Objectives: To determine the prevalence of youth with obesity, asthma, and neurological disease hospitalized with COVID-19. To identify an association between thrifty phenotype, ABO blood type, and severity of COVID-19 symptoms among youth in the United States. To understand the impact of thrifty phenotype and ABO blood type on the rate of COVID-19 within the United States among youth. Methods: There will be a narrative review for the formative research and cross-sectional data analysis of CDC’s COVID-Net data from March 1, 2020-January 31, 2021. Results: There was some evidence of a correlation between the thrifty phenotype and severity of COVID-19 symptoms. However, there is no relationship between ABO blood type and the severity of COVID-19 symptoms.
According to the CDC, the guidelines for the COVID-19 pandemic high-risk groups include older adults, pregnant women, and individuals with co-morbidities1. However, there are many younger healthier individuals that have gotten severe reactions from COVID-19. Often these younger individuals are in their early 20s and 30s and are essential workers that were infected with COVID-19 2 . Some of these younger individuals have experienced lasting symptoms2. Lasting symptoms include fatigue, shortness of breath, brain fog, sleep disorders, fevers, gastrointestinal symptoms, anxiety, and depression, which can persist for months and can range from mild to incapacitating 14 . Also, these lasting symptoms are seen in individuals with obesity, diabetes, and hypertension2.
The thrifty phenotype hypothesis was proposed by David Barker to explain the relationship between poor nutrition in early life or in utero and the development of type 2 diabetes and metabolic syndrome3. This longitudinal study followed a birth cohort over their lifetime by first measuring birth weight and seeing the development of type 2 diabetes in adulthood3. There have been several studies since the initial thrifty phenotype hypothesis, such as the Danish twin study, which further established the original findings3,4 . A nutrient-poor environment in early life causes epigenetic changes to occur that allow for individuals to retain fat5. While fat retention is a beneficial adaptation during famine times, it causes the body to be in a constant state of inflammation5. Common inflammatory diseases include allergies, asthma, obesity, diabetes, hypertension, cardiovascular disease, arthritis, and psoriasis6. These inflammatory diseases are the same comorbidities that categorize high-risk individuals for COVID-19 severe symptoms.
An individual inherits blood type from their parents passing along the ABO gene and Rh factor7. A and B are dominant genes; whereas, O is a recessive gene7. Rh positive gene is dominant; whereas, Rh negative gene is recessive7. ABO blood type could be a significant biomarker in terms of severity or risk to the COVID-19. Unlike the thrifty phenotype, blood type has genes that can be tested. Through a combination of thrifty phenotype measures and blood typing could help researchers and clinicians assess the risk or severity of COVID-19 among hospitalized youth.
A major gap in the existing research is the reason why younger and healthier individuals are being hospitalized or experiencing lasting symptoms. Analyzing inflammatory diseases of youth COVID-19 hospitalization could help researchers understand why low-risk individuals experiencing severe COVID-19 symptoms. Also, the blood type of hospitalized youth could provide researchers an understanding of the level of severity or risk based on blood type.
This study can be used as a hypothesis generating study for further research in either longitudinal cohort or retrospective cohort study.
A youth with the thrifty phenotype and blood type A or B has an increased risk of severe symptoms of COVID-19.
The methodology of testing the hypothesis is a Narrative Review of the ongoing research and data from the CDC’s COVID-Net. Questions 1, 2, and 3 will be answered through statistical analysis on the CDC’s COVID-Net via SPSS version 26. Questions 4 and 5 will be answered through narrative review research. The narrative review research will utilize Scopus, PubMed, CINAHL Complete, and Clincalkey. The key search words will include but not limited to: “Thrifty Phenotype” AND “COVID-19”, “Metabolic Disorder” AND “COVID-19”, “Metabolic Syndrome” AND “COVID-19”, “MetS” AND “COVID-19”, “ABO blood type” AND “COVID-19”, and “Blood type” AND “COVID-19.” The research will focus on more recent literature within the United States that dates back no further than 2010.
The representative sample includes all 2579 youths under the age of 17 years that have been hospitalized in 14 representative states in the United States from March 1, 2020-January 30, 20218. These 14 states are California, Connecticut, Colorado, Georgia, Maryland, Minnesota, New Mexico, New York, Oregon, Tennessee, Iowa, Michigan, Ohio, and Utah8. COVID-19 cases were confirmed through reviewing hospital records, laboratory results, and admissions into the hospital with documented positive SARS-CoV-2 Test8.
