Glucose level decline precedes dementia in elderly African Americans with diabetes

Assessing Risk Factors for Cognitive Decline Using Electronic Health Record Data: A Scoping Review

Background : The data and information contained within electronic health records (EHR) provide a rich, diverse, longitudinal view of real-world patient histories, offering valuable opportunities to study antecedent risk factors for cognitive decline. However, the extent to which such records' data have been utilized to elucidate the risk factors of cognitive decline remains unclear. Methods : A scoping review was conducted following the PRISMA guideline, examining articles published between January 2010 and April 2023, from PubMed, Web of Science, and CINAHL. Inclusion criteria focused on studies using EHR to investigate risk factors for cognitive decline. Each article was screened by at least two reviewers. Data elements were manually extracted based on a predefined schema. The studied risk factors were classified into categories, and a research gap was identified. Results : From 1,593 articles identified, 80 were selected. The majority (87.5%) were retrospective cohort studies, with 66.3% using datasets of over 10,000 patients, predominantly from the US or UK. Analysis showed that 48.8% of studies addressed medical conditions, 31.3% focused on medical interventions, and 17.5% on lifestyle, socioeconomic status, and environmental factors. Most studies on medical conditions were linked to an increased risk of cognitive decline, whereas medical interventions addressing these conditions often reduced the risk. Conclusions : EHR data significantly enhanced our understanding of medical conditions, interventions, lifestyle, socioeconomic status, and environmental factors related to the risk of cognitive decline.

The relationship between diabetes and the dementia risk: a meta-analysis

BACKGROUND

The link between diabetes and dementia risk is not well understood. This study evaluates the factors linking diabetes to dementia onset, providing guidance for preventing dementia in diabetic patients.

METHODS

This analysis utilized databases such as PubMed, Embase, Web of Science, and the Cochrane Library to review literature from January 31, 2012, to March 5, 2023. Articles were rigorously assessed using specific inclusion and exclusion criteria. The Newcastle-Ottawa Scale (NOS) was used to evaluate the quality of the studies. Data analysis was performed with STATA 15.0.

RESULTS

The study analyzed 15 articles, covering 10,103,868 patients, with 8,821,516 diagnosed with diabetes. The meta-analysis reveals a substantial association between diabetes and an increased risk of dementia [RR: 1.59, 95%CI (1.40-1.80), P < 0.01, I²=96.4%]. A diabetes duration of less than five years is linked to a higher dementia risk [RR: 1.29, 95%CI (1.20-1.39), P < 0.01, I²=92.6%]. Additionally, hypoglycemia significantly raises dementia risk [RR: 1.56, 95%CI (1.13-2.16), P < 0.01, I²=51.5%]. Analyses of blood sugar control, glycated hemoglobin, and fasting blood sugar indicated no significant effects on the onset of dementia.

CONCLUSION

Diabetes notably increases dementia risk, particularly where diabetes duration is under five years or hypoglycemia is present.

REGISTRATION

The research protocol was registered with PROSPERO and assigned the registration number CRD42023394942.

Study protocol for a prediction model for mild cognitive impairment in older adults with diabetes mellitus and construction of a nurse-led screening system: a prospective observational study

INTRODUCTION

With an increasing number of older adults in China, the number of people with cognitive impairment is also increasing. To decrease the risk of dementia, it is necessary to timely detect mild cognitive impairment (MCI), which is the preliminary stage of dementia. The prevalence of MCI is relatively high among older adults with diabetes mellitus (DM); however, no effective screening strategy has been designed for this population. This study will construct a nurse-led screening system to detect MCI in community-dwelling older adults with DM in a timely manner.

METHODS AND ANALYSIS

A total of 642 participants with DM will be recruited (n=449 for development, n=193 for validation). The participants will be divided into MCI and none-MCI groups. The candidate predictors will include demographic variables, lifestyle factors, history of diseases, physical examinations, laboratory tests and neuropsychological tests. Univariate analysis, least absolute shrinkage and selection operator regression screening, and multivariate logistic regression analysis will be conducted to identify the outcome indicators. Based on the multivariate logistic regression equation, we will develop a traditional model as a comparison criterion for the machine learning models. The Hosmer-Lemeshow goodness-of-fit test and calibration curve will be used to evaluate the calibration. Sensitivity, specificity, area under the curves and clinical decision curve analysis will be performed for all models. We will report the sensitivity, specificity, area under the curve and decision curve analysis of the validation dataset. A prediction model with better performance will be adopted to form the nurse-led screening system.

