Creatine supplementation for muscle growth: a scoping review of randomized clinical trials from to Gomez-Bruton A, Marin-Puyalto J, Muñiz-Pardos B, Matute-Llorente A, del Coso J, Gomez-Cabello A, et al.

Does acute caffeine supplementation improve physical performance in female team-sport athletes? Evidence from a systematic review and meta-analysis. van de Walle GP, Vukovich MD. The effect of nitrate supplementation on exercise tolerance and performance: a systematic review and meta-analysis. J Strength Cond Res.

Bourke BEP, Baker DF, Braakhuis AJ. Social media as a nutrition resource for athletes: a cross-sectional survey. Evaluation of general nutrition knowledge in elite Australian athletes.

Br J Nutr. Devlin BL, Belski R. Exploring general and sports nutrition and food knowledge in elite male Australian athletes. Assessing and improving general and sports nutrition knowledge of Australian athletes view project dietary intakes, nutrition knowledge and the factors influencing dietary behaviours and food choices of professional Australian football athletes view project.

Lamarche B, Morissette É, Provencher V, Valois P. Evaluation of sports nutrition knowledge and recommendations among high school coaches. Article Int J Sport Nutr Exerc Metab.

Andrews A, Wojcik JR, Boyd JM, Bowers CJ. Sports nutrition knowledge among mid-major division i university student-athletes. J Nutr Metab. Wardenaar FC, Hoogervorst D. How sports health professionals perceive and prescribe nutritional supplements to olympic and non-Olympic athletes. Int J Environ Res Public Health.

Burke LM, Meyer NL, Pearce J. National nutritional programs for the London olympic games: a systematic approach by three different countries. Nestle Nutr Inst Workshop Ser. Beck KL, Thomson JS, Swift RJ, Hurst Pv. Role of nutrition in performance enhancement and postexercise recovery.

Open Access J Sports Med. Torres-McGehee TM, Pritchett KL, Zippel D, Minton DM, Cellamare A, Sibilia M. Sports nutrition knowledge among collegiate athletes, coaches, athletic trainers, and strength and conditioning specialists. J Athl Train. Foo W, Faghy MA, Sparks A, Newbury JW, Gough LA.

The effects of a nutrition education intervention on sports nutrition knowledge during a competitive season in highly trained adolescent swimmers. Effectiveness of education interventions designed to improve nutrition knowledge in athletes: a systematic review.

Sports Med. Birkenhead KL, Slater G. Hamilton KP, Ziegler J, Samavat H, Wardenaar F, Esopenko C, Womack J, et al. Nutritional supplement use and athletic characteristics among a sample of NCAA division I and division III student-athletes.

J Diet Suppl. Wesana J, Schouteten JJ, van Acker E, Gellynck X, de Steur H. British Food J. Clark JE. Taste and flavour: their importance in food choice and acceptance. Proc Nutr Soc. Trakman GL, Forsyth A, Hoye R, Belski R. Thurecht RL, Pelly FE. Development of a new tool for managing performance nutrition: the athlete food choice questionnaire.

Corallo A, Latino ME, Menegoli M, Spennato A. A survey to discover current food choice behaviors. Sobal J, Bisogni CA. Constructing food choice decisions.

Ann Behav Med. The nutrition for sport knowledge questionnaire NSKQ : development and validation using classical test theory and Rasch analysis. Zinn C, Schofield G, Wall C. Development of a psychometrically valid and reliable sports nutrition knowledge questionnaire.

J Sci Med Sport. Ares G, Gámbaro A. Influence of gender, age and motives underlying food choice on perceived healthiness and willingness to try functional foods. Arenas-Jal M, Suñé-Negre JM, Pérez-Lozano P, García-Montoya E.

Trends in the food and sports nutrition industry: a review. Crit Rev Food Sci Nutr. Menrad K. Market and marketing of functional food in Europe. J Food Eng. Knapik JJ, Steelman RA, Hoedebecke SS, Austin KG, Farina EK, Lieberman HR. Prevalence of dietary supplement use by athletes: systematic review and meta-analysis.

Clarkson PM, Tremblay I. Exercise-induced muscle damage, repair, and adaptation in humans. Clarkson PM, Sayers SP. Etiology of exercise-induced muscle damage.