The thrifty phenotype cannot be directly tested in the population; however, certain inflammatory diseases are known to contribute to the thrifty phenotype such as obesity, Metabolic Syndrome (MetS), small for gestational age (SGA), intrauterine growth restriction, failure to thrive, and experiences of starvation/nutrition3. There is growing evidence that asthma is included within a similar category of inflammatory diseases that develops in utero9. There are some neurological delays among youths that have experienced thrifty phenotype3. Therefore, obesity, asthma, neurological disease upon admittance into the hospital will be used as the population level signifier for the thrifty phenotype. Obesity for youth 17 years and under is defined as a BMI greater than 30 for their height, weight, and sex8.
Unlike Europe and Asia, a majority of Americans do not know their blood type; therefore, population-level trends would be used as the standard distribution of ABO blood type. Thus, the ABO blood type within a general population will comprise the normal distribution and rates for analysis7.
Figure 2 displays the top six underlying comorbidities of youths hospitalized with COVID-198. Tables 1-3 have the current contingency tables of obesity, asthma, and neurological diseases8. These comorbidities were selected because of the high rate of admissions and are the signifiers of thrifty phenotype within this study. Table 4 is the Chi-Square Hypothesis Test with the null hypothesis and alternative hypothesis of the associated Table 1-3 conditions.
The prevalence for each comorbidity related hospitalization is measured as the proportion of individuals with comorbidity (obesity, asthma, and neurological disease) that are hospitalized with COVID-19 (cases).
Prevalence=Cases/Total Hospitalized Population (1)
Equation 1. This equation will calculate the prevalence of current obesity, asthma, and neurological disease diagnosed in youths that were hospitalized with COVID-19.
Figure 1. Hospitalization Underlying Comorbidities of Youths
Data: from CDC’s COVID-NET
Table 1. Current Contingency Table of Obesity
| Obese | Not Obese | Total | ||
|---|---|---|---|---|
| Youths 0-17 | Observed (Expected) |
370 (525) |
696 (541) |
1066 |
| Adults 18+ | Observed (Expected) |
77259 (77104) |
79221 (79376) |
156480 |
| Total | 77629 | 79917 | 157546 |
Table 2. Current Contingency Table of Asthma
| Asthma | No Asthma | Total | ||
|---|---|---|---|---|
| Youths 0-17 | Observed (Expected) |
259 (242) |
1772 (1789) |
2031 |
| Adults 18+ | Observed (Expected) |
18617 (18634) |
137863 (137850) |
156480 |
| Total | 18876 | 139639 | 158511 |
Table 3. Current Contingency Table of Neurological Disease
| Neurological | No Neurological | Total | ||
|---|---|---|---|---|
| Youths 0-17 | Observed (Expected) |
327 (513) |
2132 (1946) |
2459 |
| Adults 18+ | Observed (Expected) |
32861 (32675) |
123619 (123805) |
156480 |
| Total | 33188 | 125751 | 158939 |
Table 4. Current Chi-Square Hypothesis
| Associated Contingency Table | Chi-Square Hypothesis |
|---|---|
| Table 1. Obesity | H0 = Age is independent upon obesity H1= Age is dependent upon obesity Given α= 0.05 and Critical Value = 3.841 |
| Table 2. Asthma | H0 = Age is independent upon asthma H1= Age is dependent upon asthma Given α= 0.05 and Critical Value = 3.841 |
| Table 3. Neurological Disease | H0 = Age is independent upon ND H1= Age is dependent upon ND Given α= 0.05 and Critical Value = 3.841 |
Table 5. Prevalence of Youths with Comorbidity Hospitalized with COVID-19
| Comorbidity | Prevalence (%) |
|---|---|
| Obesity | 35% |
| Asthma | 15% |
| Neurological Disease | 13% |
Table 6. ABO Blood Type Susceptibility to COVID-19 10,11,12
| Comorbidity | Prevalence (%) |
|---|---|
| Type A | Highest |
| Type B | Medium |
| Type AB | Medium |
| Type O | Lowest |
In Figure 1, data was compiled from the CDC’s COVID-Net from March 1, 2020, until January 31, 20218. The figure displays the number of hospitalizations from the top six medical conditions of youths under 17 years that were hospitalized with COVID-198. Tables 1-3 are current contingency tables of Obesity, Asthma, and Neurological Diseases with the categorical variable of youths and adults hospitalization admittance8.
