Computational Nutrition is an emerging interdisciplinary field that addresses complex challenges in nutrition and health through computational methods and multimodal data. Major research directions include 1) prediction of personalized metabolic responses to foods, 2) causal inference and individualized treatment effects in nutrition and diseases, 3) dynamic assessment and monitoring of diet-related disease risks, and 4) simulation and evaluation of nutrition policies, collectively driving a paradigm shift in traditional nutrition. This paper introduces Computational Nutrition 2.0, showing how artificial intelligence (AI) agents can advance these directions. Most existing models are static, passive, and unidirectional. AI agents overcome these limitations by continuously learning from new data, autonomously perceiving physiological and behavioral states, and engaging in ongoing dialogue with users. In personalized metabolic response prediction, agentic digital twins offer a potential pathway toward dynamic dietary intervention, though this vision remains aspirational and requires further advances in agent technology, sensor reliability, and regulatory approval. In causal inference, nutritional causal agents enable in silico clinical trial simulation through automated causal graph construction and counterfactual reasoning, addressing the static and passive limitations of traditional approaches. In disease risk monitoring, agent-driven perception systems leverage non-invasive sensors for continuous monitoring and early warning, overcoming the passivity of conventional risk assessment. In nutrition policy, multi-agent systems generate Pareto-optimal policy portfolios, revealing trade-offs across health, environmental, economic, and equity dimensions. Computational Nutrition 2.0 advances the field from static models to closed-loop agentic systems for personalized nutrition, representing a paradigm shift from prediction to dynamic, proactive, and adaptive intervention. The era of AI agents offers nutrition an unprecedented opportunity to move from informing to partnering.

This article addresses the current state of global food insecurity and the rising burden of chronic diseases-both of which are nutrition-related crises-highlighting the urgent need to diversify the global dietary matrix. Currently, a mere four crops supply 60% of human energy intake, while macroeconomic barriers prevent over a third of the global population from affording a healthy diet. In this context, neglected and underutilized species (NUS) emerge as a highly sustainable and resilient alternative, given that certain species possess exceptional nutritional profiles and high adaptability to adverse climatic conditions. Consequently, future lines of research are proposed to integrate these crops into the population daily lives, aiming to explore their capacity to combat global food challenges and evaluate their medicinal properties. Ultimately, this approach seeks to promote the collective utilization of NUS within public health frameworks to improve population well-being at community, regional, and global levels.

Precision nutrition tailors dietary guidance to individual biological, genetic, metabolic, and behavioral characteristics. Artificial intelligence (AI), machine learning (ML), wearables, sensors, mobile apps, and multi-omics tools increasingly support this approach, but evidence on their effectiveness, feasibility, implementation, and equity remains fragmented. This review evaluated the effectiveness of AI/ML-enabled analytical approaches and technologies in precision nutrition across dietary, metabolic, behavioral, implementation, and equity outcomes. We conducted a PRISMA-guided systematic review and meta-analysis of studies published from January 2015 to December 2025 across six databases. Eligible studies examined AI/ML, digital tools, wearables, mobile platforms, or multi-omics approaches for personalized nutrition across the lifespan. Two reviewers independently screened studies, extracted data, and assessed bias. Random-effects meta-analyses were conducted where outcomes were comparable; remaining studies underwent qualitative and thematic synthesis. Overall, 262 studies met the inclusion criteria. Meta-analyses indicated modest pooled reductions in body weight (-1.97 kg), BMI (-0.57 kg/m[2]), waist circumference (-2.41 cm), and systolic blood pressure (-4.05 mmHg), whereas no significant pooled effects were observed for lipid outcomes or glycemic markers. Corresponding 95% confidence intervals and 95% prediction intervals were -3.02 to -0.92 and -6.69 to 2.75 kg for body weight, -0.99 to -0.15 and -2.43 to 1.29 kg/m[2] for BMI, -4.75 to -0.07 and -11.30 to 6.49 cm for waist circumference, and -6.90 to -1.19 and -13.24 to 5.15 mmHg for systolic blood pressure. Dietary intake outcomes were generally small and inconsistent, although vegetable intake showed a modest improvement. Certainty of evidence was generally low to very low because of risk-of-bias concerns, imprecision, and substantial heterogeneity; prediction intervals for several statistically significant pooled effects crossed the null. Subgroup findings were exploratory and underpowered. Technology-enabled precision nutrition may modestly improve anthropometric outcomes and systolic blood pressure, but longer, well-reported studies are needed to clarify the clinical impact of precision nutrition technologies. SYSTEMATIC REVIEW REGISTRATION: This review was registered with PROSPERO under registration number CRD420251241063.

Uterine fibroids, the most common benign tumors in women of reproductive age, are the leading cause for hysterectomies and disproportionately affect African American women. This is an updated systematic review that performs a novel meta-analysis of observational studies to quantify the association between serum vitamin D levels and uterine fibroids across a wide range of studies. Additionally, a comprehensive review is included on vitamin D-related factors in both preclinical and clinical studies to assess vitamin D's efficacy in treatment and prevention. Cochrane Library, Embase, PubMed, Scopus, and Web of Science were searched for publications from inception to April 2024. Risk of bias was assessed using Cochrane RoB 2.0 and the Newcastle-Ottawa Scale. Statistical analysis was conducted using R software and the use of a random effects model with restricted maximum-likelihood estimated the overall effect. Sixty-nine studies met the inclusion criteria and were categorized as preclinical (n=26) or clinical (n=43). One preclinical study explored downregulation of vitamin D receptors as a mechanism for pathogenesis; others reported a link between vitamin D-related polymorphisms and increased risk of fibroids. Twenty-nine clinical studies evaluated hypovitaminosis D and fibroid prevalence; twenty-six confirmed an association, while three found no significant differences. Analysis of 21 clinical studies found a large negative effect size (Mean Difference = -5.90ng/mL; I[2] = 97.4%; p < 0.001), indicating that women with fibroids had markedly lower vitamin D levels compared to controls. Supplementation was shown to improve fibroid-associated symptoms in nine clinical interventional studies, with some demonstrating a reduction in fibroid size and others showing prevention in fibroid progression. The data supports a promising role for vitamin D supplementation in regulating fibroids and highlights the need for further clinical investigation of vitamin D as a potential alternative therapy. PROSPERO REGISTRATION: CRD420261320154, 20 Feb 2026.