Precision health: A nursing perspective

Precision health refers to personalized healthcare based on a person's unique genetic, genomic, or omic composition within the context of lifestyle, social, economic, cultural and environmental influences to help individuals achieve well-being and optimal health. Precision health utilizes big data sets that combine omics (i.e. genomic sequence, protein, metabolite, and microbiome information) with clinical information and health outcomes to optimize disease diagnosis, treatment and prevention specific to each patient. Successful implementation of precision health requires interprofessional collaboration, community outreach efforts, and coordination of care, a mission that nurses are well-positioned to lead. Despite the surge of interest and attention to precision health, most nurses are not well-versed in precision health or its implications for the nursing profession. Based on a critical analysis of literature and expert opinions, this paper provides an overview of precision health and the importance of engaging the nursing profession for its implementation. Other topics reviewed in this paper include big data and omics, information science, integration of family health history in precision health, and nursing omics research in symptom science. The paper concludes with recommendations for nurse leaders in research, education, clinical practice, nursing administration and policy settings for which to develop strategic plans to implement precision health

Gene-Metabolite Interaction in the One Carbon Metabolism Pathway: Predictors of Colorectal Cancer in Multi-Ethnic Families

For personalized healthcare, the purpose of this study was to examine the key genes and metabolites in the one-carbon metabolism (OCM) pathway and their interactions as predictors of colorectal cancer (CRC) in multi-ethnic families. In this proof-of-concept study, we included a total of 30 participants, 15 CRC cases and 15 matched family/friends representing major ethnic groups in southern California. Analytics based on supervised machine learning were applied, with the target variable being specified as cancer, including the ensemble method and generalized regression (GR) prediction. Elastic Net with Akaike’s Information Criterion with correction (AICc) and Leave-One-Out cross validation GR methods were used to validate the results for enhanced optimality, prediction, and reproducibility. The results revealed that despite some family members sharing genetic heritage, the CRC group had greater combined gene polymorphism-mutations than the family controls (p < 0.1) for five genes including MTHFR C677T, MTHFR A1298C, MTR A2756G, MTRR A66G, and DHFR 19bp. Blood metabolites including homocysteine (7 µmol/L), methyl-folate (40 nmol/L) with total gene mutations (≥4); age (51 years) and vegetable intake (2 cups), and interactions of gene mutations and methylmalonic acid (MMA) (400 nmol/L) were significant predictors (all p < 0.0001) using the AICc. The results were validated by a 3% misclassification rate, AICc of 26, and >99% area under the receiver operating characteristic curve. These results point to the important roles of blood metabolites as potential markers in the prevention of CRC. Future intervention studies can be designed to target the ways to mitigate the enzyme-metabolite deficiencies in the OCM pathway to prevent cancer

Meta-Prediction of MTHFR Gene Polymorphisms and Air Pollution on the Risk of Hypertensive Disorders in Pregnancy Worldwide

Hypertensive disorders in pregnancy (HDP) are devastating health hazards for both women and children. Both methylenetetrahydrofolate reductase (MTHFR) gene polymorphisms and air pollution can affect health status and result in increased risk of HDP for women. The major objective of this study was to investigate the effect of MTHFR polymorphisms, air pollution, and their interaction on the risk of HDP by using meta-predictive analytics. We searched various databases comprehensively to access all available studies conducted for various ethnic populations from countries worldwide, from 1997 to 2017. Seventy-one studies with 8064 cases and 13,232 controls for MTHFR C677T and 11 studies with 1425 cases and 1859 controls for MTHFR A1298C were included. MTHFR C677T homozygous TT (risk ratio (RR) = 1.28, p < 0.0001) and CT plus TT (RR = 1.07, p = 0.0002) were the risk genotypes, while wild-type CC played a protective role (RR = 0.94, p = 0.0017) for HDP. The meta-predictive analysis found that the percentage of MTHFR C677T TT plus CT (p = 0.044) and CT (p = 0.043) genotypes in the HDP case group were significantly increased with elevated levels of air pollution worldwide. Additionally, in countries with higher air pollution levels, the pregnant women with wild-type CC MTHFR 677 had a protection effect against HDP (p = 0.014), whereas, the homozygous TT of MTHFR C677T polymorphism was a risk genotype for developing HDP. Air pollution level is an environmental factor interacting with increased MTHFR C677T polymorphisms, impacting the susceptibility of HDP for women

Personalized Nutrition—Genes, Diet, and Related Interactive Parameters as Predictors of Cancer in Multiethnic Colorectal Cancer Families

