Food Risk Research Articles

Ultra-processed food consumption and risk of cardiovascular mortality in Australia: preliminary results from prospective observational study

Abstract Background/Introduction A large proportion of the Australian population emigrated from countries where traditional dietary patterns are protective of cardiovascular health. Yet, recent evidence suggests that 42% of a modern Australian diet's total energy intake is comprised of ultra-processed foods. This is concerning as previous meta-analyses and umbrella reviews based on population studies have found an association between ultra-processed food consumption and adverse health outcomes, including cardiovascular disease and mortality. However, this has yet to be explored in an Australian population. Purpose To determine the association between ultra-processed food consumption and the risk of cardiovascular mortality in an Australian cohort. Methods Prospective data from the Melbourne Collaborative Cohort Study (MCCS; n = 38054) were analysed. UPF consumption at baseline (1990 to 1994) was assessed in accordance with the NOVA food classification system using data from a 121-item food frequency questionnaire. Energy-adjusted ultra-processed food consumption was then categorised into quartiles. Cardiovascular mortality was obtained through data linkage with national and local registries up to 2015 and 2019, respectively. This was done using the International Classification of Diseases (ICD) codes (390-459 and I00-I99). Unadjusted and adjusted multivariate logistic regression models were fitted to estimate associations between ultra-processed food consumption (exposure) and cardiovascular mortality (outcome). Results A total of 4,242 incident cardiovascular deaths occurred (2,272 for males and 1,970 for females) during the follow-up period. After adjusting for sociodemographic characteristics and lifestyle and health-related behaviours, higher UPF consumption was associated with a higher risk of cardiovascular mortality (aOR high (quartile 4) vs. low (quartile 1) category = 1.26, 95%CIs 1.14 to 1.39, ptrend < 0.001). Conclusion(s) In this large prospective cohort of Australian adults, higher ultra-processed food consumption was associated with an increased risk of cardiovascular mortality. These findings support previous research and guidelines recommending the reduction of ultra-processed food consumption as part of a heart-healthy diet.

Ultra-processed food intake and type 2 diabetes: A dose-response meta-analyses of prospective studies

Abstract Studies suggested a positive association between ultra-processed food (UPF) intake and risk of type 2 diabetes (T2DM). However, the magnitude of association varies across studies due to differences in population characteristics and methodological issues. It is also unclear about the exact shape of the association and whether the association is independent of diet quality and adiposity. We conducted a dose-response meta-analysis of prospective studies to summarize the association between UPF intake and T2DM risk. PubMed/MEDLINE, Web of Science, and Embase were searched through Jan. 2024 to identify relevant studies. Using random-effects models, summary relative risks (RRs) and 95% confidence intervals (CIs) were estimated for each of the following unit increment in UPF intake: 10% (of total g/d), 100-g/d, and 1-serving/d. A non-linear dose-response meta-analysis was conducted using restricted cubic spline analysis. After screening 569 publications, 12 prospective cohort studies were included in this study. The summary RRs associated with every 10%, 100-g, and 1-serving increment in UPF intake were 1.13 (95% CI = 1.10-1.16), 1.05 (95% CI = 1.03-1.06), and 1.04 (95% CI = 1.03-1.05), respectively. The positive associations remained statistically significant after additional adjustment for diet quality, BMI, and total energy intake. The dose-response curve for g/d of UPF intake suggested evidence of nonlinearity (p-nonlinearity=0.0005), showing a steeper increase in risk at UPF intake >300 g/d. However, evidence for non-linearity was not observed with % and serving/d of UPF intake. The association did not significantly vary by method and timing of UPF assessment. Higher summary RRs were observed among studies from Europe and North America compared with those from other countries (p-heterogeneity=0.048). Our data suggest that higher UPF intake may increase T2DM risk, independent of diet quality and adiposity. Key messages • Higher intake of ultra-processed food is positively associated with the risk of type 2 diabetes. • Public health strategies should encourage healthier dietary choices by emphasizing reduction of UPF intake in dietary guidelines and implementing regulations on manufacturing and labeling of UPF.