ETHICS AND DISSEMINATION

This prospective study has received institutional approval of the Medical Ethics Committee of Qidong Hospital of TCM (QDSZYY-LL-20220621). Study results will be disseminated through conference presentations, Chinese Clinical Trial Registry and publication.

TRIAL REGISTRATION NUMBER

ChiCTR2200062855.

Hypericin as a potential drug for treating Alzheimer's disease and type 2 diabetes with a view to drug repositioning

AIMS

Alzheimer's disease (AD) and type 2 diabetes (T2D) are two of the most common diseases in elderly population and they have a high rate of comorbidity. Study has revealed that T2D is a major risk factor of AD, and thus exploring therapeutic approaches that can target both diseases has drawn much interest in recent years. In this study, we tried to explore drugs that could be potentially used to prevent or treat both AD and T2D via a drug repositioning approach.

METHODS

We first searched the known drugs that may be effective to T2D treatment based on the network distance between the T2D-associated genes and drugs deposited in the DrugBank database. Then, via molecular docking, we further screened these drugs by examining their interaction with islet amyloid polypeptide (IAPP) and Aβ42 peptide, the key components involved in the pathogenesis of T2D or AD. Finally, the binding between the selected drug candidates and the target proteins was verified by molecular dynamics (MD) simulation; and the potential function of the drug candidates and the corresponding targets were analyzed.

RESULTS

From multiple resources, 734 T2D-associated genes were collected, and a list of 1109 drug candidates for T2D was obtained. We found that hypericin had the lowest binding energy and the most stable interaction with either IAPP or Aβ42 peptide. In addition, we also found that the target genes regulated by hypericin were differentially expressed in the tissues related to the two diseases.

CONCLUSION

Our results show that hypericin may be able to bind with IAPP and Aβ42 stably and prevent their accumulation, and thus could be a promising drug candidate for treating the comorbidity of AD and T2D.

Association between cardiovascular diseases and dementia among various age groups: a population-based cohort study in older adults

The link between cardiovascular (CV) risk factors or diseases and dementia is documented. There is conflicting evidence whether age moderates the association. We need to study this gap so that research and clinical initiatives target appropriate age groups. A cohort of 320,630 adult patients without dementia was built using Quebec healthcare databases (1998-2010). The CV risk factors were hypertension, diabetes and dyslipidemia, while diseases included stroke, myocardial infarction (MI), chronic heart failure (HF), and atrial fibrillation (AF). Dementia risk and CV risk factors or diseases were assessed using incidence rate ratios and Cox regression across age groups. The cohort presented by mainly female sex (67.7%) and mean age of 74.1 years. Incident rate of dementia increased with age, ranging from 4.1 to 93.5 per 1000 person-years. Diabetes, stroke, HF and AF were significantly associated with dementia risk, hazard ratios ranged from 1.08 to 3.54. The strength of association decreased in advanced age for diabetes, stroke and HF. The results suggest that prevention of diabetes, stroke, HF and AF are crucial to mitigate dementia risk. The pathophysiology of dementia in younger and older populations seems to differ, with less impact of CV risk factors in advanced age.

Nordic Walking Improves Cardiometabolic Parameters, Fitness Performance, and Quality of Life in Older Adults With Type 2 Diabetes

OBJECTIVE

To assess the effect of Nordic walking (NW) on cardiometabolic health, physical performance, and well-being in sedentary older adults with type 2 diabetes (T2D).

METHODS

Fifteen subjects with T2D (female, 5; male, 10; age, 65 ± 6.2 years [mean ± standard deviation]; body mass index, 27.3 ± 4.9 kg/m² [mean ± standard deviation]) were enrolled in a 6-month NW training program. The fasting glucose and glycosylated hemoglobin levels, lipid profile (total cholesterol, low-density lipoprotein cholesterol, high-density lipoprotein cholesterol, and triglycerides), systolic blood pressure (SBP), and diastolic blood pressures were measured before and after the intervention. Participants' quality of life (Short-Form Health Survey) and physical fitness (6-minute walking test) were also evaluated.

