Toward a Standardized Strategy of Clinical Metabolomics for the Advancement of Precision Medicine

Despite the tremendous success, pitfalls have been observed in every step of a clinical metabolomics workflow, which impedes the internal validity of the study. Furthermore, the demand for logistics, instrumentations, and computational resources for metabolic phenotyping studies has far exceeded our expectations. In this conceptual review, we will cover inclusive barriers of a metabolomics-based clinical study and suggest potential solutions in the hope of enhancing study robustness, usability, and transferability. The importance of quality assurance and quality control procedures is discussed, followed by a practical rule containing five phases, including two additional "pre-pre-" and "post-post-" analytical steps. Besides, we will elucidate the potential involvement of machine learning and demonstrate that the need for automated data mining algorithms to improve the quality of future research is undeniable. Consequently, we propose a comprehensive metabolomics framework, along with an appropriate checklist refined from current guidelines and our previously published assessment, in the attempt to accurately translate achievements in metabolomics into clinical and epidemiological research. Furthermore, the integration of multifaceted multi-omics approaches with metabolomics as the pillar member is in urgent need. When combining with other social or nutritional factors, we can gather complete omics profiles for a particular disease. Our discussion reflects the current obstacles and potential solutions toward the progressing trend of utilizing metabolomics in clinical research to create the next-generation healthcare system.11Ysciescopu

Metabolic dysfunction biomarkers as predictors of early diabetes

During the pathophysiological course of type 2 diabetes (T2D), several metabolic imbalances occur. There is increasing evidence that metabolic dysfunction far precedes clinical manifestations. Thus, knowing and understanding metabolic imbalances is crucial to unraveling new strategies and molecules (biomarkers) for the early-stage prediction of the disease’s non-clinical phase. Lifestyle interventions must be made with considerable involvement of clinicians, and it should be considered that not all patients will respond in the same manner. Individuals with a high risk of diabetic progression will present compensatory metabolic mechanisms, translated into metabolic biomarkers that will therefore show potential predictive value to differentiate between progressors/non-progressors in T2D. Specific novel biomarkers are being proposed to entrap prediabetes and target progressors to achieve better outcomes. This study provides a review of the latest relevant biomarkers in prediabetes. A search for articles published between 2011 and 2021 was conducted; duplicates were removed, and inclusion criteria were applied. From the 29 studies considered, a survey of the most cited (relevant) biomarkers was conducted and further discussed in the two main identified fields: metabolomics, and miRNA studies.This research was funded by the FCT - Fundação para a Ciência e Tecnologia (REF UID/BIM/04293/2019), and by the following scholarships: (Ref. SAICT2016/FEDER/BIO4DIA/BTI) and (SFRH/BD/146489/2019)

Optimizing Exposome-wide Assessments in Cardiometabolic Risk

This thesis is focused on cardiovascular disease (CVD) and type 2 diabetes mellitus (T2D), two concomitant conditions that appear with growing concern. In our work, we aim to improve the identification of individuals at-risk of cardiometabolic disease through the characterization of complex environmental exposures (i.e. diet, physical activity), that temporally vary, and the health effects on cardiometabolic traits and disease. Our projects were based upon the Västerbotten Health Survey (VHU) and the Malmö Diet and Cancer (MDCS) studies, which included extensive data on lifestyle, biological intermediates, and clinical outcomes. In Paper I, we utilized the so-called environmental-wide association approach (EWAS), using longitudinal data from > 31,000 adults in VHU study. Under generalized linear models, from ~ 300 candidate exposures, 11 modifiable variables were associated with most of the cardiometabolic traits; the prioritised variables belonged to smoking, coffee intake, physical activity, alcohol intake, and context-specific lifestyle domains. In Paper II, we implemented a machine learning-based model to identify individuals with variable susceptibility to lifestyle risk factors for T2D and CVD. Individuals with sensitivity to blood lipids, and blood pressure associated predictors were at higher risk to develop cardiometabolic disease. Furthermore, when pooling across sensitive groups from the two cohorts, the findings suggest a particular vulnerable subpopulation with different risk profile. In Paper III, a series of causal-inference experiments from VHU and publicly available genome-wide association study (GWAS) summary statistics were used to triangulate evidence of the direct and mediated effects by adiposity and physical activity, of macronutrient intake (fat, carbohydrates, protein and sugar) and cardiometabolic disease. Using structural equation modelling, the mediation analyses enhanced with Mendelian randomization analysis, showed a likely causal putative association between carbohydrate intake and T2D. In addition, the integrative genomic analyses suggested a candidate causal variant localized to the established T2D gene TCF7L2. In Paper IV, we conducted a systematic review and metanalysis of observational studies, complemented by Mendelian randomization analysis using GWAS summary statistics, investigating causal associations of individuals with high, yet normal, glycaemia associated with cardiovascular complications. Prediabetes was likely causally associated with coronary heart disease; suggesting higher, but not diabetic levels of blood glucose confer a risk, thus, effective preventive strategies may prove successful in prediabetes