Can J Appl Physiol. Owens DJ, Twist C, Cobley JN, Howatson G, Close GL. Exercise-induced muscle damage: what is it, what causes it and what are the nutritional solutions? Eur J Sport Sci. Harty PS, Cottet ML, Malloy JK, Kerksick CM. Nutritional and supplementation strategies to prevent and attenuate exercise-induced muscle damage: a brief review.

Sports Med Open. Hulmi JJ, Lockwood CM, Stout JR. Nutr Metab Lond. Andersen LL, Tufekovic G, Zebis MK, Crameri RM, Verlaan G, Kjær M, et al. The effect of resistance training combined with timed ingestion of protein on muscle fiber size and muscle strength.

Metab Clin Exp. Grgic J, Trexler ET, Lazinica B, Pedisic Z. Effects of caffeine intake on muscle strength and power: a systematic review and meta-analysis.

Ganio MS, Casa D, Armstrong L. Effect of caffeine on sport-specific endurance performance: a systematic review. Artic J Strength Cond Res.

Womack CJ, Saunders MJ, Bechtel MK, Bolton DJ, Martin M, Luden ND, et al. The influence of a CYP1A2 polymorphism on the ergogenic effects of caffeine. Lanhers C, Pereira B, Naughton G, Trousselard M, Lesage FX, Dutheil F. Creatine supplementation and lower limb strength performance: a systematic review and meta-analyses.

Creatine supplementation and upper limb strength performance: a systematic review and meta-analysis. Dvorak J, Junge A, Derman W, Schwellnus M.

Injuries and illnesses of football players during the FIFA world cup. Br J Sports Med. Engebretsen L, Soligard T, Steffen K, Alonso JM, Aubry M, Budgett R, et al. Sports injuries and illnesses during the London summer olympic games.

Luna S. This title is not intended to diagnose, treat, cure, or prevent any disease: why sports nutrition products should be avoided. Stanford J Public Health. Google Scholar. Lorraine K. Supplement regulation for sports nutrition supplements.

J Leg Med. Close GL, Kasper AM, Walsh NP, Maughan RJ. Lucey A, Heneghan C, Kiely ME. Guidance for the design and implementation of human dietary intervention studies for health claim submissions.

Nutr Bull. Maughan RJ, Burke LM, Dvorak J, Larson-Meyer DE, Peeling P, Phillips SM, et al. IOC Consensus statement: dietary supplements and the high-performance athlete. Hwang J, Lee K, Lin TN. Ingredient labeling and health claims influencing consumer perceptions, purchase intentions, and willingness to pay.

J Foodserv Bus Res. Vento KA, Wardenaar FC. Third-Party testing nutritional supplement knowledge, attitudes, and use among an NCAA I collegiate student-athlete population. Front Sports Act Living. Browne F, Walsh E, Walton J, Flynn A.

The contribution of nutritional supplements to micronutrient intake in Irish adults aged 18—64 years. Cashman KD, Wallace JMW, Horigan G, Hill TR, Barnes MS, Lucey AJ, et al.

Am J Clin Nutr. Martineau AR, Forouhi NG. Vitamin D for COVID a case to answer? Lancet Diabetes Endocrinol. Cashman KD, Dowling KG, Škrabáková Z, Gonzalez-Gross M, Valtueña J, de Henauw S, et al.

Vitamin D deficiency in Europe: pandemic? Owens DJ, Allison R, Close GL. Vitamin D and the athlete: current perspectives and new challenges. Torres R, Ribeiro F, Alberto Duarte J, Cabri JMH. Evidence of the physiotherapeutic interventions used currently after exercise-induced muscle damage: systematic review and meta-analysis.

Phys Ther Sport. Skinner B, Moss R, Hammond L. A systematic review and meta-analysis of the effects of foam rolling on range of motion, recovery and markers of athletic performance. J Bodyw Mov Ther. Wiewelhove T, Döweling A, Schneider C, Hottenrott L, Meyer T, Kellmann M, et al.

A meta-analysis of the effects of foam rolling on performance and recovery. Front Physiol. Vitale KC, Owens R, Hopkins SR, Malhotra A.

