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Journal articles on the topic 'Obesity – Diagnosis'

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Published: 4 June 2021
Last updated: 1 February 2022

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1

Ogawa, Wataru, and Shigeru Miyazaki. "Diagnosis criteria for obesity and obesity disease." Health evaluation and promotion 42, no. 2 (2015): 301–6. http://dx.doi.org/10.7143/jhep.42.301.

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2

Sanada, Kiyoshi. "Diagnosis of sarcopenic obesity." Japanese Journal of Physical Fitness and Sports Medicine 66, no. 3 (2017): 195–201. http://dx.doi.org/10.7600/jspfsm.66.195.

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3

Gray, David S. "Diagnosis and Prevalence of Obesity." Medical Clinics of North America 73, no. 1 (January 1989): 1–13. http://dx.doi.org/10.1016/s0025-7125(16)30688-5.

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4

Lydecker, Janet, and Carlos Grilo. "The Missed Diagnosis and Mis-Diagnosis of Pediatric Obesity." Journal of Adolescent Health 60, no. 2 (February 2017): S67—S68. http://dx.doi.org/10.1016/j.jadohealth.2016.10.316.

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5

Lissner, Lauren. "Causes, Diagnosis and Risks of Obesity." PharmacoEconomics 5, Supplement 1 (1994): 8–17. http://dx.doi.org/10.2165/00019053-199400051-00004.

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6

Bang, Chang Seok, and Jung Hwan Oh. "Diagnosis of Obesity and Related Biomarkers." Korean Journal of Medicine 94, no. 5 (October 1, 2019): 414–24. http://dx.doi.org/10.3904/kjm.2019.94.5.414.

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7

Moon, Kyung Rye. "Diagnosis and Treatment of Childhood Obesity." Korean Journal of Pediatric Gastroenterology and Nutrition 2, no. 1 (1999): 8. http://dx.doi.org/10.5223/kjpgn.1999.2.1.8.

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8

BRUNK, DOUG. "Obesity-Related Liver Disease Eludes Diagnosis." Family Practice News 37, no. 15 (August 2007): 31. http://dx.doi.org/10.1016/s0300-7073(07)70951-9.

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9

Panzer, Barry M., Sarita Dhuper, and Nitasha Gupta. "Obesity and the Dual Diagnosis Child." ICAN: Infant, Child, & Adolescent Nutrition 4, no. 5 (August 1, 2012): 310–14. http://dx.doi.org/10.1177/1941406412456206.

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Many children with excess weight and obesity struggle with comorbid psychiatric disorders and family stressors. These dual diagnosis presentations may be underestimated in epidemiologic surveys and frequently constitute exclusion criteria in childhood obesity treatment studies. As a result, clinical paradigms for this population are lacking and even multispecialty pediatric obesity centers do not provide comprehensive services to these children and their families. Hence, the need for this article, which is a preliminary exploration of possible dynamic mechanisms connecting several psychiatric diagnoses in childhood and excess weight. Based on correlations reported in the literature, depression, oppositional disorder, and attention-deficit/hyperactivity disorder are offered as examples of linear and reciprocal relationships between the two conditions. Notably, eating may be viewed as a means of regulating emotion (depression) and family conflict (oppositionalism) as well as reflecting a lack of regulation (attention-deficit/hyperactivity disorder). This article will hopefully generate subsequent research efforts in this area and enhance practitioner awareness of the complexity of providing effective services to this population.
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Wendling, Patrice. "Few Get Obesity Diagnosis From Doctor." Family Practice News 35, no. 24 (December 2005): 24. http://dx.doi.org/10.1016/s0300-7073(05)72344-6.

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11

Wong, Chiew, and Thomas H. Marwick. "Obesity cardiomyopathy: diagnosis and therapeutic implications." Nature Clinical Practice Cardiovascular Medicine 4, no. 9 (September 2007): 480–90. http://dx.doi.org/10.1038/ncpcardio0964.

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12

Grant, Paul, and David Lipscomb. "Diagnosis of obesity and clinical implications." Practical Diabetes International 25, no. 9 (November 2008): 376. http://dx.doi.org/10.1002/pdi.1313.

