Extreme malnutrition with body mass index (BMI) as low as 10 kg/m(2) is not uncommon in anorexia nervosa, with survival enabled through complex metabolic adaptations. In contrast, outcomes from hunger strikes and famines are usually fatal after weight loss to about 40% below expected body weight, corresponding to BMI 12 to 13 kg/m(2) in adults. Thus, many years of adaptation in adolescent-onset anorexia nervosa, supported by supplements of vitamins and treatment of intercurrent diseases, may allow survival at a much lower BMI. However, in the literature only a few cases of survival in patients with BMI <9 kg/m(2) have been described. We report on the case of a 29-y-old woman who was successfully treated in a specialized unit. She had a BMI of 7.8 kg/m(2). To our knowledge, this level of extreme malnutrition has not previously been reported. The present case emphasizes the importance of adherence to guidelines to decrease refeeding complications.
Download full-text PDF |
Source |
|---|---|
| http://dx.doi.org/10.1016/j.nut.2015.08.024 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
Assessing Obstetrics and Gynecology (OBGYN) Clinicians' Knowledge, Practice, and Attitudes toward Anorexia Nervosa, Atypical Anorexia Nervosa, and Amenorrhea.
J Pediatr Adolesc Gynecol
December 2024
Penn State College of Medicine, Hershey, PA, USA; Division of Adolescent Medicine.
Study Objective: Anorexia nervosa (AN) and atypical anorexia nervosa (AAN) are eating disorders (EDs) characterized by extreme restriction of energy intake. However, in contrast to AN, those with AAN are not underweight. Although individuals with either AN or AAN can experience associated functional hypothalamic amenorrhea (FHA) that may prompt them to seek gynecological care, little is known about OBGYNs' knowledge and approach to these patients; this study sought to fill this gap.
View Article and Find Full Text PDFInsight into the Environmental Health Consciousness of Medical Students Regarding the Perceived Impacts of Climate Change on Human Health.
Environ Health Insights
December 2024
Faculty of Medicine and Health Sciences, University of Bakht Alruda, Ad Duwaym, Sudan.
Climate change represents an unprecedented global public health crisis with extensive and profound implications. The Lancet Commission identified it as the foremost health challenge of the 21st century. In 2015, air pollution alone caused approximately 9 million premature deaths worldwide.
View Article and Find Full Text PDFDevelopment and Validation of a Nutritional Frailty Phenotype for Older Adults Based on Risk Prediction Model: Results From a Population-Based Prospective Cohort Study.
J Am Med Dir Assoc
December 2024
School of Nursing, Peking University, Beijing, China. Electronic address:
Objectives: Malnutrition is generally studied to be involved in outlining hazard frailty trajectories, resulting in adverse outcomes. In view of frailty's multidimensional nature, we aimed to assess the contribution of nutritional items in existing frailty tools to adverse outcomes, and develop and validate a nutritional frailty phenotype based on machine learning.
Design: A population-based prospective cohort study.
Bees remain heat tolerant after acute exposure to desiccation and starvation.
J Exp Biol
December 2024
Department of Biology, University of Central Oklahoma, Edmond, OK 73034, USA.
Organisms may simultaneously face thermal, desiccation and nutritional stress under climate change. Understanding the effects arising from the interactions among these stressors is relevant for predicting organisms' responses to climate change and for developing effective conservation strategies. Using both dynamic and static protocols, we assessed for the first time how sublethal desiccation exposure (at 16.
View Article and Find Full Text PDFPrediction of undernutrition and identification of its influencing predictors among under-five children in Bangladesh using explainable machine learning algorithms.
PLoS One
December 2024
Department of Statistics, University of Rajshahi, Rajshahi, Bangladesh.
Background And Objectives: Child undernutrition is a leading global health concern, especially in low and middle-income developing countries, including Bangladesh. Thus, the objectives of this study are to develop an appropriate model for predicting the risk of undernutrition and identify its influencing predictors among under-five children in Bangladesh using explainable machine learning algorithms.
Materials And Methods: This study used the latest nationally representative cross-sectional Bangladesh demographic health survey (BDHS), 2017-18 data.
Want AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!