Search Weight Loss Topics:

Page 675«..1020..674675676677..680690..»

Home » Page 675

Bryan Washington amongst the curry bread and migas. Illustration: Eliana Rodgers

Probably everything, says Bryan Washington, the Houston writer, when asked what he likes about Japanese food. Growing in Houston, he says, you feel like its just normal to have eight cuisines in arms reach, and Washington is a writer whose writing often explores food whether achiote or Japanese curry bread as well as queer life and his hometown. Called a lit world wunderkind by Los Angeles magazine, next week he will publish his debut novel, Memorial, which is about a maybe-ending romantic relationship and set in Houston and Osaka. Already optioned by A24, its his follow-up to the critically acclaimed Lot, for which he was named a National Book Foundation honoree. This past week, Washington spent a lot of his time signing books while watching K-dramas, recipe testing his croquettes (my lifes mission), spoiling his mom with a breakfast of migas with lump crab, and getting dim sum after drive-through voting.

Wednesday, October 14So breakfast was egg curry rice from last nights leftover curry (using the One Meal a Day recipe) that I ate with my boyfriend. I usually end up making boxed curry once a week I buy Golden Curry and get the extra hot because I think its a perfect recipe. Its super-quick, maybe ten minutes of actual work altogether. I usually make a little bit extra because I know Ill either make egg curry rice the next day, or if Im not fucking lazy that week, Ill make kare pan at some point. So, I like making a little more than Ill immediately need. Yeah, no, one curry a week isnt enough, to put it lightly, especially if its a busy week, because its just so quick and so good.

I really like One Meal a Day, and I havent tried a single recipe of theirs that didnt work. I fall into YouTube holes a few times a week, just watching people cook. I think the first OMAD one I saw was for tuna egg rice, but I dont know for sure. I mean, its just really simple, really good. I learned how to make some pretty decent rolled omelettes from them, and theyve got a really good galbi-jjim recipe. And then theres their steamed egg recipe, the drunken egg recipe

Honestly, this was kind of a strange fucking week because Ive been doing a lot of publicity for my novel Memorial, in the middle of our pandemic, so things have been pretty planned out to the hour or whatever. I did some promo after breakfast and had plans to see a friend in the park by the Rothko Chapel: Our social revolutions had been our respective significant others and parents since like March, so this was the first time we were seeing someone that wasnt them in a minute. And there arent a lot of third places in Houston that you dont need to spend cash at, so the park is in a lovely juncture: Youve got the Menil and the Rothko Chapel and a bunch of other museums in walking distance. Ive picnicked out therea lot more this year than I ever have. Its just a really nice vibe. So we ate lunch in the park: bnh m from My Baguettes, nem nng from Nem Nng & Rolls, and c ph sa from Long Coffee.

I really like My Baguettes. Its super-chill. And the nem nng place is right next door, just beside Long. Youve got hella options for boba and iced coffee in Houston, but Long Coffee is one of my favorites, and Im usually there like once a week. And theyre all within walking distance from each other, so it wasnt a a big fucking expedition. So I hit that triangle real quick and then drove back to Montrose, and then my friend and I cried for a bit and smoked for a bit and caught up and snacked on everything.

Ill order the shredded chicken bnh m most days, but, honestly, I think that the croissant sandwich from My Baguettes, with egg and pat and the rest of the fillings, is easily a top-five sandwich in the city. Easily. But I always end up passing through at 3 p.m. or 4 p.m. and by then theyre out of croissants and its always the same routine. Ill show up and ask for a croissant and theyll say, No, we dont fucking have anymore because youre too late. Im just happy to be there though, so it all works out.

That night, my boyfriend and I debated about what to cook or pick up because it was pretty late by the time we started thinking about dinner, so we ended up frying eggs and making rice with some drizzled sesame oil. And, on the side, we had some kimchee from Korean Noodle House. Its this restaurant on Longpoint Drive, super-delicious, and once a week Ill go and pick up a big tub of kimchee, and thatll just be my happiness for the week. I think, even when we were in lockdown lockdown and I was staying home, and we were all really going through it, one thing that Id do every week is pick up that tub of kimchee. It was this one solid thing I could count on, you know? Its just really fucking good.

Thursday, October 15Went to vote with my BF we did the drive-up at NRG Stadium, and it took maybe two minutes, super-organized and fluid and then afterward picked up dim sum from Fungs Kitchen: stir-fried lobster with honey-black pepper, fried squid calamari in spicy salt, Chinese broccoli with oyster sauce, and beef flat rice noodles with gravy.

Were always kind of flirting with the question of whether we actually need to pick up dim sum, because it really is a lot of food, but of course we usually end up passing through. Dim sum always wins. And well end up heading to Ocean Palace or Fungs or this one other place by the 99 Ranch out in Sugarland. So we took that home, because Im not quite sold on actually eating in restaurants just yet. Id rather just pick it up and leave a massive tip.

For lunch, I ate egg noodles and stir-fried shrimp with my BF. They were essentially leftovers from earlier in the week.And then we had leftovers from the dim sum earlier, so this served as fridge clearing in a lot ways. I cook this way pretty often. But, like, what a fucking privilege that your problem is you have to create more room for the food you have, you know? (Which would be a good time to plug the Houston Food Bank and also Mutual Aid Hou.) I hate wasting food. I hate it.

Dinner was breakfast cheeseburgers and fries from M&M Grill, takeout. M&M Grill, theyre really rad. Theyre Arabian-influenced American and Mexican food, but they also do Tex-Mex well, too, and their meat is halal. The breakfast burger is really just a cheeseburger with an egg on it. But its a solid burger and, frankly, I am just an egg person. Theres a cookbook by Rachel Khong called All About Eggs, and when it was published, I was like, This is the best fucking day, because what is better than a cookbook thats literally just egg recipes?

Friday, October 16 Breakfast was French toast made with challah from Three Bros. Bakery; eggs basted with soy sauce; sausage cooked in onions; ate with BF.

Three Bros. is maybe ten minutes from my place; theyre a local chain, and they have really good challah. So the French toast was pretty simple I just cracked an egg with some milk and sugar, mixed all of it, and let the bread chill there for a minute before I fried it up. Then the eggs basted with soy sauce is pretty simple; its fried egg with some soy sauce on it. Saying basted makes it sound like a whole fucking thing, but it isnt. I usually get Aloha soy sauce because I just really like it, but every now and then Ill opt for the usukuchi from Yamasa. Those are usually my two defaults. Ive been using sweet soy sauce lately, too, but Ive been using it sparingly because its a lot, it can overpower a dish. Or maybe Ive just got a sweet tooth.

I cook a lot of French toast though, or at least lately. Ive never cooked as much French toast as I have these past nine months. But its delicious so Im like, Okay, if the rest of this day still fucking sucks, Ill have made French toast. This can be a good thing I can count on. Theres a Chinese restaurant near me called Hong Kong Food Street, where they drizzle the French toast with condensed milk. But I dont do that at my place because I know if I started, no good would come of it. None. Id just never stop.

Im recipe-testing potato korokke for work, so I munched on those solo. Its partly for a piece Im working on, partly because I feel like my lifes mission is just to get this recipe correct. I had it once at a stall beside the Shinjuku Gyoen a few years back, and Ive been chasing the dragon ever since. But croquettes are a good way to practice deep-frying, honestly, because everything is already cooked. So youre just working on adjusting the color and crispiness to your desire. But, yeah, just trying to figure out how to make it do what I want it to do has been a challenge.

Theres a super, super-solid potato korokke recipe over at Just One Cookbook, but Ive been pulling from croquette recipe on Martha Stewarts site, too. So Ive ended up with one thats like a variation of Namis recipe from Just One Cookbook, and a variation of the Martha Stewart recipe, and I use a variation from Jo Cooks, just mixing and matching details. Im trying to figure out how to take different components from all of them and make something that works for me. Its fine if I never get there.

Ive started using lump crab meat instead of beef, which is what I originally used, and Im liking how thats turning out. So I spent much of Friday trying to do that and procrastinating around the promo I have to do. This whole week, Ive been signing a lot of books: There were 70 boxes of Memorial sitting at my place. In the weeks prior, Id just sign the bookplates, and I think there are something like 11,000 signed copies out in the world right now. I dont dwell on the number. So a lot of this cooking was also me just trying not to think about the boxes. I had to do this recipe testing, and thats a certain amount of work, but it was also not opening 70 boxes (which, all jokes aside, is actually a lovely thing to get to do).

My mom stayed with me this evening; she was in the area. I had an Asahi, and she had some wine,and I made her doria, which is pretty similar to gratin rice is the primary base of it. Just like a cream chicken dish over rice. What Ill do is make a creamy chicken stew with somerice on the side, layer the stew on the cooked rice,top it with a little bit of cheese, broil it for a bit, and add parsley. Its deeply comforting.

I also took some marinated onions out of the fridge (Our Korean Kitchen, by Rejina Pyo and Jordan Bourke, has this really great recipe that takes less than five minutes to prep, and it goes really good with grilled meats and Ill find myself making it and holding it and parceling it out), and also made miso soup and a really simple cucumber salad that an old roommate of mine taught me. Usually I make my own dashi, but I wasnt trying to do all of that this evening, so I made the powdered dashi. I started using a bunch of it since everyones been inside, and its less work and still pretty satisfying. We played with my puppy (I have a puppy surprise) and caught up for a few hours.

Saturday, October 17I cooked migas (a variation of Ford Frys recipe) with lump crab meat and salsa de aguacate for breakfast with my mom. When shes over, I usually try to cook a bunch of things, which is to say that it isnt like fucking three-day-old curry.

I usually have tortilla chips in my pantry, and theyre just chilling, waiting for something to happen. And then I had lump crab leftover from the croquettes, so I used that as a protein base and made salsa de aguacate. I moved apartments fairly recently, I guess a month and half ago now, and that experience was actually the seventh level of hell, but my one housewarming gift to myself was a Magic Bullet. I resisted getting it for a while because Im an idiot, but then I got it and it makes life easier. So. I made the salsa with that. And then I also made coffee from Third Coast beans; they have a Laos blend, and its super-good. I had it in Austin for the first time a few months back, so I just buy it whenever I see it now.

After my mom left, I signed about 20 boxes of books, and theres a show called Youns Kitchen, that I had on in the background. Its really lovely. These K-drama actors like, dumb famous in Korea are essentially running a restaurant in Spain. This season I think it was Spain. So I watched that and answered emails and signed for a bit until my wrist started to freak out and then I went to get lunch solo.

Got a croissant sandwich from Nguyen Ngo(another top-five Houston sandwich) and coffee from Tapioca House, this boba shop across the way. I think they just might make my favorite coffee in Houston. Their iced coffees super-dark, but also super-sweet, and they do it in such a way thats just absolutely delicious. So I got two coffees from them, and brought those and the food back to eat while watching Youns Kitchen and then a little bit of Romance Is a Bonus Book, which Ive already seen and love.

Dinner was shrimp tacos that my BF and I cooked. I usually have like two pounds of frozen shrimp in the freezer at all times, because were on the Gulf and shrimp is not prohibitively expensive here. Every few weeks Ill buy a few pounds and cook some them the week of and then freeze the rest in Baggied portions, thawing them whenever I need them.