RESULTS

Compared with baseline, NW significantly improved the fasting glucose level (103.5 ± 18.5 vs 168.7 ± 37.7 mg/dL, P = .01), SBP (121.8 ± 12.2 vs 133 ± 14.4 mm Hg, P = .02), physical fitness (759.88 ± 69 vs 615.5 ± 62.6 m, P < .001), and both mental health (54.5 ± 4.4 vs 45.7 ± 5.6, P < .01) and physical health (49.8 ± 4.7 vs 40.3 ± 5.9, P < .01). The levels of glycosylated hemoglobin (6.15% ± 0.8% vs 6.4% ± 1%, P = .46), total cholesterol (162.2 ± 31.2 vs 175.5 ± 28.8 mg/dL, P = .13), low-density lipoprotein cholesterol (95.2 ± 24.2 vs 106.3 ± 32.3 mg/dL, P = .43), and triglycerides (135.5 ± 60.8 vs 127.6 ± 57.4 mg/dL, P = 0.26) improved without reaching significance.

CONCLUSION

NW training improved the glycemic levels, SBP, physical fitness, and perception of quality of life in older adults with T2D. NW represents a suitable complementary strategy to improve the global health status in this population.

The common genes involved in the pathogenesis of Alzheimer's disease and type 2 diabetes and their implication for drug repositioning

BACKGROUND

The prevalences of Alzheimer's disease (AD) and type 2 diabetes (T2D) continuously increase with the aging of world population. Clinical and epidemiological studies indicate that T2D is an important risk factor for AD. However, the mechanisms underlying the linkage of the two disorders are still not fully elucidated. The aim of this study is to explore the molecular mechanisms of their comorbidity and potential drug targets for AD treatment.

METHODS

We first compiled comprehensive lists of genes associated with AD and T2D, respectively. Then, we investigated the signatures of the shared genes and screened for interactions between the hub genes. Subsequently, we used Autodock Vina to perform molecular docking to predict new drug candidates. Lastly, structure and dynamics of docking results were examined by molecular dynamics simulation to verify drug reliability.

RESULTS

We obtained 917 AD-associated genes, 631 T2D-associated genes and 175 shared genes between the two disorders for subsequent analyses. Functional analysis revealed that metabolic process, lipid and atherosclerosis, AMPK signaling pathway, insulin resistance, chemokines and cytokines were enriched in the shared genes. In addition, 50 central hub genes were identified, including IL6, TNF, INS, IL1B, AKT1, VEGFA, IL10, TP53, PTGS2, TLR4, and others. Finally, we predicted new drug candidates (verdoheme and stannsoporfin) that could be potentially used for AD treatment.

CONCLUSIONS

Our study confirmed that there are important shared genes and pathways between AD and T2D, which may provide clues to reveal the molecular mechanism underlying the pathophysiology of the two diseases and help us to discover novel drug candidates for the treatment of AD. The results may also provide clues into identification of new targets and strategies for prevention and therapy of T2D that predisposes to AD.

Altered cortical thickness, degree centrality, and functional connectivity in middle-age type 2 diabetes mellitus

PURPOSE

This study aimed to investigate the changes in brain structure and function in middle-aged patients with type 2 diabetes mellitus (T2DM) using morphometry and blood oxygen level-dependent functional magnetic resonance imaging (BOLD-fMRI).

METHODS

A total of 44 middle-aged patients with T2DM and 45 matched healthy controls (HCs) were recruited. Surface-based morphometry (SBM) was used to evaluate the changes in brain morphology. Degree centrality (DC) and functional connectivity (FC) were used to evaluate the changes in brain function.

RESULTS

Compared with HCs, middle-aged patients with T2DM exhibited cortical thickness reductions in the left pars opercularis, left transverse temporal, and right superior temporal gyri. Decreased DC values were observed in the cuneus and precuneus in T2DM. Hub-based FC analysis of these regions revealed lower connectivity in the bilateral hippocampus and parahippocampal gyrus, left precuneus, as well as left frontal sup.

CONCLUSION

Cortical thickness, degree centrality, as well as functional connectivity were found to have significant changes in middle-aged patients with T2DM. Our observations provide potential evidence from neuroimaging for analysis to examine diabetes-related brain damage.

Severe Hypoglycemia Increases Dementia Risk and Related Mortality: A Nationwide, Population-based Cohort Study

CONTEXT

There are few studies focused on the relationship between hypoglycemia and new-onset dementia in patients with type 2 diabetes and no study regarding mortality of dementia after hypoglycemia.

OBJECTIVE

We investigated the effect of severe hypoglycemia on dementia subtypes and its relation to overall mortality in patients with type 2 diabetes.

METHODS

We evaluated incident dementia, including Alzheimer disease and vascular dementia, among health checkup participants aged 40 years or older in the National Health Insurance System in Korea from January 2009 to December 2015. Episodes of severe hypoglycemia were examined for 3 years before the date of the health checkup.