Elucidating causal relationships between energy homeostasis and cardiometabolic outcomes

Energy metabolism dyshomeostasis is associated with multiple health problems. For example, abundant epidemiological data show that obesity and overweight increase the risk of cardiometabolic diseases and early mortality. Type 2 diabetes (T2D), characterized by chronically elevated blood glucose, is also associated with debilitating complications, high healthcare costs and mortality, with cardiovascular complications accounting for more than half of T2D-related deaths. Prediabetes, which is defined as elevated blood glucose below the diagnostic threshold for T2D, affects approximately 350M people worldwide, with about 35-50% developing T2D within 5 years. Further, non-alcoholic fatty liver disease, a form of ectopic fat deposition as a result of energy imbalance, is associated with increased risk of T2D, CVD and hepatocellular carcinoma. Determination of causal relationships between phenotypes related to positive energy balance and disease outcomes, as well as elucidation of the nature of these relationships, may help inform public health intervention policies. In addition, utilizing big data and machine learning (ML) approaches can improve prediction of outcomes related to excess adiposity both for research purposes and eventual validation and clinical translation. AimsIn paper 1, I set out to summarize observational evidence and further determine the causal relationships between prediabetes and common vascular complications associated with T2D i.e., coronary artery disease (CAD), stroke and renal disease. In paper 2, I studied the association between LRIG1 genetic variants and BMI, T2D and lipid biomarkers. In paper 3, we used ML to identify novel molecular features associated with non-alcoholic fatty liver disease (NAFLD). In paper 4, I elucidate the nature of causal relationships between BMI and cardiometabolic traits and investigate sex differences within the causal framework.ResultsPrediabetes was associated with CAD and stroke but not renal disease in observational analyses, whilst in the causal inference analyses, prediabetes was only associated with CAD. Common LRIG1 variant (rs4856886) was associated with increased BMI and lipid hyperplasia but a decreased risk of T2D. In paper 3, models using common clinical variables showed strong NAFLD prediction ability (ROCAUC = 0.73, p < 0.001); addition of hepatic and glycemic biomarkers and omics data to these models strengthened predictive power (ROCAUC = 0.84, p < 0.001). Finally, there was evidence of non-linearity in the causal effect of BMI on T2D and CAD, biomarkers and blood pressure. The causal effects BMI on CAD were different in men and women, though this difference did no hold after Bonferroni correction. ConclusionWe show that derangements in energy homeostasis are causally associated with increased risk of cardiometabolic outcomes and that early intervention on perturbed glucose control and excess adiposity may help prevent these adverse health outcomes. In addition, effects of novel LRIG1 genetic variants on BMI and T2D might enrich our understanding of lipid metabolism and T2D and thus warrant further investigations. Finally, application of ML to multidimensional data improves prediction of NAFLD; similar approaches could be used in other disease research

Risk factor patterns in type 2 diabetes and cardiovascular disease : exploring methods for precision medicine in public health

