Mendelian Randomization of Dyslipidemia on Cognitive Impairment Among Older Americans

Association of dyslipidemia with single nucleotide polymorphisms of the cholesteryl ester transfer protein gene and cardiovascular disease risk factors in a highly admixed population

BACKGROUND AND AIMS

Cardiovascular diseases (CVD) are major causes of mortality worldwide, leading to premature deaths, loss of quality of life, and extensive socioeconomic impacts. Alterations in normal plasma lipid concentrations comprise important risk factors associated with CVD due to mechanisms involved in the pathophysiology of atherosclerosis. Genetic markers such as single nucleotide polymorphisms (SNPs) are known to be associated with lipid metabolism, including variants in the cholesteryl ester transfer protein (CETP) gene. Thus, the study's objective was to assess the relationship among lipid profile, socioeconomic and demographic characteristics, health status, inflammatory biomarkers, and CETP genetic variants in individuals living in a highly admixed population.

METHODS

The study comprises an analysis of observational cross-sectional data representative at the population level from a highly admixed population, encompassing 901 individuals from three age groups (adolescents, adults, and older adults). Socioeconomic, demographic, health, and lifestyle characteristics were collected using semi-structured questionnaires. In addition, biochemical markers and lipid profiles were obtained from individuals' blood samples. After DNA extraction, genotyping, and quality control according to Affymetrix's guidelines, information on 15 SNPs in the CETP gene was available for 707 individuals. Lipid profile and CVD risk factors were evaluated by principal component analysis (PCA), and associations between lipid traits and those factors were assessed through multiple linear regression and logistic regression.

RESULTS

There were low linear correlations between lipid profile and other individuals' characteristics. Two principal components were responsible for 80.8 % of the total variance, and there were minor differences in lipid profiles among individuals in different age groups. Non-HDL-c, total cholesterol, and LDL-c had the highest loadings in the first PC, and triacylglycerols, VLDL-c and HDL-c were responsible for a major part of the loading in the second PC;, whilst HDL-c and LDL-c/HDL-c ratio were significant in the third PC. In addition, there were minor differences between groups of individuals with or without dyslipidemia regarding inflammatory biomarkers (IL-1β, IL- 6, IL-10, TNF-α, CRP, and MCP-1). Being overweight, insulin resistance, and lifestyle characteristics (calories from solid fat, added sugar, alcohol and sodium, leisure physical activity, and smoking) were strong predictors of lipid traits, especially HDL-c and dyslipidemia (p < 0.05). The CETP SNPs rs7499892 and rs12691052, rs291044, and rs80180245 were significantly associated with HDL-c (p < 0.05), and their inclusion in the multiple linear regression model increased its accuracy (adjusted R2 rose from 0.12 to 0.18).

CONCLUSION

This study identified correlations between lipid traits and other CVD risk factors. In addition, similar lipid and inflammatory profiles across age groups in the population suggested that adolescents might already present a significant risk for developing cardiovascular diseases in the population. The risk can be primarily attributed to decreased HDL-c concentrations, which appear to be influenced by genetic factors, as evidenced by associations between SNPs in the CETP gene and HDL-c concentrations, as well as potential gene-diet interactions. Our findings underscore the significant impact of genetic and lifestyle factors on lipid profile within admixed populations in developing countries.

Adiposity impacts cognitive function in Asian populations: an epidemiological and Mendelian Randomization study

Background

Obesity and related metabolic disturbances including diabetes, hypertension and hyperlipidemia predict future cognitive decline. Asia has a high prevalence of both obesity and metabolic disease, potentially amplifying the future burden of dementia in the region. We aimed to investigate the impact of adiposity and metabolic risk on cognitive function in Asian populations, using an epidemiological analysis and a two-sample Mendelian Randomization (MR) study.

