Dietary and policy priorities to reduce the global crises of obesity and diabetes


The world faces a global nutrition crisis, most clearly evidenced by the twin pandemics of obesity and type 2 diabetes (T2DM). Yet, substantial confusion and controversy exist about optimal dietary priorities and policy approaches to address these challenges. This paper reviews the evolution of nutritional evidence, emerging areas and corresponding policy lessons to address obesity and T2DM. This includes the complexity of diet–health pathways for long-term weight maintenance and metabolic health; a need to focus on both increasing protective foods (for example, minimally processed, phytochemical-rich foods) and reducing detrimental factors (for example, refined starches, added sugars and processed meats); and critical assessment of popular diets for weight-loss and metabolic health. Emerging evidence highlights areas for further research, including those related to food processing, non-nutritive sweeteners, emulsifiers, the microbiome, flavonoids and personalized nutrition. Evidence-based, multi-sectoral policy actions to address the global nutrition crisis are shown to span several domains, including health systems, economic incentives, school and workplace environments, quality and labelling standards, and innovation and entrepreneurship.


The ‘double burden’ of undernutrition and chronic diseases causes enormous economic losses and lost human potential across the lifespan1. Globally, poor nutrition is responsible for 41% of all deaths (3.2 million per year from child and maternal undernutrition, 10.9 million per year from chronic diseases) and 48% of lost quality-adjusted life years (327 and 255 billion per year, respectively)2,3. The food system also exacerbates diet-related health disparities, creating a vicious cycle of illness, poor work and school performance, and stunted potential4. The food sector causes 25% of greenhouse gas emissions, 32% of global energy use, 69% of freshwater consumption, 80% of deforestation, and loss of resilience of our soil and oceans5,6,7,8. The scope of these health, economic, equity and sustainability impacts are staggering—yet have remained under-recognized or accepted as status quo by governments, the public, health systems and businesses. This lack of prioritization is, however, rapidly changing—at least partly driven by recognition of the escalating health and economic costs of diet-related obesity and type 2 diabetes (T2DM). Since 1980, the number of adults with obesity has increased from 100 million to 671 million worldwide; and with T2DM, from 108 million to 422 million9,10. This is a global phenomenon: not a single nation worldwide has experienced a decline in obesity or T2DM; prevalence of T2DM in Japan (8.4%), India (9.1%) and China (9.9%) exceeds that of the United States (8.2%)10; and 55% of the rise in adiposity globally (80% in some low- and middle-income regions) is due to increases in rural, not urban, areas11. Left unchecked, these twin global pandemics will decimate population health, economic productivity and health-system capacity worldwide.

While the importance of good nutrition for health and curbing diet-related disease is appreciated, many people are confused about what constitutes a healthy diet. Like other scientific fields, nutritional science is rapidly evolving, with continuously improving methods and an increasing evidence-base12. Unlike many fields, these scientific advances in nutrition combine with deep personal and sociocultural overlays and conflicting information sources, intensifying scepticism and confusion. In addition, this evolution has occurred over less than 100 years13. The first half of the twentieth century was marked by discovery and synthesis of all the major vitamins, documentation of their roles in nutrient deficiency diseases, and recognition of a growing global population that required massive increases in food production. Together with the food shortages of the Great Depression and World War II, these scientific advances converged to emphasize the role of food as a delivery vehicle for selected vitamins and staple calories. The subsequent Green Revolution14 intentionally crafted a modern food system to maximize inexpensive commodity crops and their derivative shelf-stable, starch-rich, vitamin-fortified foods. The successes of this approach should not be understated, including remarkable reductions in global hunger and classical nutrient-deficiency diseases.

It was not until the 1980s that nutrition science and policy began to meaningfully recognize and turn toward chronic diseases. The previous reductionist strategy, so successful for nutrient-deficiency diseases, was naturally extended—for example, creating isolated focus on total fat, saturated fat and sugar. However, in the past two decades, an explosion of new studies and methodologies demonstrate that specific foods and diet quality, rather than nutrient-focused metrics, are most relevant for addressing chronic diseases like obesity and T2DM. This evolution of modern nutrition science clarifies much about the state of the field today, including the current directions of nutritional research, guidelines, policies, and areas of debate and confusion.

This paper reviews evidence, emerging areas and corresponding lessons for modern dietary and policy priorities to address obesity and T2DM. Given the scope of these issues, this Review is not intended to be exhaustive, but a synthesis of key relevant topics.

Diet quality versus diet quantity

A simplistic focus on calorie counting may achieve some success, but does not account for the complex interplay of foods and dietary patterns, on long-term weight control and metabolic health. Foods should be considered as not merely energy, but information—biologic inputs to multiple pathways that help or hinder the body’s diverse and overlapping pathways for long-term weight control. In other words, diet quality influences energy consumption and weight gain15,16,17,18,19,20. In one controlled metabolic unit trial, the availability of highly processed foods, compared to minimally processed foods, resulted in substantially greater ad libitum energy intake (+508 kcal d–1)—even when diets were otherwise matched in available energy, macronutrients, energy density, sugar, sodium and fibre—and, over just two weeks, the highly processed foods resulted in 0.9 kg spontaneous weight gain, while the minimally processed foods led to 0.9 kg spontaneous weight loss20.

Diet quality also influences energy expenditure21,22. In a controlled feeding trial among overweight adults who had achieved 12% initial weight loss, total energy expenditure after 20 weeks was nearly 100 kcal d–1 higher on a moderate carbohydrate diet and more than 200 kcal d–1 higher on a low carbohydrate diet, compared to a high carbohydrate diet22. These differences were largest among those with higher insulin secretion at baseline, supporting the relevance of carbohydrate handling and sensitivity in these effects. Explanatory mechanisms require further study and could include insulin-induced partitioning of metabolic fuels away from oxidation (and heat production) and toward storage (in adipose tissue); changes in brown fat metabolism (and subsequent heat generation); and alterations in microbiome composition, mass, nutrient utilization and thermogenesis.

Thus, diet quality appears to be a major determinant of long-term diet quantity, suggesting that long-term obesogenic effects of foods cannot be judged on the basis of caloric content alone, but also physiologic and metabolic effects that drive subsequent long-term energy intake and expenditure. In addition, diet quality influences health through a diversity of physiologic effects and biologic pathways beyond obesity (Fig. 1)23,24,25,26. While the global obesity pandemic has appropriately highlighted the central role of nutrition in health, a focus on obesity as the most relevant endpoint misses the many other health consequences of dietary habits—obesity is just one mediating pathway. Rather than diet quantity or obesity alone, the primary targets and metrics of success for clinical and population actions on nutrition should be overall diet quality and health.

Fig. 1: Nutrition-related biologic pathways for weight and metabolic health.

Diverse aspects of the diet influence numerous risk factors, making it important to consider multiple pathways as well as clinical outcomes when making conclusions and recommendations about different foods. Reproduced from ref. 24, AHA.

Complexity and pleiotropic effects of foods

For much of its history, nutrition science leveraged a reductionist strategy that emphasized isolated nutrients and their impact on single diseases or pathways13. Scientific advances make clear that foods represent complex matrices of nutrients, ingredients and processing characteristics, each with pleotropic effects on vascular, hepatic, adipocyte, pancreatic, cardiac, intestinal and brain tissues. For example, while dietary fats are commonly considered as concentrated sources of energy, they are also highly physiologically active molecules, regulating gene transcription, altering the structure and function of cellular membranes, modifying ion channel activity and electrophysiology, and influencing numerous inflammatory and other pathways through their downstream metabolites23,26. These complex physiologic effects do not fit neatly within the conventional nutritional classification of fats as saturated, monounsaturated or polyunsaturated, due to additional structural and biologic differences among fatty acids within these groups. Health effects of dietary fats appear to further vary depending on the specific food source, further complicating simplistic predictions of their potential effects on obesity, T2DM and related health outcomes26,27.

As another example, thousands of different trace phytonutrients are now being documented in foods, including more than 5,000 flavonoids with wide-ranging molecular and physiologic effects (Fig. 2; also see ‘Flavonoids’, below), which separately and together may contribute to health effects of cocoa, tea, coffee, fruits, nuts, seeds, vegetables, beans and their oils25. Similarly, metabolic effects of dairy foods have generally been considered in relation to a limited set of nutrients, such as total saturated fat, calcium and vitamin D, and a limited set of pathways, such as blood cholesterol and bone health. Yet, diverse compounds in the matrix of dairy influence a wide range of molecular and physiologic pathways25. Further complexity is evidenced in emerging areas of nutrition science related to the gut microbiome, food processing and personalized nutrition. Together, these scientific advances highlight new, food-based dietary priorities to reduce risk of obesity and T2DM, as further described in the sections below.

Fig. 2: Selected physiologic pathways and molecular mechanisms for metabolic effects of flavonoids.

These diverse compounds and their emerging complexities are likely to contribute to several of the metabolic benefits of minimally processed, phytochemical rich foods. AMPK, 5’ AMP-activated protein kinase; ERK1/2, extracellular signal-regulated kinases 1 and 2; eNOS, endothelial nitric oxide synthase; GLUT4, glucose transporter type 4; IRS2, insulin receptor substrate-2; MAPK, mitogen activated protein kinase; NF-κB, nuclear factor-κB; PGC-1α, peroxisome proliferator-activated receptor-γ coactivator-1α; PKA; protein kinase-A; PPAR-α/γ, peroxisome proliferator-activated receptor-α/γ; ROS, reactive oxygen species; SREBP-1c, sterol regulatory element–binding protein-1c; TG, triglycerides; and TLR4, Toll-like receptor 4. Reproduced from ref. 25, AHA.

Dietary priorities and protective foods

The current evidence indicates that a maximally beneficial diet pattern incorporates high intake of minimally processed, bioactive foods like fruits, nuts, seeds, non-starchy vegetables, beans/legumes, oils from these plants, whole grains, yogurt and fish; moderation in unprocessed red meats, poultry, eggs and milk; and avoidance of refined starches and sugars, processed meats, and other highly processed foods high in sodium, added sugars or trans-fat (Fig. 3)24,28. While no simple label can incorporate all the relevant characteristics of this maximally beneficial diet pattern, the most straightforward description may be a high-fat, Mediterranean-type diet emphasizing minimally processed, phytonutrient-rich foods.

Fig. 3: Dietary priorities to reduce obesity and T2DM.

Various foods appear protective, relatively neutral or harmful for obesity and T2DM. An interesting central feature of many protective foods is their role in germinating new plant life—that is, fruits, nuts, seeds, beans, whole grains and many ‘vegetables’ that are actually fruits (such as tomatoes, cucumbers, olives, squash, eggplant, peppers). The myriad of phytonutrients in these foods, jointly evolved and optimized to nurture and support new life, may be relevant to humans for optimal development and aging. Other characteristics of certain protective foods—for example, probiotics in yogurt or long-chain omega-3 fats in fish—likely contribute to their health benefits. Adapted from ref. 24, AHA

Such a dietary pattern promotes weight maintenance—fruits, non-starchy vegetables, nuts, beans, yogurt, fish and whole grains each appear to protect against chronic weight gain: the more of these foods consumed, the lower the average weight gain15,17,18,19. In contrast, increased intakes of refined grains and sugars, sugar-sweetened beverages (SSBs), potatoes, processed meats and unprocessed red meats each associate with long-term weight gain15,17,18,19. Consistent with this observational evidence, in controlled trials Mediterranean diet patterns produce significant weight loss and reduced visceral adiposity29,30,31.

