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Ate Everything: 69 Tall Giant Hafthor Bjornson Shared the Diet That Made Him Worlds Strongest Man in 2018  EssentiallySports

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Ate Everything: 69 Tall Giant Hafthor Bjornson Shared the Diet That Made Him Worlds Strongest Man in 2018 - EssentiallySports

No food is banned! Sue Cleaver lost 3st by following popular diet that includes alcohol  Express

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No food is banned! Sue Cleaver lost 3st by following popular diet that includes alcohol - Express

A Cornell University nutritional biochemist to lecture on the link between diet and disease  The Villages Daily Sun

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A Cornell University nutritional biochemist to lecture on the link between diet and disease - The Villages Daily Sun

While theres a lack of research focusing on this specific diet plan, there is an abundance of research on plant-based eating.

There is abundant research overwhelming, in fact on the general health benefits of diverse dietary patterns that emphasize whole plant foods, Dr. Katz adds.

The other fasting-related aspects of the longevity diet fasting-mimicking and intermittent fasting are less studied. It channels the science of calorie restriction and fasting, but whether this practice, twice a year, really does translate into altered longevity for humans, independently of other factors, is, of course, unknown, says Katz. But animal research suggests this style of eating may hold promise.

In theApril 2022 issue of Cell, Longo notes that fasting-mimicking diets have been linked with metabolic and anti-inflammatory effects in mice. These results could reduce risk factors for certain diseases, he writes.

A review published in October 2021 in the Annual Review of Nutrition states that intermittent fasting patterns such as time-restricted eating (which is a part of the longevity diet) is a safe way to improve metabolic health for people who are obese. Yet the jury is out regarding other benefits. For example, one study, published in April 2022 in the New England Journal of Medicine found that a time-restricted diet was not more beneficial for weight loss in people with obesity compared to a calorie restricted diet.

Heres a snapshot of some of the possible health effects of this eating plan.

Given the name of the diet, this potential perk likely comes as no surprise. The element of the longevity diet that researchers have studied most widely is plant-based eating.

Research suggests one can boost life expectancy by 3 to 13 years by replacing the Western diet of red meat and processed foods with a diet that contains more nutrient-rich foods that include vegetables, fruits, legumes, whole grains, and nuts, explains Palumbo. The research Palumbo points to, published in February 2022 in the journal PLOS Medicine, notes that when people start the diet earlier, the gains may be even greater.

Katz, though, adds a caveat. The only evidence in direct support of longevity, per se, is observation of the links between dietary intake patterns and longevity in populations such as the blue zones, he says. There are, for obvious reasons, no intervention studies or randomized trials assessing actual longevity in humans, as such trials would span the lifetimes or more of the researchers who initiated them, and few would be willing to participate as subjects, Katz adds.

Plant-based eating, which features plenty of produce, is a smart choice for heart health. As the World Health Organization points out, heart diseases are the leading causes of death worldwide.

Areview published in February 2017 in the International Journal of Epidemiology found that five servings of vegetables and fruits a day was associated with a reduced risk of cardiovascular disease. And even more servings per day (around 10) was associated with even lower risk.

Another review of research found that the more vegetables and fruits people consumed, the lower their odds of developing cardiovascular disease, compared with people who ate only 1.5 servings of vegetables per day.

Research published in June 2022 in the European Heart Journalfound that a diet rich in potassium (from longevity dietapproved foods like avocados and salmon) was associated with a lower risk of cardiovascular events, and especially helped women who had high levels of sodium in their diet.

While fish isnt necessarily a staple in a plant-based diet, it is a feature of the longevity diet, and fish is good for the heart, research suggests. For example, a study published in June 2022 inJAHA found that 3 grams of omega-3 fatty acids daily was associated with lower blood pressure. High blood pressure, or hypertension, is a risk factor for heart disease, as the CDC notes.

Plant-based eating may help protect against cancer. In the aforementioned review in the International Journal of Epidemiology, not only did researchers find that a diet rich in fruits and veggies was associated with a lower risk of cardiovascular disease, but they also found it lowered peoples odds of cancer.

In addition, research published in February 2022 in the journal BMC Medicine found that those who ate a low-meat or meat-free diet (in this study, that was defined as meat five times or less per week) had a lower overall cancer risk than those who consumed more.

Eating ample plant-based foods, like vegetables, legumes, and nuts is a key pillar in the longevity diet. And research published in April 2022 in the journal Diabetologia suggests that a higher total fruit and vegetable intake may be associated with a lower risk of type 2 diabetes in men specifically (there wasnt an association with women in this particular study).

Meanwhile, a diet high in red meat and poultry may increase your risk of type 2 diabetes, research published in May 2017 in the American Journal of Epidemiology shows.

A healthy, plant-based diet may help prevent eye diseases that can come along with old age, like cataracts and macular degeneration, according toHarvard T.H. Chan School of Public Health. For example, research suggests that high amounts of vegetables and fruits are associated with a lower risk of cataracts (yet there was no reduced risk for cataract extraction even among people who ate the highest amounts of fruits and vegetables). In the study, the high group of fruit and veggie eaters consumed around 10 servings a day, while the lowest group consumed about three servings each day.

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What Is the Longevity Diet? A Detailed Scientific Guide - Everyday Health

Does eating red meat increase ones risk of heart disease? Would eating more vegetables help? Is leaving high blood pressure untreated really a death wish? The answers might vary, depending on who a person asks, which friend or TikTok nurse, and when. Researchers at the University of Washington want to make it easier to find current, evidence-based health advice.

A new tool from the Institute for Health Metrics and Evaluation, unveiled Monday in Nature Medicine, uses a 5-star rating system to show how much evidence exists to support some diet and lifestyle changes. The researchers analyzed hundreds of studies in hopes of helping consumers, clinicians and policymakers awash in a landscape of wellness influencers, food lobbyists and quack advice cut through the chatter and know the scientific consensus. The result is what they are calling the Burden of Proof studies, since its on the research to prove something is legitimate.

Other such reviews exist, the Cochrane Library being a repository of many of them. This new tool, the authors say, is complementary to what exists, but also slightly different. Many epidemiologists assume that risk increases about the same no matter how many grams of vegetables someone eats a day, for example. Burden of Proof allows us to understand better how the risk actually changes with consumption, the authors said.

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In medicine, theres always been some skepticism about how changes to peoples behaviors can affect their long-term health, especially when it comes to recommending specific foods or activities, said Christopher Murray, senior author of the papers and founder of the IHME.

Clickbait headlines and grocery cart contents reflect the uncertainty. Cows milk is bad, and then its good. Butter nay, all fats must be gone, but then theyre back. Once the shopping cart is full, the Mediterranean, Keto, Paleo and South Beach diets compete for dominion on magazine covers in the checkout line. The peanut butter cups loom. (Is chocolate good or bad? Wait, what about peanut butter?)

