The geometric framework: An approach for studying the impact of nutrition on healthy aging

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Editors-in-Chief Jan Tornell – AstraZeneca, Sweden Andrew McCulloch – University of California, SanDiego, USA

Models for Aging Research

The geometric framework: An approach for studying the impact of nutrition on healthy aging Samantha M. Solon-Biet1, Devin Wahl1, David Raubenheimer1, Victoria C. Cogger1,2, David G. Le Couteur1,2, Stephen J. Simpson1,* 1

The Charles Perkins Centre, The University of Sydney, Sydney, Australia Centre for Education and Research on Ageing, Ageing and Alzheimers Institute and ANZAC Research Institute, Concord RG Hospital, Sydney, Australia 2

The most robust interventions that impact on the biological processes of aging and age-related diseases are nutritional (caloric restriction, protein restriction,

Section editor: Dr. Hildegard Mack – Institute for Biomedical Aging Research, University of Innsbruck, Austria.

intermittent fasting) or those pharmacological and genetic interventions that act on nutrient sensing pathways. The best established nutritional intervention is caloric restriction, but this is not feasible in most

Aging and nutrition

humans; therefore, other nutritional interventions that

Traditionally, aging has been considered to be an unmodifiable and inevitable process that inexorably increases the risk of disease and death. Modern medicine has focused on treating individual diseases as they accumulate in older people, albeit with diminishing returns while increasing the risks associated with polypharmacy and over-medicalization. The problems of old age have been described as coming as a ‘package’ where single-disease models are no longer effective [1] and when the final years of life have a trajectory that includes syndromes that are as yet resistant to pharmacological interventions such as multimorbidity, sarcopenia, dementia and frailty. More recently, it has been convincingly demonstrated that aging biology is malleable and can be influenced by many nutritional, genetic and pharmacological interventions and in many different animal models. This has raised the prospect of directly targeting aging in humans, with the aim of delaying the onset of a whole suite of age-related conditions with a single intervention. Such an approach

influence aging but do not involve protracted periods of fasting have been studied. The Geometric Framework provides a powerful research tool for disentangling the effects of various nutrients and calorie intake, in both restricted and ad libitum-fed diets, on phenotypic outcomes such as aging and lifespan. This approach can also be applied to understanding the complex network of nutrient sensing pathways, potentially identifying new targets for the development of drugs that influence healthy aging.

*Corresponding author: S.J. Simpson ([email protected])

1740-6757/$ © 2019 Elsevier Ltd. All rights reserved. https://doi.org/10.1016/j.ddmod.2019.03.002

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would increase the healthspan (a poorly defined term describing the period of life without significant disease or disability) and productivity of older people, while reducing the costs and harms associated with the current medical approach of treating multiple diseases in an individual patient with multiple treatments. There are several methods for developing therapies that target aging. First, we can attempt to understand the basic biology of aging in order to rationally design drugs based on knowledge of the structure and function of receptors and pathways that influence aging. The cellular and biological processes of aging have been categorized by the nine ‘Hallmarks of Aging’ (genomic instability, telomere attrition, epigenetic alterations, loss of proteostasis, deregulated nutrient sensing, mitochondrial dysfunction, cellular senescence, stem cell exhaustion, and altered intercellular communication) [2] and the seven ‘Pillars of Aging’ (adaptation to stress, epigenetics, inflammation, macromolecular damage, metabolism, proteostasis, stem cells and regeneration) [3]. Each of these processes represents a potential therapeutic target to delay aging, such as senolytic drugs and stem cell transplantation. Second, high throughput drug screening can be undertaken against novel targets identified through improved understanding of the mechanisms of aging (for example, screening for activity against Sirt1 led to the identification of numerous agents including resveratrol that delay aging in some animal models), or even in vitro models that reflect aging (e.g. senescent cells, C. Elegans). Third, we can harness interventions that are already well established to influence aging, primarily nutritional interventions such as caloric restriction. Caloric restriction has a robust effect on aging in many animal models, and many of the nutrient sensing pathways mediating the beneficial effects of caloric restriction have been identified [4]. It has been established for over 90 years that a reduction in food intake delays aging and increases lifespan [5,6]. Caloric restriction is undertaken by reducing food provided to animals by between 10–50% compared to ad libitum fed animals. Alternatively, caloric restriction can be undertaken by dilution of food to reduce energy intake. This approach has been utilized mostly in invertebrate models, but also at least one study in mice. Caloric restriction reduces growth and impairs reproductive output, and although the effects on lifespan vary according to species, strain and sex, there is nearly always improvements in healthspan and risk factors for disease when these have been reported (including studies in humans and non-human primates) [6–9]. The assumption that reduction in calorie intake underlies the beneficial effects of caloric restriction on aging has been challenged by studies suggesting that lifespan can be extended by restriction of individual nutrients (proteins, certain amino acids) [10,11], or by periods of fasting (intermittent fasting, every-other-day fasting). Methionine restriction, for 62

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example, was found to increase lifespan in male rats [12]. The Geometric Framework (discussed below) provides insight into how nutrients influence outcomes such as lifespan and aging, and how nutrients interact in their effects on outcomes. Recently the Geometric Framework has been applied to studies of nutrition and aging as well as underlying mechanisms [13] which has resolved some of the questions about the role of macronutrients in ad libitum-fed diets in aging and age-related health.

