Diabetic Phenotypes and Late-Life Dementia Risk: A Mechanism-specific Mendelian Randomization Study

Dementia prevention: the Mendelian randomisation perspective

Understanding the causes of Alzheimer's disease and related dementias remains a challenge. Observational studies investigating dementia risk factors are limited by the pervasive issues of confounding, reverse causation and selection biases. Conducting randomised controlled trials for dementia prevention is often impractical due to the long prodromal phase and the inability to randomise many potential risk factors. In this essay, we introduce Mendelian randomisation as an alternative approach to examine factors that may prevent or delay Alzheimer's disease. Mendelian randomisation is a causal inference method that has successfully identified risk factors and treatments in various other fields. However, applying this method to dementia risk factors has yielded unexpected findings. Here, we consider five potential explanations and provide recommendations to enhance causal inference from Mendelian randomisation studies on dementia. By employing these strategies, we can better understand factors affecting dementia risk.

Addressing unmeasured confounders in cohort studies: Instrumental variable method for a time-fixed exposure on an outcome trajectory

Instrumental variable methods, which handle unmeasured confounding by targeting the part of the exposure explained by an exogenous variable not subject to confounding, have gained much interest in observational studies. We consider the very frequent setting of estimating the unconfounded effect of an exposure measured at baseline on the subsequent trajectory of an outcome repeatedly measured over time. We didactically explain how to apply the instrumental variable method in such setting by adapting the two-stage classical methodology with (1) the prediction of the exposure according to the instrumental variable, (2) its inclusion into a mixed model to quantify the exposure association with the subsequent outcome trajectory, and (3) the computation of the estimated total variance. A simulation study illustrates the consequences of unmeasured confounding in classical analyses and the usefulness of the instrumental variable approach. The methodology is then applied to 6224 participants of the 3C cohort to estimate the association of type-2 diabetes with subsequent cognitive trajectory, using 42 genetic polymorphisms as instrumental variables. This contribution shows how to handle endogeneity when interested in repeated outcomes, along with a R implementation. However, it should still be used with caution as it relies on instrumental variable assumptions hardly testable in practice.

Mendelian randomization study of diabetes and dementia in the Million Veteran Program

INTRODUCTION

Diabetes and dementia are diseases of high health-care burden worldwide. Individuals with diabetes have 1.4 to 2.2 times higher risk of dementia. Our objective was to evaluate evidence of causality between these two common diseases.

METHODS

We conducted a one-sample Mendelian randomization (MR) analysis in the US Department of Veterans Affairs Million Veteran program. The study included 334,672 participants ≥65 years of age with type 2 diabetes and dementia case-control status and genotype data.

RESULTS

For each standard deviation increase in genetically predicted diabetes, we found increased odds of three dementia diagnoses in non-Hispanic White participants (all-cause: odds ratio [OR] = 1.07 [1.05-1.08], P = 3.40E-18; vascular: OR = 1.11 [1.07-1.15], P = 3.63E-09, Alzheimer's disease [AD]: OR = 1.06 [1.02-1.09], P = 6.84E-04) and non-Hispanic Black participants (all-cause: OR = 1.06 [1.02-1.10], P = 3.66E-03, vascular: OR = 1.11 [1.04-1.19], P = 2.20E-03, AD: OR = 1.12 [1.02-1.23], P = 1.60E-02) but not in Hispanic participants (all P > 0.05).

DISCUSSION

We found evidence of causality between diabetes and dementia using a one-sample MR study, with access to individual level data, overcoming limitations of prior studies using two-sample MR techniques.

Modifiable cardiovascular risk factors and genetics for targeted prevention of dementia

Dementia is a major global challenge for health and social care in the 21st century. A third of individuals >65 years of age die with dementia, and worldwide incidence numbers are projected to be higher than 150 million by 2050. Dementia is, however, not an inevitable consequence of old age; 40% of dementia may theoretically be preventable. Alzheimer's disease (AD) accounts for approximately two-thirds of dementia cases and the major pathological hallmark of AD is accumulation of amyloid-β. Nevertheless, the exact pathological mechanisms of AD remain unknown. Cardiovascular disease and dementia share several risk factors and dementia often coexists with cerebrovascular disease. In a public health perspective, prevention is crucial, and it is suggested that a 10% reduction in prevalence of cardiovascular risk factors could prevent more than nine million dementia cases worldwide by 2050. Yet this assumes causality between cardiovascular risk factors and dementia and adherence to the interventions over decades for a large number of individuals. Using genome-wide association studies, the entire genome can be scanned for disease/trait associated loci in a hypothesis-free manner, and the compiled genetic information is not only useful for pinpointing novel pathogenic pathways but also for risk assessments. This enables identification of individuals at high risk, who likely will benefit the most from a targeted intervention. Further optimization of the risk stratification can be done by adding cardiovascular risk factors. Additional studies are, however, highly needed to elucidate dementia pathogenesis and potential shared causal risk factors between cardiovascular disease and dementia.