Ultra-processed food consumption and risk of coronary heart diseases: UK Whitehall II cohort study

Abstract In recent decades, ultra-processed foods (UPFs) intake has increased drastically, sparking concerns about their potential impact on cardiometabolic diseases. However, large-scale cohort studies tracking UPF consumption at multiple time points remain limited. This study explores the prospective association between repeated measures of UPF intake and the risk of coronary heart disease (CHD), along with its secondary endpoints, within the UK Whitehall II study. The analytical sample tracked 7,138 midlife British participants without CHD from baseline. UPF consumption was measured using validated food frequency questionnaire (127 items) and classified by the NOVA system during three phases: 1991/1994 (phase 3), 1997/1999 (phase 5), and 2002/2003 (phase 7). This study assessed the onset of CHD and its secondary endpoints, including CHD and all-cause mortality, through medical exams and hospital records up to 2016 and 2021, respectively. Cox proportional hazards regression models adjusted for socio-demographics, lifestyle factors and total energy intake were used to explore the prospective association between cumulative average UPF intake (in quintiles) and CHD outcomes. During a median follow-up of 13 and 19 years, 589 cases and 1,314 deaths were documented. In multivariable adjusted cox models, the highest UPF consumption quintile versus the lowest quintile was associated with higher CHD incidence [HR:1.26; 95% Confidence Interval (CI): 1.02-1.55; p = 0.03]. Additional adjustment of total energy intake increased the CHD risk by 28% [HR:1.28; 95% CI: 1.03-1.58; p = 0.02]. No significant relationships were found between cumulative average UPF intake and CHD or all-cause mortality. In UK midlife adults, higher UPF intake is prospective associated with increased CHD risk. Although additional research is warranted, these findings emphasize the importance of public awareness and food policy interventions to reduce UPF intake for alleviating the population burden of CVD. Key messages • Our study suggests the need for increased efforts to implement population wide strategies on regulating food processing, such as taxation and front of package warning labelling of UPF. • Our study emphasizes UPF’s role in cardiovascular prevention, highlighting the need for nutrition counseling on UPF consumption for those at risk of CVD.

Acceptance of insects as food in Germany: risk–benefit perceptions and the role of risk preferences

PurposeWith a growing world population and climate crises, edible insects are a promising alternative to conventional livestock. However, Western consumers are largely opposed to eating insects. The aim of this study is to investigate the acceptance of insects in a German sample with a specific focus on risk–benefit perceptions and risk preferences.Design/methodology/approachData were collected via an online survey (N = 569). Established scales for food neophobia (FN), risk perception, risk preference and acceptance were used. The statistical analyses included mean comparisons regarding acceptance and risk–benefit perceptions of unprocessed and processed insects. We further examined the role of perceived benefits and risks as well as risk preferences as determinants of acceptance via hierarchical multiple regressions.FindingsRespondents indicated lower perceived risks, and higher perceived benefits and thus higher acceptance for processed insect products as compared to whole insects. Risk perception was the strongest predictor of acceptance of unprocessed as well as processed insect food. Respondents who reported to have already eaten insects before exhibited higher risk preferences than those who had not.Originality/valueThis study specifically considered the role of risk preferences, which has been so far largely overlooked in consumer research on insect foods. The findings indicate that acceptance of insect food is mainly driven by risk perception rather than individual risk preferences. However, risk preferences should not be totally overlooked as they seem to play a role especially for unprocessed insects and the decision to try insects for the first time.

Food fraud prevention strategies: Building an effective verification ecosystem.