Caloric restriction The seminal study of caloric restriction and aging was undertaken in a large cohort of rats in 1935 by McCay et al. [14]. Subsequent studies have confirmed the effects of caloric restriction on delaying age-related diseases and extending lifespan in a wide range of other species such as single-cell budding yeast, nematode worms, fruit flies, mice, rats, and non-human primates [15–17]. The mechanisms mediating the effects of caloric restriction on aging have been elucidated. Nutrient excess stimulates a cascade of anabolic responses, supporting growth, reproduction and storage of excess nutrients as fat and glycogen. Nutrient limitation activates catabolic responses that act to conserve energy and utilize energy stores. These responses are mediated by evolutionarily conserved nutrient-sensing pathways that act on a network of interconnected downstream targets [18] such as stress resistance, autophagy, mitochondrial biogenesis, immunity, inflammation, and oxidative stress [19–21]. Nutrient excess stimulates the activity of the mechanistic Target of Rapamycin (mTOR) [22] and the Insulin/Insulin-like Growth Factor 1 (Insulin/IGF-1) [23] pathways, responses linked with decreased lifespan. Conversely, nutrient limitation increases the activity of SIRT1 and 50 AMP-activated protein kinase (AMPK) pathways, implicated in the lifeextending effects of caloric restriction [19].

Invertebrate models of caloric restriction In yeast, dilution of growth media extends chronological and replicative lifespan [24,25] which has been attributed to activation of the yeast sirtuin, Sir2. Sirtuins are a family of histone deacetylases that require NAD+ as a cosubstrate. Caloric restriction increases cellular NAD+ leading to increased Sir2 activity with downstream effects on mitochondria, transcription and autophagy [25–27]. Caloric restriction in worms (C. Elegans) is achieved by manipulating bacterial density of food and increases lifespan by up to 60% [28]. Mutations that decrease the activity of Insulin/IGF-1 pathway influence lifespan and the effects of caloric restriction [26] indicating its key role in nutrient sensing and aging. Interestingly, studies in C. Elegans have shown that there is not one universal genetic pathway responsible for the lifespan extension effects of CR. Furthermore, the caloric restriction lifespan effects seen in C. Elegans may partly depend on the

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nutrient restriction regimen (i.e., bacterial, peptone, or liquid dilution) [29]. Studies in flies have (D. Melanogaster) confirm these findings by showing that lifespan is increased by dilution of dietary yeast [30] and downregulation of the insulin/ IGF-1 pathway [31]. Additional studies in invertebrate models have concluded that there are many genetic pathways that may be responsible for the lifespan extending effects of caloric restriction, and those pathways may be species dependent [32].

Mammalian models of caloric restriction As in invertebrates, the canonical pathways that mediate the effects of nutrition on aging in mammals include the AMPK, SIRT1, mTOR and insulin/IGF-1 pathways [33]. These pathways have been targeted pharmacologically in an attempt to delay aging without the need for dietary restriction, i.e. in ad libitum fed animals. Rapamycin, an mTOR antagonist increased maximal lifespan in mice even when commenced in middle age [34]. Activation of the SIRT1 pathway with resveratrol and other sirtuin-activating-compounds (STACs) increased lifespan in mice on a high fat diet, and improved healthspan (but not lifespan) in mice on standard chow diet [35]. Activation of the AMPK with metformin (administered intermittently or low concentration in chow) also increased lifespan and healthspan in mice [36], mimicking the effects of caloric restriction. Caloric restriction studies has been reported to have many beneficial effects in non-human primates [8,37,38]. Rhesus monkeys from the University of Maryland Obesity, Diabetes, and Aging Animal Resource Centre (ODAAR), National Institute on Aging (NIA) and the Wisconsin National Primate Research Centre (WNPRC) had increased healthspan demonstrated by improvements in insulin response and the rates of diabetes, cancer, cardiovascular disease, and brain health. These improvements occurred despite a lack of lifespan extension by in the NIA study [8] compared to the WNPRC study [38] or ODAAR study [37], which may be attributed to differences in study design and species specificity [39]. Despite these variable responses, improvements in health undoubtedly lead to the question of whether CR is translatable to humans.

Caloric restriction in humans Although the effects of caloric restriction on lifespan in humans is unknown, short-term randomized clinical trials show improvements in markers of health that are likely to benefit healthspan [9] including metabolic hormones (insulin, testosterone, triiodothyronine, adiponectin), blood pressure, lipid profiles, glucose levels, body fat and inflammatory cytokines [40–46]. Several recent studies have indicated that caloric restriction may be feasible for small subsets of nonobese humans and may be associated with a reduced risk for disease and anti-aging effects [47,48].