Mendelian randomization study of diabetes and dementia in the Million Veteran Program

INTRODUCTION

Diabetes and dementia are diseases of high healthcare burden worldwide. Individuals with diabetes have 1.4 to 2.2 times higher risk of dementia. Our objective was to evaluate evidence of causality between these two common diseases.

METHODS

We conducted a one-sample Mendelian randomization (MR) analysis in the U.S. Department of Veterans Affairs Million Veteran program. The study included 334,672 participants ≥65 years of age with type 2 diabetes and dementia case-control status and genotype data.

RESULTS

For each standard deviation increase in genetically-predicted diabetes, we found increased odds of three dementia diagnoses in non-Hispanic White participants (all-cause: OR=1.07[1.05-1.08], P =3.40E-18; vascular: OR=1.11[1.07-1.15], P =3.63E-09, Alzheimer's: OR=1.06[1.02-1.09], P =6.84E-04) and non-Hispanic Black participants (all-cause: OR=1.06[1.02-1.10], P =3.66E-03, vascular: OR=1.11[1.04-1.19], P =2.20E-03, Alzheimer's: OR=1.12 [1.02-1.23], P =1.60E-02) but not in Hispanic participants (all P >.05).

DISCUSSION

We found evidence of causality between diabetes and dementia using a one-sample MR study, with access to individual level data, overcoming limitations of prior studies utilizing two-sample MR techniques.

Polygenic risk of type 2 diabetes is associated with incident vascular dementia: a prospective cohort study

Type 2 diabetes and dementia are associated, but it is unclear whether the two diseases have common genetic risk markers that could partly explain their association. It is also unclear whether the association between the two diseases is of a causal nature. Furthermore, few studies on diabetes and dementia have validated dementia end-points with high diagnostic precision. We tested associations between polygenic risk scores for type 2 diabetes, fasting glucose, fasting insulin and haemoglobin A1c as exposure variables and dementia as outcome variables in 29 139 adults (mean age 55) followed for 20-23 years. Dementia diagnoses were validated by physicians through data from medical records, neuroimaging and biomarkers in cerebrospinal fluid. The dementia end-points included all-cause dementia, mixed dementia, Alzheimer's disease and vascular dementia. We also tested causal associations between type 2 diabetes and dementia through two-sample Mendelian randomization analyses. Seven different polygenic risk scores including single-nucleotide polymorphisms with different significance thresholds for type 2 diabetes were tested. A polygenic risk score including 4891 single-nucleotide polymorphisms with a P-value of <5e-04 showed the strongest association with different outcomes, including all-cause dementia (hazard ratio 1.11; Bonferroni corrected P = 3.6e-03), mixed dementia (hazard ratio 1.18; Bonferroni corrected P = 3.3e-04) and vascular dementia cases (hazard ratio 1.28; Bonferroni corrected P = 9.6e-05). The associations were stronger for non-carriers of the Alzheimer's disease risk gene APOE ε4. There was, however, no significant association between polygenic risk scores for type 2 diabetes and Alzheimer's disease. Furthermore, two-sample Mendelian randomization analyses could not confirm a causal link between genetic risk markers of type 2 diabetes and dementia outcomes. In conclusion, polygenic risk of type 2 diabetes is associated with an increased risk of dementia, in particular vascular dementia. The findings imply that certain people with type 2 diabetes may, due to their genetic background, be more prone to develop diabetes-associated dementia. This knowledge could in the future lead to targeted preventive strategies in clinical practice.