Food fraud is an ever-present threat that regulators, food business operators (FBOs), and consumers need to be aware of, prevent where possible, and address by developing mitigation strategies to detect and reduce its negative consequences. While extant literature focuses on food fraud detection, there is less attention given to prevention strategies, a knowledge gap this review seeks to address. The aim of this review was to consider food-related fraud prevention initiatives, understand what has worked well, and develop a series of recommendations on preventing food fraud, both policy related and for future research. Reactive (including intelligence based) food fraud detection dominates over prevention strategies, especially where financial, knowledge, and time resources are scarce. First-generation tools have been developed for food fraud vulnerability assessment, risk analysis, and development of food fraud prevention strategies. However, examples of integrated food control management systems at FBO, supply chain, and regulatory levels for prevention are limited. The lack of hybrid (public/private) integration of food fraud prevention strategies, as well as an effective verification ecosystem, weakens existing food fraud prevention plans. While there are several emergent practice models for food fraud prevention, they need to be strengthened to focus more specifically on capable guardians and target hardening. This work has implications for policymakers, Official Controls bodies, the food industry, and ultimately consumers who seek to consistently purchase food that is safe, legal, and authentic.

Enhancing dietary analysis: Using machine learning for food caloric and health risk assessment.

In the wake of growing concerns regarding diet-related health issues, this study investigates the application of machine learning methods to estimate the energy content and classify the health risks of foods based on the USDA National Nutrient Database. The caloric content of foods was estimated using the nutritional composition (i.e., carbohydrates, protein, total lipid, and total sugar content) and classified based on their weighted health risks, considering their carbohydrate, lipid, and glycemic index levels. The algorithms used for modeling include multiple linear regression (MLR), K-nearest neighbors, support vector machine, random forest regression (RFR), gradient-boosted regression, decision trees (DT), and deep neural networks. The MLR model demonstrated high accuracy on the training dataset (R2=0.99, mean absolute error [MAE]=7.71kcal, and root mean squared error [RMSE]=17.89kcal) and testing dataset (R2=0.99, MAE=7.75kcal, and RMSE=18kcal) in energy estimation, indicating its effectiveness in dietary assessment. The RFR and DT models were useful in categorizing foods into low-health-risk foods, but their performance was reduced in medium and high-health-risk groups. This research contributes to developing tools that could aid in personalized dietary planning and public health interventions to mitigate diet-related health risks. PRACTICAL APPLICATION: This study applies machine learning to estimate how many calories are in food and to understand the health risks different foods might have. By investigating the fats, cholesterol, and sugars in food items listed in a public database, we can better plan diets or develop apps that help people make healthier eating choices. This work aims to improve public access to nutritional information, supporting efforts to combat diet-related diseases through educational materials and applications that guide dietary choices in various settings.

A choice experiment for different preparation methods of eggs: The impact of sensory, health, and animal welfare

The risk of salmonella food poisoning increases the lower the degree of heating the eggs.This study investigated the effect of cooking methods on the willingness to pay (WTP) for sterilization method.The choice experiment consisted of eight attributes; yolk color (yellow vs. orange), grade (special vs. first grade), omega-3 (3 levels), egg sterilization method (no sterilization vs. sodium hypochlorite vs. pasteurization), hen feed (antibiotics vs. antibiotic-free vs. organic), hen’s breeding method (caged vs. cage-free), production area (in-prefecture vs. outside of prefecture), and price (5 levels).The online survey included 1,000 Japanese consumers. A within-design was employed in which everyone responded under the assumptions of eating a hard-boiled egg, a soft-boiled egg, and a raw egg.As the degree of heating decreased, the WTP for the sterilization attribute became higher. Sodium hypochlorite was always higher than pasteurization. As the degree of rawness increased, the WTP of antibiotic-free and organic feed rise. However, the organic feed had a lower WTP than the antibiotic-free feed. When eating raw eggs, the WTP of antibiotic-free was the second important compared with the sterilization. As the degree of rawness increased, the WTP of the orange color of the yolk rise, and finally reached three times higher when eating raw eggs than when eating hard-boiled eggs. The WTP of local production was not significantly different in any case. Omega-3 had a significant negative WTP only for raw eggs. Cage-free had a significant negative WTP only for boiled eggs.

