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Telo GH, Friedrich Fontoura L, Avila GO, Gheno V, Bertuzzo Brum MA, Teixeira JB, Erthal IN, Alessi J, Telo GH. Obesity bias: How can this underestimated problem affect medical decisions in healthcare? A systematic review. Obes Rev 2024; 25:e13696. [PMID: 38272850 DOI: 10.1111/obr.13696] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/04/2023] [Revised: 11/24/2023] [Accepted: 12/06/2023] [Indexed: 01/27/2024]
Abstract
INTRODUCTION Obesity is often labeled as a physical characteristic of a patient rather than a disease and it is subject to obesity bias by health providers, which harms the equality of healthcare in this population. OBJECTIVE Identifying whether obesity bias interferes in clinical decision-making in the treatment of patients with obesity. METHODS A systematic review of observational studies published between 1993 and 2023 in MEDLINE, Embase, and Cochrane Library on obesity bias and therapeutic decisions was carried out. The last search was conducted on June 30, 2023. The main outcome was the difference between clinical decisions in the treatment of individuals with and without obesity. The Newcastle-Ottawa scale for observational studies was used to assess for quality. After the selection process, articles were presented in narrative and thematic synthesis categories to better organize the descriptive analysis. RESULTS Of the 2546 records identified, 13 were included. The findings showed fewer screening exams for cancer in patients with obesity, who were also susceptible to less frequent pharmacological treatment intensification in the management of diabetes. Women with obesity received fewer pelvic exams and evidence of diminished visual contact and physician confidence in treatment adherence was reported. Some studies found no disparities in treatment for abdominal pain and tension headaches between patients presented with and without obesity. CONCLUSION The presence of obesity bias has negative effects on medical decision-making and on the quality of care provided to patients with obesity. These findings reveal the urgent necessity for reflection and development of strategies to mitigate its adverse impacts. (The protocol was registered with the international prospective register of systematic reviews, PROSPERO, under the number CRD42022307567).
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Affiliation(s)
- Guilherme Heiden Telo
- Medicine and Health Sciences Graduate Program, Pontifícia Universidade Católica do Rio Grande do Sul, Porto Alegre, Brazil
- Cardiology Division, Hospital de Clínicas de Porto Alegre, Porto Alegre, Brazil
| | - Lucas Friedrich Fontoura
- Medicine and Health Sciences Graduate Program, Pontifícia Universidade Católica do Rio Grande do Sul, Porto Alegre, Brazil
| | - Georgia Oliveira Avila
- School of Medicine, Pontifícia Universidade Católica do Rio Grande do Sul, Porto Alegre, Brazil
| | - Vicenzo Gheno
- School of Medicine, Pontifícia Universidade Católica do Rio Grande do Sul, Porto Alegre, Brazil
| | | | - Julia Belato Teixeira
- School of Medicine, Pontifícia Universidade Católica do Rio Grande do Sul, Porto Alegre, Brazil
| | - Isadora Nunes Erthal
- School of Medicine, Pontifícia Universidade Católica do Rio Grande do Sul, Porto Alegre, Brazil
| | - Janine Alessi
- Medicine and Health Sciences Graduate Program, Pontifícia Universidade Católica do Rio Grande do Sul, Porto Alegre, Brazil
- Endocrinology Division, Hospital São Lucas da Pontifícia Universidade Católica do Rio Grande do Sul, Porto Alegre, Brazil
- General Internal Medicine Division, Hospital São Lucas da Pontifícia Universidade Católica do Rio Grande do Sul, Porto Alegre, Brazil
| | - Gabriela Heiden Telo
- Medicine and Health Sciences Graduate Program, Pontifícia Universidade Católica do Rio Grande do Sul, Porto Alegre, Brazil
- School of Medicine, Pontifícia Universidade Católica do Rio Grande do Sul, Porto Alegre, Brazil
- General Internal Medicine Division, Hospital São Lucas da Pontifícia Universidade Católica do Rio Grande do Sul, Porto Alegre, Brazil
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Hu X, Li Y, Wu J, Zhang H, Huang Y, Tan X, Wen L, Zhou X, Xie P, Olasunkanmi OI, Zhou J, Sun Z, Liu M, Zhang G, Yang J, Zheng P, Xie P. Changes of gut microbiota reflect the severity of major depressive disorder: a cross sectional study. Transl Psychiatry 2023; 13:137. [PMID: 37117202 PMCID: PMC10147706 DOI: 10.1038/s41398-023-02436-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/30/2022] [Revised: 04/15/2023] [Accepted: 04/18/2023] [Indexed: 04/30/2023] Open
Abstract
Disturbed gut microbiota is a potential factor in the pathogenesis of major depressive disorder (MDD), yet whether gut microbiota dysbiosis is associated with the severity of MDD remains unclear. Here, we performed shotgun metagenomic profiling of cross-sectional stool samples from MDD (n = 138) and healthy controls (n = 155). The patients with MDD were divided into three groups according to Hamilton Depression Rating Scale 17 (HAMD-17), including mild (n = 24), moderate (n = 72) and severe (n = 42) individuals, respectively. We found that microbial diversity was closely related to the severity of MDD. Compared to HCs, the abundance of Bacteroides was significantly increased in both moderate and severe MDD, while Ruminococcus and Eubacterium depleted mainly in severe group. In addition, we identified 99 bacteria species specific to severity of depression. Furthermore, a panel of microbiota marker comprising of 37 bacteria species enabled to effectively distinguish MDD patients with different severity. Together, we identified different perturbation patterns of gut microbiota in mild-to-severe depression, and identified potential diagnostic and therapeutic targets.
