DEXA scan for assessing bone health in individuals with eating disorders -
DXA is simple, quick and noninvasive. It's also the most commonly used and the most standard method for diagnosing osteoporosis. This exam requires little to no special preparation. Tell your doctor and the technologist if there is a possibility you are pregnant or if you recently had a barium exam or received an injection of contrast material for a CT or radioisotope scan.
Leave jewelry at home and wear loose, comfortable clothing. You may be asked to wear a gown. You should not take calcium supplements for at least 24 hours before your exam.
Bone density scanning, also called dual-energy x-ray absorptiometry DXA or bone densitometry , is an enhanced form of x-ray technology that is used to measure bone loss. DXA is today's established standard for measuring bone mineral density BMD.
An x-ray exam helps doctors diagnose and treat medical conditions. It exposes you to a small dose of ionizing radiation to produce pictures of the inside of the body.
X-rays are the oldest and most often used form of medical imaging. DXA is most often performed on the lower spine and hips. In children and some adults, the whole body is sometimes scanned.
Peripheral devices that use x-ray or ultrasound are sometimes used to screen for low bone mass, mostly at the forearm. In some communities, a CT scan with special software can also be used to diagnose or monitor low bone mass QCT.
This is accurate but less commonly used than DXA scanning. DXA is most often used to diagnose osteoporosis , a condition that often affects women after menopause but may also be found in men and rarely in children. Osteoporosis involves a gradual loss of bone, as well as structural changes, causing the bones to become thinner, more fragile and more likely to break.
DXA is also effective in tracking the effects of treatment for osteoporosis and other conditions that cause bone loss. The DXA test can also assess an individual's risk for developing fractures.
The risk of fracture is affected by age, body weight, history of prior fracture, family history of osteoporotic fractures and life style issues such as cigarette smoking and excessive alcohol consumption. These factors are taken into consideration when deciding if a patient needs therapy.
The Vertebral Fracture Assessment VFA , a low-dose x-ray examination of the spine to screen for vertebral fractures that is performed on the DXA machine, may be recommended for older patients, especially if:.
On the day of the exam you may eat normally. You should wear loose, comfortable clothing, avoiding garments that have zippers, belts or buttons made of metal. Objects such as keys or wallets that would be in the area being scanned should be removed.
Remove jewelry, removable dental appliances, eyeglasses, and any metal objects or clothing that might interfere with the x-ray images. Inform your physician if you recently had a barium examination or have been injected with a contrast material for a computed tomography CT scan or radioisotope scan.
You may have to wait 10 to 14 days before undergoing a DXA test. Women should always tell their doctor and technologist if they are pregnant. Doctors will not perform many tests during pregnancy to avoid exposing the fetus to radiation.
If an x-ray is necessary, the doctor will take precautions to minimize radiation exposure to the baby. See the Radiation Safety page for more information about pregnancy and x-rays. Most of the devices used for DXA are central devices, which are used to measure bone density in the hip and spine.
They are usually located in hospitals and medical offices. Central devices have a large, flat table and an "arm" suspended overhead. Peripheral devices measure bone density in the wrist, heel or finger and are often available in drugstores and on mobile health vans in the community.
The pDXA devices are smaller than the central DXA devices, weighing only about 60 pounds. They may have a portable box-like structure with a space for the foot or forearm to be placed for imaging.
Other portable technologies such as specially designed ultrasound machines, are also sometimes used for screening. However, central DXA is the standard technique. The DXA machine sends a thin, invisible beam of low-dose x-rays with two distinct energy peaks through the bones being examined.
One peak is absorbed mainly by soft tissue and the other by bone. The soft tissue amount can be subtracted from the total and what remains is a patient's bone mineral density. DXA machines feature special software that compute and display the bone density measurements on a computer monitor.
In the central DXA examination, which measures bone density of the hip and spine, the patient lies on a padded table. An x-ray generator is located below the patient and an imaging device, or detector, is positioned above.
