A Murine Model of Maternal Micronutrient Deficiencies and Gut Inflammatory Host-microbe Interactions in the Offspring.

BACKGROUND & AIMS: Micronutrient deficiency (MND) (ie, lack of vitamins and minerals) during pregnancy is a major public health concern. Historically, studies have considered micronutrients in isolation; however, MNDs rarely occur alone. The impact of co-occurring MNDs on public health, mainly in shaping mucosal colonization by pathobionts from the Enterobacteriaceae family, remains undetermined due to lack of relevant animal models. METHODS: To establish a maternal murine model of multiple MND (MMND), we customized a diet deficient in vitamins (A, B12, and B9) and minerals (iron and zinc) that most commonly affect children and women of reproductive age. Thereafter, mucosal adherence by Enterobacteriaceae, the associated inflammatory markers, and proteomic profile of intestines were determined in the offspring of MMND mothers (hereafter, low micronutrient [LM] pups) via bacterial plating, flow cytometry, and mass spectrometry, respectively. For human validation, Enterobacteriaceae abundance, assessed via 16s sequencing of 3-month-old infant fecal samples (n = 100), was correlated with micronutrient metabolites using Spearman's correlation in meconium of children from the CHILD birth cohort. RESULTS: We developed an MMND model and reported an increase in colonic abundance of Enterobacteriaceae in LM pups at weaning. Findings from CHILD cohort confirmed a negative correlation between Enterobacteriaceae and micronutrient availability. Furthermore, pro-inflammatory cytokines and increased infiltration of lymphocyte antigen 6 complex high monocytes and M1-like macrophages were evident in the colons of LM pups. Mechanistically, mitochondrial dysfunction marked by reduced expression of nicotinamide adenine dinucleotide (NAD)H dehydrogenase and increased expression of NAD phosphate oxidase (Nox) 1 contributed to the Enterobacteriaceae bloom. CONCLUSION: This study establishes an early life MMND link to intestinal pathobiont colonization and mucosal inflammation via damaged mitochondria in the offspring.

Multiple micronutrient deficiencies in early life cause multi-kingdom alterations in the gut microbiome and intrinsic antibiotic resistance genes in mice.

Globally, ~340 million children suffer from multiple micronutrient deficiencies, accompanied by high pathogenic burden and death due to multidrug-resistant bacteria. The microbiome is a reservoir of antimicrobial resistance (AMR), but the implications of undernutrition on the resistome is unclear. Here we used a postnatal mouse model that is deficient in multiple micronutrients (that is, zinc, folate, iron, vitamin A and vitamin B12 deficient) and shotgun metagenomic sequencing of faecal samples to characterize gut microbiome structure and functional potential, and the resistome. Enterobacteriaceae were enriched in micronutrient-deficient mice compared with mice fed an isocaloric experimental control diet. The mycobiome and virome were also altered with multiple micronutrient deficiencies including increased fungal pathogens such as Candida dubliniensis and bacteriophages. Despite being antibiotic naïve, micronutrient deficiency was associated with increased enrichment of genes and gene networks encoded by pathogenic bacteria that are directly or indirectly associated with intrinsic antibiotic resistance. Bacterial oxidative stress was associated with intrinsic antibiotic resistance in these mice. This analysis reveals multi-kingdom alterations in the gut microbiome as a result of co-occurring multiple micronutrient deficiencies and the implications for antibiotic resistance.

Multiple micronutrient deficiencies alter energy metabolism in host and gut microbiome in an early-life murine model.

