Dr. Mark Hyman: Why You Should Ditch Artificial Sweeteners
“I know you’re not big on sugar and frequently tell people to cut down on it,” writes this week’s House Call. “But what about artificial sweeteners? Can I use those instead?”
Sadly, the answer is emphatically no. Human, animal, experimental and other studies show artificial sweeteners can be just as bad and maybe even worse than regular sugar.
Artificial sweeteners have long been positioned as “guilt-free,” innocuous, safe alternatives, so why would I argue they are actually worse than sugar?
Photo credit: Shutterstock
Manufacturers love to position zero-calorie sweetened foods and drinks as better because they create a “halo effect” and they know you’re more likely to buy them.
We’re surrounded by low-calorie or calorie-free foods and diet soft drinks that contain artificial sweeteners touted as healthy or consequence-free. As a result, the number of Americans who consume products that contain sugar-free sweeteners grew from 70 million in 1987 to 160 million in 2000.
At the same time, the incidence of obesity in the U.S. has doubled from 15 percent to 30 percent across all age groups, ethnic groups and social strata. And the number of overweight Americans has increased from about 30 percent to more than 65 percent of the population. The fastest growing obese population is children.
High sugar intake deservedly takes the blame here, but we frequently overlook artificial sweeteners as a potential culprit. The evidence is catching up. Recent studies have not been kind to artificial sweeteners, claiming among other problems they adversely affect gut health and glucose tolerance.
You’re probably wondering, though, how a calorie-free sweetener could make you fat. If you’ve read my blogs, you know that while calories count, other factors like hormonal imbalances contribute far more to weight gain.
Let’s briefly look at three reasons artificial sweeteners create adverse consequences to your waistline and health.
1. Artificial sweeteners increase your risk for diabesity. Studies show sugar substitutes potentially can increase your risk for weight gain, metabolic syndrome, type 2 diabetes and cardiovascular disease. One study of rats that were fed artificially sweetened food found that their metabolism slowed down and they were triggered to consume more calories and gain more weight than rats fed sugar-sweetened food. In another animal study, rats that consumed artificial sweeteners ate more food, their metabolic fire or thermogenesis slowed down and they put on 14 percent more body fat in just two weeks even if they ate fewer total calories than the rats that ate regular sugar-sweetened food.
2. Artificial sweeteners rewire your brain chemistry and metabolism. How could aspartame and other fake sweeteners make you gain weight even though they’re calorie-free? Because they stimulate your taste buds and trick them to think you’re eating real sugar. Artificial sweeteners can be 1000 times sweeter than sugar, so your body becomes confused and revs up production of insulin, your fat-storage hormone. Your metabolism slows down, you become hungry more quickly, you’re prone to eat way more food (especially carbs) and increased belly fat is the inevitable result. Because they confuse and slow down your metabolism, you burn fewer calories every day. Artificial sweeteners make you hungrier and cause you to crave even more sugar and starchy carbs, such as bread and pasta.
3. Artificial sweeteners are highly addictive. I regularly see patients who complain they can’t kick their diet-soda habit. “I have one in the morning and I can’t stop,” they say. It isn’t just their imagination: Artificial sweeteners can quickly become addictive. In an alarming study, rats offered the choice of cocaine or artificial sweeteners always picked the artificial sweetener, even if the rats were previously programmed to be cocaine addicts. The author of the study said that, “The absolute preference for taste sweetness may lead to a re-ordering in the hierarchy of potentially addictive stimuli, with sweetened diets … taking precedence over cocaine and possibly other drugs of abuse.”
Let’s consider that last point a little more closely, particularly with diet sodas, which account for a fair amount of the artificial sweeteners we consume.
One of the biggest struggles I see with patients—ironically, usually overweight or obese patients—is surrendering their diet sodas. Like artificial sweeteners, we’ve been misled to think they’re guilt-free alternatives to regular soda.
Hardly. Diet soda and diet drinks make you fat and cause type 2 diabetes.
Wait … diet soda makes people fat? Really? How does that happen?
If losing weight were all about the calories, then consuming diet drinks would seem like a good idea. That’s certainly what big-name cola companies want us to believe, judging by the ad campaigns highlighting their efforts to fight obesity. (And the other food giants making diet drinks push the same propaganda).
Soda companies proudly promote the fact that it has 180 low- or no-calorie drinks and that it has cut sales of sugared drinks in schools by 90 percent. Is that a good thing? I don’t think so. In fact, it may be worse to drink diet soda than a regular soda.
A 14-year study of 66,118 women published in the American Journal of Clinical Nutrition (and supported by many previous and subsequent studies) discovered some frightening facts that should make us all swear off diet drinks and products:
Diet sodas raised the risk of diabetes more than sugar-sweetened sodas.