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Affiliation(s)
- Xi Hu
- Department of Neurology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
- NHC Key Laboratory of Diagnosis and Treatment on Brain Functional Diseases, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Yifan Li
- Department of Neurology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
- NHC Key Laboratory of Diagnosis and Treatment on Brain Functional Diseases, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Jing Wu
- Department of Neurology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
- NHC Key Laboratory of Diagnosis and Treatment on Brain Functional Diseases, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Hanping Zhang
- Department of Neurology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
- NHC Key Laboratory of Diagnosis and Treatment on Brain Functional Diseases, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Yu Huang
- Department of Neurology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
- NHC Key Laboratory of Diagnosis and Treatment on Brain Functional Diseases, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Xunmin Tan
- Department of Neurology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
- NHC Key Laboratory of Diagnosis and Treatment on Brain Functional Diseases, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Lu Wen
- Department of Neurology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
- NHC Key Laboratory of Diagnosis and Treatment on Brain Functional Diseases, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Xingyu Zhou
- Department of Neurology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
- NHC Key Laboratory of Diagnosis and Treatment on Brain Functional Diseases, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Peijun Xie
- Department of Neurology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
- NHC Key Laboratory of Diagnosis and Treatment on Brain Functional Diseases, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | | | - Jingjing Zhou
- Beijing Key Laboratory of Mental Disorders, National Clinical Research Center for Mental Disorders & National Center for Mental Disorders, Beijing Anding Hospital, Capital Medical University, Beijing, China
- Advanced Innovation Center for Human Brain Protection, Capital Medical University, Beijing, China
| | - Zuoli Sun
- Beijing Key Laboratory of Mental Disorders, National Clinical Research Center for Mental Disorders & National Center for Mental Disorders, Beijing Anding Hospital, Capital Medical University, Beijing, China
- Advanced Innovation Center for Human Brain Protection, Capital Medical University, Beijing, China
| | - Min Liu
- Beijing Key Laboratory of Mental Disorders, National Clinical Research Center for Mental Disorders & National Center for Mental Disorders, Beijing Anding Hospital, Capital Medical University, Beijing, China
- Advanced Innovation Center for Human Brain Protection, Capital Medical University, Beijing, China
| | - Guofu Zhang
- Beijing Key Laboratory of Mental Disorders, National Clinical Research Center for Mental Disorders & National Center for Mental Disorders, Beijing Anding Hospital, Capital Medical University, Beijing, China
- Advanced Innovation Center for Human Brain Protection, Capital Medical University, Beijing, China
| | - Jian Yang
- Beijing Key Laboratory of Mental Disorders, National Clinical Research Center for Mental Disorders & National Center for Mental Disorders, Beijing Anding Hospital, Capital Medical University, Beijing, China.
- Advanced Innovation Center for Human Brain Protection, Capital Medical University, Beijing, China.
| | - Peng Zheng
- Department of Neurology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China.
- NHC Key Laboratory of Diagnosis and Treatment on Brain Functional Diseases, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China.
| | - Peng Xie
- Department of Neurology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China.
- NHC Key Laboratory of Diagnosis and Treatment on Brain Functional Diseases, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China.
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Puzhko S, Schuster T, Barnett TA, Renoux C, Munro K, Barber D, Bartlett G. Difference in patterns of prescribing antidepressants known for their weight-modulating and cardiovascular side effects for patients with obesity compared to patients with normal weight. J Affect Disord 2021; 295:1310-1318. [PMID: 34706445 DOI: 10.1016/j.jad.2021.08.018] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/27/2021] [Revised: 08/03/2021] [Accepted: 08/06/2021] [Indexed: 11/17/2022]
Abstract
BACKGROUND Patients with depression and comorbid obesity may be more prone to weight modulating and cardiovascular side effects of selected antidepressants (AD). It is important to ascertain whether these AD prescriptions differ by patient weight status. METHODS Canadian Primary Care Sentinel Surveillance Network (CPCSSN) electronic medical records were used. Participants were adults with depression prescribed an AD in 2000-2016, with weight categories established before the first prescription. Logistic regression and mixed effects models were applied to examine associations between obesity and AD prescribing, adjusted for sex, age, and comorbidities. Machine learning algorithm random forest (RF) was used to evaluate the importance of weight in predicting prescribing patterns. RESULTS Of 26,571 participants, 72.4% were women, mean age was 38.9 years (standard deviation (SD)=14.2) and mean BMI 27.0 kg/m2 (SD = 6.5); 9.5% had ≥ 1 comorbidity. Patients with obesity, compared to normal weight patients, were more likely to receive bupropion (adjusted odds ratio (aOR) 1.24, 95%CI: 1.09,1.42), fluoxetine (aOR 1.14, 95%CI: 0.97,1.34), and amitriptyline (aOR 1.13, 95%CI: 0.93,1.36), and less likely to receive mirtazapine (aOR 0.55, 95%CI: 0.44,0.68) and escitalopram (aOR 0.88, 95%CI: 0.80, 0.97). RF analysis showed that weight was among the most important predictors of prescribing patterns, equivalent to age and more important than sex. CONCLUSIONS AD prescribing patterns for patients with obesity appear to be different for selected AD types, including AD known for their weight-modulating and cardiovascular side effects. Longitudinal studies are needed to examine whether these prescribing patterns are associated with significant health outcomes.