To personalize nutrition, the purpose of this study was to examine five key genes in the folate metabolism pathway, and dietary parameters and related interactive parameters as predictors of colorectal cancer (CRC) by measuring the healthy eating index (HEI) in multiethnic families. The five genes included methylenetetrahydrofolate reductase (MTHFR) 677 and 1298, methionine synthase (MTR) 2756, methionine synthase reductase (MTRR 66), and dihydrofolate reductase (DHFR) 19bp, and they were used to compute a total gene mutation score. We included 53 families, 53 CRC patients and 53 paired family friend members of diverse population groups in Southern California. We measured multidimensional data using the ensemble bootstrap forest method to identify variables of importance within domains of genetic, demographic, and dietary parameters to achieve dimension reduction. We then constructed predictive generalized regression (GR) modeling with a supervised machine learning validation procedure with the target variable (cancer status) being specified to validate the results to allow enhanced prediction and reproducibility. The results showed that the CRC group had increased total gene mutation scores compared to the family members (p < 0.05). Using the Akaike’s information criterion and Leave-One-Out cross validation GR methods, the HEI was interactive with thiamine (vitamin B1), which is a new finding for the literature. The natural food sources for thiamine include whole grains, legumes, and some meats and fish which HEI scoring included as part of healthy portions (versus limiting portions on salt, saturated fat and empty calories). Additional predictors included age, as well as gender and the interaction of MTHFR 677 with overweight status (measured by body mass index) in predicting CRC, with the cancer group having more men and overweight cases. The HEI score was significant when split at the median score of 77 into greater or less scores, confirmed through the machine-learning recursive tree method and predictive modeling, although an HEI score of greater than 80 is the US national standard set value for a good diet. The HEI and healthy eating are modifiable factors for healthy living in relation to dietary parameters and cancer prevention, and they can be used for personalized nutrition in the precision-based healthcare era

Predictors of the Healthy Eating Index and Glycemic Index in Multi-Ethnic Colorectal Cancer Families

For personalized nutrition in preparation for precision healthcare, we examined the predictors of healthy eating, using the healthy eating index (HEI) and glycemic index (GI), in family-based multi-ethnic colorectal cancer (CRC) families. A total of 106 participants, 53 CRC cases and 53 family members from multi-ethnic families participated in the study. Machine learning validation procedures, including the ensemble method and generalized regression prediction, Elastic Net with Akaike’s Information Criterion with correction and Leave-One-Out cross validation methods, were applied to validate the results for enhanced prediction and reproducibility. Models were compared based on HEI scales for the scores of 77 versus 80 as the status of healthy eating, predicted from individual dietary parameters and health outcomes. Gender and CRC status were interactive as additional predictors of HEI based on the HEI score of 77. Predictors of HEI 80 as the criterion score of a good diet included five significant dietary parameters (with intake amount): whole fruit (1 cup), milk or milk alternative such as soy drinks (6 oz), whole grain (1 oz), saturated fat (15 g), and oil and nuts (1 oz). Compared to the GI models, HEI models presented more accurate and fitted models. Milk or a milk alternative such as soy drink (6 oz) is the common significant parameter across HEI and GI predictive models. These results point to the importance of healthy eating, with the appropriate amount of healthy foods, as modifiable factors for cancer prevention

A Meta-Prediction of Methylenetetrahydrofolate-Reductase Polymorphisms and Air Pollution Increased the Risk of Ischemic Heart Diseases Worldwide

Ischemic heart disease (IHD) is among the leading causes of death worldwide. Methylenetetrahydrofolate reductase (MTHFR) polymorphisms have been associated with IHD risk, but the findings presented with heterogeneity. The purpose of the present meta-analysis was to provide an updated evaluation by integrating machine-learning based analytics to examine the potential source of heterogeneity on the associations between MTHFR polymorphisms and the risk of various subtypes of IHD, as well as the possible impact of air pollution on MTHFR polymorphisms and IHD risks. A comprehensive search of various databases was conducted to locate 123 studies (29,697 cases and 31,028 controls) for MTHFR C677T, and 18 studies (7158 cases and 5482 controls) for MTHFR A1298C. Overall, MTHFR 677 polymorphisms were risks for IHD (TT: Risk ratio (RR) = 1.23, p < 0.0001; CT: RR = 1.04, p = 0.0028, and TT plus CT: RR = 1.09, p < 0.0001). In contrast, MTHFR 677 CC wildtype was protective against IHD (RR = 0.91, p < 0.00001) for overall populations. Three countries with elevated IHD risks from MTHFR C677T polymorphism with RR >2 included India, Turkey, and Tunisia. Meta-predictive analysis revealed that increased air pollution was associated with increased MTHFR 677 TT and CT polymorphisms in both the case and control group (p < 0.05), with the trend of increased IHD risk resulting from increased air pollution. These results associate the potential inflammatory pathway with air pollution and the folate pathway with MTHFR polymorphism. Future intervention studies can be designed to mitigate MTHFR enzyme deficiencies resulting from gene polymorphisms to prevent IHDs for at-risk populations

