Science Says Specific People Should Avoid Carbs—Are You One of Them?

Understanding the intricate interplay between genetics, insulin, and diet damage multiplier for that bowl of pasta, this puts calories in the passenger seat, where calorie imbalance results not from just eating too many calories, but from the hormonally stimulated growth of fat cells. We're gonna empower you with knowledge about genetics. Is obesity a genetic disease? Well, no, but also yes. I say no insofar as most cases of obesity are not caused by a single genetic mutation. However, the answer is yes insofar as obesity is a complex disease where different individuals have different genetic susceptibilities that are unveiled through lifestyle. In fact, any two unrelated people vary from each other at about 4 million sites within our genome. Yes, 4 million. So there's enormous genetic diversity, and this does impact our predispositions to diseases, obesity included. So in this video, we will not only provide evidence of this fact, but also explain how this knowledge can be used to more precisely and effectively treat obesity in our modern world. We're going to empower you with knowledge about genetics. Now, what the researchers of this study did was test a key prediction of the carbohydrate-insulin model using something called Mendelian randomization. Now, let's unpack those terms together. The carbohydrate-insulin model is a mechanistic model of obesity that works as follows. When somebody eats a high glycemic load diet, meaning a diet that tends to spike blood sugar and blood insulin more This gives a hormonal signal to the body to store energy as fat tissue. In other words, energy calories comes in and they are triaged preferentially towards fat rather than in energy expenditure or towards lean tissue. And as a downstream consequence, energy expenditure goes down and hunger increases. Thus, while calories in, calories out equals weight change and thermodynamics are maintained, the calorie imbalance is a result result of a primary hormonal insulin disturbance. In very simple terms, calories count, but they do not cause fat gain and obesity. Rather, the calorie surplus associated with weight gain is the result of hormonal insulin signaling to fat cells to get fat. And this model importantly is supported by multiple lines of evidence, including everything from preclinical animal mechanistic studies to multiple human randomized controlled trials. However, an obvious question remains: why do some people report ballooning up when they eat any carbs, and other people seem to be resistant to carbs in terms of weight gain? They can eat pasta, bagels, and cookies to their heart's content without gaining excess body fat. It's not fair, right? But it's a legitimate question, and one researchers sought to address in this Mendelian randomization study. Okay, now what is Mendelian randomization? Mendelian randomization is a method that scientists use to study whether a certain factor, like insulin secretion in response to carbs, causes a particular outcome, in this case obesity or weight gain. And it relies on genetic variation. Remember those 4 million variable genetic sites at the beginning of the video? Basically, the genetic variation is assigned randomly using nature's random experiment, a genetic a coin toss, you could say, and this helps to uncover cause-effect relationships. So in this study, the researchers asked the question: does carbohydrate-stimulated insulin secretion, the amount of insulin released in response to a carbohydrate load, does that predict obesity? And if it does, it provides more evidence for the carbohydrate-insulin model using genetics as a tool. So in this study, they used MAGIC. No, actually, they relied on data from a meta-analysis of Glucose and Insulin-Related Traits Consortium. That was one of the cohorts. MAGIC is the acronym. It was a previously published meta-analysis on insulin secretion, including 26,037 people. And they also used data from the UK Biobank cohort, including 138,541 participants. Now, using prior knowledge about variations in the human genome, they created genetic risk scores for traits including carbohydrate-stimulated insulin secretion. They basically created a genetic risk score for how much insulin a person secretes in response to carbs as determined by their genetics and a genetic risk score for body mass index, BMI. And as a quick aside on the methods, the researchers did do their due diligence to validate their genetic risk scores in a separate population basically confirming that their genetic risk scores for insulin secretion after a carbohydrate bolus, a carbohydrate challenge, in this case 75 grams of carbs in the form of glucose, did indeed predict the amount of insulin that was released and that persisted in circulation in the blood at 30 minutes. So, the genetic risk score was validated. Anyway, what did they find in their Mendelian randomization? They indeed found higher insulin release genetic risk score did predict higher BMI. In fact, they even tested slightly different insulin release genetic risk scores in different populations and consistently found, yes, more insulin release as predicted by genetics