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Affiliation(s)
- S Puzhko
- Department of Family Medicine, Faculty of Medicine, McGill University, 5858 Chemin de la Côte-des-Neiges, suite 300, H3S 1Z1, Montréal, Qc, Canada.
| | - T Schuster
- Department of Family Medicine, Faculty of Medicine, McGill University, 5858 Chemin de la Côte-des-Neiges, suite 300, H3S 1Z1, Montréal, Qc, Canada.
| | - T A Barnett
- Department of Family Medicine, Faculty of Medicine, McGill University, 5858 Chemin de la Côte-des-Neiges, suite 300, H3S 1Z1, Montréal, Qc, Canada.
| | - C Renoux
- Department of Neurology & Neurosurgery, McGill University, 3801 Rue Université, H3A 2B4, Montréal, Qc, Canada; Department of Epidemiology and Biostatistics, McGill University, Montréal, Qc, Canada; Centre for Clinical Epidemiology, Lady Davis Institute for Medical Research, Jewish General Hospital, Montréal, Qc, Canada.
| | - K Munro
- Department of Family Medicine, Faculty of Medicine, McGill University, 5858 Chemin de la Côte-des-Neiges, suite 300, H3S 1Z1, Montréal, Qc, Canada.
| | - D Barber
- Department of Family Medicine, Faculty of Medicine, Queen's University, 220 Bagot Street, K7L 3G2, Kingston, On, Canada.
| | - G Bartlett
- School of Medicine, University of Missouri, 7 Hospital Drive, Medical Sciences Building, Suite MA306N, 65211, Columbia, MO, United States.
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Peluzio MDCG, Dias MDME, Martinez JA, Milagro FI. Kefir and Intestinal Microbiota Modulation: Implications in Human Health. Front Nutr 2021; 8:638740. [PMID: 33693024 PMCID: PMC7938729 DOI: 10.3389/fnut.2021.638740] [Citation(s) in RCA: 24] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2020] [Accepted: 01/26/2021] [Indexed: 12/17/2022] Open
Abstract
In the last decades changes in the pattern of health and disease in Latin America and in the world has been observed, with an increase in cases of chronic non-communicable diseases. Changes in intestinal microbiota composition can contribute to the development of these diseases and be useful in their management. In this context, the consumption of fermented foods with probiotic properties, such as kefir, stands out due to its gut microbiota-modulating capacity. There is an increasing interest in the commercial use of kefir since it can be marketed as a natural beverage containing health-promoting bacteria and has been gaining international popularity in Latin America. Also the consumption of these drinks in Latin America seems to be even more relevant, given the socioeconomic situation of this population, which highlights the need for disease prevention at the expense of its treatment. In this narrative review, we discuss how kefir may work against obesity, diabetes mellitus, liver disease, cardiovascular disorders, immunity, and neurological disorders. Peptides, bioactive compounds and strains occurring in kefir, can modulate gut microbiota composition, low-grade inflammation and intestinal permeability, which consequently may generate health benefits. Kefir can also impact on the regulation of organism homeostasis, with a direct effect on the gut-brain axis, being a possible strategy for the prevention of metabolic diseases. Further studies are needed to standardize these bioactive compounds and better elucidate the mechanisms linking kefir and intestinal microbiota modulation. However, due to the benefits reported, low cost and ease of preparation, kefir seems to be a promising approach to prevent and manage microbiota-related diseases in Latin America and the rest of the world.
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Affiliation(s)
| | | | - J Alfredo Martinez
- Department of Nutrition, Food Science and Physiology, Center for Nutrition Research, University of Navarra, Pamplona, Spain.,Centro de Investigación Biomédica en Red de la Fisiopatología de la Obesidad y Nutrición (CIBERobn), Carlos III Health Institute, Madrid, Spain.,Instituto de Investigación Sanitaria de Navarra, Navarra Institute for Health Research, Pamplona, Spain.,Madrid Institute of Advanced Studies (IMDEA Food), Food Institute, Madrid, Spain
| | - Fermín I Milagro
- Department of Nutrition, Food Science and Physiology, Center for Nutrition Research, University of Navarra, Pamplona, Spain.,Centro de Investigación Biomédica en Red de la Fisiopatología de la Obesidad y Nutrición (CIBERobn), Carlos III Health Institute, Madrid, Spain.,Instituto de Investigación Sanitaria de Navarra, Navarra Institute for Health Research, Pamplona, Spain
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