Putting the Balance Back in Diet
2015; Cell Press; Volume: 161; Issue: 1 Linguagem: Inglês
10.1016/j.cell.2015.02.033
ISSN1097-4172
AutoresStephen J. Simpson, David G. Le Couteur, David Raubenheimer,
Tópico(s)Nutritional Studies and Diet
ResumoThe notion of dietary balance is fundamental to health yet is not captured by focusing on the intake of energy or single nutrients. Advances in nutritional geometry have begun to unravel and integrate the interactive effects of multiple nutrients on health, lifespan, aging, and reproduction. The notion of dietary balance is fundamental to health yet is not captured by focusing on the intake of energy or single nutrients. Advances in nutritional geometry have begun to unravel and integrate the interactive effects of multiple nutrients on health, lifespan, aging, and reproduction. One of the most important and prominent public health messages is to eat a healthy, balanced diet. But what does that mean? Balanced with respect to what—and when during the life course? What are the consequences of failing to achieve a balanced diet? These are fundamental questions that remain less well answered than is necessary to devise effective public health policy to combat the pandemic of obesity and metabolic disease (Simpson and Raubenheimer, 2012Simpson S.J. Raubenheimer D. The nature of nutrition. A unifying framework form animal adaption to human obesity. Princeton University Press, Princeton2012Crossref Google Scholar). Here, we show that advances from nutritional ecology are providing new ways to address these problems. The classical approach to understanding diet balance has been painstakingly to derive individual estimates for required intakes of each of the dozens of macro- and micronutrients that are needed for health and wellbeing. Such “one variable at a time” (OVAT) approaches (Box et al., 1978Box G.E.P. Hunter W.G. Hunter J.S. Statistics for Experimenters. John Wiley & Sons, New York1978Google Scholar) have provided the foundations of nutrition science. The evidence-base has been built from a combination of animal studies in which single constituents have been manipulated in experimental diets, epidemiological analysis of the associations between intakes of single nutrients and health outcomes in human populations, and single-variable clinical trials. These data have in turn informed national dietary guidelines with associated recommended daily intakes (RDIs) for micro- and macronutrients. Clinical practice, food labeling policies, and public health strategies have followed. There is an abundant literature showing that fats, sugars, salt, vitamins, etc. contribute to health outcomes, but one consequence of taking a single-variable approach has been to promote adversarial debate between proponents of single-nutrient causes (or solutions) to diet-related health problems. This is nowhere better illustrated than in the long running debate over the roles of sugar and saturated fats in obesity and metabolic disease (Feinman, 2011Feinman R.D. Fad diets in the treatment of diabetes.Curr. Diab. Rep. 2011; 11: 128-135Crossref PubMed Scopus (18) Google Scholar, Willett, 2011Willett W.C. The great fat debate: total fat and health.J. Am. Diet. Assoc. 2011; 111: 660-662Abstract Full Text Full Text PDF PubMed Scopus (15) Google Scholar). As a result, public confusion reigns—even (perhaps especially) among the well-educated populace—fuelled by commercial interests in the food sectors and the fad diet industry (Simpson and Raubenheimer, 2014Simpson S.J. Raubenheimer D. Perspective: Tricks of the trade.Nature. 2014; 508: S66Crossref PubMed Scopus (29) Google Scholar). The fundamental problem with OVAT approaches is that they fail to capture the multidimensional essence of nutrition (Ruohonen and Kettunen, 2004Ruohonen K. Kettunen J. Effective experimental designs for optimizing fish feeds.Aquacult. Nutr. 2004; 10: 145-151Crossref Scopus (25) Google Scholar). It is axiomatic that diets are more than the sum of their components; they are combinations of foods, each comprising mixtures of nutrients and other constituents. Changing the concentration of one component in the diet can alter the character of the entire blend. In simple statistical terms, OVAT looks only at the main effects of single nutrients and does not account for the interactions between nutrients within diets—neither the non-independence of dietary constituents within mixtures nor the interactive effects of nutrients on health outcomes. We need an approach that explicitly takes account of the interactions among nutrients within foods and diets and is able to define