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While a small percentage of humans may have the ‘‘willpower’’ to maintain a caloric restricted diet, voluntary caloric restriction is not sustainable in most humans, and there are potential adverse effects including osteoporosis and impairments in reproductive function, immune function and wound healing [49]. Therefore, other approaches to reduced caloric intake such as intermittent fasting (IF), every other day feeding (EODF) and the Fasting Mimicking Diet (FMD) are being evaluated. These interventions might influence aging by periods of fasting and effects on circadian rhythm rather than via reduced calorie intake [50]. In the short-term, these diets are beneficial for brain health [51], cardiovascular disease [52], immunity [53], glucose levels and insulin sensitivity [54].

Restriction of dietary protein and amino acids Many studies have found that restriction of dietary proteins or certain amino acids can increase lifespan in animals on ad libitum-fed diets [10,55,56]. This suggests that some of the benefits of caloric restriction may be mediated via reduced intake of protein and/or specific amino acids. In fruit flies, reducing protein intake by providing a diet with a protein to carbohydrate ratio of 1:16 increased lifespan, while choice experiments revealed that fruit flies regulate their nutritional intake to a dietary protein to carbohydrate ratio of 1:4 which optimizes reproduction [57]. Moreover there is evidence from observational studies that reduced protein (particularly meatbased protein) intake in humans is associated with better health outcomes [58–63]. In yeast, restriction of leucine, serine, threonine, valine and methionine increased lifespan [64,65]. Methionine restriction also extends lifespan in fruit flies [66,67] while methionine or tryptophan restriction increases lifespan in rodents [55,68–71]. Many of these studies have focused on the role of the mTOR, GCN2 and Insulin/IGF-1 nutrient sensing pathways, which are known to be sensitive to amino acid availability [60,72]. Most studies investigating the effects on protein restriction in rodents occur in controlled laboratory settings where the manipulation of macronutrients is strictly monitored. However, in the wild it has been shown that rodents consume a targeted amount of protein (18–24% crude protein), largely to maintain optimal growth and reproductive capabilities, likely to the expense of extended lifespan [73].

Limitations of one-variable-at-a-time (OVAT) studies Most nutritional research has focused on the effects of excess or insufficient intake of individual nutrients, a one-variableat-a-time (OVAT) approach. In aging research, this has led to conclusions about the effects of individually perturbing energy intake, or the intake of each of the macronutrients or specific amino acids. However, food is a complex mixture where changing the concentration of one dietary constituent www.drugdiscoverytoday.com

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influences the availability of other constituents and the ratios (or interactions) between nutrients. To address this issue, an integrative approach is required that accounts for independent and interactive effects of dietary components on outcomes. The Geometric Framework (GF) was designed for this purpose [74]. It was first applied to studies of insects [75] and subsequently applied to a wide range of outcomes including appetite, gut microbiome, reproduction and aging. The GF has been utilized in organisms ranging from slime molds to rodents [11,76–80] and humans. The GF has also been applied to obesity in humans [81], conservation of endangered species [82] and linking nutritional ecology to the evolution of aging [83,84].

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In traditional caloric restriction and aging experiments, animals are given a fixed aliquot of food [14,94], whereas in GF experiments animals are usually provided ad libitum access to food to allow physiological compensatory feeding responses to occur. In order to determine the effects of caloric restriction, some animals are provided with food that has been diluted to an extent where even with compensatory increases in food intake, energy intake is reduced [11].

(a) Female Drosophila (Lee et.al. 2008) Lifespan

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(b) Female field cricket (Maklakov et al 2008) Lifespan

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Animals regulate their intake of nutrients and energy [57,82,85–87]. Dietary studies show that animals will titrate their intake of energy and macronutrients to meet endogenous targets for energy or specific macro- or micronutrients [96]. The GF provides an integrative platform in which to visualize and analyze these responses. Previous work applying the GF in insects [88], mice [89] and humans [87], shows that animals prioritize the intake of protein in a nutritionally imbalanced environment – this is termed ‘protein leverage’ [81]. Thus, restriction to imbalanced foods result in costs as a result of ingested excess of some nutrients, and deficits of others. For example, ingesting excess carbohydrates to maintain a protein target results in obesity in humans, rodents and insects [81,90], while over-ingesting protein to maintain a carbohydrate target shortens lifespan [11,57,91–93]. When provided with a choice of foods containing differing amounts of macronutrients, animals consume a combination of foods that together meet all their dietary targets. Thus, previous studies of the effects of a low protein diet on lifespan are not just studying reduced protein intake, but also the effects of increased food intake, and reduced ratios of protein to the other macronutrients. In the GF, determining outcomes as a result of dietary manipulations is achieved by plotting response surfaces across an n-dimensional nutrient space. Most experiments have been performed by randomizing animals to one of multiple different ad libitum-fed diets varying in content of energy, protein, carbohydrates and fat. Response surfaces are derived from individual data points describing phenotypic traits such as metabolic health, lifespan and reproductive function, or mechanistic parameters such as gene or protein expression, mitochondrial function and telomere length. Careful measurement of food intake must be undertaken so that the outcomes can be plotted against food consumption thus accounting for nutrient regulation and compensatory feeding.