Development of in silico methodologies to predict the toxicity of novel proteins in the context of food and feed risk assessment

Abstract This report is the outcome of an EFSA procurement (OC/EFSA/GMO/2021/02 – LOT1) aiming at developing an in silico strategy to predict the toxicity of (novel) proteins. Up‐to‐date, commercially available tools predicting protein toxicity based on primary structures were evaluated for their accuracy and usability, using a curated dataset of annotated toxins and non‐toxins from UniProt. ToxinPred2 and Toxify emerged as the top performers, showing both high accuracy and suitability for integration into an automated pipeline. Additional bioinformatics methods were explored, which provide sequence similarity‐based information rather than direct predictions (BLAST, InterPro HMM profiles). By converting their outputs into features for machine learning models, a high prediction accuracy was achieved, though there is potential for improvement to reduce overfitting risks. An Artificial Intelligence (AI)‐based consensus pipeline, integrating results from ToxinPred2, Toxify, and our machine learning models was developed. This consensus model reached a 95% accuracy rate in distinguishing toxins from non‐toxins. Noteworthy, our BLAST‐based machine learning model ‐ although performance‐wise comparable to BLAST ‐ offers higher sensitivity and specificity across diverse queries than BLAST; it relies on database‐based evolutionary relationships, which may significantly limit its applicability to novel or mutated toxins. Structure‐based prediction methods are deemed impractical due to their resource intensity and reliance on accurate structural data; AI‐driven structure prediction methods ‐ like Rosetta and AlphaFold ‐ are promising, however they are still under development and may not be suitable for the regulatory context yet. Recommendations are provided, including enhancement of the proposed consensus pipeline to create an independent open‐source, user‐friendly tool for evaluating the safety of (novel) proteins in food and feed; regular updates of the proposed databases and models; incorporation of 3D structures and in general validation of AI and machine learning models for regulatory uses.

Geospatial analysis of alarmingly increasing flood vulnerability and disaster risk within the northeast Himalaya Region of India

Geoenvironmetally the eastern part of the Himalaya region is highly vulnerable to flood and other natural disasters as it consists of fragmented and tectonically active geology and geomorphology, very high monsoon rainfall (>360cm) and subsequent runoff, rugged hilly terrain with high ranges of elevation and slope, dense drainage density etc. Other hand, the unplanned developmental activities keep going and scaling up this vulnerability and risk to floods and other disasters. Addressing this burning issue, a geospatial technology-based case study of the Kohima district, Nagaland state (India), is presented here. The geospatial "technology-based" analyses employed in the study are thematic GIS mapping of flood hazard, vulnerability and risk controlling factors; and performing overlay operation using the AHP model in GIS software to generate spatio-temporal map layers of flood vulnerability and disaster risk. Results reveal that the region is under a high rate of monsoon climate change (increasing temperature, rainfall, rainy days, rainfall events and flood events with an annual rate of 0.35%, 1.12%, 0.36%, 2.67% and 4% respectively), land use degradation (increasing built-up area with 0.60%, annual rate decreasing forest, shrubs and water bodies with 0.80% accumulated annual rates respectively) and demographic changes (increasing urban as well and rural population density with 0.53-2.10% and 0.55-2.14% respectively). Accumulated impacts of climate change, land use degradation and demographic changes causing an increase in flood hazard, vulnerability and disaster risk. Flood hazard zones and vulnerability zones extending with 0.50% (4.89km2) and 0.84% (8.16km2) annual rates respectively, subsequently the food risk zones categorized as a moderate, high and very high potential risk, have been spreading out with a yearly rate of 0.07% (0.65km2), 0.13% (1.31km2) and 0.03% (0.33km2) respectively. It decreases the area under low and very low-risk zones by 0.17% (1.63km2) and 0.07% (0.65km2) annual rates respectively. Following up on these annual rates, the spatial distribution of flood hazard, vulnerability and risk zones for the next decade (2031-2040) have also been projected, revealing alarming situations, if flood disaster risk reduction (F-DRR) measures were not implemented in timely. It is strongly believed that the proposed study will be very useful for district-level planners and administrators to implement sustainable development planning, for the scientific fraternity to enhance their research work in the field of flood disaster management and for individuals for their safety in terms of life and property.