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Diet research is really challenging, said Jeffrey Stanaway, assistant professor of global health and lead author on the groups analysis of vegetable health studies. It is difficult for researchers to measure how much people eat, to do so over time, and to separate their diet from other health factors (people who eat lots of fruits and vegetables are more likely to exercise, for example).

And yet, diet and other behaviors play a significant role in disease prevention. About half of the U.S. population has a chronic condition, and long-term illnesses like heart disease, diabetes and cancer are major drivers of disability and death worldwide. The vast majority of what makes you healthy happens outside the doctors office, said Georges Benjamin, executive director of the American Public Health Association.

By evaluating the available data for any link between vegetable eating and five different health outcomes, Stanaway could come to a conclusion: The evidence on vegetables is pretty good, he said. Even a conservative interpretation of the evidence, which the IHME tool uses, showed eating more vegetables is tied to a reduced risk of chronic disease, though future studies could affect that. The model is meant to be updated, and will be, as additional research becomes available, the team said.

A three-star relationship between an increase in non-starchy, fibrous vegetable consumption and ischemic stroke was the strongest link of the bunch. Data suggest increasing vegetable consumption from one to four servings per day carried about a 23% reduction in stroke risk. The analysis also showed a two-star rating for vegetable-eating and heart disease (two on the verge of three, Stanaway said). The study did not include starchy vegetables, such as potatoes, sweet potatoes or corn, and also excluded cured and pickled vegetables (kimchee, sauerkraut).

For the most part, dietary habits landed between one and three stars, indicating a need for more rigorous research. I was very surprised at how many of the diet-risk relationships were much weaker than expected, Murray said. He has a slight bit more tolerance for eating red meat after seeing those results, he said.

All evidence on red meat and its links to disease were weak. That wasnt unexpected to Benjamin, who wasnt involved in the research. The things that have always been kind of fuzzy still look kind of fuzzy, he said.

The strongest ratings on a meat-heavy diet were two stars, for colon and rectum cancer, breast cancer, ischemic heart disease and type 2 diabetes. In the case of strokes, the researchers found a diet high in red meat could actually have some protective effects, and gave that evidence one-star ratings. Low star-ratings should be seen as areas for research investment, the IHME team said a large, well-designed study on people with diets high in red meat could make a big impact.

Tobacco is often the place where all of the fiery debate comes to rest. There is wide consensus among health professionals that smoking tobacco is bad for humans. IHMEs tool found evidence for strong or very strong links across eight diseases or outcomes, including larynx cancer, aortic aneurysm, peripheral arterial disease of the lower limbs, tracheal, bronchus and lung cancer, chronic obstructive pulmonary disease, and others.

It is irrefutable that tobacco is a major risk to health and really has a broad set of impacts across multiple cardiovascular and cancer outcomes, all in all, Murray said.

Still, there was less robust evidence on the connection between smoking and numerous other illnesses, including ischemic heart disease, esophageal cancer, stroke, type 2 diabetes, and others. Strangely, there was a one-star-rated link between smoking and asthma, a finding that surprised the researchers. Cannabis smoking was not included in the analysis.

The risk of ischemic heart disease was strongly linked to high systolic blood pressure a five-star rating validating both common dogma among clinicians and the IHME tools accuracy, the researchers said in a news conference.

The IHME team has already analyzed nearly 200 other risk-outcome combinations, ranging from alcohol drinking, air pollution and high body-mass index, to other diet factors, such as eating whole grains and legumes. Those results will be published in the future, Murray said.

Benjamin said it will take time for clinicians, policymakers and patients to see the value of this tool the data alone might not be enough to sway the publics understanding of risk.

Where the rating system could be useful in the long run is the doctors office, when a clinician is crafting a care plan for a patient with multiple risk factors (say, smoking, high blood pressure and low vegetable consumption). If what we know about those risks can be weighed against each other, then the doctor and patient might have a better sense of what to prioritize, Benjamin said. The less things you give people to do, the better, and the more likely they are to comply, he said.

STATs coverage of chronic health issues is supported by a grant fromBloomberg Philanthropies. Our financial supportersare not involved in any decisions about our journalism.

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Which diet and health habits are backed by science? Now there's a tool for that - STAT

More than half of people who become pregnant are overweight or obese at the time of conception, and obesity during pregnancy is associated with progeny who develop metabolic syndrome later in life.

Studies of humans and mammalian animal models have shown, for example, that high-fat diets during pregnancy and while nursing result in offspring more likely to develop nonalcoholic fatty liver disease and to have altered bile acid homeostasis.

Scientists at the Washington University School of Medicine in St. Louis recently undertook a study to learn more about how maternal obesity might influence the development of cholestasis, a liver disease for which therapies are limited.

In cholestasis, bile cannot reach the duodenum, the first portion of the small intestine, where it is supposed to facilitate food digestion. The disease can be brought on by several factors, including duct obstructions or narrowing, toxic compounds, infection and inflammation, disturbance of intestinal microbiota, and genetic abnormalities.

In their study, published in the Journal of Lipid Research, Michael D. Thompson and collaborators at Washington University fed female mice conventional chow or a high-fat, high-sucrose diet and bred them with lean males.

They fed the offspring DDC, which is short for 3,5-diethoxycarbonyl-1,4-dihydrocollidine, for two weeks to induce cholestasis. After this feeding period, the offspring ate conventional chow for 10 more days. They found that offspring from females on the high-fat, high-sucrose diet had increased fine branching of the bile duct and enhanced fibrotic response to DDC treatment and delayed recovery times from it.

Earlier this year, the team reported changes to offspring microbiome after maternal consumption of high-fat, high-sucrose chow, so they decided to feed antibiotic-treated mice cecal contents from the offspring that had been fed conventional chow or high-fat, high-sucrose, followed by DDC for two weeks. They found that cholestatic liver injury is transmissible in these mice models, further supporting the role of the microbiome in this disease.

For those reasons and others, a lot of research has been done and continues to this day on the effects of maternal diet on offspring.

Davidson et al./JLR

The term cholestasis is derived from the Greek phrase meaning bile halting. The graphic above shows how the researchers bred, fed and completed cecal microbiome transplantation. HF/HS is short for high-fat, high-sucrose, and DDC is short for 3,5-diethoxycarbonyl-1,4-dihydrocollidine.

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Maternal diet's effects on liver disease in offspring - ASBMB Today

Erling Haaland has revealed some of the food he eats to ensure his body is in perfect condition, including heart and liver.