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(c) Female Queensland fruit fly (Fanson and Taylor 2012) Lifetime eggs

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(d) Female Drosophila (Jensen et al. 2015) Lifetime eggs

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Fig. 1. Response surfaces showing the effects of protein and carbohydrate intake on lifespan and reproductive output of insects. Lifespan and lifetime egg production are shown for the fruit fly Drosophila melanogaster [57,97], the field cricket Teleogryllus commodus [90], and the Queensland fruit fly Bactrocera tryoni [98]. Response surfaces show highest values in red and decrease as the colors turn to blue. For all plots, lifespan in maximized at very low protein to carbohydrate (P:C) ratios. Lifetime egg production is optimized at higher P:C ratios and show that the nutritional optima for lifespan differ to that for reproduction. Data are replotted from the cited studies [99].

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Fig. 2. Response surfaces showing the effects of protein and carbohydrate intake on lifespan and various cardiometabolic outcomes in mice [11,77,99]. Response surfaces show highest values in red and decrease as the colors turn to blue. Low P:C intakes increase (A) lifespan and improve (C) glucose tolerance, (D) insulin levels, (E) immunity, (F) triglyceride levels despite an increase in adiposity (B). A low P:C intake also (G) decreases hepatic mTOR activation and (H) increases circulating FGF21 levels. Data are replotted from the cited studies.

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Geometric framework and aging in invertebrate models

Conclusions

One seminal study used the GF approach to study nutrition and aging the fruit fly [57]. More than 1000 mated female flies were provided ad libitum access to one of 28 different diets that varied in protein to carbohydrate ratio at one of four total concentrations. Intake was measured using a capillary feeding assay [95] and response surfaces for lifespan, mortality rates and reproductive measures were plotted onto a nutrient array of protein and carbohydrate intakes. Response surfaces are heat maps where regions with highest elevation show the highest value for a given response and are colored in red, and the lowest in blue, while statistical significance is calculated using generalized additive models. Two of these surfaces, showing lifespan and lifetime egg production, are shown in Fig. 1. The surface for lifespan reveals the ratio of dietary protein to carbohydrate was the main factor influencing lifespan, while energy intake had no effect. The longest lifespans, indicated by the solid red line, occurred when flies were maintained on a diet with a low protein to carbohydrate ratio of 1:16. On the other hand, lifetime egg production was optimized in diets with a protein to carbohydrate ration of 1:4. When allowed to self-select between nutritionally complimentary diets, flies converged on a diet that maximized reproduction. Subsequent studies on numerous different insect species have reported similar results [96].

Several nutritional interventions delay aging through acting on nutrient signaling pathways that influence many of the hallmarks of aging such as mitochondrial function, autophagy and gene expression. The Geometric Framework has provided new insights into the relationship between nutrition and aging by demonstrating that when provided ad libitum access to food, aging is influenced by the interaction between dietary protein and carbohydrates rather than calories. The Geometric Framework can also be applied to other animal studies of aging and nutrition, dissecting out the effects of other nutritional dimensions (e.g. the types of carbohydrates (sucrose, starch etc.) or specific amino acids (methionine, tryptophan, serine, citrulline). Mapping the mechanisms and responses of animals to dietary balance will help to define nutritional interventions that can be translated into humans, while identifying mechanisms that can be targeted pharmacologically.

Geometric framework and aging in mice One study has used the GF methodology to study health and aging in mice [11]. Nearly 900 mice were given ad libitum access to one of 25 different diets that varied in protein, carbohydrate and fat, and provided at one of three energy densities through the addition of non-digestible cellulose. Food intake was recorded throughout, and at 15 months, one third of mice were sacrificed to assess body composition, cardiometabolic health, immune function, gut microbiome, nutrient signaling and reproduction, while the remaining mice were maintained for determination of lifespan. Lifespan results were similar to those seen in insects, with longest lifespans occurring on a lower protein to carbohydrate ratio (Fig. 2). Low calorie intake, achieved by dilution, resulted in shorter lifespans. Markers of metabolic health (including liver function) immune function and pathways linking diet to aging (mTOR, FGF21, IGF1, telomeres) paralleled the lifespan data [11,76,77,79]. As in insects, measures for reproductive potential (testes mass and epididymal sperm counts in males and uterine mass and estrous cycling in females) were optimized on diets with a higher protein to carbohydrate ratio [78]. Dietary fat did not appear to have a large effect on these mice, suggesting that the effects of protein and carbohydrate on health, reproduction and lifespan are evolutionarily conserved [97]. 66