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13

Sunwoo, Bernie Young. "Obesity Hypoventilation: Pathophysiology, Diagnosis, and Treatment." Current Pulmonology Reports 8, no. 2 (April 11, 2019): 31–39. http://dx.doi.org/10.1007/s13665-019-0223-x.

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14

Butturini, Anna M., Frederick J. Dorey, Beverly J. Lange, David W. Henry, Paul S. Gaynon, Cecilia Fu, Janet Franklin, et al. "Obesity and Outcome in Pediatric Acute Lymphoblastic Leukemia." Journal of Clinical Oncology 25, no. 15 (May 20, 2007): 2063–69. http://dx.doi.org/10.1200/jco.2006.07.7792.

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PurposeTo evaluate the effect of obesity (defined as a body mass index > 95th percentile for age and sex at diagnosis) on outcome of pediatric acute lymphoblastic leukemia (ALL).Patients and MethodsWe retrospectively analyzed data from 4,260 patients with newly diagnosed ALL enrolled from 1988 to 1995 onto five concurrent Children's Cancer Group studies. Results were verified in a second cohort of 1,733 patients enrolled onto a sixth study from 1996 to 2002.ResultsThe 1988 to 1995 cohort included 343 obese and 3,971 nonobese patients. The 5-year event-free survival rate and risk of relapse in obese versus nonobese patients were 72% ± 2.4% v 77% ± 0.6% (P = .02) and 26 ± 2.4 v 20 ± 0.6 (P = .02), respectively. After adjusting for other prognostic variables, obesity's hazard ratios (HRs) of events and relapses were 1.36 (95% CI, 1.04 to 1.77; P = .021) and 1.29 (95% CI, 1.02 to 1.56; P = .04), respectively. The effect of obesity was prominent in the 1,003 patients ≥ 10 years old at diagnosis; in this subset, obesity's adjusted HRs of events and relapses were 1.5 (95% CI, 1.1 to 2.1; P = .009) and 1.5 (95% CI, 1.2 to 2.1; P = .013), respectively. In a second cohort of 1,160 patients ≥ 10 years old, obesity's adjusted HRs of events and relapses were 1.42 (95% CI, 1.03 to 1.96; P = .032) and 1.65 (95% CI, 1.13 to 2.41; P = .009), respectively. The effect of obesity on outcome was unrelated to changes in chemotherapy doses, length of intervals between chemotherapy cycles, or incidence and severity of therapy-related toxicity.ConclusionObesity at diagnosis independently predicts likelihood of relapse and cure in preteenagers and adolescents with ALL.
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15

KAWAMURA, ISAO. "Obesity : Advances on diagnosis and treatment.5.Surgical treatment of obesity." Nihon Naika Gakkai Zasshi 84, no. 8 (1995): 1295–99. http://dx.doi.org/10.2169/naika.84.1295.

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16

Kim, Jae Hyun. "Overview of pediatric obesity: diagnosis, epidemiology, and significance." Journal of the Korean Medical Association 64, no. 6 (June 10, 2021): 401–9. http://dx.doi.org/10.5124/jkma.2021.64.6.401.

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Background: The prevalence of pediatric obesity has increased over the past several decades worldwide and in Korea. Childhood obesity has become a serious social problem.Current Concepts: Diagnosis of obesity is based on body mass index (BMI) in children and adolescents aged ≥2 years. Overweight and obese are defined as BMI ≥85th percentile to <95th percentile and BMI ≥95th percentile, respectively, corresponding to sex and age. Obesity is further classified as Class I (BMI ≥95th percentile to <120% of 95th percentile), Class II (BMI ≥120% of 95th percentile to <140% of 95th percentile), and Class III (BMI ≥140% of 95th percentile). Waist circumference and waist-height ratio are used to evaluate abdominal obesity. Pediatric obesity can cause childhood comorbidities, including type 2 diabetes, dyslipidemia, non-alcoholic fatty liver disease, and hypertension. Adult obesity, cardiovascular diseases, and other adult comorbidities, together with increased medical costs are additional consequences of pediatric obesity.Discussion and Conclusion: Prevention, diagnosis, and proper management of pediatric obesity are important.
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17

TANAKA, SHUN'ICHI. "Obesity : Advances on diagnosis and treatment.3.Obesity and associated diseases.6.Obesity and immunity." Nihon Naika Gakkai Zasshi 84, no. 8 (1995): 1267–72. http://dx.doi.org/10.2169/naika.84.1267.