A Guide for Selection of Genetic Instruments in Mendelian Randomization Studies of Type 2 Diabetes and HbA1c: Toward an Integrated Approach

In this study we examine the instrument selection strategies currently used throughout the type 2 diabetes and HbA1c Mendelian randomization (MR) literature. We then argue for a more integrated and thorough approach, providing a framework to do this in the context of HbA1c and diabetes. We conducted a literature search for MR studies that have instrumented diabetes and/or HbA1c. We also used data from the UK Biobank (UKB) (N = 349,326) to calculate instrument strength metrics that are key in MR studies (the F statistic for average strength and R2 for total strength) with two different methods ("individual-level data regression" and Cragg-Donald formula). We used a 157-single nucleotide polymorphism (SNP) instrument for diabetes and a 51-SNP instrument (with partition into glycemic and erythrocytic as well) for HbA1c. Our literature search yielded 48 studies for diabetes and 22 for HbA1c. Our UKB empirical examples showed that irrespective of the method used to calculate metrics of strength and whether the instrument was the main one or included partition by function, the HbA1c genetic instrument is strong in terms of both average and total strength. For diabetes, a 157-SNP instrument was shown to have good average strength and total strength, but these were both substantially lesser than those of the HbA1c instrument. We provide a careful set of five recommendations to researchers who wish to genetically instrument type 2 diabetes and/or HbA1c. In MR studies of glycemia, investigators should take a more integrated approach when selecting genetic instruments, and we give specific guidance on how to do this.

A Diabetes Genetic Risk Score Is Associated With All-Cause Dementia and Clinically Diagnosed Vascular Dementia in the Million Veteran Program

OBJECTIVE

Diabetes and dementia are diseases of high health care burden worldwide, and studies have shown that diabetes is associated with an increased relative risk of dementia. We set out to examine whether type 2 diabetes-associated genetic variants were associated with dementia and whether they differed by race/ethnicity or clinical dementia diagnosis.

RESEARCH DESIGN AND METHODS

We evaluated associations of two type 2 diabetes genetic risk scores (GRS and GRS-nonAPOE: a score without rs429358, a variant associated with Alzheimer disease [AD]) with three classifications of clinical dementia diagnoses in the Million Veteran Program (MVP): all-cause dementia, vascular dementia (VaD), and AD. We conducted our analysis stratified by European (EUR), African (AFR), and Hispanic (HIS) races/ethnicities.

RESULTS

In EUR, we found associations of the GRS with all-cause dementia (odds ratio [OR] 1.06, P = 1.60e-07) and clinically diagnosed VaD (OR 1.12, P = 5.2e-05) but not with clinically diagnosed AD (OR 1.02, P = 0.43). The GRS was not associated with any dementia outcome in AFR or HIS. When testing with GRS-nonAPOE, we found that effect size estimates in EUR increased and P values decreased for all-cause dementia (OR 1.08, P = 2.6e-12), for VaD (OR 1.14, P = 7.2e-07), and for AD (OR 1.06, P = 0.018). For AFR, the association of GRS-nonAPOE and clinically diagnosed VaD (OR 1.15, P = 0.016) was statistically significant. There were no significant findings for HIS.

CONCLUSIONS

We found evidence suggesting shared genetic pathogenesis of diabetes with all-cause dementia and clinically diagnosed VaD.

Causal effect of insulin resistance on small vessel stroke and Alzheimer's disease: A Mendelian randomization analysis

BACKGROUND AND PURPOSE

The causal effect of insulin resistance on small vessel stroke and Alzheimer's disease (AD) was controversial in previous studies. We therefore applied Mendelian randomization (MR) analyses to identify the causal effect of insulin resistance on small vessel stroke and AD.

METHODS

We selected 12 single-nucleotide polymorphisms (SNPs) associated with fasting insulin levels and five SNPs associated with "gold standard" measures of insulin resistance as instrumental variables in MR analyses. Summary statistical data on SNP-small vessel stroke and on SNP-AD associations were derived from studies by the Multi-ancestry Genome-Wide Association Study of Stroke consortium (MEGASTROKE) and the Psychiatric Genomics Consortium-Alzheimer Disease Workgroup (PGC-ALZ) in individuals of European ancestry. Two-sample MR estimates were conducted with inverse-variance-weighted, robust inverse-variance-weighted, simple median, weighted median, weighted mode-based estimator, and MR pleiotropy residual sum and outlier (MR-PRESSO) methods.