Bayesian network modeling applied to food risks: Data from General Administration of Customs of China as an example

We introduce a multidimensional Bayesian Network (BN) modeling approach as an alternative to the classical multivariate regression approach commonly used for risk factor analysis. BN modeling is a rich and flexible analytical technique capable of elucidating complex food data. It is a data-driven graphical modeling technique and exploratory tool that visually presents dependencies and causal associations while retaining statistical rigor in holistic level inference. We used data on monthly unauthorized food imports to China for demonstrating a full Additive BN model in the open-source R language with all code necessary to reproduce our analyses. Compared to the classical regression model, the Additive BN model of this study described more relationships between variables, and overcome the problems of classical discretization of general BN. The analysis showed that food category and import weight were two actionable drivers directly related to risk and can be controlled to manage risk. The risk was also related to where the food is produced, but not where it is likely to be sold. The BN model also is able to systematically elaborate uncertainty and obtain information through probabilistic inference, which might be useful for giving additional new insights potentially not captured by classical methods. Finally, we discuss the potential and evolution of BN, such as dynamic BN.

Feature extraction contributing to risk-acceptance prediction by machine learning models in food benefit-risk communication in Japan

To protect public health, it is crucial to reduce the perception gap that exists between experts and the public. This can ensure appropriately informed food choices based on scientific evidence. This study aimed to build a machine learning model to predict public risk acceptance to ensure the promotion of effective food risk communication between experts and the public for bringing peace of mind to the public. We developed a machine learning model for public risk acceptance and evaluated its accuracy with risk acceptance as the output data and background information and included age, gender, health status, numeracy score, trust in public institutions, personal characteristics (self-motivation, controllability, involvement, and anxiety), favorability toward fish, risk perception, benefit perception, frequency of fish consumption, and message type as input data. We used the data from a randomized controlled study conducted in 2022 in Japan among 7200 participants. In total, 3324 of 3600 men and 3295 of 3600 women were in the top (high-benefit and low-risk) perception category. The accuracy of the machine learning model for the prediction of risk acceptance of the public was high for both men and women with F values of 0.926 (SD: 0.000) and 0.911 (SD: 0.000), respectively. This is the first study using a machine learning approach in food safety. The accuracy of the risk-acceptance estimation model was high. The findings can help public agencies pre-design communication before disseminating health information, thereby contributing to effective benefit-risk communication via a machine learning model.

Terrestrial Animal Source Foods and Health Outcomes for Those with Special Nutrient Needs in the Life Course.

Background. Animal source foods are under scrutiny for their role in human health, yet some nutritionally vulnerable populations are largely absent from consideration. Methods. Applying a Population Intervention/Exposure Comparator Outcome (PICO/PECO) framework and prioritizing systematic review and meta-analyses, we reviewed the literature on terrestrial animal source foods (TASFs) and human health, by life course phase. Results. There were consistent findings for milk and dairy products on positive health outcomes during pregnancy and lactation, childhood, and among older adults. Eggs were found to promote early childhood growth, depending on context. Unprocessed meat consumption was associated with a reduced risk for anemia during pregnancy, improved cognition among school-age children, and muscle health in older adults. Milk and eggs represent a risk for food sensitivities/allergies, though prevalence is low, and individuals tend to outgrow the allergies. TASFs affect the human microbiome and associated metabolites with both positive and negative health repercussions, varying by type and quantity. Conclusions. There were substantial gaps in the evidence base for studies limiting our review, specifically for studies in populations outside high-income countries and for several TASF types (pig, poultry, less common livestock species, wild animals, and insects). Nonetheless, sufficient evidence supports an important role for TASFs in health during certain periods of the life course.

