Relationship between HbA1c and all-cause mortality in older patients with insulin-treated type 2 diabetes: results of a large UK Cohort Study

Perspectives on deprescribing in older people with type 2 diabetes and/or cardiovascular conditions: challenges from healthcare provider, patient and caregiver perspective, and interventions to support a proactive approach

INTRODUCTION

For people with type 2 diabetes and/or cardiovascular conditions, deprescribing of glucose-lowering, blood pressure-lowering and/or lipid-lowering medication is recommended when they age, and their health status deteriorates. So far, deprescribing rates of these so-called cardiometabolic medications are low. A review of challenges and interventions addressing these challenges in this population is pertinent.

AREAS COVERED

We first provide an overview of relevant deprescribing recommendations. Next, we review challenges for healthcare providers (HCPs) to deprescribe cardiometabolic medication and provide insight in the patient and caregiver perspective on deprescribing. We summarize findings from research on implementing deprescribing of cardiometabolic medication and reflect on strategies to enhance deprescribing. We have used a combination of methods to search for relevant articles.

EXPERT OPINION

There is a need for rigorous development and evaluation of intervention strategies aimed at proactive deprescribing of cardiometabolic medication. To address challenges at different levels, these should be multifaceted interventions. All stakeholders must become aware of the relevance of deintensifying medication in this population. Education and training for HCPs and patients should support patient-centered communication and shared decision-making. Development of procedures and tools to select eligible patients and conduct targeted medication reviews are important for implementation of deprescribing in routine care.

Prospective cohort studies underscore the association of abnormal glycemic measures with all-cause and cause-specific mortalities

The role of fasting plasma glucose (FPG), glycated hemoglobin (HbA1c), and triglyceride-glucose index (TyG index) in predicting all-cause and cause-specific mortalities remains elusive. This study included 384,420 adults from the Shanghai cohort and the UK Biobank (UKB) cohort. After multivariable adjustment in the Cox models, FPG ≥7.0 mmol/L or HbA1c ≥ 6.5% increased the risk of all-cause mortality, FPG ≥5.6 mmol/L or HbA1c ≥ 6.5% increased CVD-related mortality, and higher or lower TyG index increased all-cause and CVD-related mortalities in the Shanghai cohort; FPG ≥5.6 mmol/L, HbA1c ≥ 5.7%, TyG index <8.31 or ≥9.08 increased the risks of all-cause, CVD-related, and cancer-related mortalities in the UKB cohort. FPG or HbA1c increased the discrimination of the conventional risk model in predicting all-cause and CVD-related mortalities in both cohorts. Thus, increased levels of FPG and HbA1c and U-shaped TyG index increase the risks of all-cause especially CVD-related mortalities.

Development of oculomics artificial intelligence for cardiovascular risk factors: A case study in fundus oculomics for HbA1c assessment and clinically relevant considerations for clinicians

Artificial Intelligence (AI) is transforming healthcare, notably in ophthalmology, where its ability to interpret images and data can significantly enhance disease diagnosis and patient care. Recent developments in oculomics, the integration of ophthalmic features to develop biomarkers for systemic diseases, have demonstrated the potential for providing rapid, non-invasive methods of screening leading to enhance in early detection and improve healthcare quality, particularly in underserved areas. However, the widespread adoption of such AI-based technologies faces challenges primarily related to the trustworthiness of the system. We demonstrate the potential and considerations needed to develop trustworthy AI in oculomics through a pilot study for HbA1c assessment using an AI-based approach. We then discuss various challenges, considerations, and solutions that have been developed for powerful AI technologies in the past in healthcare and subsequently apply these considerations to the oculomics pilot study. Building upon the observations in the study we highlight the challenges and opportunities for advancing trustworthy AI in oculomics. Ultimately, oculomics presents as a powerful and emerging technology in ophthalmology and understanding how to optimize transparency prior to clinical adoption is of utmost importance.

Association between glycated hemoglobin and risk of all-cause mortality in community patients with type 2 diabetes: A prospective cohort study

AIMS/INTRODUCTION

To analyze the association between HbA1c level and the risk of all-cause mortality in community patients with type 2 diabetes mellitus, and to provide a scientific basis for the management of type 2 diabetes mellitus in the community.

MATERIALS AND METHODS

Based on a Zhejiang rural community type 2 diabetes mellitus cohort, a total of 10,310 patients with type 2 diabetes mellitus with complete baseline and follow-up data were selected. The Cox proportional hazards regression model and the restricted cubic spline model were used to evaluate the relationship between the HbA1c level and the risk of all-cause mortality.