RESULTS

Genetically predicted higher insulin resistance had a higher odds ratio (OR) of small vessel stroke (OR 1.23, 95% confidence interval [CI] 1.05-1.44, p = 0.01 using fasting insulin; OR 1.25, 95% CI 1.07-1.46, p = 0.006 using gold standard measures of insulin resistance) and AD (OR 1.13, 95% CI 1.04-1.23, p = 0.004 using fasting insulin; OR 1.02, 95% CI 1.00-1.03, p = 0.03 using gold standard measures of insulin resistance) using the inverse-variance-weighted method. No evidence of pleiotropy was found using MR-Egger regression.

CONCLUSION

Our findings provide genetic support for a potential causal effect of insulin resistance on small vessel stroke and AD.

Type 2 Diabetes and Cognitive Status in the Health and Retirement Study: A Mendelian Randomization Approach

Background

Type 2 diabetes mellitus (T2DM) and dementia are leading causes of mortality and disability in the US. T2DM has been associated with dementia; however, causality has not been clearly established. This study tested inferred causality between T2DM and dementia status using a Mendelian randomization approach.

Methods

Participants (50+ years) from the 2010 wave of the Health and Retirement Study of European or African genetic ancestry were included (n = 10,322). History of T2DM was self-reported. Cognitive status (dementia, cognitive impairment non-dementia, or normal cognition) was defined from clinically validated cognitive assessments. Cumulative genetic risk for T2DM was determined using a polygenic score calculated from a European ancestry T2DM genome-wide association study by Xue et al. (2018). All models were adjusted for age, sex, education, APOE-ε4 carrier status, and genetic principal components. Multivariable logistic regression was used to test the association between cumulative genetic risk for T2DM and cognitive status. To test inferred causality using Mendelian randomization, we used the inverse variance method.

Results

Among included participants, 20.9% had T2DM and 20.7% had dementia or cognitive impairment. Among European ancestry participants, T2DM was associated with 1.66 times odds of cognitive impairment non-dementia (95% confidence interval: 1.55–1.77) relative to normal cognition. A one standard deviation increase in cumulative genetic risk for T2DM was associated with 1.30 times higher odds of T2DM (95% confidence interval: 1.10–1.52). Cumulative genetic risk for T2DM was not associated with dementia status or cognitive-impaired non-dementia in either ancestry (P > 0.05); lack of association here is an important assumption of Mendelian randomization. Using Mendelian randomization, we did not observe evidence for an inferred causal association between T2DM and cognitive impairment (odds ratio: 1.04; 95% confidence interval: 0.90–1.21).

Discussion

Consistent with prior research, T2DM was associated with cognitive status. Prevention of T2DM and cognitive decline are both critical for public health, however, this study does not provide evidence that T2DM is causally related to impaired cognition. Additional studies in other ancestries, larger sample sizes, and longitudinal studies are needed to confirm these results.

The Diabetes Syndrome - A Collection of Conditions with Common, Interrelated Pathophysiologic Mechanisms

The four basic pathophysiologic mechanisms which damage the β-cell within diabetes (ie, genetic and epigenetic changes, inflammation, an abnormal environment, and insulin resistance [IR]) also contribute to cell and tissue damage and elevate the risk of developing all typical diabetes-related complications. Genetic susceptibility to damage from abnormal external and internal environmental factors has been described including inflammation and IR. All these mechanisms can promote epigenetic changes, and in total, these pathophysiologic mechanisms interact and react with each other to cause damage to cells and tissues ultimately leading to disease. Importantly, these pathophysiologic mechanisms also serve to link other common conditions including cancer, dementia, psoriasis, atherosclerotic cardiovascular disease (ASCVD), nonalcoholic fatty liver disease (NAFLD), and nonalcoholic steatohepatitis (NASH). The “Diabetes Syndrome”, an overarching group of interrelated conditions linked by these overlapping mechanisms, can be viewed as a conceptual framework that can facilitate understanding of the inter-relationships of superficially disparate conditions. Recognizing the association of the conditions within the Diabetes Syndrome due to common pathophysiologies has the potential to provide both benefit to the patient (eg, prevention, early detection, precision medicine) and to the advancement of medicine (eg, driving education, research, and dynamic decision-based medical practice).

Impact of metabolic dysfunction on cognition in humans

PURPOSE OF REVIEW

The current review evaluates the recent literature on the impact of metabolic dysfunction in human cognition, focusing on epidemiological studies and meta-analyses of these.