Sleep hygiene for optimizing recovery in athletes: review and recommendations. Int J Sports Med. Conversely, it is also possible that meta-analyses have overadjusted estimates so that the hazard ratios are closer to the null than the true effects.

To take these effects into account, we conducted a new set of sensitivity analyses. In these analyses, we calculated alternative hazard ratios HR a based on HR 0 , the hazard ratio from the meta-analyses for a given change of intake for a given food group.

Data on background mortality from for specific countries and regions were obtained from the freely available GBD cause of death database [ 12 ].

We extracted data for the United States, China, and Europe, as these are the regions from where most of the nutritional studies providing mortality estimates originate.

Region-specific estimates on total mortality rates in 5-year age groups were also available from GBD. These were converted to single-year age-specific mortality rates in our model.

To assess the quality of evidence for each food group from the meta-analyses, we use NutriGrade, a version of GRADE adapted to nutritional studies [ 22 ]. We further constructed an overall quality score by taking the mean of the NutriGrade scores for each of the food groups weighted by their absolute contribution to LE.

The quality of the meta-analyses was assessed with the AMSTAR—2 tool [ 23 ]. The quality of the meta-analyses was rated as high for studies on all included meta-analyses [ 2 — 5 , 15 ], except for the meta-analysis on white meat that was rated as moderate [ 14 ].

In the left food panel i. In this paper, we present estimated gain in LE when changing from a typical diet to OD or FA for , , , and year-old adults from the United States, China, and Europe.

Graphs including forest plots are calculated in Stata SE Only publicly available data sources have been used, and thus no ethical permission is required. We adhered to the transparent reporting of a multivariable prediction model for individual prognosis or diagnosis TRIPOD; see S1 TRIPOD Checklist [ 24 ].

In this section, we will focus on the United States, but the results for China and Europe were generally very similar can be found in S2 — S15 Figs. Table 1 and Fig 1 estimate the life expectancies at different ages associated with a typical Western diet, a feasibility approach diet, and an optimized diet.

As seen, an increase in LE of up to Corresponding numbers for and year-old females are Similar findings were seen for China and the United States.

Changing from a typical diet to the feasibility approach diet would also give substantial gains for all age groups. Gain in LE when changing from a typical Western diet to a feasibility approach or optimized diet is also indicated.

Estimates per food groups and total change in LE is presented with uncertainty intervals UI. Note that lines for LE for red and processed meat changes are overlapping and similarly also for white meat and added oils.

LE, life expectancy. When changing from a typical Western to an optimized diet, the largest gains in LE could be made by eating more legumes, whole grains, and nuts, as well as eating less red meat and processed meat, with gradual reduction in effect with increasing age Fig 2 and S2 Table.

For a year-old from the United States, LE would increase by more than 1 year for each of these food groups. Fruits and vegetables as well as fish had substantial positive impact, but the intake in a typical diet is closer to an optimal intake than for legumes, whole grains, and nuts.

S3 Table indicates that when increasing time to full effect from 10 years to 30 years, gains in LE were reduced by less than 1 year for year-olds i. Conversely, decreasing time to full effect from 10 years to 5 years S16 Fig , S3 Table , health gains for year-old females and males increased by 0.

The overall quality of evidence was moderate for the optimized diet NutriGrade score: 6. In this paper, we present a method for estimating the impact of food choices on LE. This method has been implemented in a tool that is freely available online—the Food4HealthyLife calculator. Our results indicate that for individuals with a typical Western diet, sustained dietary changes at any age may give substantial health benefits, although the gains are the largest if changes start early in life.

Eating more legumes, whole grains, and nuts, and eating less red meat and processed meats were estimated to be the most effective ways to increase LE for individuals with a typical diet. This reflects a combination of the health effect for each food group combined with the difference between typical and optimal intakes.

Meta-analyses have also shown strong positive health effects from fruits, vegetables, and fish [ 2 , 5 ]. However, for these food groups, the typical intake was closer to optimal intake than for other food groups, particularly for vegetables. One could argue that for some food groups such as legumes, an optimal diet requires large intake and that such intakes might be unfeasible for many.

Thus, we have also presented feasibility approach diet estimates that are closer to what we may realistically expect from diet changes of most people in most settings where ideals often are difficult to follow in practice.