Is AI Food a Gimmick or the Future Direction of Food Production?-Predicting Consumers' Willingness to Buy AI Food Based on Cognitive Trust and Affective Trust.

In recent years, artificial intelligence (AI) has been developing rapidly and has had a broad impact on the food industry, with food produced from AI-generated recipes already appearing to actually go on sale. However, people's trust and willingness to purchase AI food are still unclear. This study builds an integrated theoretical model based on cognitive trust and affective trust, taking into account consumers' quality value orientations, social norms, and perceived risks of AI food, with the aim of predicting and exploring consumers' trust and acceptance of AI food. This study utilized the questionnaire method and 315 questionnaires were collected. The results of structural equation modeling (PLS-SEM) indicated that food quality orientation, subjective norms, perceived trust, and affective trust all had a significant positive effect on consumers' purchase intentions. Perceived risk had a negative effect on affective trust and consequently on consumers' purchase intention, but the effect on cognitive trust was not significant. The results also suggest that cognitive trust is the basis of affective trust and that consumer trust and acceptance of AI food can be enhanced by augmenting two antecedents of cognitive trust (food quality orientation and subjective norms). Possible practical implications and insights from the current findings are discussed.

The association between the number of food kinds and risk of depression in U.S. adults

BackgroundThe association between the number of food kinds and the risk of depression in adults was examined.MethodsAccording to the inclusion and exclusion criteria, a total of 4593 adults were included in the study. The number of food kinds was collected via 24‒hour dietary recalls. Depression was assessed using the Patient Health Questionnaire‒9. Logistic regression and restricted cubic spline models were applied to assess the association between the number of food kinds and the risk of depression.ResultsThis study included 4593 study participants, 451 of whom were diagnosed with depression. The revised advantage ratios (with corresponding confidence intervals) for the prevalence of depression among individuals in the fourth quartiles of the number of food kinds (Q4) in comparison to the lowest quartile (Q1) were determined to be 0.59 (0.36‒0.96), respectively. According to our subgroup analyses, the number of food kinds was negatively associated with the risk of depression in females, participants aged 18‒45 and 45‒65 years, and participants with a body mass index (BMI) of 25 to 24.9 kg/m2. According to our dose‒response analysis, the number of food kinds was linearly associated with the risk of depression (Pfor nonlinear=0.5896).ConclusionThe risk of depression exhibited a linear and negative correlation with the number of food kinds. The results indicated that a diversified diet was an effective nonpharmacological approach that deserved further generalization.

Ultra-processed foods consumption and risk of age-related eye diseases: a prospective cohort study with UK biobank.

Consumption of ultra-processed foods (UPF) has been associated with increased risks of various age-related diseases. However, the potential association between UPF consumption and age-related eye diseases (AREDs) remains unclear. We aim to assess the associations between consumption of UPF and risk of AREDs including age-related macular degeneration (AMD), cataract and glaucoma. We included 156,232 individuals aged 50 or older, who were free from AREDs from UK biobank study. Dietary intake data were collected using 24-h dietary assessments. UPF is defined according to the NOVA classification, and all participants are divided into four quartiles based on the weight proportion (%) of UPF. During a median of 10years of follow-up. Cox proportional hazards were used to estimate the association between the proportion of UPF in the diet and the subsequent risk of various AREDs. After adjusting for multiple variables, individuals in the highest quartiles for UPF consumption exhibited an increased risk of AMD (hazard ratio (HR): 1.28; 95% confidence interval (CI): 1.01-1.63; p = 0.03), cataract (HR: 1.10; 95% CI: 1.01-1.20; p = 0.04), and glaucoma (HR: 1.27; 95% CI: 0.98-1.63; p = 0.06) compared to those in the lowest quartiles. Moreover, a 10% increase in the weight of UPF in diet was associated with an 8% higher risk of AMD (HR: 1.08; 95% CI: 1.01-1.15; p = 0.03), a 3% higher risk of cataract (HR: 1.03; 95% CI: 1.00-1.06; p = 0.04), and a 7% higher risk of glaucoma (HR: 1.07; 95% CI: 1.00-1.15; p = 0.05). Our results suggest that a higher proportion of UPF in the diet was significantly link with an elevated risk of AMD and cataract. While additional research is necessary to validate these findings in diverse populations and settings, these results offer initial evidence to endorse public health initiatives that encourage limiting consumption of UPF.

