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Alzheimer’s disease is the most common type of dementia that currently affects over 6.5 million people in the U.S. Currently there is no cure and the existing drug therapies attempt to delay the mental decline and improve cognitive abilities. Two of the most commonly prescribed such drugs are Donepezil and Memantine. We formally tested and confirmed the presence of a beneficial drug-drug interaction of Donepezil and Memantine using a causal inference analysis. We applied doubly robust estimators to one of the largest and high-quality medical databases to estimate the effect of two commonly prescribed Alzheimer’s disease (AD) medications, Donepezil and Memantine, on the average number of hospital or emergency department visits per year among patients diagnosed with AD. Our results show that, compared to the absence of medication scenario, the Memantine monotherapy, and the Donepezil monotherapy, the combined use of Donepezil and Memantine treatment significantly reduces the average number of hospital or emergency department visits per year by 0.078 (13.8%), 0.144 (25.5%), and 0.132 days (23.4%), respectively. The assessed decline in the average number of hospital or emergency department visits per year is consequently associated with a substantial reduction in medical costs. As of 2022, according to the Alzheimer’s Disease Association, there were over 6.5 million individuals aged 65 and older living with AD in the US alone. If patients who are currently on no drug treatment or using either Donepezil or Memantine alone were switched to the combined used of Donepezil and Memantine therapy, the average number of hospital or emergency department visits could decrease by over 613 thousand visits per year. This, in turn, would lead to a remarkable reduction in medical expenses associated with hospitalization of AD patients in the US, totaling over 940 million dollars per year.

Results from the Multiethnic Cohort Study demonstrated significant differences in lung cancer risk among cigarette smokers from five different ethnic/racial groups. For the same number of cigarettes smoked, and particularly among light smokers, African Americans and Native Hawaiians had the highest risk for lung cancer, Whites had intermediate risk, while Latinos and Japanese Americans had the lowest risk. We analyzed urine samples from 331-709 participants from each ethnic group in this study for metabolites of phenanthrene, a surrogate for carcinogenic polycyclic aromatic hydrocarbon exposure. Consistent with their lung cancer risk and our previous studies of several other carcinogens and toxicants of cigarette smoke, African Americans had significantly (p<0.0001) higher median levels of the two phenanthrene metabolites 3-hydroxyphenanthrene (3-PheOH, 0.931 pmol/ml) and phenanthrene tetraol (PheT, 1.13 pmol/ml) than Whites (3-PheOH, 0.697 pmol/ml; PheT, 0.853 pmol/ml) while Japanese-Americans had significantly (p = 0.002) lower levels of 3-PheOH (0.621 pmol/ml) than Whites. PheT levels (0.838 pmol/ml) in Japanese-Americans were not different from those of Whites. These results are mainly consistent with the lung cancer risk of these three groups, but the results for Native Hawaiians and Latinos were more complex. We also carried out a genome wide association study in search of factors that could influence PheT and 3-PheOH levels. Deletion of GSTT1 explained 2.2% of the variability in PheT, while the strongest association, rs5751777 (p = 1.8x10-62) in the GSTT2 gene, explained 7.7% of the variability in PheT. These GWAS results suggested a possible protective effect of lower GSTT1 copy number variants on the diol epoxide pathway, which was an unexpected result. Collectively, the results of this study provide further evidence that different patterns of cigarette smoking are responsible for the higher lung cancer risk of African Americans than of Whites and the lower lung cancer risk of Japanese Americans, while other factors appear to be involved in the differing risks of Native Hawaiians and Latinos.

Research from the Multiethnic Cohort (MEC) demonstrated that, for the same quantity of cigarette smoking, African Americans and Native Hawaiians have a higher lung cancer risk than Whites, while Latinos and Japanese Americans are less susceptible. We collected urine samples from 2,239 cigarette smokers from five different ethnic groups in the MEC and analyzed each sample for S-phenylmercapturic acid (SPMA), a specific biomarker of benzene uptake. African Americans had significantly higher (geometric mean [SE] 3.69 [0.2], p<0.005) SPMA/ml urine than Whites (2.67 [0.13]) while Japanese Americans had significantly lower levels than Whites (1.65 [0.07], p<0.005). SPMA levels in Native Hawaiians and Latinos were not significantly different from those of Whites. We also conducted a genome-wide association study in search of genetic risk factors related to benzene exposure. The glutathione S-transferase T1 (GSTT1) deletion explained between 14.2-31.6% (p = 5.4x10-157) and the GSTM1 deletion explained between 0.2%-2.4% of the variance (p = 1.1x10-9) of SPMA levels in these populations. Ethnic differences in levels of SPMA remained strong even after controlling for the effects of these two deletions. These results demonstrate the powerful effect of GSTT1 status on SPMA levels in urine and show that uptake of benzene in African American, White, and Japanese American cigarette smokers is consistent with their lung cancer risk in the MEC. While benzene is not generally considered a cause of lung cancer, its metabolite SPMA could be a biomarker for other volatile lung carcinogens in cigarette smoke.

