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Diabetes & Obesity International Journal Research Article 10 min read

Obesity: Good and Bad News

Cornelli U*, Belcaro G and Recchia M
* Corresponding author
ISSN: 2574-7770  10.23880/doij-16000246  Received: July 20, 2021  Published: August 16, 2021
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Keywords
Obesity COVID-19 Deaths Ecology Lifestyle Demography
Abstract

Objective: To determine if obesity is a risk factor for COVID-19 death. Methods: The COVID-19 data were taken from the John Hopkins records updated to February 17, 2021. The obesity prevalence in the 187 countries was that of 2016 (the only data available). LEEDELS (life expectancy, ecological, demographic and lifestyle) data for 2016 were also analyzed for all the countries. The countries were analyzed in two sets: the 49 countries whose death registries the WHO consider reliable (49 SCs) and the remaining 138 countries. The correlations between COVID-19 and obesity were calculated using Spearman's ρ. The same was done for the correlations between LEEDELS data and obesity. Results: No correlation was found between obesity and COVID-19 deaths in the 49 SCs (good news) and in the other 138 countries the correlation was positive (bad news). Obesity seems to be independent of the LEEDELS data in the 49 SCs, apart from GDP1 (Gross Domestic Product type 1). A strong correlation was found in the remaining 138 countries with all the variables that reflect prosperity such as GDP, cars, mobile phones and internet connection. Conclusions: Obesity cannot be considered a risk factor for COVID-19 deaths. Since obesity is linked to different LEEDELS data in the 49 SCs and the rest of the world, it is possible that every country shows a different pattern.

Introduction

The link between obesity and COVID-19 has been the object of several investigations since it was shown to be the cause of poor immune response and outcomes during respiratory diseases [1].

Excess fatty mass in obesity is related to metabolic dysregulation and is considered a unifying risk factor for severe COVID-19 infections [2].

One of the first observations of the impact of obesity on the number of COVID-19 deaths was reported in a small study conducted in Seattle where a larger proportion of overweight diabetic patients died in comparison to those with normal weight [3, 4].

In one Chinese region (Shenzhen), it was shown that obese patients were more likely to progress towards severe COVID-19 [5]. Similarly, in Italy, it was found that overweight and obese patients had a greater need for noninvasive ventilation during COVID-19 pneumonia [6].

Subjects with BMI > 35 kg/m2 were highly prevalent among those requiring invasive mechanical ventilation in a study on 124 patients in France [7], and obesity was shown to be a risk factor for the worsening of COVID-19 and ICU admission [8].

After old age, BMI > 40 was the strongest predictor among 5279 patients suffering from COVID-19 in New York City [9], and the same was shown when considering the data from 10929 deaths in the UK [9], where the risk ratio paralleled the increase in obesity class from I to III [10].

It has recently been stated that patients with obesity, diabetes type II, and cardiovascular diseases risk a poor outcome from COVID-19 infection [11].

From all these data, it seems that obesity is a risk factor for COVID-19 severity and deaths. However, the data reported refer to a few developed countries, and the correlation between obesity and COVID-19 might be different in countries characterized by different LEEDELS (life expectancy, ecological, demographic, and lifestyle) data. The aim of this research is to determine if obesity prevalence is correlated with the number of COVID-19 deaths in the world and which LEEDELS data determine this correlation.

Methods

The obesity prevalence data for 2016 were taken from the CIA Factbook [12] since this was the only source where data were available for every country. A total of 187 countries were considered. This figure was determined by the availability of obesity and LEEDELS variables (life expectancy, ecological, demographic and lifestyle) data for 2016, taken from the Atlante Geografico De Agostini [13].

The COVID-19 death figures were taken from the John Hopkins records updated to February 14, 2021 [14]. The data for all countries were considered. However, the evaluation of the relationship between obesity prevalence and obesity was mainly based on the data from the 49 countries considered by the WHO as having reliable death registries (49 selected countries or SC) [15]. These two sets of countries were also separated when considering the LEEDELS variables.