However, for most food groups, our estimates in the feasibility approach are within ranges that are common in cohort studies. There are also substantial individual variations in diet profile, which has impact on the potential health gain for each food group.

As an example, some people have diets that are relatively similar to optimized diets and can expect less additional benefits from optimizing diets compared to individuals with a typical Western diet.

For several of the food groups, more than one meta-analysis is available. For red and processed meats, a more recent meta-analysis from than the one used in our estimates has been published [ 6 ]. However, this did not present dose—response data for red and processed meats separately, and the supplemental data for these groups combined indicated similar results as for the meta-analysis by Schwingshackl and colleagues.

It is worthy to note that meta-analyses indicate worse outcomes on LE from processed meat than nonprocessed red meat when compared by weight, but if the consumption of unprocessed red meat consumption is double as high as for processed meat, the total effect is probably similar. For fish, whole grains, and legumes, more recent but smaller and less comprehensive meta-analyses were omitted from our data [ 25 — 27 ].

These also provided similar effect estimates to the estimates we used. For some food groups such as dairy products, fruits, and vegetables, systematic reviews of meta-analyses were available and supported the selection of the data sources [ 28 , 29 ].

For added oils, there were mixed results depending on type of oil, where monounsaturated fatty acids such as olive oil have been reported to have beneficial effects [ 15 , 30 , 31 ]. As most added oils contain a combination of different types of fatty acids, the general trend for health impact of added oils is often neutral [ 15 ].

Many of the background studies were adjusted for other food groups. It can be argued that food groups are interrelated and thus not independent. Studies presenting outcome measures with and without adjustment for other food groups have generally indicated minimal changes in the outcome measures [ 32 — 34 ].

To account for this possibility, we added sensitivity analyses model adjustment. Our method has several strengths. First, our food impact estimates are from the most comprehensive and recent meta-analyses presenting dose—response data on diet patterns and mortality.

We also have developed methodology that integrates different aspects such as time to full effects and potentially some degree of overlapping with sensitivity analyses and uncertainty intervals.

Our method also has several limitations. Meta-analyses present associations and some caution must be used when interpreting these. Still, meta-analyses are in many cases the best available evidence available as trials on diets could be challenging and, in several cases, could be unethical.

Thus, emphasized several sensitivity analyses. For some food groups, meta-analyses presenting dose—response data were not available, which yield more uncertainty in model output. The meta-analyses used in these data had high quality [ 23 ], while the meta-evidence ranged from very low eggs and white meat to high whole grains with most in the moderate quality category [ 22 ].

The overall meta-evidence was estimated as moderate for the optimal and feasibility approach diets. Still, the quality of the evidence for diet changes mostly involving eggs and white meat would be lower than when diet changes are dominated by whole grains, fish, processed meat, and nuts. This is reported in the tool for transparency.

For added oils, it is likely that olive oils that are rich in monounsaturated fatty acids have beneficial effects and are probably superior to several other added oils [ 15 , 30 , 31 ]. However, we did not have sufficient data to present different oils separately.

GBD provides background epidemiological data for the populations we have presented but involves a combination of background data and modeling. We have no information on the impact on past morbidity experienced due to disease, and this was therefore not included in the model, although different health profiles may be associated with different impact of food choices.

Thus, our estimates are based on population distributions of health indicators and do not account for differences in risk factors nor genetic vulnerability. The time perspective of diet change adds another layer of uncertainty. The duration of changes in the studies varies, and it is likely that short-term changes yield weaker effects than those presented in this article.

We assumed 10 years to achieve full effects while conducting sensitivity analyses for both 5, 30, and 50 years. Still, progress in development of medical treatments and continuous changes in lifestyle can affect the impact of diet on LE and thus add uncertainty to our estimates [ 35 ].

Thus, the methodology is not meant as individualized forecasting of life years gained, but rather population estimates under certain assumptions. Although we do not model nonfatal effects, LE is correlated with healthy life years.

Most of the background data are adjusted for factors such as smoking, exercise, age, and sex. However, some residual confounding may still affect the estimates. Further, we have not considered any long-term health consequences that are due to sustained excessive intake of food with high levels of toxins, such as dioxins and polychlorinated biphenyls, which are relevant for some types of fish and sea foods [ 40 , 41 ].