To assess the spine, the patient's legs are supported on a padded box to flatten the pelvis and lower lumbar spine. To assess the hip, the patient's foot is placed in a brace that rotates the hip inward. In both cases, the detector is slowly passed over the area, generating images on a computer monitor.
You must hold very still and may need to hold your breath for a few seconds while the technologist takes the x-ray. This helps reduce the possibility of a blurred image. Anorexia nervosa is an eating disorder that causes low body weight and an intense fear of weight gain.
Affected people limit the amount they eat and drink, even though they are underweight and not getting the food they need. Anorexia is most common in young women. But at least 1 in 10 people with an eating disorder — possibly as many as 1 in 4 — are men.
There are other types of eating disorder, such as bulimia nervosa. People with bulimia follow a pattern of 'bingeing' eating lots of food and then 'purging', either by making themselves sick, taking drugs such as laxatives, or doing lots of exercise to help them lose weight.
It can do, as it can affect healthy bone growth and lead to low bone density. If you have low bone density, your bones may be weaker and more likely to break.
Eating disorders often develop in teenagers. Childhood and the early adult years are a very important time for our bones. They quickly get bigger and stronger as you grow, especially during puberty.
Changes in your levels of sex and growth hormones during these years play an important part in building healthy bones. Your bones usually reach their maximum strength around the end of your 20s. If you have anorexia you probably will have low bone density.
This is also possible, but less likely, if you have bulimia. There are a number of reasons for this. If your anorexia has caused low bone density, your bones may be weaker and you may be more likely to break a bone. Anorexia is often not spotted or diagnosed as early in men, which means both the eating disorder and any impact on your bone health may go untreated for longer.
This is because your periods stopping can be a sign of low oestrogen, which can affect your bones. Your risk of fractures will also depend on other things, such as your age if you are over 50 and whether you've already broken bones easily.
Not everyone with anorexia needs a bone density scan. This is recommended for any older person with risk factors for osteoporosis. Last edited dd mmm yyyy. Authoring team. Add information to this page that would be handy to have on hand during a consultation, such as a web address or phone number.
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Hezlth Knopf, MD and Individualw S. Mehler, MD Eating Recovery Center, Denver, Colorado. Medical induviduals Energy-boosting foods frequently seen in patients with anorexia nervosa AN Mental focus and learning bulimia Hydrating sports drinks BN. It eaitng a well-established consequence of Boost thermogenesis naturally nutritional assessihg, and is particularly concerning for the developing bodies of adolescent patients. The time of onset, type and duration of the eating disorder, as well as the degree of nutritional deficit, will determine whether peak adult bone mass can be achieved. Since low bone density is most often associated with AN, BN is not considered a risk factor. These include long-term increased risk of fractures, osteoporosis, and limited longitudinal growth, impairing eventual height.DEXA scan for assessing bone health in individuals with eating disorders -
Take the first step in your health journey. We're here to guide you towards the best version of yourself. Osteoporosis and Eating Disorders. Osteoporosis and Eating Disorders By Rebecca Jaspan and the LCWNS Team Among the many factors involved in eating disorder treatment, one that cannot be overlooked is bone health.
Bachrach LK, Gordon CM. Bone Densitometry in Children and Adolescents. A systematic review and meta-analysis of the association between eating disorders and bone density.
Osteoporos Int. The Epidemiology and Pathogenesis of Osteoporosis. In: Feingold KR, Anawalt B, Boyce A, et al.
Endotext [Internet]. South Dartmouth MA : MDText. com, Inc. Epidemiology of osteoporosis. Clin Cases Miner Bone Metab. Department of Health and Human Services, www. Eating Disorders Review. Read More.
A New Way to Monitor Your PCOS — Continuous Glucose Monitoring Read More ». PCOS and Eating Disorders Read More ». Power Smoothie Read More ».
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Adjusting for age, height and pubertal stage, ED patients had significantly lower whole-body FM, FFM, protein mass PM and mineral mass MM compared with controls.
