management of CVD in T2D.

Precision Prognostics for Cardiovascular Disease in Type 2 Diabetes: A Systematic Review and Meta-analysis

Background Precision medicine has the potential to improve cardiovascular disease (CVD) risk prediction in individuals with type 2 diabetes (T2D). Methods We conducted a systematic review and meta-analysis of longitudinal studies to identify potentially novel prognostic factors that may improve CVD risk prediction in T2D. Out of 9380 studies identified, 416 studies met inclusion criteria. Outcomes were reported for 321 biomarker studies, 48 genetic marker studies, and 47 risk score/model studies. Results Out of all evaluated biomarkers, only 13 showed improvement in prediction performance. Results of pooled meta-analyses, non-pooled analyses, and assessments of improvement in prediction performance and risk of bias, yielded the highest predictive utility for N-terminal pro b-type natriuretic peptide (NT-proBNP) (high-evidence), troponin-T (TnT) (moderate-evidence), triglyceride-glucose (TyG) index (moderate-evidence), Genetic Risk Score for Coronary Heart Disease (GRS-CHD) (moderate-evidence); moderate predictive utility for coronary computed tomography angiography (low-evidence), single-photon emission computed tomography (low-evidence), pulse wave velocity (moderate-evidence); and low predictive utility for C-reactive protein (moderate-evidence), coronary artery calcium score (low-evidence), galectin-3 (low-evidence), troponin-I (low-evidence), carotid plaque (low-evidence), and growth differentiation factor-15 (low-evidence). Risk scores showed modest discrimination, with lower performance in populations different from the original development cohort. Conclusions Despite high interest in this topic, very few studies conducted rigorous analyses to demonstrate incremental predictive utility beyond established CVD risk factors for T2D. The most promising markers identified were NT-proBNP, TnT, TyG and GRS-CHD, with the highest strength of evidence for NT-proBNP. Further research is needed to determine their clinical utility in risk stratification and management of CVD in T2D.

Long-term quality-of-care score for predicting the occurrence of acute myocardial infarction in patients with type 2 diabetes mellitus

BACKGROUND

Cardiovascular disease (CVD) is the leading cause of death globally, and diabetes mellitus (DM) is a well-established risk factor. Among the risk factors for CVD, DM is a major modifiable factor. In the fatal CVD outcomes, acute myocardial infarction (AMI) is the most common cause of death.

AIM

To develop a long-term quality-of-care score for predicting the occurrence of AMI among patients with type 2 DM on the basis of the hypothesis that good quality of care can reduce the risk of AMI in patients with DM.

METHODS

Using Taiwan's Longitudinal Cohort of Diabetes Patient Database and the medical charts of a medical center, we identified incident patients diagnosed with type 2 DM from 1999 to 2003 and followed them until 2011. We constructed a summary quality-of-care score (with values ranging from 0 to 8) with process indicators (frequencies of HbA1c and lipid profile testing and urine, foot and retinal examinations), intermediate outcome indicators (low-density lipoprotein, blood pressure and HbA1c), and co-morbidity of hypertension. The associations between the score and the incidence of AMI were evaluated using Cox regression models.

RESULTS

A total of 7351 patients who had sufficient information to calculate the score were enrolled. In comparison with participants who had scores ≤ 1, those with scores between 2 and 4 had a lower risk of developing AMI [adjusted hazard ratio (AHR) = 0.71; 95% confidence interval (95%CI): 0.55-0.90], and those with scores ≥ 5 had an even lower risk (AHR = 0.37; 95%CI: 0.21-0.66).

CONCLUSION

Good quality of care can reduce the risk of AMI in patients with type 2 DM. The quality-of-care score developed in this study had a significant association with the risk of AMI and thus can be applied to guiding the care for these patients.

Clinical impacts of frailty on 123,172 people with diabetes mellitus considering the age of onset and drugs of choice: a nationwide population-based 10-year trajectory analysis

AIMS

Frailty substantially increased the risk of adverse clinical outcomes, which was also critical in diabetes management. This study aimed to investigate the interrelationships between the age of onset, frailty, anti-diabetic medications and clinical outcomes in people with diabetes mellitus (DM).