Such minimally processed, bioactive foods are also consistently linked to better cardiometabolic outcomes28. In the large Women’s Health Initiative, women who consumed healthier overall diet patterns rich in protective foods experienced significantly lower risk of T2DM32. In contrast, the randomized low-fat intervention did not reduce onset of T2DM or improve insulin resistance over 8.5 years33. These observed long-term benefits are supported by controlled trials utilizing dietary patterns rich in these foods24,34. For example, in the PREDIMED clinical trial, participants assigned to Mediterranean-type diets supplemented with extra-virgin olive oil or mixed nuts had less visceral adiposity and lower incidence of T2DM and cardiovascular disease, compared with a control low-fat diet31,35,36.

While effects of specific subcategories of protective foods are less well established, those richest in phytochemicals (for example, nuts, berries and virgin olive oil) appear to be particularly potent. For example, a meta-analysis of controlled trials of tree nuts or peanuts identified favourable effects on insulin resistance and fasting insulin, although not statistically significant changes in HbA1c (glycated haemoglobin) or fasting glucose37. A meta-analysis of controlled trials of berries found modest but significant improvements in HbA1C, body mass index, systolic blood pressure, low-density lipoprotein (LDL)-cholesterol and tumour necrosis factor-α38. Similarly, a meta-analysis of controlled trials supports glycaemic benefits of extra-virgin olive oil, compared with various control fats or low-fat diets, on fasting glucose and HbA1c in diabetic patients39.

Carbohydrate quality

As a proportion of the diet, refined starches and sugars from processed foods represent one of the largest global challenges for obesity and T2DM. Major sources include white bread, white rice, white potatoes, breakfast cereals and crackers, refined pastas, chips and fries, soda, candy, muffins and sweet bakery products. Across diverse foods and beverages, those richest in starches and sugars most strongly associate with long-term weight gain15 and T2DM risk40. Together with evidence from metabolic feeding studies on harms of processed, rapidly digestible carbohydrates41, and interventional trials demonstrating substantial weight loss and improved glycaemia on low-carbohydrate (low-carb) diets42,43,44, these findings make clear that poor-quality carbohydrates should be avoided to optimize weight and metabolic health.

Long-term health effects of simple and refined complex carbohydrates in foods appear similarly adverse15,24,45. Both are rapidly digested and produce very similar dose-dependent glycaemic responses. These similarities are consistent with adverse metabolic associations of high-glycaemic-load diets40. Thus, from a health perspective, refined complex carbohydrate (that is, starch, which is essentially 100% glucose) may be considered similar to ‘hidden sugar’—pervasive and insidious in the global food supply. Added sugars in beverages appear even more deleterious, with adverse effects on weight gain and, independently, body composition, fatty liver and T2DM, perhaps owing to a combination of large portion sizes, rapid intake patterns and limited effects on satiety24. Yet, not all carbohydrates should be avoided. For example, fruits, bean, legumes, whole grains and yogurt all contain some sugar or starch, yet are linked to metabolic and cardiovascular benefits as well as long-term weight maintenance24. These benefits appear related to a combination of factors (Box 1), rather than any one characteristic24,46. Glycaemic responses of carbohydrates can be further mitigated by food order or mixed meals, such as by adding fats or proteins preceding or accompanying the meal, or even by a healthier long-term background diet47,48.

Foods containing whole grains or dietary fibre are associated with lower risk of T2DM and weight gain24,28,46. While some of these benefits are likely related to displacement of poor-quality carbohydrates in the diet, evidence supports additional metabolic benefits of whole grains and dietary fibre, such as related to the germ in whole grains (containing minerals, fatty acids and phytochemicals) and to microbial fermentation of dietary fibre (for example, related to production of bioactive short-chain fatty acids such as acetate, butyrate and propionate)49.

Resistant starches are also of growing interest but are understudied. Starches can be resistant to digestion due to physical inaccessibility (for example, intact whole grains), crystalline form (for example, raw potatoes, green bananas high amylose maize), retrogradation (realignment of cooked, gelatinized starches during cooling, for example, stale bread or cold rice) or chemical modification (for example, many emulsifiers, stabilizers and thickening agents)50. Like dietary fibre, resistant starches reach the large intestine where bacterial fermentation produces short-chain fatty acids and other metabolites. Two recent meta-analyses identified only small, short-term trials of resistant starch, conducted in mixed patient populations51,52. Evaluating body weight, satiety and glucose-insulin homeostasis, some benefits were identified, but of uncertain relevance given the small number of studies, heterogeneity and uncertain risk of bias.

Because an array of different factors may influence carbohydrate quality, there is no single accepted metric or definition of a healthy carbohydrate-rich food. Contents of total carbohydrate, soluble fibre, insoluble fibre, resistant starch, net carbs, whole grains, added sugar, glycaemic index and glycaemic load may each be relevant but also not tell the whole story. A holistic approach should first focus on food categories to be encouraged (for example, fruits and beans) versus avoided (such as sugar-sweetened beverages, white bread, white rice and sugary breakfast cereals). Secondarily, for distinguishing among processed and packaged foods (such as different types of commercially produced whole-grain breads, cereals, crackers, granola bars, energy bars and bakery products), the ratio of total carbohydrate to fibre is empirically useful. While not perfect, a ratio of 10:1 or lower succeeds as a practical ‘rule-of-thumb’ by implicitly balancing the relative proportion of starch and sugar versus whole grain, bran and added fibre53,54.

Dietary fats

For decades, low-fat diets and foods were the cornerstone of recommendations for weight loss and weight control. Based on multiple lines of new evidence, several organizations including the 2015 United States Dietary Guidelines Advisory Committee have concluded that evidence no longer supports any upper limit on total fat consumption34. However, other organizations like the World Health Organization have not yet discarded outdated perspectives on harms of total fat27, contributing to public and policy confusion.

Dietary fats comprise highly diverse compounds with robust and complex effects on cell membrane structure and function, trans-membrane receptors and ion channels, gene expression, and regulatory metabolites23,26. Health effects of fats appear further modified by the food source, for example due to accompanying nutrients, food matrices, intramolecular and supramolecular lipid structures, and processing26,27,55. Consistent with this complexity, total dietary fat consumption is not related to risk of T2DM (or other major health outcomes) across large ranges (~20–40%) of energy56. Low-fat diets are inferior to low-carb diets for weight loss and glycaemic control42,43,44.

Among major fat subclasses, total saturated fat intake has similar effects on glycaemic responses as total carbohydrate57 and is not associated with risk of T2DM58. In contrast, unsaturated fats reduce both HbA1c and HOMA-IR (homeostatic model assessment of insulin resistance), whether compared to saturated fat or carbohydrate; while polyunsaturated fats further improve insulin secretion capacity57. Consistently, estimated dietary consumption and circulating blood biomarkers of linoleic acid (the predominant dietary omega-6 polyunsaturated fat) are associated with lower incidence of T2DM, with 35% lower risk across the interquintile range of blood linoleic acid levels59,60. These benefits are further supported by a recent Mendelian randomization study of genetic variants associated with higher linoleic acid levels61. Together, these finding support the benefits of foods rich in unsaturated fats, such as nuts, seeds, avocados, and oils from fruits (for example, olive and avocado), beans (such as soybean or canola) and seeds (for example, safflower and grapeseed), to improve glycaemic control, reduce insulin resistance and lower risk of T2DM.

Circulating biomarkers of dairy fat consumption, including both odd-chain saturated fats and a natural ruminant trans-fat, are also consistently associated with lower risk of T2DM, with about 20–35% lower risk across their interquintile ranges62. While such objective biomarkers have several advantages, they cannot distinguish between different food sources, and such benefits could relate to other aspects of foods rich in dairy fat25.

Metabolic effects of omega-3 fatty acids remain uncertain. In meta-analyses of trials, seafood-derived (long-chain) omega-3 fats reduce triglycerides, heart rate and blood pressure; improve endothelial function; and increase adiponectin23. However, long-chain omega-3 fats do not significantly affect glycaemia or insulin sensitivity in trials63. Prospective cohort studies generally find little to no association of long-chain omega-3 consumption from fish with risk of T2DM, except for protective associations in Asian populations64. Few trials have evaluated effects of plant-derived omega-3 fats on glucose-insulin homeostasis; and their associations with T2DM risk in observational studies remain inconsistent64.

Other minor fatty acids may influence risk of T2DM. For instance, very long-chain saturated fats (20 to 24 carbons) are of growing interest, with significant inverse associations between their circulating levels and risk of T2DM65, as well as other health outcomes. Very long-chain saturated fats can be endogenously synthesized through elongation of long-chain saturated fats or consumed from a handful of foods such as canola oil, peanuts and macadamia nuts. These fats are key components of, and may alter the biologic effects of, ceramides and sphingomyelin, which play roles in insulin resistance, inflammation and liver homeostasis66.

Dietary protein

Increased dietary protein plus strength-training increases muscle mass and strength more than strength-training alone in generally healthy, middle-aged and older populations67,68. Given the relevance of lean muscle mass for insulin sensitivity, this suggests that protein consumption with strength training could improve metabolic health. However, studies of dietary protein and satiety, weight control or metabolic health show mixed findings. In meta-analysis of randomized trials, increased protein consumption had little effect on metabolic risk factors, including adiposity, lipids, blood pressure, inflammation or glucose69. And, in a meta-analysis of 21 prospective cohorts including 487,956 participants with 38,350 incident cases of T2DM, total protein intake was associated with higher risk of T2DM70. When food sources were separately evaluated, animal protein was associated with higher risk, while plant protein was associated with a trend toward lower risk. In interventional studies, high-protein diets induce variable effects on the gut microbiome, again with differences for animal compared to plant sources71. Given the broadly similar amino acid profiles of animal and plant proteins (indeed, the former are typically more complete and bioavailable), the difference in risk suggests effects on T2DM of animal compared to plant foods are unrelated to protein content. This is not unexpected: similar to total dietary fat or carbohydrate, dietary protein is derived from highly diverse food sources with divergent health effects. Based on current evidence, a focus on dietary protein per se appears less relevant than on specific types of foods to encourage or avoid; and the addition of strength training may modify effects.