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18

San Giovanni, Christine B., Myla Ebeling, Robert A. Davis, C. Shaun Wagner, and William T. Basco. "Sensitivity of Clinical Pediatric Obesity Diagnosis Documented in Electronic Health Records." Clinical Pediatrics 59, no. 14 (July 24, 2020): 1274–81. http://dx.doi.org/10.1177/0009922820941640.

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Objective. This study tested the sensitivity of obesity diagnosis in electronic health records (EHRs) using body mass index (BMI) classification and identified variables associated with obesity diagnosis. Methods. Eligible children aged 2 to 18 years had a calculable BMI in 2017 and had at least 1 visit in 2016 and 2017. Sensitivity of clinical obesity diagnosis compared with children’s BMI percentile was calculated. Logistic regression was performed to determine variables associated with obesity diagnosis. Results. Analyses included 31 059 children with BMI at or above 95th percentile. Sensitivity of clinical obesity diagnosis was 35.81%. Clinical obesity diagnosis was more likely if the child had a well visit, had Medicaid insurance, was female, Hispanic or Black, had a chronic disease diagnosis, and saw a provider in a practice in an urban area or with academic affiliation. Conclusion. Sensitivity of clinical obesity diagnosis in EHR is low. Clinical obesity diagnosis is associated with nonmodifiable child-specific factors but also modifiable practice-specific factors.
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19

Vasyukova, Olga V. "Obesity in children and adolescents: diagnosis criteria." Obesity and metabolism 16, no. 1 (June 20, 2019): 70–73. http://dx.doi.org/10.14341/omet10170.

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Currently in the world the main diagnostic parameter for assessing obesity is the magnitude of body mass index. In children, taking into account the growth and body weight indicators that dynamically change as the child grows up, it is common to use not absolute, but relative values of body mass index percentiles or standard deviations. The lecture examined various systems and methods for assessing the physical development of children in the world and in Russia domestic ones, R.N. Dorokhova and I.I. Bakhraha, World Health Organization (WHO), International Group for the Study of Obesity. A comparative analysis of the existing systems and the validity of the currently adopted Federal recommendations on the diagnosis of obesity in children based on the recommendations of WHO has been carried out.
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20

Frolova, E. V. "OBESITY: DIAGNOSIS AND TREATMENT IN GENERAL PRACTICE." Russian Family Doctor 20, no. 4 (December 15, 2016): 5. http://dx.doi.org/10.17816/rfd201645-25.

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21

Frühbeck, Gema, Dimitrios N. Kiortsis, and Victoria Catalán. "Precision medicine: diagnosis and management of obesity." Lancet Diabetes & Endocrinology 6, no. 3 (March 2018): 164–66. http://dx.doi.org/10.1016/s2213-8587(17)30312-1.

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22

Gupta, Deepak. "Moribund Obesity as a Palliative Care Diagnosis." Journal of Palliative Medicine 12, no. 6 (June 2009): 515–16. http://dx.doi.org/10.1089/jpm.2009.0043.

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23

DIXON, BRUCE K. "Obesity, Hypertension, Apnea Confound Diagnosis of PAH." Family Practice News 36, no. 6 (March 2006): 56. http://dx.doi.org/10.1016/s0300-7073(06)72869-9.

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24

Maclellan, Reid A., and Arin K. Greene. "Obesity-Induced Lymphedema: Presentation, Diagnosis, and Management." Journal of the American College of Surgeons 219, no. 3 (September 2014): S89—S90. http://dx.doi.org/10.1016/j.jamcollsurg.2014.07.213.

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25

Abbey, I., and M. Rudolf. "Do paediatricians miss the diagnosis of obesity?" Archives of Disease in Childhood 95, Suppl 1 (April 2010): A51.2—A52. http://dx.doi.org/10.1136/adc.2010.186338.114.

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26

Greene, Arin K. "Diagnosis and Management of Obesity-Induced Lymphedema." Plastic and Reconstructive Surgery 138, no. 1 (July 2016): 111e—118e. http://dx.doi.org/10.1097/prs.0000000000002258.