We made those with a red salsa, some Sriracha, and some cheese, and then we watched the Blackpink documentary, which was cool as hell, and then a few episodes of Greenleaf, which is basicallya K-drama set in Memphis.

Sunday, October 18Woke up pretty late, past breakfast time. I had the rest of the books to sign, because they had to be shipped by Monday, and I would simply have to walk into the ocean if they werent finished, so I made banana-nut scones, and while they were in the oven, I started signing again and queued upsome Ghibli movies in the background. Once the scones were done, I chewed on them with some coffee and alternated between signing and emails.I usually have the coffee concentrate from Lees; its a half-gallon or gallon, basically liquid gold.

I wasnt hungry until later that evening, so dinner was stir-fried eggs and tomatoes with crab (the last of the lump meat), stir-fried ground pork with basil and peppers, and rice that I cooked with my BF. I love crab, but its a bit more expensive than shrimp. But I had a lot of crab; I bought too much for these croquettes and it goes bad quickly.

For the eggs, theres this recipe from somebodys mom on YouTube that is simply a stunner, and I spent like two years trying to replicate it, but now I cant find that video anymore. But lately Ive been using the Chinese Cooking Demystified version, and then I stir-fried crab with it, and we also had the stir-fried pork.

Im really fortunate in that, while the neighborhood where I grew up was hella white, the street we lived on and the street immediately adjacent to it were deeply diverse, and my parents friends were deeply diverse. We ate a lot of Cuban food, a lot of Filipino food, a good amount of Japanese food; we ate quite a lot of Jamaican food, a lot of Nigerian food. A lot of that was just being in close proximity to friends and loved ones eating a lot of different stuff. The diversity of cuisines and the allowance for the diversity of cuisines in Houston is objectively astounding, but, among Houstonians, its not terribly remarkable. It never struck me as something that was noteworthy. Then you get older, and then you get more context to see not everyone has fuckingeight different cuisines lined up next to one another in every strip mall.

My mom is Jamaican, and my dad is from Florida. They met in Florida. Houston feels very much like home. But Ive been really fortunate to be able to travel a little bit, and Ive come around to thinking many places can seem like home. Being open to different places is definitely something I think about often. Just being around a bunch of different folks who are from a litany of places, the idea of being rooted to one place is definitely lovely and viable, but not essential for me or from my standpoint. Although I will say a lot of people who leave Houston and then they end up coming back because its so much itself I do wonder if that would be me, if I ever choose to leave full time. Maybe home is actually just a feeling, wherever you end up finding it.

Sign up for the Grub Street newsletter.

Originally posted here:
Bryan Washington Requires More Than One Curry Per Week - Grub Street

Read More..

There are few better feelings in life than being totally well rested. Unfortunately, though, our sleeping patterns dont always play ball, and there are a number of different factors in our day-to-day lives that can make it difficult to doze off, including everything from stress to our central heating.

One key thing that is often overlooked when it comes to getting a good nights sleep is our diets. Studies have found that our dietary patterns and consumption of specific foods can have a significant impact on our sleep quality, with some foods having the potential to really disrupt our time in the land of Nod.

As Brielle explains, eating healthy, well balanced meals supports your bodys pathways when regulating your hormones, some of which impact how much sleep you get and how well you sleep. This means that eating foods containing nutrients that support sleep and in the right amounts is crucial to ensuring you are able to drift off when your head hits the pillow.

In the same way that your diet can influence your sleep, poor sleep is in turn associated with unhealthy eating habits, says Aishah. Therefore, if one becomes compromised, this will have a knock-on effect on the other. But your sleep and diet are supposed to support one another, because both influence your energy levels throughout the day. SoBrielle recommends trying to keep a consistent sleeping and eating pattern, so that they are able to work together to supply your body with energy.

Aishah explains that there is growing evidence to suggest that the time you eat can impact your circadian rhythm, also known as your sleep-wake cycle. As a result, eating too close to when you go to bed can potentially impact your sleep routine, and so its best to eat earlier in the evening, allowing yourself a couple of hours to digest your meal before going to sleep.

However, there is no hard and fast rule for when you should be eating. As Brielle explains, its important to listen to your body and how you feel, and aim to feel satisfied when you eat in the evening, but not stuffed or hungry. Really, it all depends on what works well for you.

While some foods are fine to eat throughout the day, whether because they are good fuel or a tasty pick-me-up, they may not always be a good idea to eat close to bedtime. Both Brielle and Aishah particularly warn against the consumption of caffeine too late in the day, because it stimulates the brain and gives you energy, thus keeping you awake, says Brielle.

Foods high in sugar can be stimulating as well. As Aishah explains, they heighten arousal and give you a dopamine hit, which keeps you feeling alert, and so she recommends you stay away from sugary foods like sweets, chocolate and cakes at least two to three hours before going to bed.

According to Brielle, some foods cause disrupted sleep simply because they take longer for the body to digest. If youre struggling with your sleep, try to stay away from heavy proteins in your evening meals, and minimise the carb-rich foods like rice and pasta. Instead, she recommends trying plant-based alternatives, which are lighter and less likely to keep you awake.

Brielle explains that kiwis are excellent sleep aids. In fact, in a study, participants who started consuming kiwis regularly before bed were able to fall asleep 42% faster than they did before. Foods high in melatonin are great as well, because melatonin is the hormone that helps to regulate your circadian rhythm. Aishah recommends cherries and nuts such as walnuts and almonds as good sources of this crucial hormone.

Fatty fish such as salmon and tuna are a good option for your evening meal. They are a great source of vitamin D and omega 3 fatty acids, which help to regulate serotonin, the hormone that is responsible for maintaining sleep, explains Brielle. Milk contains vitamin D, too, as well as tryptophan, both of which have been linked to supporting sleep.

Follow @StrongWomenUK on Instagram for the latest workouts, delicious recipes and motivation from your favourite fitness experts.

Images: Getty

Continued here:
How our diets disrupt our sleep, and what to eat before bed - Stylist Magazine

Read More..

The sweepstakes are easy to enter. Just fill in your boss's first and last name, their email address, and 500 characters or less about why you think your boss deserves the grand prize. Then you just need to fill in your own name, email address, and date of birth, and you're good to go. Entrants have until 11:59 pm ET on November 16th, a month after National Boss Day, to submit their forms, according to theDiet Coke National Boss's Day Sweepstakes Official Rules.

You can nominate up to four times before the deadline, but each nomination has to be unique. So basically, if you work under multiple people and want to nominate a few of them, go ahead you don't have to choose. But for obvious reasons, you can't nominate any of them more than once.

Don't sweat about composing theperfect description of your boss's Diet-Coke-worthiness. The judging organization "will select the names of the potential winning Nominees in a random drawing of all eligible entries received during the Promotion Period."So go ahead, enter your boss. If they're areally good boss, maybe they'll share a couple of their 365 cans of Diet Coke with you.

See original here:
How to win a year's worth of free Diet Coke - Mashed

Read More..

As Prime Minister Yoshihide Suga prepares for the start of an extraordinary Diet session, set to open Monday, he also has to contend with the growing question of when he will call an election.

Sugas tenure as Liberal Democratic Party (LDP) president will end next September and the terms of Lower House lawmakers will expire the following month, leaving Suga with limited wiggle room to form a strategy on dissolving the Lower House.

Calling an election at the right moment has been always an arduous task for prime ministers. For Suga, who was appointed to the role just a month ago, it is an important test of his acumen as a politician and, depending on the outcome, could embolden or weaken his standing within the ruling party.

Buoyed by optimism and high expectations for the new administration that were reflected in early polls, political spectators in Nagatacho, the nations political center, had anticipated Suga would pull the trigger in the early days of his administration. A Kyodo News survey conducted shortly after he took office in mid-September showed the approval rating for his Cabinet at 66.4%, with disapproval at just 16.2%.

But Suga himself seemed to extinguish the prospect of an early Lower House election.

I want to get some work done, since I just assumed the role of LDP president, Suga said in a news conference immediately after his victory in the partys leadership contest, adding that he needed to take the pandemic situation into consideration as well.

If the prime minister decides to hold an election next year, he will have very little flexibility on when to do so as several important events are already scheduled. The postponed Olympic Games are expected to take place in July, and the Tokyo assembly election is set to take place around that time as well.

The Tokyo vote is vital for Komeito, the LDPs junior coalition partner, and it has been widely noted that Komeito is averse to holding a general election immediately before or after a local campaign as it would wish to concentrate its efforts on the latter.

If the summer of 2021 is out of the picture, only three viable scenarios remain: at the start of next years Diet session, in January; immediately after the fiscal 2021 budget is passed, in the spring, or close to the expiration of Lower House lawmakers terms, in the fall.

Besides cooperation with Komeito, Suga may also contemplate working with Nippon Ishin no Kai, a right-leaning opposition party with whose leaders the prime minister has strong working relationships, said Jun Iio, a professor of Japanese politics at the National Graduate Institute for Policy Studies.

Suga, the professor predicted, will probably not dissolve the Lower House this year, as keeping the decision available to him as long as possible, like a trump card, would help maintain his power.

Even though Suga may be tempted to call for a snap vote if his Cabinets approval rating slips, the LDP holds a commanding lead in all major polls on approval compared to other parties.

In a Kyodo News poll this month, the LDPs approval rating was 45.8% far ahead of the rating for the Constitutional Democratic Party of Japan (CDP), the largest opposition party, which saw the approval of just 6.4%.

Nippon Ishin, which had a 4.2% approval rating in the Kyodo poll, is currently preoccupied with Novembers referendum on the Osaka metropolis plan. Ichiro Matsui, head of the party, said in September he would prefer the general election to be held the same day as the referendum to boost voter turnout.

But the prime minister might have been dissuaded from such move, since Nippon Ishin and the LDPs Osaka chapter are divided on the issue. The administration has been ambivalent on whether it supports the metropolis proposal, and holding the general election on the same day could be taken as an implicit nod for the plan.

Like most prime ministers, Suga has been tight-lipped on when he may call a vote. In maintaining this uncertainty, it is convenient for the prime minister to hint at the possibility of a snap election whenever he thinks it necessary to shake things up within the party, Iio said.

Suga is a self-assured individual who doesnt believe his administrations popularity will decline, as he believes he is getting work done (on lowering cell phone bills and promoting digitalization) and would dare to challenge anyone who seeks to replace him, Iio said. Itd be advantageous to hold on to the right to call a general election, to avoid the possibility of being forced out by (the LDPs) factional dynamics.

Traditionally, prime ministers from the LDP are members of one of its factions, to maintain their status and amass support. But Suga doesnt belong to any of them, leaving him without a solid support base and more vulnerable to friction between caucuses.

Political parties have begun preparations to field candidates for each electoral district. The CDP, which acquired new lawmakers from the Democratic Party for the People through a merger this summer, is looking to work with other opposition parties to back the same candidates. Some within the party, though, are unwilling to cooperate further with the Japanese Communist Party.

The opposition parties undertaking for a unified counterforce against Suga could break down, Iio said, if Nippon Ishin puts forward candidates across the country to divide up votes.