Red and processed meats

Intakes of red and processed meat are each linked to higher incidence of T2DM, with about double the risk, gram-for-gram, for processed compared to unprocessed meats72. Given their otherwise generally similar nutrient profiles, this risk difference implicates harms of preservatives (for example, sodium and nitrites) or other aspects of processing (for example, high-temperature cooking)73,74,75. For unprocessed red meats, harms may relate to excess haeme iron, a generally underappreciated risk for T2DM based on animal experiments, studies of gestational diabetes and genetic disorders of iron metabolism76,77. In experimental studies, iron generates oxidative stress, impairs pancreatic β-cell and mitochondrial function, and may increase skeletal muscle and adipose tissue insulin resistance77. Both unprocessed red and processed meat intake are also positively associated with long-term weight gain15,18. Based on these findings, processed meats should be avoided, while unprocessed red meats should be minimized (for example, up to 1–2 servings per week) to optimize metabolic health. Interestingly, the particular harms of processed meats appear underrecognised—in the United States, for example, consumption of unprocessed red meat has declined by nearly 20% since 2000, while consumption of processed meat remains unchanged78.

Dairy foods

While dairy foods are often grouped together, the health effects of different subtypes (milk, cheese, yogurt or butter) appear to vary25. Implicated compounds include probiotics, vitamin K1 and K2 (menoquinones), milk fat globule membrane (MFGM), specific amino acids, medium-chain triglycerides, odd-chain saturated fats, unsaturated fats, branched-chain fats, natural trans-fats, vitamin D and calcium. For example, growing evidence supports benefits of probiotics, such as those in yogurt, fermented milk and certain cheeses, for weight control, glycaemia and perhaps non-alcoholic fatty liver disease79,80,81. Cheese is also a rich source of menoquinones, produced by bacterial fermentation, which have higher bioavailability and longer half-lives than vitamin K1. Through carboxylation of osteocalcin, menoquinones may influence β-cell proliferation, insulin expression and adiponectin production82. Uniquely found in dairy, MFGM is a fascinating tri-layered membrane that naturally encloses milk triglyceride globules during extrusion from the mammary gland. Rich in bioactive polar lipids (phospholipids and sphingolipids) and proteins, MFGM at usual levels in cheese or cream reduces intestinal absorption of dietary cholesterol, blunts rises in blood LDL-cholesterol and alters gene expression83,84,85, while higher doses of MFGM actually improve blood lipids and reduce post-prandial insulin86,87,88. In contrast to cream or cheese, butter contains very little MFGM, which is discarded as buttermilk during its production.

In short-term randomized trials, consumption of total dairy or milk products increases lean muscle mass and reduces body fat, especially in the setting of energy-restricted weight-loss diets89. Among children, observational studies suggest that dairy consumption associates with lower risk of obesity, with limited and mixed findings by type of dairy89. No long-term trials have been performed in children, other than rare multi-component interventions that preclude inference on dairy alone90. Among adults, observational relationships between dairy intake and long-term weight and T2DM vary by food type not dairy fat content15,17,18,91,92. For example, consumption of yogurt and fermented milk, but not regular reduced-fat or whole milk, associates with lower incidence of T2DM; while cheese associates with lower incidence of T2DM in many but not all studies91,92,93,94. Consistent with this, neither reduced-fat milk nor whole milk appreciably relates to long-term weight gain among adults15,17,18; changes in milk fat appear unconsciously compensated with carbohydrates long-term18. Cheese intake is associated with less long-term weight gain when replacing refined carbohydrates, but with weight gain when accompanied by refined carbohydrates18. Yogurt consistently associates with lower long-term weight gain15,17,18, even for sugar-sweetened yogurts, although with about half the benefits lost compared with unsweetened yogurt18.

Coffee and tea

Both coffee and tea are observationally associated with modest improvements in long-term weight maintenance16 and lower risk of T2DM95,96. Emerging studies suggest that phytonutrients, rather than caffeine, in these bean, leaf and fruit extracts may be most relevant25. However, controlled trials have not yet confirmed physiologic effects to account for the magnitude of these associations, with mixed and inconsistent findings for coffee and tea and glycaemia97,98,99. Green and black tea may modestly lower blood pressure100 and LDL-cholesterol101,102, while green tea may improve glycaemia99. Mendelian randomization studies of genetic variants linked to coffee intake did not find associations with cardiometabolic risk factors or T2DM103,104. Overall, observational studies support potential cardiometabolic benefits of coffee and tea, but further research is needed to confirm such benefits and corresponding mechanisms.

Popular diets to treat obesity and T2DM

Among diet patterns evaluated and advocated for weight-loss and glycaemic control, increasing attention is being paid to Mediterranean, low-carb, ketogenic and paleo diets. For diet patterns, health effects cannot be attributed to any single food or nutrient, but to the overall pattern.

Mediterranean diets

In a network meta-analysis of 56 randomized trials evaluating popular diet patterns (for example, low-fat, vegetarian, Mediterranean, paleo, low-carb, low glycaemic and high-protein) in patients with T2DM, Mediterranean, paleo, and vegetarian diets appeared most effective to reduce fasting glucose; while low-carb, Mediterranean and paleo diets appeared most effective to reduce HbA1c105. In subgroup analyses, low-carb diets appeared more effective in shorter-term studies, smaller studies and older individuals (age 60+ years), while Mediterranean diets appeared more effective in longer-term studies, larger studies and younger adults (age <60 years). For weight loss in patients with T2DM, a meta-analysis of 20 randomized trials of various popular diets found significant weight loss only with a Mediterranean diet30. Most of these trials did not exceed one year, raising questions about long-term effects. The PREDIMED trial supports long-term benefits of a Mediterranean diet; after 5 years, the Mediterranean-type diet supplemented with extra-virgin olive oil or nuts reduced visceral adiposity as well as risks of T2DM and cardiovascular disease, compared with a low-fat diet36,106,107.

The health effects of individual foods (Fig. 3), together with the above results, provide strong evidence for a Mediterranean-type diet for long-term weight control and metabolic health. The key characteristics of such a diet pattern are not any specific regional cuisine but an abundance of minimally processed foods and plant oils rich in phytochemicals, moderate fish and dairy, occasional meat, and low intakes of highly processed foods including refined starches, sugars and salt. The specific choices of foods meeting these criteria can be adapted to local availability and culture.

Low-carb and ketogenic diets

In trials with equal-intensity dietary interventions, low-carb (high-fat) diets produce similar or greater weight-loss than low-fat (high-carb) diets, with corresponding improvements in blood pressure, lipids and glycaemic control42,43. Meta-analyses further suggest that low-carb diets may be superior to low-fat diets for glycaemic control in patients with T2DM44,108,109. Such benefits occur even though most low-carb (for example, Atkins) diets lack calorie guidance or restriction, while low-fat diets include the additional interventions of portion control and calorie-restriction. In one trial comparing ad libitum low-carb versus low-fat diets (that is, testing the effects of diet composition alone), the low-carb diet reduced body weight and body fat, while the low-fat diet had small effects on weight and reduced lean muscle mass110.

A ‘low-carb’ focus can be a simple rule to help reduce exposure to ultra-processed foods rich in refined starches and sugars, which likely explains HbA1c reductions105. Yet, carbohydrate food sources and other characteristics (that is, processing, food structure, accompanying nutrients, dose and flux) are also relevant. For example, both low-carb–high-fat and high-carb–low-fat diets lead to weight loss, without calorie counting, when they emphasize minimally processed, bioactive-rich foods20,111. Overall, a Mediterranean-type diet, rich in minimally processed foods and healthy fats, and low in ultra-processed foods and refined starches and sugars, appears optimal.

Extreme low-carb (that is, ketogenic) diets can lead to meaningful weight loss and metabolic benefits112. However, such diets may be challenging to sustain and do not leverage health benefits of fruits, non-starchy vegetables, beans/legumes and minimally processed whole grains. Also, the specific long-term requirement for ketosis per se (versus simply reducing refined starches and sugars) remains unclear. Extreme low-carb diets may be most useful for initial weight-loss (for example, over 6–12 months), followed by transitions toward slowly incorporating carbs from minimally processed, bioactive-rich foods as tolerated. Potential long-term health effects require further investigation.

Paleo diets

Paleo diets aim to conform to foods consumed during human evolution over millennia. Benefits include avoidance of poor quality carbohydrates (refined starches and sugars) and other ultra-processed foods; and positive emphasis on non-starchy vegetables, nuts and fish; which together can produce weight-loss and corresponding metabolic benefits113. Yet, some interpretations of paleo diets include liberal intakes of red meats (including non-paleo processed meats), lard and salt, as well as avoidance of protective plant oils, legumes and dairy; which may reduce net benefits.

Selected emerging areas

Many exciting scientific areas relating to nutrition and metabolic health are in their relative infancy. In the coming years, rigorous further investigation of such topics will greatly expand our understanding and armamentarium to better address obesity, T2DM and other diet-related disorders. Four of these areas are highlighted below.

Food processing

Over the past 70 years, changes in plant and livestock breeding, agricultural practices and food processing methods have transformed the global food supply. The potential health implications of the new processing and manufacturing techniques are receiving increasing attention20,114,115,116,117, with certain food classification systems and even national guidelines advocating for avoidance of highly processed foods118,119. Processed meats and refined grains, starches and sugars are convincingly linked to metabolic harms28. However, nearly all foods must undergo some form of processing for human consumption—for example, milling, refining, heating, cooking, smoking, drying, salting, fermenting or preserving (some exceptions include fruits, nuts, seeds and certain vegetables). Thus, rather than focusing on processing per se, the key issue is to understand which aspects of modern processing are detrimental and define optimal processing of different foods for health.

Processing can increase palatability, nutrient bioavailability, shelf life and convenience, and reduced risk of food-borne pathogens. Processing may also reduce fibre, phenolics, minerals, fatty acids, vitamins and other bioactives; increase the doses and flux of starch and sugar; and introduce compounds such as sodium, other preservatives and additives, trans-fats, heterocyclic amines and advanced glycation end-products (AGEs). Pathways related to the microbiome—including prebiotics, probiotics, non-nutritive sweeteners, emulsifiers and thickeners—are reviewed in the next section.

Health effects of AGEs represent a promising but substantially understudied area. AGEs, formed during high-temperature cooking and browning, are experimentally implicated in pathways related to cardiometabolic risk73,120. A few small studies suggest benefits of low-AGE diets in subjects with overweight, obesity and prediabetes120. In the largest trial, among 100 subjects with obesity and metabolic syndrome, a low- versus high-AGE diet for one year significantly reduced body weight, waist circumference, insulin resistance, and biomarkers of oxidative stress and inflammation121.

On average, most highly processed food products have adverse metabolic effects (for example, SSBs, refined grains and cereals, and processed meats), while most minimally processed foods are protective (for example, fruits, nuts and seeds) (Fig. 3). On the other hands, certain more ‘natural’ foods such as eggs, butter and unprocessed red meats do not appear to improve metabolic health, while other more processed products (for example, yogurt, cheese, plant oils and margarines, canned fish, nut and fruit-rich snacks) are beneficial. In addition, while newer industrial processing methods have received the most media and public attention, certain traditional processing methods may also have adverse health effects. For example, the centuries-old practice of making butter removes MFGM, a potentially beneficial compound83,84,85,86,87,88,122,123. And, as described above, AGEs are formed during cooking and heating, used by humans for millennia.