Introduction: Micronutrients perform a wide range of physiological functions essential for growth and development. However, most people still need to meet the estimated average requirement worldwide. Globally, 2 billion people suffer from micronutrient deficiency, most of which are co-occurring deficiencies in children under age five. Despite decades of research, animal models studying multiple micronutrient deficiencies within the early-life period are lacking, which hinders our complete understanding of the long-term health implications and may contribute to the inefficacy of some nutritional interventions. Evidence supporting the Developmental Origins of Health and Disease (DOHaD) theory demonstrates that early-life nutritional deficiencies carry life-long consequences mediated through various mechanisms such as abnormal metabolic programming, stunting, altered body composition, and the gut microbiome. However, this is largely unexplored in the multiple micronutrient deficient host. Methods: we developed a preclinical model to examine undernutrition's metabolic and functional impact on the host and gut microbiome early in life. Three-week-old weanling C57BL/6N male mice were fed a low-micronutrient diet deficient in zinc, folate, iron, vitamin A, and vitamin B12 or a control diet for 4-weeks. Results: Our results showed that early-life multiple micronutrient deficiencies induced stunting, altered body composition, impaired glucose and insulin tolerance, and altered the levels of other micronutrients not depleted in the diet within the host. In addition, functional metagenomics profiling and a carbohydrate fermentation assay showed an increased microbial preference for simple sugars rather than complex ones, suggestive of a less developed microbiome in the low-micronutrient-fed mice. Moreover, we found that a zinc-only deficient diet was not sufficient to induce these phenotypes, further supporting the importance of studying co-occurring deficiencies. Discussion: Together, these findings highlight a previously unappreciated role of early-life multiple micronutrient deficiencies in shaping the metabolic phenome of the host and gut microbiome through altered glucose energy metabolism, which may have implications for metabolic disease later in life in micronutrient-deficient survivors.

Nonalcoholic Fatty Liver Disease and the Gut-Liver Axis: Exploring an Undernutrition Perspective.

Nonalcoholic fatty liver disease (NAFLD) is a chronic condition affecting one quarter of the global population. Although primarily linked to obesity and metabolic syndrome, undernutrition and the altered (dysbiotic) gut microbiome influence NAFLD progression. Both undernutrition and NAFLD prevalence are predicted to considerably increase, but how the undernourished gut microbiome contributes to hepatic pathophysiology remains far less studied. Here, we present undernutrition conditions with fatty liver features, including kwashiorkor and micronutrient deficiency. We then review the gut microbiota-liver axis, highlighting key pathways linked to NAFLD progression within both overnutrition and undernutrition. To conclude, we identify challenges and collaborative possibilities of emerging multiomic research addressing the pathology and treatment of undernourished NAFLD.

When a pandemic and an epidemic collide: COVID-19, gut microbiota, and the double burden of malnutrition.

BACKGROUND: It is estimated that the COVID-19 pandemic will drastically increase all forms of malnutrition. Of particular concern, yet understated, is the potential to increase the double burden of malnutrition (DBM) epidemic. This coexistence of undernutrition together with overweight and obesity, or diet-related non-communicable disease (NCD), within low- to middle-income countries (LMICs) is increasing rapidly. Although multiple factors contribute to the DBM, food insecurity (FI) and gut microbiota dysbiosis play a crucial role. Both under- and overnutrition have been shown to be a consequence of food insecurity. The gut microbiota has also been recently implicated in playing a role in under- and overnutrition, with altered community structure and function common to both. The pandemic has already caused significant shifts in food availability which has immediate effects on the gut microbiome. In this opinion paper, we discuss how COVID-19 may indirectly exacerbate the DBM through food insecurity and the gut microbiome. MAIN TEXT: The World Food Programme (WFP) estimates that 265 million people in LMICs will experience acute hunger in 2020 due to the pandemic, nearly doubling the original projection of 135 million. Global border closures to food trade, loss of food production, and stark decline in household income will exacerbate starvation while simultaneously necessitating that families resort to calorie-dense, nutrient-poor foods, thereby increasing obesity. While food insecurity, which is the persistent lack of consistent access to adequate and nutrient-rich foods, will primarily drive nutrition behavior, the gut microbiome is perhaps a key biological mechanism. Numerous human and animal studies describe low diversity and an increase in inflammatory species as characteristic features of the undernourished and overnourished gut microbiota. Indeed, fecal transplant studies show that microbiota transfer from undernourished and overnourished humans to germ-free mice lacking a microbiome transfers the physical and metabolic phenotype, suggesting a causal role for the microbiota in under- and overnutrition. The observed microbiome dysbiosis within severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) coupled with the DBM presents a viscous cycle. CONCLUSION: Low- to mid-income countries will likely see an increase in the DBM epidemic. Providing access to nutritious foods and protecting individuals' gut microbiome to "flatten the curve" of the DBM trajectory should be prioritized.