WHAT HAPPENED? Haaland has made an electrifying start to life in the Premier League, netting 15 times already this season. The secret behind his incredible start may lie in his diet, which he disclosed recently - including eating heart!

WHAT HE SAID: In an a documentary named 'Haaland: The Big Decision' the striker revealed some of the more bizarre elements of his diet, including heart and liver. He said: "You [other people] don't eat this, but I am concerned with taking care of my body. I think eating quality food that is as local as possible is the most important. People say meat is bad for you, but which? The meat you get at McDonald's? Or the local cow eating grass right over there? I eat the heart and the liver."

THE BIGGER PICTURE: The Norwegian striker eats a home-cooked lasagne made by his dad, Alfie, before every home game, and manager Pep Guardiola joked about the meal after their victory over Southampton. He said: "We can make an offer for Erlings father to cook for us. If this is the secret of Erlings goals, I will convince [chairman] Khaldoon [Al Mubarak] to bring him here! But I dont think theres just one secret."

AND WHAT'S MORE: Haaland reportedly consumes around 6,000 calories a day to keep himself in tip-top shape. He also incorporates some rather odd practices into his daily routine, including the filtration of his water and getting sunlight in his eyes immediately after waking up. What ever he does, it seems to be working!

IN THREE PHOTOS:

WHAT NEXT FOR HAALAND? Manchester City face Copenhagen in the Champions League on October 11 before a mammoth Premier League clash against Liverpool. Haaland's first game for City was against the Reds in the Community Shield, where he was rather underwhelming, but he has since proved any doubter wrong, and probably will again on October 16.

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'You don't eat this' - Haaland reveals bizarre diet behind roaring start to Man City career - Goal.com

What is culture? According to the Oxford Learners Dictionary, it is the customs, arts, social institutions, and achievements, of a particular nation, people or social group. During the months of September and October, the United States celebrates National Hispanic Heritage Month to show support for the impact and contributions that Hispanic communities have made to this country. While it is important to appreciate the great food, arts, music and familia, the Hispanic community should take a step back and critically think of the impact their culinary culture has on their mental health.

In the Hispanic community, eating disorders are often neglected as a health issue and, instead, are treated like tantrums. They are swept under the rug mostly to avoid the spread of word that someone in the family is ill, or worse, crazy. There is an enormous amount of importance placed on what others will think or say rather than helping people to solve their issues, but there is also the firm, antiquated mentality of We dont talk about that nonsense. Hispanic people are taught from a young age that problems are not meant to be shared. Instead, one should silently deal with them on their own in an effort to not be a burden to others. This results in private issues evolving into generational trauma and being normalized amongst the community.

Restrictive eating disorders like anorexia and bulimia can start from a young age when girls are complimented for their small size and thinness, causing them to internalize that validation and restrict their eating habits so they can maintain their physique. But it is also often contradicted by their families when told that they should eat more because they are too thin and that nobody is going to love them. Sadly, there is an underlying cultural obsession that women should be thin and small, so they can attract a prospective husband and get married. That underlying obsession has become normalized, and in some cases encouraged, leading young women to develop an unhealthy relationship with food.

The unhealthy relationship that is created by commentary is overall an all-over-the-place contradiction that has no beginning and no end. Moms and tias are constantly judging, criticizing and scrutinizing their daughters and nieces bodies. Constant comments like eat a little less or youll get fat, are you sure you want to eat that, or have you gained weight, create food insecurity that affects self-esteem, eating habits and mental health.

While many people will dismiss the harmful comments stating that it is our culture, it does not make it okay. Once again, it creates unresolved problems that will be passed down through the generations due to a stubborn and dangerous mentality. The Hispanic community should and has been aiming to create and hold a conversation regarding these issues to create a solution in order to break that cultural aspect.

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It is not a diet, it is a problem - UTSA The Paisano

The results highlight both the need for routine blood bile acid level testing as well as caution when individuals with high bile acid levels consume fiber.

Fiber-enriched foodsare often consumed by many individuals to promote weight loss and fend against chronic diseases like cancer and diabetes.

Consuming highly refined fiber, however, may raise the risk of liver cancer in certain people, especially those with a silent vascular deformity, according to a recent study from The University of Toledo.

The finding, which is described in a report published in the journal Gastroenterology, adds to UToledos expanding body of knowledge about the undervalued role that our gut plays in the origin of disease.

We have worked for a long time on this idea that all diseases start from the gut, said Dr. Matam Vijay-Kumar, a professor in the Department of Physiology and Pharmacology in the College of Medicine and Life Sciences and the papers senior author. This study is a notable advancement of that concept. It also provides clues that may help identify individuals at a higher risk for liver cancer and potentially enable us to lower that risk with simple dietary modifications.

From left, Dr. Matam Vijay-Kumar, a professor in the Department of Physiology and Pharmacology, and Dr. Beng San Yeoh, a postdoctoral fellow. Credit: University of Toledo

Vijay-Kumars team published a major paper in the journal Cell in 2018 that revealed a large proportion of mice with immune system defects developed liver cancer after being given an inulin-fortified diet.

Inulin is a refined, plant-based fermentable fiber that is sold in supermarkets as a health-promoting prebiotic. Additionally, it is often found in processed foods.

Vijay-Kumar and colleagues found that around one in ten regular, otherwise healthy lab mice got liver cancer after consuming the inulin-containing diet, despite the fact that inulin promotes metabolic health in the majority of those who consume it.

That was very surprising, given how rarely liver cancer is observed in mice, said Vijay-Kumar, who is also director of the UToledo Microbiome Consortium. The findings raised real questions about the potential risks of certain refined fibers, but only now do we understand why the mice were developing such aggressive cancer.

The new study offers a clear explanation and may have implications that go beyond laboratory animals.

As the team furthered its investigation, the researchers discovered all mice that developed malignant tumors had high concentrations of bile acids in their blood caused by a previously unnoticed congenital defect called a portosystemic shunt.

Normally, blood leaving the intestines goes into the liver where it is filtered before returning to the rest of the body. When a portosystemic shunt is present, blood from the gut is detoured away from the liver and back into the bodys general blood supply.

The vascular defect also allows the liver to continuously synthesize bile acids. Those bile acids eventually spill over and enter circulation instead of going into the gut.

Blood thats diverted away from the liver contains high levels of microbial products that can stimulate the immune system and cause inflammation.

To check that inflammation, which can be damaging to the liver, the mice react by developing a compensatory anti-inflammatory response that dampens the immune response and reduces their ability to detect and kill cancer cells.

While all mice with excess bile acids in their blood were predisposed to liver injury, only those fed inulin progressed to hepatocellular carcinoma, a deadly primary liver cancer.