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27

Truswell, A. S. "ABC of nutrition. Obesity: diagnosis and risks." BMJ 291, no. 6496 (September 7, 1985): 655–57. http://dx.doi.org/10.1136/bmj.291.6496.655.

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28

Cândido, Ana PC, Sílvia N. Freitas, and George LL Machado-Coelho. "Anthropometric measurements and obesity diagnosis in schoolchildren." Acta Paediatrica 100, no. 9 (April 18, 2011): e120-e124. http://dx.doi.org/10.1111/j.1651-2227.2011.02296.x.

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29

Narkiewicz, K. "Diagnosis and management of hypertension in obesity." Obesity Reviews 7, no. 2 (May 2006): 155–62. http://dx.doi.org/10.1111/j.1467-789x.2006.00226.x.

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30

SAITO, YASUSHI. "Obesity : Advances on diagnosis and treatment.1.Type and criterion of obesity." Nihon Naika Gakkai Zasshi 84, no. 8 (1995): 1217–20. http://dx.doi.org/10.2169/naika.84.1217.

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31

IKEDA, YOSHIO. "Obesity : Advances on diagnosis and treatment.4.Treatment of obesity.4.Anorectics." Nihon Naika Gakkai Zasshi 84, no. 8 (1995): 1290–94. http://dx.doi.org/10.2169/naika.84.1290.

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32

Bardia, Aditya, Shernan G. Holtan, Jeffrey M. Slezak, and Warren G. Thompson. "Diagnosis of Obesity by Primary Care Physicians and Impact on Obesity Management." Mayo Clinic Proceedings 82, no. 8 (August 2007): 927–32. http://dx.doi.org/10.4065/82.8.927.

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33

Wang, S., L. M. Sparks, H. Xie, F. L. Greenway, L. de Jonge, and S. R. Smith. "Subtyping obesity with microarrays: implications for the diagnosis and treatment of obesity." International Journal of Obesity 33, no. 4 (February 3, 2009): 481–89. http://dx.doi.org/10.1038/ijo.2008.277.

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34

Dogra, Shilpa, Joseph Baker, and Chris I. Ardern. "Role of Age at Asthma Diagnosis in the Asthma-Obesity Relationship." Canadian Respiratory Journal 17, no. 5 (2010): e97-e101. http://dx.doi.org/10.1155/2010/679716.

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OBJECTIVE: To determine whether age at asthma diagnosis has an impact on the previously described relationship between asthma and obesity.METHODS: Data were provided from Cycle 1.1 (2000/2001) of the Canadian Community Health Survey, a nationally representative health survey that included 6871 participants (2464 males and 4407 females) with asthma. Body mass index was used to categorize participants as normal weight (18.5 kg/m2to 24.9 kg/m2), overweight (25 kg/m2to 29.9 kg/m2) or obese (30 kg/m2or greater). Multivariate logistic regression analyses were used to estimate the odds of overweight and obesity by self-reported age at asthma diagnosis, after accounting for current age and other covariables.RESULTS: In fully adjusted models, males diagnosed with asthma during adolescence (12 to 20 years of age) were at elevated odds of obesity (OR 1.58; 95% CI 1.03 to 2.43) compared with asthmatic patients diagnosed during childhood (0 to 11 years of age). Women diagnosed with asthma in mid life (21 to 44 years of age) and later life (45 to 64 years of age) were 43% (OR 1.43; 95% CI 1.08 to 1.90) and 56% (OR 1.56; 95% CI 1.00 to 2.44) more likely to be obese than those diagnosed in childhood, respectively.CONCLUSIONS: The impact of age at asthma diagnosis on the asthma-obesity relationship differed between males and females. However, the identification of high-risk groups of asthmatic patients may strengthen primary prevention strategies for obesity and related comorbidities at multiple levels of influence.
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TOKUNAGA, KATSUTO. "Obesity : Advances on diagnosis and treatment.2.Origin and disease state of obesity.3.Hypothalamic obesity." Nihon Naika Gakkai Zasshi 84, no. 8 (1995): 1231–35. http://dx.doi.org/10.2169/naika.84.1231.

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36

MURATA, MITSUNORI. "Obesity : Advances on diagnosis and treatment.2.Origin and disease state of obesity.4.Infantile obesity." Nihon Naika Gakkai Zasshi 84, no. 8 (1995): 1236–40. http://dx.doi.org/10.2169/naika.84.1236.

