IntroductionThe Alzheimer's Disease Research Summits of 2012 and 2015 incorporated experts from academia, industry, and nonprofit organizations to develop new research directions to transform our... Show moreIntroductionThe Alzheimer's Disease Research Summits of 2012 and 2015 incorporated experts from academia, industry, and nonprofit organizations to develop new research directions to transform our understanding of Alzheimer's disease (AD) and propel the development of critically needed therapies. In response to their recommendations, big data at multiple levels are being generated and integrated to study network failures in disease. We used metabolomics as a global biochemical approach to identify peripheral metabolic changes in AD patients and correlate them to cerebrospinal fluid pathology markers, imaging features, and cognitive performance.MethodsFasting serum samples from the Alzheimer's Disease Neuroimaging Initiative (199 control, 356 mild cognitive impairment, and 175 AD participants) were analyzed using the AbsoluteIDQ-p180 kit. Performance was validated in blinded replicates, and values were medication adjusted.Results Multivariable-adjusted analyses showed that sphingomyelins and ether-containing phosphatidylcholines were altered in preclinical biomarker-defined AD stages, whereas acylcarnitines and several amines, including the branched-chain amino acid valine and α-aminoadipic acid, changed in symptomatic stages. Several of the analytes showed consistent associations in the Rotterdam, Erasmus Rucphen Family, and Indiana Memory and Aging Studies. Partial correlation networks constructed for Aβ1–42, tau, imaging, and cognitive changes provided initial biochemical insights for disease-related processes. Coexpression networks interconnected key metabolic effectors of disease.DiscussionMetabolomics identified key disease-related metabolic changes and disease-progression-related changes. Defining metabolic changes during AD disease trajectory and its relationship to clinical phenotypes provides a powerful roadmap for drug and biomarker discovery. Show less
Treating chronic diseases such as rheumatoid arthritis and type 2 diabetes mellitus is a hot topic that has been discussed widely and investigated extensively, but never solved, due in part to... Show moreTreating chronic diseases such as rheumatoid arthritis and type 2 diabetes mellitus is a hot topic that has been discussed widely and investigated extensively, but never solved, due in part to their high complexity. Integrating disease-related information using a systems approach may help improve our knowledge of stages of the disease, thus improving the accuracy of diagnosing chronic disease. With respect to integrative thinking, traditional Chinese medicine (TCM)‒based concepts may provide a suitable holistic model, as TCM describes disease syndromes/phenotypes as an experience-based reference from the systems level. Systems-based metabolomics provides a comprehensive picture of small molecular metabolites as a readout and provides biological interpretations of the pathophysiology of disease. The rapid, highly sensitive, non-invasive measurement of ultra-weak photon emission (UPE) -- which measures spontaneously emitted photons at the surface of the skin--has been proposed for supporting TCM-based diagnostics and for reflecting the whole body’s physiological and pathological status. Combining metabolomics with TCM-based diagnostics will provide a robust model for investigating the biological processes that underlie UPE. This thesis aimed to investigate system-wide perturbations by using/combining metabolomics, UPE and TCM-based diagnostics, to provide i) a systems view of chronic disease, and ii) personalized phenotyping guided by TCM-based principles. Show less
On the basis of systems thinking, in this thesis metabolomics, Chinese medicine (CM), as well as Western medicine (WM) were combined to achieve a more comprehensive systems diagnosis of patients... Show moreOn the basis of systems thinking, in this thesis metabolomics, Chinese medicine (CM), as well as Western medicine (WM) were combined to achieve a more comprehensive systems diagnosis of patients with chronic diseases. Specifically, metabolomics has been applied to characterize patients by small molecule profiles, which can be applied in phenotyping patients and matching these with optimal therapies. Chinese and Western medicine have different perspectives on diagnosis and could be highly complementary to each other, so combining both could have advantages in personalized diagnosis. Therefore, in this thesis a combination of systems approaches is used, including metabolomics, CM-based diagnosis principles as well as WM to provide systems diagnosis of patients with chronic diseases, in particular rheumatoid arthritis (RA). With systems diagnosis, we could better identify potential biomarkers to predict the WM therapeutic response of patients with RA and monitoring disease progression. Show less