Suga doesnt believe hed win an election without machinating on various fronts, he added.

PHOTO GALLERY (CLICK TO ENLARGE)

Original post:
Speculation over Japan election timing grows ahead of Diet session - The Japan Times

Read More..

One can simply extract the aloe vera gel from the plant.

Highlights

There are many prized ingredients in our nature that comes with wonderful health benefits. One such example is of aloe vera (ghritkumari). Ever since the bright green, succulent plant found value and mention in Ayurveda, health and beauty remedies by experts, it has taken over gardens and window shelves of many homes around the world. No wonder there are tonnes of gels, creams and juices being made with the wonderful plant.

But did you know, as much as there are amazing benefits of using aloe vera for skin, there are some health benefits of consuming aloe vera as well?! The nutritionally dense plant has countless benefits that are enough for us to start finding ways to include it in our diet. One of the most important benefits of aloe vera is that it may also help you in weight loss! Besides being loaded with vitamins and minerals, aloe vera is believed to have certain active compounds, which may help you shed a few pounds. It is also known to boost metabolism, which helps burn more of body fat aiding weight loss. Its laxative properties aid digestion when consumed in small quantities that can lead to weight loss too.

There are many ways to consume aloe vera; however, it must be consumed in small quantities and avoided by pregnant women and those with frequent tummy troubles and problems like diarrhoea or loose motions. Aloe vera juice is one of the most popular ways to reap in the many benefits of the plant. Not only is it easy, but is known to boost the body's immunity. The wide range of antioxidants, present in aloe vera also helps fight cell damage caused by free-radical activity and strengthens your immunity.

(Also Read:Aloe Vera Juice Benefits: 7 Amazing Reasons To Drink Aloe Vera Juice Everyday)

Aloe vera must be consumed in small quantities.

One can simply buy a pack of aloe vera juice from the market or extract the aloe vera gel from the plant. It might be bitter in taste but you can always add a teaspoon of honey for taste. Here we have a quick and simple aloe vera juice recipe packed with the goodness of lemon and honey that may help you shed a few kilos along with boosting your immunity!

Ingredients-

. Aloe vera gel- 2 tsp

. Lemon- 1 (juiced)

. Honey- 1 tsp

. Mint leaves (chopped)- 5-6

Method-

. All you need to do is blend all the ingredients well till smooth and serve.

Promoted

As per many health experts, one can even combine aloe vera with other healthy herbs such as giloy, amla or tulsi for more health benefits. Be cautious about not consuming too much of aloe vera since it can have side effects.

Try this aloe vera, lemon and honey juice for weight loss empty stomach every morning. Share your experience with us in the comments section below.

About Aanchal MathurAanchal doesn't share food. A cake in her vicinity is sure to disappear in a record time of 10 seconds. Besides loading up on sugar, she loves bingeing on FRIENDS with a plate of momos. Most likely to find her soulmate on a food app.

See the rest here:
This Aloe Vera Juice With Lemon And Honey May Work Wonders For Weight Loss - NDTV Food

Read More..

Abstract

The mismatch between evolved human physiology and Western lifestyles is thought to explain the current epidemic of cardiovascular disease (CVD) in industrialized societies. However, this hypothesis has been difficult to test because few populations concurrently span ancestral and modern lifestyles. To address this gap, we collected interview and biomarker data from individuals of Turkana ancestry who practice subsistence-level, nomadic pastoralism (the ancestral way of life for this group), as well as individuals who no longer practice pastoralism and live in urban areas. We found that Turkana who move to cities exhibit poor cardiometabolic health, partially because of a shift toward Western diets high in refined carbohydrates. We also show that being born in an urban area independently predicts adult health, such that life-long city dwellers will experience the greatest CVD risk. By focusing on a substantial lifestyle gradient, our work thus informs the timing, magnitude, and evolutionary causes of CVD.

It has become increasingly clear that the spread of Western, industrialized lifestyles is contributing to a rapid rise in metabolic and cardiovascular diseases (CVDs) worldwide (15). Since the Industrial Revolution, modern advancements in agriculture, transportation, and manufacture have had a profound impact on human diets and activity patterns, such that calorie-dense food is often easily accessible and adequate nutrition can be achieved with a sedentary lifestyle. This state of affairs, which is typical in Western, industrialized societies but rapidly spreading across developing countries, stands in stark contrast to the ecological conditions experienced over the vast majority of our evolutionary history. Consequently, the mismatch between human physiologywhich evolved to cope with a mixed plant- and meat-based diet, activity-intensive foraging, and periods of resource scarcityand Western, industrialized lifestyles has been hypothesized to explain the current epidemic of cardiometabolic disease (14).

Attempts to test the evolutionary mismatch hypothesis thus far have largely focused on comparing cardiometabolic health outcomes between industrialized nations and small-scale, subsistence-level groups (e.g., hunter-gatherers, horticulturalists, and pastoralists). Arguably, the diets and activity patterns of these subsistence-level groups are relatively in line with their recent evolutionary history, and these populations can thus be thought of as matched to their evolutionary past (1, 5). In support of the evolutionary mismatch hypothesis, essentially, all subsistence-level populations studied to date show minimal type 2 diabetes, hypertension, obesity, and heart disease relative to the United States and Europe (513). Two other classes of studies provide further support: (i) Indigenous populations that have recently transitioned to market-based economies show higher rates of obesity and metabolic syndrome compared to subsistence-level groups [e.g., (14, 15)] and (ii) comparisons between rural and urban areas in developing countries have found higher rates of hypertension, type 2 diabetes, and obesity in the urban, industrialized setting (1620).

Despite the groundwork that has been laid so far in understanding how Western lifestyles influence health, most prior studies leave two major gaps. First, the participants genetic backgrounds are either heterogeneous (in the case of urban versus rural comparisons within a country) or confounded with lifestyle (in the case of subsistence-level versus U.S. or Europe comparisons). This makes it difficult to disentangle genetic versus environmental contributions to health. A more robust study design would be to compare health between individuals living their ancestral, traditional way of life versus individuals from the same genetic background living a modern, industrialized lifestyle. This type of natural experiment is difficult to come by [but see (13)], and large-scale work that has assessed acculturation effects on cardiometabolic health within a single group has therefore been limited to more modest lifestyle gradients [e.g., work with the Tsimane (15, 17), Shuar (10), or Yakut (18, 21)]. A second major gap is that research to date has focused on industrialization and acculturation effects at particular life stages, mainly in adulthood, despite strong evidence that early-life conditions influence adult health and that life-course perspectives are likely important (19, 20). Of particular relevance is the hypothesis that individuals use cues during development to predict what the adult environment will be like and develop phenotypes well suited for those conditions. Under such a predictive adaptive response (PAR) framework, industrial transitions are thought to be especially detrimental because individuals may be born in resource-poor environments but exposed to resource-rich environments as adults; individuals are thus phenotypically prepared for scarcity but encounter plenty instead, leading to a within-lifetime environmental mismatch and subsequent cardiometabolic disease (2224). Despite the popularity and potential significance of this idea, little work has robustly and empirically tested it against other evolutionary explanations for why early-life resource scarcity is commonly associated with poor adult cardiometabolic health (20, 2528). In particular, the developmental constraints (DC) hypothesis alternatively predicts that early-life nutritional challenges will be unavoidably costly and associated with poor health outcomes no matter the adult environment (20, 25, 28).

To address these gaps, we collected interviews and cardiometabolic health biomarker data from the Turkana, a subsistence-level, pastoralist population from a remote desert in northwest Kenya (Fig. 1) (29, 30). The Turkana and their ancestors have practiced nomadic pastoralism in arid regions of East Africa for thousands of years (30), and present-day, traditional Turkana still rely on livestock for subsistence: 62% of calories are derived from fresh or fermented milk, and another 12% of calories come from animal meat, fat, or blood (29) [specifically, the Turkana herd dromedary camels, zebu cattle, fat-tailed sheep, goats, and donkeys (29)]. The remaining calories are derived from wild foods or products obtained through occasional trade (e.g., cereals, tea, and oil) (29). However, as infrastructure in Kenya has improved in the past few decades, small-scale markets have expanded into northwest Kenya, leading some Turkana to no longer practice nomadic pastoralism and to rely more heavily on the market economy; specifically, these individuals make and sell charcoal or woven baskets or keep animals in a fixed location for trade rather than subsistence. In addition to the emergence of this nonpastoralist (but still relatively subsistence-level) subgroup, some individuals have left the Turkana homelands entirely and now live in highly urbanized parts of central Kenya (Fig. 1). The Turkana situation thus presents a unique opportunity, in that individuals of the same genetic background can be found across a substantial lifestyle gradient ranging from relatively matched to extremely mismatched with their recent evolutionary history. Further, because many Turkana are currently migrating between rural and urban areas within their lifetime, we were able to empirically test the PAR hypothesis by asking whether individuals who experienced rural conditions in early life but urban conditions in adulthood exhibited worse cardiometabolic health than individuals whose early and adult environments were similar. We tested this idea against the DC hypothesis, which predicts that early-life challenges incur simple long-term costs that are not contingent on the adult environment (20, 25, 28).

(A) Sampling locations throughout northern and central Kenya are marked with red dots; the county borders are marked with dashed lines. In both Laikipia and Turkana counties, the largest city (which is generally central within each county) is marked with a black dot. (B) Schematic describing the three lifestyle groups that were sampled as part of this study. (C) The proportion of people from each lifestyle group who reported that they consumed a particular item regularly, defined as one to two times per week, more than two times per week, or every day. People who reported that they consumed a particular item rarely or never were categorized as not consuming the item regularly. Animal products are a staple of the traditional pastoralist diet (85), while carbohydrates and added nutrients, which can only be obtained through trade, are indicative of market integration.

Capitalizing on this natural experiment, we sampled 1226 adult Turkana in 44 locations from the following groups: (i) individuals practicing subsistence-level pastoralism in the Turkana homelands, (ii) individuals that do not practice pastoralism but live in the same remote, rural area, and (iii) individuals living in urban centers (Fig. 1). We found that cardiometabolic profiles across 10 biomarkers were favorable in pastoralist Turkana, and rates of obesity and metabolic syndrome were low, similar to other subsistence-level populations (612). Comparisons within the Turkana revealed a nonlinear relationship between the extent of industrialization or evolutionary mismatch and cardiometabolic health: No significant biomarker differences were found between pastoralists and nonpastoralists from rural areas. However, we found strong, sometimes sex-specific, differences in health between these two groups and nonpastoralists living in urban areas, although metabolic dysfunction among urban Turkana did not reach the levels observed in the United States. Using formal mediation analyses (31, 32), we show that consumption of processed, calorically dense foods (primarily carbohydrates and cooking fats) and indices of market integration may explain health shifts in urban Turkana. Last, we show that a proxy of urbanization (population density) experienced around the time of birth is associated with worse adult cardiometabolic health, independent of adult lifestyle. In other words, the health costs of living an industrialized lifestyle in early life and adulthood are additive, such that within-lifetime environmental mismatches do not appear to exacerbate health issues as has been previously suggested (22, 24).