Comparison of Nutrigenomics Technology Interface Tools for Consumers and Health Professionals: A Sequential Explanatory Mixed Methods Investigation.

BACKGROUND: Nutrigenomics forms the basis of personalized nutrition by customizing an individual's dietary plan based on the integration of life stage, current health status, and genome information. Some common genes that are included in nutrition-based multigene test panels include CYP1A2 (rate of caffeine break down), MTHFR (folate usage), NOS3 (risk of elevated triglyceride levels related to omega-3 fat intake), and ACE (blood pressure response in related to sodium intake). The complexity of gene test-based personalized nutrition presents barriers to its implementation. OBJECTIVE: This study aimed to compare a self-driven approach to gene test-based nutrition education versus an integrated practitioner-facilitated method to help develop improved interface tools for personalized nutrition practice. METHODS: A sequential, explanatory mixed methods investigation of 55 healthy adults (35 to 55 years) was conducted that included (1) a 9-week randomized controlled trial where participants were randomized to receive a standard nutrition-based gene test report (control; n=19) or a practitioner-facilitated personalized nutrition intervention (intervention; n=36) and (2) an interpretative thematic analysis of focus group interview data. Outcome measures included differences in the diet quality score (Healthy Eating Index-Canadian [HEI-C]; proportion [%] of calories from total fat, saturated fat, and sugar; omega 3 fatty acid intake [grams]; sodium intake [milligrams]); as well as health-related quality of life (HRQoL) scale score. RESULTS: Of the 55 (55/58 enrolled, 95%) participants who completed the study, most were aged between 40 and 51 years (n=37, 67%), were female (n=41, 75%), and earned a high household income (n=32, 58%). Compared with baseline measures, group differences were found for the percentage of calories from total fat (mean difference [MD]=-5.1%; Wilks lambda (λ)=0.817, F1,53=11.68; P=.001; eta-squared [η²]=0.183) and saturated fat (MD=-1.7%; λ=0.816; F1,53=11.71; P=.001; η²=0.18) as well as HRQoL scores (MD=8.1 points; λ=0.914; F1,53=4.92; P=.03; η²=0.086) compared with week 9 postintervention measures. Interactions of time-by-group assignment were found for sodium intakes (λ=0.846; F1,53=9.47; P=.003; η²=0.15) and HEI-C scores (λ=0.660; F1,53=27.43; P<.001; η²=0.35). An analysis of phenotypic and genotypic information by group assignment found improved total fat (MD=-5%; λ=0.815; F1,51=11.36; P=.001; η²=0.19) and saturated fat (MD=-1.3%; λ=0.822; F1,51=10.86; P=.002; η²=0.18) intakes. Time-by-group interactions were found for sodium (λ=0.844; F3,51=3.09; P=.04; η²=0.16); a post hoc analysis showed pre/post differences for those in the intervention group that did (preintervention mean 3611 mg, 95% CI 3039-4182; postintervention mean 2135 mg, 95% CI 1564-2705) and did not have the gene risk variant (preintervention mean 3722 mg, 95% CI 2949-4496; postintervention mean 2071 mg, 95% CI 1299-2843). Pre- and postdifferences related to the Dietary Reference Intakes showed increases in the proportion of intervention participants within the acceptable macronutrient distribution ranges for fat (pre/post mean difference=41.2%; P=.02). Analysis of textual data revealed 3 categories of feedback: (1) translation of nutrition-related gene test information to action; (2) facilitation of eating behavior change, particularly for the macronutrients and sodium; and (3) directives for future personalized nutrition practice. CONCLUSIONS: Although improvements were observed in both groups, healthy adults appear to derive more health benefits from practitioner-led personalized nutrition interventions. Further work is needed to better facilitate positive changes in micronutrient intakes. TRIAL REGISTRATION: ClinicalTrials.gov NCT03310814; http://clinicaltrials.gov/ct2/show/NCT03310814. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): RR2-10.2196/resprot.9846.