Non-communicable diseases, including type 2 diabetes and cardiovascular disease, are leading contributors to the global burden of disease and an important public health challenge. At an individual level, there is important variability in the risk of these conditions. However, public health interventions often adopt a generalized one-size-fits-all approach. The overall aim of this thesis was to explore the utility of a precision medicine approach to public health and epidemiology, by applying different analytical methods to classify individuals into similar sub-populations based on their individual level characteristics. In study I, I investigated the patterns of weight changes from childhood to early adulthood and how they relate to the occurrence of type 2 diabetes later in life. The results indicate that exposure to overweight/obesity during early adulthood explains a large proportion of the cases of type 2 diabetes, highlighting the importance of public health interventions during this period. In study II, I used different methods for mediation analysis to study the importance of different mechanisms linking low socioeconomic status and type 2 diabetes. The findings show that around 50% of the association between socioeconomic status and type 2 diabetes could be reduced if unhealthy behaviors and metabolic exposures were removed. Interestingly, the results were similar across the different mediation methods. Finally, in studies III and IV, I used data-driven methods to identify sub-groups of healthy adults based on simple clinical characteristics and laboratory values. The findings show that this method was equally effective, or even better, than those commonly used in clinical practice, and could improve the way we define who is at high risk of type 2 diabetes or cardiovascular disease. In conclusion, these studies provide evidence that precision medicine can be a useful approach to guide development and implementation of public health interventions

Machine-learning based exploration of determinants of gray matter volume in the KORA-MRI study

To identify the most important factors that impact brain volume, while accounting for potential collinearity, we used a data-driven machine-learning approach. Gray Matter Volume (GMV) was derived from magnetic resonance imaging (3T, FLAIR) and adjusted for intracranial volume (ICV). 93 potential determinants of GMV from the categories sociodemographics, anthropometric measurements, cardio-metabolic variables, lifestyle factors, medication, sleep, and nutrition were obtained from 293 participants from a population-based cohort from Southern Germany. Elastic net regression was used to identify the most important determinants of ICV-adjusted GMV. The four variables age (selected in each of the 1000 splits), glomerular filtration rate (794 splits), diabetes (323 splits) and diabetes duration (122 splits) were identified to be most relevant predictors of GMV adjusted for intracranial volume. The elastic net model showed better performance compared to a constant linear regression (mean squared error = 1.10 vs. 1.59, p&lt;0.001). These findings are relevant for preventive and therapeutic considerations and for neuroimaging studies, as they suggest to take information on metabolic status and renal function into account as potential confounders

Doctor of Philosophy

dissertationSeveral studies have demonstrated an association between prediabetes (preDM) and the incidence of Type II Diabetes Mellitus (T2DM). Many preventable factors can contribute to this association, namely behavioral and environmental conditions that lead to physiological changes and symptomology. Earlier identification of disease through combining common laboratory studies that demonstrate an elevated fasting glucose may be one mechanism to identify the vast majority of patients who are unaware of their preDM condition. Also, it has been widely demonstrated that T2DM can be effectively prevented or delayed with interventions geared towards weight management, physical activity, goal setting, and stress management. However, it is not entirely known whether education provided within a healthcare delivery system is effective in supporting patients to reach a 5% weight loss while reducing their overall incidence of T2DM disease. Furthermore, study is needed to evaluate such health interventions beyond effectiveness, to better identify effect and transferability through measuring the reach, adoption, and implementation. The objective of this dissertation was to determine: (a) the risk of T2DM among patients with confirmed and unconfirmed preDM relative to an at-risk group; (b) the association of a 5% weight loss with participation in the Intermountain Healthcare (IH) Diabetes Prevention Program (DPP); and, subsequently, (c) the reach, effectiveness, adoption, and implementation of the IH DPP intervention. The IH Enterprise Data Warehouse was utilized to evaluate these objectives. Patients with unconfirmed preDM iv (HR 1.74; CI 1.59, 1.91; p<0.0001) and confirmed preDM (HR 2.77; CI 2.38, 3.23; p<0.0001) were more likely to develop T2DM when compared to at-risk patients. DPP participants were more likely to achieve a 5% weight loss within 6 months (OR 1.72; 95% CI 1.29, 2.34; p<0.001) and less likely to have incident T2DM (OR 0.45; 95% CI 0.24, 0.84; p=0.012) when compared to the no-DPP group. Lastly, DPP-based lifestyle interventions deployed within IH's delivery system demonstrated moderate effectiveness in the short term, yet the proportion of patients (8%) who enrolled was low. Broad adoption across regions by providers and leadership revealed organizational buy-in (194 providers at 53 clinics referred patients), while demonstrating that much of the clinical effect was seen when patients participated in interventions that were far less resource intensive (only 2.3 DPP counseling encounters on average). In conclusion, confirmed and unconfirmed preDM was associated with T2DM, however when patients participated in a DPP-based intervention, there was significant weight loss and reduction in T2DM incidence. Finally, the IH DPP demonstrated encouraging potential when evaluating organizational adoption and short-term effectiveness, yet may benefit from leveraging technology to scale these established interventions for those at risk for disease

How can older adults combat diabetes to achieve a longer and healthier life?