This is more likely to overestimate than underestimate effect sizes. There is also a risk of overadjustment as some of the studies included in meta-analyses adjusted for potential intermediate factors. This may contribute to underestimating the full impact on dietary changes on health.

Model development often have iterative improvements that will gradually give more precise estimates; however, the main messages are likely to be robust. Our sensitivity analyses indicate how the estimated changes in LE due to dietary changes vary if the true effects are over- or underestimated. Even the most conservative approaches indicate strong effects.

In conclusion, sustained change from a typical to an optimized diet from early age could translate into an increase in LE of more than 10 years.

Gains are reduced substantially with delayed initiation of changes, particularly when approaching the age of 80 years. An increase in the intake of legumes, whole grains, and nuts, and a reduction in the intake of red meat and processed meats, contributed most to these gains.

Fruits and vegetables also have a positive health impact, but for these food groups, the intake in a typical Western diet is closer to the optimal intake than for the other food groups. The Food4HealthyLife calculator could be a useful tool for both clinicians, policy makers, and laypeople to understand impact of various food choices.

FA, feasibility approach diet; LE, life expectancy; OD, optimized diet; TW, typical Western diet. Uncertainty intervals for some food groups have rounding differences compared to corresponding S2 Table due to symmetrical adjustment in the admetan package in Stata.

Estimates for change in LE is presented with sensitivity adjusted uncertainty intervals using lower interval as model adjustment of 0.

EU, Europe; LE, life expectancy; US, United States. Article Authors Metrics Comments Media Coverage Peer Review Reader Comments Figures. Correction 25 Mar Fadnes LT, Økland JM, Haaland ØA, Johansson KA Correction: Estimating impact of food choices on life expectancy: A modeling study.

Abstract Background Interpreting and utilizing the findings of nutritional research can be challenging to clinicians, policy makers, and even researchers. Conclusions A sustained dietary change may give substantial health gains for people of all ages both for optimized and feasible changes. Author summary Why was this study done?

Food is fundamental for health, and globally dietary risk factors are estimated to cause 11 million deaths and million disability-adjusted life years annually. The Global Burden of Diseases, Injuries, and Risk Factors study GBD provides summary measures of population health that are relevant when comparing health systems but does not estimate the impact of alterations in food group composition and respective health benefits.

The EAT—Lancet commission did present a planetary diet, but it gives limited information on the health impact of other diets, and few people are able to adhere to strict health maximization approaches. What did the researchers do and find? Our modeling methodology using meta-analyses, data from the Global Burden of Disease study and life table methodology showed that life expectancy LE gains for prolonged changes from typical Western to optimizing diets could translate into more than a decade for young adults.

The largest gains would be made by eating more legumes, whole grains and nuts, and less red and processed meat.

For older people, the gains would be smaller but substantial. What do these findings mean? Understanding the relative health potential of different food groups could enable people to make feasible and significant health gains.

The Food4HealthyLife calculator could be a useful tool for clinicians, policy makers, and laypeople to understand the health impact of dietary choices. Funding: The authors received no specific funding for this work.

Introduction Food is fundamental for health. Methods The LE at a certain age is the number of years an individual at that age is expected to live before they die given a set of age-specific mortality rates. Conceptually, our approach can be summed up as follows: Let LE age D be the age-specific LE with prolonged change to diet D.

Therefore, following a healthy dietary pattern and making every bite count is particularly important to this age group. The Healthy Eating Index HEI measures diet quality based on the Dietary Guidelines for Americans.

Compared to other age ranges, older adults have the highest diet quality, with an HEI score of 63 out of Eating more fruits, vegetables, whole grains, and dairy improves diet quality — as does cutting down on added sugars, saturated fat, and sodium.

Support from health professionals, friends, and family can help older adults meet food group and nutrient recommendations.

Eating enough protein helps prevent the loss of lean muscle mass. But older adults often eat too little protein — especially adults ages 71 and older. These protein sources also provide additional nutrients, such as calcium, vitamin D, vitamin B12, and fiber.