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References [1] Fontana L, Kennedy BK, Longo VD, Seals D, Melov S. Medical research: treat ageing. Nature 2014;511(7510):405–7. [2] Lo´pez-Otı´n C, Blasco MA, Partridge L, Serrano M, Kroemer G. The hallmarks of aging. Cell 2013;153(6):1194–217. [3] Kennedy BK, Berger SL, Brunet A, Campisi J, Cuervo AM, Epel ES, et al. Geroscience: linking aging to chronic disease. Cell 2014;159(4):709–13. [4] Roth GS, Ingram DK, Joseph JA. Nutritional interventions in aging and age-associated diseases. Ann N Y Acad Sci 2007;1114:369–71. [5] Lawler DF, Ballam JM, Meadows R, Larson BT, Li Q, Stowe HD, et al. Influence of lifetime food restriction on physiological variables in labrador retriever dogs. Exp Gerontol 2007;42(3):204–14. [6] Speakman JR, Mitchell SE. Caloric restriction. Mol Aspects Med 2011;32 (3):159–221. [7] Mitchell SJ, Madrigal-Matute J, Scheibye-Knudsen M, Fang E, Aon M, Gonzalez-Reyes JA, et al. Effects of sex, strain, and energy intake on hallmarks of aging in mice. Cell Metab 2016;23(6):1093–112. [8] Mattison JA, Roth GS, Beasley TM, Tilmont EM, Handy AM, Herbert RL, et al. Impact of caloric restriction on health and survival in rhesus monkeys from the NIA study. Nature 2012;489(7415):318–21. [9] Heilbronn LK, de Jonge L, Frisard MI, DeLany JP, Larson-Meyer DE, Rood J, et al. Effect of 6-month calorie restriction on biomarkers of longevity, metabolic adaptation, and oxidative stress in overweight individuals: a randomized controlled trial. JAMA 2006;295(13):1539–48. [10] Piper MD, Mair W, Partridge L. Counting the calories: the role of specific nutrients in extension of life span by food restriction. J Gerontol A Biol Sci Med Sci 2005;60(5):549–55. [11] Solon-Biet SM, McMahon AC, Ballard JW, Ruohonen K, Wu LE, Cogger VC, et al. The ratio of macronutrients, not caloric intake, dictates cardiometabolic health, aging, and longevity in ad libitum-fed mice. Cell Metab 2014;19(3):418–30. [12] Orentreich N, Matias JR, DeFelice A, Zimmerman JA. Low methionine ingestion by rats extends life span. J Nutr 1993;123(2):269–74. [13] Simpson SJ, Le Couteur DG, Raubenheimer D. Putting the balance back in diet. Cell 2015;161(1):18–23. [14] McCay CM, Crowell MF, Maynard LA. The effect of retarded growth upon the length of life span and upon the ultimate body size. J Nutr 1935;10 (1):63–79. [15] Lopez-Lluch G, Navas P. Calorie restriction as an intervention in ageing. J Physiol 2016;594(8):2043–60. [16] Picca A, Pesce V, Lezza AMS. Does eating less make you live longer and better? An update on calorie restriction. Clin Interv Aging 2017;12:Error: FPage (1887) is higher than LPage (1902)!.