Women who drank one 12-ounce diet soda a week had a 33 percent increased risk of type 2 diabetes and women who drank one 20-ounce soda a week had a 66 percent increased risk.
Women who drank diet sodas drank twice as much as those who drank sugar-sweetened sodas because artificial sweeteners are more addictive than regular sugar. The average diet soda drinker consumes three diet drinks a day.
The bottom line is you can’t outsmart Mother Nature. Fooling your brain into thinking you are getting something sweet plays dirty tricks on your metabolism. Artificial sweeteners disrupt the normal hormonal and neurological signals that control hunger and satiety (feeling full).
The use of artificial sweeteners, as well as “food porn,” the sexy experience of sweet, fat and salt in your mouth, alters your food preferences. Your palate shifts from being able to enjoy fruits and vegetables and whole foods to liking only the sexy stuff.
Sugar or Artificial Sweeteners: What’s the Answer?
Let’s be clear here that I am not letting sugar off the hook. Of the more than 600,000 food products—note I said food products, not foods—80 percent have added sugar. That’s where the trouble begins.
We went from eating about 10 pounds of sugar per person, per year in 1800 to 152 pounds of sugar (and 146 pounds of flour) per person, per year today. Think about it: On average we eat about one pound of sugar every day!
Those sugar-laden foods literally become drugs: Pharmacological doses that hijack our metabolism and make us fat and sick.
Adding a teaspoon of sugar to your coffee or having an occasional dessert doesn’t make you fat and sick. Added sugars in even so-called healthy foods or non-sweet tasting foods creates the real, cumulative damage.
I realize this can all become confusing. Here are five ways I recommend making sense about sweeteners:
1. Have a little. If you like sugar and want a little bit, fine, but eat real food and then have sweet things. Consider sugar a recreational drug that you can partake of in moderation like red wine or tequila. Put a little sugar in your coffee because at least you’re aware about how much you’re getting. Likewise, you’re not going to overeat cake, because you know it’s bad for you. One caveat: If you know a little sugar will become a slippery slope for overeating, stay away from the sweet stuff period.
2. Become aware of hidden sugars even in so-called healthy foods. Read ingredients and realize sugar lurks even in foods that don’t taste sweet or that are positioned as healthy.
3. Learn to appreciate natural sweetness. Fruit, nuts and other real foods contain natural sweetness without processed foods’ sugar overload or the detrimental effects of artificial sweeteners.
4. Stop confusing your body. If you have a desire for something sweet have a little sugar, but stay away from “fake” foods. Eating a whole-foods diet that has a low-glycemic load and is rich in phytonutrients and indulging in a few real sweet treats once in a while is a better alternative than tricking your body with artificial sweeteners, which leads to wide scale metabolic rebellion and obesity.
5. Judiciously use this one sweetener. Among sweeteners, I make one exception with stevia. A little bit in your coffee or tea should be fine, but be judicious. Besides, overdoing stevia creates a bitter effect, so you’re more likely not to get carried away. If you partake, make sure you’re buying 100 percent pure organic stevia, not the stuff that contains bulking agents like maltodextrin (corn) and nebulous natural flavors.
If you really want to break free from the addictive grip of artificial sweeteners and sugar, as well as food sensitivities, I highly recommend doing The Blood Sugar Solution 10-Day Detox Diet. I’ve witnessed patients curb their worst sugar and artificial sweetener cravings and learn to appreciate the natural sweetness of real, whole foods in just 10 days.
Whatever you do, stay away from artificial sweeteners. I recommend giving up aspartame, sucralose, sugar alcohols such as xylitol and maltitol and all of the other heavily used and marketed sweeteners unless you want to slow down your metabolism, gain weight and become an addict. Use a little stevia if you must, but skip out on the others.
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Disasters stemming from hazards like floods, wildfires, and disease often garner attention because of their extreme conditions and heavy societal impacts. Although the nature of the damage may vary, major disasters are alike in that socially vulnerable populations often experience the worst repercussions. For example, we saw this following Hurricanes Katrina and Harvey, each of which generated widespread physical damage and outsized impacts to low-income and minority survivors.