To characterize the health of the Turkana people, we collected extensive interview and biomarker data from adult Turkana sampled throughout Kenya (Table 1 and Fig. 1). We measured body mass index (BMI), waist circumference, total cholesterol, triglycerides, high- and low-density lipoproteins (HDLs and LDLs), body fat percentage, systolic and diastolic blood pressure, and blood glucose levels (Table 2). We also created a composite measure of health, defined as the proportion of measured biomarkers that exceed cutoffs set by the U.S. Centers for Disease Control and Prevention (CDC) or the American Heart Association as being indicative of disease (see Supplementary Materials and Methods).

NHANES, National Health and Nutrition Examination Survey; MI, market integration. M, Male; F, Female.

BP, blood pressure.

As has been observed in other subsistence-level populations (5), we found extremely low levels of cardiometabolic disease among traditional, pastoralist Turkana: No individuals met the criteria for obesity (BMI > 30) or metabolic syndrome (33), and only 6.4% of individuals had hypertension [blood pressure > 135/85 (33)]. Further, across eight cardiometabolic biomarkers that have been measured consistently in other subsistence-level populations (612), the means observed in the Turkana were generally within the range of what others have reported (table S1, A and B). Mean body fat percentage (mean SD for females = 20.45 4.57%) and BMI (19.99 2.14 kg/m2) were on the lower extremes but were similar to other pastoralists (mean BMI in the Fulani and the Maasai = 20.2 and 20.7 kg/m2, respectively) and to a small study of the Turkana conducted in the 1980s [mean BMI = 17.7 kg/m2 (34)]. Notably, the only biomarkers that were strongly differentiated in traditional, pastoralist Turkana were HDL (72.69 14.72 mg/dl) and LDL cholesterol levels (60.89 20.22 mg/dl), both of which were even more favorable than what has been observed in other subsistence-level groups, including the Fulani and the Maasai (range of reported means for HDL = 34.45 to 49.11 mg/dl and LDL = 72.70 to 92.81 mg/dl). It remains to be seen why the Turkana HDL/LDL profiles appear as strong and consistent outliers relative to other subsistence-level groups, but one possibility is that there has been selection on Turkana lipid traits as a result of their unique diet, ecology, and lifestyle. This possibility could be explored in future evolutionary genetic and metabolic studies.

Next, we sought to understand the shape of the relationship between industrialization and cardiometabolic health within the Turkana, by comparing biomarker values across the three lifestyle categories. Using linear models controlling for age and sex, we found that Turkana practicing traditional pastoralism did not differ in any of the 10 measured biomarkers relative to nonpastoralist Turkana living in similarly rural areas (all P values > 0.05; Fig. 2 and table S2A), despite there being major dietary difference between these groups (Fig. 1). Pastoralist and rural nonpastoralist Turkana did significantly differ in our composite measure, with nonpastoralist Turkana exhibiting more biomarker values above clinical cutoffs [average proportion of biomarkers above cutoffs = 4.02 and 6.82% for pastoralists and nonpastoralists, respectively; P value = 1.39 103; false discovery rate (FDR) < 5%; Fig. 2].

(A) Effect sizes for contrasts between pastoralist, rural nonpastoralist, and urban nonpastoralist Turkana (from linear models controlling for age and sex; table S2A). Effect sizes are standardized, such that the x axis represents the difference in terms of SDs between groups. BP, blood pressure. (B) Standardized effect sizes for contrasts between rural Turkana (pastoralist and rural nonpastoralist grouped together), urban nonpastoralist Turkana, and the U.S. (from linear models controlling for age and sex; table S2B). In (A) and (B), lighter colored bars represent effect sizes that were not significant [false discovery rate (FDR) > 5%], and analyses of body fat and blood glucose focus on females only (see Supplementary Materials and Methods). Symbols correspond to FDR significance thresholds as follows: *FDR < 0.1%, FDR < 1%, and +FDR < 5%. (C) Predicted values for a typical rural Turkana (pastoralist and rural nonpastoralist grouped together), urban Turkana, and U.S. individual are shown for a subset of significant biomarkers. Estimates and error bars were obtained using coefficients and their SEs from fitted models, for a female of average age (see Supplementary Materials and Methods).

Notably, biomarker values for both pastoralist and nonpastoralist, rural Turkana were consistently more favorable than among Turkana living in urban areas in central Kenya. People living in urban areas exhibited composite measures indicative of worse cumulative cardiometabolic health (average proportion of biomarkers above cutoffs = 13.42%), higher BMIs and body fat percentages, larger waist circumferences, higher blood pressure, and higher levels of total cholesterol, triglycerides, and blood glucose (all FDR < 5%; Fig. 2 and table S2A). The only tested variables that did not exhibit differences between urban and rural Turkana (both pastoralists and nonpastoralists) were the HDL and LDL cholesterol levels, which were favorable in all Turkana regardless of lifestyle (tables S1A and S2A). Using standardized effect sizes, we found that the biomarkers that differed most between the two rural groups and urban residents were blood glucose, triglycerides, and BMI (Fig. 2). For example, the average urban Turkana resident has a 9.69 and 8.43% higher BMI relative to pastoralist and nonpastoralist rural Turkana, respectively.

For all 11 measures, we explored the possibility of age by lifestyle category and sex by lifestyle category interactions. We found no evidence that age modifies the response to lifestyle change (FDR > 5% for all biomarkers; likelihood ratio test comparing models with versus without the interaction term). However, we did find that inclusion of a sex by lifestyle category term improved the model fit for blood glucose levels (P value from a likelihood ratio test = 1.838 104) and body fat percentage (P value = 4.234 103; table S2A). In both cases, women experienced worse health with increasing market integration and industrialization, while men did not (fig. S1). The nature of this interaction is consistent with several previous studies (10, 21, 35, 36); however, the specific reasons behind the heightened sensitivity of women to acculturation (in our study and elsewhere) remain unknown. Previous work has speculated that these sex-specific effects are driven by social and behavioral factors that affect diet and activity patterns (e.g., rate of acquisition of wage labor jobs) and that change more markedly for women versus men during industrial transitions (10). It is likely that this general explanation applies to the Turkana as well, although follow-up work is needed to understand the specifics.

We next asked whether the biomarker levels observed among urban Turkana approached those observed in a fully Western, industrialized society (specifically, the United States). We note that a caveat of these analyses is that they must include different genetic backgrounds since Turkana individuals are rarely found in fully industrialized countries.

To compare metabolic health between the U.S., rural Turkana (grouping pastoralists and nonpastoralists since these groups were minimally differentiated in previous analyses), and urban Turkana, we downloaded data from the CDCs National Health and Nutrition Examination Survey (NHANES) conducted in 2006 (37), focusing on adults (ages 18-65) to recapitulate the age distribution of our Turkana dataset (see Table 1 for sample sizes). Comparisons between NHANES and our Turkana dataset revealed that, while urban Turkana exhibit biomarker values indicative of poorer health than rural Turkana, urban Turkana have more favorable metabolic profiles than the U.S. (Fig. 2, figs. S2 and S3, and table S2B). This pattern held for all measures except (i) blood glucose levels, where no differences were observed (P value for U.S. versus rural Turkana = 0.166, U.S. versus urban Turkana = 0.074); (ii) triglycerides, where urban Turkana could not be distinguished from the U.S. (P = 0.627); (iii) systolic blood pressure, where urban Turkana exhibited higher values than the U.S. (4.55% higher; P = 3.02 106; FDR < 5%); and (iv) diastolic blood pressure, where mean values for both urban and rural Turkana were unexpectedly higher than the U.S. (rural Turkana, 8.77% higher than U.S., P = 4.43 1065; urban Turkana, 11.69% higher than U.S., P = 3.58 1031, FDR < 5% for both comparisons; fig. S4). These differences in diastolic blood pressure remained after removing all U.S. individuals taking cardiometabolic medications (rural Turkana, 6.91% higher than U.S., P = 3.27 1067; urban Turkana, 10.37% higher than U.S., P = 2.21 1035). However, two pieces of evidence suggest that the higher diastolic blood pressure values observed in the Turkana are not pathological: (i) Values for rural Turkana (77.43 15.22 mmHg) are similar to estimates from other subsistence-level populations without cardiometabolic disease (range of published means = 70.9 to 79.9 mmHg; table S1A) and (ii) few rural Turkana meet the criteria for hypertension relative to the U.S. (fig. S3). Future work is needed to understand the environmental and/or genetic sources of the observed differences in blood pressure between Turkana and U.S. individuals.

For measures that exhibited differences between urban Turkana and the U.S. in the expected directions, these effect sizes were consistently much larger in magnitude than the differences that we observed between rural and urban Turkana (Fig. 2). For example, while the average urban Turkana experiences a 9% higher BMI than their rural counterparts, the average U.S. individual has a BMI that is 44 and 32% higher than rural and urban Turkana, respectively. Similarly, while the average proportion of biomarkers above clinical cutoffs is 6.22% in rural Turkana and 13.42% in urban Turkana, this number rises to 38.84% in the U.S.

We next sought to identify the specific dietary, lifestyle, or environmental inputs that drive differential health outcomes between urban and rural Turkana. To do so, we turned to interview data collected for each individual (see Supplementary Materials and Methods), which revealed substantial variation in diet, market access, and urbanicity [a term that we use to mean living in an urbanized area and engaging in an urban lifestyle, following (38); Figs. 1 and 3]. We paired these interview data with mediation analyses (31, 32) to formally test whether the effect of a predictor (X) on an outcome (Y) was direct or, instead, indirectly explained by a third variable (M) such that XMY (Fig. 3). Using this statistical framework, we tested whether the following factors could explain the decline in metabolic health observed in urban Turkana: increased consumption of market-derived, calorically dense foods (e.g., carbohydrates such as soda, bread, rice, as well as fats such as cooking oil), reduced consumption of traditional animal products, poorer health habits, ownership of more market-derived goods and modern amenities (e.g., cell phone, finished floor, and electricity), occupation that is more market integrated (e.g., formal employment), and residence in a more populated or developed area (measured via population density, distance to a major city, and female education levels) (see Supplementary Materials and Methods). In particular, we predicted that lifestyle effects on health would be mediated by a shift toward a diet that incorporates more carbohydrates and fewer animal products in urban Turkana. These analyses focused on biomarkers for which our sample sizes were the largest since dietary data were not available for all individuals (see table S3 for sample sizes).

(A) Key measures of urbanicity and market integration used in mediation analyses, with means and distributions shown for urban and rural Turkana. (B) Schematic of mediation analyses. Specifically, mediation analyses test the hypothesis that lifestyle effects on health are explained by an intermediate variable, such as consumption of particular food items (red arrows); alternatively, lifestyle effects on health may be direct (black arrow) or mediated by a variable that we did not measure. (C) Summary of mediation analysis results, where colored squares indicate a variable that was found to significantly explain urban-rural health differences in a given biomarker. Significant mediators are colored on the basis of how much the lifestyle effect (urban/rural) decreased when a given mediator was included in the model. MI, market integration. Full results and sample sizes for mediation analyses are presented in table S3.