Overall, seeking minimally processed, phytochemical-rich foods, and avoiding more processed foods, is a strong general—but not absolute rule—for good health. Given the size, expertise and reach of the global agriculture and food industry, a major increase in private and public research investment is needed to better define and understand pathways for optimal food processing.

Gut microbiome

Nutritional choices exert large, rapid effects on gut microbial composition and function, with implications on host health124,125,126,127. For example, several protective foods (Fig. 3) have prebiotic or probiotic characteristics. Prebiotics feed the microbiome, such as dietary fibres, fructans (for example, inulin in chicory root) and other oligosaccharides, resistant starch, and certain phenolics (for example, cocoa-derived flavonols)46,126. Probiotics are live bacteria or yeasts that favourably alter gut microbial composition127, found in fermented foods like yogurt; cheddar, cottage, gouda and mozzarella cheeses; and kefir (milk), kimchi (cabbage and other vegetables), kombucha (tea), miso (soybeans), natto (soybeans), sauerkraut (cabbage) and tempeh (soybeans). Trials of probiotic-containing foods and supplements demonstrate benefits on weight control, glycaemia and possibly non-alcoholic fatty liver disease79,80,81.

Conversely, metabolic harms of highly processed foods may partly relate to adverse microbial effects. Common processing methods (for example, milling and refining) strip away key prebiotics. Even if reconstituted (for example, added bran and fibre), the loss of intact food structure (termed ‘acellular nutrition’) may alter digestion and absorption in the proximal gut117 and also deprive the (dominant) distal gut microbiome of relevant prebiotics128. Foods can also be intentionally processed to retain or supplement prebiotic contents.

Food additives like non-nutritive sweeteners, emulsifiers and thickeners may also influence the microbiome117,128. In some animal models and limited human experiments, artificial sweeteners alter host microbial composition and adversely influence satiety, glucose–insulin homeostasis, caloric intake and weight gain129,130. Non-nutritive sweeteners may also influence taste preferences and learned behaviours, especially among children; and trigger digestive tract sweet-taste receptors that influence glucose absorption and insulin secretion131. In a meta-analysis of short-term trials, non-nutritive sweeteners significantly reduced postprandial blood glucose at 2 to 3.5 hours, compared with baseline132. The long-term implications of such effects, which could induce counter-regulatory hunger or other hormonal responses, are unclear. In one small trial, participants who consumed a drink with non-nutritive sweeteners, compared with a sugar-sweetened drink, ate significantly more one hour later when provided ad libitum lunch, eliminating (but not overtaking) the initial caloric deficit of the non-nutritive-sweetened drink133. Some long-term observational studies find that baseline frequency of diet soda intake associates positively with weight gain and T2DM134, but studies of changes in intake (less susceptible to bias and reverse causation) find very small inverse associations16. In sum, evidence on harms of artificial sweeteners is mixed, while no long-term studies have assessed the newer, natural non-nutritive and low-calorie sweeteners. Based on the breadth and depth of their use and uncertain long-term effects, the global food sector may be said to have “embarked on a massive, uncontrolled, and inadvertent public health experiment”134. Further research on their effects is urgently needed. For now, these compounds may best be considered a bridge for consumers and the food sector away from added sugars and toward naturally sweet or unsweetened foods, rather than a final destination.

Emulsifiers and thickeners are used to alter the appearance, texture or mouthfeel of processed foods135. Common emulsifiers include carrageenan, guar gum, lecithin (soy, egg), mono- and diglycerides, and polysorbates. Food thickeners include proteins (for example, collagen, egg whites and gelatin), starches (for example, cornstarch, potato starch, sago, wheat flour and tapioca), sugar polymers (such as agar and pectin), and vegetable gums (for example, guar and xanthan). In some experimental models, emulsifiers and thickeners influence the gut microbiome, the gut mucosa and related inflammatory pathways135. For example, in a mouse model, two common emulsifiers disrupted the gut mucosal barrier, altered microbial composition and increased bacterial translocation, leading to low-grade inflammation, weight gain and metabolic syndrome136. Such effects appear partly mediated by direct effects on microbial composition and pro-inflammatory potential137. As with artificial sweeteners, the long-term metabolic effects of emulsifiers and thickeners remain uncertain and controversial.


Flavonoids represent more than 5,000 different compounds in fruits, nuts, seeds, vegetables, beans and their oils, with wide-ranging molecular and physiologic effects25. Oleocanthal is a flavonoid in extra-virgin olive oil that causes the common burning sensation at the back of the throat when the oil is directly consumed. The similarity of this sensation to swallowing a chewed uncoated aspirin is no coincidence: oleocanthal binds the same irritant transient receptor potential A1 channel in the throat as many non-steroidal anti-inflammatory drugs138,139. Likewise, oleocanthal inhibits cyclooxygenase 1 and 2 isoenzymes throughout the body, with stronger dose-dependent anti-inflammatory effects than ibuprofen at equimolar concentrations138,139. Thus, while metabolic effects of olive oil are often considered only through the lens of its monounsaturated fat content, trace phytonutrients such as oleocanthal are likely also important.

Individual foods and diet patterns rich in dietary flavonoids and other phytochemicals consistently associate with better weight control and lower risk of T2DM24,140,141. Animal and experimental studies demonstrate effects of flavonoids on a number of pathways related to metabolic health (Fig. 2). Supplementation with flavonoids prevents diet-induced weight gain in several animal models25, even on calorie-matched diets142,143,144,145, suggesting possible additional effects on pathways related to energy expenditure, such as in the gut microbiome or brown fat.

Given the diversity of naturally occurring flavonoids identified to date146, observed effects on molecular pathways for certain flavonoids are unlikely to be generalizable to others. The complexities in flavonoid bioavailability and metabolism, including effects of microbiome-produced flavonoid metabolites, which often have longer half-lives and achieve higher circulating concentrations147, remain to be fully explored. Based on their promise for metabolic health, additional mechanistic, experimental and clinical studies of flavonoids and their metabolites are urgently needed to further elucidate their typology, bioavailability, metabolism and health effects.

Personalized nutrition

The investigation of gene–diet interactions for obesity and T2DM has resulted in many findings, but disappointingly small effect sizes and reproducibility148,149. Personalization based on other characteristics—for example, sociodemographics, cultural factors, the microbiome, medical history, physiologic parameters and epigenetics—appears more promising150,151,152,153,154. For example, glycaemic responses to poor quality carbohydrates may be especially detrimental in women155 compared with men. Similarly, patients with T2DM, insulin resistance or atherogenic dyslipidaemia may benefit most from reducing refined carbs and increasing dietary fibre, proteins and plant oils22,153,154,156,157. The gut microbiome is also promising for personalization: an individual’s gut microbial composition may help predict personalized glycaemic and weight responses to different foods152,158,159,160,161. This could relate, for example, to differential digestion of dietary fibres by Bacteroides, Prevotella and other gut species, with corresponding varying production of short-chain fatty acids161.

In addition to identifying optimal foods, personalized nutrition could theoretically inspire larger or more sustained behavioural changes compared with more general recommendations. For example, strategies that assess and incorporate a person’s cognitive–behavioural stages, and cultural and socioeconomic background, may increase effectiveness of general behaviour-change strategies162,163—but limited evidence currently supports this concept for nutrition behaviours164. Moreover, personalized interventions could increase health disparities if they are costly or difficult to access due to required genomic, metabolomic and other high-dimensional data150.

Overall, personalized nutrition remains an interesting concept deserving of greater investigation. However, the massive, rapid global shifts in obesity and T2DM across and within populations165 demonstrate the dominant influence of generalized environmental determinants and the corresponding importance of population approaches to address these factors. Such systems strategies can also reduce health disparities, compared with individual-based approaches166,167.

Multisectoral policies and best-buy priorities

Given the core role of nutrition in health, healthcare costs, disparities and sustainability, multi-sectoral policies for better nutrition should be a top priority for governments, businesses, health systems and payers168,169,170,171. Effective actions span several domains: health systems, economic incentives for consumers and industry, school and workplace environments, government quality standards and labeling, and innovation and entrepreneurship (Table 1)171,172,173,174,175,176,177,178,179,180,181,182,183,184,185.

Table 1 Effective multi-sectoral actions to improve nutritiona

For most of human history and through the twentieth century ‘Green Revolution’14, governments aimed to combat the challenge of insufficient calories by promoting production and distribution of staple crops. With the unprecedented recent rise in global diet-related chronic diseases, government policies have largely failed to adapt, emphasizing agricultural production of major commodities and support for large food companies as motivated by traditional trade and economic perspectives. However, the continued double burden of diet-related illness plus a new sustainability agenda has begun to shift this dynamic—for example, the majority of the United Nations 2030 Sustainable Development Goals incorporate or are heavily influenced by food and nutrition186.

In formulating dietary policies to address obesity and T2DM, many governments and public health experts have adapted principles from the World Health Organization 2005 Framework Convention on Tobacco Control, the first contemporary framework convention with specific public health objectives187. This includes an emphasis on taxation, warning labels, marketing restrictions, access constraints and limitations on content levels of harmful compounds. For example, SSB taxes have now passed in seven United States jurisdictions and multiple nations, including Barbados, Belgium, Brunei, Chile, Dominica, Ecuador, France, India, Ireland, Kiribati, Mauritius, Mexico, Norway, Peru, the Philippines, Portugal, Saudi Arabia, South Africa, Spain (Catalonia), St Helena, St Vincent and the Grenadines, Sri Lanka, Thailand, the United Arab Emirates, the United Kingdom and Vanuatu188. While such tax policies can be fiscally regressive, they are progressive for improving health disparities. Fiscal regressivity can be further offset by utilizing the tax revenues for subsidies on healthier foods, an approach that has been recommended189 but not yet implemented by any nation. A diversity of countries have also implemented mandatory or voluntary food front-of-package or other warning labels190, including Chile’s notorious new ‘black box’ warning labels191. Several nations, including Belgium, Canada (Quebec), Chile, Ireland, Israel, France, Mexico, Sweden, Taiwan and the United Kingdom, have also instituted restrictions on food marketing to children192,193. Countries such as the United States and Mexico constrain access to soda and/or junk food in schools; while Canada, Denmark, Switzerland, Turkey, the United Kingdom and the United States aim to limit contents of additives such as trans-fats, sodium or added sugars194,195.