and quantify the consequences of different diet compositions on multiple measures of health across the life course. In this essay we illustrate such an approach, known as the geometric framework, which originated from the field of nutritional ecology (Raubenheimer et al., 2009Raubenheimer D. Simpson S.J. Mayntz D. Nutrition, ecology and nutritional ecology: toward an integrated framework.Funct. Ecol. 2009; 23: 4-16Crossref Scopus (404) Google Scholar). Nutritional geometry integrates not only multiple diet components, but also scales across molecules, cells, organs, organisms, populations, and ecosystems (Simpson and Raubenheimer, 2012Simpson S.J. Raubenheimer D. The nature of nutrition. A unifying framework form animal adaption to human obesity. Princeton University Press, Princeton2012Crossref Google Scholar). Starting with the ideas of nutrient-specific appetites and regulatory priorities, we introduce the concept of nutritional response landscapes using model organisms including Drosophila and mouse, and then discuss the application of nutritional geometry in humans. A fundamental requirement for considering the multilayer interactive effects of nutrients is to establish the extent to which the intakes of different nutrients are specifically regulated by the animal. In other words, are there so-called “nutrient-specific appetites” distinct from intake control merely based on total dietary energy or volume? Nutritional geometry provides a series of simple yet powerful concepts and experimental designs for addressing this question. One example has been to explore whether an animal has the capacity to regulate its intake of two nutrients simultaneously when challenged with different pairwise combinations of nutritionally complementary foods varying in their ratio and/or concentrations of the two focal nutrients. If animals converge upon the same ratio and amounts of the nutrients eaten (“intake target”) across experimental food pairings, in each case ingesting the unique amount of each food required to do so on that particular pairing, it is then evident that the animal has separate regulatory systems controlling intake of the two nutrients. Similar types of experimental design have been used to show that organisms from acellular slime molds all the way to primates possess nutrient-specific appetite systems for macronutrients, such as proteins, carbohydrates, and fats, as well as for at least two micronutrients, sodium and calcium (Simpson and Raubenheimer, 2012Simpson S.J. Raubenheimer D. The nature of nutrition. A unifying framework form animal adaption to human obesity. Princeton University Press, Princeton2012Crossref Google Scholar). However, most micronutrients do not seem to be specifically regulated; rather, their intakes are maintained within healthy limits by a combination of correlation in foods with other regulated nutrients and non-specific mechanisms such as learned aversion to foods associated with development of a micronutrient deficiency, coupled with heightened attraction to novel foods (Simpson and Raubenheimer, 2012Simpson S.J. Raubenheimer D. The nature of nutrition. A unifying framework form animal adaption to human obesity. Princeton University Press, Princeton2012Crossref Google Scholar). Having demonstrated that specific appetites exist for certain nutrients, the question arises as to how these are prioritized when the animal is restricted to a diet composition that does not allow the intake target to be reached for all regulated nutrients simultaneously. Under such circumstances, the animal must balance eating too little of some nutrients against over-consuming others relative to the intake target. Understanding how animals prioritize different nutrients under these circumstances is of considerable importance for appreciating or predicting the health impacts of shifts in diet (Lihoreau et al., 2014Lihoreau M. Buhl J. Charleston M.A. Sword G.A. Raubenheimer D. Simpson S.J. Modelling nutrition across organizational levels: from individuals to superorganisms.J. Insect Physiol. 