Mapping Social Vulnerability<p>Figure 1a is a typical map of social vulnerability across the United States at the census tract level based on the Social Vulnerability Index (SoVI) algorithm of <a href="https://onlinelibrary.wiley.com/doi/abs/10.1111/1540-6237.8402002" target="_blank"><em>Cutter et al.</em></a> . Spatial representation of the index depicts high social vulnerability regionally in the Southwest, upper Great Plains, eastern Oklahoma, southern Texas, and southern Appalachia, among other places. With such a map, users can focus attention on select places and identify population characteristics associated with elevated vulnerabilities.</p>
Fig. 1. (a) Social vulnerability across the United States at the census tract scale is mapped here following the Social Vulnerability Index (SoVI). Red and pink hues indicate high social vulnerability. (b) This bivariate map depicts social vulnerability (blue hues) and annualized per capita hazard losses (pink hues) for U.S. counties from 2010 to 2019.<p>Many current indexes in the United States and abroad are direct or conceptual offshoots of SoVI, which has been widely replicated [e.g., <a href="https://link.springer.com/article/10.1007/s13753-016-0090-9" target="_blank"><em>de Loyola Hummell et al.</em></a>, 2016]. The U.S. Centers for Disease Control and Prevention (CDC) <a href="https://www.atsdr.cdc.gov/placeandhealth/svi/index.html" target="_blank">has also developed</a> a commonly used social vulnerability index intended to help local officials identify communities that may need support before, during, and after disasters.</p><p>The first modeling and mapping efforts, starting around the mid-2000s, largely focused on describing spatial distributions of social vulnerability at varying geographic scales. Over time, research in this area came to emphasize spatial comparisons between social vulnerability and physical hazards [<a href="https://doi.org/10.1007/s11069-009-9376-1" target="_blank"><em>Wood et al.</em></a>, 2010], modeling population dynamics following disasters [<a href="https://link.springer.com/article/10.1007%2Fs11111-008-0072-y" target="_blank" rel="noopener noreferrer"><em>Myers et al.</em></a>, 2008], and quantifying the robustness of social vulnerability measures [<a href="https://doi.org/10.1007/s11069-012-0152-2" target="_blank" rel="noopener noreferrer"><em>Tate</em></a>, 2012].</p><p>More recent work is beginning to dissolve barriers between social vulnerability and environmental justice scholarship [<a href="https://doi.org/10.2105/AJPH.2018.304846" target="_blank" rel="noopener noreferrer"><em>Chakraborty et al.</em></a>, 2019], which has traditionally focused on root causes of exposure to pollution hazards. Another prominent new research direction involves deeper interrogation of social vulnerability drivers in specific hazard contexts and disaster phases (e.g., before, during, after). Such work has revealed that interactions among drivers are important, but existing case studies are ill suited to guiding development of new indicators [<a href="https://doi.org/10.1016/j.ijdrr.2015.09.013" target="_blank" rel="noopener noreferrer"><em>Rufat et al.</em></a>, 2015].</p><p>Advances in geostatistical analyses have enabled researchers to characterize interactions more accurately among social vulnerability and hazard outcomes. Figure 1b depicts social vulnerability and annualized per capita hazard losses for U.S. counties from 2010 to 2019, facilitating visualization of the spatial coincidence of pre‑event susceptibilities and hazard impacts. Places ranked high in both dimensions may be priority locations for management interventions. Further, such analysis provides invaluable comparisons between places as well as information summarizing state and regional conditions.</p><p>In Figure 2, we take the analysis of interactions a step further, dividing counties into two categories: those experiencing annual per capita losses above or below the national average from 2010 to 2019. The differences among individual race, ethnicity, and poverty variables between the two county groups are small. But expressing race together with poverty (poverty attenuated by race) produces quite different results: Counties with high hazard losses have higher percentages of both impoverished Black populations and impoverished white populations than counties with low hazard losses. These county differences are most pronounced for impoverished Black populations.</p>
Fig. 2. Differences in population percentages between counties experiencing annual per capita losses above or below the national average from 2010 to 2019 for individual and compound social vulnerability indicators (race and poverty).<p>Our current work focuses on social vulnerability to floods using geostatistical modeling and mapping. The research directions are twofold. The first is to develop hazard-specific indicators of social vulnerability to aid in mitigation planning [<a href="https://doi.org/10.1007/s11069-020-04470-2" target="_blank" rel="noopener noreferrer"><em>Tate et al.</em></a>, 2021]. Because natural hazards differ in their innate characteristics (e.g., rate of onset, spatial extent), causal processes (e.g., urbanization, meteorology), and programmatic responses by government, manifestations of social vulnerability vary across hazards.</p><p>The second is to assess the degree to which socially vulnerable populations benefit from the leading disaster recovery programs [<a href="https://doi.org/10.1080/17477891.2019.1675578" target="_blank" rel="noopener noreferrer"><em>Emrich et al.</em></a>, 2020], such as the Federal Emergency Management Agency's (FEMA) <a href="https://www.fema.gov/individual-disaster-assistance" target="_blank" rel="noopener noreferrer">Individual Assistance</a> program and the U.S. Department of Housing and Urban Development's Community Development Block Grant (CDBG) <a href="https://www.hudexchange.info/programs/cdbg-dr/" target="_blank" rel="noopener noreferrer">Disaster Recovery</a> program. Both research directions posit social vulnerability indicators as potential measures of social equity.</p>