The ability to absorb vitamin B12 can decrease with age and with the use of certain medicines. Older adults should talk with their health care provider about the use of dietary supplements to increase vitamin B12 intake.

Healthy Beverage Choices for Older Adults. Unsweetened fruit juices and low-fat or fat-free milk or fortified soy beverages can also help meet fluid and nutrient needs. Health care providers can remind older patients to enjoy beverages with meals and throughout the day.

If older adults choose to drink alcohol, they should only drink in moderation — 2 drinks or less in a day for men and 1 drink or less in a day for women. Remember that this population may feel the effects of alcohol more quickly than they did when they were younger, which could increase the risk of falls and other accidents.

Supporting Older Adults in Healthy Eating. Similar to other life stages, health professionals, family, and friends can support older adults in achieving a healthy dietary pattern that fits with their budget, preferences, and traditions.

Healthy Beverage Choices for Older Adults. Unsweetened fruit juices and low-fat or fat-free milk or fortified soy beverages can also help meet fluid and nutrient needs.

Health care providers can remind older patients to enjoy beverages with meals and throughout the day. If older adults choose to drink alcohol, they should only drink in moderation — 2 drinks or less in a day for men and 1 drink or less in a day for women. Remember that this population may feel the effects of alcohol more quickly than they did when they were younger, which could increase the risk of falls and other accidents.

Supporting Older Adults in Healthy Eating. Similar to other life stages, health professionals, family, and friends can support older adults in achieving a healthy dietary pattern that fits with their budget, preferences, and traditions.

Additional factors to consider when supporting healthy eating for older adults include:. Find Resources to Help Older Adults Eat Healthy. There are a number of government resources that health professionals can use to support older individuals in accessing and achieving a healthy dietary pattern.

Choosing healthy foods and actively using nutrition resources can help people make every bite count, no matter their age.

For more information about these resources for older adults, check out Nutrition Programs for Seniors from Nutrition. Linking to a non-federal website does not constitute an endorsement by ODPHP or any of its employees of the sponsors or the information and products presented on the website.

Nutrition as We Age: Healthy Eating with the Dietary Guidelines. Special Considerations for Older Adults The Healthy Eating Index HEI measures diet quality based on the Dietary Guidelines for Americans. Supporting Older Adults in Healthy Eating Similar to other life stages, health professionals, family, and friends can support older adults in achieving a healthy dietary pattern that fits with their budget, preferences, and traditions.

Additional factors to consider when supporting healthy eating for older adults include: Enjoyment of food — Sharing meals with friends and family can increase food enjoyment and provide a great opportunity to share a lifetime of stories, all while improving dietary patterns.

However, you also need to ensure that you are likewise sustaining your own physical and mental health. How do you keep your standards high and your teaching schedule full while also remaining healthy and injury-free? Read on to find out how three avid veteran instructors achieve balance.

Their experiences and advice model best self-care practices for a long and successful career. It was a sprained ankle that spurred Ami McMullen, owner of Studio Hustle in Louisville, Kentucky, into perfecting her coaching and cuing skills.

And that skill has paid off; after all, the more telling you do, the less showing you have to do—and too much showing can lead to repetitive stress injuries.

Better cuing and coaching not only guard against overuse but also allow students to get the most out of your class. And if you have an injury, skilled cuing can save the class from falling to pieces, because people will be used to following your voice.

Of course, some formats, such as indoor cycling, are best done with the class. In addition, she finds that instructors who are constantly catching colds or the flu are often experiencing an imbalance. Another option is to keep updating your skills and educate yourself on new moves, advises McMullen.

McMullen also recommends investing in the right equipment so you can vary your routine with, say, kettlebell swings or medicine ball tosses. An often-overlooked area of instructor wellness relates to hearing and vocal injuries.

She finds that the current tendency in the industry is to play music much higher than the recommended decibel level, a practice that can hurt your hearing and strain your voice.

And while cardio and resistance are important components of any program, range of motion should rate, too, she says.

Flexibility work should incorporate a range of options. This is reported in the tool for transparency. For added oils, it is likely that olive oils that are rich in monounsaturated fatty acids have beneficial effects and are probably superior to several other added oils [ 15 , 30 , 31 ].