2014; 69: 2-11Crossref PubMed Scopus (34) Google Scholar, Raubenheimer and Simpson, 1997Raubenheimer D. Simpson S.J. Integrative models of nutrient balancing: application to insects and vertebrates.Nutr. Res. Rev. 1997; 10: 151-179Crossref PubMed Scopus (335) Google Scholar). As a premise, we need first to be able to map nutritional response landscapes. Drosophila provides a simple system for illustrating how to map the consequences of nutrition in multiple, potentially interacting nutrient and response dimensions. Lee et al., 2008Lee K.P. Simpson S.J. Clissold F.J. Brooks R. Ballard J.W. Taylor P.W. Soran N. Raubenheimer D. Lifespan and reproduction in Drosophila: New insights from nutritional geometry.Proc. Natl. Acad. Sci. USA. 2008; 105: 2498-2503Crossref PubMed Scopus (707) Google Scholar used nutritional geometry to disentangle the effects of calories from those of macronutrients in the context of increased lifespan upon caloric restriction (Curtis and de Cabo, 2013Curtis J. de Cabo R. Utilizing calorie restriction to evaluate the role of sirtuins in healthspan and lifespan of mice.Methods Mol. Biol. 2013; 1077: 303-311Crossref PubMed Scopus (8) Google Scholar, Everitt et al., 2010Everitt A.V. Rattan S.I. Le Couteur D.G. de Cabo R. Calorie Restriction, Aging and Longevity. Springer Press, New York2010Crossref Scopus (15) Google Scholar, Mercken et al., 2012Mercken E.M. Carboneau B.A. Krzysik-Walker S.M. de Cabo R. Of mice and men: the benefits of caloric restriction, exercise, and mimetics.Ageing Res. Rev. 2012; 11: 390-398Crossref PubMed Scopus (208) Google Scholar, Speakman and Mitchell, 2011Speakman J.R. Mitchell S.E. Caloric restriction.Mol. Aspects Med. 2011; 32: 159-221Crossref PubMed Scopus (548) Google Scholar) and also explored the basis for the frequently reported trade-off between aging and reproduction (Tatar, 2011Tatar M. The plate half-full: status of research on the mechanisms of dietary restriction in Drosophila melanogaster.Exp. Gerontol. 2011; 46: 363-368Crossref PubMed Scopus (64) Google Scholar). Flies offer several advantages for this type of analysis. First, their dietary calories come principally from two macronutrient sources—protein and carbohydrate (lipids, although essential, provide only a small caloric contribution)—thereby defining a two-dimensional nutrient space. Second, flies are small and short-lived, making large numbers of dietary treatments in a longevity study feasible. In this study, flies were confined throughout their lifetime with ad libitum access to one of 28 diets, comprising seven protein to carbohydrate ratios (P:C), each at one of four total concentrations. Response landscapes for longevity and reproductive output were mapped onto an array of individual P:C intakes recorded for more than 1,000 flies, thereby allowing the consequences of nutrient and energy intakes to be visualized and analyzed. The results were striking (Figure 1A). Low-calorie intake per se was not associated with prolonged lifespan in ad libitum-fed flies; rather, lifespan was a function of the ratio of protein to carbohydrate ingested, declining as P:C increased. Second, lifespan and reproduction had differently shaped response landscapes with peaks in different places on the protein-carbohydrate intake plane—the diet composition that sustained longest life led to a lower intake of protein than needed to support maximal reproductive success. When allowed to compose their own diet by selecting among complementary food pairings, flies chose to mix a diet maximizing reproductive output rather than lifespan. Subsequent studies have shown that the trade-off between lifespan and reproduction is not obligatory or causal, but simply reflects differing nutritional optima for the two traits (Grandison et al., 2009Grandison R.C. Piper M.D. Partridge L. Amino-acid imbalance explains extension of lifespan by dietary restriction in Drosophila.Nature. 2009; 462: 1061-1064Crossref PubMed Scopus (543) Google Scholar, Tatar, 2011Tatar M. The plate half-full: status of research on the mechanisms of dietary restriction in Drosophila melanogaster.Exp. Gerontol. 2011; 46: 363-368Crossref PubMed Scopus (64) Google Scholar). A similar experiment has been conducted in mice (Solon-Biet et al., 2014Solon-Biet S.M. McMahon A.C. Ballard J.W.O. Ruohonen K. Wu L.E. Cogger V.C. Warren A. Huang X. Pichaud N. Melvin R.G. et al.The ratio of macronutrients, not caloric intake, dictates cardiometabolic health, aging, and longevity in ad libitum-fed mice.Cell Metab. 2014; 19: 418-430Abstract Full Text Full Text PDF PubMed Scopus (590) Google Scholar). Here, the aim was to extend the use of nutritional geometry to quantify, inter alia, the impacts of macronutrients on food intake, body composition, lifespan, reproductive potential, cardio-metabolic health, immune status, mitochondrial function, gut microbiota, and nutrient signaling pathways. Nine hundred mice were confined from weaning with ad libitum access to one of 30 diets. These comprised ten protein to carbohydrate to fat ratios (P:C:F), which systematically sampled the 3D macronutrient mixture space, each ratio provided at one of three total energy densities by dilution with cellulose. Of the 30 diets, five that were very low (5%) in protein, high in fat, and low in energy density failed to sustain growth in young mice and were discontinued. Food intake was recorded throughout the experiment. Mice, like other animals, possess separate macronutrient appetites (Sørensen et al., 2008Sørensen A. Mayntz D. Raubenheimer D. Simpson S.J. Protein-leverage in mice: the geometry of macronutrient balancing and consequences for fat deposition.Obesity (Silver Spring). 