Remarkably, 100% of the mice with high bile acids in their blood went on to develop cancer when fed inulin. None of the mice with low bile acids developed cancer when fed the same diet.

Dietary inulin is good in subduing inflammation, but it can be subverted into causing immunosuppression, which is not good for the liver, said Dr. Beng San Yeoh, a postdoctoral fellow and the new papers first author.

Dr. Bina Joe, Distinguished University Professor and chair of the Department of Physiology and Pharmacology, and a co-author of the study said the high-impact publication demonstrates the pioneering research being done at UToledo.

The role of the gut and gut bacteria in health and disease is an exciting and important area of research, and our team is providing new insights on the leading edge of this field, she said.

Beyond the laboratory, UToledos research could provide insight that might help clinicians identify people who are at higher risk of liver cancer years in advance of any tumors forming.

Portosystemic shunts in humans are relatively rare the documented incidence is only one in 30,000 people at birth. However, given that they generally cause no noticeable symptoms, the true incidence may be many times greater. Portosystemic shunting also commonly develops following liver cirrhosis.

Theorizing that high bile acid levels might serve as a viable marker for liver cancer risk, Vijay-Kumars team tested bile acid levels in serum samples collected between 1985 and 1988 as part of a large-scale cancer prevention study.

In the 224 men who went on to develop liver cancer, their baseline blood bile acid levels were twice as high as men who did not develop liver cancer. Statistical analysis also found individuals with the highest blood bile acid levels had a more than four-fold increase in the risk of liver cancer.

The research team also sought to examine the relationship between fiber consumption, bile acid levels, and liver cancer in humans.

While existing epidemiological studies dont differentiate between soluble and non-soluble fiber, researchers could look at fiber consumption in concert with blood bile acids.

There are two basic types of naturally occurring dietary fiber, soluble and insoluble. Soluble fibers are fermented by gut bacteria into short-chain fatty acids. Insoluble fibers pass through the digestive system unchanged.

Intriguingly, researchers found high total fiber intake reduced the risk of liver cancer by 29% in those whose serum bile acid levels were in the lowest quartile of their sample.

However, in men whose blood bile acid levels placed them in the top quarter of the sample, high fiber intake conferred a 40% increased risk of liver cancer.

Taken together, Yeoh and Vijay-Kumar say the findings suggest both the need for regular blood bile acid level testing and a cautious approach to fiber intake in individuals who know they have higher-than-normal levels of bile acids in their blood.

Serum bile acids can be measured by a simple blood test developed over 50 years ago. However, the test is usually only performed in some pregnant women, Vijay-Kumar said. Based on our findings, we believe this simple blood test should be incorporated into the screening measurements that are routinely performed to monitor health.

And while the researchers are not arguing broadly against the health-promoting benefits of fiber, they are urging attention to what kind of fiber certain individuals eat, underscoring the importance of personalized nutrition.

All fibers are not made equal, and all fibers are not universally beneficial for everyone. People with liver problems associated with increased bile acids should be cautious about refined, fermentable fiber, Yeoh said. If you have a leaky gut liver, you need to be careful of what you eat, because what you eat will be handled in a different way.

References: Enterohepatic Shunt-Driven Cholemia Predisposes to Liver Cancer by Beng San Yeoh, Piu Saha, Rachel M. Golonka, Jun Zou, Jessica L. Petrick, Ahmed A. Abokor, Xia Xiao, Venugopal R. Bovilla, Alexis C.A. Bretin, Jess Rivera-Esteban, Dominick Parisi, Andrea A. Florio, Stephanie J. Weinstein, Demetrius Albanes, Gordon J. Freeman, Amira F. Gohara, Andreea Ciudin, Juan M. Perics, Bina Joe, Robert F. Schwabe, Katherine A. McGlynn, Andrew T. Gewirtz and Matam Vijay-Kumar, 18 August 2022, Gastroenterology.DOI: 10.1053/j.gastro.2022.08.033

Dysregulated Microbial Fermentation of Soluble Fiber Induces Cholestatic Liver Cancer by Vishal Singh, Beng San Yeoh, Benoit Chassaing, Xia Xiao, Piu Saha, Rodrigo Aguilera Olvera, John D. Lapek Jr., Limin Zhang, Wei-Bei Wang, Sijie Hao, Michael D. Flythe, David J. Gonzalez, Patrice D. Cani, Jose R. Conejo-Garcia, Na Xiong, Mary J. Kennett, Bina Joe, Andrew D. Patterson, Andrew T. Gewirtz and Matam Vijay-Kumar, 18 October 2018, Cell.DOI: 10.1016/j.cell.2018.09.004

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Diets High in Processed Fiber May Increase Cancer Risk - SciTechDaily

Untargeted plasma metabolites in Dutch cohorts

In this study, we examined plasma metabolomes in 1,679 fasting plasma samples from 1,368 individuals from two LLD5 sub-cohorts (LLD1 and LLD2) and the GoNL6 cohort (Extended Data Fig. 1 and Supplementary Table 1). The LLD1 cohort was the discovery cohort, with information about genetics, diet and the gut microbiome available for 1,054 participants. Moreover, 311 LLD1 subjects were followed up 4years later (LLD1 follow-up). We also included two independent replication cohorts: 237 LLD2 participants for whom we had genetic and dietary data and 77 GoNL participants for whom only genetic data were available (Extended Data Fig. 1 and Supplementary Table 1). Untargeted metabolomics profiling was done using flow-injection time-of-flight mass spectrometry (FI-MS)10,11, which yielded plasma levels of 1,183 metabolites (Supplementary Table 2). These metabolites covered a wide range of lipids, organic acids, phenylpropanoids, benzenoids and other metabolites (Extended Data Fig. 2a). As we observed weak (absolute rSpearman<0.2) correlations among the 1,183 metabolites (Extended Data Fig. 2b), data reduction was not required and, consequently, all metabolites were subjected to subsequent analyses. We validated the identification and quantification of some metabolites (for example, bile acids, creatinine, lactate, phenylalanine and isoleucine) by comparing their abundance levels from FI-MS with those previously determined by liquid chromatography with tandem mass spectrometry (LC-MS/MS)12 or NMR13 (rSpearman>0.62; Extended Data Fig. 2c,d).