However, we did not have sufficient data to present different oils separately. GBD provides background epidemiological data for the populations we have presented but involves a combination of background data and modeling. We have no information on the impact on past morbidity experienced due to disease, and this was therefore not included in the model, although different health profiles may be associated with different impact of food choices.

Thus, our estimates are based on population distributions of health indicators and do not account for differences in risk factors nor genetic vulnerability.

The time perspective of diet change adds another layer of uncertainty. The duration of changes in the studies varies, and it is likely that short-term changes yield weaker effects than those presented in this article. We assumed 10 years to achieve full effects while conducting sensitivity analyses for both 5, 30, and 50 years.

Still, progress in development of medical treatments and continuous changes in lifestyle can affect the impact of diet on LE and thus add uncertainty to our estimates [ 35 ]. Thus, the methodology is not meant as individualized forecasting of life years gained, but rather population estimates under certain assumptions.

Although we do not model nonfatal effects, LE is correlated with healthy life years. Most of the background data are adjusted for factors such as smoking, exercise, age, and sex. However, some residual confounding may still affect the estimates.

Further, we have not considered any long-term health consequences that are due to sustained excessive intake of food with high levels of toxins, such as dioxins and polychlorinated biphenyls, which are relevant for some types of fish and sea foods [ 40 , 41 ].

This is more likely to overestimate than underestimate effect sizes. There is also a risk of overadjustment as some of the studies included in meta-analyses adjusted for potential intermediate factors.

This may contribute to underestimating the full impact on dietary changes on health. Model development often have iterative improvements that will gradually give more precise estimates; however, the main messages are likely to be robust. Our sensitivity analyses indicate how the estimated changes in LE due to dietary changes vary if the true effects are over- or underestimated.

Even the most conservative approaches indicate strong effects. In conclusion, sustained change from a typical to an optimized diet from early age could translate into an increase in LE of more than 10 years.

Gains are reduced substantially with delayed initiation of changes, particularly when approaching the age of 80 years. An increase in the intake of legumes, whole grains, and nuts, and a reduction in the intake of red meat and processed meats, contributed most to these gains.

Fruits and vegetables also have a positive health impact, but for these food groups, the intake in a typical Western diet is closer to the optimal intake than for the other food groups.

The Food4HealthyLife calculator could be a useful tool for both clinicians, policy makers, and laypeople to understand impact of various food choices. FA, feasibility approach diet; LE, life expectancy; OD, optimized diet; TW, typical Western diet.

Uncertainty intervals for some food groups have rounding differences compared to corresponding S2 Table due to symmetrical adjustment in the admetan package in Stata.

Estimates for change in LE is presented with sensitivity adjusted uncertainty intervals using lower interval as model adjustment of 0. EU, Europe; LE, life expectancy; US, United States. Article Authors Metrics Comments Media Coverage Peer Review Reader Comments Figures.

Correction 25 Mar Fadnes LT, Økland JM, Haaland ØA, Johansson KA Correction: Estimating impact of food choices on life expectancy: A modeling study.

Abstract Background Interpreting and utilizing the findings of nutritional research can be challenging to clinicians, policy makers, and even researchers.

Conclusions A sustained dietary change may give substantial health gains for people of all ages both for optimized and feasible changes. Author summary Why was this study done? Food is fundamental for health, and globally dietary risk factors are estimated to cause 11 million deaths and million disability-adjusted life years annually.

The Global Burden of Diseases, Injuries, and Risk Factors study GBD provides summary measures of population health that are relevant when comparing health systems but does not estimate the impact of alterations in food group composition and respective health benefits.

The EAT—Lancet commission did present a planetary diet, but it gives limited information on the health impact of other diets, and few people are able to adhere to strict health maximization approaches. What did the researchers do and find? Our modeling methodology using meta-analyses, data from the Global Burden of Disease study and life table methodology showed that life expectancy LE gains for prolonged changes from typical Western to optimizing diets could translate into more than a decade for young adults.

The largest gains would be made by eating more legumes, whole grains and nuts, and less red and processed meat. For older people, the gains would be smaller but substantial.

What do these findings mean? Understanding the relative health potential of different food groups could enable people to make feasible and significant health gains. The Food4HealthyLife calculator could be a useful tool for clinicians, policy makers, and laypeople to understand the health impact of dietary choices.