2008; 16: 566-571Crossref PubMed Scopus (146) Google Scholar), and when these were forced to compete by restricting animals to a single diet composition, total food intake was driven principally by protein, increasing as percent protein in the diet fell (consistent with compensatory feeding to stabilize protein intake). Compensatory feeding for carbohydrate was also apparent, with intake increasing as percent carbohydrate fell in the diet but to a somewhat lesser degree than for protein. Unlike protein and carbohydrate, however, the concentration of dietary fat had little influence over total food intake. Consequently, total food and energy intakes were maximal on diets combining low percent protein with high percent fat. Energy intakes in turn corresponded to the body composition of mice, with adiposity increasing as a function of energy intake. Even though mice on low P:C diets were moderately adipose (although not to the extent of low-protein, high-fat fed mice), they lived longest (Figure 1B). Indeed, longevity mirrored the pattern seen in flies, being greatest on low P:C diets. Markers of metabolic health (insulin, glucose tolerance) and immune function at 15 months of age were consistent with the longevity data, being best on low P:C diets and worst on high-protein and high-fat diets (Le Couteur et al., 2014Le Couteur D.G. Tay S.S. Solon-Biet S. Bertolino P. McMahon A.C. Cogger V.C. Colakoglu F. Warren A. Holmes A.J. Pichaud N. et al.The Influence of Macronutrients on Splanchnic and Hepatic Lymphocytes in Aging Mice.J. Gerontol. A Biol. Sci. Med. Sci. 2014; Google Scholar, Solon-Biet et al., 2014Solon-Biet S.M. McMahon A.C. Ballard J.W.O. Ruohonen K. Wu L.E. Cogger V.C. Warren A. Huang X. Pichaud N. Melvin R.G. et al.The ratio of macronutrients, not caloric intake, dictates cardiometabolic health, aging, and longevity in ad libitum-fed mice.Cell Metab. 2014; 19: 418-430Abstract Full Text Full Text PDF PubMed Scopus (590) Google Scholar; Figure 2). By contrast, measures of reproductive potential in both males and females were highest on a higher-protein diet, consistent with results from flies. There was no evidence for prolongation of lifespan on ad libitum diets that restricted calorie intake by reducing the energy density of the diet. The standard regime for restricting calorie intake that is well known to extend lifespan involves providing mice with a daily aliquot of food, which is soon eaten, leaving the animal deprived for the rest of the day (Curtis and de Cabo, 2013Curtis J. de Cabo R. Utilizing calorie restriction to evaluate the role of sirtuins in healthspan and lifespan of mice.Methods Mol. Biol. 2013; 1077: 303-311Crossref PubMed Scopus (8) Google Scholar, Everitt et al., 2010Everitt A.V. Rattan S.I. Le Couteur D.G. de Cabo R. Calorie Restriction, Aging and Longevity. Springer Press, New York2010Crossref Scopus (15) Google Scholar). By inference, then, the results of Solon-Biet et al., 2014Solon-Biet S.M. McMahon A.C. Ballard J.W.O. Ruohonen K. Wu L.E. Cogger V.C. Warren A. Huang X. Pichaud N. Melvin R.G. et al.The ratio of macronutrients, not caloric intake, dictates cardiometabolic health, aging, and longevity in ad libitum-fed mice.Cell Metab. 2014; 19: 418-430Abstract Full Text Full Text PDF PubMed Scopus (590) Google Scholar imply that extension of lifespan with standard caloric restriction protocols may not entirely be secondary to reduction in calories; rather, other factors may contribute such as periods of fasting (Mattson et al., 2014Mattson M.P. Allison D.B. Fontana L. Harvie M. Longo V.D. Malaisse W.J. Mosley M. Notterpek L. Ravussin E. Scheer F.A.J.L. et al.Meal frequency and timing in health and disease.Proc. Natl. Acad. Sci. USA. 2014; 111: 16647-16653Crossref PubMed Scopus (330) Google Scholar), and reduction in protein intake that ensues once the mouse has eaten its daily food allocation. A major conclusion from the geometric experiments on flies and mice is that the balance of macronutrients in the diet has a profound impact on food and energy intake, metabolic health, lifespan, immune function, and reproduction. The