Trunk and limb FM and limb lean soft tissue were significantly lower in ED patients. However, no significant difference in the hydration of FFM was detected. Our study confirms that ED patients are depleted not only in FM but also in FFM, PM and MM. DXA has limitations for estimating body composition in individual young female ED patients.
However, the literature on fat-free mass FFM depletion and its components is inconsistent, in particular the extent to which muscle wasting occurs in these patients. Some studies found lower FFM in AN patients compared with controls, 2 , 3 , 4 , 5 , 6 whereas others reported no difference.
In dialysis patients with chronic renal failure, some studies found reduced FFM compared with controls, 12 , 13 whereas another study found no difference. Some studies have further reported that FFM components, including mineral 2 , 17 , 18 , 19 and protein mass PM , 2 , 4 , 20 , 21 are significantly reduced in AN patients.
PM declines in proportion with disease severity; 4 , 21 however, the protein component of FFM may be rapidly replenished after weight regain. Body mass index BMI is the most widely used measure of nutritional status in ED patients and gains in BMI during treatment are considered a sign of improved health.
The primary aim of this study was to compare body composition between adolescent girls with EDs and healthy controls. It also allows possible differences in PM and mineral mass MM to be examined. The secondary aim was to evaluate the utility of BMI and DXA for estimating body composition in ED patients, using the 4C model as the reference method.
Subjects included 13 female ED patients involving substantial weight loss and female controls, aged between 10 and 18 years.
The patients were recruited from a specialist clinic at Great Ormond Street Hospital, London, a tertiary referral hospital. All had been diagnosed previously and were referred either because of clinical deterioration or because, despite medical stabilization, they were not responding to treatment.
All patients met full criteria for AN at some point and would therefore be diagnosed with AN using DSM5 criteria. Controls were recruited from an ongoing body composition research project for healthy children and adolescents.
They also had no history of EDs. All subjects over 16 years of age gave written consent before participating in the study; parents or guardians gave written consent for younger subjects, who also gave verbal assent. Body weight was measured with minimum clothing to 0. Circumferences of the mid-upper arm, waist, hips, thigh and calf were also measured using a non-stretchable flexible tape.
Height, weight, BMI and waist circumference were converted into s. scores using current UK reference data. Total body water TBW was determined by deuterium oxide, using an oral dose equivalent to 0.
Isotopic enrichment of saliva samples was determined by isotope-ratio mass spectrometry. Body volume was measured by air-displacement plethysmography using BODPOD instrumentation Life Measurement Instruments, Concord, CA, USA. The subjects wore a close-fitting swimming costume and swimming cap.
Two complete tests that is, a minimum of four raw volume measurements were performed on each subject. Bone mineral content was measured using DXA Lunar Progidy, Madison, WI, USA. The subjects removed objects and clothes containing metal before undergoing the scan.
Subjects lay supine, with arms and legs at their sides. The estimated radiation exposure was 2. Data on whole body and regional FM, bone mineral content and non-bone lean soft tissue were extracted.
The 4C model differentiates weight into FM, water, protein and mineral. Using the raw measurements described above, FM can be calculated using the following equation: Total body MM was calculated as bone mineral content × 1. The protein:mineral ratio indicates the degree of mineralization of fat-free tissue.
A Hattori plot 35 was generated to express the relative contributions of FFMI versus FMI to BMI in the ED patients and controls. Whole-body FM and FFM by the 4C model, and regional fat and lean soft tissue by DXA, were expressed as z -scores relative to the UK reference data. We aimed to recruit 16 ED patients, which would be adequate to identify a 1 s.
difference in any outcome relative to 16 controls. However, to increase statistical power of the comparison and to be able to address a wide range of size and pubertal status in the controls, we analyzed all available data from a study of healthy girls, which have been published as the UK body composition reference data.