To compare the relative importance of diet, genetics and the gut microbiome in explaining inter-individual plasma metabolome variability, we calculated the proportion of variance explained by these three factors for the whole plasma metabolome profile and for the individual metabolites separately. We have detailed information on 78 dietary habits (Supplementary Table 3), 5.3million human genetic variants and the abundances of 156 species and 343 MetaCyc pathways for each individual of the LLD1 cohort. Diet, genetics and the gut microbiome could explain 9.3, 3.3 and 12.8%, respectively, of inter-individual variations in the whole plasma metabolome, without adjusting for covariates (see the Methods section Distance matrix-based variance estimation; false discovery rate (FDR)<0.05; Fig. 1a and Supplementary Table 4), whereas intrinsic factors (age, sex and body mass index (BMI)) and smoking collectively explained 4.9% of the variance. Together, these factors explain 25.1% of the variance in the plasma metabolome (Fig. 1a).

a, Inter-individual variation in the whole plasma metabolome explained by the indicated factors, estimated using the PERMANOVA method. All, all of the indicated factors combined; smk, smoking status. b, Venn diagram indicating the number of metabolites whose inter-individual variation was significantly explained by diet, genetics or the gut microbiome, as estimated using the linear regression method (FDRF-test<0.05). c, Inter-individual variations in metabolites explained by diet, genetics or the gut microbiome, as estimated using the linear regression method (the lasso regression method was applied for feature selection) with a significant estimated adjusted r2>5% (FDRF-test<0.05). The blue bars represent dietary contributions to metabolite variations, the yellow bars indicate genetic contributions and the orange bars indicate microbial contributions. The other colors indicate the metabolic categories of metabolites (see legend). The yaxis indicates the proportion of variation explained. TMAO, trimethylamine N-oxide.

Next, we tested for pairwise associations between each metabolite and the dietary variables, genetic variants and microbial taxa. We observed 2,854 associations with dietary habits (Supplementary Table 5), 48 associations with 40 unique genetic variants (metabolite quantitative trait loci (mQTLs); Supplementary Table 6), 1,373 associations with gut bacterial species (Supplementary Table 7) and 2,839 associations with bacterial MetaCyc pathways (Supplementary Table 8) (see the Methods sections Associations with dietary habits, QTL mapping and Microbiome-wide associations). In total, 769 metabolites were significantly associated with at least one factor (Fig. 1b and Supplementary Tables 58). We then performed interaction analysis to assess the role of dietmicrobiome, geneticsmicrobiome and dietgenetics interactions in regulating the human metabolome using an interaction term in the linear model (see the Methods section Interaction analysis). Among these, 185 metabolites were associated with multiple factors and seven were affected by either geneticsmicrobiome, geneticsdiet or dietmicrobiome interactions (Supplementary Table 9).

As interactions were limited, we further assessed the proportion of variance of each metabolite that was explained by these factors using an additive model with the least absolute shrinkage and selection operator (lasso) method (see the Methods section Estimating the variance of individual metabolites). In general, the inter-individual variations in 733 metabolites could be explained by at least one of the three factors (FDRF-test<0.05; Supplementary Table 10). In detail, dietary habits contributed 0.435% of the variance in 684 metabolites; microbial abundances contributed 0.725% of the variance in 193 metabolites; and genetic variants contributed 328% of the variance in 44 metabolites (adjusted r2; FDRF-test<0.05; Supplementary Table 10). We also estimated the explained variance of metabolites using Elastic Net14, which is designed for highly correlated features, and found that the estimated explained variances were comparable between linear regression and the Elastic Net regression (Supplementary Fig. 1).

We further compared the variance explained by each type of factor (diet, genetics or the microbiome) and assigned the dominant factor for each metabolite if one factor explained more variance than the other two. Inter-individual variations in 610 metabolites were mostly explained by diet, 85 were explained by the gut microbiome and 38 were explained by genetics (Supplementary Table 10). Hereafter, we refer to these as diet-dominant, microbiome-dominant and genetics-dominant metabolites, respectively. The dominant factors of metabolites highlight their origin. For instance, ten out of the 21 diet-dominant metabolites for which diet explained >20% of the variance (FDRF-test<0.05; Supplementary Table 10) were food components based on their annotation in the Human Metabolome Database (HMDB)15. Similarly, of the 85 microbiome-dominant metabolites, 23 were annotated in the HMDB as microbiome-related metabolites (including 15 uremic toxins). Furthermore, out of the 38 genetics-dominant metabolites, ten were lipid species and eight were amino acids. Taken together, our analysis highlights that one factoreither dietary, genetic or microbialcan have a dominant effect over the other two in explaining the variances of plasma metabolites, with diet or the microbiome being particularly dominant. However, we also found that the variances in 185 metabolites were significantly attributable to more than one factor (Supplementary Table 10), including six metabolites associated with both genetics and the microbiome and 153 metabolites associated with both diet and the microbiome. For example, genetics and the microbiome explained 4 and 5%, respectively, of the variance in plasma 5-carboxy--chromanol (Fig. 1c)a dehydrogenated carboxylate product of 5-hydroxy--tocopherol16 that may reduce cancer and cardiovascular risk17. Another example is hippuric acida uremic toxin that can be produced by bacterial conversion of dietary proteins18, with 13% of its variance explained by diet and 13% explained by the microbiome (Fig. 1c).

Temporal changes in plasma metabolites can reflect changes in an individuals diet, gut microbiome and health status. When assessing the plasma metabolome in the 311 LLD1 follow-up samples, we indeed observed a significant shift in the plasma metabolome, with a significant difference in the second principal component (PPC1 paired Wilcoxon=0.1 and PPC2 paired Wilcoxon=1.3105; Fig. 2a). Baseline genetics, diet and microbiome, together with age, sex and BMI, could explain 59.4% of the variance in the follow-up plasma metabolome (PPERMANOVA=0.004) (Supplementary Fig. 2). We also observed that temporal stability can vary substantially between different metabolites (see the Methods section Temporal consistency of individual metabolites; Supplementary Table 11). Previously, we had assessed the changes in the gut microbiome in the LLD1 follow-up cohort and linked these to changes in the plasma metabolome7. Here, we further checked the temporal variability of the plasma metabolome and assessed the stability of diet-, microbiome- and genetics-dominant metabolites over time. Interestingly, the temporal correlation of the microbiome-dominant metabolites was similar to that of the genetics-dominant metabolites (PWilcoxon=0.51; Fig. 2b), whereas the temporal correlation between diet-dominant metabolites was significantly lower than between microbiome- and genetics-dominant metabolites (PWilcoxon<3.4105; Fig. 2b). However, the dominant dietary, microbial and genetic factors identified at baseline also explained similar variance in metabolic levels in the follow-up samples (Extended Data Fig. 3 and Supplementary Table 10). Our data also revealed a positive correlation between stability and the amount of variance that could be explained: the more variance explained, the more stable a metabolite is over time (Fig. 2c). For a few metabolites, we could not replicate the variance explained at baseline at the second time point, and these metabolites also showed weak or no correlation in their abundances between the two time points. For example, N-acetylgalactosamine showed very weak correlation between the two time points (r=0.13; P=0.02), and its genetic association was not replicated at the second time point.