diet composition that best supports longevity is not the same as that which sustains maximal reproductive output or leanness. The question arises as to whether these conclusions apply to humans. The evidence suggests that they do. For mice on diets differing in the ratio and concentrations of protein, carbohydrate, and fat, food intake was driven most strongly by the concentration of protein in the diet, but with a strong competing feedback emanating from signals associated with the specific appetite for carbohydrate. The data from population survey analyses (Austin et al., 2011Austin G.L. Ogden L.G. Hill J.O. Trends in carbohydrate, fat, and protein intakes and association with energy intake in normal-weight, overweight, and obese individuals: 1971-2006.Am. J. Clin. Nutr. 2011; 93: 836-843Crossref PubMed Scopus (237) Google Scholar, Austin and Krueger, 2013Austin G.L. Krueger P.M. Increasing the percentage of energy from dietary sugar, fats, and alcohol in adults is associated with increased energy intake but has minimal association with biomarkers of cardiovascular risk.J. Nutr. 2013; 143: 1651-1658Crossref PubMed Scopus (9) Google Scholar, Martinez-Cordero et al., 2012Martinez-Cordero C. Kuzawa C.W. Sloboda D.M. Stewart J. Simpson S.J. Raubenheimer D. Testing the Protein Leverage Hypothesis in a free-living human population.Appetite. 2012; 59: 312-315Crossref PubMed Scopus (29) Google Scholar), compendia of controlled trials (Gosby et al., 2014Gosby A.K. Conigrave A.D. Raubenheimer D. Simpson S.J. Protein leverage and energy intake.Obes. Rev. 2014; 15: 183-191Crossref PubMed Scopus (116) Google Scholar), and detailed clinical studies involving foods formulated to disguise their macronutrient composition (Gosby et al., 2011Gosby A.K. Conigrave A.D. Lau N.S. Iglesias M.A. Hall R.M. Jebb S.A. Brand-Miller J. Caterson I.D. Raubenheimer D. Simpson S.J. Testing protein leverage in lean humans: a randomised controlled experimental study.PLoS ONE. 2011; 6: e25929Crossref PubMed Scopus (120) Google Scholar) indicate that prioritization of protein intake may be even stronger in humans. Humans compensate for reduction in the available proportion of dietary energy contributed by protein by increasing food intake, and in so doing over-ingest fats and carbohydrates. Since the percentage of energy from protein in the diet is always smaller than that from fats and carbohydrates combined, compensatory adjustments in intake that redress relatively small deficits in protein “gear up” to relatively large excess of fats and carbohydrates, and thus energy intake overall—what we have termed “protein leverage” (Simpson and Raubenheimer, 2005Simpson S.J. Raubenheimer D. Obesity: the protein leverage hypothesis.Obes. Rev. 2005; 6: 133-142Crossref PubMed Scopus (365) Google Scholar). Studies have shown that total energy intake is, indeed, a negative function of percent protein in the diet across the range seen in all human populations measured to date with food sufficiency, namely 10%–25% protein of total energy. Above ca. 20%–25% protein the reduction in intake with rising percent protein becomes attenuated (Gosby et al., 2014Gosby A.K. Conigrave A.D. Raubenheimer D. Simpson S.J. Protein leverage and energy intake.Obes. Rev. 2014; 15: 183-191Crossref PubMed Scopus (116) Google Scholar), presumably because of increasingly strong opposing feedbacks arising from deficiency of other nutrients, notably carbohydrate, driving increased intake. At the other extreme, clinical trials using 5% protein (Martens et al., 2013Martens E.A. Lemmens S.G. Westerterp-Plantenga M.S. Protein leverage affects energy intake of high-protein diets in humans.Am. J. Clin. Nutr. 2013; 97: 86-93Crossref PubMed Scopus (60) Google Scholar, Martens et al., 2014aMartens E.A. Tan S.Y. Dunlop M.V. Mattes R.D. Westerterp-Plantenga M.S. Protein leverage effects of beef protein on energy intake in humans.Am. J. Clin. Nutr. 2014; 99: 1397-1406Crossref PubMed Scopus (34) Google Scholar, Martens et al., 2014bMartens E.A. Tan S.-Y. 