All statistical analyses were performed using Statistical Package for the Social Sciences SPSS v. Independent sample t -tests and χ 2 -tests were used to test for differences in background characteristics, age and anthropometry between the patients and controls.
General linear models were used to test for differences in body circumferences, FM, FFM and the various components of FFM including water, protein and mineral, and regional body composition by DXA between patients and controls, adjusting for potential confounding factors age, height and pubertal stage.
Bland—Altman analyses were used to assess the bias in body composition measurements of the ED patients by DXA, using 4C data as the reference method.
of the mean bias. The correlation between the bias and mean of measured values was also determined. The bias was expressed as percent of the mean by natural log transformation of the bias of individual values and multiplying by The control girls and ED patients were all White Europeans, except for one ED patient who was Indian.
This patient did not significantly differ in age or any of the anthropometric or body composition measurements compared with the rest of the patients; therefore, her data were included in the analyses.
All subjects had data for the 4C model, except for one ED patient who only had a DXA scan. Therefore, the main comparisons of ED versus controls, and of the accuracy of DXA, involved 12 ED patients and controls; however, an additional ED data point was available for the regional DXA comparison.
Characteristics of the ED patients and controls are presented in Table 1. The patients were aged between 10 and 18 years with a mean of Control girls were also aged between 10 and 18 years, but were on average 1.
As expected, the ED group had significantly lower weight and BMI s. The controls were 2. The mean BMI s. scores of 0. In terms of background characteristics, there were no significant differences for birth weight and parental BMI between patients and controls.
However, all anthropometric measurements differed significantly between the groups, including body circumferences. Table 2 presents the body composition outcomes. The only statistically significant differences were lower body volume, FM, FFMI and FMI in the ED patients.
The s. scores for FM and FFM were also significantly lower in the patients. Figure 1 shows a Hattori plot of FMI versus FFMI in ED patients and controls. The ED patients were all low on the graph, confirming their uniformly low FMI compared with the controls.
They were also grouped towards the left-hand side of the graph, indicating a lower range of FFMI. However, due to the difference between groups in mean age, further statistical adjustments were required to test for differences in regional body composition and FFM composition.
The hydration value of A Hattori plot illustrating differences in FMI and FFMI by the 4C model between ED patients and controls. As FMI and FFMI add up to BMI, downward-crossing diagonal lines express constant BMI values.
The ED patients have reduced FMI and FFMI relative to the control population. After adjusting for height and age, the ED patients had mean deficits of These data indicate a greater depletion of upper arm and upper leg tissue compared with abdominal regions.
Table 3 illustrates crude whole-body composition differences between ED patients, after adjusting for differences in age and finally after adjusting for differences in age, pubertal status and height. In all models, ED patients had significantly lower FM, FFM, MM and PM.
After adjusting for age, height and puberty, the deficits compared with controls were equivalent to However, differences in hydration or density of FFM did not reach significance and no difference in the protein:mineral ratio was found.
Regional body composition, assessed by DXA, was also compared between ED patients and controls Table 3. After adjusting for age, sex and pubertal stage, the percentage deficit in FM was Equivalent deficits for the bone were As with body girths, these data indicate greater loss of fat in the arm compared with the trunk, and greater loss of lean tissue in the limbs compared with the trunk.
Using the new UK reference data, mean s. The utility of BMI and DXA for body composition assessment was then assessed using the 4C model as the reference method. The evaluation of the accuracy of DXA for body composition assessment is given in Table 4.
In the ED patients, DXA was found to overestimate FM and underestimate FFM by 4. Even though the bias was not very high, the limits of agreement for FM and FFM were ± Similarly, compared with the 4C model, DXA was found to overestimate FM and underestimate FFM in the control girls. The magnitude of the biases in FM and FFM did not differ significantly between ED patients and controls.
A graphical illustration of the findings for the ED patients is shown in Figure 2. Bland—Altman plots to evaluate the accuracy of DXA for body composition assessment in ED patients. a Fat mass — controls; b Fat mass — ED patients; c Fat-free mass — controls; d Fat-free mass — ED patients.