Social Vulnerability as a Measure of Equity<p>Given their focus on social marginalization and economic barriers, social vulnerability indicators are attracting growing scientific interest as measures of inequity resulting from disasters. Indeed, social vulnerability and inequity are related concepts. Social vulnerability research explores the differential susceptibilities and capacities of disaster-affected populations, whereas social equity analyses tend to focus on population disparities in the allocation of resources for hazard mitigation and disaster recovery. Interventions with an equity focus emphasize full and equal resource access for all people with unmet disaster needs.</p><p>Yet newer studies of inequity in disaster programs have documented troubling disparities in income, race, and home ownership among those who <a href="https://eos.org/articles/equity-concerns-raised-in-federal-flood-property-buyouts" target="_blank">participate in flood buyout programs</a>, are <a href="https://www.eenews.net/stories/1063477407" target="_blank" rel="noopener noreferrer">eligible for postdisaster loans</a>, receive short-term recovery assistance [<a href="https://doi.org/10.1016/j.ijdrr.2020.102010" target="_blank" rel="noopener noreferrer"><em>Drakes et al.</em></a>, 2021], and have <a href="https://www.texastribune.org/2020/08/25/texas-natural-disasters--mental-health/" target="_blank" rel="noopener noreferrer">access to mental health services</a>. For example, a recent analysis of federal flood buyouts found racial privilege to be infused at multiple program stages and geographic scales, resulting in resources that disproportionately benefit whiter and more urban counties and neighborhoods [<a href="https://doi.org/10.1177/2378023120905439" target="_blank" rel="noopener noreferrer"><em>Elliott et al.</em></a>, 2020].</p><p>Investments in disaster risk reduction are largely prioritized on the basis of hazard modeling, historical impacts, and economic risk. Social equity, meanwhile, has been far less integrated into the considerations of public agencies for hazard and disaster management. But this situation may be beginning to shift. Following the adage of "what gets measured gets managed," social equity metrics are increasingly being inserted into disaster management.</p><p>At the national level, FEMA has <a href="https://www.fema.gov/news-release/20200220/fema-releases-affordability-framework-national-flood-insurance-program" target="_blank">developed options</a> to increase the affordability of flood insurance [Federal Emergency Management Agency, 2018]. At the subnational scale, Puerto Rico has integrated social vulnerability into its CDBG Mitigation Action Plan, expanding its considerations of risk beyond only economic factors. At the local level, Harris County, Texas, has begun using social vulnerability indicators alongside traditional measures of flood risk to introduce equity into the prioritization of flood mitigation projects [<a href="https://www.hcfcd.org/Portals/62/Resilience/Bond-Program/Prioritization-Framework/final_prioritization-framework-report_20190827.pdf?ver=2019-09-19-092535-743" target="_blank" rel="noopener noreferrer"><em>Harris County Flood Control District</em></a>, 2019].</p><p>Unfortunately, many existing measures of disaster equity fall short. They may be unidimensional, using single indicators such as income in places where underlying vulnerability processes suggest that a multidimensional measure like racialized poverty (Figure 2) would be more valid. And criteria presumed to be objective and neutral for determining resource allocation, such as economic loss and cost-benefit ratios, prioritize asset value over social equity. For example, following the <a href="http://www.cedar-rapids.org/discover_cedar_rapids/flood_of_2008/2008_flood_facts.php" target="_blank" rel="noopener noreferrer">2008 flooding</a> in Cedar Rapids, Iowa, cost-benefit criteria supported new flood protections for the city's central business district on the east side of the Cedar River but not for vulnerable populations and workforce housing on the west side.</p><p>Furthermore, many equity measures are aspatial or ahistorical, even though the roots of marginalization may lie in systemic and spatially explicit processes that originated long ago like redlining and urban renewal. More research is thus needed to understand which measures are most suitable for which social equity analyses.</p>
Challenges for Disaster Equity Analysis<p>Across studies that quantify, map, and analyze social vulnerability to natural hazards, modelers have faced recurrent measurement challenges, many of which also apply in measuring disaster equity (Table 1). The first is clearly establishing the purpose of an equity analysis by defining characteristics such as the end user and intended use, the type of hazard, and the disaster stage (i.e., mitigation, response, or recovery). Analyses using generalized indicators like the CDC Social Vulnerability Index may be appropriate for identifying broad areas of concern, whereas more detailed analyses are ideal for high-stakes decisions about budget allocations and project prioritization.</p>
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