Author & Editor: Leah Farquharson 

This article discusses the implications of Obesity on COVID-19 risks and infections.


Obesity is a widespread epidemic affecting over two billion people worldwide (Callabero, 2019). An individual with a body mass index (BMI) equal to or greater than 30 is considered to be obese (Caballero, 2019).

There are four heterogeneous subtypes of obesity: metabolically healthy obesity (MHO), metabolically abnormal obesity (MAO), metabolically obese with normal weight (MONW), and sarcopenic obesity (Mayoral et al., 2020).

Multifactorial polygenic obesity, another subtype of obesity, involves several polymorphic genes that can be altered due to environmental factors (Mayoral et al., 2020). There are also many risk factors such as genetic factors, endocrine factors, and socioeconomic factors that may increase the risk of developing obesity (Apovian, 2016) (Caballero, 2019). An individual may need to undergo tests such as methylation studies as well as a physical examination to determine the diagnosis (Caballero, 2019).

If prevention is no longer applicable, treatments include lifestyle modifications such as changes in diet and physical activity habits, pharmacological treatments, and weight loss surgery as a last resort (Wirth et al., 2014). 


Obesity is the result of genetic, environmental, behavioral, social, and cultural factors that result in energy mismatch (Racette, 2003). The main cause of obesity is a sustained period of energy indifference, in which the amount of energy expended is less than the amount of energy consumed through nutrients; behavioral factors result when an individual intakes more than they expend, and excess energy is stored in an individual as fat (Arroyo-Johnson & Mincey, 2016)


Obesity currently affects over 2 billion people worldwide (Caballero, 2019). Obesity data is generally based on body mass index (BMI) which is calculated by dividing an individual’s weight (kg) by meters squared (Caballero, 2019).

A normal BMI range is 18.5-24.9 kg/m^2, overweight BMI range is 25-29.9 kg/m^2, and obese BMI range is greater or equal to 30 kg/m^2 (Caballero, 2019). 

BMI cut-offs and BMI ranges associated with obesity may vary with ethnicity (Mayoral et al., 2020).


  • BMI above or equal to 30 (Caballero, 2019). 

Risk Factors

  • Energy imbalance (Arroyo-Johnson & Mincey, 2016)
  • Genetics: body fat may be strongly associated with genetic factors (Apovian, 2016). Leptin resistance may also be an inherited trait (Apovian, 2016).
  • Polymorphic gene products: one study shows that there are 12 obesity susceptible loci that may have a cumulative effect on obesity measures or even act as obesity predictors (Apovian, 2016). 
  • Gut microbiome: individuals with an “obese microbiome” may perform greater energy harvesting compared to individuals with a “lean microbiome” (Apovian, 2016). 
  • Chronodisruption, which can be induced by sleep disturbances (e.g., shift work, sleep deprivation) or shifting of normal eating time to night hours (Apovian, 2016). 
  • Socioeconomic status (Caballero, 2019).
  • Childhood obesity (Caballero, 2019)
  • Sedentary jobs (Mohammed et al., 2018)
  • Social determinants of health: neighborhood and the built environment, health and health care, social and community context, education, and economic stability (Arroyo-Johnson & Mincey, 2016)


BMI may be determined based on the patient’s height and weight, and then compared to general cut-off values (Mayoral et al., 2020). Individuals with a BMI greater than or equal to 30 kg/m^2 are classified as obese (Apovian, 2016). A Dual-energy X-ray absorptiometry (DEXA) scan may be required to determine adiposity (Mayoral et al., 2020).

The waist-to-hip ratio may be calculated to determine the level of abdominal adiposity, and individuals with a waist-to-hip ratio of 0.5 may be classified as having high abdominal adiposity (Ashwell M et al, 2012, Mayoral et al., 2020). Methods such as BMI calculation and waist to hip ratio may not be as accurate across races, or in more muscular individuals (Mayoral et al., 2020). 

Methylation studies may be required to determine other related comorbidities (Caballero, 2019). 


Clinical features

A basic diagnosis for individuals with obesity is a BMI greater than or equal to 30 kg/m^2 (Mayoral et al., 2020). 

Individuals with MHO have an absence of metabolic disorders (diabetes, hypertension, etc) with a BMI classified as obese, and is generally diagnosed by inflammatory markers and cut-off values (e.g., BMI) (Mayoral et al., 2020). MAO is diagnosed by BMI as well as metabolic status (Mayoral et al., 2020). 

MONW is diagnosed when an individual has a BMI in the normal range or below 25 kg/m^2, but the individual has an obese metabolic status (Mayoral et al., 2020). Individuals with MONW may have increased body fat percentages, decreased body water and skeletal muscle percentages (Mayoral et al., 2020). Some metabolic status commonly seen in individuals with MONW are hyperinsulinemia, atherogenic lipid profile, hypertriglyceridemia, unfavorable adipokine profile, and hypertension (Mayoral et al., 2020). 