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Testing protein leverage in lean humans: a randomised controlled experimental study.PLoS ONE. 2011; 6: e25929Crossref PubMed Scopus (120) Google Scholar showed that the 12% increase in ad libitum energy intake among subjects confined to a 10% protein diet relative to 15% or 25% protein diets was due to increased consumption of savory-flavored foods between meals. The seeking of savory cues is indicative of protein hunger, and is reflected in increased activity in brain regions associated with reward, such as the inferior orbitofrontal cortex and striatum (Griffioen-Roose et al., 2014Griffioen-Roose S. Smeets P.A. van den Heuvel E. Boesveldt S. Finlayson G. de Graaf C. Human protein status modulates brain reward responses to food cues.Am. J. Clin. Nutr. 2014; 100: 113-122Crossref PubMed Scopus (53) Google Scholar). These results indicate that protein status influences gustatory pathways in a way that affects protein intake in humans. In insects, feedbacks onto gustatory responses occur at the periphery, through direct modulation of taste receptors, as well as via learning of nutrient-specific cues (Simpson and Raubenheimer, 2012Simpson S.J. Raubenheimer D. The nature of nutrition. A unifying framework form animal adaption to human obesity. Princeton University Press, Princeton2012Crossref Google Scholar). The mediating nutrient signaling systems controlling protein appetite are thought to involve both circulating free amino acids and lean hormonal signals such as FGF 21 (Laeger et al., 2014Laeger T. Henagan T.M. Albarado D.C. Redman L.M. Bray G.A. Noland R.C. Münzberg H. Hutson S.M. Gettys T.W. Schwartz M.W. Morrison C.D. FGF21 is an endocrine signal of protein restriction.J. Clin. Invest. 2014; 124: 3913-3922Crossref PubMed Scopus (366) Google Scholar). Controlled, prospective experiments testing the effects of multiple diets, equivalent to those performed in animals, are not feasible in humans. Nevertheless, there is growing evidence from observational studies and quasi-interventional trials indicating that health and lifespan are influenced by the balance of macronutrients and can be best interpreted using nutritional geometry. In a systematic review of human dietary studies (Pedersen et al., 2013Pedersen A.N. Kondrup J. Børsheim E. Health effects of protein intake in healthy adults: a systematic literature review.Food Nutr Res. 2013; 57https://doi.org/10.3402/fnr.v57i0.21245Crossref PubMed Google Scholar), it was concluded that long-term, high-protein, low-carbohydrate diets and increased mortality are associated. In addition, long-term, high-protein, high-fat and low-carbohydrate diets increased the risk of type 2 diabetes mellitus. Consistent with this notion, Fung and colleagues (Fung et al., 2010Fung T.T. van Dam R.M. Hankinson S.E. Stampfer M. Willett W.C. Hu F.B. Low-carbohydrate diets and all-cause and cause-specific mortality: two cohort studies.Ann. Intern. 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Ballard J.W. Taylor P.W. Soran N. Raubenheimer D. Lifespan and reproduction in Drosophila: New insights from nutritional geometry.Proc. Natl. Acad. Sci. USA. 2008; 105: 2498-2503Crossref PubMed Scopus (707) Google Scholar, Solon-Biet et al., 2014Solon-Biet S.M. McMahon A.C. Ballard J.W.O. Ruohonen K. Wu L.E. Cogger V.C. Warren A. Huang X. Pichaud N. Melvin R.G. et al.The ratio of macronutrients, not caloric intake, dictates cardiometabolic health, aging, and longevity in ad libitum-fed mice.Cell Metab. 2014; 19: 418-430Abstract Full Text Full Text PDF PubMed Scopus (590) Google Scholar). These conclusions are indirectly supported by associations between increased mortality and low-carbohydrate diets in humans (Noto et al., 2013Noto H. Goto A. Tsujimoto T. Noda M. Low-carbohydrate diets and all-cause mortality: a systematic review and meta-analysis of observational studies.PLoS ONE. 2013; 8: e55030Crossref PubMed Scopus (143) Google Scholar) and a recent study showing increased mortality and cancer on high-protein diets (Levine et al., 2014Levine M.E. Suarez J.A. Brandhorst S. Balasubramanian P. Cheng C.W. Madia F. Fontana L. Mirisola M.G. Guevara-Aguirre J. Wan J. et al.Low protein intake is associated with a major reduction in IGF-1, cancer, and overall mortality in the 65 and younger but not older population.Cell Metab. 2014; 19: 407-417Abstract Full Text Full Text PDF PubMed Scopus (551) Google Scholar). In demonstrating that both high and low P:C diets have benefits and risks, these data clearly illustrate the importance of dietary balance. But a conundrum remains (Figure 2). Whereas a low P:C diet appears beneficial for longevity and late life health, protein leverage on such a diet tends to drive overconsumption of total energy and risk of obesity, thereby mitigating the health benefits of low-protein intake. Another consideration is that overweight in humans might be associated with poor outcomes if caused by low-protein, high-fat diets, but better outcomes when low-protein, high-carbohydrate diets apply. Managing these counterposing effects might include reducing the intake of proteins with high concentrations of sulfur- and branched chain amino acids linked to pro-aging and disease pathways (Hine et al., 2015Hine C. Harputlugil E. Zhang Y. Ruckenstuhl C. Lee B.C. Brace L. Longchamp A. Treviño-Villarreal J.H. Mejia P. Ozaki C.K. et al.Endogenous hydrogen sulfide production is essential for dietary restriction benefits.Cell. 2015; 160: 132-144Abstract Full Text Full Text PDF PubMed Scopus (353) Google Scholar, Solon-Biet et al., 2014Solon-Biet S.M. McMahon A.C. Ballard J.W.O. Ruohonen K. Wu L.E. Cogger V.C. Warren A. Huang X. Pichaud N. Melvin R.G. et al.The ratio of macronutrients, not caloric intake, dictates cardiometabolic health, aging, and longevity in ad libitum-fed mice.Cell Metab. 