The LEEDELS data consist of:

  • Life expectancy (years)
  • Population density (inhabitants/Km2)
  • Urban population (% of inhabitants in cities)
  • Unemployment (% of people not working compared with the total active population)
  • GDP (Gross Domestic Product in USD)
  • GDP1 (% of GDP from agriculture, animal husbandry, fishing and exploitation of forests)
  • GDP3 (% of GDP from commerce, transport, communication, credit, insurance, and tourism)
  • Education (% of GDP for education)
  • Hospital beds (number of beds/1000 inhabitants)
  • PM 2.5-10 (mg/m3)
  • Cars (number of cars/1000 inhabitants)
  • Mobile phones (number of mobile phones/1000 inhabitants)
  • Internet (number of connections/1000 inhabitants)

Statistical Evaluation

The mean values and standard deviations were calculated for all the LEEDELS data. The statistically significant differences between the 49 SCs and the other countries were calculated using the Mann–Whitney U test. Spearman’s ρ was used for correlation. All the analysis was done using JMP14 Pro software produced by the SAS institute.

Results

On February 17 2021 the total deaths for COVID-19 in the world were about 2,24 million and for the 68.7 % they were recorded in the 49 SC. The average values of the LEEDELS data in the two sets of countries are shown in table 1.

CountryCOVID-19
2021 Feb 17		Obesity
2016		CountryCOVID-19
2021 Feb 17		Obesity
2016		CountryCOVID-19
2021 Feb 17		Obesity
2016
Afganistan0.0935.5Gibuti0.07210.9Oman0.40227.0
Albania0.56721.7Greece0.56113.5Pakistan0.0688.6
Algeria0.07527.4Grenada0.00924.9Palau0.00055.3
Andorra1.39125.6Guatemala0.39021.3Panama1.46922.7
Angola0.0208.2Guinea0.08021.2Papua New
Guinea		0.00121.3
Antigua0.11118.9Guinea Bissau0,0307.7Paraguay0.43820.3
Argentina1.18228.3Guinea
Equatorial		0.1159.5Peru1.43019.7
Armenia1.04620.2Guyana0.2358.0Philippines0.1146.4
Australia0.03929.0Haiti0.02420.2Poland1.07323.1
Austria0.97420.1Honduras0.44822.7Portugal1.50820.8
Azerbaigian0.33619.9Hungary1.41021.4Qatar0.11635.1
Bahamas0.48631.6Iceland0.08824.1Romania0.98222.5
Bahrein0.31229.8India0.12521.9Russia0.54923.1
Bangladesh0.0533.6Indonesia0.1343.9Rwanda0.0205.8
Barbados0.09823.1Iran0.7786.9Saint Kitts and
Nevis		0.00022.9
Belgium1.95122.1Iraq0.39525.8San Marino2.169nr
Belize0.87224.1Ireland0.86330.4Santa Lucia0.12819.7
Benin0.0079.6Israel0.65625.3Saint Vincent
Grenadinas		0.05523.7
Bhutan0.0016.4Italy1.54926.1Samoa0.00022.5
Belarus0.19824.5Jamaica0.14019.9Sao Tomè and
Principe		0.09812.4
Bolivia1.05620.2Japan0.05724.7Saudi Arabia0.20935.4
Bosnia1.28317.9Jordan0.6884.3Senegal0.0578.8
Bostwana0.11218.9Kazakhstan0.18035.5Serbia0.59821.5
Brazil1.18822.1Kenia0.04121.0Seychelles0.11114.0
Brunei0.00714.1Kiribati0.0007.1Sierra Leone0.0138.7
Bulgaria1.34725.0Korea Northnr46.0Singapore0.0076.1
Burkina
Faso		0.0085.6Korea South0.0316.8Slovakia1.13820.5
Burundi0.0005.4Kuwait0.2944.7Slovenia1.81520.2
Cambogia0.0003.9Kyrgyzstan0.24537.9Solomon
Islands		0.00022.5
Camerun0.02311.4Laos0.00016.6Somalia0.0168.3
Canada0.59929.4Latvia0.7535.3South Africa0.89528.3
Cabo Verde0.27011.8Lebanon0.91623.6Spain1.42423.8
Centr. Afr.
Republic		0.0147.5Lesotho0.12232.0Sri Lanka0.0205.2
Chiad0.0106.1Liberia0.02016.6Sudan0.0506.6
Chile1.10228.0Libya0.3329.9Suriname0.31026.4
China0.0046.2Liechtenstein1.42732.5Sweden1.32720.6
Colombia1.21622.3Lithuania1.508nrSwaziland0.507nr
Comoros0.1747.8Luxembourg1.11326.3Switzerland1.19419.5
Congo0.0289.6Madagascar0.01322.6Syria0.04327.8
Congo D.R.0.0106.7Malawi0.0615.3TaJikistan0.01114.2
Core D’Ivore0.00810.3Malaysia0.0335.8Tanzania0.0008.4
Costa Rica0.57325.7Maldives0.17015.6Taywan0.000nr
Croatia1.26324.4Mali0.0218.6Thailand0.00110.0
Czechia1.76526.0Malta0.7018.6Timor Leste0.0003.8
Cuba0.02424.6Marshall0.00028.9Togo0.0128.4
Cyprus0.26221.8Mauritania0.12252.9Tongo0.00048.2
Denmark0.40919.7Mauritius0.00812.7Trinidad
Tobago		0.12418.6
Dominica0.00027.9Mexico1.46910.8Tunisia0.69429.9
Dominican
R.		0.30627.6Micronesia0.00028.9Turkey0.35632.1
Equador0.96019.9Moldova0.90545.8Turkmenistan0.00018.6
Egypt0.11532.0Mongolia0.00118.9Tuvalu0.00051.6
El Salvador0.27624.6Montenegro1.46520.6Uganda0.0095.3
Equatorial
Guinea		0.1158.0Morocco0.25523.3Ukraina0.60724.1
Eritrea0.0015.0Mozambique0.02226.1United Arab
Emirates		0.18731.7
Estonia0.38821.2Myammar0.0627.2United
Kingdom		1.83127.8
Ethiopia0.0254.5Namibia0.175nrUnited States1.53136.2
Fiji0.00230.2Nepal0.07417.2Uruguay0.15927.9
Finland0.13222.2Netherland0.9004.1Uzbekistan0.02016.6
France1.29821.6New Zealand0.00620.4Vanautu0.00025.2
Gabon0.04315.0Nicaragua0.02830.8Venezuela0.04325.6
Gambia0.07310.3Niger0.00923.7Vietnam0.0002.1
Georgia0.90921.7Nigeria0.0108.9Yemen0.02417.1
Germany0.82022.3North
Macedonia		1.45222.4Zambia0.0658.1
Ghana0.0215.5Norway0.11823.1Zimbawe0.10015.5
ρ Spearman
= 0.3729 p<
0.001