This ‘tobacco playbook’ makes sense for certain food categories (for example, soda and junk foods) and additives (for example, trans fats, sodium and added sugars). However, such policies have much less relevance for increasing the consumption of protective foods. Insufficient intakes of such foods cause at least as much disease as excess intakes of harmful foods and nutrients2,28. This can represent an important positive message for the public, policy makers and industry—one that celebrates the power of good nutrition. To increase the availability, affordability and consumption of protective foods, a more nuanced, multi-sectoral set of actions will be required (Table 1). For instance, the Rockefeller Foundation recently outlined a set of priorities toward such goals, including smart investments in value chain infrastructure and efficiency, advances in the use of artificial intelligence and data analytics, increased investments in research and innovation, and coordinated efforts for public awareness and innovation to increase demand for, and desirability of, protective foods196. Given the Rockefeller Foundation’s central role in the ‘Green Revolution’ more than 70 years ago, a highly successful effort that increased global food production and reduced global hunger, this new recognition and focus on protective foods represents a powerful new chapter in the effort to reduce diet-related illness and its consequences.


The food system is crucial for well-being, healthcare costs, health disparities and planetary sustainability. While diet influences many diseases, the global pandemics of obesity and T2DM are particularly notable. In less than a century, modern nutrition science has advanced remarkably, highlighting key priorities to address obesity and T2DM. The significant impacts of the food system on health, the economy, equity and the environment, together with mounting public and food-industry recognition of these issues, have created an opportunity for leadership to create meaningful and lasting solutions. Such efforts must be catalyzed by multi-sectoral policies, with governments playing a special role. This includes an urgent need for greatly expanded food and nutrition discovery and innovation, that is coordinated and mission-oriented toward the health of people and the planet.


  1. 1.

    Bloom, D. et al. The Global Economic Burden of Noncommunicable Diseases (World Economic Forum, 2011).

  2. 2.

    GBD 2017 Risk Factor Collaborators. Global, regional, and national comparative risk assessment of 84 behavioural, environmental and occupational, and metabolic risks or clusters of risks for 195 countries and territories, 1990–2017: a systematic analysis for the Global Burden of Disease Study 2017. Lancet 392, 1923–1994 (2018).

  3. 3.

    Wang, D. D. et al. Global improvement in dietary quality could lead to substantial reduction in premature death. J. Nutrit. 149, 1065–1074 (2019).

  4. 4.

    Perez-Escamilla, R. et al. Nutrition disparities and the global burden of malnutrition. BMJ 361, k2252 (2018).

  5. 5.

    Bustamante, M. et al. Co-benefits, trade-offs, barriers and policies for greenhouse gas mitigation in the agriculture, forestry and other land use (AFOLU) sector. Glob. Chang. Biol. 20, 3270–3290 (2014).

  6. 6.

    Sims, R. E. H. et al. Energy-smart food for people and climate (FAO, 2011).

  7. 7.

    AQUASTAT (FAO, 2016);

  8. 8.

    Kissinger, G., Herold, M. & De Sy, V. Drivers of Deforestation and Forest Degradation: A Synthesis Report for REDD+ Policymakers (Government of the UK & Government of Norway, 2012).

  9. 9.

    NCD-RisC. Worldwide trends in body-mass index, underweight, overweight, and obesity from 1975 to 2016: a pooled analysis of 2416 population-based measurement studies in 128.9 million children, adolescents, and adults. Lancet 390, 2627–2642 (2017).

  10. 10.

    NCD-RisC. Worldwide trends in diabetes since 1980: a pooled analysis of 751 population-based studies with 4.4 million participants. Lancet 387, 1513–1530 (2016).

  11. 11.

    NCD-RisC. Rising rural body-mass index is the main driver of the global obesity epidemic in adults. Nature 569, 260–264 (2019).

  12. 12.

    Mozaffarian, D. & Forouhi, N. G. Dietary guidelines and health-is nutrition science up to the task? BMJ 360, k822 (2018).

  13. 13.

    Mozaffarian, D., Rosenberg, I. & Uauy, R. History of modern nutrition science-implications for current research, dietary guidelines, and food policy. BMJ 361, k2392 (2018).

  14. 14.

    Pingali, P. L. Green revolution: impacts, limits, and the path ahead. Proc. Natl Acad. Sci. USA 109, 12302–12308 (2012).

  15. 15.

    Mozaffarian, D., Hao, T., Rimm, E. B., Willett, W. C. & Hu, F. B. Changes in diet and lifestyle and long-term weight gain in women and men. N. Engl. J. Med. 364, 2392–2404 (2011).

  16. 16.

    Pan, A. et al. Changes in water and beverage intake and long-term weight changes: results from three prospective cohort studies. Int. J. Obes. 37, 1378–1385 (2013).

  17. 17.

    Wang, H. et al. Longitudinal association between dairy consumption and changes of body weight and waist circumference: the Framingham Heart Study. Int. J. Obes. 38, 299–305 (2014).

  18. 18.

    Smith, J. D. et al. Changes in intake of protein foods, carbohydrate amount and quality, and long-term weight change: results from 3 prospective cohorts. Am. J. Clin. Nutr. 101, 1216–1224 (2015).

  19. 19.

    Bertoia, M. L. et al. Changes in intake of fruits and vegetables and weight change in United States men and women followed for up to 24 years: analysis from three prospective cohort studies. PLoS Med. 12, e1001878 (2015).

  20. 20.

    Hall, K. D. et al. Ultra-processed diets cause excess calorie intake and weight gain: an inpatient randomized controlled trial of ad libitum food intake. Cell Metab. 30, 226 (2019).

  21. 21.

    Ebbeling, C. B. et al. Effects of dietary composition on energy expenditure during weight-loss maintenance. JAMA 307, 2627–2634 (2012).

  22. 22.

    Ebbeling, C. B. et al. Effects of a low carbohydrate diet on energy expenditure during weight loss maintenance: randomized trial. BMJ 363, k4583 (2018).

  23. 23.

    Mozaffarian, D. & Wu, J. H. Omega-3 fatty acids and cardiovascular disease: effects on risk factors, molecular pathways, and clinical events. J. Am. Coll. Cardiol. 58, 2047–2067 (2011).

  24. 24.

    Mozaffarian, D. Dietary and policy priorities for cardiovascular disease, diabetes, and obesity: a comprehensive review. Circulation 133, 187–225 (2016).

  25. 25.

    Mozaffarian, D. & Wu, J. H. Y. Flavonoids, dairy foods, and cardiovascular and metabolic health: a review of emerging biologic pathways. Circ. Res. 122, 369–384 (2018).

  26. 26.

    Wu, J. H. Y., Micha, R. & Mozaffarian, D. Dietary fats and cardiometabolic disease: mechanisms and effects on risk factors and outcomes. Nat. Rev. Cardiol. 16, 581–601 (2019).

  27. 27.

    Astrup, A. et al. WHO draft guidelines on dietary saturated and trans fatty acids: time for a new approach? BMJ 366, l4137 (2019).

  28. 28.

    Micha, R. et al. Etiologic effects and optimal intakes of foods and nutrients for risk of cardiovascular diseases and diabetes: Systematic reviews and meta-analyses from the Nutrition and Chronic Diseases Expert Group (NutriCoDE). PLoS ONE 12, e0175149 (2017).

  29. 29.

    Shai, I. et al. Weight loss with a low-carbohydrate, Mediterranean, or low-fat diet. N. Engl. J. Med. 359, 229–241 (2008).

  30. 30.

    Ajala, O., English, P. & Pinkney, J. Systematic review and meta-analysis of different dietary approaches to the management of type 2 diabetes. Am. J. Clin. Nutr. 97, 505–516 (2013).

  31. 31.

    Estruch, R. et al. Effect of a high-fat Mediterranean diet on bodyweight and waist circumference: a prespecified secondary outcomes analysis of the PREDIMED randomised controlled trial. Lancet Diabet. Endocrinol. 7, 6–17 (2019).

  32. 32.

    Cespedes, E. M. et al. Multiple healthful dietary patterns and type 2 diabetes in the women’s health initiative. Am. J. Epidemiol. 183, 622–633 (2016).

  33. 33.

    Howard, B. V. et al. Low-fat dietary pattern and risk of cardiovascular disease: the women’s health initiative randomized controlled dietary modification trial. JAMA 295, 655–666 (2006).

  34. 34.

    Dietary Guidelines Advisory Committee. Scientific Report of the 2015 Dietary Guidelines Advisory Committee (USDA, 2015).

  35. 35.

    Salas-Salvado, J. et al. Prevention of diabetes with Mediterranean diets: a subgroup analysis of a randomized trial. Ann. Intern. Med. 160, 1–10 (2014).

  36. 36.

    Estruch, R. et al. Primary prevention of cardiovascular disease with a Mediterranean diet supplemented with extra-virgin olive oil or nuts. N. Engl. J. Med. 378, e34 (2018).

  37. 37.

    Tindall, A. M., Johnston, E. A., Kris-Etherton, P. M. & Petersen, K. S. The effect of nuts on markers of glycemic control: a systematic review and meta-analysis of randomized controlled trials. Am. J. Clin. Nutr. 109, 297–314 (2019).

  38. 38.

    Huang, H., Chen, G., Liao, D., Zhu, Y. & Xue, X. Effects of berries consumption on cardiovascular risk factors: a meta-analysis with trial sequential analysis of randomized controlled trials. Sci. Rep. 6, 23625 (2016).

  39. 39.

    Schwingshackl, L. et al. Olive oil in the prevention and management of type 2 diabetes mellitus: a systematic review and meta-analysis of cohort studies and intervention trials. Nutr. Diabet. 7, e262 (2017).

  40. 40.

    Livesey, G. et al. Dietary glycemic index and load and the risk of type 2 diabetes: a systematic review and updated meta-analyses of prospective cohort studies. Nutrients 11, 1280 (2019).

  41. 41.

    Ludwig, D. S. Aways Hungry? (Grand Central Life and Style, 2016).

  42. 42.

    Hu, T. et al. Effects of low-carbohydrate diets versus low-fat diets on metabolic risk factors: a meta-analysis of randomized controlled clinical trials. Am. J. Epidemiol. 176, S44–54 (2012).

  43. 43.

    Tobias, D. K. et al. Effect of low-fat diet interventions versus other diet interventions on long-term weight change in adults: a systematic review and meta-analysis. Lancet Diabet. Endocrinol. 3, 968–979 (2015).

  44. 44.

    Huntriss, R., Campbell, M. & Bedwell, C. The interpretation and effect of a low-carbohydrate diet in the management of type 2 diabetes: a systematic review and meta-analysis of randomised controlled trials. Eur. J. Clin. Nutr. 72, 311–325 (2018).

  45. 45.

    Vasilaras, T. H., Raben, A. & Astrup, A. Twenty-four hour energy expenditure and substrate oxidation before and after 6 months’ ad libitum intake of a diet rich in simple or complex carbohydrates or a habitual diet. Int. J. Obes. Relat. Metab. Disord. 25, 954–965 (2001).

  46. 46.

    Weickert, M. O. & Pfeiffer, A. F. H. Impact of dietary fiber consumption on insulin resistance and the prevention of type 2 diabetes. J. Nutrition 148, 7–12 (2018).

  47. 47.

    Shukla, A. P., Iliescu, R. G., Thomas, C. E. & Aronne, L. J. Food order has a significant impact on postprandial glucose and insulin levels. Diabetes Care 38, 98–99 (2015).

