Browsing by Subject "Metabolic biomarkers"

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    Associations of Nutritional, Environmental, and Metabolic Biomarkers with Diabetes-Related Mortality in U.S. Adults: The Third National Health and Nutrition Examination Surveys between 1988–1994 and 2016
    (MDPI, 2022-06-24) Zhang, Xi; Ardeshirrouhanifard, Shirin; Li, Jing; Li, Mingyue; Dai, Hongji; Song, Yiqing; Epidemiology, School of Public Health
    Background: Nutritional, environmental, and metabolic status may play a role in affecting the progression and prognosis of type 2 diabetes. However, results in identifying prognostic biomarkers among diabetic patients have been inconsistent and inconclusive. We aimed to evaluate the associations of nutritional, environmental, and metabolic status with disease progression and prognosis among diabetic patients. Methods: In a nationally representative sample in the NHANES III (The Third National Health and Nutrition Examination Survey, 1988−1994), we analyzed available data on 44 biomarkers among 2113 diabetic patients aged 20 to 90 years (mean age: 58.2 years) with mortality data followed up through 2016. A panel of 44 biomarkers from blood and urine specimens available from NHANES III were included in this study and the main outcomes as well as the measures are mortalities from all-causes. We performed weighted logistic regression analyses after controlling potential confounders. To assess incremental prognostic values of promising biomarkers beyond traditional risk factors, we compared c-statistics of the adjusted models with and without biomarkers, separately. Results: In total, 1387 (65.2%) deaths were documented between 1988 and 2016. We observed an increased risk of all-cause mortality associated with higher levels of serum C-reactive protein (p for trend = 0.0004), thyroid stimulating hormone (p for trend = 0.04), lactate dehydrogenase (p for trend = 0.02), gamma glutamyl transferase (p for trend = 0.02), and plasma fibrinogen (p for trend = 0.03), and urine albumin (p for trend < 0.0001). In contrast, higher levels of serum sodium (p for trend = 0.005), alpha carotene (p for trend = 0.006), and albumin (p for trend = 0.005) were associated with a decreased risk of all-cause mortality. In addition, these significant associations were not modified by age, sex, or race. Inclusion of thyroid stimulating hormone (p = 0.03), fibrinogen (p = 0.01), and urine albumin (p < 0.0001), separately, modestly improved the discriminatory ability for predicting all-cause mortality among diabetic patients. Conclusions: Our nationwide study findings provide strong evidence that some nutritional, environmental, and metabolic biomarkers were significant predictors of all-cause mortality among diabetic patients and may have potential clinical value for improving stratification of mortality risk.
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    Predictive metabolic networks reveal sex- and APOE genotype-specific metabolic signatures and drivers for precision medicine in Alzheimer's disease
    (Wiley, 2023) Chang, Rui; Trushina, Eugenia; Zhu, Kuixi; Zaidi, Syed Shujaat Ali; Lau, Branden M.; Kueider-Paisley, Alexandra; Moein, Sara; He, Qianying; Alamprese, Melissa L.; Vagnerova, Barbora; Tang, Andrew; Vijayan, Ramachandran; Liu, Yanyun; Saykin, Andrew J.; Brinton, Roberta D.; Kaddurah-Daouk, Rima; Alzheimer’s Disease Neuroimaging Initiative; Alzheimer’s Disease Metabolomics Consortium; Radiology and Imaging Sciences, School of Medicine
    Introduction: Late-onset Alzheimer's disease (LOAD) is a complex neurodegenerative disease characterized by multiple progressive stages, glucose metabolic dysregulation, Alzheimer's disease (AD) pathology, and inexorable cognitive decline. Discovery of metabolic profiles unique to sex, apolipoprotein E (APOE) genotype, and stage of disease progression could provide critical insights for personalized LOAD medicine. Methods: Sex- and APOE-specific metabolic networks were constructed based on changes in 127 metabolites of 656 serum samples from the Alzheimer's Disease Neuroimaging Initiative cohort. Results: Application of an advanced analytical platform identified metabolic drivers and signatures clustered with sex and/or APOE ɛ4, establishing patient-specific biomarkers predictive of disease state that significantly associated with cognitive function. Presence of the APOE ɛ4 shifts metabolic signatures to a phosphatidylcholine-focused profile overriding sex-specific differences in serum metabolites of AD patients. Discussion: These findings provide an initial but critical step in developing a diagnostic platform for personalized medicine by integrating metabolomic profiling and cognitive assessments to identify targeted precision therapeutics for AD patient subgroups through computational network modeling.