a, Principal component analysis of metabolite levels at two time points (Euclidean dissimilarity). The green dots indicate baseline samples and the orange dots indicate follow-up samples (n=311 biologically independent samples). The KruskalWallis test (two sided) was used to check differences between baseline and follow-up. b, Temporal stability of metabolites stratified by the dominantly associated factor for each metabolite. The Wilcoxon test (two sided) was used to check the differences between groups. Each dot represents one metabolite. The yaxis indicates the Spearman correlation coefficient of abundances of each metabolite between two time points (n=311 biologically independent samples). In a and b, the box plots show the median and first and third quartiles (25th and 75th percentiles) of the first and second principal components (a) or correlation coefficients (b); the upper and lower whiskers extend to the largest and smallest value no further than 1.5 the interquartile range (IQR), respectively; and outliers are plotted individually. c, Correlation between metabolite stability and the metabolite variance explained by diet (left), genetics (middle) and the microbiome (right). The xaxis indicates the inter-individual variation explained by each factor and the yaxis indicates the Spearman correlation coefficient (two sided) of abundances of each metabolite between the two time points. The dashed white lines show the best fit and the gray shading represents the 95% confidence interval (CI) (n=311 biologically independent samples).

Having established the variances in metabolites explained by diet, genetics and the gut microbiome and the dominant factors that explained most of this variance, we focused on detailing specific associations and on the potential implications of our findings for assessing diet quality and improving our understanding of the genetic risk of complex diseases and the interaction and causality relationships among diet, the microbiome, genetics and metabolism.

We observed 2,854 significant associations (FDRSpearman<0.05) between 74 dietary factors and 726 metabolites (Fig. 3a and Supplementary Table 5; see the Methods section Lifelines diet quality score prediction). Associations with food-specific metabolites can, in theory, be used to verify food questionnaire data. For instance, the strongest association we observed was between quinic acid levels and coffee intake (rSpearman=0.54; P=1.61080; Fig. 3b). Quinic acid is found in a wide variety of different plants but has a particularly high concentration in coffee. Another example is 2,6-dimethoxy-4-propylphenol, which was strongly associated with fish intake (rSpearman=0.53; P=1.51076; Fig. 3c). This association is expected as this compound is particularly present in smoked fish according to HMDB annotation15. In addition, we also detected associations between dietary factors and metabolic biomarkers of some diseases. For example, 1-methylhistidine is a biomarker for cardiometabolic diseases including heart failure19 that is enriched in meat, and we observed significant associations between 1-methylhistidine and meat (rSpearman=0.12; P=7.2105) and fish intake (rSpearman=0.11; P=3.1104) as well as a lower level of 1-methylhistidine in vegetarians (rSpearman=0.15; P=9.7107; Fig. 3d).

a, Summary of the associations between diet and metabolites. The bars represent dietary habits, with the bar order sorted by the number of significant associations. Association directions are colored differently: orange indicates a positive association, whereas blue indicates a negative association. The length of each bar indicates the number of significant associations at FDR<0.05 (Spearman; two sided). b, Association between plasma quinic acid levels and coffee intake. The x and yaxes indicate residuals of coffee intake and the metabolic abundance after correcting for covariates, respectively (n=1,054 biologically independent samples). c, Association between plasma 2,6-dimethoxy-4-propylphenol levels and fish intake frequency (n=1,054 biologically independent samples). The x and yaxes refer to residuals of fish intake and metabolic abundance after correcting for covariates, respectively. d, Differential plasma levels of 1-methylhistidine between vegetarians and non-vegetarians (n=1,054 biologically independent samples). The yaxis indicates normalized residuals of metabolic abundance. The Pvalue from the Wilcoxon test (two sided) is shown. The box plots show the median and first and third quartiles (25th and 75th percentiles) of the metabolite levels. The upper and lower whiskers extend to the largest and smallest value no further than 1.5 the IQR, respectively. Outliers are plotted individually. e, Association between the diet quality score predicted by the plasma metabolome (yaxis) and the diet quality score assessed by the FFQ (xaxis) (n=237 biologically independent samples). In b, c and e, each gray dot represents one sample, the dark gray dashed line shows the linear regression line and the gray shading represents the 95% CI. In b and c, the association strength was assessed using Spearman correlation (two sided; the correlation coefficient and Pvalue are reported) and in e, the prediction performance was assessed with linear regression (F-test; two sided; the adjusted r2 value and Pvalue are reported).

Given the relationship between diet, metabolism and human health, we wondered whether the plasma metabolome could predict diet quality. For each of the Lifelines participants, we constructed a Lifelines Diet Score based on food frequency questionnaire (FFQ) data that reflected the relative diet quality based on dietdisease relationships8. To build a metabolic model to predict an individuals diet quality, we used LLD1 as the training set and LLD2 as the validation set. The resulting metabolic model included 76 metabolites, 51 of which were dominantly associated with diet. The diet score predicted by metabolites showed a significant association with the real diet score assessed by the FFQ in the validation set (r2adjusted=0.27; PF-test=3.5105; Fig. 3e). We also tested four other dietary scores (the Alternate Mediterranean Diet Score20, Healthy Eating Index (HEI)21, Protein Score22 and Modified Mediterranean Diet Score23) and found that the HEI predicted by plasma metabolites was also significantly associated with the FFQ-based HEI (r2adjusted=0.23; PF-test=6.5105; Supplementary Table 12).

Genetic associations of plasma metabolites may provide functional insights into the etiologies of complex diseases. After correcting for the first two genetic principal components, age, sex, BMI, smoking, 78 dietary habits, 40 diseases and 44 medications, QTL mapping in LLD1 identified 48 study-wide, independent genetic associations between 44 metabolites and 40 single-nucleotide polymorphisms (SNPs) (PSpearman<4.21011; clumping r2=0.05; clumping window=500kilobases (kb); Fig. 4a and Supplementary Table 6). All 48 genetic associations were replicated in either LLD1 follow-up or the two independent replication datasets (LLD2 and GoNL; Supplementary Fig. 3 and Supplementary Table 6). We also assessed the impact of physical activity, as assessed by questionnaires24, on the genetics association of metabolism, but found its influence to be negligible (Supplementary Fig. 4). Functional mapping and annotation (FUMA) of genome-wide association studies (GWAS)25 analysis revealed that the identified mQTLs were enriched in genes expressed in the liver and kidney (Extended Data Fig. 4) and related to metabolic phenotypes (Supplementary Table 6).

