When studying drug effects using observational data, time-related biases such as immortal time bias, time-window bias, and time lag bias may exist and result in spurious associations. Many of the recent studies investigating the effects of metformin on dementia risk were based on large health care administrative databases and might be subject to time-related biases. This systematic review aims to assess if time-related biases exist in previous studies investigating the association between metformin use and dementia risk among diabetes patients. The electronic databases of PubMed, Web of Science, and ProQuest were searched for the terms “Metformin” AND “dementia” OR “Alzheimer’s Disease” OR “cognitive decline” OR “cognitive impairment.” These databases were searched from inception through 03/10/2021. Only English language articles and human subjects research were eligible. In total, twelve retrospective cohort studies, two case-control studies, and two nested case-control studies were identified. Twelve studies reported a reduced risk of dementia associated with metformin use, two articles reported increased risk, and two articles indicated no significant association between metformin use and dementia risk. In these sixteen studies, immortal time bias existed in eleven articles, time lag bias was found in seven articles, time-window bias was identified in three articles, while only one article was not identified any time-related biases. Most previous studies investigating the association between metformin and dementia risk did not carefully considered time-related biases. Future observational studies may need to take these time- related biases into consideration.

Hypertension is a risk factor for cardiovascular diseases including myocardial infarction, stroke, heart failure, angina, and coronary heart disease. It is estimated that over one third of the world population is experiencing hypertension, however, only half of the hypertensive population had blood pressure under control. In 2017, ACC/AHA published an updated high blood pressure guideline and redefine people who had their systolic blood pressure between 130-140 mmHg or diastolic blood pressure between 80-90 mmHg as having hypertension. The recommended treatment target became 130/80 mmHg instead of 140/90 mmHg. The aim of the study is to predict and compare the number of preventable CVD events if blood pressure can be successfully lowered to 140/90 mmHg and 130/80 mmHg, respectively, in an ideal scenario, using 2013-2016 National Health and Nutrition Survey (NHANES). Around 385,000 CVD events could be potentially prevented in 10 years if blood pressures were controlled under 130/80 mmHg. Around 208,000 CVD events could be potentially prevented in 10 years the target was 140/90 mmHg. Middle-aged subgroups (50-69 years old), whites, Framingham high-risk subgroup (FRS > 20%), and comorbidity high-risk subgroup tended to have the highest increase in the number of preventable CVD events. Based on PAR, older persons, people who had FRS > 20%, and people who were in the comorbidity low-risk subgroup tended to benefit more than their respective comparison groups. This estimation provided us a with better understanding of the implications of the 2017 ACC/AHA guideline and the importance of controlling blood pressure to the new target.

Background: The worldwide growth in the elderly population is anticipated to be accompanied by significant growth in the prevalence of dementia. The identification of individuals at high risk of dementia is critical for early diagnosis and intervention and, therefore, numerous dementia prediction models have been developed. However, limited models have been developed using electronic health record (EHR) data and no models have been developed or validated for use among American Indian and Alaska Native (AI/AN) individuals. Focusing on a national sample of Indian Health Service (IHS) users, this investigation aimed to develop and internally validate a two-year, all-cause incident dementia risk prediction model using EHR data and machine learning (ML) algorithms. Methods: Seven years of data from the IHS National Data Warehouse and related EHR databases were extracted for this investigation. Five years of baseline health and service use data from fiscal year (FY) 2007-2011 were utilized to predict the risk of dementia diagnosed between FY 2012-2013. The study cohort included 17,451 IHS users aged 65 years and above without a recorded diagnosis of dementia by the end of FY 2011. Three algorithms were compared: Logistic Regression (LR) with Backwards Stepwise Selection, Least Absolute Shrinkage and Selection Operator (LASSO) Regression, and eXtreme Gradient Boosting (XGBoost). For each algorithm, a literature-based and an extended model were developed. Model performance was compared using area under the receiver operating charactering curve (AUC) and model-specific feature importance was examined. Results: During the outcome assessment period, 631 (3.6%) individuals were diagnosed with dementia. Extended XGBoost and LASSO models exhibited the best discriminatory performance (AUC of 0.807 and 0.803, respectively). Compared to LR and LASSO, XGBoost required significantly less investigator-driven data preprocessing. Significant predictors identified by different algorithms had substantial overlaps: all three algorithms shared 10 common predictors among the top 15 predictors identified. Novel predictors identified included the frequency of inpatient hospitalizations, emergency room visits, and hospital observations occurring during 5-year period preceding diagnosis Conclusions: Routinely collected EHR data can be used to predict the two-year risk of incident, all-cause dementia among IHS users. Following external validation, the developed risk prediction model(s) could serve as a clinical tool to aid IHS clinicians in the identification of AI/AN individuals at high risk for dementia.