Table 1: COVID-19 Deaths x 103 at February 17 2021 and obesity prevalence 2016 in all the 187 world countries with available data

The data concerning the 49 SC are reported in Table 2.

COVID-19 Death x 103 Feb-17Obesity Prevalence 2016CountryCOVID-19 Death x 103 Feb-17Obesity Prevalence 2016
Armenia1.04620.2Kyrgyzstan0.24516.6
Australia0.03929.0Latvia0.75323.6
Austria0.97420.1Lithuania1.05826.3
Bahamas0.48631.6Luxembourg1.11322.6
Belgium1.95122.1Malta0.70128.9
Brazil1.18822.1Mauritius0.00810.8
Brunei0.00714.1Mexico1.46928.9
Canada0.59929.4Netherlands0.90020.4
Chile1.10228.0New Zealand0.00630.6
Croatia1.26324.4Norway0.11823.1
Cuba0.02424.6Republic of Chorea0.0314.7
Czechia1.76526.0Moldova0.90518.9
Denmark0.40919.7Romania0.98222.5
Estonia0.38821.2Saint Vincent & Grenadinas0.05523.7
Finland0.13222.2Slovakia1.13820.5
France1.29821.6Slovenia1.81520.2
Germany0.82022.3Spain1.42423.8
Grenada0.00921.3Sweden1.32720.6
Guatemala0.39021.2Switzerland1.19419.5
Hungary1.41024.1Macedonia1.45222.4
Iceland0.08821.9Trinidad and Tobago0.12418.6
Ireland0.86325.3United Kingdom1.83127.8
Israel0.65626.1USA1.53136.2
Italy1.54919.9Uzbekistan0.02016.6
Japan0.0574.3ρ Spearman-0.1730 p > 0.05

Table 2: COVID-19 Deaths x 103 at February 17 2021 and obesity prevalence 2016 in the 49 SC.

