BackgroundPhenylalanine and tyrosine are precursor amino acids required for the synthesis of dopamine, the main neurotransmitter implicated in the neurobiology of schizophrenia. Inflammation, increasingly implicated in schizophrenia, can impair the function of the enzyme Phenylalanine hydroxylase (PAH; which catalyzes the conversion of phenylalanine to tyrosine) and thus lead to elevated phenylalanine levels and reduced tyrosine levels. This study aimed to compare phenylalanine, tyrosine, and their ratio (a proxy for PAH function) in a relatively large sample of schizophrenia patients and healthy controls.MethodsWe measured non-fasting plasma phenylalanine and tyrosine in 950 schizophrenia patients and 1000 healthy controls. We carried out multivariate analyses to compare log transformed phenylalanine, tyrosine, and phenylalanine:tyrosine ratio between patients and controls.ResultsCompared to controls, schizophrenia patients had higher phenylalanine (p<0.0001) and phenylalanine: tyrosine ratio (p<0.0001) but tyrosine did not differ between the two groups (p = 0.596).ConclusionsElevated phenylalanine and phenylalanine:tyrosine ratio in the blood of schizophrenia patients have to be replicated in longitudinal studies. The results may relate to an abnormal PAH function in schizophrenia that could become a target for novel preventative and interventional approaches.
Objectives Assessing client-level food waste is a priority for hunger relief organizations to effectively address food insecurity. Our objectives were: 1) to measure the amount of, and reasons for leftover food at the household level after receiving food from urban food pantries; 2) to assess differences in the amount of leftover food associated with different food pantry distribution models. Methods This was a prospective, observational study. Food-pantry clients (n = 53) were surveyed from four food pantries in Baltimore, MD. 28 of those clients were followed-up with 2 weeks later. Half of the follow-up sample used a client-choice food pantry in which clients select their own food, while the other half received pre-packed bags. At baseline, we recorded the brand, type, and weight of each product in client bags, and grouped them into Food Assortment Scoring Tool (FAST) categories. FAST scores were calculated for each bag by multiplying each category's gross weight share by a healthfulness parameter and summing the categories. At follow-up, clients estimated the percentage of each product that was consumed by their household, and reported what happened to the unused portion, and why it was unused. Results The average client choice bag weighed 27.8 ± 14.8 lbs, whereas the average pre-packed bag weighed 18.3 ± 5.3 lbs. Clients from client-choice food pantries had 22.6% of their bag leftover at follow-up; clients from traditional pantries had 34.1% of their bag leftover (P = .0375). At baseline, FAST scores were higher among traditional bags (70.3 ± 5.2) compared to client choice bags (63.5 ± 7.3) (P = .007). FAST scores of foods client-choice visitors used by follow-up was 66.7 ± 7.8, higher than scores of their baseline food selections (P = .014), suggesting use of healthy foods first. The greatest proportion of leftover food was beverages. The smallest proportion of leftover food was processed fruits and vegetables. The most common reason for not using an item was “Plan to use later” (80% of leftover items). Conclusions Food pantries distributing foods via a prepackaged bag model should consider switching to a client choice method to reduce leftover food, which may eventually be wasted. Further research should expand on this association using larger sample sizes and follow-up periods >2 weeks. Funding Sources Funded by the Bloomberg American Health Initiative Evidence Generation Awards.
Objectives To date, no intervention has attempted to improve the availability and accessibility of healthful options at food pantries in Baltimore. Our objective is to: 1) test the feasibility of various policy, educational and environmental strategies to improve the stocking and distribution of healthy options at food pantries, 2) assess the impact of a food pantry-based nutrition intervention at the pantry and client levels. Methods 2 small, 2 medium, and 3 large pantries were randomly selected from Maryland Food Bank's community-based network partners in Baltimore (n = 102) out of eligible and interested pantries. 1 small, 1 medium and 1 large pantry received intervention in 3 phases, each focusing on one food group: lean & low-sodium proteins; fruits & vegetables; healthy carbohydrates. Each phase used policy (stocking guidelines and switch to client choice distribution), educational (trainings for pantry staff; taste tests and cooking demos for clients), environmental strategies (shelf rearrangement; print materials for clients). Intervention had medium reach, high dose delivered, and high fidelity. Pantry-level impact was measured via variety scores for the 3 promoted food groups using data from Food Pantry Environmental Checklist. Client-level impact was measured via Food Assortment Scoring Tool scores for client bags. Paired t-tests were used to assess differences between intervention and comparison groups. Results For lean & low-sodium proteins, the difference in variety scores between post-intervention and baseline was 6.00 +/–5.29 for intervention pantries, and –1.00 +/–5.03 for comparison pantries (P = 0.13). For fruits & vegetables, the difference in variety scores between post-intervention and baseline was 1.67 +/–7.51 for intervention pantries, and 3.25 +/–7.46 for comparison pantries (P = 0.80). For healthy carbohydrates, the difference in variety scores between post-intervention and baseline was 5.33 +/–9.50 for intervention pantries, and 1.75 +/–6.50 for comparison pantries (P = 0.61). Conclusions Policy, educational and environmental strategies may increase the stocking of some healthful options at Baltimore food pantries. The impact of feasible strategies should be studied with a larger sample size to reach statistically significant conclusions. Funding Sources Johns Hopkins University Lerner Center for Public Health Promotion.
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