Genome-wide association studies (GWAS) have laid the foundation for investigations into the biology of complex traits, drug development and clinical guidelines. However, the majority of discovery efforts are based on data from populations of European ancestry 1 – 3 . In light of the differential genetic architecture that is known to exist between populations, bias in representation can exacerbate existing disease and healthcare disparities. Critical variants may be missed if they have a low frequency or are completely absent in European populations, especially as the field shifts its attention towards rare variants, which are more likely to be population-specific 4 – 10 . Additionally, effect sizes and their derived risk prediction scores derived in one population may not accurately extrapolate to other populations 11 , 12 . Here we demonstrate the value of diverse, multi-ethnic participants in large-scale genomic studies. The Population Architecture using Genomics and Epidemiology (PAGE) study conducted a GWAS of 26 clinical and behavioural phenotypes in 49,839 non-European individuals. Using strategies tailored for analysis of multi-ethnic and admixed populations, we describe a framework for analysing diverse populations, identify 27 novel loci and 38 secondary signals at known loci, as well as replicate 1,444 GWAS catalogue associations across these traits. Our data show evidence of effect-size heterogeneity across ancestries for published GWAS associations, substantial benefits for fine-mapping using diverse cohorts and insights into clinical implications. In the United States—where minority populations have a disproportionately higher burden of chronic conditions 13 —the lack of representation of diverse populations in genetic research will result in inequitable access to precision medicine for those with the highest burden of disease. We strongly advocate for continued, large genome-wide efforts in diverse populations to maximize genetic discovery and reduce health disparities. Genetic analyses of ancestrally diverse populations show evidence of heterogeneity across ancestries and provide insights into clinical implications, highlighting the importance of including ancestrally diverse populations to maximize genetic discovery and reduce health disparities.

Circulating lipid concentrations are clinically associated with cardiometabolic diseases. The phenotypic variance explained by identified genetic variants remains limited, highlighting the importance of searching for additional factors beyond genetic sequence variants. DNA methylation has been linked to lipid concentrations in previous studies, although most of the studies harbored moderate sample sizes and exhibited underrepresentation of non-European ancestry populations. In addition, knowledge of nongenetic factors on lipid profiles is extremely limited. In the Population Architecture Using Genomics and Epidemiology (PAGE) Study, we performed methylome-wide association analysis on 9,561 participants from diverse race and ethnicity backgrounds for HDL-c, LDL-c, TC, and TG levels, and also tested interactions between smoking or alcohol intake and methylation in their association with lipid levels. We identified novel CpG sites at 16 loci ( P  < 1.18E-7) with successful replication on 3,215 participants. One additional novel locus was identified in the self-reported White participants ( P  = 4.66E-8). Although no additional CpG sites were identified in the genome-wide interaction analysis, 13 reported CpG sites showed significant heterogeneous association across smoking or alcohol intake strata. By mapping novel and reported CpG sites to genes, we identified enriched pathways directly linked to lipid metabolism as well as ones spanning various biological functions. These findings provide new insights into the regulation of lipid concentrations.

Background Lung cancer is the leading cause of cancer-related death. While cigarette smoking is the primary cause of this malignancy, risk differs across racial/ethnic groups. For the same number of cigarettes smoked, Native Hawaiians compared to whites are at greater risk and Japanese Americans are at lower risk of developing lung cancer. DNA methylation of specific CpG sites (e.g., in AHRR and F2RL3 ) is the most common blood epigenetic modification associated with smoking status. However, the influence of internal smoking dose, measured by urinary nicotine equivalents (NE), on DNA methylation in current smokers has not been investigated, nor has a study evaluated whether for the same smoking dose, circulating leukocyte DNA methylation patterns differ by race. Methods We conducted an epigenome-wide association study (EWAS) of NE in 612 smokers from three racial/ethnic groups: whites ( n = 204), Native Hawaiians ( n = 205), and Japanese Americans ( n = 203). Genome-wide DNA methylation profiling of blood leukocyte DNA was measured using the Illumina 450K BeadChip array. Average β value, the ratio of signal from a methylated probe relative to the sum of the methylated and unmethylated probes at that CpG, was the dependent variables in linear regression models adjusting for age, sex, race (for pan-ethnic analysis), and estimated cell-type distribution. Results We found that NE was significantly associated with six differentially methylated CpG sites (Bonferroni corrected p < 1.48 × 10−7): four in or near the FOXK2, PBX1, FNDC7, and FUBP3 genes and two in non-annotated genetic regions. Higher levels of NE were associated with increasing methylation beta-valuesin all six sites. For all six CpG sites, the association was only observed in Native Hawaiians, suggesting that the influence of smoking dose on DNA methylation patterns is heterogeneous across race/ethnicity ( p interactions < 8.8 × 10−8). We found two additional CpG sites associated with NE in only Native Hawaiians. Conclusions In conclusion, internal smoking dose was associated with increased DNA methylation in circulating leukocytes at specific sites in Native Hawaiian smokers but not in white or Japanese American smokers.