RESULTS

Among 2 032 689 participants (1 172 271 men, 860 418 women), 14 443 (0.7%) experienced severe hypoglycemia, during a mean follow-up period of 6.9 ± 1.7 years. Individuals in the severe hypoglycemia group were more likely to be diagnosed with dementia compared to individuals without severe hypoglycemia (23.3% vs 7.3%; P < .001) and the overall incidence of Alzheimer disease was higher than vascular dementia. Dementia risk rose with increasing number of severe hypoglycemic episodes (1 episode [hazard ratio (HR) = 1.54; 95% CI, 1.48-1.60], 2 or more episodes [HR = 1.80; 95% CI, 1.66-1.94]). Overall mortality was higher in participants with dementia, but without severe hypoglycemia (HR = 2.03; 95% CI, 1.96-2.10) and severe hypoglycemia, but without dementia (HR = 4.24; 95% CI, 4.29-4.40), and risk of death was highest in those with both severe hypoglycemia and dementia (HR = 5.08; 95% CI, 4.83-5.35).

CONCLUSION

Severe hypoglycemia is associated with dementia, especially Alzheimer disease and mortality; together, they have an additive effect on overall mortality.

On shared gamma-frailty conditional Markov model for semicompeting risks data

Semicompeting risks data are a mixture of competing risks data and progressive state data. This type of data occurs when a nonterminal event is subject to truncation by a well-defined terminal event, but not vice versa. The shared gamma-frailty conditional Markov model (GFCMM) has been used to analyze semicompeting risks data because of its flexibility. There are two versions of this model: the restricted and the unrestricted model. Maximum likelihood estimation methodology has been proposed in the literature. However, we found through numerical experiments that the unrestricted model sometimes yields nonparametrically biased estimation. In this article, we provide a practical guideline for using the GFCMM in the analysis of semicompeting risk data that includes: (a) a score test to assess if the restricted model, which does not exhibit estimation problems, is reasonable under a proportional hazards assumption, and (b) a graphical illustration to justify whether the unrestricted model yields nonparametric estimation with substantial bias for cases where the test provides a statistical significant result against the restricted model. This guideline was applied to the Indianapolis-Ibadan Dementia Project data as an illustration to explore how dementia occurrence changes mortality risk.

Changes of glucose levels precede dementia in African-Americans with diabetes but not in Caucasians

INTRODUCTION

Changes in glucose levels may represent a powerful metabolic indicator of dementia in African-Americans with diabetes. It is unclear whether these changes also occur in Caucasians.

METHODS

A secondary data analysis using electronic medical records from 5228 African-Americans and Caucasians aged ≥65 years was carried out. Mixed effects models with repeated serum glucose measurements were used to compare changes in glucose levels between African-Americans and Caucasian patients with and without incident dementia.

RESULTS

African-Americans and Caucasians with diabetes had significantly different changes in glucose levels by dementia status (P < .0001). African-Americans experienced a significant decline in glucose levels before the dementia diagnosis (estimated glucose decline 1.3421 mg/dL per year, P < .0001) than those who did not develop dementia. Caucasians with and without dementia showed stable glucose levels over time (P = .3071).

DISCUSSION

Significant changes in glucose levels precede dementia in African-American patients with diabetes but not in Caucasians.

Population Neuroscience: Dementia Epidemiology Serving Precision Medicine and Population Health

Over recent decades, epidemiology has made significant contributions to our understanding of dementia, translating scientific discoveries into population health. Here, we propose reframing dementia epidemiology as "population neuroscience," blending techniques and models from contemporary neuroscience with those of epidemiology and biostatistics. On the basis of emerging evidence and newer paradigms and methods, population neuroscience will minimize the bias typical of traditional clinical research, identify the relatively homogenous subgroups that comprise the general population, and investigate broader and denser phenotypes of dementia and cognitive impairment. Long-term follow-up of sufficiently large study cohorts will allow the identification of cohort effects and critical windows of exposure. Molecular epidemiology and omics will allow us to unravel the key distinctions within and among subgroups and better understand individuals' risk profiles. Interventional epidemiology will allow us to identify the different subgroups that respond to different treatment/prevention strategies. These strategies will inform precision medicine. In addition, insights into interactions between disease biology, personal and environmental factors, and social determinants of health will allow us to measure and track disease in communities and improve population health. By placing neuroscience within a real-world context, population neuroscience can fulfill its potential to serve both precision medicine and population health.