There were chi-square tests of independence from each current contingency table to determine if these three co-morbidities (Obesity, Asthma, and Neurological Diseases) were influenced by age when individuals were admitted into the hospital for COVID-19. Table 4 has the Chi-Square Test with the null hypothesis and alternative hypothesis. Although cross-sectional studies usually do not conduct Chi-Square Tests of independence, this was done to establish if a further narrative review is needed to understand the potential relationship between ABO blood type with the thrifty phenotype.
In Table 1 obesity current contingency table, the test statistic was 𝚾2=90.78, which is larger than the critical value of 3.841. Therefore, the null hypothesis is rejected. The age of the individual is dependent upon obesity at the time of admittance. The risk of obese youths being hospitalized 0.35. The risk of obese adults being hospitalized 0.49. The relative risk of an obese adult being hospitalized is 1.4 more likely than an obese youth.
In Table 2 asthma current contingency table, the test statistic was 𝚾2=1.37, which is less than the critical value of 3.841. Therefore, there is a failure to reject the null hypothesis. The age of the individual is independent of asthma at the time of admittance. The risk of an asthmatic youth being hospitalized is 0.13. The risk of an asthmatic adult being hospitalized is 0.12. The relative risk of an asthmatic youth being hospitalized is 1.08 more likely than an asthmatic adult.
In Table 3 neurological disease current contingency table, the test statistic was 𝚾2=86.55, which is greater than the critical value of 3.841. Therefore, the null hypothesis is rejected. The age of the individual is dependent upon neurological disease at the time of admittance. The risk of a youth with a neurological disease being hospitalized is 0.13. The risk of an adult with a neurological disease being hospitalized is 0.21. The relative risk of an adult with a neurological disease being hospitalized is 1.62 more likely than youth with neurological disease.
In Table 5, obesity related hospitalization had a prevalence of 35%, asthma related hospitalization had a prevalence of 15%, and neurological disease related hospitalization had a prevalence of 13%. These three factors are related to the thrifty phenotype. However, environmental risk factors and other social determinants of health contribute to the higher percentage of obese youths being hospitalized.
Overall, obesity and neurological diseases are both age-dependent admittance into the hospital for COVID-19. The relative risk of obese adults being hospitalized is 1.4 more likely than obese youths, and the relative risk of adults with a neurological disease being hospitalized is 1.62 more likely than youths with neurological disease. Asthma is age independent upon admittance into the hospital. The relative risk of an asthmatic youth being hospitalized is 1.08 more likely than an asthmatic adult.
The study of the relationship between COVID-19 and blood type is still ongoing. Table 6 was based on multiple studies, which had the susceptibility to COVID-19 based on blood type10,11,12. These studies showed a correlation between COVID-19 and blood type. The most recent study dispels previous studies’ findings of the correlation between blood type and COVID-19. The main findings from Latz et al. were those symptomatic individuals with blood types B and AB that were Rh positive were more likely to test positive for COVID-19; whereas, blood type O were less likely to test positive13. These findings should be further studied to determine if blood type could confer protection or induce risk in individuals13. Also, Latz et al. showed there was no statistically significant connection between blood type and severity of disease or between blood type and the need for hospitalization, positioning for intubation, or inflammatory markers13. Previous studies were concerned with COVID-19 causing systemic inflammation, which leads to morbidity and death10,11,12. However, in the Latz et al. study, inflammation was similar across all infected patients regardless of blood type13. Latz et al. study concluded that researchers or clinicians should not consider ABO blood type as prognostic in patients with COVID-1913.
The prevalence of youths hospitalized with COVID-19 that had obesity was the highest at 35% followed by asthma at 15% and neurological disease at 13%. There should be further research to understand the relationship. Obesity and neurological disease were dependent on age, with adults having a higher relative risk. However, asthma was independent on age, with youths 1.08 more likely to be hospitalized when compared to adults. There was some evidence of a correlation between the thrifty phenotype and severity of COVID-19 symptoms. Although this correlation between thrifty phenotype and severity of COVID-19 symptoms should be further investigated.