  48. 48.

    Kim, Y., Keogh, J. B. & Clifton, P. M. Differential effects of red meat/refined grain diet and dairy/chicken/nuts/whole grain diet on glucose, insulin and triglyceride in a randomized crossover study. Nutrients 8, 687 (2016).

  49. 49.

    Kasubuchi, M., Hasegawa, S., Hiramatsu, T., Ichimura, A. & Kimura, I. Dietary gut microbial metabolites, short-chain fatty acids, and host metabolic regulation. Nutrients 7, 2839–2849 (2015).

  50. 50.

    Birt, D. F. et al. Resistant starch: promise for improving human health. Adv. Nutr. 4, 587–601 (2013).

  51. 51.

    Snelson, M. et al. Metabolic effects of resistant starch type 2: a systematic literature review and meta-analysis of randomized controlled trials. Nutrients 11, 1833 (2019).

  52. 52.

    Wang, Y. et al. Effects of the resistant starch on glucose, insulin, insulin resistance, and lipid parameters in overweight or obese adults: a systematic review and meta-analysis. Nutr. Diabetes 9, 19 (2019).

  53. 53.

    Mozaffarian, R. S. et al. Identifying whole grain foods: a comparison of different approaches for selecting more healthful whole grain products. Public Health Nutr. 16, 2255–2264 (2013).

  54. 54.

    Ghodsian, B. & Madden, A. M. Evaluating the ≤10:1 wholegrain criterion in identifying nutrient quality and health implications of UK breads and breakfast cereals. Public Health Nutr. 21, 1186–1193 (2018).

  55. 55.

    Michalski, M. C. et al. Multiscale structures of lipids in foods as parameters affecting fatty acid bioavailability and lipid metabolism. Prog. Lipid Res. 52, 354–373 (2013).

  56. 56.

    Alhazmi, A., Stojanovski, E., McEvoy, M. & Garg, M. L. Macronutrient intakes and development of type 2 diabetes: a systematic review and meta-analysis of cohort studies. J. Am. Coll. Nutr. 31, 243–258 (2012).

  57. 57.

    Imamura, F. et al. Effects of saturated fat, polyunsaturated fat, monounsaturated fat, and carbohydrate on glucose-insulin homeostasis: a systematic review and meta-analysis of randomised controlled feeding trials. PLoS Med. 13, e1002087 (2016).

  58. 58.

    Micha, R. & Mozaffarian, D. Saturated fat and cardiometabolic risk factors, coronary heart disease, stroke, and diabetes: a fresh look at the evidence. Lipids 45, 893–905 (2010).

  59. 59.

    Wu, J. H. Y. et al. Omega-6 fatty acid biomarkers and incident type 2 diabetes: pooled analysis of individual-level data for 39 740 adults from 20 prospective cohort studies. Lancet Diabet. Endocrinol. 5, 965–974 (2017).

  60. 60.

    Zong, G. et al. Associations between linoleic acid intake and incident type 2 diabetes among U.S. men and women. Diabetes Care 42, 1406–1413 (2019).

  61. 61.

    Zhao, J. V. & Schooling, C. M. Effect of linoleic acid on ischemic heart disease and its risk factors: a Mendelian randomization study. BMC Med. 17, 61 (2019).

  62. 62.

    Imamura, F. et al. Fatty acid biomarkers of dairy fat consumption and incidence of type 2 diabetes: a pooled analysis of prospective cohort studies. PLoS Med. 15, e1002670 (2018).

  63. 63.

    Abbott, K. A., Burrows, T. L., Thota, R. N., Acharya, S. & Garg, M. L. Do omega-3 PUFAs affect insulin resistance in a sex-specific manner? a systematic review and meta-analysis of randomized controlled trials. Am. J. Clin. Nutr. 104, 1470–1484 (2016).

  64. 64.

    Wu, J. H. et al. Omega-3 fatty acids and incident type 2 diabetes: a systematic review and meta-analysis. Br. J. Nutr. 107, S214–227 (2012).

  65. 65.

    Fretts, A. M. et al. Associations of circulating very-long-chain saturated fatty acids and incident type 2 diabetes: a pooled analysis of prospective cohort studies. Am. J. Clin. Nutr. 109, 1216–1223 (2019).

  66. 66.

    Lemaitre, R. N. et al. Plasma phospholipid very-long-chain saturated fatty acids and incident diabetes in older adults: the Cardiovascular Health Study. Am. J. Clin. Nutr. 101, 1047–1054 (2015).

  67. 67.

    Morton, R. W. et al. A systematic review, meta-analysis and meta-regression of the effect of protein supplementation on resistance training-induced gains in muscle mass and strength in healthy adults. Br. J. Sports Med. 52, 376–384 (2018).

  68. 68.

    Liao, C. D. et al. Effects of protein supplementation combined with resistance exercise on body composition and physical function in older adults: a systematic review and meta-analysis. Am. J. Clin. Nutr. 106, 1078–1091 (2017).

  69. 69.

    Schwingshackl, L. & Hoffmann, G. Long-term effects of low-fat diets either low or high in protein on cardiovascular and metabolic risk factors: a systematic review and meta-analysis. Nutr. J. 12, 48 (2013).

  70. 70.

    Ye, J. et al. Dietary protein intake and subsequent risk of type 2 diabetes: a dose-response meta-analysis of prospective cohort studies. Acta Diabetol. 56, 851–870 (2019).

  71. 71.

    Blachier, F. et al. High-protein diets for weight management: interactions with the intestinal microbiota and consequences for gut health. a position paper by the my new gut study group. Clin. Nutr. 38, 1012–1022 (2019).

  72. 72.

    Pan, A. et al. Red meat consumption and risk of type 2 diabetes: 3 cohorts of US adults and an updated meta-analysis. Am. J. Clin. Nutr. 94, 1088–1096 (2011).

  73. 73.

    Uribarri, J. et al. Advanced glycation end products in foods and a practical guide to their reduction in the diet. J. Am. Diet. Assoc. 110, 911–916 (2010).

  74. 74.

    Micha, R., Michas, G., Lajous, M. & Mozaffarian, D. Processing of meats and cardiovascular risk: time to focus on preservatives. BMC Med. 11, 136 (2013).

  75. 75.

    Rohrmann, S. & Linseisen, J. Processed meat: the real villain? Proc. Nutr. Soc. 75, 233–241 (2016).

  76. 76.

    Zhao, Z. et al. Body iron stores and heme-iron intake in relation to risk of type 2 diabetes: a systematic review and meta-analysis. PLoS ONE 7, e41641 (2012).

  77. 77.

    Fernandez-Real, J. M., McClain, D. & Manco, M. Mechanisms linking glucose homeostasis and iron metabolism toward the onset and progression of type 2 diabetes. Diabetes Care 38, 2169–2176 (2015).

  78. 78.

    Zeng, L. et al. Trends in processed meat, unprocessed red meat, poultry, and fish consumption in the United States, 1999–2016. J. Acad. Nutr. Diet. 119, 1085–1098 (2019).

  79. 79.

    Zhang, Q., Wu, Y. & Fei, X. Effect of probiotics on body weight and body-mass index: a systematic review and meta-analysis of randomized, controlled trials. Int. J. Food Sci. Nutr. 67, 571–580 (2015).

  80. 80.

    Sun, J. & Buys, N. J. Glucose- and glycaemic factor-lowering effects of probiotics on diabetes: a meta-analysis of randomised placebo-controlled trials. Br. J. Nutr. 115, 1167–1177 (2016).

  81. 81.

    Loman, B. R., Hernandez-Saavedra, D., An, R. & Rector, R. S. Prebiotic and probiotic treatment of nonalcoholic fatty liver disease: a systematic review and meta-analysis. Nutr. Rev. 76, 822–839 (2018).

  82. 82.

    Kanazawa, I. Osteocalcin as a hormone regulating glucose metabolism. World J. Diabetes 6, 1345–1354 (2015).

  83. 83.

    Rosqvist, F. et al. Potential role of milk fat globule membrane in modulating plasma lipoproteins, gene expression, and cholesterol metabolism in humans: a randomized study. Am. J. Clin. Nutr. 102, 20–30 (2015).

  84. 84.

    Hjerpsted, J., Leedo, E. & Tholstrup, T. Cheese intake in large amounts lowers LDL-cholesterol concentrations compared with butter intake of equal fat content. Am. J. Clin. Nutr. 94, 1479–1484 (2011).

  85. 85.

    Soerensen, K. V., Thorning, T. K., Astrup, A., Kristensen, M. & Lorenzen, J. K. Effect of dairy calcium from cheese and milk on fecal fat excretion, blood lipids, and appetite in young men. Am. J. Clin. Nutr. 99, 984–991 (2014).

  86. 86.

    Demmer, E. et al. Addition of a dairy fraction rich in milk fat globule membrane to a high-saturated fat meal reduces the postprandial insulinaemic and inflammatory response in overweight and obese adults. J. Nutr. Sci. 5, e14 (2016).

  87. 87.

    Beals, E. et al. Addition of milk fat globule membrane-enriched supplement to a high-fat meal attenuates insulin secretion and induction of soluble epoxide hydrolase gene expression in the postprandial state in overweight and obese subjects. J. Nutr. Sci. 8, e16 (2019).

  88. 88.

    Vors, C. et al. Milk polar lipids reduce lipid cardiovascular risk factors in overweight postmenopausal women: towards a gut sphingomyelin-cholesterol interplay. Gut (2019).

  89. 89.

    Geng, T., Qi, L. & Huang, T. Effects of dairy products consumption on body weight and body composition among adults: an updated meta-analysis of 37 randomized control trials. Mol. Nutr. Food Res. 62, 1700410 (2018).

  90. 90.

    Nupponen, M. et al. Metabolic syndrome from adolescence to early adulthood: effect of infancy-onset dietary counseling of low saturated fat: the Special Turku Coronary Risk Factor Intervention Project (STRIP). Circulation 131, 605–613 (2015).

  91. 91.

    Sluijs, I. et al. The amount and type of dairy product intake and incident type 2 diabetes: results from the EPIC-InterAct Study. Am. J. Clin. Nutr. 96, 382–390 (2012).

  92. 92.

    Chen, M. et al. Dairy consumption and risk of type 2 diabetes: 3 cohorts of US adults and an updated meta-analysis. BMC Med. 12, 215 (2014).

  93. 93.

    Ericson, U. et al. Food sources of fat may clarify the inconsistent role of dietary fat intake for incidence of type 2 diabetes. Am. J. Clin. Nutr. 101, 1065–1080 (2015).

  94. 94.

    Diaz-Lopez, A. et al. Dairy product consumption and risk of type 2 diabetes in an elderly Spanish Mediterranean population at high cardiovascular risk. Eur. J. Nutr. 55, 349–360 (2015).

  95. 95.

    Ding, M., Bhupathiraju, S. N., Chen, M., van Dam, R. M. & Hu, F. B. Caffeinated and decaffeinated coffee consumption and risk of type 2 diabetes: a systematic review and a dose-response meta-analysis. Diabet. Care 37, 569–586 (2014).