Our study demonstrates that female adolescents with EDs involving substantial weight loss have reduced FM and FFM compared with controls, after adjusting for differences in age, height and pubertal stage.
In addition, there were significant deficits in the protein and mineral components of FFM, although not of sufficient magnitude to indicate differences in the physical or biological properties of fat-free tissue.
In addition to differences in whole-body composition, weight loss differed by region. ED patients had bone and FM deficits in their trunks and limbs, and lower lean soft tissue in their limbs. Although consistent with anthropometric data on girths, the magnitudes of these regional DXA soft tissue differences are less reliable than the equivalent whole-body 4C data, as we further demonstrated poor accuracy of DXA for estimating body composition, especially fatness, in ED patients.
Previous studies describing body composition differences between AN patients and controls have been inconsistent. DXA has been used in several studies of ED patients; 2 , 5 , 37 however, these studies have not demonstrated the validity of DXA in ED patients using an accurate reference method.
In healthy children and adolescents, the accuracy of DXA has been shown to vary across the range of nutritional status. These findings support our earlier conclusion that DXA may have limitations for monitoring change in body composition in individuals gaining or losing tissue masses.
PM is expected to correlate with height and, independently, to increase during pubertal maturation. An important component of our work was therefore to disentangle the greater age and more advanced pubertal stage of our patients relative to the controls, from their shorter height.
After these adjustments, PM was significantly lower in the ED patients. This agrees with the findings from several previous studies of adolescents with EDs, which estimated total body nitrogen by in-vivo neutron activation analysis. Protein depletion in children with chronic renal failure contributed to growth failure and severe stunting, 40 which have also been described in patients with AN.
We also observed depleted MM in the ED patients, consistent with some previous studies, 43 , 44 although another study showed no significant deficit. More generally, the duration of malnutrition is considered an important predictor of bone loss.
Most work on the nutritional status of ED patients has relied on weight and height indices. This is consistent with findings from previous studies from healthy subjects 47 and AN patients, 25 showing that BMI does not accurately reflect body composition, especially fatness, and may be especially poor for monitoring disease progression and response to treatment.
The main limitation of our study is the small sample size for the ED patients and we did not manage to recruit our target of 16 patients.
However, owing to the powerful effect of EDs on body composition and the large number of control subjects, we were able to detect statistically significant differences in almost all outcomes compared with healthy controls.
Equally, our evaluation of DXA is complemented by previous work demonstrating differential bias across the entire range of nutritional status. Hence, despite the small sample size, our main conclusions are likely to be robust. Another limitation is the use of self-reported puberty status 48 rather than physical examination.
However, adjustment for puberty made little difference to the results, whereas size was more important. In summary, our study using the gold standard 4C model demonstrates reduction in FM, FFM, PM and MM in adolescent ED patients, relative to healthy controls.
Previously, FFM and PM have been shown to predict clinical outcome and morbidity during starvation and other illnesses of malnutrition. Hence, accurate measurements of body composition in ED patients may be important for monitoring the success of their treatment and in preventing future complications such as bone loss and infertility.
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Journal of Asseasing Disorders assessjng 11 dlsorders, DEXA scan for assessing bone health in individuals with eating disorders number: 44 Jn this article. Beta-alanine and resistance training details. Compared assessibg other eating disorders, ARFID is observed to have an earlier childhood onset and chronic course without intervention. Childhood represents a sensitive period for longitudinal growth and bone accrual, setting the stage for long-term health outcomes associated with longevity and quality of life, including risk for fracture and osteoporosis. Reviewing what is known of clinical data from anorexia nervosa AN and similar cohorts, the chronicity and etiology of dietary restriction observed in ARFID are hypothesized to compromise bone health significantly.Healthh of Eating Disorders Energy-boosting foods 11 Weight gain tips, Article number: DXEA Cite this article. Metrics scaj. Compared to other eating disorders, ARFID is observed to have an earlier ineividuals onset and chronic course without intervention.