There was no correlation between obesity and risk of death from COVID-19.

We tried to understand this discrepancy on the correlations between the two sets of counties, and the possible determinants of obesity in relation to the LEEDELS were analyzed (Table 3).

Measure49 SC138 countriesPa
Life expectancyYears79.5 ± 3.9768.4 ± 8.89< 0.05
Densityinhabitants/Km²165.7 ± 222.35289 ± 16174,1< 0.05
Urban population% of inhabitants70.7 ± 19.2251.7 ± 22.31< 0.05
Unemployment% of inhabitants8.4 ± 9.9410.3 ± 8.58< 0.05
GDP totalUSD/inhabitant32254 ± 25339.28738 ± 18422.6< 0.05
GDP 1a% of the total8.4± 9.3431.9 ± 24.83< 0.05
GDP 3b% of the total68.3 ± 11.5347.9 ± 21.50< 0.05
Instruction% GDP total5.3 ± 1.714.5 ± 2.53< 0.05
Hospital bedsN/1000 inhabitants4.7 ± 2.282.5 ± 2.31< 0.05
PM 2.5-10.0mg/m³29.1 ± 16.1248.6 ± 39.44< 0.05
CarsN/1000 inhabitants361.7 ± 187.9898.3. ± 160.34< 0.05
Cell PhoneN/1000 inhabitants1173.2 ± 239.51992.4 ± 429.89< 0.05
InternetConnections/1000 inhabitants722.2 ± 198.77335.4 ± 252.2< 0.05
Covid-19 deaths1537839700843
Total populationN x 10³14364865792701

Table 3: LEEDELS variables in the 49 SCs compared to the other 138 countries with available data: mean values ± SD. a = Mann–Whit

All the variables in the 49 SC were significantly different from those in the remaining 138 countries.

As regards the LEEDELS data, obesity showed a statistically significant negative correlation only with GDP1 (Table 4), which measures GDP from agriculture, animal husbandry, fishing, and forest exploitation. Total GDP and GDP3 (GDP from transport, communications, credit, insurance and tourism) were not correlated, and neither were all the other LEEDELS variables.

Measure49 SC by WHO Spearman ρp138 countries Spearman ρp
Life expectancyYears0.0059>0.050.1618>0.05
Densityinhabitants/Km²0.2114>0.05-0.0482>0.05
Urban population% of inhabitants-0.0270>0.050.5793<0.001
Unemployment% in relation to the workers0.1235>0.050.2635<0.001
GP totalUSD/inhabitant0.0054>0.050.5789<0.001
GP 1a% of the total-0.3055<0.05-0.6195<0.001
GP 3b% of the total0.0864>0.050.5580<0.001
Instruction% GDP total-0.2384>0.050.2036<0.001
Hospital bedsN/1000 inhabitants0.2577>0.050.3303<0.001
PM 2.5-10.0mg/m³0.0968>0.05-0.2309<0.001
CarsN/1000 inhabitants0.1751>0.050.5419<0.001
Cell PhoneN/1000 inhabitants0.2469>0.050.3342<0.001
InternetConnections/1000 inhabitants0.1401>0.050.5364<0.001

Table 4: Correlation between LEEDELS data and obesity in the 49 SCs and in the remaining 138 countries in the world with availabl

Table 4: Correlation between LEEDELS data and obesity in the 49 SCs and in the remaining 138 countries in the world with available data. a = % of GDP from agriculture, animal husbandry, fishing, and forest exploitation. b = % of GDP from transport, communications, credit, insurance and tourism. The patterns of the two sets of countries are extremely different. In particular, apart from life expectancy and population density, all the variables were directly or indirectly correlated with obesity in the group of 138 countries.