Funding: The authors received no specific funding for this work. Introduction Food is fundamental for health. Methods The LE at a certain age is the number of years an individual at that age is expected to live before they die given a set of age-specific mortality rates.

Conceptually, our approach can be summed up as follows: Let LE age D be the age-specific LE with prolonged change to diet D. LE age D is calculated using standard age-specific lifetable methodology, where annual mortality rates are adjusted according to the selected diet i.

The baseline diet yields LE age D 0. Life years gained or lost because of change from the baseline diet to diet D is now LE age D —LE age D 0.

In each case, dietary intake was improved from the TW through feasible to optimal levels rounded off : Whole grains fresh weight : TW 50 g, FA Results In this section, we will focus on the United States, but the results for China and Europe were generally very similar can be found in S2 — S15 Figs.

Download: PPT. Table 1. LE for males and females at different ages from the United States, China, and Europe for different diets.

Fig 1. Expected life years gained for year-old female adults left forest plot and males right forest plot from the United States who change from a typical Western diet to an optimized or feasible approach diet with changes indicated in grams per day. Fig 2. Fig 3. Expected increase in LE for optimizing different food groups with diet changes initiating from various ages between 20 and 80 years of age left plot.

Discussion In this paper, we present a method for estimating the impact of food choices on LE. Supporting information. S1 Text. s PDF. S2 Text.

String used in PubMed to identify meta-analyses for setting hazard ratios. S3 Text. Estimated intake of various food groups in the United States and Norway. S1 Table. S2 Table.

Increase in LE for each food group change for and year-old female and male adults from the United States, who change from a TW to OD or FA.

S3 Table. Absolute and relative change in LE with delay to full effects of 10 default , 5, 30, and 50 years for , , , and year-old females and males from the United States. S1 Fig. Example of calculator input and output. S2 Fig. Expected life years gained for year-old female adults from China who change from a typical Western diet to an optimized or feasible approach diet with changes indicated in grams.

s PNG. S3 Fig. Expected life years gained for year-old male adults from China who change from a typical Western diet to an optimized or feasible approach diet with changes indicated in grams.

S4 Fig. Expected life years gained for year-old female adults from Europe who change from a typical Western diet to an optimized or feasible approach diet with changes indicated in grams.

S5 Fig. Expected life years gained for year-old male adults from Europe who change from a typical Western diet to an optimized or feasible approach diet with changes indicated in grams. S6 Fig. Expected life years gained for year-old female adults from the United States who change from a typical Western diet to an optimized or feasible approach diet.

S7 Fig. Expected life years gained for year-old male adults from the United States who change from a typical Western diet to an optimized or feasible approach diet.

S8 Fig. S9 Fig. S10 Fig. S11 Fig. S12 Fig. S13 Fig. S14 Fig. S15 Fig. S16 Fig. Expected increase in LE for optimizing different food groups with diet changes initiating from various ages between 20 and 80 years of age time to full effect: 5 years.

S17 Fig. Expected increase in LE for optimizing different food groups with diet changes initiating from various ages between 20 and 80 years of age time to full effect: 30 years. S1 TRIPOD Checklist. Checklist for prediction model development. Acknowledgments Thanks to Arngeir Berge for assistance with images.

References 1. GBD Diet Collaborators. Health effects of dietary risks in countries, — a systematic analysis for the Global Burden of Disease Study Epub Apr 8. Aune D, Giovannucci E, Boffetta P, Fadnes LT, Keum N, Norat T, et al.

Fruit and vegetable intake and the risk of cardiovascular disease, total cancer and all—cause mortality—a systematic review and dose—response meta—analysis of prospective studies. Int J Epidemiol. Epub Mar Aune D, Keum N, Giovannucci E, Fadnes LT, Boffetta P, Greenwood DC, et al.

Whole grain consumption and risk of cardiovascular disease, cancer, and all cause and cause specific mortality: systematic review and dose—response meta—analysis of prospective studies. Epub Jun Nut consumption and risk of cardiovascular disease, total cancer, all—cause and cause—specific mortality: a systematic review and dose—response meta—analysis of prospective studies.