In support of our predictions, urban-rural differences in waist circumference, BMI, and our composite measure of health were mediated by greater consumption of processed, calorically dense foods (including carbohydrates and fats) and lower consumption of traditional animal products (milk and blood) in urban Turkana (Fig. 3 and table S3). Notably, the total number of different carbohydrate items that an individual consumed was a strong and consistent predictor across these three biomarkers, suggesting that individual dietary components may matter less than overall exposure to refined carbohydrates. However, it is important to note that refined carbohydrates are commonly processed with oil or other additives, and it is therefore likely a combined effect of exposure to both carbohydrates and fats that drives the negative health effects that we observe.

Contrary to our predictions, we did not find that dietary differences mediated urban-rural differences in systolic blood pressure, diastolic blood pressure, or body fat percentage. Instead, these measures were explained by variables that captured how industrialized and market-integrated a given individuals lifestyle was, which was also important for waist circumference, BMI, and our composite measure of health in addition to dietary effects. For example, fine-scale measures of population density and degree of market reliance of occupation both significantly mediated five of six tested biomarkers (Fig. 3 and table S3). Further, these indices of urbanicity and market integration tended to be stronger mediators than dietary variables (Fig. 3).

To understand the degree to which the mediators that we identified explain the relationship between lifestyle and a given biomarker, we compared the magnitude of the lifestyle effect in our original models (controlling for age and sex, without any mediators) to the effect estimated in the presence of all significant mediators. If the mediators fully explain the relationship between lifestyle and a given biomarker, then we would expect the estimate of the lifestyle effect to be zero in the second model. These analyses revealed that the mediators that we identified explain most of the relationship between lifestyle and waist circumference (effect size decrease = 90.7%), BMI (79.9%), systolic blood pressure (74.9%), and composite health (64.1%) but explain only a small portion of lifestyle effects on diastolic blood pressure (10.0%) and body fat (23.5%; table S3).

Last, we were interested in understanding whether early-life conditions had long-term effects on health, above and beyond the effects of adult lifestyle that we had already identified. We were motivated to ask this question because work in humans and nonhuman animals has demonstrated strong associations between diet and ecology during the first years of life and fitness-related traits measured many years later (20, 25, 39, 40). Two major hypotheses have been proposed to explain why this embedding of early-life conditions into long-term health occurs. First, the PAR hypothesis posits that organisms adjust their phenotype during development in anticipation of predicted adult conditions. Individuals that encounter adult environments that match their early conditions are predicted to gain a selective advantage, whereas animals that encounter mismatched adult environments should suffer a fitness cost (19, 22, 25, 41, 42). In contrast, the DC or silver spoon hypothesis predicts a simple relationship between early environmental quality and adult fitness: Individuals born in high-quality environments experience a fitness advantage regardless of the adult environment (25, 28, 43). Under DC, poor-quality early-life conditions cannot be ameliorated by matching adult and early-life environments; instead, the effects of environmental adversity accumulate across the life course.

We found no evidence that individuals who experienced matched early-life and adult environments had better metabolic health in adulthood than individuals who experienced mismatched early-life and adult conditions (P > 0.05 for all biomarkers). In particular, we tested for interaction effects between the population density of each individuals birth location (estimated for their year of birth) and a binary factor indicating whether the adult environment was urban or rural (table S4A; see table S4B for parallel analyses using population density to define the adult environment as a continuous measure). This analysis was possible given the within-lifetime migrations of many Turkana between urban and rural areas: Only 19.52 and 33.01% of urban and rural Turkana, respectively, were sampled within 10 km of their birthplace, and the correlation between birth and sampling location population densities was low (R2 = 0.115; P < 1016; fig. S5).

While we observed no evidence for interaction effects supporting PAR, we did find strong main effects of early-life population density on adult waist circumference (b = 0.272, P = 7.33 108), BMI (b = 0.266, P = 1.35 107), body fat (b = 0.306, P = 1.57 105), diastolic blood pressure (b = 0.124, P = 2.01 102), and our composite measure of health (b = 0.296, P = 3.40 104; all FDR < 5%), in support of DC. For all biomarkers, being born in a densely populated location was associated with poorer adult metabolic health (Fig. 4 and table S4A). Furthermore, the early-life environment effect was on the same order of magnitude as the effect of living in an urban versus rural location in adulthood (see table S4A and Supplementary Materials and Methods). For example, BMIs are 5.69% higher in urban versus rural areas, while individuals born in areas from the 25th versus 75th percentile of the early-life population density distribution exhibit BMIs that differ by 3.34%. Similarly, the effect of the adult environment (urban compared to rural) on female body fat percentages is 11.14%, while the effect of early-life population density (25th compared to 75th percentile) is 20.7% (table S4A).

The relationship between the population density of each individuals birth location and (A) BMI, (B) our composite measure of health, (C) waist circumference, and (D) diastolic blood pressure are shown for individuals sampled in rural and urban locations, respectively. Notably, while the intercept for a linear fit between early-life population density and each biomarker differs between rural and urban sampling locations (indicating mean differences in biomarker values as a function of adult lifestyle), the slope of the line does not. In other words, we find no evidence that the relationship between early-life conditions and adult health is contingent on the adult environmnt (as predicted by PAR). Instead, being born in an urban location predicts poorer metabolic health regardless of the adult environment.

By sampling a relatively endogamous population across a substantial lifestyle gradient, we show that (i) traditional, pastoralist Turkana exhibit low levels of cardiometabolic disease and (ii) increasing industrialization, in both early life and adulthood, has detrimental, additive effects on metabolic health (in opposition of popular PAR models that have rarely been tested empirically in humans) (20, 22, 24). Our findings offer strong support for the evolutionary mismatch hypothesis, more so than existing studies that cannot disentangle lifestyle and genetic background effects (612, 44, 45, 46) or that assess lifestyle effects across much more modest gradients (10, 17, 21, 47, 48). Our work also provides some of the first multidimensional, large-scale data on acculturation and industrialization effects on cardiometabolic health in pastoralists [see also (34, 49, 50)], which have received less attention than other subsistence modes [e.g., horticulturalists such as the Shuar and Tsimane (10, 15, 51, 52)].

Our observation that pastoralist Turkana do not suffer from cardiometabolic diseases echoes long-standing findings from other subsistence-level groups (612). However, it also provides empirical support for a more recent and controversial hypothesis: that many types of mixed plant- and meat-based diets are compatible with cardiometabolic health (1, 53, 54) and that mismatches between the distant human hunter-gatherer past and the subsistence-level practices of horticulturalists or pastoralists do not lead to disease (55). In other words, contemporary hunter-gatherers are most aligned with human subsistence practices that evolved ~300 thousand years ago (56), but they do not exhibit better cardiometabolic health relative to horticulturalists or pastoralists, whose subsistence practices evolved ~12 thousand years ago (tables S1A to S3) (57). Instead, we find evidence consistent with the idea that extreme mismatches between the recent evolutionary history of a population and lifestyle are needed to produce the chronic diseases now prevalent worldwide; in the Turkana, this situation appears to manifest in urban, industrialized areas but not in rural areas with changing livelihoods but limited access to the market economy.

Because our study assessed health in individuals who experience no, limited, or substantial access to the market economy, we were able to determine that industrialization has nonlinear effects on health in the Turkana. In particular, we find no differences between pastoralists and nonpastoralists in rural areas for 10 of 11 variables (Fig. 2), despite nonpastoralists consuming processed carbohydrates that are atypical of traditional practices (Fig. 1 and table S1A). Nevertheless, rural nonpastoralists still live in remote areas, engage in activity-intensive subsistence activities, and rely far less heavily on markets than urban Turkana. Given the mosaic of lifestyle factors that can change with modernization, often in concert, our results suggest that this type of lifestyle has not crossed the threshold necessary to produce cardiometabolic health issues. This threshold model may help explain heterogeneity in previous studies, where small degrees of evolutionary mismatch and market integration have produced inconsistent changes in cardiometabolic health biomarkers (10, 58, 59).

While our dataset does not capture every variable that mediates urban-rural health differences in the Turkana, we were able to account for a substantial portion (>60%) of lifestyle effects on waist circumference, BMI, systolic blood pressure, and composite health. In line with our expectations, increases in these biomarkers in urban areas were mediated by greater reliance on processed, calorically dense foods (i.e., refined carbohydrates and cooking fats) and reduced consumption of animal products (Figs. 1 and 3). However, our mediation analyses also show that broader measures of lifestyle modernization (e.g., population density, distance to a major city, and female education levels) have stronger explanatory power than diet alone. It is likely that these indices serve as proxies for unmeasured, more proximate mediators, such as psychosocial stress, nutrient balance, total caloric intake, or total energy expenditure, all of which vary by industrialization and can affect health (1, 12, 6063). The fact that the number of meals eaten per day (which is typically one in rural areas and two to three in urban areas) was a strong mediator for three of six variables points to total caloric intake, while the importance of occupation suggests activity levels are also probably key. More generally and as expected, our mediation analyses suggest that the link between lifestyle change and health is complex, multifactorial (e.g., driven by a suite of dietary and other factors), and potentially quite different for different biomarkers. Work with the Turkana is under way to address some of the unmeasured sources of variance that we hypothesize to be especially critical, namely, total caloric intake and total energy expenditure.

In addition to the pervasive influence of adult lifestyle on metabolic physiology that we observe in the Turkana, our analyses also revealed appreciable effects of early-life environments. In particular, controlling for the adult environment (urban or rural), birth location population density was a significant predictor of BMI, waist circumference, diastolic blood pressure, our composite measure of health, and body fat. Further, the impact of early-life and adult conditions appears to be on the same order of magnitude, although they do, in some cases, vary up to twofold. In particular, we observed 2 to 6% differences in BMI, waist circumference, and diastolic blood pressure, as a function of each life stage, while body fat and our composite measure show changes in the 11 to 20% range (note, however, that the measures that we used to quantify early-life and adult conditions are not the same, making direct effect size comparisons difficult; table S4A).

We did not find evidence that individuals who grew up in rural versus urban conditions were more prepared for these environments later in life, as predicted by PAR. These findings agree with work in preindustrial human populations and long-lived mammals, which have found weak or no support for PARs (26, 27, 6466). Together, these findings suggest that because early-life ecological conditions are often a poor predictor of adult environments for long-lived organisms, a strategy matching individual physiology to an unpredictable adult environment is unlikely to evolve (6769). Instead, our work joins others in concluding that challenging early-life environments simply incur long-term health costs (26, 27, 6466). While previous work in subsistence-level groups has clearly shown an effect of early-life environments (including acculturation) on health outcomes (52, 59), it has not explicitly tested whether PAR versus DC models explain these associations. Our attempt to do so here in the context of urbanicity exposure suggests that rapid industrial transitions are unlikely to create health problems because of within-lifetime environmental mismatches (26, 27, 70). Instead, our findings suggest that greater cumulative exposure to urban, industrialized environments across the life course will create the largest burdens of cardiometabolic disease.