The variables which may be considered typical of developed countries were all correlated: in a negative way for GPD1, and PM; and in a positive way for all the others (education, hospital beds, cars, mobile phones, and internet). Urban population and unemployment were also correlated.

Discussion

The main limitation of this study is that the data relating to obesity prevalence are those of 2016, while the number of COVID-19 deaths refers to 2021. In a previous study [16], it was shown that there is a strong correlation between years for any disease. Therefore, if we compare data from 2016 with data from 2021 we still obtain reliable information.

The results of this analysis indicate that obesity cannot be considered a risk factor for COVID-19 deaths in the developed countries, despite the extensive literature reporting a correlation between obesity and COVID-19 [2-

11]. A part of the studies done in China [5], all the other was conducted in developed countries (e.g. USA, Italy, France).

However, we should consider that obesity prevalence does not consider the severity of the condition. It has been shown that the risk of COVID-19 death doubles when BMI is > 40 kg/m2 (class III) and due to aging. [10]. According to the many studies carried out on COVID-19, severe disease and aging are risk factors, but this falls a long way short of considering overweight young or adult people < 30 kg/m2 at risk. Furthermore, obesity is an important medical condition to take care of, and plenty of resources are devoted to improving all aspects of this condition. All these efforts may also help counteract COVID-19 infection, giving obese people the same possibility to tackle the viral spread as the healthier population has. Despite the difference in the reliability of the death registries in the two sets of countries (49 SC and other 138 countries), the correlation with obesity was different. It was shown that obesity has a very different pattern in relation to the LEEDELS variables in the more developed countries (49 SC) than it has in the other 138 countries.

In the 49 SC, only GPD 1 was negatively correlated with obesity, while in the rest of the world all the variables reflecting wealth were significantly involved. Increase in obesity was directly correlated with number of cars, internet connections, and mobile phones, which all reflect the prosperity of a country, and the negative correlation with PM can also be considered to be connected with the tentative to reach prosperity increasing the industrial activity (which is bound to pollution).

There is a sort of “ceiling effect” in the 49 SC: the relatively high LEEDELS values (Table 3) do not allow obesity to be determined, while in developing countries, the same variables have much greater weight in determining obesity prevalence.

The different LEEDELS/obesity and the obesity/ COVID-19 patterns indicates that the number of deaths from the infection in the 49 SC is relatively independent of the LEEDLEs data. This was confirmed by the lack of correlation between the number of COVID-19 deaths and any of the LEEDELS values apart from GDP1.

However, this is not the first time the conclusions are not in line with common findings once the bigger picture is considered. For instance, considering the diabetes type II values in the 49 SC no correlation was found [17, 18, 19, 20], at the opposite a protection was shown (not statistically significant) that become statistically significant considering all the world countries (data not reported but available). However, we cannot rule out that each country may have peculiarities, and therefore specific outcomes cannot be considered general rules even if they are found in important or large countries.

Conclusion

On the basis of the analysis of obesity prevalence in 187 an increase in the number of deaths due to COVID-19 seems to be evident (the bad news), while in the 49 SC more developed countries this correlation is absent (the good news). Obesity shows different patterns with different LEEDELS variables. In the poorest countries, the variables linked to prosperity are positively correlated with the condition. However, studies carried out in one country, no matter how important it is in the world, cannot be considered valid for all other countries.

Funding

This research received no financial support.

Author Contributions

UC conceived the trial; GB and UC retrieved all the data; MR carried out the statistical evaluation; UC wrote the text.

Conflict of Interest

There are no conflicts of interest.