a, Manhattan plot showing 48 independent mQTLs identified linking 44 metabolites and 40 genetic variants with P<4.21011 (Spearman; two sided). Representative genes for the SNPs with significant mQTLs are labeled. b, Association between a tag SNP (rs1495741) of the NAT2 gene and plasma AFMU levels. c, Association between a SNP (rs13100173) within the HYAL3 gene and plasma levels of N-acetylgalactosamine-4-sulfate. d, Association between a tag SNP (rs17789626) of the SCLT1 gene and plasma mizoribine levels. e, Differences in coffee intake between participants with different genotypes at rs1495741. f, Correlations between coffee intake and AFMU in participants with different genotypes at rs1495741. g, Differences in bacterial fatty acid -oxidation pathway abundance in participants with different genotypes at rs67981690. h, Correlations between bacterial fatty acid -oxidation pathway abundance and 5-carboxy--chromanol in participants with different genotypes at rs67981690. In be and g, the xaxis indicates the genotype of the corresponding SNP and the yaxis indicates normalized residuals of the corresponding metabolic abundance (n=927 biologically independent samples). Each dot represents one sample. The box plots show the median and first and third quartiles (25th and 75th percentiles) of the metabolite levels. The upper and lower whiskers extend to the largest and smallest value no further than 1.5 the IQR, respectively. Outliers are plotted individually. The association strength is shown by the Spearman correlation coefficient and corresponding Pvalue (two sided). In f and h, the xaxis indicates the normalized abundance of coffee intake (f) or the bacterial fatty acid -oxidation pathway (h) and the yaxis indicates the normalized residuals of the corresponding metabolic abundance. Each dot represents one sample (n=927 biologically independent samples). The lines indicate linear regressions for each genotype group separately. Areas with light gray shading indicate the 95% CI of the linear regression lines. The association strength per genotype is shown by the Spearman correlation and the corresponding Pvalue (two sided).

The strongest association we found was between the caffeine metabolite 5-acetylamino-6-formylamino-3-methyluracil (AFMU) and SNP rs1495741 near the N-acetyltransferase 2 (NAT2) gene (rSpearman=0.52; P=1.71066; Fig. 4b), which showed strong linkage disequilibrium (r2=0.98) with a SNP, rs35246381, that was recently reported to be associated with urinary AFMU26. AFMU is a direct product of NAT2 activity and has been associated with bladder cancer risk27. Interestingly, the plasma level of AFMU was associated not only with coffee intake (rSpearman=0.29; P=9.21022; Supplementary Table 5) and the genotype of rs1495741, but also with their interactions (Supplementary Table 9). Individuals with a homologous AA genotype had a similar level of coffee intake, but their correlation between coffee intake and plasma AFMU level was significantly lower compared with individuals with GG and GA genotypes (Fig. 4e,f).

Pleotropic mQTL effects were also observed at several loci, including SLCO1B1, FADS2, KLKB1 and PYROXD2 (Supplementary Table 6). For example, three associations (related to three metabolites, two of them lipids) were observed for two SNPs (rs67981690 and rs4149067; linkage disequilibrium r2=0.72 in Northern Europeans from Utah) in SLCO1B1, which encodes the solute carrier organic anion transporter family member 1B1. Expression of the SLCO1B1 protein is specific to the liver, where this transporter is involved in the transport of various endogenous compounds and drugs, including statins28, from blood into the liver. The SLCO1B1 locus has also been linked to plasma levels of fatty acids and to statin-induced myopathy29. Furthermore, we detected a geneticsmicrobiome interaction between rs67981690 and microbial fatty acid oxidation pathways in regulating plasma levels of 5-carboxy--chromanol (P=1.5103), where the association of the bacterial fatty acid oxidation pathway with plasma levels of 5-carboxy--chromanol was dependent on the genotype of rs67981690 (Fig. 4g,h).

To identify novel mQTLs, we performed a systematic search of all published mQTL studies from 2008 onwards (Supplementary Table 13). This approach identified three novel mQTLs in our datasets (Supplementary Table 13) that were either not located close to previously reported mQTLs (distance>1,000kb) or not in linkage disequilibrium (r2<0.05). The first two novel SNPsrs13100173 at HYAL3 and rs11741352 at ARSBwere associated with N-acetylgalactosamine-4-sulfate (Fig. 4c,d), which is associated with mucopolysaccharidosis30. Interestingly, N-acetylgalactosamine-4-sulfate can bind to HYAL proteins (HYAL1, HYAL2, HYAL3 and HYAL4), suggesting that mQTLs can also pinpoint potential metaboliteprotein interactions. The third novel mQTL was rs17789626 at SCLT1, which was associated with mizoribinea compound used to treat nephrotic syndrome31.

We established 4,212 associations between 208 metabolites and 314 microbial factors (114 species and 200 MetaCyc pathways) (FDRLLD1<0.05; PLLD1 follow-up<0.05; Supplementary Tables 7 and 8). Interestingly, many of the metabolites that were associated with microbial species and MetaCyc pathways are also known to be gut microbiome related based on their HMDB annotations15. For instance, we observed 919 associations with 25 uremic toxins, 142 associations with thiamine (vitamin B1) and 117 associations with five phytoestrogens (FDR<0.05; Supplementary Tables 7 and 8). Uremic toxins and thiamine have been shown to be related to various diseases, including chronic kidney disease and cardiovascular diseases32,33. Phytoestrogens are a class of plant-derived polyphenolic compounds that can be transformed by gut microbiota into metabolites that promote the hosts metabolism and immune system33,34.

To assess whether gut microbiome composition causally contributes to plasma metabolite levels, we carried out bi-directional MR analyses (see the Methods section Bi-directional MR analysis). Here, we focused on the 37 microbial features that were associated with at least three independent genetic variants at P<1105 and with 45 metabolites (Supplementary Table 14). At FDR<0.05 (corresponding to P=2103 obtained from the inverse variance weighted (IVW) test)35, we observed four potential causal relationships at baseline that could also be found in the follow-up in the microbiomes to metabolites direction (Fig. 5ad and Supplementary Tables 15 and 16) but not in the opposite direction (Supplementary Table 17), and these outcomes were maintained following weighted median testing (P<0.03; Supplementary Fig. 5). To ensure that the data followed MR assumptions, we performed several sensitivity analyses, including checking for horizontal pleiotropy (MR-Egger36 intercept P>0.05; Supplementary Table 15) and heterogeneity (Cochrans Q test P>0.05; Supplementary Table 15) and leave-one-out analysis (Extended Data Fig. 5). We did not use causal estimates derived using the MR-Egger method to filter the results, as its power to detect causality is known to be low36. These sensitivity checks further confirmed the reliability of these four MR causal estimates.

a, Analysis of the association between adenosylcobalamin biosynthesis pathway abundance and 5-hydroxytryptophol levels. b, Glycogen biosynthesis pathway abundance versus 5-sulfo-1,3-benzenedicarboxylic acid levels. c, E. rectale abundance versus hydrogen sulfite levels. d, Veillonella parvula abundance versus 2,3-dehydrosilybin levels. In the top panels of ad, the xaxis shows the SNP exposure effect, and the yaxis shows the SNP outcome effect and each dot represents a SNP. Error bars represent the s.e. of each effect size. The bottom panels of ad, show the MR effect size (center dot) and 95% CI for the baseline (blue) and follow-up (green) datasets of the LLD1 cohort, estimated with the IVW MR approach (two sided) (n=927 biologically independent samples at baseline and n=311 biologically independent samples at follow-up).