RECENT FINDINGS

Worldwide around 50 million people live with dementia, a number projected to triple by 2050. Recent reports from the Lancet Commission suggest that 40% of dementia cases may be preventable primarily by focusing on well established metabolic dysfunction components and cardiovascular risk factors.

SUMMARY

There is robust evidence that type 2 diabetes and midlife hypertension increase risk of dementia in late life. Obesity and elevated levels of LDL cholesterol in midlife probably increase risk of dementia, but further research is needed in these areas. Physical activity, diet, alcohol, and smoking might also influence the risk of dementia through their effect on metabolic dysfunction. A key recommendation is to be ambitious about prevention, focusing on interventions to promote healthier lifestyles combating metabolic dysfunction. Only comprehensive multidomain and staff-requiring interventions are however efficient to maintain or improve cognition in at-risk individuals and will be unrealistic economic burdens for most societies to implement. Therefore, a risk score that identifies high-risk individuals will enable a targeted early intensive intervention toward those high-risk individuals that will benefit the most from a prevention against cardiovascular risk factors and metabolic dysfunction.

Polygenic Score Models for Alzheimer's Disease: From Research to Clinical Applications

The high prevalence of Alzheimer's disease (AD) among the elderly population and its lack of effective treatments make this disease a critical threat to human health. Recent epidemiological and genetics studies have revealed the polygenic nature of the disease, which is possibly explainable by a polygenic score model that considers multiple genetic risks. Here, we systemically review the rationale and methods used to construct polygenic score models for studying AD. We also discuss the associations of polygenic risk scores (PRSs) with clinical outcomes, brain imaging findings, and biochemical biomarkers from both the brain and peripheral system. Finally, we discuss the possibility of incorporating polygenic score models into research and clinical practice along with potential challenges.

Glycemic traits and Alzheimer's disease: a Mendelian randomization study

Previous observational studies have reported an association between impaired glucose metabolism and Alzheimer’s disease. This study aimed to examine the causal association of glycemic traits with Alzheimer’s disease. We used a two-sample Mendelian randomization approach to evaluate the causal effect of six glycemic traits (type 2 diabetes, fasting glucose, fasting insulin, hemoglobin A1c, homeostasis model assessment- insulin resistance and HOMA-β-cell function) on Alzheimer’s disease. Summary data on the association of single nucleotide polymorphisms with these glycemic traits were obtained from genome-wide association studies of the DIAbetes Genetics Replication And Meta-analysis and Meta-Analyses of Glucose and Insulin-related traits Consortium. Summary data on the association of single nucleotide polymorphisms with Alzheimer’s disease were obtained from the International Genomics of Alzheimer's Project. The Mendelian randomization analysis showed that 1-standard deviation higher fasting glucose and lower HOMA-β-cell function (indicating pancreatic β-cell dysfunction) were causally associated with a substantial increase in risk of Alzheimer’s disease (odds ratio=1.33, 95% confidence interval: 1.04-1.68, p=0.02; odds ratio=1.92, 95% confidence interval: 1.15-3.21, p=0.01). However, no significant association was observed for other glycemic traits. This Mendelian randomization analysis provides evidence of causal associations between glycemic traits, especially high fasting glucose and pancreatic β-cell dysfunction, and high risk of Alzheimer's disease.

Mendelian randomisation for nutritional psychiatry

Nutritional psychiatry is a growing area of research, with several nutritional factors implicated in the cause of psychiatric ill-health. However, nutritional research is highly complex, with multiple potential factors involved, highly confounded exposures and small effect sizes for individual nutrients. This Personal View considers whether Mendelian randomisation provides a solution to these difficulties, by investigating causality in a low-risk and low-cost way. We reviewed studies using Mendelian randomisation in nutritional psychiatry, along with the potential opportunities and challenges of using this approach for investigating the causal effects of nutritional exposures. Several studies have identified nutritional exposures that are potentially causal by using Mendelian randomisation in psychiatry, offering opportunities for further mechanistic research, intervention development, and replication. The use of Mendelian randomisation as a foundation for intervention development facilitates the best use of resources in an emerging discipline in which opportunities are rich, but resources are often poor.