2014; 19: 418-430Abstract Full Text Full Text PDF PubMed Scopus (590) Google Scholar), decreasing dietary P:C by replacing dietary fats with healthy carbohydrates, periods of intermittent fasting, and drug development targeting nutrient-sensing pathways (Le Couteur et al., 2012Le Couteur D.G. McLachlan A.J. Quinn R.J. Simpson S.J. de Cabo R. Aging biology and novel targets for drug discovery.J. Gerontol. A Biol. Sci. Med. Sci. 2012; 67: 168-174Crossref PubMed Scopus (45) Google Scholar, Baur et al., 2012Baur J.A. Ungvari Z. Minor R.K. Le Couteur D.G. de Cabo R. Are sirtuins viable targets for improving healthspan and lifespan?.Nat. Rev. Drug Discov. 2012; 11: 443-461Crossref PubMed Scopus (322) Google Scholar, Mattson et al., 2014Mattson M.P. Allison D.B. Fontana L. Harvie M. Longo V.D. Malaisse W.J. Mosley M. Notterpek L. Ravussin E. Scheer F.A.J.L. et al.Meal frequency and timing in health and disease.Proc. Natl. Acad. Sci. USA. 2014; 111: 16647-16653Crossref PubMed Scopus (330) Google Scholar). Age itself is a major determinant of what constitutes an optimal diet. Hence, whereas low P:C diets benefit late life health and longevity (Levine et al., 2014Levine M.E. Suarez J.A. Brandhorst S. Balasubramanian P. Cheng C.W. Madia F. Fontana L. Mirisola M.G. Guevara-Aguirre J. Wan J. et al.Low protein intake is associated with a major reduction in IGF-1, cancer, and overall mortality in the 65 and younger but not older population.Cell Metab. 2014; 19: 407-417Abstract Full Text Full Text PDF PubMed Scopus (551) Google Scholar), they are not optimal for somatic growth and reproduction earlier in life, which require higher protein intakes. In addition to age, a network of interacting factors need to be considered to determine an optimal diet, including genotype, epigenotype, sex, health, and immune status, commensal ecology, societal context, physical environment, and the level of activity. Mapping response landscapes as a function of multiple nutrient dimensions offers a step-change in understanding the nutritional phenotype of an animal, compared to energy or single-nutrient-based single-dimensional approaches. The same potential applies to deciphering cellular and molecular pathways. The concept that appetite and metabolism respond to specific nutrients and nutrient ratios is transformative for dissecting cellular mechanisms for these processes, evidenced by the recent discovery of FGF 21 as the first known candidate endocrine signal in the control of protein intake (e.g., Laeger et al., 2014Laeger T. Henagan T.M. Albarado D.C. Redman L.M. Bray G.A. Noland R.C. Münzberg H. Hutson S.M. Gettys T.W. Schwartz M.W. Morrison C.D. FGF21 is an endocrine signal of protein restriction.J. Clin. Invest. 2014; 124: 3913-3922Crossref PubMed Scopus (366) Google Scholar). A geometric analysis can also better aid interpretation of the effects of genetic or pharmacological manipulations (Piper et al., 2011Piper M.D.W. Partridge L. Raubenheimer D. Simpson S.J. Dietary restriction and aging: a unifying perspective.Cell Metab. 2011; 14: 154-160Abstract Full Text Full Text PDF PubMed Scopus (131) Google Scholar). As an example of the use of nutritional geometry, a number of interacting nutrient-sensing pathways are considered to mediate the link between diet and aging, including mTOR, AMPK, insulin/IGF1/GH, and SIRT1. The effects of dietary P:C on lifespan in mice and flies led to the prediction that these pathways, either individually or in combination, are responsive to P:C ratio rather than to energy or single nutrients (Simpson and Raubenheimer, 2009Simpson S.J. Raubenheimer D. Macronutrient balance and lifespan.Aging (Albany, N.Y. Online). 