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37

Kleinendorst, Lotte, Maarten P. G. Massink, Mellody I. Cooiman, Mesut Savas, Olga H. van der Baan-Slootweg, Roosje J. Roelants, Ignace C. M. Janssen, et al. "Genetic obesity: next-generation sequencing results of 1230 patients with obesity." Journal of Medical Genetics 55, no. 9 (July 3, 2018): 578–86. http://dx.doi.org/10.1136/jmedgenet-2018-105315.

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BackgroundObesity is a global and severe health problem. Due to genetic heterogeneity, the identification of genetic defects in patients with obesity can be time consuming and costly. Therefore, we developed a custom diagnostic targeted next-generation sequencing (NGS)-based analysis to simultaneously identify mutations in 52 obesity-related genes. The aim of this study was to assess the diagnostic yield of this approach in patients with suspected genetic obesity.MethodsDNA of 1230 patients with obesity (median BMI adults 43.6 kg/m2; median body mass index-SD children +3.4 SD) was analysed in the genome diagnostics section of the Department of Genetics of the UMC Utrecht (The Netherlands) by targeted analysis of 52 obesity-related genes.ResultsIn 48 patients pathogenic mutations confirming the clinical diagnosis were detected. The majority of these were observed in the MC4R gene (18/48). In an additional 67 patients a probable pathogenic mutation was identified, necessitating further analysis to confirm the clinical relevance.ConclusionsNGS-based gene panel analysis in patients with obesity led to a definitive diagnosis of a genetic obesity disorder in 3.9% of obese probands, and a possible diagnosis in an additional 5.4% of obese probands. The highest yield was achieved in a selected paediatric subgroup, establishing a definitive diagnosis in 12 out of 164 children with severe early onset obesity (7.3%). These findings give a realistic insight in the diagnostic yield of genetic testing for patients with obesity and could help these patients to receive (future) personalised treatment.
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Dr. Minor, Courtney. "1247 Rare hypoventilation syndrome identified in obese adolescent." Sleep 43, Supplement_1 (April 2020): A475. http://dx.doi.org/10.1093/sleep/zsaa056.1241.

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Abstract Introduction With the obesity epidemic persisting in the United States today, it is no surprise that morbidly obese patients with poor sleep are often linked to the diagnoses of obstructive sleep apnea and/or obesity hypoventilation syndrome before testing is even completed. It is important to spread awareness among pediatric sleep providers of the rare hypoventilation syndromes for which most physicians likely have never seen a case. Missed diagnoses may lead to not only increased morbidity and mortality in patients, but under-reporting of cases will further delay crucial research needed to identify the precise pathophysiology that is currently unknown. Report of Case Authors encountered a 16 year-old male who had re-presented to pediatric sleep clinic after a two-year period of being lost to follow-up. The patient’s past medical history included Angelman’s syndrome, idiopathic central adrenal insufficiency, autonomic instability, seizure disorder, aggressive conduct disorder, morbid obesity, and hypoventilation without discrete obstructive sleep apnea as diagnosed on sleep study at age 10. Central hypoventilation was designated as idiopathic. The family had now returned to care due to concern for worsening episodes of bradypnea, bradycardia, and cyanosis requiring aggressive tactile stimulation. Review of records over the interim revealed multiple ER visits for stress dose steroids, as well as hospitalizations for hypothermia, bradycardia, and seizures. Consideration was taken that his multiple diagnoses were consistent with the unified diagnosis of Rapid-onset obesity with hypothalamic dysfunction, hypoventilation, and autonomic dysregulation (ROHHAD), and his hypoventilation thus could be accurately characterized as a syndrome of central hypoventilation rather than idiopathic or related to obesity. In addition, his progressive aggression and severe developmental delay could more accurately be attributed to chronic intermittent hypoxia, thus prompting more urgent need for non-invasive ventilation. Conclusion With approximately 100 reported cases, ROHHAD is a rare, clinically distinct entity from other etiologies of hypoventilation. Improved awareness of the diagnosis would hopefully lead to earlier diagnosis and improved anticipatory guidance able to be provided to the family.
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Santos, Orianny Nágela Batista, Bruna Yhang da Costa Silva, Thais Ariele Lima Chaves, Ozianne Kelly Vidal Oliveira, Jane Karine da Silva, and Samanta Naje Rodrigues de Castro. "Prevalência de risco de sarcopenia e obesidade sarcopênica entre idosos não-institucionalizados do interior do Ceará." out-dez 4, no. 35 (January 20, 2021): 384–91. http://dx.doi.org/10.37111/braspenj.2020354010.