Type 2 diabetes (hereafter, diabetes) and prediabetes are very common in older adults and constitute a great health concern for this population. The objective of this project is to investigate the impact of prediabetes and diabetes on health and survival among older adults, and to identify modifiable factors that may attenuate the risk of diabetes on disability and mortality to prolong survival with independence. Data used in this project were derived from the ongoing population-based Swedish National study on Aging and Care in Kungsholmen (SNAC-K). Study I described the natural history of prediabetes and identified prognostic factors related to different outcomes of prediabetes. We found that among 918 participants with prediabetes at baseline, 204 (22%) reverted back to normoglycemia, 119 (13%) developed diabetes, and 215 (23%) died during the 12-year follow-up. Lower systolic blood pressure, and weight loss, and the absence of heart diseases were associated with the reversion of prediabetes to normoglycemia, whereas obesity was related to its progression to diabetes. Study II examined the association of prediabetes and diabetes with the risk of stroke and subsequent dementia. Among 2,655 dementia-free participants at baseline, a stroke-free cohort and a prevalent stroke cohort were identified based on prevalent stroke. In the stroke-free cohort, 236 participants developed ischemic stroke and 47 developed post-stroke dementia. Diabetes was associated with a higher risk of ischemic stroke and post-stroke dementia. In the prevalent stroke cohort, diabetes was also related to dementia risk. We did not find a significant association between prediabetes and stroke or post-stroke dementia. Study III assessed the association of prediabetes and diabetes with physical function decline and disability progression and explored whether cardiovascular diseases (CVDs) mediate these associations. During a 12-year follow-up, prediabetes accelerated the deterioration in chair stand performance, walking speed, and disability progression, independent of the future development of diabetes. Diabetes led to a faster decline than prediabetes, especially among those with uncontrolled diabetes. CVDs mediated 7.1%, 7.8%, and 20.9% of the associations between prediabetes and chair stand performance, walking speed, and disability progression, respectively. Study IV examined the association of prediabetes and diabetes on a composite outcome of disability or death and further identified modifiable factors that may prolong disability-free survival. Diabetes, but not prediabetes, was associated with a higher risk of disability or death. Compared to diabetes-free participants with a favorable lifestyle profile including the presence of at least one of the healthy behaviours, active leisure activities, or moderate-to-rich social network, those with diabetes and an unfavorable profile had 2.46 times higher risk of the outcomes. However, among participants with diabetes, the risk of the outcome was attenuated (HR 1.19, 95% CI 0.93 to 1.53) in those with a favorable profile, which prolonged disability-free survival by 3 years compared to those with an unfavorable profile. Conclusions. In addition to its associations with stroke and cardiovascular diseases, diabetes could increase the risk of dementia secondary to stroke and accelerate decline in physical function. This decline in physical function might start already during prediabetes. Yet, one out of five older adults with prediabetes could revert back to normoglycemia with lifestyle modifications such as weight management. Diabetes is related to the risk of disability or death among older adults, but a healthy and socially active lifestyle may attenuate this risk and prolong disability-free survival

Implementing A Prediabetes Screening Algorithm To Improve Identification And Referrals In Primary Care

Almost half (49%) of the United States population has prediabetes or type 2 diabetes. Type 2 diabetes has many associated comorbidities and is the seventh leading cause of death in the United States. It is also the most expensive chronic condition in the nation. Identifying patients with prediabetes allows for early intervention to prevent or delay the onset of type 2 diabetes. The objective of this quality improvement project was to develop and implement a screening algorithm in the primary care setting using the Prediabetes Risk Test and point of care HemoglobinA1c testing to improve identification of patients with prediabetes and increase referrals to lifestyle intervention. Over the 12-week implementation period, fifteen patients were identified as having prediabetes, three agreed to a referral to lifestyle intervention, and one was started on metformin. This was a marked increase compared to two prior recent years. The algorithm was feasible and effective at improving identification of prediabetes, in addition to improving staff and provider knowledge and retention. Future studies should include a broader patient population in a variety of locations with longitudinal follow-up. Updating the Prediabetes Risk Test to specify physical activity for future studies may also be beneficial