  96. 96.

    Yang, W. S., Wang, W. Y., Fan, W. Y., Deng, Q. & Wang, X. Tea consumption and risk of type 2 diabetes: a dose-response meta-analysis of cohort studies. Br. J. Nutr. 111, 1329–1339 (2014).

  97. 97.

    Zheng, X. X. et al. Effects of green tea catechins with or without caffeine on glycemic control in adults: a meta-analysis of randomized controlled trials. Am. J. Clin. Nutr. 97, 750–762 (2013).

  98. 98.

    Li, Y. et al. Effects of tea or tea extract on metabolic profiles in patients with type 2 diabetes mellitus: a meta-analysis of 10 randomized controlled trials. Diabet. Metab. Res. Rev. 32, 2–10 (2015).

  99. 99.

    Kondo, Y. et al. Effects of coffee and tea consumption on glucose metabolism: a systematic review and network meta-analysis. Nutrients 11, 48 (2018).

  100. 100.

    Liu, G. et al. Effects of tea intake on blood pressure: a meta-analysis of randomised controlled trials. Br. J. Nutr. 112, 1043–1054 (2014).

  101. 101.

    Onakpoya, I., Spencer, E., Heneghan, C. & Thompson, M. The effect of green tea on blood pressure and lipid profile: a systematic review and meta-analysis of randomized clinical trials. Nutr. Metab. Cardiovasc. Dis. 24, 823–836 (2014).

  102. 102.

    Wang, D., Chen, C., Wang, Y., Liu, J. & Lin, R. Effect of black tea consumption on blood cholesterol: a meta-analysis of 15 randomized controlled trials. PLoS ONE 9, e107711 (2014).

  103. 103.

    Nordestgaard, A. T., Thomsen, M. & Nordestgaard, B. G. Coffee intake and risk of obesity, metabolic syndrome and type 2 diabetes: a Mendelian randomization study. Int. J. Epidemiol. 44, 551–565 (2015).

  104. 104.

    Kwok, M. K., Leung, G. M. & Schooling, C. M. Habitual coffee consumption and risk of type 2 diabetes, ischemic heart disease, depression and Alzheimer’s disease: a Mendelian randomization study. Sci. Rep. 6, 36500 (2016).

  105. 105.

    Schwingshackl, L., Chaimani, A., Hoffmann, G., Schwedhelm, C. & Boeing, H. A network meta-analysis on the comparative efficacy of different dietary approaches on glycaemic control in patients with type 2 diabetes mellitus. Eur. J. Epidemiol. 33, 157–170 (2018).

  106. 106.

    Estruch, R. et al. Effect of a high-fat Mediterranean diet on bodyweight and waist circumference: a prespecified secondary outcomes analysis of the PREDIMED randomised controlled trial. Lancet Diabetes Endocrinol. 4, 666–676 (2016).

  107. 107.

    Salas-Salvado, J. et al. Reduction in the incidence of type 2 diabetes with the Mediterranean diet: results of the PREDIMED-Reus nutrition intervention randomized trial. Diabet. Care 34, 14–19 (2011).

  108. 108.

    Kodama, S. et al. Influence of fat and carbohydrate proportions on the metabolic profile in patients with type 2 diabetes: a meta-analysis. Diabet. Care 32, 959–965 (2009).

  109. 109.

    Schwingshackl, L. & Hoffmann, G. Comparison of the long-term effects of high-fat v. low-fat diet consumption on cardiometabolic risk factors in subjects with abnormal glucose metabolism: a systematic review and meta-analysis. Br. J. Nutr. 111, 2047–2058 (2014).

  110. 110.

    Bazzano, L. A. et al. Effects of low-carbohydrate and low-fat diets: a randomized trial. Ann. Intern. Med. 161, 309–318 (2014).

  111. 111.

    Gardner, C. D. et al. Effect of low-fat vs low-carbohydrate diet on 12-month weight loss in overweight adults and the association with genotype pattern or insulin secretion: the DIETFITS randomized clinical trial. JAMA 319, 667–679 (2018).

  112. 112.

    Hallberg, S. J. et al. Effectiveness and safety of a novel care model for the management of type 2 diabetes at 1 year: an open-label, non-randomized, controlled study. Diabetes Ther. 9, 583–612 (2018).

  113. 113.

    Ghaedi, E. et al. Effects of a Paleolithic diet on cardiovascular disease risk factors: a systematic review and meta-analysis of randomized controlled trials. Adv. Nutr. 10, 634–646 (2019).

  114. 114.

    Hoffman, R. & Gerber, M. Food processing and the Mediterranean diet. Nutrients 7, 7925–7964 (2015).

  115. 115.

    Louzada, M. L. et al. Consumption of ultra-processed foods and obesity in Brazilian adolescents and adults. Prev. Med. 81, 9–15 (2015).

  116. 116.

    Dobarganes, C. & Marquez-Ruiz, G. Possible adverse effects of frying with vegetable oils. Br. J. Nutr. 113, S49–57 (2015).

  117. 117.

    Zinocker, M. K. & Lindseth, I. A. The Western diet-microbiome-host interaction and its role in metabolic disease. Nutrients 10, 365 (2018).

  118. 118.

    Monteiro, C. A., Moubarac, J. C., Cannon, G., Ng, S. W. & Popkin, B. Ultra-processed products are becoming dominant in the global food system. Obes. Rev. 14, 21–28 (2013).

  119. 119.

    da Silva Oliveira, M. S. & Silva-Amparo, L. Food-based dietary guidelines: a comparative analysis between the dietary guidelines for the Brazilian population 2006 and 2014. Public Health Nutr. 21, 210–217 (2018).

  120. 120.

    Luevano-Contreras, C., Gomez-Ojeda, A., Macias-Cervantes, M. H. & Garay-Sevilla, M. E. Dietary advanced glycation end products and cardiometabolic risk. Curr. Diabet. Rep. 17, 63 (2017).

  121. 121.

    Vlassara, H. et al. Oral AGE restriction ameliorates insulin resistance in obese individuals with the metabolic syndrome: a randomised controlled trial. Diabetologia 59, 2181–2192 (2016).

  122. 122.

    Arranz, E. & Corredig, M. Invited review: Milk phospholipid vesicles, their colloidal properties, and potential as delivery vehicles for bioactive molecules. J. Dairy Sci. 100, 4213–4222 (2017).

  123. 123.

    Bourlieu, C. et al. Polar lipid composition of bioactive dairy co-products buttermilk and butterserum: emphasis on sphingolipid and ceramide isoforms. Food Chem. 240, 67–74 (2018).

  124. 124.

    Brunkwall, L. & Orho-Melander, M. The gut microbiome as a target for prevention and treatment of hyperglycaemia in type 2 diabetes: from current human evidence to future possibilities. Diabetologia 60, 943–951 (2017).

  125. 125.

    Canfora, E. E., Meex, R. C. R., Venema, K. & Blaak, E. E. Gut microbial metabolites in obesity, NAFLD and T2DM. Nat. Rev. Endocrinol. 15, 261–273 (2019).

  126. 126.

    Davani-Davari, D. et al. Prebiotics: definition, types, sources, mechanisms, and clinical applications. Foods 8, 92 (2019).

  127. 127.

    Yoo, J. Y. & Kim, S. S. Probiotics and prebiotics: present status and future perspectives on metabolic disorders. Nutrients 8, 173 (2016).

  128. 128.

    Reese, A. T. & Carmody, R. N. Thinking outside the cereal box: noncarbohydrate routes for dietary manipulation of the gut microbiota. Appl. Environ. Microbiol. 85, e02246-18 (2019).

  129. 129.

    Suez, J., Korem, T., Zilberman-Schapira, G., Segal, E. & Elinav, E. Non-caloric artificial sweeteners and the microbiome: findings and challenges. Gut Microbes 6, 149–155 (2015).

  130. 130.

    Pearlman, M., Obert, J. & Casey, L. The association between artificial sweeteners and obesity. Curr. Gastroenterol. Rep. 19, 64 (2017).

  131. 131.

    Pepino, M. Y. Metabolic effects of non-nutritive sweeteners. Physiol. Behav. 152, 450–455 (2015).

  132. 132.

    Nichol, A. D., Holle, M. J. & An, R. Glycemic impact of non-nutritive sweeteners: a systematic review and meta-analysis of randomized controlled trials. Eur. J. Clin. Nutr. 72, 796–804 (2018).

  133. 133.

    Tey, S. L., Salleh, N. B., Henry, J. & Forde, C. G. Effects of aspartame-, monk fruit-, stevia- and sucrose-sweetened beverages on postprandial glucose, insulin and energy intake. Int. J. Obes. 41, 450–457 (2017).

  134. 134.

    Ludwig, D. S. Artificially sweetened beverages: cause for concern. JAMA 302, 2477–2478 (2009).

  135. 135.

    Halmos, E. P., Mack, A. & Gibson, P. R. Review article: emulsifiers in the food supply and implications for gastrointestinal disease. Aliment Pharmacol. Ther. 49, 41–50 (2019).

  136. 136.

    Chassaing, B. et al. Dietary emulsifiers impact the mouse gut microbiota promoting colitis and metabolic syndrome. Nature 519, 92–96 (2015).

  137. 137.

    Chassaing, B., Van de Wiele, T., De Bodt, J., Marzorati, M. & Gewirtz, A. T. Dietary emulsifiers directly alter human microbiota composition and gene expression ex vivo potentiating intestinal inflammation. Gut 66, 1414–1427 (2017).

  138. 138.

    Beauchamp, G. K. et al. Phytochemistry: ibuprofen-like activity in extra-virgin olive oil. Nature 437, 45–46 (2005).

  139. 139.

    Scotece, M. et al. New drugs from ancient natural foods. Oleocanthal, the natural occurring spicy compound of olive oil: a brief history. Drug Discov. Today 20, 406–410 (2015).

  140. 140.

    Guo, X., Yang, B., Tan, J., Jiang, J. & Li, D. Associations of dietary intakes of anthocyanins and berry fruits with risk of type 2 diabetes mellitus: a systematic review and meta-analysis of prospective cohort studies. Eur. J. Clin. Nutr. 70, 1360–1367 (2016).

  141. 141.

    Xu, H., Luo, J., Huang, J. & Wen, Q. Flavonoids intake and risk of type 2 diabetes mellitus: a meta-analysis of prospective cohort studies. Medicine 97, e0686 (2018).

  142. 142.

    Mulvihill, E. E. et al. Naringenin prevents dyslipidemia, apolipoprotein B overproduction, and hyperinsulinemia in LDL receptor-null mice with diet-induced insulin resistance. Diabetes 58, 2198–2210 (2009).

  143. 143.

    Hoek-van den Hil, E. F. et al. Quercetin decreases high-fat diet induced body weight gain and accumulation of hepatic and circulating lipids in mice. Genes Nutr. 9, 418 (2014).

  144. 144.

