“If I’m not overweight, do I need to become concerned about obesity and other health issues?” asks this week’s House Call. “Even though I drink soda and eat whatever I want, I don’t gain weight. Should I be worried?”
The short answer is yes and here’s why.
One study published in the Journal of the American Medical Association found nearly one in four skinny people have pre-diabetes and are “metabolically obese.” In other words, about 25 percent of the population fit the skinny-fat syndrome, also known as thin on the outside, fat on the inside.
Skinny-fat means just what it sounds like: You look thin but inside you’re fat. You’ve got organ fat (the more dangerous type of fat) coating your liver, kidneys and other organs. You are under-lean but over-fat, meaning not enough muscle and too much fat (especially belly fat).
Most people assume if you’re overweight, you’re unhealthy and if you are thin, you are healthy. Unfortunately, the reality isn’t so simple.
Even though it might sound crazy, being skinny-fat might become more dangerous than being overweight.
Let’s say you go to your annual doctor visit and you are overweight or obese. He or she will probably run blood tests, become concerned about type 2 diabetes and ask you to lose some weight.
If you arrive thin, your doctor might not conduct those blood tests or otherwise acknowledge underlying issues. He or she might assume things are normal rather than checking under the hood for pre-diabetes and other problems that pave the way for detrimental repercussions.
That’s unfortunate because if you are a skinny-fat person and get diagnosed with diabetes, you have twice the risk of death than if you are overweight when diagnosed with diabetes. People who are thin but have pre-diabetes and high sugar also increase their risk for heart disease and early death.
In my medical practice, I see skinny-fat syndrome all the time. Jim provides an excellent example. He came in for a “wellness checkup” and felt happy about his weight. His body mass index (BMI) was 22, which seemed within the normal range.
Jim never seemed to gain weight and felt he could “tolerate” a diet that included lots of bread, pasta and sugar. He liked his two sodas a day and a few glasses of wine at night. He walked but didn’t do much vigorous exercise or weight training.
When we looked under the hood, we found Jim’s blood sugar was 117 mg/dl (pre-diabetes). His triglycerides were 350 mg/dl and his HDL was 35 mg/dl. His blood pressure was 148/96 mmHg. Normal is less than 110/75 mmHg.
When we measured his insulin levels after taking a sugar drink, they were sky high.
The culprit for many of these elevated numbers was inulin, the fat-storage hormone. Insulin stores belly fat and leads to hormonal and metabolic changes that cause muscle loss and inflammation, furthering the vicious cycle of pre-diabetes, or worse, developing type 2 diabetes whether you are skinny or fat.
You might be surprised how I addressed these conditions.
How Do You Know if You’re Skinny-Fat?
Signs of skinny-fat syndrome include family history of type 2 diabetes, early onset of heart disease or even having a little potbelly.
But signs can also be subtler and I would rather you test than guess. That is why I strongly recommend blood tests to reveal skinny-fat syndrome. Ask your doctor to do these tests (ideal ranges are in parenthesis):
- Fasting blood sugar or glucose (normal less than 90 mg/dl)
- Triglycerides (normal less than 100 mg/dl)
- HDL—the good cholesterol (normal greater than 60 mg/dl)
- Blood pressure (normal less than 120/80, ideal less than 115/75)
You might also ask your doctor for these two tests that help detect diabesity and other problems before they become problems:
1. An insulin response test that measures glucose (blood sugar) and insulin levels. This test is conducted in two steps: first, levels are checked after fasting; then levels are checked at one- and two-hour intervals after consuming a 75-gram glucose drink—the equivalent of two sodas. Glucose should be less than 90 mg/dl after fasting and should never go above 120 mg/dl at the one- and two-hour marks. More than 140 mg/dl indicates pre-diabetes and more than 200 mg/dl indicates type 2 diabetes. Insulin should be less than 10 after fasting and should never go above 25 or 30 after the sugar drink. Many with "diabesity" and skinny-fat syndrome can have levels of over 50, 100 or even 200.
2. NMR Lipid Particle (from Labcorp) or Cardio IQ Test (from Quest). This is the 21st-century cholesterol test that proves far more accurate at predicting heart disease and other factors than the traditional test which looks at total cholesterol levels. This measures the size and number of cholesterol particles. You should have less than 1000 total LDL particles and less than 500 small LDL particles (the small, dense, dangerous kind). When you are a skinny fat person with diabesity, you have too many LDL and these are destructive and can cause leaky gut.
Curbing Skinny Fat Syndrome with these Strategies
Earlier, I discussed Jim, my skinny-fat patient who was shocked to find he had "diabesity." I worked with Jim and numerous other skinny-fat patients with the exact same strategies as someone who is overweight, because both paths lead to "diabesity" and all its repercussions. The solution requires getting blood sugar and insulin levels under control. These eight strategies always help:
- Cut out refined sugar. We eat an average of 152 pounds of sugar and 146 pounds of flour (which convert to sugar) per person, per year in the U.S. These pharmacological doses crash our metabolism, spike insulin levels and wreak all sorts of havoc.
- Eat more fat! Healthy fat will cut your cravings. Olive oil, avocados, coconut butter, fish fat and grass-fed lamb or beef all have good fats that boost your health while normalizing insulin and other hormones.
- Increase fiber intake. Load up on plenty of fiber-rich plant foods like non-starchy veggies, legumes, nuts, seeds and lower-sugar fruits like berries.
- Exercise regularly. For skinny-fat syndrome, I particularly like strength training and high intensity interval training to build muscle and reverse insulin resistance.
- Don’t drink your calories. That includes energy drinks, fruit juices and alcohol. One recent study found sugar-sweetened beverages account for 184,000 deaths every year.
- Avoid artificial sweeteners. Check out my recent blog where I discussed the numerous dangers of these fake sugars.
- Get good sleep. Sleep deprivation alters metabolism and increases cravings for carbs and sugar. Make time for eight hours of quality sleep every night. You can find 19 of my top sleep tips here.
- Supplement smartly. Supplements help to maintain the optimal amounts of nutrients you need to optimize metabolism, help burn calories and balance blood sugar. We get a lot of these from our healthy food choices. However, I always recommend a basic protocol to ensure optimal levels. At the very least, you should be taking a good multivitamin, fish oil (EPA/DHA) and vitamin D. You can find these and other professional-quality supplements in my store.
If you suspect you’re skinny-fat, don’t just assume everything is okay. Go to your doctor and check under the hood. Don’t be complacent simply because you are not overweight. Ask for the tests I mention in this blog and implement the eight strategies to reverse this health-robbing condition.
The smartest way I know to reverse skinny-fat syndrome is by following The Blood Sugar Solution 10-Day Detox Diet, which allows you to gain control of your health and lose dangerous “skinny fat” in just 10 days. You can also find tons of free information about controlling insulin levels and reversing diabesity on my blog page.
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By Eric Tate and Christopher Emrich
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>
By Jessica Corbett
Sen. Bernie Sanders on Tuesday was the lone progressive to vote against Tom Vilsack reprising his role as secretary of agriculture, citing concerns that progressive advocacy groups have been raising since even before President Joe Biden officially nominated the former Obama administration appointee.