Methods

The Health for Life in Singapore (HELIOS) Study is a population-based cohort of South-East-Asian men and women in Singapore, aged 30-84 years. We analyzed 8769 participants with metabolic and cognitive data collected between 2018 and 2021. Whole-body fat mass was quantified with Dual X-Ray Absorptiometry (DEXA). Cognition was assessed using a computerized cognitive battery. An index of general cognition ' g ' was derived through factor analysis. We tested the relationship of fat mass indices and metabolic measures with ' g ' using regression approaches. We then performed inverse-variance-weighted MR of adiposity and metabolic risk factors on ' g ', using summary statistics for genome-wide association studies of BMI, visceral adipose tissue (VAT), waist-hip-ratio (WHR), blood pressure, HDL cholesterol, triglycerides, fasting glucose, HbA1c, and general cognition.

Findings

Participants were 58.9% female, and aged 51.4 (11.3) years. In univariate analysis, all 29 adiposity and metabolic measures assessed were associated with ' g ' at P < 0.05. In multivariable analyses, reduced ' g ' was consistently associated with increased visceral fat mass index and lower HDL cholesterol (P < 0.001), but not with blood pressure, triglycerides, or glycemic indices. The reduction in ' g ' associated with 1SD higher visceral fat, or 1SD lower HDL cholesterol, was equivalent to a 0.7 and 0.9-year increase in chronological age respectively (P < 0.001). Inverse variance MR analyses showed that reduced ' g ' is associated with genetically determined elevation of VAT, BMI and WHR (all P < 0.001). In contrast, MR did not support a causal role for blood pressure, lipid, or glycemic indices on cognition.

Interpretation

We show an independent relationship between adiposity and cognition in a multi-ethnic Asian population. MR analyses suggest that both visceral adiposity and raised BMI are likely to be causally linked to cognition. Our findings have important implications for preservation of cognitive health, including further motivation for action to reverse the rising burden of obesity in the Asia-Pacific region.

Funding

The Nanyang Technological University-the Lee Kong Chian School of Medicine, National Healthcare Group, National Medical Research Council, Ministry of Education, Singapore.

Metabolic syndrome and cognition: A systematic review across cognitive domains and a bibliometric analysis

The aim of this review is to investigate the association between metabolic syndrome (MetS) and cognitive decline in distinct cognitive domains, and to perform a complementary study description through the bibliometric analysis. PubMed and Scopus databases were searched from inception to 15 December 2021 to identify longitudinal studies that examined the association of MetS with incident decline, in order to prevent reverse causality. The Preferred Reporting Items for Systematic Review and Meta-Analysis checklist was used to conduct the present systematic review. Thirty studies were included and results were analyzed across the cognitive domains of global cognition, memory, executive functions, attention, visuoconstructive abilities, and language. The majority of the studies reviewed did not report statistically significant results for most cognitive domains investigated, and decline in specific cognitive domains was not consistently associated with the presence of MetS. Meta-analyses were not conducted due to the high degree of between-study heterogeneity regarding the MetS definitions, the cognitive domains examined, the specific tests used for each cognitive domain and the different measures of association used. Bibliometric analysis revealed that most studies are conducted by research teams from USA and China, and that cognitive tasks that reflect real-life abilities are rarely examined. Future studies should employ larger sample sizes, longer follow-up periods, a global consensus for MetS definition and standardized tests of the above mentioned cognitive domains as well as problem-solving tasks with high sensitivity and specificity to clarify the impact of MetS on cognition and its underlying mechanisms.

Phenome-Wide Association Study of Polygenic Risk Score for Alzheimer's Disease in Electronic Health Records