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[17] Fontana L, Nehme J, Demaria M. Caloric restriction and cellular senescence. Mech Ageing Dev 2018;176:19–23. [18] Piper MD, Bartke A. Diet and aging. Cell Metab 2008;8(2):99–104. [19] Solon-Biet SM, Mitchell SJ, de Cabo R, Raubenheimer D, Le Couteur DG, Simpson SJ. Macronutrients and caloric intake in health and longevity. J Endocrinol 2015;226(1):R17–28. [20] Masoro EJ. Overview of caloric restriction and ageing. Mech Age Dev 2005;126(9):913–22. [21] Weindruch R, Naylor PH, Goldstein AL, Walford RL. Influences of aging and dietary restriction on serum thymosin alpha 1 levels in mice. J Gerontol 1988;43(2):B40–2. [22] Stanfel MN, Shamieh LS, Kaeberlein M, Kennedy BK. The TOR pathway comes of age. Biochim Biophysa Acta 2009;1790(10):1067–74. [23] Fontana L, Partridge L, Longo VD. Extending healthy life span—from yeast to humans. Science 2010;328(5976):321–6. [24] Kaeberlein M, Powers RW, Steffen KK, Westman EA, Hu D, Dang N, et al. Regulation of yeast replicative life span by tor and sch9 in response to nutrients. Science 2005;310(5751):1193–6. [25] Lin S-J, Defossez P-A, Guarente L. Requirement of NAD and SIR2 for lifespan extension by calorie restriction in saccharomyces cerevisiae. Science 2000;289(5487):2126–8. [26] Guarente L, Kenyon C. Genetic pathways that regulate ageing in model organisms. Nature 2000;408(6809):255–62. [27] Tahara EB, Cunha FM, Basso TO, Della Bianca BE, Gombert AK, Kowaltowski AJ. Calorie restriction hysteretically primes aging saccharomyces cerevisiae toward more effective oxidative metabolism. PLoS One 2013;8(2):11. [28] Hosono R, Nishimoto S, Kuno S. Alterations of life span in the nematode Caenorhabditis elegans under monoxenic culture conditions. Exp Gerontol 1989;24(3):251–64. [29] Greer EL, Brunet A. Different dietary restriction regimens extend lifespan by both independent and overlapping genetic pathways in C. elegans. Aging Cell 2009;8(2):113–27. [30] Partridge L, Piper MD, Mair W. Dietary restriction in Drosophila. Mech Ageing Dev 2005;126(9):938–50. [31] Broughton S, Partridge L. Insulin/IGF-like signalling, the central nervous system and aging. Biochem J 2009;418(1):1–12. [32] Mair W, Dillin A. Aging and survival: the genetics of life span extension by dietary restriction. Annu Rev Biochem 2008;77:727–54. [33] Huffman DM, Farias Quipildor G, Mao K, Zhang X, Wan J, Apontes P, et al. Central insulin-like growth factor-1 (IGF-1) restores whole-body insulin action in a model of age-related insulin resistance and IGF-1 decline. Aging Cell 2016;15(1):181–6. [34] de Cabo R, Le Couteur DG. The biology of aging. In: Kasper DL, et al., editors. Harrison’s principles of internal medicine 19/e, Vol. 1 & Vol. 2. McGraw-Hill Education; 2015. p. 94e1–7. [35] Baur JA, Pearson KJ, Price NL, Jamieson HA, Lerin C, Kalra A, et al. Resveratrol improves health and survival of mice on a high-calorie diet. Nature 2006;444(7117):337–42. [36] Martin-Montalvo A, Mercken EM, Mitchell SJ, Palacios HH, Mote PL, Scheibye-Knudsen M, et al. Metformin improves healthspan and lifespan in mice. Nat Commun 2013;4(2192). [37] Bodkin NL, Alexander TM, Ortmeyer HK, Johnson E, Hansen BC. Mortality and morbidity in laboratory-maintained rhesus monkeys and effects of long-term dietary restriction. J Gerontol A Biol Sci Med Sci 2003;58(3):212–9. [38] Colman RJ, Anderson RM, Johnson SC, Kastman EK, Kosmatka KJ, Beasley TM, et al. Caloric restriction delays disease onset and mortality in rhesus monkeys. Science 2009;325(5937):201–4. [39] Mattison JA, Colman RJ, Beasley TM, Allison DB, Kemnitz JW, Roth GS, et al. Caloric restriction improves health and survival of rhesus monkeys. Nat Commun 2017;8:14063. [40] Fontana L, Klein S. Aging, adiposity, and calorie restriction. JAMA 2007;297(9):986–94. [41] Fontana L, Meyer TE, Klein S, Holloszy JO. Long-term calorie restriction is highly effective in reducing the risk for atherosclerosis in humans. Proc Natl Acad Sci U S A 2004;101(17):6659–63.

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[42] Su HY, Lee HC, Cheng WY, Huang SY. A calorie-restriction diet supplemented with fish oil and high-protein powder is associated with reduced severity of metabolic syndrome in obese women. Eur J Clin Nutr 2015;69(3):322–8. [43] Moreno B, Bellido D, Sajoux I, Goday A, Saavedra D, Crujeiras AB, et al. Comparison of a very low-calorie-ketogenic diet with a standard lowcalorie diet in the treatment of obesity. Endocrine 2014;47(3):793–805. [44] Jonker JT, Djaberi R, van Schinkel LD, Hammer S, Bus MT, Kerpershoek G, et al. Very-low-calorie diet increases myocardial triglyceride content and decreases diastolic left ventricular function in type 2 diabetes with cardiac complications. Diabet Care 2019;37(1):e1–2. [45] Klempel MC, Kroeger CM, Bhutani S, Trepanowski JF, Varady KA. Intermittent fasting combined with calorie restriction is effective for weight loss and cardio-protection in obese women. Nutr J 2012;11 (98):1475–2891. [46] Svendsen PF, Jensen FK, Holst JJ, Haugaard SB, Nilas L, Madsbad S. The effect of a very low calorie diet on insulin sensitivity, beta cell function, insulin clearance, incretin hormone secretion, androgen levels and body composition in obese young women. Scand J Clin Lab Invest 2012;72(5):410–9. [47] Das SK, Balasubramanian P, Weerasekara YK. Nutrition modulation of human aging: the calorie restriction paradigm. Mol Cell Endocrinol 2017;455:148–57. [48] Most J, Tosti V, Redman LM, Fontana L. Calorie restriction in humans: an update. Ageing Res Rev 2017;39:36–45. [49] Fontana L, Partridge L. Promoting health and longevity through diet: from model organisms to humans. Cell 2015;161(1):106–18. [50] Di Francesco A, Di Germanio C, Bernier M, de Cabo R. A time to fast. Science 2018;362(6416):770–5. [51] Wahl D, Cogger VC, Solon-Biet SM, Waern RV, Gokarn R, Pulpitel T, et al. Nutritional strategies to optimise cognitive function in the aging brain. Ageing Res Rev 2016;31:80–92. [52] Wan R, Ahmet I, Brown M, Cheng A, Kamimura N, Talan M, et al. Cardioprotective effect of intermittent fasting is associated with an elevation of adiponectin levels in rats. J Nutr Biochem 2010;21(5):413–7. [53] Choi IY, Piccio L, Childress P, Bollman B, Ghosh A, Brandhorst S, et al. A diet mimicking fasting promotes regeneration and reduces autoimmunity and multiple sclerosis symptoms. Cell Rep 2016;15(10):2136–46. [54] Varady KA. Impact of intermittent fasting on glucose homeostasis. Curr Opin Clin Nutr Metab Care 2016;19(4):300–2. [55] Zimmerman JA, Malloy V, Krajcik R, Orentreich N. Nutritional control of aging. Exp Gerontol 2003;38(1–2):47–52. [56] Mair W, Piper MDW, Partridge L. Calories do not explain extension of life span by dietary restriction in Drosophila. PLoS Biol 2005;3(7)e223. [57] Lee KP, Simpson SJ, Clissold FJ, Brooks R, Ballard JW, Taylor PW, et al. Lifespan and reproduction in Drosophila: new insights from nutritional geometry. Proc Natl Acad Sci U S A 2008;105(7):2498–503. [58] Longo VD, Antebi A, Bartke A, Barzilai N, Brown-Borg HM, Caruso C, et al. Interventions to slow aging in humans: are we ready? Aging Cell 2015;14 (4):497–510. [59] Lynch CJ, Adams SH. Branched-chain amino acids in metabolic signalling and insulin resistance. Nat Rev Endocrinol 2014;10(12):723–36. [60] Soultoukis GA, Partridge L. Dietary protein, metabolism, and aging. Annu Rev Biochem 2016;85:5–34. [61] Waern RV, Cumming RG, Blyth F, Naganathan V, Allman-Farinelli M, Le Couteur D, et al. Adequacy of nutritional intake among older men living in Sydney, Australia: findings from the concord health and ageing in men project (CHAMP). Br J Nutr 2015;114(5):812–21. [62] Le Couteur DG, Solon-Biet S, Wahl D, Cogger VC, Willcox BJ, Willcox DC, et al. New horizons: dietary protein, ageing and the Okinawan ratio. Age Ageing 2016;45(4):443–7. [63] Levine ME, Suarez Jorge A, Brandhorst S, Balasubramanian P, Cheng C-W, Madia F, 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(3):407–17. [64] Wu Z, Song L, Liu SQ, Huang D. Independent and additive effects of glutamic acid and methionine on yeast longevity. PLoS One 2013;8(11) e79319.

