Obesity and climate change: co-crises with common solutions
Bibliographic record
Abstract
The global obesity crisis involves an unprecedented and rapid change to the human phenotype. Conferring vast levels of avoidable morbidity and mortality at enormous cost, it has proved refractory to previous policy-led action. This article reviews recent developments in our understanding of obesity and its links to the climate co-crisis, aiming to inform evidence-based, societal-level actions to address both. Recent therapeutic developments now offer transformative interventions for millions of people living with obesity. However, treating all affected adults and children with major bariatric surgery or lifelong anti-obesity medication is unsustainable given the risks and costs. The obesity crisis has been driven primarily by the transformation of our food environment toward diets dominated by ultra-processed foods (UPFs) that exert multiple addictive and obesogenic mechanisms. Emerging evidence shows that not all UPFs have the same impact: processed meat and low-fiber, energy-dense UPFs are linked with poorer outcomes compared with less energy-dense, high-fiber, plant-rich UPFs, indicating that more nuanced classifications would be helpful. This food system also contributes significantly to climate change and other environmental harms, primarily through ruminant meat consumption. Both climate change and obesity are driven by unsustainable, but profitable, consumption. Solutions exist but have not been adequately implemented owing to a lack of political will. They require food system reforms that replace energy-dense UPFs with unprocessed foods and reduce animal-sourced foods. Accumulating evidence supports prioritizing actions to remove market distortions via increasing cost transparency, taxing unhealthy foods (redirecting the proceeds to public health), combating marketing, effective food labeling, facilitating healthy food choices, promoting healthy living environments, and public and professional education. New economic models, market demand shifts, and technological innovation should all be harnessed to overcome economic and political barriers, and food system reform should be integral to future actions to achieve the Sustainable Development Goals. This transformation to improve both human and planetary health will require interdisciplinary scientific advocacy and coalition-building across society. During the COVID-19 pandemic, societies recognized how rapid, concerted, science-led action can effectively address a global threat; a similar societal shift is required to motivate the political action needed to address the obesity crisis.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.005 | 0.011 |
| Scholarly communication | 0.014 | 0.018 |
| Open science | 0.002 | 0.018 |
| Research integrity | 0.012 | 0.016 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".