    Tsutsumi, R. et al. Sudachitin, a polymethoxylated flavone, improves glucose and lipid metabolism by increasing mitochondrial biogenesis in skeletal muscle. Nutr. Metab. 11, 32 (2014).

  145. 145.

    Zhang, X. et al. Dietary luteolin activates browning and thermogenesis in mice through an AMPK/PGC1alpha pathway-mediated mechanism. Int. J. Obes. 40, 1841–1849 (2016).

  146. 146.

    Kawser Hossain, M. et al. Molecular mechanisms of the anti-obesity and anti-diabetic properties of flavonoids. Int. J. Mol. Sci. 17, 569 (2016).

  147. 147.

    Warner, E. F. et al. Common phenolic metabolites of flavonoids, but not their unmetabolized precursors, reduce the secretion of vascular cellular adhesion molecules by human endothelial cells. J. Nutr. 146, 465–473 (2016).

  148. 148.

    Heianza, Y. & Qi, L. Gene-diet interaction and precision nutrition in obesity. Int. J. Mol. Sci. 18, 787 (2017).

  149. 149.

    Ortega, A., Berna, G., Rojas, A., Martin, F. & Soria, B. Gene-diet interactions in type 2 diabetes: the chicken and egg debate. Int. J. Mol. Sci. 18, 1188 (2017).

  150. 150.

    Ordovas, J. M., Ferguson, L. R., Tai, E. S. & Mathers, J. C. Personalised nutrition and health. BMJ 361, bmj.k2173 (2018).

  151. 151.

    de Toro-Martin, J., Arsenault, B. J., Despres, J. P. & Vohl, M. C. Precision nutrition: a review of personalized nutritional approaches for the prevention and management of metabolic syndrome. Nutrients 9, 913 (2017).

  152. 152.

    Christensen, L., Roager, H. M., Astrup, A. & Hjorth, M. F. Microbial enterotypes in personalized nutrition and obesity management. Am. J. Clin. Nutr. 108, 645–651 (2018).

  153. 153.

    Kabisch, S. et al. Fasting glucose state determines metabolic response to supplementation with insoluble cereal fibre: a secondary analysis of the optimal fibre trial (OptiFiT). Nutrients 11, 2385 (2019).

  154. 154.

    Hjorth, M. F. et al. Pretreatment fasting glucose and insulin as determinants of weight loss on diets varying in macronutrients and dietary fibers-the POUNDS LOST study. Nutrients 11, 586 (2019).

  155. 155.

    Mirrahimi, A. et al. Associations of glycemic index and load with coronary heart disease events: a systematic review and meta-analysis of prospective cohorts. J. Am. Heart Assoc. 1, e000752 (2012).

  156. 156.

    Dong, J. Y., Zhang, Z. L., Wang, P. Y. & Qin, L. Q. Effects of high-protein diets on body weight, glycaemic control, blood lipids and blood pressure in type 2 diabetes: meta-analysis of randomised controlled trials. Br. J. Nutr. 110, 781–789 (2013).

  157. 157.

    Viana, L. V., Gross, J. L. & Azevedo, M. J. Dietary intervention in patients with gestational diabetes mellitus: a systematic review and meta-analysis of randomized clinical trials on maternal and newborn outcomes. Diabet. Care 37, 3345–3355 (2014).

  158. 158.

    Zeevi, D. et al. Personalized nutrition by prediction of glycemic responses. Cell 163, 1079–1094 (2015).

  159. 159.

    Korem, T. et al. Bread affects clinical parameters and induces gut microbiome-associated personal glycemic responses. Cell Metab. 25, 1243–1253 (2017).

  160. 160.

    Rothschild, D. et al. Environment dominates over host genetics in shaping human gut microbiota. Nature 555, 210–215 (2018).

  161. 161.

    Hjorth, M. F. et al. Prevotella-to-Bacteroides ratio predicts body weight and fat loss success on 24-week diets varying in macronutrient composition and dietary fiber: results from a post-hoc analysis. Int. J. Obes. 43, 149–157 (2019).

  162. 162.

    Artinian, N. T. et al. Interventions to promote physical activity and dietary lifestyle changes for cardiovascular risk factor reduction in adults: a scientific statement from the American Heart Association. Circulation 122, 406–441 (2010).

  163. 163.

    Spring, B. et al. Better population health through behavior change in adults: a call to action. Circulation 128, 2169–2176 (2013).

  164. 164.

    Celis-Morales, C. et al. Effect of personalized nutrition on health-related behaviour change: evidence from the Food4Me European randomized controlled trial. Int. J. Epidemiol. 46, 578–588 (2017).

  165. 165.

    Capewell, S. & O’Flaherty, M. Rapid mortality falls after risk-factor changes in populations. Lancet 378, 752–753 (2011).

  166. 166.

    McGill, R. et al. Are interventions to promote healthy eating equally effective for all? Systematic review of socioeconomic inequalities in impact. BMC Public Health 15, 457 (2015).

  167. 167.

    Guzman-Castillo, M. et al. The contribution of primary prevention medication and dietary change in coronary mortality reduction in England between 2000 and 2007: a modelling study. BMJ Open 5, e006070 (2015).

  168. 168.

    Mozaffarian, D. in Rising Health Care Costs: Drivers, Challenges and Solutions 8–25 (NAIC & CIPR, 2018).

  169. 169.

    Huang, Y. et al. Adoption and design of emerging dietary policies to improve cardiometabolic health in the US. Curr. Atheroscler. Rep. 20, 25 (2018).

  170. 170.

    Swinburn, B. A. et al. The global syndemic of obesity, undernutrition, and climate change: the Lancet commission report. Lancet 393, 791–846 (2019).

  171. 171.

    Mozaffarian, D. et al. Population approaches to improve diet, physical activity, and smoking habits: a scientific statement from the American Heart Association. Circulation 126, 1514–1563 (2012).

  172. 172.

    Li, F. et al. Built environment and 1-year change in weight and waist circumference in middle-aged and older adults: Portland Neighborhood Environment and Health Study. Am. J. Epidemiol. 169, 401–408 (2009).

  173. 173.

    Powell, L. M. & Bao, Y. Food prices, access to food outlets and child weight. Econ. Hum. Biol. 7, 64–72 (2009).

  174. 174.

    Boone-Heinonen, J. et al. Fast food restaurants and food stores: longitudinal associations with diet in young to middle-aged adults: the CARDIA study. Arch. Intern. Med. 171, 1162–1170 (2011).

  175. 175.

    Block, J. P., Christakis, N. A., O’Malley, A. J. & Subramanian, S. V. Proximity to food establishments and body mass index in the Framingham Heart Study offspring cohort over 30 years. Am. J. Epidemiol. 174, 1108–1114 (2011).

  176. 176.

    Gibson, D. M. The neighborhood food environment and adult weight status: estimates from longitudinal data. Am. J. Public Health 101, 71–78 (2011).

  177. 177.

    Shier, V., An, R. & Sturm, R. Is there a robust relationship between neighbourhood food environment and childhood obesity in the USA? Public Health 126, 723–730 (2012).

  178. 178.

    Wang, R. & Shi, L. Access to food outlets and children’s nutritional intake in urban China: a difference-in-difference analysis. It. J. Pediatrics 38, 30 (2012).

  179. 179.

    Boone-Heinonen, J. et al. The neighborhood energy balance equation: does neighborhood food retail environment + physical activity environment = obesity? The CARDIA study. PLoS ONE 8, e85141 (2013).

  180. 180.

    Smith, D., Cummins, S., Clark, C. & Stansfeld, S. Does the local food environment around schools affect diet? Longitudinal associations in adolescents attending secondary schools in East London. BMC Public Health 13, 700 (2013).

  181. 181.

    Cummins, S., Flint, E. & Matthews, S. A. New neighborhood grocery store increased awareness of food access but did not alter dietary habits or obesity. Health Affairs (Project Hope) 33, 283–291 (2014).

  182. 182.

    Elbel, B. et al. Assessment of a government-subsidized supermarket in a high-need area on household food availability and children’s dietary intakes. Public Health Nutr. 18, 2881–2890 (2015).

  183. 183.

    Dubowitz, T. et al. Diet and perceptions change with supermarket introduction in a food desert, but not because of supermarket use. Health Affairs (Project Hope) 34, 1858–1868 (2015).

  184. 184.

    Ghosh-Dastidar, M. et al. Does opening a supermarket in a food desert change the food environment? Health Place 46, 249–256 (2017).

  185. 185.

    Haspel, T. Food deserts don’t cause obesity. But that doesn’t mean they don’t matter. Washington Post (August 2018).

  186. 186.

    Sustainable Development Goals: 17 Goals to Transform our World (UN, 2015).

  187. 187.

    WHO Framework Convention on Tobacco Control (WHO, 2019).

  188. 188.

    Silver, L. Sugary drink taxes – the new normal. World Cancer Research Fund International (June 2018).

  189. 189.

    Mozaffarian, D., Rogoff, K. S. & Ludwig, D. S. The real cost of food: can taxes and subsidies improve public health? JAMA 312, 889–890 (2014).

  190. 190.

    Pomeranz, J., Mozaffarian, D. & Micha, R. Mandating front-of-package food labels in the U. S. – What are the First Amendment obstacles? Food Policy 86, (2019).

  191. 191.

    Jacobs, A. In sweeping war on obesity, Chile slays Tony the tiger. New York Times (February 2018).

  192. 192.

    Food Directions LLC. Restrictions of Food Marketing and Advertisements Aimed at Children (2017).

  193. 193.

    Association of National Advertisers. Three New International Food Advertising Restrictions (2019).

  194. 194.

    USFDA. Trans Fat (2018).

  195. 195.

    Hyseni, L. et al. Systematic review of dietary salt reduction policies: Evidence for an effectiveness hierarchy? PLoS ONE 12, e0177535 (2017).

  196. 196.

    Flor, R. Focusing on “Protective Foods” to Reduce the Global Burden of Disease. Rockefeller Foundation (April 2019).

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D.M. acknowledges support from the National Health, Lung, and Blood Institute (grant no. R01 HL115189), National Institutes of Health. D.M. also acknowledges research funding from the National Institutes of Health and the Gates Foundation; personal fees from GOED, Nutrition Impact, Pollock Communications, Bunge, Indigo Agriculture, Amarin, Acasti Pharma, Cleveland Clinic Foundation, America’s Test Kitchen, and Danone; scientific advisory board, Brightseed, DayTwo, Elysium Health and Filtricine; and chapter royalties from UpToDate—all outside the submitted work.

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Correspondence to Dariush Mozaffarian.

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Tufts University holds patents US8889739 and US9987243 (unlicensed), listing D.M. as a co-inventor, for use of trans-palmitoleic acid to prevent and treat insulin resistance, type 2 diabetes and related conditions, as well as reduce metabolic risk factors.

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Mozaffarian, D. Dietary and policy priorities to reduce the global crises of obesity and diabetes. Nat Food 1, 38–50 (2020).

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