Pathological features

Obese individuals may have low circulating Peptide YY levels and high serum leptin concentrations (Apovian, 2016). Obese individuals may also have an adiponectin deficiency which is associated with decreased insulin resistance (Apovian, 2016). 

Individuals with MHO have decreased circulating complement C3, high sensitivity C reactive protein (hsCRP), tumor necrosis factor-alpha (TNF-α), IL-6, and plasminogen activator inhibitor-1 as well as increased adiponectin (Mayoral et al., 2020). 

Individuals with MAO have increased uric acid and increased visceral adiposity (Mayoral et al., 2020). The T45T adiponectin genotype may be associated with the increase of metabolic disorders in patients with MAO (Mayoral et al., 2020).

Individuals with MONW may have an increase in uric acid and alanine transaminase, an increase in circulating hsCRP, cystatin C and leukocytes, and an increased production of triglycerides and glucose (Mayoral et al., 2020). Triglyceride production and glucose levels can be determined by TyG index (Mayoral et al., 2020). The TCGA and CTAT genes, which are disparate haplotypes of the FTO gene, may be involved in individuals with MONW (Mayoral et al., 2020). 

Individuals with sarcopenic obesity may have increased serum hsCRP (Mayoral et al., 2020). The PTPRD, CDK14, and IMMP2L genes may be involved in sarcopenic obesity (Mayoral et al., 2020). 

Treatment protocol

Preventative measures should be taken to avoid obesity if possible. 

To prevent childhood obesity, the following is recommended: exclusive breastfeeding, appropriate weaning foods, healthy dietary practices, and introduction of physical activity as a daily habit at a young age (Caballero, 2019). Some games such as “time to eat” can help kids combat childhood obesity, where they get specific email reminders and must upload pictures of their meals to feed their pets, and are then given a score on their meal (Mohammed et al., 2018).

Treatment plans must be individualized and patient-specific in order to have an effective outcome (Wirth et al., 2014). The patient must also agree to the lifestyle modifications recommended by their healthcare practitioner and based on their goals (Wirth et al., 2014). 

In order to prevent obesity, individuals must intake according to their caloric needs as well as get regular exercise (Wirth et al., 2014). Weight loss is recommended for individuals considered obese (greater or equal to BMI 30 kg/m^2) and may be recommended for overweight individuals (BMI 25-30 kg/m^2) (Wirth et al., 2014). Generally, a calorie deficit of 500-600 calories is recommended and will result in losing around 0.5 kg per week (Wirth et al., 2014).

Individuals may be recommended to eat less high-calorie dense foods and more low-calorie foods (Wirth et al., 2014). This is because lower-calorie foods, such as vegetables, are more filling and have a low energy content, likely due to the high water and fiber content (Wirth et al. 2014). 

The recommendation on the consumption of fast food, alcohol, and sugary drinks is to reduce intake (Wirth et al., 2014). Endurance-based exercise is recommended for at least two hours per week (Wirth et al., 2014). 

Long-term weight stabilization is important

Patients may be recommended to maintain their weights after a period of weight loss (Wirth et al., 2014). It is important in the maintenance stage to maintain physical activity habits (Wirth et al., 2014). 

Weight-reducing drugs, such as orlistat, may be recommended for individuals above BMI 28 kg/m^2 and have other comorbidities (Wirth et al., 2014). Orlistat may also be recommended if the obese individual is not undergoing adequate weight loss (Wirth et al., 2014).

Surgery may be required for some very obese individuals (Wirth et al., 2014). Surgery has been shown to be more effective and efficient than lifestyle changes such as diet and exercise, but is very invasive and therefore not recommended to every obese individual (Wirth et al., 2014). 

Individuals that may be recommended for weight loss surgeries include individuals with BMI of 40 kg.m^2 or higher, individuals with BMI between 35-40 kg/m^2 as well as significant comorbidities, or in special cases, individuals with 30-35 kg/m^2 and type 2 diabetes (Wirth et al., 2014).

Types of surgery include:

  • gastric bypass
  • gastric banding
  • sleeve gastronomy (Wirth et al., 2014).

For obese individuals, metabolic syndromes and other comorbidities must be addressed (Caballero, 2019). Individuals with obesity as well as type 2 diabetes mellitus may be recommended metformin, GLP-1 mimetics, or SGLT2 inhibitors (Wirth et al., 2014). 