METHODS

A total of 123,172 people aged 40 years and older who were newly diagnosed with DM were identified and categorised into four frailty subgroups (robust, mild, moderate and severe) based on the multimorbidity frailty index (mFI). Cox proportional hazards models were used to examine associations between frailty and clinical outcomes at different ages of DM onsets (40-64, 65-74, 75-84 and 85+ years). Outcomes of interest included generic outcomes (mortality and unplanned hospitalisation) and DM-related outcomes (cardiovascular disease-related mortality, major adverse cardiovascular events (MACEs), diabetes-related hospitalisation and hypoglycaemia).

RESULTS

The proportion of frailty increased with age at diagnosis amongst people with incident DM and the mFI scores increased significantly during the 10-year follow-up. Amongst people with diabetes, those with mild, moderate and severe frailty were associated with greater risks of all-cause mortality (mild: adjusted hazard ratio (aHR) 1.69 [95% confidence interval (CI) 1.60-1.80], P < 0.01; moderate: aHR 2.46 [2.29-2.65], P < 0.01; severe frailty: aHR 3.40 [3.16-3.65], P < 0.01) compared with the robust group. Similar results were found in unplanned hospitalisations, cardiovascular disease-related mortality, MACEs and hypoglycaemia.

CONCLUSIONS

Our study quantified the prevalence of frailty, captured its dynamic changes and examined its impacts on various clinical outcomes amongst people with diabetes at different ages at onset. Frailty assessment and management should be implemented into routine diabetes care.

All-cause mortality, cardiovascular mortality, major cardiovascular events and hypoglycaemia of patients with diabetes onset at an older age: results from the 10-year nationwide cohort study

BACKGROUND

Longitudinal adverse outcomes are unclear among adults with diabetes according to the age of onset.

OBJECTIVE

To investigate the longitudinal diabetes-related outcomes in adults with new-onset diabetes stratified by age.

DESIGN

Retrospective cohort study.

SETTING

Taiwan National Health Insurance Research Database claims data from 2000 to 2015.

SUBJECTS

In total, 115,751 participants aged ≥40 years with new-onset diabetes in 2003 were recruited and stratified by the ages 40-64 (64.3%), 65-74 (21.2%), 75-84 (11.8%) and ≥85 years (2.7%) at the time of diagnosis.

METHODS

Time-varying multivariate Cox proportional hazards model adjusted for covariates was used to examine the associations between the ages of the patients at diabetes onset and the outcomes of interest [all-cause mortality, cardiovascular (CV) mortality, major cardiovascular events (MACE) and hypoglycaemia] during a 10-year follow-up period.

RESULTS

The results showed that compared with those patients aged 40-64 at diagnosis, patients with older-onset diabetes had significantly higher comorbidities (P < 0.01) and a higher diabetes severity (P < 0.01). Patients with older-onset diabetes had a higher risk of all-cause mortality [adjusted hazard ratio (aHR) 2.28, 4.48 and 10.07 in 65-74, 75-84 and ≥85 years old, respectively], CV mortality (aHR = 2.82, 6.06 and 15.91), MACE (aHR = 2.19, 3.01 and 4.15) and hypoglycaemia (aHR = 2.41, 3.59 and 4.62) than patients aged 40-64 during a 10-year follow-up period.

CONCLUSIONS

Patients with diabetes onset at an older age was associated with increased risks of all-cause mortality, CV mortality, MACE and hypoglycaemia after adjusting for the severity of diabetes and anti-diabetic treatment.