References

  1. Sheridan PA, Paich HA, Handy J, Karlsson EA, Hudgens MG, et al. (2012) Obesity is associated with impaired immune response to influenza vaccination in humans. Int J Obes 36(8): 1072-1077.
  2. Sattar N, McInnes IB, McMurray JJV (2020) Obesity a risk factor for severe COVID-19 infection: Multiple potential mechanism. Circulation 142(1): 4-6.
  3. Bhatraju PK, Ghassemieh BJ, Nichols M, Kim R, Jerome KR, et al. (2020) Covid-19 in critically ill patents in the Seattle region-Case Series. NEJM 382(21): 2012-2022.
  4. Stefan N, Birkenfeld AL, Sculze MB, Ludwig DS (2020) Obesity and impaired metabolic health in patients with COVID-19. Nature Rev Endocrinol 16(7): 341-342.
  5. Cai Q, Chen F, Wang T, Luo F, Liu X, et al. (2020) Obesity and COVID-19 severity in a designated hospital in Shenzhen, China. Diabetes care 43(7): 1392-1398.
  6. Busetto L, Bettini S, Fabris R, Serra R, Pra CD, et al. (2020) Obesity and COVID-19: an Italian snapshot. Obesity 28(9): 1600-1605.
  7. Simonnet A, Chetboun M, Poyssi J, Raverdy V, Noulette J, et al. (2020) High prevalence of obesity in severe acute respiratory syndrome coronavirus-2 (SARS Cov- 2) requiring invasive mechanical ventilation. Obesity 28(7): 1195-1199.
  8. Caussy C, Pattou F, Wallet F, Simon C, Chalopin S, et al. (2020) Prevalence of obesity among adult inpatients with COVID-19 in France. Lancet Diabetes Endocrinol 8(7): 562-564.
  9. Petrilli CM, Jones SA, Yang J, Rajagopalan H, O’Donnell L, et al. (2020) Factors associated with hospital admission and critical illnesses among 5279 people with coronavirus disease 2019 in New York City: prospective cohort study. BMJ 369: m1966.
  10. Williamson EJ, Walker AJ, Bhaskaran K (2020) Factors associated with COVID-19-relates death using OpenSAFELY. Nature 584(7821): 430-436.
  11. O’Heam M, Liu J, Cudhea F, Micha R, Mozaffarian D (2021) Coronavirus disease 2019 hospitalizations attributable to cardiometabolic conditions in the United States: a comparative risk assessment analysis. JAHA 10(5): e0119259.
  12. The CIA World Factbook 2020-2021. Skyhorse Publishing, NewYork.
  13. Atlante Geografico De Agostini 2017, Geo4Map srl, Novara, Italy.
  14. JHCHS Survey Results, John Hopkins Coronavirus Resource Center.
  15. WHO (2018) Global Health Estimated. World Health Organization.
  16. Cornelli U, Belcaro G, Martino R (2020) Diabetes Type II in correlation to non-cancer and cancer diseases in 49 WHO selected countries (SCs) on the basis of Age Standardized Death Rate (ASDR). J Med Clin Res 4(3): 1-11.
  17. Cornelli U, Belcaro G, Cesarone MR, Martin R, Roberto C (2012) Analysis of COVID-19 ; where do we stand? J Preg Wom Heal Car 1(1): JPWHC.MS.ID.000504.
  18. Holan N, Knigton P, Kar Partha, Jackie O’Keefe, Matt Curley, et al. (2020) Risk factors for COVID-19-related mortality with type 1 and type 2 diabetes in England: a population-based cohort study. Lancet Diab Endocrinol 8(10): 823-833.
  19. Hayden MR (2020) Endothelial activation and dysfunction in metabolic syndrome, type 2 diabetes and coronavirus disease. J Int Med Res 48 (7): 300060520939746.
  20. Zhu L, She ZG, Qin JJ, Cheng Xu, Zhang XJ, et al. (2020) Association of blood glucose and outcomes in patients with COVID-19 and pre-existing Types 2 diabetes. Metab Cella 31(6): 1068-1077.e3.
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@article{cornelli2021,
  title   = {Obesity: Good and Bad News},
  author  = {Cornelli U, Belcaro G and Recchia M},
  journal = {Diabetes & Obesity International Journal},
  year    = {2021},
  volume  = {6},
  number  = {3},
  doi     = {10.23880/doij-16000246}
}
Cornelli U, Belcaro G and Recchia M (2021). Obesity: Good and Bad News. Diabetes & Obesity International Journal, 6(3). https://doi.org/10.23880/doij-16000246
TY  - JOUR
TI  - Obesity: Good and Bad News
AU  - Cornelli U, Belcaro G and Recchia M
JO  - Diabetes & Obesity International Journal
PY  - 2021
VL  - 6
IS  - 3
DO  - 10.23880/doij-16000246
ER  -