did predict higher BMI. And this is consistent with the carbohydrate-insulin model of obesity. Where higher insulin release tells fat cells to store fat, leading to the development of obesity. This puts calories in the passenger seat, where calorie imbalance results not from just eating too many calories, but from the hormonally stimulated growth of fat cells that leads to downstream calorie imbalance. So keeping it as simple as possible, Carbs spike insulin, insulin tells fat cells to grow, and this leads downstream to an increase in hunger and decreased energy output and calorie imbalance. So there you have it, case closed. But not really. The researchers took another very important step. You see, a limitation of Mendelian randomization is it can confuse causal direction. In other words, to really be certain that these results support the carbohydrate-insulin model in the strongest sense, you'd want to show that insulin secretion in response to carbs predicts BMI, but also that the flip side is not true, i.e., that genetically influenced BMI isn't predicting insulin secretion in response to carbs. This is actually called a— ready for it— bidirectional Mendelian randomization, because you're asking about the relationship in both directions: genetically influenced insulin secretion influencing higher BMI, and genetically influenced higher BMI affecting insulin secretion. But long story short, they found no evidence for the latter. So TL;DR, too long didn't read, going over your head, this supports the carbohydrate-insulin model. Now getting to this study's clear and practical takeaways for you or maybe loved ones. People who secrete more insulin in response to carbs as a result of, you could say, unlucky genetics, they will benefit more from carbohydrate restriction in terms of weight loss and fat loss than those who, because of their lucky genetics, let's call it, secrete less insulin. In effect, genetically determined insulin secretion in response to carbs acts as a damage multiplier for that bowl of pasta or that donut as far as weight gain is concerned. In fact, this conclusion is also supported by other studies, including a major human randomized control trial called DIETFITS, where those who tended to secrete more insulin were found to benefit more from the intervention of a reduced glycemic load, reduced carbohydrate diet. So, if your DNA dictates that you secrete a lot of insulin in response to carbs, it's worth being mindful of that fact, the fact that carbs will hit you harder in terms of weight gain. And I apologize on behalf of biology. Mother Nature isn't always fair. But the good news is awareness and knowledge, they're power. And having this knowledge enables a precision nutrition approach. And is hopefully motivation for those who would benefit all the more from swapping their breakfast of oatmeal and bananas for smoked salmon and avocado, or swapping their dinner of shepherd's pie for a nice filet mignon and roasted vegetables. I think these data are optimistic, not fatalistic. They're empowering. But what do you think? In closing, I want to mention some limitations of the study. As all studies have limitations. First, the populations studied were predominantly European in ancestry. Diversifying population pools in future studies will help generalize these findings, especially to underrepresented minority populations that may be at especially high risk for obesity, possibly owing to dynamics in insulin secretion in these populations and also socioeconomic factors, which are important as well. Second, these data do not argue that obesity is completely explained by genetically determined insulin secretion interacting with dietary carbohydrates in the modern food environment. In fact, in absolute amounts, and admittedly insulin secretion only predicted a modest proportion of the variation in BMI. This is to be expected in a complex trait like obesity, but it is nevertheless worth highlighting. There's always more to the story. And third, the human genome isn't completely annotated, isn't completely understood, so we are necessarily missing pieces of the genetic puzzle. Repeating this sort of analysis in 5 or 10 years may reveal even stronger relationships and may reveal more actionable genes and genetic insights. Now, understanding The intricate interplay between genetics, insulin, and diet— really, it's a game changer in the fight against obesity. This study not only provides compelling evidence for the carbohydrate-insulin model, adding to that pile of literature, but also highlights the potential for precision nutrition to revolutionize how we approach weight and obesity management. While our genetic makeups may predispose us to certain challenges as individuals, it also offers a roadmap for personalized interventions. With this knowledge in hand, we have the power to make informed, personally informed dietary choices, turning what might feel like a biological disadvantage into an opportunity for a targeted, effective action— targeted, effective approach. So let's use these insights to take control of our health and redefine what's possible. Stay curious, I hope you learned something, and I'll see you in the next one.