High-dimensionality Data Analysis of Pharmacological Systems Associated with Complex Diseases

It is widely accepted that molecular reductionist views of highly complex human physiologic activity, e.g., the aging process, as well as therapeutic drug efficacy are largely oversimplifications. Currently some of the most effective appreciation of biologic disease and drug response complexity is achieved using high-dimensionality (H-D) data streams from transcriptomic, proteomic, metabolomics, or epigenomic pipelines. Multiple H-D data sets are now common and freely accessible for complex diseases such as metabolic syndrome, cardiovascular disease, and neurodegenerative conditions such as Alzheimer's disease. Over the last decade our ability to interrogate these high-dimensionality data streams has been profoundly enhanced through the development and implementation of highly effective bioinformatic platforms. Employing these computational approaches to understand the complexity of age-related diseases provides a facile mechanism to then synergize this pathologic appreciation with a similar level of understanding of therapeutic-mediated signaling. For informative pathology and drug-based analytics that are able to generate meaningful therapeutic insight across diverse data streams, novel informatics processes such as latent semantic indexing and topological data analyses will likely be important. Elucidation of H-D molecular disease signatures from diverse data streams will likely generate and refine new therapeutic strategies that will be designed with a cognizance of a realistic appreciation of the complexity of human age-related disease and drug effects. We contend that informatic platforms should be synergistic with more advanced chemical/drug and phenotypic cellular/tissue-based analytical predictive models to assist in either de novo drug prioritization or effective repurposing for the intervention of aging-related diseases. SIGNIFICANCE STATEMENT: All diseases, as well as pharmacological mechanisms, are far more complex than previously thought a decade ago. With the advent of commonplace access to technologies that produce large volumes of high-dimensionality data (e.g., transcriptomics, proteomics, metabolomics), it is now imperative that effective tools to appreciate this highly nuanced data are developed. Being able to appreciate the subtleties of high-dimensionality data will allow molecular pharmacologists to develop the most effective multidimensional therapeutics with effectively engineered efficacy profiles.

Both pre-frailty and frailty increase healthcare utilization and adverse health outcomes in patients with type 2 diabetes mellitus

Background

Diabetes mellitus (DM) correlates with accelerated aging and earlier appearance of geriatric phenotypes, including frailty. However, whether pre-frailty or frailty predicts greater healthcare utilization in diabetes patients is unclear.

Methods

From the Longitudinal Cohort of Diabetes Patients in Taiwan (n = 840,000) between 2004 and 2010, we identified 560,795 patients with incident type 2 DM, categorized into patients without frailty, or with 1, 2 (pre-frail) and ≥ 3 frailty components, based on FRAIL scale (Fatigue, Resistance, Ambulation, Illness, and body weight Loss). We examined their long-term mortality, cardiovascular risk, all-cause hospitalization, and intensive care unit (ICU) admission.

Results

Among all participants (56.4 ± 13.8 year-old, 46.1% female, and 84.8% community-dwelling), 77.8% (n = 436,521), 19.2% (n = 107,757), 2.7% (n = 15,101), and 0.3% (n = 1416) patients did not have or had 1, 2 (pre-frail), and ≥ 3 frailty components (frail), respectively, with Fatigue and Illness being the most common components. After 3.14 years of follow-up, 7.8% patients died, whereas 36.6% and 9.1% experienced hospitalization and ICU stay, respectively. Cox proportional hazard modeling discovered that patients with 1, 2 (pre-frail), and ≥ 3 frailty components (frail) had an increased risk of mortality (for 1, 2, and ≥ 3 components, hazard ratio [HR] 1.05, 1.13, and 1.25; 95% confidence interval [CI] 1.02–1.07, 1.08–1.17, and 1.15–1.36, respectively), cardiovascular events (HR 1.05, 1.15, and 1.13; 95% CI 1.02–1.07, 1.1–1.2, and 1.01–1.25, respectively), hospitalization (HR 1.06, 1.16, and 1.25; 95% CI 1.05–1.07, 1.14–1.19, and 1.18–1.33, respectively), and ICU admission (HR 1.05, 1.13, and 1.17; 95% CI 1.03–1.07, 1.08–1.14, and 1.06–1.28, respectively) compared to non-frail ones. Approximately 6–7% risk elevation in mortality and healthcare utilization was noted for every frailty component increase.

Conclusion

Pre-frailty and frailty increased the risk of mortality and cardiovascular events, and entailed greater healthcare utilization in patients with type 2 DM.

Electronic supplementary material

The online version of this article (10.1186/s12933-018-0772-2) contains supplementary material, which is available to authorized users.