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Early identification and sasessing of threats to bone mass diskrders have significant personal adsessing population-level implications.
For Powerful antifungal herbs with ARFID, delayed identification and intervention to address feeding disturbances may have a long-lasting impact on various body systems and processes, including disofders relating to Best post-workout snacks growth and bone mass accrual.
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Research in individuals with ARFID DEXA scan for assessing bone health in individuals with eating disorders individulas longitudinal changes in Hypertension and inflammation, bone micro-structure, and bone strength during clinical intervention are required.
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The treatment of ARFID broadly aims to ensure inclusion of sufficient food volume and variety boje improve Managing blood sugar crashes health and their relationship to eatjng and disorrders, varying across individual patients based on unique CGM sensor technology. As an example, treatment for assesing who are heath or formula dependent begins with a focus on witn overall volume and variety of foods consumed, individuasl individuals tor nutritional deficiencies need only to focus individuxls a variety of foods consumed.
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Thus, thorough assessment disordes comorbid medical wwith, particularly those that cause pain or discomfort with eating, is crucial to ensure individuxls diagnosis and treatment [ 23456 ]. It is generally understood that ARFID may portend numerous health consequences secondary to malnutrition, High-intensity interval training (HIIT) observed Menopausal fat distribution other eating disorders such as AN [ 7 ].
The symptom wih of ARFID is typically reported in hexlth childhood and persists bealth time [ 8 ]. ARFID disoredrs from other eating disorders ehalth dietary restriction helath. Additionally, patients zssessing ARFID likely have different macronutrient and micronutrient indivoduals profiles compared to other eating disorders.
For example, patients who individualz with severe food selectivity indiiduals consume diets high in processed foods, carbohydrates, and Thermogenesis for fat burning sugars, with low intake of fruits, vegetables, and proteins [ 9 ].
These nutritional shortfalls are of particular concern in ARFID given the assezsing age of onset and individuqls chronicity of highly restrictive eating patterns. Consequently, disorfers processes dependent upon Heart health guidelines adequacy could be healthh.
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This narrative review summarizes available ehalth evidence involving ARFID and related nutritional deficiencies and bone health outcomes Wlth. Articles were Liver detoxification drinks by searching Pubmed, disordsrs to the best of our Dark chocolate extravaganza, only two studies reported BMD outcomes in individuals with ARFID.
Readily accessible demographic e. Dual-energy X-ray absorptiometry DXA is the gold standard clinical and research tool used to assess BMD in pediatric and adult patients.
The ability of DXA to assess BMD at various anatomic regions is a strength of this technique since the composition of the skeleton with respect to cortical and trabecular bone varies across regions. In pediatric patients, the International Society for Clinical Densitometry ISCD recommends assessing the total body excluding the head and the lumbar spine, which are skeletal regions primarily comprised of cortical and trabecular bone, respectively [ 18 ].
Due to the close relationship between BMD and stature, the ISCD also recommends adjusting BMD measures for height to minimize potential confounding, particularly in children and adolescents with pubertal delay or short stature. This includes computing height Z-score-adjusted BMD Z-scores using published calculations [ 19 ], as well as calculating lumbar spine bone mineral apparent density BMAD.
Pediatric growth charts for lumbar spine BMAD for American children were recently published. BMAD demonstrates strong tracking across childhood and adolescence, similar to other BMD measures, while helping minimize stature-related artifacts in BMD [ 14 ].
Robust reference ranges help account for variability in BMD during the dynamic growing years. Similar to growth references from the Centers for Disease Control and Prevention and growth standards from the World Health Organization, BMD growth charts help account for the non-linear increases in bone density that occur in childhood, in addition to sex and race differences.
Standard deviation scores, or Z-scores, are calculated from reference datasets and facilitate the interpretation of measurements such as BMD. A Z-score indicates the number of standard deviations above or below the age-specific mean that a BMD value sits on the growth chart.