RESULTS

During a mean follow-up of 5.5 years, 971 patients died. With HbA1c levels of 6.5-7.0% as the reference, after adjusting for relevant confounding factors, the HR(95%CI) of all-cause mortality with HbA1c levels of <5.5%, 5.5-6.5%, 7.0-8.0%, 8.0-9.0%, and ≥9.0% were 1.53 (1.08-2.15), 0.97 (0.79-1.21), 1.14 (0.92-1.41), 1.44 (1.14-1.83), and 2.08 (1.68-2.58), respectively. The HbA1c level was associated with the risk of all-cause mortality in a "J-shaped" manner. The risk of all-cause mortality was lowest when the HbA1c was 6.5-7.0%, and increased significantly when the HbA1c was ≥ 8.0% and the HbA1c was < 5.5% (P < 0.05). The risk of all-cause death in the HbA1c 5.5-6.5% group and the 7.0-8.0% group was not significant compared with the reference group (P > 0.05).

CONCLUSIONS

The HbA1c levels were associated with the risk of all-cause mortality in type 2 diabetes mellitus in a "J-shaped" manner, a too high or a too low HbA1c level could increase the risk of death. Attention should be paid to the individual evaluation of patients and the setting of appropriate glycemic control goals.

Terminal trajectory of HbA1c for 10 years supports the HbA1c paradox: a longitudinal study using Health and Retirement Study data

Objectives

We aimed to assess the potential time-varying associations between HbA1c and mortality, as well as the terminal trajectory of HbA1c in the elderly to reveal the underlying mechanisms.

Design

The design is a longitudinal study using data from the Health and Retirement Study.

Setting and participants

Data were from the Health and Retirement Study. A total of 10,408 participants aged ≥50 years with available HbA1c measurements at baseline (2006/2008) were included.

Methods

Longitudinal HbA1c measured at 2010/2012 and 2014/2016 were collected. HbA1c values measured three times for their associations with all-cause mortality were assessed using Cox regression and restricted cubic splines. HbA1c terminal trajectories over 10 years before death were analyzed using linear mixed-effect models with a backward time scale.

Results

Women constitute 59.6% of the participants with a mean age of 69 years, with 3,070 decedents during the follow-up (8.9 years). The mortality rate during follow-up was 29.5%. Increased mortality risk became insignificant for the highest quartile of HbA1c compared to the third quartile (aHR 1.148, 1.302, and 1.069 for a follow-up of 8.9, 6.5, and 3.2 years, respectively) with a shorter follow-up, while it became higher for the lowest quartile of HbA1c (aHR 0.986, 1.068, and 1.439 for a follow-up of 8.9, 6.5, and 3.2 years, respectively). Accordingly, for both decedents with and without diabetes, an initial increase in HbA1c was followed by an accelerating terminal decline starting 5-6 years before death.

Conclusions and implications

The time-varying association between HbA1c and mortality mapped to the terminal trajectory in HbA1c. High and low HbA1c may have different clinical relationships with mortality. The HbA1c paradox may be partially explained by reverse causation, namely, early manifestation of death.

Management of Glucose-Lowering Therapy in Older Adults with Type 2 Diabetes: Challenges and Opportunities

The population of older adults (≥65 years) with type 2 diabetes mellitus (T2DM) is diverse, encompassing individuals with varying functional capabilities, living arrangements, concomitant medical conditions, and life expectancies. Hence, their categorization into different patient profiles (ie, good health, intermediate health, poor health) may aid in clinical decision-making when establishing glycemic goals and pharmacological treatment strategies. Further granularity in assessing each patient profile through interdisciplinary collaboration may also add precision to therapeutic and monitoring decisions. In this review, we discuss with a multidisciplinary approach how to deliver the best benefit from advanced diabetes therapies and technologies to older adults with T2DM according to each patient profile. There remain however several areas that deserve further research in older adults with T2DM, including the efficacy and safety of continuous glucose monitoring and automated insulin delivery systems, the switch to once-weekly insulin, the effectiveness of multidisciplinary care models, and the use of supported telemedicine and remote blood glucose monitoring in the oldest-old (≥85 years) who particularly require the assistance of others.

Relationship of Glycated Hemoglobin A1c with All-Cause and Cardiovascular Mortality among Patients with Hypertension

