The current review will examine the field of food intake biomarkers and the potential use of such biomarkers. Biomarkers of food intake have the potential to be objective measures of intake thus addressing some of the limitations associated with self-reported dietary assessment methods. They are typically food-derived biomarkers present in biological samples and distinct from endogenous metabolites. To date, metabolomic profiling has been successful in identifying several putative food intake biomarkers. With respect to food intake biomarkers, there has been a proliferation of publications in this field. However, caution is needed when interpreting these as food intake biomarkers. Many have not been validated thus hampering their use. While much of the focus to date is on discovery of food intake biomarkers there are excellent examples of how to utilise these biomarkers in nutrition research. Applications include but are not limited to: (1) measurement of adherence to diets in intervention studies (2) objectively predicting intake with no reliance on self-reported data and (3) calibrating self-reported data in large epidemiological studies. Examples of these applications will be covered in this review. While significant progress is achieved to date in the food intake biomarkers field there are a number of key challenges that remain. Examples include lack of databases focused on food-derived metabolites thus hindering the discovery of new biomarkers and the need for new statistical approaches to deal with multiple biomarkers for single foods. Addressing these and other key challenges will be key to development of future opportunities.

Sexual and gender–based violence (SGBV) is a multifaceted, endemic, and nefarious phenomenon that remains poorly measured and understood, despite greater global awareness of the issue. While efforts to improve data collection methods have increased–including the implementation of the Demographic and Health Survey (DHS) in some countries–the lack of reliable SGBV data remains a significant challenge to developing targeted policy interventions and advocacy initiatives. Using a recent mixed–methods research project conducted by the authors in Sierra Leone as a case study, this paper discusses the current status of SGBV data, challenges faced, and potential research a pproaches.

Climate change exacerbates existing risks and vulnerabilities for people globally, and migration is a longstanding adaptation response to climate risk. The mechanisms through which climate change shapes human mobility are complex, however, and gaps in data and knowledge persist. In response to these gaps, the United Nations Development Programme’s (UNDP) Predictive Analytics, Human Mobility, and Urbanization Project employed a hybrid approach that combined predictive analytics with participatory foresight to explore climate change-related mobility in Pakistan and Viet Nam from 2020 to 2050. Focusing on Karachi and Ho Chi Minh City, the project estimated temporal and spatial mobility patterns under different climate change scenarios and evaluated the impact of such in-migration across key social, political, economic, and environmental domains. Findings indicate that net migration into these cities could significantly increase under extreme climate scenarios, highlighting both the complex spatial patterns of population change and the potential for anticipatory policies to mitigate these impacts. While extensive research exists on foresight methods and theory, process reflections are underrepresented. The innovative approach employed within this project offers valuable insights on foresight exercise design choices and their implications for effective stakeholder engagement, as well as the applicability and transferability of insights in support of policymaking. Beyond substantive findings, this paper offers a critical reflection on the methodological alignment of data-driven and participatory foresight with the aim of anticipatory policy ideation, seeking to contribute to the enhanced effectiveness of foresight practices.