Articles on misdiagnosis

Lydecker, J. A., & Grilo, C. M. (2017). The Missed Diagnosis and Misdiagnosis of Pediatric Obesity. Psychotherapy and psychosomatics, 86(3), 173–174. https://doi.org/10.1159/000452501

Patel, A. I., Madsen, K. A., Maselli, J. H., Cabana, M. D., Stafford, R. S., & Hersh, A. L. (2010). Underdiagnosis of pediatric obesity during outpatient preventive care visits. Academic pediatrics, 10(6), 405–409. https://doi.org/10.1016/j.acap.2010.09.004 

Zhu, Q., Huang, B., Li, Q., Huang, L., Shu, W., Xu, L., Deng, Q., Ye, Z., Li, C., & Liu, P. (2020). Body mass index and waist-to-hip ratio misclassification of overweight and obesity in Chinese military personnel. Journal of physiological anthropology, 39(1), 24. https://doi.org/10.1186/s40101-020-00236-8 


Obesity and COVID-19

Recent studies have shown that obese individuals may be at higher risk for COVID-19 infection and prognosis (Popkin et al., 2020). 

  • Obese individuals are 46% more likely to be COVID-19 positive compared to their normal-weight counterparts (Popkin et al., 2020). 
  • COVID-19 positive obese individuals are 113% more likely to require hospital admission in comparison to COVID-19 positive normal-weight individuals (Popkin et al., 2020). 
  • In comparison to COVID-19 positive normal-weight individuals, obese individuals with COVID-19 are 74% more likely to require ICU admission and see an overall increase in deaths by 48% (Popkin et al., 2020). 
  • Obesity may cause a six-fold increase in risk for contraction of severe COVID-19 in adults (Popkin et al., 2020). 

Because obesity is a metabolic disease, individuals with obesity may have an impaired immune response due to hormone and nutrient dysregulation (Popkin et al., 2020). Uncontrolled serum glucose due to hyperglycemia significantly increases COVID-19 mortality by decreasing immune cell efficacy (Popkin et al., 2020). 

Other hormones involved in glucose regulation, such as insulin and leptin, are involved in the body’s inflammatory response and uncontrolled levels may influence the immune response (Popkin et al., 2020). Diets high in fatty acids may result in an inappropriate or insufficient inflammatory response (Popkin et al., 2020). 

Other comorbidities such as sleep apnea may further exacerbate the COVID-19 condition (Popkin et al., 2020). The difficulty of care may be increased for individuals with obesity due to a greater body mass which may lower their care quality (Popkin et al., 2020). A large body mass may also interfere with the prone position and other supportive therapies (Popkin et al., 2020). 


Apovian C. M. (2016). Obesity: definition, comorbidities, causes, and burden. The American journal of managed care, 22(7 Suppl), s176–s185.

Arroyo-Johnson, C., & Mincey, K. D. (2016). Obesity Epidemiology Worldwide. Gastroenterology clinics of North America, 45(4), 571–579. https://doi.org/10.1016/j.gtc.2016.07.012

Caballero B. (2019). Humans against Obesity: Who Will Win?. Advances in nutrition (Bethesda, Md.), 10(suppl_1), S4–S9. https://doi.org/10.1093/advances/nmy055 

Mayoral, L. P., Andrade, G. M., Mayoral, E. P., Huerta, T. H., Canseco, S. P., Rodal Canales, F. J., Cabrera-Fuentes, H. A., Cruz, M. M., Pérez Santiago, A. D., Alpuche, J. J., Zenteno, E., Ruíz, H. M., Cruz, R. M., Jeronimo, J. H., & Perez-Campos, E. (2020). Obesity subtypes, related biomarkers & heterogeneity. The Indian journal of medical research, 151(1), 11–21. https://doi.org/10.4103/ijmr.IJMR_1768_17 

Mohammed, M. S., Sendra, S., Lloret, J., & Bosch, I. (2018). Systems and WBANs for Controlling Obesity. Journal of healthcare engineering, 2018, 1564748. https://doi.org/10.1155/2018/1564748 

Popkin, B. M., Du, S., Green, W. D., Beck, M. A., Algaith, T., Herbst, C. H., Alsukait, R. F., Alluhidan, M., Alazemi, N., & Shekar, M. (2020). Individuals with obesity and COVID-19: A global perspective on the epidemiology and biological relationships. Obesity reviews : an official journal of the International Association for the Study of Obesity, 21(11), e13128. https://doi.org/10.1111/obr.13128 

Wirth, A., Wabitsch, M., & Hauner, H. (2014). The prevention and treatment of obesity. Deutsches Arzteblatt international, 111(42), 705–713. https://doi.org/10.3238/arztebl.2014.0705 

Ashwell M, Gunn P, Gibson S. Waist-to-height ratio is a better screening tool than waist circumference and BMI for adult cardiometabolic risk factors: systematic review and meta-analysis. Obes Rev. 2012 Mar;13(3):275-86. doi: 10.1111/j.1467-789X.2011.00952.x. Epub 2011 Nov 23. PMID: 22106927

Susan B Racette, Susan S Deusinger, Robert H Deusinger, Obesity: Overview of Prevalence, Etiology, and Treatment, Physical Therapy, Volume 83, Issue 3, 1 March 2003, Pages 276–288, https://doi.org/10.1093/ptj/83.3.276




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