Should You Drink Coffee With Coconut Oil?
Millions of people around the world depend on a morning cup of coffee to get their day started.
Coffee is not only a great source of caffeine that provides a convenient boost of energy but also has many beneficial antioxidants and nutrients.
A recent trend is to add coconut oil to coffee to reap the health benefits of this popular fat, too.
However, you may wonder whether this practice is healthy.
This article tells you whether you should drink coffee with coconut oil.
May Help You Stay in Ketosis
Coconut oil has become increasingly popular among people following the high-fat, very-low-carb ketogenic diet.
Adding it to your coffee can help you reach or maintain ketosis, a metabolic state in which your body uses ketones — molecules produced from fat breakdown — as fuel instead of glucose, a type of sugar (1).
Coconut oil can help you stay in ketosis as it's loaded with fats called medium-chain triglycerides (MCTs).
Compared to other fats, MCTs are rapidly absorbed and immediately delivered to your liver. Here, they're either used as a source of energy or converted into ketone bodies (5).
Interestingly, MCT oils are more easily converted to ketones than long-chain triglycerides, another type of fat found in foods (6).
Research shows that MCTs can help you stay in ketosis — even if you eat slightly more protein and carbs than recommended on a classic ketogenic diet (6).
Coconut oil has 4 types of MCTs, and 50% of its fat comes from the MCT lauric acid (7).
Lauric acid appears to make ketones at a slower but more sustained rate as it's metabolized more steadily than other MCTs. Therefore, adding coconut oil to your coffee is an effective way to help you stay in ketosis (7, 8).
Coconut oil helps your body make ketones. If you follow a ketogenic diet, adding it to your cup of coffee may help you reach and stay in ketosis.
Health Benefits and Downsides
Adding coconut oil to your coffee is an easy way to reap the health benefits of both.
Here are some ways in which adding coconut oil to your coffee may improve health:
- May speed up your metabolism. Studies show that MCTs in coconut oil and caffeine in coffee may speed up your metabolism, which can increase the number of calories you burn in a day (9, 10, 11).
- May improve energy levels. Coffee contains caffeine, which can help you feel less tired. Coconut oil packs MCTs, which are transported straight to your liver and can act as a quick source of energy as well (12, 13).
- May help keep your bowels regular. Coconut oil MCTs and coffee compounds like caffeine and chlorogenic acids may help stimulate your bowels and keep your digestive system healthy (14, 15).
- May help raise HDL (good) cholesterol. Several studies have found that coconut oil can raise levels of HDL cholesterol, which is protective against heart disease (16, 17).
However, adding coconut oil to coffee also has its drawbacks.
For starters, many people who add it to their morning coffee use it as a breakfast replacement. Doing so means that you may miss out on many important nutrients that you would get from eating a more balanced breakfast.
While coconut oil has some nutrients, it won't have as many as a nutritious breakfast that contains many different food groups.
What's more, coconut oil is high in calories, providing 121 calories per tablespoon (14 grams). Most people who add it to coffee tend to use 2 tablespoons — an extra 242 calories (18).
If this doesn't sound like much, note that it would take a 155-pound (70-kg) person nearly 50 minutes of walking at a brisk pace (3.5 miles or 5.6 km per hour) to burn that many calories (19).
Additionally, while the combined effect of coconut oil and coffee may slightly boost your metabolism, it's more likely to make you gain weight if you don't account for the added calories.
The calories in a few tablespoons of coconut oil are likely to exceed the calories expended due to the small metabolism increase related to the ingestion of the MCTs and caffeine.
Coconut oil is much more effective when you use it to replace less healthy fats in your diet, such as those from processed foods, rather than on top of the fats you're currently consuming.
Adding coconut oil to coffee can offer some health benefits. Still, it has potential drawbacks, such as replacing a more nutritious meal and adding too many calories. Plus, certain medical conditions may make it necessary to limit your fat intake.
How Much Coconut Oil Should You Use?
If you want to try coconut oil in your cup of joe, start small by adding 1 tablespoon (14 grams) to hot coffee and stirring it thoroughly to ensure that the oil incorporates well.
Some people prefer to blend the oil with coffee in a blender to make a delicious tropical-style beverage.
Eventually, you can work your way up to 2 tablespoons (28 grams) of coconut oil if you would like to increase your fat intake. This may be most appropriate for those attempting to reach and maintain ketosis.
Avoid adding too much coconut oil too quickly, especially if you follow a low- to moderate-fat diet, as it may cause nausea and laxative-like symptoms.
Start by adding 1 tablespoon (14 grams) of coconut oil to your hot coffee. You can slowly work your way up to twice as much. Note that adding too much coconut oil too quickly may cause unpleasant side effects.
The Bottom Line
If you're watching your calorie or fat intake for medical or personal reasons, avoid putting coconut oil into your coffee.
Still, if you follow a ketogenic diet or want to include this healthy fat in your diet, then adding it to your coffee can be an easy way to increase your intake.
To avoid unpleasant side effects, start slowly and add no more than 1 tablespoon (14 grams) of coconut oil at first.
Reposted with permission from our media associate Healthline.
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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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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.