www.drugdiscoverytoday.com

67

Drug Discovery Today: Disease Models | Models for Aging Research

[65] Mirzaei H, Suarez JA, Longo VD. Protein and amino acid restriction, aging and disease: from yeast to humans. Trends Endocrinol Metab 2014;25 (11):558–66. [66] Lee BC, Kaya A, Ma S, Kim G, Gerashchenko MV, Yim SH. Methionine restriction extends lifespan of Drosophila melanogaster under conditions of low amino-acid status. Nat Commun 2014;5:3592. [67] Lee BC, Kaya A, Gladyshev VN. Methionine restriction and life-span control. Ann N Y Acad Sci 2016;1363:116–24. [68] Minor RK, Allard JS, Younts CM, Ward TM, de Cabo R. Dietary interventions to extend life span and health span based on calorie restriction. J Gerontol A Biol Sci Med Sci 2010;65(7):695–703. [69] Gallinetti J, Harputlugil E, Mitchell JR. Amino acid sensing in dietaryrestriction-mediated longevity: roles of signal-transducing kinases GCN2 and TOR. Biochem J 2013;449(1):1–10. [70] Peng W, Robertson L, Gallinetti J, Mejia P, Vose S, Charlip A, et al. Surgical stress resistance induced by single amino acid deprivation requires Gcn2 in mice. Sci Transl Med 2012;4(118). 118ra11. [71] Xiao F, Huang Z, Li H, Yu J, Wang C, Chen S, et al. Leucine deprivation increases hepatic insulin sensitivity via GCN2/mTOR/S6K1 and AMPK pathways. Diabetes 2011;60(3):746–56. [72] Mirzaei H, Raynes R, Longo VD. The conserved role of protein restriction in aging and disease. Curr Opin Clin Nutr Metab Care 2016;19(1):74–9. [73] National Research Council Subcommittee on Laboratory Animal, N. Fourth Revised Edition, Nutrient requirements of laboratory animals, 1995, Fourth Revised Edition National Academies Press (US); 1995. [74] Simpson SJ, Raubenheimer D. The nature of nutrition. A unifying framework form animal adaption to human obesity. Princeton: Princeton University Press; 2012. [75] Raubenheimer D, Simpson SJ. The geometry of compensatory feeding in the locust. Anim Behav 1993;45(5):953–64. [76] Le Couteur DG, Tay SS, Solon-Biet SM, Bertolino P, McMahon AC, Cogger VC, et al. The influence of macronutrients on splanchnic and hepatic lymphocytes in aging mice. J Gerontol A Biol Sci Med Sci 2014;70 (12):1499–507. [77] Solon-Biet SM, Cogger VC, Pulpitel T, Heblinski M, Wahl D, McMahon AC, et al. Defining the nutritional and metabolic context of FGF21 using the Geometric Framework. Cell Metabol 2016;24(4):555–65. [78] Solon-Biet SM, Walters KA, Simanainen UK, McMahon AC, Ruohonen K, Ballard JWO, et al. Macronutrient balance, reproductive function, and lifespan in aging mice. Proc Natl Acad Sci U S A 2015;112(11):3481–6. [79] Cogger VC, Mohamad M, Solon-Biet S, Senior AM, Warren A, O’Reilly JN, et al. Dietary macronutrients and the aging liver sinusoidal endothelial cell. Am J Physiol Heart Circul Physiol 2016;310(9):H1064–70. [80] Holmes AJ, Chew YV, Colakoglu F, Cliff JB, Klaassens E, Read MN, et al. Diet-microbiome interactions in health are controlled by intestinal nitrogen source constraints. Cell Metabol 2017;25(1):140–51. [81] Simpson SJ, Raubenheimer D. Obesity: the protein leverage hypothesis. Obesity Rev 2005;6(2):133–42. [82] Felton AM, Felton A, Raubenheimer D, Simpson SJ, Foley WJ, Wood JT, et al. Protein content of diets dictates the daily energy intake of a freeranging primate. Behav Ecol 2009;20(4):685–90.