2009; 1: 875-880PubMed Google Scholar). This hypothesis was supported by response surface analyses indicating that circulating insulin levels were strongly influenced by dietary P:C, and that hepatic mTOR activation was a positive function of the ratio of circulating branched chain amino acids and glucose (Solon-Biet et al., 2014Solon-Biet S.M. McMahon A.C. Ballard J.W.O. Ruohonen K. Wu L.E. Cogger V.C. Warren A. Huang X. Pichaud N. Melvin R.G. et al.The ratio of macronutrients, not caloric intake, dictates cardiometabolic health, aging, and longevity in ad libitum-fed mice.Cell Metab. 2014; 19: 418-430Abstract Full Text Full Text PDF PubMed Scopus (590) Google Scholar). Here we have focused on the relationships among diet composition, intake, and health, but nutritional geometry has also been used to investigate the broader causes of variance in diet composition of humans and other animals, including developmental, economic, evolutionary, and ecological (Raubenheimer et al., 2015Raubenheimer D. Machovsky-Capuska G.E. Gosby A.K. Simpson S. Nutritional ecology of obesity: from humans to companion animals.Br. J. Nutr. 2015; 113: S26-S39Crossref PubMed Scopus (62) Google Scholar). This intake-focused approach is not an alternative to theories of human nutrition that center on variation in biological responses to ingested nutrients, for example the propensity to store fat (Wells, 2006Wells J.C.K. The evolution of human fatness and susceptibility to obesity: an ethological approach.Biol. Rev. Camb. Philos. Soc. 2006; 81: 183-205Crossref PubMed Scopus (168) Google Scholar). Rather, as stressed by Speakman, 2014Speakman J.R. If body fatness is under physiological regulation, then how come we have an obesity epidemic?.Physiology (Bethesda). 2014; 29: 88-98Crossref PubMed Scopus (39) Google Scholar, nutrient intake and its consequences are best modeled as part of the same system, enabling the understanding, prediction, and management of organism- and population-level responses to different environments (Lihoreau et al., 2014Lihoreau M. Buhl J. Charleston M.A. Sword G.A. Raubenheimer D. Simpson S.J. Modelling nutrition across organizational levels: from individuals to superorganisms.J. Insect Physiol. 2014; 69: 2-11Crossref PubMed Scopus (34) Google Scholar). We stress, further, that nutrient combinations entered into a geometric model should be considered on a case-by-case basis. To date many questions have been addressed by modeling interactions among the macronutrients (Simpson and Raubenheimer, 2012Simpson S.J. Raubenheimer D. The nature of nutrition. A unifying framework form animal adaption to human obesity. Princeton University Press, Princeton2012Crossref Google Scholar), but in other cases mineral micronutrients and vitamins (e.g., Blumfield et al., 2012Blumfield M. Hure A. MacDonald-Wicks L. Smith R. Simpson S. Raubenheimer D. Collins C. The association between the macronutrient content of maternal diet and the adequacy of micronutrients during pregnancy in the Women and Their Children’s Health (WATCH) study.Nutrients. 2012; 4: 1958-1976Crossref PubMed Scopus (24) Google Scholar) or specific amino acids (Solon-Biet et al., 2014Solon-Biet S.M. McMahon A.C. Ballard J.W.O. Ruohonen K. Wu L.E. Cogger V.C. Warren A. Huang X. Pichaud N. Melvin R.G. et al.The ratio of macronutrients, not caloric intake, dictates cardiometabolic health, aging, and longevity in ad libitum-fed mice.Cell Metab. 2014; 19: 418-430Abstract Full Text Full Text PDF PubMed Scopus (590) Google Scholar) have been integrated into the model. The quality of macronutrients (types of fats, carbohydrates, and proteins) is another important aspect of diet that is amenable to geometric analysis, yet remains uncharted. It is only through acknowledging the complexity of nutrition and systematically charting its implications from the food environment to dietary choices and health consequences that we can hope to tame the epidemic of obesity-related diseases that has arisen over recent decades. We acknowledge funding support from the National Health and Medical Research Council, Australian Research Council, Gravida (The National Research Centre for Growth and Development, New Zealand), and the Ageing and Alzheimers Institute for supporting the fruit fly and mice studies discussed in this essay. We also thank our colleagues who contributed to these studies. Since the submission of this manuscript, an additional paper has shown that reproductive function is best supported in male and female mice on a higher-protein diet. Solon-Biet, S.M., Walters, K.A., Simanainen, U., McMahon, A.C., Ruohonen, K., Ballard, J.W.O., Raubenheimer, D., Handelsman, D.J., Le Couteur, D.G., and Simpson, S.J. (2015). Macronutrient balance, reproductive function and lifespan in aging mice. Proceedings of the National Academy of Science, USA 112, 3481–3486.
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