We further found that increased abundance of microbial adenosylcobalamin biosynthesis (coenzyme B12) was associated with reduced plasma levels of 5-hydroxytryptophol (Fig. 5a)a uremic toxin related to Parkinsons disease37. We also found that plasma hydrogen sulfite levels were related to Eubacterium rectale (Fig. 5c)a core gut commensal species38 that is highly prevalent (presence rate=97%) and abundant (mean abundance=8.5%) in both our cohorts and in other populations39,40,41. As a strict anaerobe, E. rectale promotes the hosts intestinal health by producing butyrate and other short-chain fatty acids from non-digestible fibers42, and a reduced abundance of this species has been observed in subjects with inflammatory bowel disease39,43 and colorectal cancer44 compared with healthy controls. As a toxin, hydrogen sulfite interferes with the nervous system, cardiovascular functions, inflammatory processes and the gastrointestinal and renal system45. Our results thus reveal a potential new beneficial effect of E. rectale.

To further investigate the metabolic potential of individual bacterial species, we applied newly developed pipelines to identify microbial primary metabolic gene clusters (gutSMASH pathways)46 and microbial genomic structural variants (SVs)47. These two tools profile microbial genomic entities that are implicated in metabolic functions. By associating 1,183 metabolites with 3,075 gutSMASH pathways and 6,044 SVs (1,782 variable SVs (vSVs) and 4,262 deletion SVs (dSVs); see Methods), we observed 23,662 associations with gutSMASH pathways and 790 associations with bacterial SVs (FDRLLD1<0.05; PLLD1 follow-up<0.05; Supplementary Tables 1820). These associations connect the genetically encoded functions of microbes with metabolites, thereby providing putative mechanistic information underlying the functional output of the gut microbiome. In one example, we observed that the microbial uremic toxin biosynthesis pathways, including the glycine cleavage pathway (in Olsenella and Clostridium species) and the hydroxybenzoate-to-phenol pathway (in Clostridium species) responsible for hippuric acid and phenol sulfate biosynthesis, were associated with the hippuric acid (Olsenella species: rSpearman=0.15; P=9.3107; Clostridium species: rSpearman=0.18; P=5.9109) and phenol sulfate (rSpearman=0.17; P=4.2108; Extended Data Fig. 6a) levels measured in plasma, respectively (FDRLLD1<0.05 and PLLD1 follow-up<0.05; Extended Data Fig. 6b).

Next, we carried out a mediation analysis to investigate the links between diet, the microbiome and metabolites. For 675 microbial features that were associated with both dietary habits and metabolites (FDR<0.05), we applied bi-directional mediation analysis to evaluate the effects of microbiome and metabolites for diet (see the Methods section Bi-directional mediation analysis). This approach established 146 mediation linkages: 133 for the dietary impact on the microbiome through metabolites and 13 for the dietary impact on metabolites through the microbiome (FDRmediation<0.05 and Pinverse-mediation>0.05; Fig. 6a,b and Supplementary Table 21). Most of these linkages were related to the impact of coffee and alcohol on microbial metabolic functionalities (Fig. 6a).

a, Parallel coordinates chart showing the 133 mediation effects of plasma metabolites that were significant at FDR<0.05. Shown are dietary habits (left), plasma metabolites (middle) and microbial factors (right). The curved lines connecting the panels indicate the mediation effects, with colors corresponding to different metabolites. freq., frequency; PFOR, pyruvate:ferredoxin oxidoreductase; OD, oxidative decarboxylation; HGD, 2-hydroxyglutaryl-CoA dehydratase; TPP, thiamine pyrophosphate. b, Parallel coordinates chart showing the 13 mediation effects of the microbiome that were significant at FDR<0.05. Shown are dietary habits (left), microbial factors (middle) and plasma metabolites (right). For the microbial factors column, number ranges represent the genomic location of microbial structure variations (SVs) in kilobyte unit, and colons represent the detailed annotation of certain gutSMASH pathway. c, Analysis of the effect of coffee intake on the abundance of M. smithii as mediated by hippuric acid. d, Analysis of the effect of beer intake on the C. methylpentosum Rnf complex pathway as mediated by hulupinic acid. e, Analysis of the effect of fruit intake on urolithin B in plasma as mediated by a vSV in Ruminococcus species (300305kb). In ce, the gray lines indicate the associations between the two factors, with corresponding Spearman coefficients and Pvalues (two sided). Direct mediation is shown by a red arrow and reverse mediation is shown by a blue arrow. Corresponding Pvalues from mediation analysis (two sided) are shown. inv., inverse; mdei., mediation.

Coffee contains various phenolic compounds that can be converted to hippuric acid by colonic microflora48. Hippuric acid is an acyl glycine that is associated with phenylketonuria, propionic acidemia and tyrosinemia49. We observed that hippuric acid can mediate the impact of drinking coffee on Methanobrevibacter smithii abundance (Pmediation=2.21016; Fig. 6c). We also observed that hulupinic acid, which is commonly detected in alcoholic drinks, can mediate the impact of beer consumption on the Clostridium methylpentosum ferredoxin:NAD+ oxidoreductase (Rnf) complex (Pmediation=2.21016; Fig. 6d)an important membrane protein in driving the ATP synthesis essential for all bacterial metabolic activities50.

Of the dietary impacts on metabolites through the microbiome (Fig. 6b and Supplementary Table 21), one interesting example is a Ruminococcus species vSV (300305kb) that encodes an ATPase responsible for transmembrane transport of various substrates51. This Ruminococcus species vSV mediated the effect of fruit consumption on plasma levels of urolithin B (Pmediation=2.21016; Fig. 6e). Urolithin B is a gut microbiota metabolite that protects against myocardial ischemia/reperfusion injury via the p62/Keap1/Nrf2 signaling pathway52. Taken together, our data provide potential mechanistic underpinnings for dietmetabolite and dietmicrobiome relationships.

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