Higher sweet beverage consumption was associated with increased gestational weight gain and birth weight: A Chinese cohort study

Diet during pregnancy is crucial to maternal metabolism and fetal development, so exploring the most potent food risk factor could improve maternal and child health. In this study, we investigated the diet and lifestyle of 833 healthy pregnant women in the second trimester from November 2020 to August 2021. Based on the Tianjin Antenatal Care System in China, we followed up with these women and recorded their gestational weight gain (GWG) and newborn birth weight. We conducted a dietary survey through FFQ based on the food groups recommended by the Chinese Dietary Guidelines and included common ultra-processed foods. We collected 219 semi-quantitative FFQs and 614 self-reported FFQs for analysis. According to the consumption frequency of 12 food groups, 4 dietary patterns were extracted by principal component analysis. We analyzed the associations of food energy, consumption frequency, and dietary patterns with GWG and birth weight, especially GWG in the first and second trimesters (f-GWG). The results showed that f-GWG was positively correlated with food energy. Beverage consumption was associated with f-GWG (r = 0.288, P = .026) in obese pregnant women. A dietary pattern that favors high consumption of ultra-processed foods (fried foods, baked desserts, and sweet beverages) was associated with increased GWGs. Non-obesity women with high consumption of baked desserts and sweet beverages had higher GWGs (P < .05). After adjusting for confounding factors (including total energy, physical activity, and sleep quality), only sweet beverage consumption was associated with f-GWG (β 0.498, 95%CI 0.153-0.843) and birth weight (β 0.124, 95%CI 0.009-0.240). Sweet beverage consumption is a key adjustable risk factor for prenatal care.

Mycotoxin management: exploring natural solutions for mycotoxin prevention and detoxification in food and feed.

Mycotoxins, secondary metabolites produced by various fungi, pose a significant threat to food and feed safety worldwide due to their toxic effects on human and animal health. Traditional methods of mycotoxin management often involve chemical treatments, which may raise concerns about residual toxicity and environmental impact. In recent years, there has been growing interest in exploring natural alternatives for preventing mycotoxin contamination and detoxification. This review provides an overview of the current research on the use of natural products for mitigating mycotoxin risks in food and feed. It encompasses a wide range of natural sources, including plant-derived compounds, microbial agents, and enzymatic control. The mechanisms underlying the efficacy of these natural products in inhibiting mycotoxin synthesis, adsorbing mycotoxins, or enhancing detoxification processes are discussed. Challenges and future directions in the development and application of natural products for mycotoxin management are also addressed. Overall, this review highlights the promising role of natural products as sustainable and eco-friendly alternatives for combating mycotoxin contamination in the food and feed supply chain.

Food Insecurity Prevalence and Risk Factors among Persons with HIV in a Southeastern US Clinical Care Setting.

