Ultra-Processed Food Consumption Associated with Incident Hypertension among Chinese Adults-Results from China Health and Nutrition Survey 1997-2015

Who consumes ultra-processed food? A systematic review of sociodemographic determinants of ultra-processed food consumption from nationally representative samples

Ultra-processed food (UPF) intake is associated with increased non-communicable disease risks. However, systematic reports on sociodemographic predictors of UPF intake are lacking. This review aimed to understand UPF consumption based on sociodemographic factors, using nationally representative cohorts. The systematic review was pre-registered (PROSPERO:CRD42022360199), following PRISMA guidelines. PubMed/MEDLINE searches (‘ultra-processed/ultraprocessed’ and ‘ultra-processing/ultraprocessing’) until 7 September 2022 retrieved 1131 results. Inclusion criteria included: observational, nationally representative adult samples, in English, in peer-reviewed journals, assessing the association between sociodemographics and individual-level UPF intake defined by the NOVA classification. Exclusion criteria included: not nationally representative, no assessment of sociodemographics and individual-level UPF intake defined by NOVA. Risk of bias was assessed using the Newcastle–Ottawa Scale (NOS). Fifty-five papers were included, spanning thirty-two countries. All thirteen sociodemographic variables identified were significantly associated with UPF intake in one or more studies. Significant differences in UPF intake were seen across age, race/ethnicity, rural/urbanisation, food insecurity, income and region, with up to 10–20% differences in UPF intake (% total energy). Higher UPF intakes were associated with younger age, urbanisation and being unmarried, single, separated or divorced. Education, income and socioeconomic status showed varying associations, depending on country. Multivariate analyses indicated that associations were independent of other sociodemographics. Household status and gender were generally not associated with UPF intake. NOS averaged 5·7/10. Several characteristics are independently associated with high UPF intake, indicating large sociodemographic variation in non-communicable disease risk. These findings highlight significant public health inequalities associated with UPF intake, and the urgent need for policy action to minimise social injustice-related health inequalities.

Machine learning accurately predicts food exchange list and the exchangeable portion

Introduction

Food Exchange Lists (FELs) are a user-friendly tool developed to help individuals aid healthy eating habits and follow a specific diet plan. Given the rapidly increasing number of new products or access to new foods, one of the biggest challenges for FELs is being outdated. Supervised machine learning algorithms could be a tool that facilitates this process and allows for updated FELs-the present study aimed to generate an algorithm to predict food classification and calculate the equivalent portion.

Methods

Data mining techniques were used to generate the algorithm, which consists of processing and analyzing the information to find patterns, trends, or repetitive rules that explain the behavior of the data in a food database after performing this task. It was decided to approach the problem from a vector formulation (through 9 nutrient dimensions) that led to proposals for classifiers such as Spherical K-Means (SKM), and by developing this idea, it was possible to smooth the limits of the classifier with the help of a Multilayer Perceptron (MLP) which were compared with two other algorithms of machine learning, these being Random Forest and XGBoost.

Results

The algorithm proposed in this study could classify and calculate the equivalent portion of a single or a list of foods. The algorithm allows the categorization of more than one thousand foods with a confidence level of 97% at the first three places. Also, the algorithm indicates which foods exceed the limits established in sodium, sugar, and/or fat content and show their equivalents.

Discussion

Accurate and robust FELs could improve implementation and adherence to the recommended diet. Compared with manual categorization and calculation, machine learning approaches have several advantages. Machine learning reduces the time needed for manual food categorization and equivalent portion calculation of many food products. Since it is possible to access food composition databases of various populations, our algorithm could be adapted and applied in other databases, offering an even greater diversity of regional products and foods. In conclusion, machine learning is a promising method for automation in generating FELs. This study provides evidence of a large-scale, accurate real-time processing algorithm that can be useful for designing meal plans tailored to the foods consumed by the population. Our model allowed us not only to distinguish and classify foods within a group or subgroup but also to perform the calculation of an equivalent food. As a neural network, this model could be trained with other food bases and thus improve its predictive capacity. Although the performance of the SKM model was lower compared to other types of classifiers, our model allows selecting an equivalent food not from a group previously classified by machine learning but with a fully interpretable algorithm such as cosine similarity for comparing food.

Association between ultra-processed food intake and risk of colorectal cancer: a systematic review and meta-analysis

Background

Although some epidemiological studies have shown a positive relationship between high intake of ultra-processed food (UPF) and risk of colorectal cancer (CRC), the results remain inconsistent. Therefore, we conducted this systematic review and meta-analysis to clarify the association between UPF intake and CRC risk.

Methods

PubMed/MEDLINE, Web of Science, EMBASE, China National Knowledge Infrastructure (CNKI) and Wan fang databases were used to search the relevant studies published up to February 2023. The summary relative risks (RRs) with the corresponding 95% confidence intervals (CIs) were estimated by comparing the highest category vs. the lowest category of UPF intake, using the random-effects models (DerSimonian-Laird method). Heterogeneity between studies was explored using the Cochran's Q test and I-square (I²). Publication bias was assessed by examining the funnel plots, and quantified by Begg's or Egger's tests.

Results

A total of seven articles (three cohort and four case-control studies), involving 18,673 CRC cases and 462,292 participants, were included in our study. Combining nine effect sizes from seven articles, an increased risk of CRC was shown in the highest compared with the lowest category of UPF intake (RR = 1.26; 95%CI:1.14-1.38, p < 0.0001). Subgroup analyses showed a positive association between UPF intake and CRC risk in case-control studies (RR = 1.41; 95%CI: 1.22-1.63, p < 0.0001). When we conducted analyses separately by study area, there was a significant association between UPF intake and CRC risk in developed countries (RR = 1.20; 95%CI: 1.11-1.30, p < 0.0001).