Meta-Prediction of the Effect of Methylenetetrahydrofolate Reductase Polymorphisms and Air Pollution on Alzheimer’s Disease Risk

Background: Alzheimer’s disease (AD) is a significant public health issue. AD has been linked with methylenetetrahydrofolate reductase (MTHFR) C677T polymorphism, but the findings have been inconsistent. The purpose of this meta-predictive analysis is to examine the associations between MTHFR polymorphisms and epigenetic factors, including air pollution, with AD risk using big data analytics approaches. Methods and Results: Forty-three studies (44 groups) were identified by searching various databases. MTHFR C677T TT and CT genotypes had significant associations with AD risk in all racial populations (RR = 1.13, p = 0.0047; and RR = 1.12, p < 0.0001 respectively). Meta-predictive analysis showed significant increases of percentages of MTHFR C677T polymorphism with increased air pollution levels in both AD case group and control group (p = 0.0021–0.0457); with higher percentages of TT and CT genotypes in the AD case group than that in the control group with increased air pollution levels. Conclusions: The impact of MTHFR C677T polymorphism on susceptibility to AD was modified by level of air pollution. Future studies are needed to further examine the effects of gene-environment interactions including air pollution on AD risk for world populations

Gene Environment Interactions and Predictors of Colorectal Cancer in Family-Based, Multi-Ethnic Groups

For the personalization of polygenic/omics-based health care, the purpose of this study was to examine the gene–environment interactions and predictors of colorectal cancer (CRC) by including five key genes in the one-carbon metabolism pathways. In this proof-of-concept study, we included a total of 54 families and 108 participants, 54 CRC cases and 54 matched family friends representing four major racial ethnic groups in southern California (White, Asian, Hispanics, and Black). We used three phases of data analytics, including exploratory, family-based analyses adjusting for the dependence within the family for sharing genetic heritage, the ensemble method, and generalized regression models for predictive modeling with a machine learning validation procedure to validate the results for enhanced prediction and reproducibility. The results revealed that despite the family members sharing genetic heritage, the CRC group had greater combined gene polymorphism rates than the family controls (p < 0.05), on MTHFR C677T, MTR A2756G, MTRR A66G, and DHFR 19 bp except MTHFR A1298C. Four racial groups presented different polymorphism rates for four genes (all p < 0.05) except MTHFR A1298C. Following the ensemble method, the most influential factors were identified, and the best predictive models were generated by using the generalized regression models, with Akaike’s information criterion and leave-one-out cross validation methods. Body mass index (BMI) and gender were consistent predictors of CRC for both models when individual genes versus total polymorphism counts were used, and alcohol use was interactive with BMI status. Body mass index status was also interactive with both gender and MTHFR C677T gene polymorphism, and the exposure to environmental pollutants was an additional predictor. These results point to the important roles of environmental and modifiable factors in relation to gene–environment interactions in the prevention of CRC

Meta-Prediction of MTHFR Gene Polymorphism and Air Pollution on the Risks of Congenital Heart Defects Worldwide: A Transgenerational Analysis

Congenital heart disease (CHD) is the leading cause of death in children, and is affected by genetic and environmental factors. To investigate the association of air pollution with methylene-tetrahydrofolate reductase (MTHFR) polymorphisms and the risk of CHD, we included 58 study groups of children and parents, with 12,347 cases and 18,106 controls worldwide. Both MTHFR C677T (rs 1801133) and A1298C (rs 1801131) gene polymorphisms were risks for CHD in children with transgenerational effects from their parents. Countries with greater risks of CHD with a pooled risk ratio (RR) > 2 from MTHFR 677 polymorphisms included Germany, Portugal, China, and Egypt for children; and Brazil, Puerto Rico, Mexico, China, and Egypt for mothers. Whereas, countries with greater risk of CHD with RR > 2 from MTHFR 1298 polymorphisms included Taiwan, Turkey, and Egypt for children; and Brazil, China, and Egypt for mothers. Additionally, meta-prediction analysis revealed that the percentages of MTHFR 677TT and TT plus CT polymorphisms together were increased in countries with higher levels of air pollution, with a trend of increased CHD risks with higher levels of air pollution for children (p = 0.07). Our findings may have significant implications for inflammatory pathways in association with MTHFR polymorphisms and future intervention studies to correct for folate-related enzyme deficits resulted from MTHFR polymorphisms to prevent CHDs for future generations