As of the most recent study, the research and medical community concluded that ABO blood type should not be considered prognostic in patients with COVID-1913. There is no relationship between ABO blood type and severity of COVID-19 symptoms13. There should be further research to understand why more AB and B blood types test positive for the COVID-19 virus; whereas, fewer O blood types do not test positive for the COVID-19 virus.
This was a cross-sectional study based upon observed population-level data and should not be used as guidance for doctors or healthcare providers. Another limitation of the study is the reliance on secondary research methods and the inability to control for confounders or effect modification such as the social determinants of health and gender. Also, these associations were unable to establish causation, static, and subject to potential misclassification bias.
There should be more research conducted on the connection between acute inflammatory diseases or the thrifty phenotype and COVID-19. There should be further research to understand why more AB and B blood types test positive for the virus; whereas, fewer O blood types do not test positive for the virus.
I would like to take this time to recognize Tracy Flood M.D., Ph.D., Brianna Desharnais, fellow research leads, fellow researchers, and general COVID-19 Data project members. I appreciate the opportunity to work, grow, and learn from these colleagues during such a difficult moment in history.
1. Do I need to take extra precautions against covid-19. https://www.cdc.gov/coronavirus/2019-ncov/need-extra-precautions/index.html. Published March 15, 2021. Accessed March 28, 2021.
2. Maragakis LL. Coronavirus and COVID-19: Younger Adults Are at Risk, Too. Johns Hopkins Medicine. https://www.hopkinsmedicine.org/health/conditions-and-diseases/coronavirus/coronavirus-and-covid-19-younger-adults-are-at-risk-too. Accessed January 12, 2021.
3. Hales CN, Barker DJ. The thrifty phenotype hypothesis. British Medical Bulletin. 2001;60(1):5-20. doi:10.1093/bmb/60.1.5
4. Petersen I, Nielsen MM, Beck-Nielsen H, Christensen K. No evidence of a higher 10 year period prevalence of diabetes among 77,885 twins compared with 215,264 singletons from the Danish birth cohorts 1910–1989. Diabetologia. 2011;54(8):2016-2024. doi:10.1007/s00125-011-2128-2
5. Tiffon C. The Impact of Nutrition and Environmental Epigenetics on Human Health and Disease. International Journal of Molecular Sciences. 2018;19(11):3425. doi:10.3390/ijms19113425
6. Inflammation: Types, symptoms, causes, and treatment. Medical News Today. https://www.medicalnewstoday.com/articles/248423#causes. Accessed January 9, 2021.
7. Blood Type Genetics and Compatibility. Baylor Scott & White Health. https://www.bswhealth.com/patient-tools/blood-center/blood-type-genetics-and-compatibility. Accessed January 9, 2021.
8. COVID-19 Hospitalizations. Centers for Disease Control and Prevention. https://gis.cdc.gov/grasp/COVIDNet/COVID19_5.html. Accessed April 1, 2021.
9. Yang KD. Perinatal Programming of Childhood Asthma. Clinical and Developmental Immunology. 2012;2012:1-2. doi:10.1155/2012/438572
10. Goel R, Bloch EM, Pirenne F, et al. ABO blood group and COVID‐19: a review on behalf of the ISBT COVID‐19 working group. Vox Sanguinis. 2021. doi:10.1111/vox.13076
11. Silva-Filho JC, Melo CG, Oliveira JL. The influence of ABO blood groups on COVID-19 susceptibility and severity: A molecular hypothesis based on carbohydrate-carbohydrate interactions. Medical Hypotheses. 2020;144:110155. doi:10.1016/j.mehy.2020.110155
12. Ray JG, Schull MJ, Vermeulen MJ, Park AL. Association Between ABO and Rh Blood Groups and SARS-CoV-2 Infection or Severe COVID-19 Illness. Annals of Internal Medicine. 2021;174(3):308-315. doi:10.7326/m20-4511
13. Latz CA, DeCarlo C, Boitano L, et al. Blood type and outcomes in patients with COVID-19. Annals of Hematology. 2020;99(9):2113-2118. doi:10.1007/s00277-020-04169-1
14. NIH launches new initiative to study "Long COVID". National Institutes of Health. https://www.nih.gov/about-nih/who-we-are/nih-director/statements/nih-launches-new-initiative-study-long-covid. Published February 23, 2021. Accessed February 30, 2021.