Our study has several limitations. First, with the exception of our biomarker measurements, most of our data are self-reported. It is possible that recall may be imperfect, answers may be exaggerated to appear impressive (e.g., in interviews about the ownership of market goods), or participants may not wish to reveal private details (e.g., in interviews about health habits or covariates). On the basis of our conversations with study participants, we expect intentionally provided misinformation to be rare, but there are two areas where self-reporting contributes to specific limitations worthy of discussion. First, because our age data are self-reported, this key covariate is likely noisy and more so for rural than for urban participants (who are more likely to know their exact birth date). We do not have a reason to believe that this issue affects our estimate of the lifestyle-cardiometabolic health relationship, but it does likely complicate our ability to identify age by lifestyle interactions, which is a critical area for future study. Second, because our diet data are self-reported, we are currently unable to tease apart the precise nutritional components that drive health variation in the Turkana. Our mediation analyses reveal that several market-derived foods are key contributors, suggesting that broad exposure to processed, high-energy foods (including both carbohydrates and fats) is important for cardiometabolic health. Future work that estimates the total caloric intake and the intake of fat, protein, carbohydrates, and micronutrients [as in (71)] in the Turkana are planned.

A second limitation is that we do not know how our biomarker values are related to outcomes such as heart disease or mortality in the Turkana. We are relying on work in Western cohorts that has related lipid profiles, blood glucose, blood pressure, and measures of adiposity to these outcomes (72), but it is possible that those relationships are different in the Turkana [e.g., work in the United States has already demonstrated how the shape of the BMI-mortality curve may differ by race/ethnicity (73)]. Further, certain biomarkers may not linearly track disease and mortality risk: Notably, in Western cohorts, the effect of BMI on all-cause mortality risk is J shaped, such that underweight individuals experience some increase in risk, normal BMI individuals experience the lowest risk, and overweight and obese individuals experience the greatest risk (74). It is therefore difficult to draw conclusions about the relationship between any one biomarker, lifestyle change, and long-term outcomes in the present study. However, two pieces of evidence suggest that the changing biomarker profiles that we observe in urban Turkana are meaningful. First, work in Western countries has consistently shown that when individuals simultaneously cross clinical thresholds for several biomarkers, as we observe in urban Turkana, risk of cardiovascular events and all-cause mortality increases (72). Second, Kenya has seen a marked rise in CVD in recent decades, with 13% of hospital deaths in 2014 attributed to CVD. CVD risk is much higher in urban relative to rural areas across the country, with hypertension and type 2 diabetes estimated to be at least fourfold more prevalent in urban settings (75).

Last, a third limitation of our study is that we lack data on several key factors known to modify or mediate the relationship between lifestyle change and cardiometabolic health, such as total energy expenditure (1), total caloric intake and nutritional composition of the diet (71), and parasite load (76). Our ongoing research with the Turkana is in the process of gathering data on these sources of variance.

The hypothesis that mismatches between evolved human physiology and Western lifestyles cause disease has become a central tenet of evolutionary medicine, with potentially profound implications for how we study, manage, and treat a long list of conditions thought to arise from evolutionary mismatch (77). However, this hypothesis has been difficult to robustly test in practice because of inadequate population comparisons and the multiple types of mismatch to be considered. Leveraging the lifestyle change currently occurring in the Turkana population, we show that cardiometabolic health is worse in urban relative to rural areas but that small deviations from traditional, ancestral practices in rural areas do not produce health effects. To build upon our results, we advocate for more within-population comparisons spanning large lifestyle gradients, combined with longitudinal sampling designs [e.g., (72)]. Longitudinal study of other populations undergoing industrial transitions would also be invaluable for assessing the generality of the early-life effects on adult cardiometabolic health that we observe here and for identifying the specific early-life ecological, social, or behavioral factors that drive long-term variation in health.

Data were collected between April 2018 and March 2019 in Turkana and Laikipia counties in Kenya. During this time, researchers visited locations where individuals of Turkana ancestry were known to reside (Fig. 1). At each sampling location, healthy adults (>18 years old) of self-reported Turkana ancestry were invited to participate in the study, which involved a structured interview and measurement of 10 cardiometabolic biomarkers. Participation rates of eligible adults were high (>75%). GPS coordinates were recorded on a handheld Garmin GPSMAP 64 device at each sampling location. Additional details on the sampling procedures can be found in Supplementary Materials and Methods.

This study was approved by Princeton Universitys Institutional Review Board for Human Subjects Research (Institutional Review Board no. 10237) and Maseno Universitys Ethics Review Committee (MSU/DRPI/MUERC/00519/18). We also received county-level approval from both Laikipia and Turkana counties for research activities and research permits from Kenyas National Commission for Science, Technology, and Innovation (NACOSTI/P/18/46195/24671). Written, informed consent was obtained from all participants after the study goals, sampling procedures, and potential risks were discussed with community elders and explained to participants in their native language (by both a local official, usually the village chief, and by researchers or field assistants).

Individuals were excluded from analyses if they met any of the following criteria: (i) pregnancy, (ii) extreme outlier values for a given biomarker, (iii) missing data on primary subsistence activity, (iv) missing interview data, and (v) missing gender or age. For the early-life effects analyses, we also excluded individuals that did not report a birth location or for whom GPS coordinates for the reported birth location could not be identified on a map. Those missing birth locations had similar health profiles as individuals for whom a birth location could be assigned (all FDR > 5% for linear models testing for an effect of birth location missingness on each biomarker, controlling for age, sex, and lifestyle; table S4C). Thus, although the sample sizes for our early-life effects analyses are smaller than for analyses focused on current environmental/lifestyle effects (table S4A), this sample size reduction is not systematically biased in a way that is likely to affect the results.

Before statistical analyses, all biomarkers (except the composite measure of health) were mean centered and scaled by their SD, using the scale function in R (78). Consequently, all reported effect sizes are standardized and represent the effect of a given variable on the outcome in terms of increases in SDs.

Testing for lifestyle effects on measured biomarkers. For each of the 10 measured biomarkers, we used the following linear model to test for effects of lifestyle controlling for covariatesyi=0+lil+aia+sis+ei(1)where yi is the normalized (mean centered and scaled by the SD) biomarker value for individual i, li is lifestyle (pastoralist; nonpastoralist, rural; or nonpastoralist, urban), ai is age (in years), si is sex (male or female), and ei represents residual error. To determine whether a given biomarker exhibited a lifestyle by sex interaction, we used a likelihood ratio test to compare the fit of model 1 with the following modelyi=0+lil+aia+sis+(lisi)ls+ei(2)

In model 2, l s represents a lifestyle by sex interaction effect. If the P value for the likelihood ratio test comparing models 1 and 2 was less than 0.05, then we concluded that a lifestyle by sex interaction existed, and we tested for the effects of lifestyle within each gender separately (controlling for age). These analyses revealed lifestyle associations with body fat and blood glucose in females but not males (fig. S1). All additional analyses for these biomarkers therefore analyzed data from females alone.

Analyses of blood glucose levels included a covariate noting whether the individual had fasted overnight before the time of blood collection (which was always in the morning). For analyses of the composite measure of health, we used the same approach and the same main and interaction effects described for models 1 and 2 paired with generalized linear models with a binomial link function to accommodate count data. Specifically, the composite measure of health was modeled as the number of biomarkers that exceeded clinical cutoffs/the number of biomarkers measured for a given individual. Only individuals with three or more measured biomarkers were included in this analysis.

For all 11 measures (10 biomarkers and 1 composite measure), we extracted the P values associated with the lifestyle effect (l) from our models and corrected for multiple hypothesis testing using a Benjamini-Hochberg FDR (79). We considered a given lifestyle contrast to be significant if the FDR-corrected P value was less than 0.05 (equivalent to a 5% FDR threshold). The results of all final models are presented in table S2A.

Identifying factors that mediate lifestyle effects on measured biomarkers. For each biomarker that was significantly associated with lifestyle (table S2A), we were interested in identifying specific variables that mediated urban-rural differences in health. However, we did not perform mediation analyses for lipid traits and for blood glucose, as sample sizes for these biomarkers were smaller to begin with, and, after overlapping with our dietary data, we could only include 50 to 60 urban individuals. Therefore, we focused mediation analyses on waist circumference, BMI, diastolic and systolic blood pressure, body fat, and our composite measure of health (sample sizes for mediation analyses are presented in table S3).

To implement mediation analyses, we used an approach similar to (80, 81) to estimate the indirect effect of lifestyle on a given biomarker through the following potential mediating variables: alcohol and tobacco use (yes/no); consumption of meat, milk, blood, cooking oil, sugar, salt, bread, rice, ugali, potatoes, soda, fried foods, and sweets (frequency of use measured on a 0 to 4 scale); total number of unique carbohydrate items consumed; number of meals eaten per day; distance to the nearest city (in kilometers); log10 population density; main subsistence activity; proportion of mothers in the sampling location with no formal education; and a tally of the number of market-derived amenities an individual had (see Supplementary Materials and Methods). Occupation was coded to reflect integration in the market economy as follows: 0 = animal keeping, farming, fishing, hunting, and gathering; 1 = charcoal burning and mat making; 2 = casual worker, petty trade, and self-employment; and 3 = formal employment. To estimate female education levels in a given area, we calculated the fraction of women sampled in a given area with >0 children who reported having received no education. Population density, distance to a city, the proportion of mothers with no formal education, and the number of owned market goods are all measures that have been used in the literature to describe how urban a given individual/location is (82, 83). Population density estimates were derived from NASAs Socioeconomic Data and Applications Center (https://doi.org/10.7927/H49C6VHW). Specifically, we used the Gridded Population of the World database (version 4.11) to estimate the number of persons per square kilometer for each sampling location based on our GPS coordinates (see the Supplementary Materials).

For all mediation analyses, we used two categories to describe lifestyle, urban and rural, given minimal health differences between pastoralist and nonpastoralist Turkana living in rural areas. For biomarkers with no sex by lifestyle interaction, we estimated the strength of the indirect effect of each mediator as the difference between the effect of lifestyle (urban versus rural) in two linear models: the unadjusted model that did not account for the mediator (equivalent to model 1 in the Testing for lifestyle effects on measured biomarkers section) and the effect of lifestyle in an adjusted model that also incorporated the mediator. If a given variable is a strong mediator, then the effect of lifestyle will decrease when this variable is included in the model and absorbs variance otherwise attributed to lifestyle. For each biomarker, the adjusted model was implemented as followsyi=0+lil+aia+sis+mim+ei(3)

Where m represents the effect of the potential mediator on the outcome variable (all other variables are as defined in model 1). For body fat percentage, which displayed lifestyle effects in females but not males (fig. S1), we modeled females only and removed the sex term (s) from models 1 and 3. For the composite measure of health, we used generalized linear models with a binomial link function instead of linear models.

To assess the significance of each mediating variable, we estimated the decrease in l in model 1 relative to model 3 across 1000 iterations of bootstrap resampling. We deemed a variable to be a significant mediator if the lower bound of the 95% confidence interval (for the decrease in l) did not overlap with 0. As a measure of effect size, we report the proportion of 1000 bootstrap resampling iterations for which the effect of lifestyle (l) was reduced when the potential mediating variable was included in the model (table S3); a proportion of >0.975 is equivalent to a 95% confidence interval that does not overlap with 0. As another measure of effect size, we report the percent change in the lifestyle effect estimated from model 1 relative to model 3 for each biomarker-mediator pair (without bootstrapping and using the full dataset to estimate each effect size; Fig. 3). Further, to understand the degree to which the total set of mediators that we identified explain the relationship between lifestyle and a given biomarker, we report the percent change in effect size for the lifestyle effect estimated from model 1 versus a model that included all the same covariates and all significant mediators for a given biomarker.