Investigating the Relationship Between Type 2 Diabetes and Dementia Using Electronic Medical Records in the GoDARTS Bioresource

OBJECTIVE

To investigate the impact of type 2 diabetes on incidence of major dementia subtypes, Alzheimer and vascular dementia, using electronic medical records (EMR) in the GoDARTS bioresource.

RESEARCH DESIGN AND METHODS

GoDARTS (Genetics of Diabetes Audit and Research in Tayside Scotland) comprises a large case-control study of type 2 diabetes with longitudinal follow-up in EMR. Dementia case subjects after recruitment were passively identified in the EMR, and using a combination of case note review, an Alzheimer-specific weighted genetic risk score (wGRS), and APOE4 genotype, we validated major dementia subtypes. We undertook a retrospective matched cohort study to determine the risk of type 2 diabetes status for incident dementia accounting for competing risk of death.

RESULTS

Type 2 diabetes status was associated with a significant risk of any dementia (cause-specific hazard ratio [csHR] 1.46, 95% CI 1.31-1.64), which was attenuated, but still significant, when competing risk of death was accounted for (subdistribution [sd]HR 1.26, 95% CI 1.13-1.41). The accuracy of EMR-defined cases of Alzheimer or vascular dementia was high-positive predictive value (PPV) 86.4% and PPV 72.8%, respectively-and wGRS significantly predicted Alzheimer dementia (HR 1.23, 95% CI 1.12-1.34) but not vascular dementia (HR 1.02, 95% CI 0.91-1.15). Conversely, type 2 diabetes was strongly associated with vascular dementia (csHR 2.47, 95% C 1.92-3.18) but not Alzheimer dementia, particularly after competing risk of death was accounted for (sdHR 1.02, 95% CI 0.87-1.18).

CONCLUSIONS

Our study indicates that type 2 diabetes is associated with an increased risk of vascular dementia but not with an increased risk of Alzheimer dementia and highlights the potential value of bioresources linked to EMR to study dementia.

Which Risk Factors Causally Influence Dementia? A Systematic Review of Mendelian Randomization Studies

Background:

Numerous risk factors for dementia are well established, though the causal nature of these associations remains unclear.

Objective:

To systematically review Mendelian randomization (MR) studies investigating causal relationships between risk factors and global cognitive function or dementia.

Methods:

We searched five databases from inception to February 2017 and conducted citation searches including MR studies investigating the association between any risk factor and global cognitive function, all-cause dementia or dementia subtypes. Two reviewers independently assessed titles and abstracts, full-texts, and study quality.

Results:

We included 18 MR studies investigating education, lifestyle factors, cardiovascular factors and related biomarkers, diabetes related and other endocrine factors, and telomere length. Studies were of predominantly good quality, however eight received low ratings for sample size and statistical power. The most convincing causal evidence was found for an association of shorter telomeres with increased risk of Alzheimer’s disease (AD). Causal evidence was weaker for smoking quantity, vitamin D, homocysteine, systolic blood pressure, fasting glucose, insulin sensitivity, and high-density lipoprotein cholesterol. Well-replicated associations were not present for most exposures and we cannot fully discount survival and diagnostic bias, or the potential for pleiotropic effects.

Conclusions:

Genetic evidence supported a causal association between telomere length and AD, whereas limited evidence for other risk factors was largely inconclusive with tentative evidence for smoking quantity, vitamin D, homocysteine, and selected metabolic markers. The lack of stronger evidence for other risk factors may reflect insufficient statistical power. Larger well-designed MR studies would therefore help establish the causal status of these dementia risk factors.

Parkinson's disease and Alzheimer's disease: a Mendelian randomization study

BACKGROUND

Alzheimer's disease (AD) and Parkinson's disease (PD) are the top two common neurodegenerative diseases in elderly. Recent studies found the α-synuclein have a key role in AD. Although many clinical and pathological features between AD and PD are shared, the genetic association between them remains unclear, especially whether α-synuclein in PD genetically alters AD risk.

RESULTS

We did not obtain any significant result (OR = 0.918, 95% CI: 0.782-1.076, P = 0.291) in MR analysis between PD and AD risk. In MR between α-synuclein in PD with AD risk, we only extracted rs356182 as the IV through a strict screening process. The result indicated a significant association based on IVW method (OR = 0.638, 95% CI: 0.485-0.838, P = 1.20E-03). In order to examine the robustness of the IVW method, we used other three complementary analytical methods and also obtained consistent results.