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Introduction: The loss of muscle mass is an expected consequence of aging, but excess adiposity has also been increasing in this age group. Both in isolation compromise health and quality of life, but a greater impact happens when they associate. Objective: To verify the prevalence of risk for sarcopenia and sarcopenic obesity among elderly in the municipality of Morada Nova-CE. Methods: Quantitative, transversal, descriptive and analytical study. Simple Questionnaire to Rapidly Diagnosed Sarcopenia (SARC-F) was applied. After, weight, height, calf circumference (CP) and triceps skinfolds (DCT), bicipital, subscapular and supra iliac were collected for diagnoses of nutritional status, sarcopenia and sarcopenic obesity. In descriptive statistics, mean and standard deviation were calculated. In inferential statistics, Pearson’s correlation tests, ANOVA and chisquare were applied. Results: A total of 121 elderly people aged 60 or over and of both sexes participated in the study. The majority of the elderly people (72%, n = 87) were female, with a mean age of 68.8 ± 6.18 years, ranging from 60 to 86 years. The main findings were: predominance of BMI eutrophy, obesity due to DCT adequacy and percentage of fat (% GC), absence of muscle mass depletion, sarcopenia and sarcopenic obesity. Conclusions: Most elderly people did not have sarcopenia or sarcopenic obesity. No association was found between these two diagnoses, nor between the results of SARC-F and CP. Women were more likely to have sarcopenia than men. We suggest studies that allow the establishment of consensus bridges for the diagnosis of sarcopenia and sarcopenic obesity, as well as studies involving the use of SARC-F for its diffusion and evaluation of its sensitivity for the diagnosis of sarcopenia.
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OIKAWA, SHIN'ICHI. "Obesity :Advancement of treatment and diagnosis.3.Obesity and dieseases related to it.2.Obesity and angiopathy." Nihon Naika Gakkai Zasshi 84, no. 8 (1995): 1246–50. http://dx.doi.org/10.2169/naika.84.1246.

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OUCHI, YASUYOSHI. "Obesity : Advance in diagnosis and treatment.3.Obesity and related diseases.1.Hypertension." Nihon Naika Gakkai Zasshi 84, no. 8 (1995): 1241–45. http://dx.doi.org/10.2169/naika.84.1241.

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KOIZUMI, JUNJI. "Obesity : Advances on treatments and diagnosis.3.Obesity and associated diseases.4.Hyperlipidemia." Nihon Naika Gakkai Zasshi 84, no. 8 (1995): 1256–61. http://dx.doi.org/10.2169/naika.84.1256.

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AKAOKA, IEO. "Obesity : Advances on diagnosis and treatment.3.Obesity and associated diseases.5.Gout." Nihon Naika Gakkai Zasshi 84, no. 8 (1995): 1262–66. http://dx.doi.org/10.2169/naika.84.1262.

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NAKAMURA, TEIJI. "Obesity : Advances on diagnosis and treatment.4.Treatment of obesity.1.Diet therapy." Nihon Naika Gakkai Zasshi 84, no. 8 (1995): 1273–78. http://dx.doi.org/10.2169/naika.84.1273.

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ABE, RYUZO. "Obesity : Advances on diagnosis and treatment.4.Obesity and treatment.2.Exercise therapy." Nihon Naika Gakkai Zasshi 84, no. 8 (1995): 1279–83. http://dx.doi.org/10.2169/naika.84.1279.

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SAKATA, TOSHIIE. "Obesity : Advances on diagnosis and treatment.4.Treatment of obesity.3.Behavior therapy." Nihon Naika Gakkai Zasshi 84, no. 8 (1995): 1284–89. http://dx.doi.org/10.2169/naika.84.1284.