Both low and high glycated hemoglobin A1c (HbA1c) levels are well-established causal risk factors for all-cause and cardiovascular mortality in the general population and diabetic patients. However, the relationship between HbA1c with all-cause and cardiovascular mortality among patients with hypertension is unclear. We used NHANES data from 1999 to 2014 as the basis for this population-based cohort study. Based on HbA1c levels (HbA1c > 5, HbA1c > 5.5, HbA1c > 6, HbA1c > 6.5, HbA1c > 7%), hypertensive patients were divided into five groups. An analysis of multivariable Cox proportional hazards was conducted based on hazard ratios (HRs) and respective 95% confidence intervals (CIs). The relationship between HbA1c and mortality was further explored using Kaplan–Meier survival curves, restricted cubic spline curves, and subgroup analyses. In addition, 13,508 patients with hypertension (average age 58.55 ± 15.56 years) were included in the present analysis, with 3760 (27.84%) all-cause deaths during a follow-up of 127.69 ± 57.9 months. A U-shaped relationship was found between HbA1c and all-cause and cardiovascular mortality (all p for likelihood ratio tests were 0.0001). The threshold value of HbA1c related to the lowest risk for all-cause and cardiovascular mortality was 5.3% and 5.7%, respectively. Below the threshold value, increased HbA1c levels reduced the risk of all-cause mortality (HR 0.68, 95% CI 0.51–0.90, p = 0.0078) and cardiovascular mortality (HR 0.77, 95% CI 0.57–1.05, p = 0.0969). Inversely, above the threshold value, increased HbA1c levels accelerated the risk of all-cause mortality (HR 1.14, 95% CI 1.11–1.18, p < 0.0001) and cardiovascular mortality (HR 1.22, 95% CI 1.16–1.29, p < 0.0001). In conclusion, A U-shape relationship was observed between HbA1c and all-cause and cardiovascular mortality among hypertensive patients.

Diabetes and cognitive decline

Epidemiologic studies have documented an association between diabetes and increased risk of cognitive decline in the elderly. Based on animal model studies, several mechanisms have been proposed to explain such an association, including central insulin signaling, neurodegeneration, brain amyloidosis, and neuroinflammation. Nevertheless, the exact mechanisms in humans remain poorly defined. It is reasonable, however, that many pathways may be involved in these patients leading to cognitive impairment. A major aim of clinicians is identifying early onset of neurologic signs and symptoms in elderly diabetics to improve quality of life of those with cognitive impairment and reduce costs associated with long-term complications. Several biomarkers have been proposed to identify diabetics at higher risk of developing dementia and diagnose early stage dementia. Although biomarkers of brain amyloidosis, neurodegeneration and synaptic plasticity are commonly used to diagnose dementia, especially Alzheimer disease, their role in diabetes remains unclear. The aim of this review is to explore the molecular mechanisms linking diabetes with cognitive decline and present the most important findings on the clinical use of biomarkers for diagnosing and predicting early cognitive decline in diabetics.

Intensive glycemic control and macrovascular, microvascular, hypoglycemia complications and mortality in older (age ≥60years) or frail adults with type 2 diabetes: a systematic review and meta-analysis from randomized controlled trial and observation studies

INTRODUCTION

Guidelines for type 2 diabetes (T2D) recommend individualized HbA1c targets to take into account patient age or frailty. We synthesized evidence from randomized controlled trials and observational studies for intensive glycemic control (HbA1c target ≤58 mmol/mol) versus standard care, in elderly (age ≥60 years) or frail adults with T2D.

METHODS

Searches were performed utilizing recognized terms for T2D, frailty, older age, and HbA1c control and outcomes of interest. Meta-analysis was performed where possible. Primary outcomes included all-cause mortality, severe hypoglycemia, and hospital admission rates. Vascular complications, cognitive decline, and falls/fractures were secondary outcomes.

RESULTS

7,528 studies were identified of which 15 different clinical studies were selected. No difference was noted in all-cause mortality with intensive control (pooled hazard ratio 0.96, 95% confidence interval 0.90-1.03), but risk of severe hypoglycemia increased (2.45, 2.22-2.72). Intensive control was associated reductions in microvascular (0.73, 0.68-0.79) and macrovascular complications (0.84, 0.79-0.89). Outcome data for risk of hospitalization, cognition, and falls/fractures were limited.

CONCLUSION

Intensive glycemic control was associated with reduced rates of complications but increased severe hypoglycemia. Significant heterogeneity exists and the impact of different drug regimens is unclear. Caution is needed when setting glycemic targets in elderly or frail individuals.

Development of a predictive risk model for all-cause mortality in patients with diabetes in Hong Kong

INTRODUCTION

Patients with diabetes mellitus are risk of premature death. In this study, we developed a machine learning-driven predictive risk model for all-cause mortality among patients with type 2 diabetes mellitus using multiparametric approach with data from different domains.

RESEARCH DESIGN AND METHODS

This study used territory-wide data of patients with type 2 diabetes attending public hospitals or their associated ambulatory/outpatient facilities in Hong Kong between January 1, 2009 and December 31, 2009. The primary outcome is all-cause mortality. The association of risk variables and all-cause mortality was assessed using Cox proportional hazards models. Machine and deep learning approaches were used to improve overall survival prediction and were evaluated with fivefold cross validation method.