Cardiovascular disease (CVD) stands as the predominant cause of mortality on a global scale, with diabetes mellitus (DM) emerging as a prominent risk factor for CVD. Notably, adults afflicted by DM face a nearly twofold increased risk of CVD-related mortality compared to their non-DM counterparts. The present dissertation undertakes the task of formulating pre- ventive strategies for CVD within the DM patient population. The first chapter of this study endeavors to ascertain the residual risk of CVD stemming from hypertriglyceridemia (HTG) in the DM population, drawing data from the National Health Nutrition and Examination Survey (NHANES) spanning the years 2007 to 2014. Interestingly, it is observed that despite the implementation of first-line lipid-lowering therapy involving statin usage and optimal low- density lipoprotein cholesterol (LDL-C) control, approximately one-fifth of this population continues to exhibit HTG. Additionally, three-quarters of DM patients possess a moderate to high 10-year risk for CVD. The second chapter aims to assess the potential impact of two novel DM medications, namely liraglutide (glucagon-like peptide-1 receptor agonists (GLP-1 RAs)) (Chapter 2) and empagliflozin (sodium-glucose cotransporter 2 inhibitor (SGLT-2i)) (Chapter 3), on CVD prevention. The evaluation is conducted in terms of the preventive capacity for CVD events while on treatment, utilizing the NAHNES database. Followingix the meticulous application of key inclusion and exclusion criteria derived from corresponding clinical trials, an estimated 4.2 million and 1.86 million US DM patients are deemed eligi- ble for these treatments, respectively. Moreover, approximately 21,209 and 12,066 major cardiovascular events, comprising a composite outcome of cardiovascular death, myocardial infarction, and stroke, could be averted annually, respectively. The forth chapter endeav- ors to explore the impact of multifactorial risk control—encompassing body mass index, blood glucose, blood pressure, blood lipid levels, physical activity, nicotine exposure, sleep health, and dietary patterns—on the relative risk of CVD development among DM patients based on the UK Biobank database. Notably, a strikingly low percentage of only 2.2% of DM patients demonstrate ideal control over at least six of the above risk factors, and this achievement is associated with an 84% lower risk of incident CVD when compared to individ- uals without any risk factor control. The combined findings of the four chapters underscore the paramount importance of adopting an integrative approach to CVD prevention, partic- ularly in the context of DM patients. The insights gained from this research endeavor hold significant implications for shaping comprehensive strategies aimed at mitigating CVD risks and enhancing patient outcomes within this vulnerable population.

PFAS and air pollution are two groups of modifiable risk factors in the environment. According to the National Health and Nutrition Examination Survey, PFAS are ubiquitous in the serum of the general US population. Contaminated seafood and drinking water, packaged fast food, stain-resistant or water-repellant consumer products, dust, and air are the major exposure routes to humans. In addition, ambient air pollution remains the greatest environmental risk factor and is associated with 100,000-200,000 deaths annually in the US, although the air quality in the US has dramatically improved over the past few decades compared to its historical levels and other countries in the world. Infants, pregnant women, and elderly people are three typical populations that are especially susceptible to these environmental pollutants. In Chapter 2, we examined the associations between serum PFAS concentrations and type of residential flooring among the general US population aged 12 years and older using the 2005-2006 NHANES survey data. We concluded that low pile carpeting was associated with increased serum concentrations of PFHxS and MeFOSAA in the general US population. In Chapters 3 and 4, we conducted two nationwide studies investigating the impacts of PFAS detected in drinking water on birthweight and HDP status, respectively. We used county-level aggregated PFAS data from EPA UCMR3 and county-level multiple-stratified birth/pregnancy data from CDC WONDER. We concluded that the PFAS mixture found in drinking water may contribute to low birthweight and hypertensive disorder of pregnancy in the US. In Chapters 5 and 6, we further conducted two subgroup analyses using data from the C8 and IHS projects, respectively, focusing on two population subgroups with significantly higher environmental exposure to PFOA and O3 in the US due to local industries. Our studies suggest PFOA is moderately associated with preeclampsia in the C8 project and O3 is associated with a higher risk of dementia in American Indians. Future efforts to reduce exposure to PFAS and air pollution might help lower risks of low birthweight, hypertensive disorders of pregnancy/preeclampsia, and dementia in the US.

While there is evidence in previous epidemiological studies that sleep duration is an important contributor to morbidity and mortality, variability by race/ethnicity in the association between sleep duration and adverse health outcomes has not been extensively studied in the literature. In particular, prior studies have essentially ignored sleep duration and its association with cardiometabolic diseases within the American Indian and Alaska Native (AI/AN) population, a group that exhibits an alarmingly high rate of diabetes and relevant cardiometabolic conditions. In this dissertation, I investigated the relationship between sleep duration and cardiometabolic outcomes using two cohorts of AI/ANs: 1) a unique longitudinal lifestyle intervention project; 2) a large cross-sectional survey. The main findings of this dissertation include: 1) suboptimal sleep duration is prevalent in the AI/AN population, as with other minority populations; 2) among AI/ANs with prediabetes undergoing lifestyle intervention, those with adequate sleep benefit more from the intervention than those with short sleep duration; 3) the association between suboptimal sleep and diabetes is stronger in AI/ANs than other race/ethnic groups; and 4) adherence to a set of healthy lifestyle factors confers significant reduction in risk of diabetes and CVD in AI/ANs. This work represents an important step forward in systematically characterizing sleep duration and its cardiometabolic consequences in the AI/AN population and may have a significant impact for future public health interventions in this severely underserved population.

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