Conclusion

Our results show that high UPF intake is significantly associated with a higher risk of CRC, in the absence, however, of a dose-response association. Further studies in particular of large prospective cohort studies are necessary to confirm these results.

Ultra-Processed Foods, Diet Quality and Human Health

The increase in the volume of industrially processed products in the global food supply has coincided with an increasing prevalence of obesity and non-communicable diseases in many countries, suggesting that ultra-processed foods (UPF) consumption may be detrimental to human health [...].

Ultra-processed food consumption and increased risk of metabolic syndrome: a systematic review and meta-analysis of observational studies

Background

Although higher consumption of ultra-processed food (UPF) has been linked to a higher risk of metabolic syndrome (MetS), the results remain controversial. Herein, we performed a systematic review and meta-analysis of observational studies to clarify the relationship between UPF consumption defined by the NOVA framework and risk of MetS.

Methods

An extensive literature search on PubMed, ISI Web of Science, EBSCO and China National Knowledge Infrastructure (CNKI) databases was conducted to search for the relevant articles published priori to January 2023, and newly published articles between January 2023 and March 2023 were re-searched. Random-effects or fixed-effects models were adopted to calculate the pooled relative risks (RRs) and 95% confidence intervals (CIs). The between-study heterogeneity was explored using the Cochran's Q test and I-square (I²). Publication bias was investigated using the visual inspection of asymmetry in funnel plots and Begg's and Egger's tests.

Results

Nine studies (six cross-sectional and three prospective cohort studies) totaling 23,500 participants with 6,192 MetS cases were included in the final analysis. The pooled effect size for the highest vs. lowest categories of UPF consumption indicated a positive association with the risk of MetS (RR: 1.25, 95%CI: 1.09-1.42, P < 0.0001). Subgroup analyses revealed a positive association between consumption of UPF and MetS risk in cross-sectional studies (RR: 1.47, 95%CI: 1.16-1.87, P = 0.002), and no significant association in cohort studies (RR: 1.10, 95%CI: 0.96-1.27, P = 0.104), respectively. In addition, a more significant association between UPF consumption and increased risk of MetS was found in the subgroups of study quality <7 (RR: 2.22; 95%CI: 1.28-3.84, P = 0.004) than study quality ≥7 (RR: 1.20; 95%CI: 1.06-1.36, P = 0.005). Similarly, when we performed analyses separately by sample size, there was a significant association between UPF consumption and MetS risk in sample size ≥5,000 (RR: 1.19; 95%CI: 1.11-1.27, P < 0.0001), and in sample size <5,000 (RR: 1.43; 95%CI: 1.08-1.90, P = 0.013), respectively.

Conclusions

Our findings suggest that higher consumption of UPF is significantly associated with an increased risk of MetS. Further longitudinal studies are needed to confirm the effect of UPF consumption on MetS.

Association between Ultra-Processed Food Consumption and Metabolic Syndrome among Adults in China-Results from the China Health and Nutrition Survey

The prevalence of metabolic syndrome (MetS) is increasing and the relationship between ultra-processed food (UPF) consumption and MetS remains uncertain in Chinese adults. This study aimed to examine the longitudinal association of UPF consumption with the risk of MetS and its components in Chinese adults. Adults aged 18 years and above who participated in at least two waves of the China Health and Nutrition Survey (CHNS) in 2009, 2015, and 2018 were included in this analysis. Dietary intake data were collected by three consecutive 24 h dietary recalls and weighing household foods and condiments. Depending on the purpose and extent of food processing, UPFs were classified using the NOVA food classification system. A multivariate Cox proportional risk model was used to explore the association between UPF consumption (grouped by quartile: quartile 1 (Q1), quartile 2 (Q2), quartile 3 (Q3), and quartile 4 (Q4)) and risk of MetS and its components. A total of 5147 adults were included. During a median (IQR) 6.0 (3.0, 9.0) year follow-up with 31,878 person-years, 1712 MetS cases were identified, with an incidence of 33.26%. After multivariable adjustment, the risk of MetS was increased by 17% in the highest quartile with UPF consumption (HR: 1.17, 95% CI: 1.01-1.35, p trend: 0.047), with the lowest quartile as a reference. For the components of MetS, the risk of central obesity, raised triglycerides (TG), reduced high-density lipoprotein cholesterol (HDL-C), and raised blood pressure (BP) was increased by 33% (HR: 1.33, 95% CI: 1.18-1.51, p trend: <0.001), 26% (HR: 1.26, 95% CI: 1.08-1.48, p trend: 0.003), 25% (HR: 1.25, 95% CI: 1.07-1.46, p trend: 0.007), and 16% (HR: 1.16, 95% CI: 1.03-1.32, p trend: 0.018) in the highest quartile with UPF consumption, respectively. Adults aged 45-59 years and living in urban areas with higher UPF consumption had higher odds of MetS. These results indicate that higher long-term UPF consumption was associated with an increased risk of MetS in Chinese adults. Further studies such as intervention trials are needed to confirm the mechanism of correlation between UPF consumption and health-related outcomes. Nutritional education actions are warranted to promote a balanced diet and improve the overall dietary quality of residents to reduce the risk of MetS effectively.