RESULTS

A total of 273 678 patients (mean age: 65.4±12.7 years, male: 48.2%, median follow-up: 142 (IQR=106-142) months) were included, with 91 155 deaths occurring on follow-up (33.3%; annualized mortality rate: 3.4%/year; 2.7 million patient-years). Multivariate Cox regression found the following significant predictors of all-cause mortality: age, male gender, baseline comorbidities, anemia, mean values of neutrophil-to-lymphocyte ratio, high-density lipoprotein-cholesterol, total cholesterol, triglyceride, HbA1c and fasting blood glucose (FBG), measures of variability of both HbA1c and FBG. The above parameters were incorporated into a score-based predictive risk model that had a c-statistic of 0.73 (95% CI 0.66 to 0.77), which was improved to 0.86 (0.81 to 0.90) and 0.87 (0.84 to 0.91) using random survival forests and deep survival learning models, respectively.

CONCLUSIONS

A multiparametric model incorporating variables from different domains predicted all-cause mortality accurately in type 2 diabetes mellitus. The predictive and modeling capabilities of machine/deep learning survival analysis achieved more accurate predictions.

Glycemic and lipid variability for predicting complications and mortality in diabetes mellitus using machine learning

INTRODUCTION

Recent studies have reported that HbA1c and lipid variability is useful for risk stratification in diabetes mellitus. The present study evaluated the predictive value of the baseline, subsequent mean of at least three measurements and variability of HbA1c and lipids for adverse outcomes.

METHODS

This retrospective cohort study consists of type 1 and type 2 diabetic patients who were prescribed insulin at outpatient clinics of Hong Kong public hospitals, from 1st January to 31st December 2009. Standard deviation (SD) and coefficient of variation were used to measure the variability of HbA1c, total cholesterol, low-density lipoprotein cholesterol (LDL-C), high-density lipoprotein cholesterol (HDL-C) and triglyceride. The primary outcome is all-cause mortality. Secondary outcomes were diabetes-related complications.

RESULT

The study consists of 25,186 patients (mean age = 63.0, interquartile range [IQR] of age = 15.1 years, male = 50%). HbA1c and lipid value and variability were significant predictors of all-cause mortality. Higher HbA1c and lipid variability measures were associated with increased risks of neurological, ophthalmological and renal complications, as well as incident dementia, osteoporosis, peripheral vascular disease, ischemic heart disease, atrial fibrillation and heart failure (p <  0.05). Significant association was found between hypoglycemic frequency (p <  0.0001), HbA1c (p <  0.0001) and lipid variability against baseline neutrophil-lymphocyte ratio (NLR).

CONCLUSION

Raised variability in HbA1c and lipid parameters are associated with an elevated risk in both diabetic complications and all-cause mortality. The association between hypoglycemic frequency, baseline NLR, and both HbA1c and lipid variability implicate a role for inflammation in mediating adverse outcomes in diabetes, but this should be explored further in future studies.

Predictions of diabetes complications and mortality using hba1c variability: a 10-year observational cohort study

INTRODUCTION

Emerging evidence suggests that HbA1c variability, in addition to HbA1c itself, can be used as a predictor for mortality. The present study aims to examine the predictive power of mean HbA1c and HbA1c variability measures for diabetic complications as well as mortality.

METHODS

The retrospective observational study analyzed diabetic patients who were prescribed insulin at outpatient clinics of the Prince of Wales Hospital and Shatin Hospital, Hong Kong, from 1 January to 31 December, 2009. Standard deviation (SD), root mean square (RMS), and coefficient of variation were used as measures of HbA1c variability. The primary outcomes were all-cause and cardiovascular mortality. Secondary outcomes were diabetes-related complications.

RESULTS

The study cohort consists of 3424 patients, including 3137 patients with at least three HbA1c measurements. The low mean HbA1c subgroup had significantly shorter time-to-death for all-cause mortality (P < 0.001) but not cardiovascular mortality (P = 0.920). The high Hba1c subgroup showed shorter time-to-death for all-cause (P < 0.001) and cardiovascular mortality (P < 0.001). Mean Hba1c and Hba1c variability predicted all-cause as well as cardiovascular-specific mortality. In terms of secondary outcomes, mean HbA1c and HbA1c variability significantly predicted diabetic ketoacidosis/hyperosmolar hyperglycemic state/diabetic coma, neurological, ophthalmological, and renal complications. A significant association between dichotomized HbA1c variability and hypoglycemia frequency was found (P < 0.0001).

CONCLUSION

High HbA1c variability is associated with increased risk of all-cause and cardiovascular mortality, as well as diabetic complications. The association between hypoglycemic frequency, HbA1c variability, and mortality suggests that intermittent hypoglycemia resulting in poorer outcomes in diabetic patients.