Alzheimer's disease (AD) is the most common form of dementia and a growing public health burden in the United States. Significant progress has been made in identifying genetic risk for AD, but limited studies have investigated how AD genetic risk may be associated with other disease conditions in an unbiased fashion. In this study, we conducted a phenome-wide association study (PheWAS) by genetic ancestry groups within a large academic health system using the polygenic risk score (PRS) for AD. PRS was calculated using LDpred2 with genome-wide association study (GWAS) summary statistics. Phenotypes were extracted from electronic health record (EHR) diagnosis codes and mapped to more clinically meaningful phecodes. Logistic regression with Firth's bias correction was used for PRS phenotype analyses. Mendelian randomization was used to examine causality in significant PheWAS associations. Our results showed a strong association between AD PRS and AD phenotype in European ancestry (OR = 1.26, 95% CI: 1.13, 1.40). Among a total of 1,515 PheWAS tests within the European sample, we observed strong associations of AD PRS with AD and related phenotypes, which include mild cognitive impairment (MCI), memory loss, and dementias. We observed a phenome-wide significant association between AD PRS and gouty arthropathy (OR = 0.90, adjusted p = 0.05). Further causal inference tests with Mendelian randomization showed that gout was not causally associated with AD. We concluded that genetic predisposition of AD was negatively associated with gout, but gout was not a causal risk factor for AD. Our study evaluated AD PRS in a real-world EHR setting and provided evidence that AD PRS may help to identify individuals who are genetically at risk of AD and other related phenotypes. We identified non-neurodegenerative diseases associated with AD PRS, which is essential to understand the genetic architecture of AD and potential side effects of drugs targeting genetic risk factors of AD. Together, these findings expand our understanding of AD genetic and clinical risk factors, which provide a framework for continued research in aging with the growing number of real-world EHR linked with genetic data.

Impact of nonrandom selection mechanisms on the causal effect estimation for two-sample Mendelian randomization methods

Nonrandom selection in one-sample Mendelian Randomization (MR) results in biased estimates and inflated type I error rates only when the selection effects are sufficiently large. In two-sample MR, the different selection mechanisms in two samples may more seriously affect the causal effect estimation. Firstly, we propose sufficient conditions for causal effect invariance under different selection mechanisms using two-sample MR methods. In the simulation study, we consider 49 possible selection mechanisms in two-sample MR, which depend on genetic variants (G), exposures (X), outcomes (Y) and their combination. We further compare eight pleiotropy-robust methods under different selection mechanisms. Results of simulation reveal that nonrandom selection in sample II has a larger influence on biases and type I error rates than those in sample I. Furthermore, selections depending on X+Y, G+Y, or G+X+Y in sample II lead to larger biases than other selection mechanisms. Notably, when selection depends on Y, bias of causal estimation for non-zero causal effect is larger than that for null causal effect. Especially, the mode based estimate has the largest standard errors among the eight methods. In the absence of pleiotropy, selections depending on Y or G in sample II show nearly unbiased causal effect estimations when the casual effect is null. In the scenarios of balanced pleiotropy, all eight MR methods, especially MR-Egger, demonstrate large biases because the nonrandom selections result in the violation of the Instrument Strength Independent of Direct Effect (InSIDE) assumption. When directional pleiotropy exists, nonrandom selections have a severe impact on the eight MR methods. Application demonstrates that the nonrandom selection in sample II (coronary heart disease patients) can magnify the causal effect estimation of obesity on HbA1c levels. In conclusion, nonrandom selection in two-sample MR exacerbates the bias of causal effect estimation for pleiotropy-robust MR methods.

It is well known that nonrandom selection in one-sample Mendelian Randomization (MR) can result in biased estimates and inflated type I error rates. Actually, two-sample MR analyses are more prone to be affected by nonrandom selection than one-sample MR analyses, because two samples for genome-wide association studies (GWAS) may be selected each under different mechanisms from the source population. Summary-level genetic association statistics in two-sample MR may be derived from different study designs such as case-control, case-only and cohort studies, which further inevitably affect the causal effect estimation of exposure on outcome. In this study, we firstly propose a theorem for causal effect invariance under different selection mechanisms. In the simulation, we design 49 combinations of nonrandom selection mechanisms in sample I and sample II, which are widespread in practical applications. The simulation results reveal that the selection mechanisms in sample II have a larger influence on biases and type I error rates than those in sample I. As an illustrative example, we find the nonrandom selection in sample II (coronary heart disease patients) can magnify the causal effect estimation of obesity on the HbA1c levels.