Food insecurity (FI) is associated with adverse health outcomes for persons with HIV (PWH). Little is known about FI among PWH in southern or non-urban settings. We examined FI prevalence, risk factors, and access to services in a southeastern HIV clinic. Among PWH in the UNC CFAR HIV Clinical Cohort who were screened for FI as part of HIV care between 2021 and 2022, we estimated unadjusted prevalence ratios (PRs) comparing the probability of reporting FI by demographic and clinical characteristics. The 479 PWH screened for FI were 65% cisgender men, 62% non-Hispanic Black PWH, a median of 54 years old (IQR 41-62), and 93% with an HIV viral load (VL) < 200 copies/mL. FI prevalence was 36.3% (95% CI 32.3%-40.9%). Cisgender women and transgender adults were more likely to report FI than cisgender men (PRs 1.24 [95% CI 0.97-1.59] and 2.03 [1.32-3.12], respectively). Compared with White PWH, the PR was 1.71 (1.20-2.42) for Black and 2.44 (1.56-3.82) for Hispanic PWH. The PR was 1.42 (0.98-2.05) for PWH with VL ≥ 200 versus < 200 copies/mL. Having no or public versus private health insurance was also associated with FI. PWH with FI had a high prevalence of comorbidities including hypercholesterolemia (49%) and hypertension (48%), though these were not associated with FI. Almost half of PWH with FI were not accessing a food pantry or nutrition assistance program. Identifying FI in PWH is critical as FI is common and may contribute to viral non-suppression, poor comorbidity control, and gender and racial/ethnic health disparities in PWH.

Defining Risk: Hepatitis A Virus in Philippine Shellfish (Oyster and Mussels)

The hepatitis A virus (HAV) endemicity in the Philippines ranges from moderate to high. Despite this, health risks related to local seafood consumption are associated with red tide or mass fish kill. However, government agencies should consider the safety of local shellfish and culture sites to HAV. This article examines the current trends relevant to the presence of HAV along the local shellfish supply chain – including production levels, microbial concentration, consumer behavior, and food safety regulations. Despite the susceptibility of local shellfish to HAV, the study identified crucial data gaps: [1] reliance on bacterial indicators, [2] absence of monitoring records for HAV concentration in local shellfish products and culturing sites, and [3] limited studies regarding the effect of local cooking practices on HAV inactivation. The synthesis of this analysis will guide the national food safety risk managers in addressing the identified data gaps to facilitate a full-blown food risk analysis of HAV in Philippine shellfish.

Food Consumption and Risk of Islet Autoimmunity and Type 1 Diabetes in Children at Increased Genetic Susceptibility for Type 1 Diabetes

BackgroundProspective longitudinal evidence considering the entire childhood food consumption in relation to the development of islet autoimmunity (IA or) type 1 diabetes is lacking. ObjectivesWe studied the associations of consumption of various foods and their combinations with IA and type 1 diabetes risk. MethodsChildren with genetic susceptibility to type 1 diabetes born in 1996–2004 were followed from birth up to ≤6 y of age in the prospective birth cohort type 1 diabetes prediction and prevention study (n = 5674). Exposure variables included 34 food groups covering the entire diet based on repeated 3-d food records at ages 3 mo to 6 y. Endpoints were islet cell antibodies plus biochemical IA (n = 247), multiple biochemical IA (n = 206), and type 1 diabetes (n = 94). We analyzed associations between longitudinally observed foods and risk of IA/type 1 diabetes using a Bayesian approach to joint models in 1-food and multi-food models adjusted for energy intake, sex, human leukocyte antigen genotype, and familial diabetes. ResultsThe final multi-food model for islet cell antibodies plus biochemical IA included oats [hazard ratio (HR): 1.09; 95% credible interval (CI): 1.04, 1.14], banana (HR: 1.07; 95% CI: 1.03, 1.11), and cruciferous vegetables (HR: 0.83; 95% CI: 0.73, 0.94). The final model for multiple biochemical IA included, in addition to the above-mentioned foods, fermented dairy (HR: 1.42; 95% CI: 1.12, 1.78) and wheat (HR: 1.10; 95% CI: 1.03, 1.18). The final multi-food model for type 1 diabetes included rye (HR: 1.27; 95% CI: 1.07, 1.50), oats (HR: 1.15; 95% CI: 1.03, 1.26), fruits (HR: 1.05; 95% CI: 1.01, 1.09), and berries (HR: 0.67; 95% CI: 0.50, 0.93). ConclusionsHigher consumption of oats, gluten-containing cereals, and fruits was associated with increased that of cruciferous vegetables with decreased risk of several type 1 diabetes-related endpoints when considering all the foods in combination. Further etiological and mechanistic studies are warranted.