BMD growth charts are typically specific for sex and race, with some exceptions. Additional strengths of DXA include wide-spread availability, low cost, marginal radiation dose, fast scan times, and the assessment of lean and fat mass. Understanding these assessment methodologies is important to contextualize findings currently reported in the literature related to bone health in people with ARFID.
Consideration of available technologies also informs the development of recommendations for clinical use. As defined by the DSM-5, restrictive eating disorders include AN-Restrictive AN-RAN-Atypical AN-Aand ARFID [ 20 ].
AN-R and AN-A are characterized by intentional energy restriction and fear of weight gain, with AN-R associated with more severe low body weight. There is considerable variability in body size within the ARFID population, as poor growth is not required to meet diagnostic criteria for ARFID.
Longitudinal growth depends upon adequate nutrition, therefore height serves as an indicator of healthy development and nutritional status.
In addition to its association with shorter adult stature, growth stunting during the formative years is associated with many adverse outcomes, including disturbances in puberty and sexual maturation, cognitive deficits, and poor academic performance.
Many key nutrients and food groups involved in longitudinal growth are not consumed in adequate amounts in people with severe food selectivity such as in ARFID. This includes, but is not limited to, protein, zinc, iodine, calcium, vitamin D, and dairy [ 92122 ].
Although no studies thus far have explicitly focused on longitudinal growth in children and adolescents with ARFID, several studies have reported descriptive statistics relating to stature, suggesting that children and adolescents with ARFID have shorter stature than healthy reference datasets.
Average BMI Z-score, calculated using United Kingdom growth reference data, was greater than 1. Height Z-scores in the ARFID group were slightly lower than the AN group, but did not differ significantly. Interestingly, stature deficits in the ARFID group were more pronounced in males compared to females.
This is an important finding since boys are more prone to fracture compared to girls [ 13 ], and ARFID is observed at higher rates in males than in females, in contrast to other eating disorders. In this study, children with ARFID tended to be shorter than those without ARFID, and differences in height between AFRID subtypes were evident.
These differences support the notion that ARFID has varying effects on growth in stature depending upon its subtype, with the most pronounced deficits evident in those diagnosed with ARFID subtypes A1—A3.
Studies involving bone health in individuals with eating disorders have primarily focused on people with AN, revealing relatively consistent bone deficits in these patients [ 172526 ].
Individuals with AN-R and AN-A have bone deficits when compared to non-AN healthy controls, with AN-R typically having lower BMD compared to those with AN-A [ 27 ]. The lower BMD in both AN groups suggests that body weight alone does not fully account for threats to bone health and underscores the importance of studying the effects of all types of restrictive intake on bone health.
In one of the first studies of bone outcomes in people with ARFID, Schorr and colleagues conducted a cross-sectional study of adults ages 18—63 years with clinically diagnosed eating disorders from two hospitals in the United States [ 27 ].
It is important to note that the specific ARFID subtype of these patients was not stated in the article. Classification of ARFID was based on the presence of restricted eating without psychological symptoms consistent with AN.
There were no body weight criteria for ARFID classification, but the ARFID group had a similar BMI to the AN-R group and a lower BMI than the AN-A group.
Since ARFID typically manifests in childhood, the lack of specificity with regards to ARFID manifestation and the older age of these patients complicates the translation of these findings.
The study by Alberts and colleagues discussed above expanded upon the results of Schorr et al. In the main analyses, bone Z-scores were generally low in both groups, but did not differ significantly. Overall, bone Z-scores tended to be lower in the ARFID versus the AN group, but these differences were not statistically significant.
Accordingly, these findings suggest that bone deficits in youth with ARFID are of a similar magnitude as those observed in youth with AN. In addition, because bone deficits were evident when considering lumbar spine BMAD, which helps account for short stature and pubertal delay, this suggests that threatened BMD in children with ARFID is likely independent of stature-related artifacts.