Comparison of Nutrigenomics Technology Interface Tools for Consumers and Health Professionals: Protocol for a Mixed-Methods Study.

BACKGROUND: Although nutrition interventions are a widely accepted resource for the prevention of long-term health conditions, current approaches have not adequately reduced chronic disease morbidity. Nutrigenomics has great potential; however, it is complicated to implement. There is a need for products based on nutrition-related gene test results that are easily understood, accessible, and used. OBJECTIVE: The primary objective of this study was to compare a nonpractitioner-assisted direct-to-consumer self-driven approach to nutrigenomics versus an integrated and personalized practitioner-led method. METHODS: This 4-month study used a mixed-methods design that included (1) a phase 1 randomized controlled trial that examined the effectiveness of a multifaceted, nutrition-based gene test (components assessed included major nutrients, food tolerances, food taste and preferences, and micronutrients) in changing health behaviors, followed by (2) a qualitative investigation that explored participants' experiences. The study recruited 55 healthy males and females (aged 35-55 years) randomized as a 2:1 ratio where 36 received the intervention (gene test results plus integrated and personalized nutrition report) and 19 were assigned to the control group (gene test results report emailed). The primary outcomes of interest measures included changes in diet (nutrients, healthy eating index), changes in measures on General Self-efficacy and Health-Related Quality of Life scales, and anthropometrics (body mass index, waist-to-hip ratio) measured at baseline, post intervention (3 and 6 weeks), and the final visit (week 9 post intervention). RESULTS: Of the 478 individuals who expressed interest, 180 were invited (37.7%, 180/478) and completed the eligibility screening questionnaire; 73 of the 180 invited individuals (40.5%) were deemed eligible. Of the 73 individuals who were deemed to be eligible, 58 completed the baseline health questionnaire and food records (79%). Of these 58 individuals, 3 were excluded either because they did not complete all required data collection forms or were later found to be ineligible. The final sample (n=55) was mostly female (75%), married (85%), and those who had completed postsecondary education (62%). CONCLUSIONS: This study will leverage quantitative and qualitative findings, which will guide the development of nutrigenomics-based products in electronic formats that are user-friendly for consumers and health professionals. Although the quantitative data have not been analyzed yet, the overwhelming interest in the study and the extremely high retention rate show that there is a great degree of interest in this field. Given this interest and the fact that nutrigenomics is an evolving science, a need for continued research exists to further the understanding of the role of genetic variation and its role and applications in nutrition practice. TRIAL REGISTRATION: Clinicaltrials.gov NCT03310814; http://clinicaltrials.gov/ct2/show/NCT03310814 (Archived by WebCite at http://www.webcitation.org/6yGnU5deB). REGISTERED REPORT IDENTIFIER: RR1-10.2196/9846.

Nurses and the control of infectious disease. Understanding epidemiology and disease transmission is vital to nursing care.

Epidemiology examines the distribution and source of a disease in a population. Understanding epidemiology and disease transmission is vital to nursing care. Infectious disease transmission requires three components: an agent (virus, bacterium, parasite or other microbe), a vulnerable host and a conducive environment. Disease spread can occur through direct contact or via indirect methods (airborne droplets, vectors, fomites, water or food). Intervention can occur by attacking the agent (e.g., using microbicides), changing the environment (e.g., providing negative pressure rooms) or strengthening the host (e.g., vaccination). Three epidemiologically relevant microbes are the SARS (severe acute respiratory syndrome)-associated coronavirus, methicillin-resistant Staphylococcus aureus (MRSA) and Clostridium difficile (C. difficile). The first is an emerging pathogen, and the latter two are existing agents that have mutated such that they are resistant to their standard treatments. For SARS, control measures include screening for possible cases and appropriate triage, respiratory and barrier precautions within the healthcare facility, and voluntary isolation in the community for contacts or healthcare workers who exhibit symptoms. Control measures for MRSA include the screening of patient lesions, isolating or cohorting patients who are already infected, covering wounds with impermeable dressings, treating staff and patient carriers with antibiotics, and improved hygiene. Control measures for C. difficile Control measures include paying close attention to the hygiene of the clinical setting, disinfecting using bleach and the isolation of infected patients.