Testing for early-life effects on biomarkers of adult health. Several research groups (26, 27, 64, 65, 84) have operationalized the PAR and DC hypotheses by asking whether there is evidence for interaction effects between early-life and adult environments (in support of PAR) or whether early-life adversity is instead consistently associated with compromised adult health (in support of DC). We took a similar approach to disentangle these hypotheses. For biomarkers with no sex and lifestyle interaction, we first asked whether there was any evidence for interaction effects between adult lifestyle and lifestyle/urbanicity during early life using the following linear modelyi=0+lil+aia+sis+did+(lidi)ld+ei(4)where di represents the log10 population density for the birth location of individual i during their birth year, li represents adult lifestyle (urban versus rural), and ld captures the interaction effect between these two variables. For the two variables with lifestyle effects on health in females but not males (body fat percentage and blood glucose levels), we modeled females only and removed the sex term (s). For the composite measure of health, we used generalized linear models with a binomial link function instead of linear models. For each of the 11 models, we extracted the P value associated with ld and corrected for multiple hypothesis testing (79). In all cases, the nominal and FDR-corrected P value was >0.05, suggesting that PARs do not explain early-life effects on health in the Turkana.

Next, we reran the appropriate version of model 4 for each measure after removing the interaction effect (ld). For biomarkers with no sex and lifestyle interaction, this model was equivalent toyi=0+lil+aia+sis+did+ei(5)

For each of the 11 models, we extracted the P value associated with the early-life effect (d) and corrected for multiple hypothesis testing (79). We considered a given variable to show support for the DC hypothesis if the FDR-corrected P value was less than 0.05. Results for all models described in this section are presented in table S4A. Results for parallel analyses that use log10 population density for the sampling location to define the adult environment (rather than a binary urban/rural lifestyle variable) are presented in table S4B. All statistical analyses were performed in R (78).

W. Leonard, in Evolution in Health and Disease, S. Stearns, J. Koella, Eds. (Oxford Univ. Press, 2010), pp. 265276.

P. Baker, J. Hanna, T. Baker, The Changing Somoans: Behavior and Health in Transition (Oxford Univ. Press, 1986).

A. Grafen, in Reproductive success, T. H. Clutton-Brock, Ed. (University of Chicago Press, 1988), pp. 454471.

National Health and Nutrition Examination Survey Data from the Centers for Disease Control and Prevention (CDC) and the National Center for Health Statistics (NCHS) (2019) (Hyattsville, MD).

E. Fratkin, E. A. Roth, As Pastoralists Settle: Social, Health, and Economic Consequences of Pastoral Sedentarization in Marsabit District, Kenya (Kluwer Academic Publishers, 2004).

A.E. Caldwell, S. Eaton, M. Konner, in Oxford Handbook of Evolutionary Medicine, M. Brune, W. Schiefenhoevel, Eds. (Oxford Univ. Press, 2019), pp. 209267.

D. E. Lieberman, The Story of the Human Body: Evolution, Health, and Disease (Pantheon Books, 2013).

G. Conroy, H. Pontzer, Reconstructing Human Origins: A Modern Synthesis (Norton, 2012).

T. McDade, C. Nyberg, in Human Evolutionary Biology, M. P. Muehlenbein, Ed. (Cambridge Univ. Press, 2010), pp. 581602.

Kenya STEPwise Survey for non communicable diseases risk factors (2015).

R Core Development Team, R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2015).

K. Galvin, M. Little, in Turkana Herders of the Dry Savanna: Ecology and Biobehavioral Response of Nomads to an uncertain Environment, M. A. Little, P. Leslie, Eds. (Oxford Univ. Press, 1999), pp. 125146.

M. A. Little, B. Johnson, in South Turkana Nomadism: Coping with an Unpredictably Varying Environment, R. Dyson-Hudson, J. T. McCabe, Eds. (HRAFlex Books, 1985).

R. J. Hijmans, raster: Geographic Data Analysis and Modeling. (2019).

R. J. Hijmans, geosphere: Spherical Trigonometry. (2017).

See the original post:
Urbanization and market integration have strong, nonlinear effects on cardiometabolic health in the Turkana - Science Advances

Read More..

In addition to math and reading lessons, many third graders in Alabamas low-income communities learn about nutrition from animated characters like Shining Rainbow, who loves colorful vegetables, and Muscle Max, who eats plenty of lean protein. The students also take the Vow of the Warrior in their classrooms. I will enter into the quest for health, strength, and wisdom. I will try new fruits and vegetables, the vow begins.

Its all part of a state SNAP-Ed curriculum called Body Quest, which applies what Sondra Parmer, the administrator of SNAP-Ed programs for the Alabama Cooperative Extension System, calls multilevel intervention and it turns out it has had a significant impact on children and families since its launch in 2010.

Most people are familiar with SNAP (Supplemental Nutrition Assistance Program) benefits, which help address food insecurity among vulnerable populations. SNAP-Ed is a companion program that provides comprehensive nutrition education to many of the same families, who may be struggling to put together healthy meals on a limited budget.

When we look at the data for the program, we can say with certaintybecause were comparing a treatment and a control groupthat because of Body Quest, these kids are eating better, said Parmer.

Now, a new study has aggregated data across eight states in the Southeast to evaluate the broader impact of programs like these for the first time. Published in the Journal of Nutritional Science at the end of September, the study found adults and children in SNAP-Ed programs are more likely to make a number of positive behavior changes, including eating more fruit and vegetables.

And while the data is from 2017, the results come at a time when advocates say helping food-insecure families eat well is more important than ever. Since the pandemic began, millions of Americans have lost their jobs and joined the ranks of those struggling to feed their families, prompting various calls for an increase in SNAP benefits. One analysis found nearly a quarter of American households faced food insecurity during the pandemic, more than double the number that did before COVID-19. In households with children, food insecurity tripled.

In the face of hunger, prioritizing healthy eating is even harder, especially in low-income communities where few nutritious foods are even available. And those communities have long suffered higher rates of diet-related diseases such as diabetes.

Those statistics now also point to risk factors for COVID-19. COVID has really highlighted the impact of underlying conditions like heart disease, diabetes, high blood pressure, and obesity, said Tracy Fox, a nutritionist by training who has been working on federal nutrition and nutrition education policy for more than 20 years. They have such a significant impact on whether or not you get COVID and how well you handle it.

Based on the study results, then, SNAP-Ed may be one effective tool to help people in low-income communities eat more of the foods that prevent diet-related diseases and the devastating impact of COVID-19.

How Does SNAP-Ed Work?

The entire SNAP program is funded by the farm bill; about 95 percent of the money goes directly to SNAP benefits, and the small remaining slice includes funding for SNAP-Ed. While states began to operate the education program as far back as 1998, it transformed during the Obama administration to focus on evidence-based projects and emphasize community and public health approaches to nutrition education.

The U.S. Department of Agriculture (USDA) distributes annual funds to states, which then administer the educational programs through cooperative extension services at land-grant universities, public health departments, and nonprofits. In 2020, the USDA distributed $441 million for the program. (Because SNAP-Ed funding is distributed entirely separately from benefits themselves, calls to raise benefits would not affect SNAP-Ed.)

The programs aim to educate SNAP recipients, but there is a lot of flexibility in terms of what each program looks like.

They include direct education programs such as lessons and cooking classes, and social marketing campaigns to disseminate messages about healthy eating. And in recent years, there has been emphasis placed on the implementation of policy, systems, and environmental (PSE) changesor long-term shifts that make healthy choices easier. For example, a school might ban soda and other sugary beverages (policy), install new water-bottle-filling fountains with promotional posters nearby (environment), and make a plan to stock vending machines with healthier alternatives (systems).

In Alabama, the Body Quest program includes direct education in the form of classroom nutrition lessons as well as many PSE changes, such as lunchroom posters with animated characters encouraging healthy choices and school wellness committees that create action plans to make school environments healthier. For example, at a school in Conecuh County, the committee identified a need for daily physical activity breaks, and the SNAP-Ed educator trained teachers in how to conduct them.

Body Quest is just one cog in the wheel when it comes to SNAP-Ed programs in the state, Parmer noted. Educators also plant and maintain teaching gardens, teach food bank clients how to cook with produce they are unfamiliar with, and more.

Evidence of Impacts

The flexibility given to each state to craft programs that meet the needs of its unique communities is one of SNAP-Eds biggest strengths, Fox said. But it also makes collecting consistent data and evaluating that data in a uniform way difficult.

To undertake the research, the Public Health Institute created a working group with representatives from SNAP-Ed agencies in eight states: Alabama, Florida, Georgia, Kentucky, Mississippi, North Carolina, South Carolina, and Tennessee.

We selected the common indicators, and then we came up with a plan on how to gather that information from everyone, explained Amy DeLisio, the director of the Center for Wellness and Nutrition at the Public Health Institute and a co-author of the study. The 25 participating agencies used pre- and post-tests with SNAP-Ed participants, and then re-coded the results to match standardized indicators.

Results showed participants ate about a third of a cup of more fruit and a quarter of a cup more vegetables per day than they had before participating in the programs. And while that little bump might not sound significant, experts said its more meaningful than it may appear.

It may seem like a very small amount of fruits and vegetables on your plate, said Julia McCarthy, interim deputy director at the Laurie M. Tisch Center for Food, Education and Policy, but it is a significant increase, especially given most Americans fall far short of meeting dietary guidelines in this realm. Furthermore, behavior change is slow and hard to come by, she said.

Researchers also found that individuals in the study reported that they were more likely to increase the variety of produce in their diets, drink more water and fewer sugary beverages, and read nutrition labels while shopping.

The study was limited by the lack of a control group, DeLisio said. But in general, [the data] is showing SNAP-Ed works, she concluded.

McCarthy said she was excited to find more than 700 policy, system, and environmental changes being used within the SNAP-Ed programs they analyzed, which she thought pointed to the fact that changing peoples environments is a crucial component of nutrition education.

You cant teach people how to eat well without healthy foods, just like you cant teach people how to read without books, she added.

And the fact that the study aggregated data across states in the entire Southeast region, Fox said, made it much more impactful and interesting. You have higher numbers reporting, and therefore you have a little more confidence in the data . . . and what theyre showing, she said. I think its a really good model for other regions, hopefully, to use.

Timely Information

All the experts said the study was a starting point for more research that needs to be done across the country. But at this moment in time, the results are especially meaningful.

There are a lot of Americans who have lost their jobs and are now in poverty, and they might not know how to stretch their food dollars or select healthier foods on a budget, DeLisio said. Its relevant to that new population.

SNAP-Ed programs have also been affected by the pandemic in significant ways, since most are facilitated in person. Some programs have moved online, while some educators have had to pause their efforts entirely.

The USDA has so far denied state requests for waivers that would allow SNAP-Ed educators to temporarily participate in hunger relief efforts that dont directly include nutrition education. Fox has been working with groups who are asking Congress to step in to allow that flexibility, and while a draft of the second HEROES Act did include language to allow for that, the legislation is still a work in progress and negotiations are currently stalled.