A summary of these studies is presented in Table 1. The skeleton is responsive to static and dynamic loading from standing, locomotion, physical activity, and muscle contractions [ 1228 ]. In studies of patients with AN, current body weight and a history of low body weight are among the strongest determinants of BMD [ 1729 ].
Similar findings have been reported in people with ARFID. Alberts and colleagues report strong associations between BMI, BMI Z-score, and underweight duration as predictors of BMD Z-score, and BMI and BMI Z-score as predictors of BMAD Z-score [ 23 ].
Similarly, in Schorr et al. In addition, these authors report lower lean mass and percent body fat in adults with ARFID compared to controls and found significant positive correlations between appendicular lean mass and bone Z-score. These studies highlight the role of body weight in ARFID-related bone deficits and suggest skeletal muscle deficits might be involved in these relationships.
Measures of body composition should be considered in future studies since fat mass and lean mass play a prominent role in peak bone mass attainment and maintenance.
Vitamin C deficiency is among the most common nutrient deficiencies in patients with ARFID in the form of severe food selectivity [ 30 ]. The skeletal matrix comprises an integrated framework of type 1 collagen, which serves as scaffolding for the mineral hydroxyapatite component of bone.
Although vitamin C deficiency is uncommon in the general population, several recent clinical case reports discuss the development of scurvy in patients with ARFID and its subsequent effects on bone health [ 3033 ].
Scurvy is associated with numerous musculoskeletal manifestations evident through radiographic testing and patient observation, such as joint swelling and bone pain. Based on the pattern of food restriction, another historically associated vitamin deficiency that can occur in patients with ARFID is vitamin D deficiency rickets.
Vitamin D is a pro-hormone nutrient involved in modulating bone turnover and augmenting intestinal calcium absorption. Cutaneous conversion of 7-dehydrocholesterol to cholecalciferol via UVB radiation is the primary source of vitamin D for humans, but dietary sources include fatty fish, eggs, soy, dairy, and juices [ 34 ].
Under-mineralized bone in children with vitamin D deficiency may lead to the development of rickets [ 35 ]. Children with rickets exhibit bowing of the legs, curving of the spine, and deficits of dentition [ 36 ].
Vitamin D deficiency in childhood can also cause growth restriction and skeletal deformities, increasing the risk of fracture [ 3537 ]. In the previously discussed study by Schorr and colleagues, serum vitamin D was measured alongside BMD in men with AN and ARFID [ 27 ].
Recently, associations between low vitamin D status, allergies, and autoimmune disorders have been recognized in children [ 37 ].
Allergies and autoimmune dysfunction are common comorbidities in children with ARFID [ 38 ]. Although vitamin D status in individuals with ARFID has not been extensively studied, several published clinical cases report vitamin D deficiency rickets in children and adolescents with autism spectrum disorder ASD with severe food selectivity.
: DEXA scan for assessing bone health in individuals with eating disorders| Low bone mineral density in people with anorexia nervosa | Open Access This article is licensed under a Creative Commons Attribution 4. Preliminary results using high resolution peripheral computed tomography. DXA scans are also interpreted by other physicians such as rheumatologists and endocrinologists. DXA machines feature special software that compute and display the bone density measurements on a computer monitor. Osteoporosis is extremely prevalent in eating disorders and can lead to severe problems. |
| Orthopedic Complications of Anorexia Nervosa | ACUTE | The radiation dose for ofr procedure varies. Should I ij an osteoporosis drug? Bone density loss is one of the more common complications associated with AN. He is author of scientific publications, including 3 textbooks. Am J Clin Nutr ; 51 : — |
| Anorexia and osteoporosis risk | Howard JT, Walick KS, Rivera JC. Please contact your physician with specific medical questions or for a referral to a radiologist or other physician. References Association AP. Update on the medical management of eating disorders in adolescents. Steinman, J. Provided by the Springer Nature SharedIt content-sharing initiative. |
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