CONCLUSION

The overall PD genetic risk factors did not predict AD risk, but the α-synuclein susceptibility genetic variants in PD reduce the AD risk. We believe that our findings may help to understand the association between them, which may be useful for future genetic studies for both diseases.

Lack of genetic support for shared aetiology of Coronary Artery Disease and Late-onset Alzheimer's disease

Epidemiological studies suggest a positive association between coronary artery disease (CAD) and late-onset Alzheimer's disease (LOAD). This large-scale genetic study brings together 'big data' resources to examine the causal impact of genetic determinants of CAD on risk of LOAD. A two-sample Mendelian randomization approach was adopted to estimate the causal effect of CAD on risk of LOAD using summary data from 60,801 CAD cases from CARDIoGRAMplusC4D and 17,008 LOAD cases from the IGAP Consortium. Additional analyses assessed the independent relevance of genetic associations at the APOE locus for both CAD and LOAD. Higher genetically determined risk of CAD was associated with a slightly higher risk of LOAD (Odds Ratio (OR) per log-odds unit of CAD [95% CI]: 1.07 [1.01-1.15]; p = 0.027). However, after exclusion of the APOE locus, the estimate of the causal effect of CAD for LOAD was attenuated and no longer significant (OR 0.94 [0.88-1.01]; p = 0.072). This Mendelian randomization study indicates that the APOE locus is the chief determinant of shared genetic architecture between CAD and LOAD, and suggests a lack of causal relevance of CAD for risk of LOAD after exclusion of APOE.

Eliminating Survivor Bias in Two-stage Instrumental Variable Estimators

Mendelian randomization studies commonly focus on elderly populations. This makes the instrumental variables analysis of such studies sensitive to survivor bias, a type of selection bias. A particular concern is that the instrumental variable conditions, even when valid for the source population, may be violated for the selective population of individuals who survive the onset of the study. This is potentially very damaging because Mendelian randomization studies are known to be sensitive to bias due to even minor violations of the instrumental variable conditions. Interestingly, the instrumental variable conditions continue to hold within certain risk sets of individuals who are still alive at a given age when the instrument and unmeasured confounders exert additive effects on the exposure, and moreover, the exposure and unmeasured confounders exert additive effects on the hazard of death. In this article, we will exploit this property to derive a two-stage instrumental variable estimator for the effect of exposure on mortality, which is insulated against the above described selection bias under these additivity assumptions.

High Hemoglobin A1c and Diabetes Predict Memory Decline in the Health and Retirement Study

BACKGROUND

Type 2 diabetes (T2D) is an established risk factor for dementia, but evidence for T2D and memory decline is less consistent. Understanding how T2D and blood glucose relate to memory decline is crucial to elucidating the mechanisms linking T2D and dementia.

MATERIALS AND METHODS

For 8888 Health and Retirement Study participants aged 50+, glycosylated hemoglobin (HbA1c) was measured in either 2006 or 2008 and physician's diagnosis of diabetes was self-reported in the same year. Composite memory (z scored) was assessed biennially through 2012 using immediate and delayed word list recall or the Informant Questionnaire for Cognitive Decline. Marginal mean regression models for repeated outcomes were specified to predict memory decline as a function of diabetes or HbA1c, using age as the timescale and adjusting for health and social confounders.

RESULTS

Diabetes was associated with a 10% faster rate of memory decline [β=-0.04 per decade; 95% confidence interval (CI), -0.06 to -0.01). A 1 U increase in HbA1c corresponded with a 0.05 SD decrease in memory score per decade (95% CI, -0.08 to -0.03). Even among individuals with HbA1c<6.5% (threshold for diabetes), higher HbA1c was associated with memory decline (β=-0.05 per decade; 95% CI, -0.08 to -0.03).

DISCUSSION

Diabetes accelerated memory loss and higher HbA1c predicted memory decline even in nondiabetics.

Type 2 Diabetes, Cognition, and Dementia in Older Adults: Toward a Precision Health Approach

IN BRIEF There has been a concurrent dramatic rise in type 2 diabetes and dementia in the United States, and type 2 diabetes shares common genetic and environmental risk factors and underlying pathology with both vascular and Alzheimer's dementias. Given the ability to identify this at-risk population and a variety of potential targeted treatments, type 2 diabetes represents a promising focus for a precision health approach to reduce the impact of cognitive decline and dementia in older adults.