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47

Nimptsch, Katharina, Stefan Konigorski, and Tobias Pischon. "Diagnosis of obesity and use of obesity biomarkers in science and clinical medicine." Metabolism 92 (March 2019): 61–70. http://dx.doi.org/10.1016/j.metabol.2018.12.006.

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48

Bleich, Sara N., Jeanne M. Clark, Suzanne M. Goodwin, Mary Margaret Huizinga, and Jonathan P. Weiner. "Variation in Provider Identification of Obesity by Individual- and Neighborhood-Level Characteristics among an Insured Population." Journal of Obesity 2010 (2010): 1–7. http://dx.doi.org/10.1155/2010/637829.

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Objective. The purpose of this study was to examine whether neighborhood- and individual-level characteristics affect providers' likelihood of providing an obesity diagnosis code in their obese patients' claims.Methods. Logistic regressions were performed with obesity diagnosis code serving as the outcome variable and neighborhood characteristics and member characteristics serving as the independent variables (N= 16,151 obese plan members).Results. Only 7.7 percent of obese plan members had an obesity diagnosis code listed in their claims. Members living in neighborhoods with the largest proportions of Blacks were 29 percent less likely to receive an obesity diagnosis (P<.05). The odds of having an obesity diagnosis code were greater among members who were female, aged 44 or below, hypertensive, dyslipidemic, BMI ≥ 35 kg/m2, had a larger number of provider visits, or who lived in an urban area (allP<.05).Conclusions. Most health care providers do not include an obesity diagnosis code in their obese patients' claims. Rates of obesity identification were strongly related to individual characteristics and somewhat associated with neighborhood characteristics.
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49

Volevodz, N. N., I. A. Eremina, and T. V. Semicheva. "Early diagnosis of Bardet-Biedl syndrome associated with obesity." Obesity and metabolism 5, no. 1 (March 15, 2008): 39–43. http://dx.doi.org/10.14341/omet2008139-43.

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One of the urgent problems of modern health care is the increase in the prevalence of obesity among children and adolescents. Late diagnosis and delayed initiation of treatment lead to serious complications such as hypertension, type 2 diabetes mellitus. At present there are quite rare syndromes associated with obesity: Prader-Willi syndrome, Bardet-Biedl, Alström. Bardet-Biedl syndrome, - a disease characterized by obesity central origin, retinitis pigmentosa, polydactyly, mental retardation, hypogonadism, and renal dysfunction.
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50

Juo, Yen-Yi, Melinda A. Maggard Gibbons, Erik Dutson, Anne Y. Lin, Jane Yanagawa, O. Joe Hines, Guido Eibl, and Yijun Chen. "Obesity Is Associated with Early Onset of Gastrointestinal Cancers in California." Journal of Obesity 2018 (September 19, 2018): 1–6. http://dx.doi.org/10.1155/2018/7014073.

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Abstract:
Background. Although it is well known that obesity is a risk factor for gastrointestinal (GI) cancer, it is not well established if obesity can cause earlier GI cancer onset. Methods. A cross-sectional study examining the linked 2004–2008 California Cancer Registry Patient Discharge Database was performed to evaluate the association between obesity and onset age among four gastrointestinal cancers, including esophageal, gastric, pancreatic, and colorectal cancers. Regression models were constructed to adjust for other carcinogenic factors. Results. The diagnosis of obesity (BMI > 30) was associated with a reduction in diagnosis age across all four cancer types: 3.25 ± 0.53 years for gastric cancer, 4.56 ± 0.18 years for colorectal cancer, 4.73 ± 0.73 years for esophageal cancer, and 5.35 ± 0.72 for pancreatic cancer. The diagnosis of morbid obesity (BMI > 40) was associated with a more pronounced reduction in the age of diagnosis: 5.48 ± 0.96 years for gastric cancer, 7.75 ± 0.30 years for colorectal cancer, 7.67 ± 1.26 years for esophageal cancer, and 8.19 ± 1.25 years for pancreatic cancer. Both morbid obesity and obesity remained strongly associated with earlier cancer diagnosis for all four cancer types even after adjusting for other available cancer risk factors. Conclusions. The diagnosis of obesity, especially morbid obesity, was associated with a significantly earlier gastrointestinal cancer onset in California. Further research with prospective cohort data may be required to establish the causal relationship between obesity and cancer onset age.
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