For example, following a very low-carb diet for a long time may disrupt hormones in some women. Eating too few carbs has been associated with disruptions to the menstrual cycle, fertility problems and poor sleep quality. It's also been linked to poor bone health, anxiety and depression. Some women even report weight loss resistance or weight gain.
This article explores how low-carb diets may affect women's hormones.
Low-Carb and Low-Calorie Diets May Affect Women's Adrenals
Your hormones are regulated by three major glands:
- Hypothalamus: located in the brain.
- Pituitary: located in the brain.
- Adrenals: located at the top of the kidneys.
The HPA axis is responsible for regulating your stress levels, mood, emotions, digestion, immune system, sex drive, metabolism, energy levels and more.
The glands are sensitive to things like calorie intake, stress and exercise levels.
Long-term stress can cause you to overproduce the hormones cortisol and norepinephrine, creating an imbalance that increases pressure on the hypothalamus, pituitary and adrenal glands (2).
This ongoing pressure may eventually lead to HPA axis dysfunction, sometimes controversially referred to as “adrenal fatigue" (3).
Symptoms include fatigue, a weakened immune system and greater risk of long-term health problems such as hypothyroidism, inflammation, diabetes and mood disorders.
Many sources suggest that a diet too low in calories or carbs can also act as a stressor, causing HPA dysfunction.
Bottom Line: Eating too few carbs or calories and experiencing chronic stress may disrupt the HPA axis, causing hormonal problems.
A Low-Carb Diet May Cause Irregular Menstrual Cycles or Amenorrhea in Some Women
If you don't eat enough carbs, you may experience irregular menstrual cycles or amenorrhea.
Amenorrhea is defined as a woman's menstrual cycle being absent for 3 months or more.
The most common cause of amenorrhea is hypothalamic amenorrhea, caused by too few calories, too few carbs, weight loss, stress or too much exercise (6).
Amenorrhea occurs due to the drop in levels of many different hormones, such as gonadotropin-releasing hormone (GnRH), which starts the menstrual cycle (7).
These changes can slow some functions in the hypothalamus, the region of the brain responsible for hormone release.
Low levels of leptin, a hormone produced by fat cells, is another potential cause of amenorrhea and irregular menstruation. Evidence suggests that women need a certain level of leptin to maintain normal menstrual function (9, 10).
If your carb or calorie consumption is too low, it can suppress your leptin levels and interfere with leptin's ability to regulate your reproductive hormones. This is particularly true for underweight or lean women on a low-carb diet.
However, evidence on amenorrhea on low-carb diets is scarce. Studies that report amenorrhea as a side effect were usually only done in women following a predominately low-carb diet for a long period of time (11).
Bottom Line: Following a very low-carb (ketogenic) diet over a long period of time may cause irregular menstrual cycles or amenorrhea.
Carbs Can be Beneficial for Thyroid Function
Your thyroid gland produces two hormones: thyroxine (T4) and triiodothyronine (T3).
These two hormones are necessary for a wide range of bodily functions.
These include breathing, heart rate, the nervous system, body weight, temperature control, cholesterol levels and the menstrual cycle.
Reverse T3 is a hormone that blocks the action of T3. Some studies have shown that ketogenic diets reduce T3 levels.
One study found that T3 levels dropped by 47 percent over 2 weeks in people consuming a no-carb diet. In contrast, people consuming the same calories but at least 50 grams of carbs daily experienced no changes in T3 levels (14).
Low T3 and high rT3 levels can slow your metabolism, resulting in symptoms such as weight gain, fatigue, lack of concentration, low mood and more.
One study found that, after 1 year, a diet consisting of moderate carbs (46 percent of total energy intake) had more positive effects on mood than a long-term diet of very low carbs (4 percent of total energy intake) in overweight and obese adults (15).
Bottom Line: Very low-carb diets may cause a drop in thyroid function in some people. This may result in fatigue, weight gain and low mood.
Low-Carb Diets May Affect Fertility
The amount and type of carbs consumed are associated with women's fertility levels.
For example, consuming both too many and too few carbs has been associated with reduced fertility (16).
Following a very low-carb diet for an extended period of time can disrupt hormones, causing amenorrhea or irregular menstrual cycles. This can lower fertility and make it harder for a woman to get pregnant (10, 17, 18, 19).
Bottom Line: Some evidence suggests that following a very low-carb diet for a long period of time can affect menstruation and fertility in women.
How Many Carbs Should You Eat?
The optimal amount of dietary carbs varies for each individual.
Many experts in the field recommend that you consume 15–30 percent of your total calories as carbs.
For most women, this usually equates to around 75–150 grams daily, although some may find a higher or lower carb intake to be more beneficial.
A Moderate Carb Intake May Be Better for Some Women
Certain women may do better consuming a moderate amount of carbs or around 100–150 grams daily. This includes women who:
- Are very active and struggle to recover after training.
- Have an underactive thyroid, despite taking medication (14).
- Struggle to lose weight or start gaining weight, even on a low-carb diet.
- Have stopped menstruating or are having an irregular cycle.
- Have been on a very low-carb diet for an extended period of time.
- Are pregnant or breastfeeding.
For these women, benefits of a moderate-carb diet may include weight loss, better mood and energy levels, normal menstrual function and better sleep.
Other women, such as athletes or those trying to gain weight, may find a daily carb intake of more than 150 grams appropriate.
Bottom Line: A moderate carb intake may benefit some women, including those who are very active or have menstrual problems.
A Low Carb Intake May Be Better for Others
Certain women may do better sticking to a low-carb diet that is under 100 grams per day. This includes women who:
- Are overweight or obese.
- Are very sedentary.
- Have epilepsy (20).
- Have polycystic ovarian syndrome (PCOS), fibroids or endometriosis (21).
- Experience yeast overgrowth.
- Are diagnosed with type 1 or type 2 diabetes (22).
- Have a neurodegenerative disease such as Alzheimer's or Parkinson's (23).
- Have certain forms of cancer (23).
Here is more info about how many carbs you should eat.
Bottom Line: A lower carb intake may benefit women with obesity, epilepsy, diabetes, polycystic ovarian syndrome (PCOS) and other conditions.
Take Home Message
Evidence suggests that women's hormones are sensitive to energy availability, meaning that too few calories or carbs can cause imbalances.
Such imbalances can have very serious consequences, including impaired fertility, low mood and even weight gain.
However, most evidence suggests these effects are generally seen only in women on a long-term, very low-carb diet (under 50 grams per day).
Everyone is different and the optimal carb intake varies greatly between individuals. There is no one-size-fits-all solution in nutrition.
Some people function best on a very low-carb diet, while others function best on a moderate- to high-carb diet.
To figure out what works best for you, you should experiment and adjust your carb intake depending on how you look, feel and perform.
This article was reposted from our media associate Authority Nutrition.
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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.