This dissertation provides an in-depth exploration into the treatment effectiveness for aplastic anemia using causal inference methods, structured around three pivotal research papers. Each paper contributes to a nuanced understanding of treatment impacts, specifically focusing on bone marrow transplantation (BMT) and prescription drugs, and the identification of optimal treatment strategies.The first paper, \"Causal Inference Analysis for Assessing the Effect of Bone Marrow Transplantation on the One-Year Survival of Adult and Pediatric Aplastic Anemia Patients,\" sets the foundation. It examines the short-term effectiveness of BMT in both adult and pediatric patients, providing crucial insights into how this treatment affects survival rates within the first-year post-diagnosis. This analysis is instrumental in understanding the implications of BMT across different age groups.In the second paper, \"Assessing the Effect of Bone Marrow Transplantation on the Survival of Adult Aplastic Anemia Patients for the First Five-Years After Initial Diagnosis: A Causal Inference Study,\" the focus shifts to a longer-term perspective. This study extends the survival analysis to five years, offering a comprehensive view of the sustained effects of BMT. This longitudinal approach helps in understanding the durability of the treatment's impact.The third paper, \"Identification of Optimal Treatment on the One-Year Survival of Aplastic Anemia Patients: A Causal Inference Study,\" broadens the scope by comparing various treatment options. It seeks to identify the most effective treatment strategy for improving one-year survival rates in aplastic anemia patients, contributing to the decision-making process in clinical settings.These three papers collectively offer an in-depth, varied analysis of treatment effects in aplastic anemia, employing causal inference techniques to navigate complex data. Utilizing robust, clinically significant methods such as doubly robust estimators, our research reveals a marked improvement in survival rates for BMT recipients, particularly adults, over one year. The studies also recognize the typically more severe stages of illness in BMT patients, underscoring the need for assessing disease severity. Despite constraints like small sample sizes limiting variable adjustments, this research substantially progresses our comprehension of aplastic anemia treatments. This methodological approach guarantees solid, clinically pertinent insights, enhancing our grasp of treatment efficacy in this field.

Type 2 Diabetes (T2D) is an emerging public health problem in Asia. An Asian mitochondrial DNA variation m.1382A>C (rs111033358) leads to a K14Q amino acid replacement in MOTS-c, an insulin sensitizing mitochondrial-derived peptide. Meta-analysis of three cohorts (n=27,527, J53 MICC, MEC, and TMM) showed that males but not females with the C-allele exhibit a higher prevalence of T2D. Furthermore, in J-MICC, only males with the C-allele in the lowest tertile of physical activity increased their prevalence of T2D, demonstrating a kinesio-genomic interaction. High-fat fed, male mice injected with MOTS-c showed reduced weight and improved glucose tolerance, but not K14Q-MOTS-c treated mice. Like the human data, female mice were unaffected. Mechanistically, K14Q-MOTS-c leads to diminished insulin-sensitization in vitro. Thus, the m.1382A>C polymorphism is associated with susceptibility to T2D in men, possibly interacting with exercise, and contributing to the risk of T2D in sedentary males by reducing the activity of MOTS-c.

Data-driven computational analysis is becoming increasingly important in biomedical research, as the amount of data being generated continues to grow. However, the lack of practices of sharing research outputs, such as data, source code and methods, affects transparency and reproducibility of studies, which are critical to the advancement of science. Many published studies are not reproducible due to insufficient documentation, code, and data being shared. We conducted a comprehensive analysis of 453 manuscripts published between 2016-2021 and found that 50.1% of them fail to share the analytical code. Even among those that did disclose their code, a vast majority failed to offer additional research outputs, such as data. Furthermore, only one in ten papers organized their code in a structured and reproducible manner. We discovered a significant association between the presence of code availability statements and increased code availability (p=2.71×10 ). Additionally, a greater proportion of studies conducting secondary analyses were inclined to share their code compared to those conducting primary analyses (p=1.15*10 ). In light of our findings, we propose raising awareness of code sharing practices and taking immediate steps to enhance code availability to improve reproducibility in biomedical research. By increasing transparency and reproducibility, we can promote scientific rigor, encourage collaboration, and accelerate scientific discoveries. We must prioritize open science practices, including sharing code, data, and other research products, to ensure that biomedical research can be replicated and built upon by others in the scientific community.