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www.drugdiscoverytoday.com

Vol. 27, 2018

[83] Raubenheimer D, Simpson SJ, Le Couteur DG, Solon-Biet SM, Coogan SCP. Nutritional ecology and the evolution of aging. Exp Gerontol 2016;23(1):4–16. [84] Raubenheimer D, Simpson SJ, Mayntz D. Nutrition, ecology and nutritional ecology: toward an integrated framework. Funct Ecol 2009;23 (1):4–16. [85] Dussutour A, Latty T, Beekman M, Simpson SJ. Amoeboid organism solves complex nutritional challenges. Proc Natl Acad Sci U S A 2010;107 (10):4607–11. [86] Sorensen A, Mayntz D, Raubenheimer D, Simpson SJ. Protein-leverage in mice: the geometry of macronutrient balancing and consequences for fat deposition. Obesity 2008;16(3):566–71. [87] Gosby AK, Conigrave AD, Lau NS, Iglesias MA, Hall RM, Jebb SA, et al. Testing protein leverage in lean humans: a randomised controlled experimental study. PLoS One 2011;6(10)e25929. [88] Chambers PG, Raubenheimer D, Simpson SJ. The functional significance of switching interval in food mixing by Locusta migratoria. J Insect Physiol 1998;44(1):77–85. [89] Sørensen A, Mayntz D, Raubenheimer D, Simpson SJ. Protein-leverage in mice: the geometry of macronutrient balancing and consequences for fat deposition. Obesity 2008;16(3):566–71. [90] Maklakov AA, Simpson SJ, Zajitschek F, Hall MD, Dessmann J, Clissold F, et al. Sex-specific fitness effects of nutrient intake on reproduction and lifespan. Curr Biol 2008;18(14):1062–6. [91] Fanson BG, Weldon CW, Pe´rez-Staples D, Simpson SJ, Taylor PW, et al. Nutrients, not caloric restriction, extend lifespan in queensland fruit flies (Bactrocera tryoni). Aging Cell 2009;8(5):514–23. [92] Dussutour A, Simpson SJ. Communal nutrition in ants. Curr Biol 2009;19 (9):740–4. [93] Dussutour A, Simpson SJ. Ant workers die young and colonies collapse when fed a high-protein diet. Proc R Soc B Biol Sci 2012;279(1737):Error: FPage (2402) is higher than LPage (2408)!. [94] Weindruch R, Walford RL, Fligiel S, Guthrie D. The retardation of aging in mice by dietary restriction: longevity, cancer, immunity and lifetime energy intake. J Nutr 1986;116(4):641–54. [95] Ja WW, Carvalho GB, Mak EM, de la Rosa NN, Fang AY, Liong JC, et al. Prandiology of Drosophila and the CAFE assay. Proc Natl Acad Sci U S A 2007;104(20):8253–6. [96] Le Couteur DG, Solon-Biet S, Cogger VC, Mitchell SJ, Senior A, de Cabo R, et al. The impact of low-protein high-carbohydrate diets on aging and lifespan. Cell Mol Life Sci 2016;73(6):1237–52. [97] Jensen K, McClure C, Priest NK, Hunt J. Sex-specific effects of protein and carbohydrate intake on reproduction but not lifespan in Drosophila melanogaster. Aging cell 2015;14(4):605–15. [98] Fanson BG, Taylor PW. Protein:carbohydrate ratios explain life span patterns found in Queensland fruit fly on diets varying in yeast:sugar ratios. Age (Dordr) 2012;34(6):1361–8. [99] Solon-Biet SM, Wahl D, Raubenheimer D, Le Couteur D, Simpson SJ. A framework for uncovering the roles of calories and macronutrients in health and aging. In: Ram JL, Conn PM, editors. Conn’s handbook on models for human aging. New York: Elsevier; 2018.