Regardless of what the future brings, DeLisio said she believes the data supports ongoing funding for SNAP-Ed.McCarthy echoed that sentiment, emphasizing the unnecessary division that has often existed between hunger and nutrition work.

Families want to feed their members healthy, delicious food, and any sort of food insecurity efforts that dont consider nutrition are not going far enough, she said. COVID-19 has exposed just how vulnerable diet-related diseases have made us. Healthy eating has to be a top priority.

More here:
Nutrition Education is Helping Low-Income Families Eat Healthier - Civil Eats

Read More..

Actress Natalie Portman appeared on The Tonight Show with Jimmy Fallon via video call from Australia to dish about her intense workouts and vegan diet for her role in the upcoming Thor: Love and Thunder movie. (She is in the country prepping for shooting.) The fourth film in the Thor series is based on the popular comic book The Mighty Thor, where Jane Foster (played by Natalie) becomes Thor.

"I don't know if people understand the training that goes into these movies. Are you doing these crazy workouts and stuff?" Jimmy asked Natalie.

"Im trying!" Natalie responded, laughing.

"It's insane!" Jimmy said.

"Ive had like months of pandemic, eating baked goods and laying in bed and feeling sorry for myself. Im, like, super tired after working out. And during. And dreading before," the actress told Jimmy about what it's been like getting back to her workouts.

This content is imported from YouTube. You may be able to find the same content in another format, or you may be able to find more information, at their web site.

This content is imported from {embed-name}. You may be able to find the same content in another format, or you may be able to find more information, at their web site.

Jimmy also asked her about Natalie's vegan cooking show, which she regularly posts on her Instagram.

"I'm obsessed with your cooking videos. You should do a show! I would watch it very single week, I love it," Jimmy told her.

"That's so nice! I don't really have a lot of skill, so I always feel like if I can do it, anyone can do it," Natalie said. "I've gotten so many great recipes from Instagram from other people that I follow. And it's definitely easier that we're cooking every meal pretty much."

She also opened up more about her vegan diet: "I'm vegan, and a lot of people think we're eating alfalfa, so I like showing that there's really delicious, varied, easy things that you can do at home that your kids will eat that are plant-based. And I've been lucky enough to learn a lot of other people I admire a lot."

This content is created and maintained by a third party, and imported onto this page to help users provide their email addresses. You may be able to find more information about this and similar content at piano.io

View original post here:
Natalie Portman Talks About Her Tough 'Thor' Workouts And Vegan Cooking On The Tonight Show - Women's Health

Read More..

In his latest YouTube video, British bodybuilder and CrossFit athlete Obi Vincent put his cardio fitness and aerobic endurance to the test when he took on a VO2 max workout. The VO2 max gives an indicator of a person's fitness by measuring their energy output based on the oxygen consumed and carbon dioxide produced during exercise.

"I've never tested my fitness," says Obi. "Throughout school into my twenties, I didn't do any sports whatsoever. Lazy as anything. I did bodybuilding for years, then CrossFit for two and a half years now, and conditioning. I think if I'd done this two years ago, it would have been a lot worse, trust me. So this is a good lesson. I almost avoided doing any fitness test because I was more scared of what it would look like."

After 12 minutes (and the equivalent of 3.6 miles) on the assault bike, Obi's VO2 output is 57 millileters per minute per kilo. "I should not have done this on the assault bike," says Obi, breathless from the workout. "That was a bad idea."

This content is imported from YouTube. You may be able to find the same content in another format, or you may be able to find more information, at their web site.

Performance nutritionist Luke points out that the easiest way to up your VO2 max is to lose some weight, which for the stacked Obi would mean losing muscle mass. However, he is already leaner at 242 pounds than he was previously, when he weighed in at 253 pounds, and likes his physique the way it is now, so he's reluctant to get any smaller.

In addition to highlighting areas of his own personal fitness that he wants to work on, Obi adds that he has learned a lot from Luke in terms of fueling his body with the right energy sources for workouts. "I'm actually really disappointed with my score... It'll be interesting to go and start applying some of the things I've learned, and see how I get on and do a retest."

This content is created and maintained by a third party, and imported onto this page to help users provide their email addresses. You may be able to find more information about this and similar content at piano.io

See the original post here:
Watch a Top CrossFitter and Bodybuilder Get Wrecked Taking the VO2 Max Fitness Test - menshealth.com

Read More..

The coronavirus has made a routine trip to the gym feel like a health threat.

Many epidemiologists consider gyms to be among the highest-risk environments, and they were some of the last businesses to reopen in New York City in early September.

Now gyms must comply with a long list of regulations. Checking in requires a health screening; masks are mandatory, even during the most strenuous workouts; only one-third of normal occupancy is allowed; and everyone must clean, then clean some more.

At a Planet Fitness in Brooklyn, Dinara Izmagambetova, who wore a floral black face mask and had a sheen of sweat after completing a two-hour workout, said she was thrilled to be back in a gym. But safety measures had made it a less sociable experience, she said.

I could ask someone how to use a machine before the outbreak, Ms. Izmagambetova said. Now Im doing a lot of Googling.

Despite all the safety guidelines, some fitness enthusiasts are reluctant to go back and many have adapted to virtual workouts and exercising outdoors. Sales of fitness equipment like kettlebells and Peloton bikes have skyrocketed as people brought their workouts home.

Christopher Carbone plans to cancel his membership at a Planet Fitness branch near his home on Staten Island because of concerns about people who touch the same equipment many times and excess sweat and breathing in range of others.

Instead of going to the gym, Mr. Carbone will keep working out at home with a small set of hand weights.

In normal times, gyms often served as places of solace, where fitness buffs and casual exercisers could sweat out the stresses of the day.

Many former patrons are eager to return to their routines, and gym owners desperately need their business.

But even as gyms have reopened, their future remains unclear. Some of them have had to shut down again after Gov. Andrew M. Cuomo recently designated parts of Brooklyn and Queens coronavirus hot spots.

A Retro Fitness location in Rego Park, Queens, formerly in one of Mr. Cuomos red zones, expressed regret about closing on its Facebook page.

We have done our best to stay open as long as possible to serve you, the post said, adding, We support the city/countys decision as being in the best interest of our members, staff, and community to help curb the spread of Coronavirus.

The gym was recently allowed to reopen as some restrictions were eased.

Despite scientists concerns, infection clusters connected to gyms in the United States have been relatively rare so far, though they have been reported in Hawaii and California.

Were not seeing outbreaks tied to gyms as heavily as something like a bar or school, said Saskia Popescu, an epidemiologist from George Mason University.

Still, a number of the 2,000 or so gyms in New York State and fitness centers across the country face a fight for life. At least one-fourth of the more than 40,000 gyms in the United States could close by the end of the year, according to the International Health, Racquet and Sportsclub Association, an industry group. A study by Yelp said that more than 2,600 already had.

Many of those that have closed are smaller, independently owned businesses that have fewer resources than large national chains like Planet Fitness, L.A. Fitness and Equinox.

Marco Guanilo, who owns Momentum Fitness on the Upper West Side of Manhattan, said he had struggled during the long months he was closed, but that about 50 percent of his business had returned since he reopened.

Still, he was $300,000 in debt, much of it from back rent payments he could not pay. Mr. Guanilo said that he thought his business would endure as long as he could stay open. The recent state-imposed closures have made him anxious.

Im scared of another shutdown, Mr. Guanilo said, because that will put us under.

While major chains may have deeper pockets, many are also in dire straits. Golds Gym, 24 Hour Fitness and Town Sports International the parent company of New York Sports Clubs have all filed for bankruptcy.

Planet Fitness, which has more than 2,000 locations around the world and 40 in New York City, has also faced serious challenges. Its revenue was down nearly 80 percent from the same period last year, according to the companys second quarter earnings report

Despite the bleak numbers, Chris Rondeau, Planet Fitnesss chief executive, said the company has managed to weather the pandemic.

Cancels are a little bit higher, for sure, Mr. Rondeau said, but, he added, people are joining at the same clip they were this time last year.

Planet Fitness furloughed most of its employees during the pandemic, but about 85 percent of them have returned to work and no locations closed, Mr. Rondeau said.

Across the country, states have imposed different regulations to reopen gyms safely. Most require occupancy limitations and at least six feet of social distancing, though some states mandate as much as 14 feet. Requirements for face coverings vary.

Regulations differ even in the states neighboring New York: New Jersey only allows gyms to operate at 25 percent capacity, while Connecticut permits twice that.

Before gyms in New York can reopen they must undergo an inspection over video with an official from the citys Health Department, showing that they have posted safety plans, have spaced machines apart and are using an up-to-code air filtration system.

Fulfilling the requirements and stockpiling cleaning supplies and personal protective equipment can cost more than $10,000, a significant burden after months of inactivity.

As of the beginning of October, the city had inspected more than 1,000 gyms, and only 11 had failed. Failing gyms can reopen once they fix the issues they were cited for. In-person inspections might begin in the near future, officials said.

Dr. Popescu said she believed that the virtual approach to inspections is frankly better than nothing, which is what many have done.

Whatever the risk factor, gyms are certainly different these days.

On a recent weekend at a large Planet Fitness branch in Brooklyn, a masked greeter asked clients whether they had coronavirus symptoms, then collected their contact information.

Television screens flashed reminders to disinfect workout stations, and every other treadmill and elliptical machine was blocked out with yellow-and-purple signs that said, Were practicing social fitnessing. This machine is not available for use. Even so, there were few people working out.

One of them was Dana Fagan, a bookkeeper, 41, who said she was pleased by the precautions being taken.

Im cleaning more the whole thing is wet and Im fine with that, she said about disinfecting the equipment. You can never have enough.

Mr. Guanilos boutique gym normally offers group classes, physical therapy and individual sessions with trainers. The more controlled atmosphere at his gym, where patrons have individual sessions if theyre not in a group class, appeals to people who are concerned about infection, like Joshua Rubin, a 38-year-old director at an accounting firm.

Theres not people wandering around using different machines, Mr. Rubin said. Theres only two to three of us at a time.

Nearby, Jesse Damon, 46, stretched his arms while a trainer verbally guided him, keeping several feet away.

Theyre very safe here, this is a private gym, he said, adding that he went to a gym in Wyoming during a visit in June and it was a lot of 20-year-olds not wearing masks.

Fitness classes normally make up nearly half of Mr. Guanilos income, but the city still does not allow them indoors because officials say they are too risky.

While he was shut, Mr. Guanilo was able to recover some of his lost business through virtual sessions and group fitness classes in Central Park, which involved hauling hundreds of pounds of equipment on a hand truck.

Mr. Guanilos clients want him to succeed, but some are not comfortable returning. Richard Stanger, a 70-year-old business consultant, said he would not go back to Momentum Fitness until there was a reliable treatment for the virus.

We all want life to return to normal, and normal to me would be working out with Marco, Mr. Stanger said. And Im hoping we get there, but Im not optimistic that we can get there before the first of the year.

Read this article:
Relieved to Be Back at the Gym, but Is It Safe? - The New York Times

Read More..

Contact Us Today

Page 675«..1020..674675676677..680690..»

Home » Page 675