Variation in the rate of deaths due to COVID-19 has been detected in different countries (
; accessed on May 30, 2022). Since COVID-19 is an infectious disease, presumably more cases and higher death rates should be found in densely populated countries. The population density varies between 36/km² to 92/km² in the USA, Spain and Greece, whereas, in India, it is 428/km https://covid19.who.int/table ( 2 ; accessed on May 18, 2022). Therefore, in principle, India should have had a higher number of COVID-19 cases and deaths. However, in reality, these western countries have shown five to eight times higher death rates compared to India ( https//www.worldpopulationreview.com ; accessed on May 30, 2022). Therefore, identifying factors that could explain such differences remain important. https://covid19.who.int/table
Existing comorbid conditions and their risk related to COVID-19 severity and death have been well established
. Host genetic polymorphisms are also associated with severe symptoms and deaths from COVID-19 1 . Plant-based foods, pescatarian and Mediterranean diets and low consumption of red and processed meat have been shown to lower the susceptibility to moderate-to-severe COVID-19 disease 2 3 , . Reported diets supplemented with vitamins and zinc may reduce COVID-19 severity 4 . On the other hand, higher consumption of a western diet was found to be associated with increased COVID-19 risk and severity 5 4 , . 6
Gene expression profiles of SARS-CoV-2-infected individuals have been used to identify susceptibility, symptoms, severity, disease pathways and drugs for COVID-19 patients
. In this study, we aimed to identify specific foods, diets, metabolites or nutrients associated with the observed differences in severity and death rates due to COVID-19 in the western and Indian populations using available transcriptome data and nutrigenome approaches. 7–10 Material &Methods
Selection of datasets: RNA sequencing (RNA-Seq) data from COVID-19 patients’ blood were obtained from public domain through Gene Expression Omnibus (GEO; ) and PubMed ( https://www.ncbi.nlm.nih.gov/geo/ ) databases and were grouped into two categories based on the country-specific death rates (death/100,000 people) from COVID-19. The USA, Greece and Spain were the selected countries with high death rates representing western samples, whereas data from India was used for a country with a low COVID-19 death rate. For the USA, 29 severe COVID-19 samples and nine healthy controls were collected from Bioproject: PRJNA634489 https://pubmed.ncbi.nlm.nih.gov and GSE189990 7 . For Greece, the GSE152641 dataset of 62 cases and 24 controls were included 11 , and for Spain, the GSE180594 dataset (18 cases and 7 controls) 12 was used. Two datasets were chosen for India; the south Indian (Karnataka) dataset (GSE196822) of 49 expression profiles of four distinct COVID-19 conditions including asymptomatic (n=8), mild (n=9), moderate (n=10), severe (n=7) and control (n=9) and the north Indian (Haryana) dataset (GSE181439) had asymptomatic (n=9) and severe (n=9) cases. 13
Obtaining differentially expressed genes (DEGs) from RNA-Seq data: The GO2R tool ( ; accessed on May 16, 2022) was used for differentially expressed gene (DEG) profiles of cases https://www.ncbi.nlm.nih.gov/geo/geo2r vs. control for GSE180594 and for the south Indian dataset GSE196822. The Limma-Voom package version 4.2 (Bioconductor, Victoria, Australia) was used in R Studio to analyze asymptomatic 14 vs. control, mild vs. control, moderate vs. control and severe vs. control. Other DEGs were obtained from the corresponding publications 7 , 11 , 12 , . In all cases, the fold change (Log 15 2) >1 was considered upregulated and <1 was considered downregulated at adjusted P<0.05.
DEG analysis for metabolites and pathways: A modified method of our previously established DEG analysis using only the upregulated gene sets was applied which gave us >90 per cent accuracy . DEGs of each country sample were separately analyzed using Enrichr (release March 29, 2021; New York, USA) 8–10 . However, two USA samples and two Indian datasets were clubbed to make combined USA and Indian samples, respectively. Since for the other countries, only one sample was used, the two Indian samples were combined to make one combined sample for India and the two USA samples were combined to make one combined USA samples for our analysis. All western country samples were also combined for an integrated and comparative analysis with the combined Indian samples. Some analyses with the downregulated genes were also considered to cross verify the reliability of data sets. For example, while using the ‘COVID-19 Related Gene Sets 2021’ database in the Enrichr for upregulated gene sets, it required first to be enriched by giving n number of genes upregulated by SARS-CoV-2 infection. In our cross verification of data set reliability, it was found that all these DEGs were associated with SARS-CoV-2 infection and influenza. Therefore, we proceeded with our DEGs for further analysis. 16
In Enrichr, the ‘COVID-19 Related Gene Sets 2021’ database was first used to validate if our applied gene set was up or downregulated in COVID-19. In addition, ‘Disease perturbations from GEO up’ and ‘Disease perturbations from GEO down’ were also used to cross-verify the reliability of datasets. In the second step, the human metabolites database (HMDB) was used to identify the metabolites associated with the given gene set. Three pathway databases, WikiPathway 2021 Human, KEGG 2021 Human, and Reactome 2016 were used to identify pathways commonly enriched by at least two databases to interpret our results. The ‘Drug perturbations from GEO up’ and ‘Drug perturbations from GEO down’ databases were also used to correlate the results with identified pathways. In all enrichment analyses, top 10 enrichment results were only considered for interpretation.
Nutrigenomics analysis of DEG: NutriGenomeDB (release November 21, 2021; Madrid, Spain) and its phenotype-centered analysis was used for nutrigenomics analysis and selected 17 Homo sapiens as organism. Each dataset was analyzed individually and in combination with complete DEGs (up + downregulated genes). Only the blood based gene expression signatures of different foods, nutrients and bioactive compounds from this database were considered. Furthermore, net enrichment score (NES) were used to predict the final results as NES typically gives better accuracy compared to the number of overlapping gene (NOS) calculations. Analyses of western and Indian foods and diet: Data and literature mining approaches were used to understand the food consumption among western and Indian populations. Furthermore, various databases and corporate reports were used to understand the differences between dietary habits in the western and Indian populations ( Supplementary Table I). Supplementary Table I:
Dietary intakes of key foods and nutrients in adults aged 20 yr, national data, per capita g/day
A flow diagram of overall study design is given in
Figure 1. Fig. 1:
Schematic flow chart of overall methodology or study design.
Differences between Indian and western dietary intakes: Twelve key food components were found in the Indian diet, which were considerably different from that in the western populations ( Supplementary Table I). At the national level (mean intake per capita, g/day), western populations consumed 10-25 times more red meat, 8-12 times more processed foods, 5-7 times more dairy products, 3-8 times more fish, 10-12 times more coffee and two times more alcohol than Indians. On the other hand, Indians used 1.5 times more legumes and vegetables and four times more whole grains than western populations. Most importantly, while western populations used nil or negligible amounts of tea and turmeric, Indians consumed an average 1.2 and 2.5 g tea and turmeric per person per day, respectively ( Table I and Supplementary Figure). In south India, the main staple foods were Idli and Dosa (fermented food, rice and black gram 2:1 ratio) with Sambar (lentil-based stew) and rice with Rasam (spicy soup) . 18 Sambar and Rasam contain several spices including turmeric, chili pepper, cumin, curry leaves, mustard, coriander, asafoetida, sea salt, etc. 19 , . Similarly, in north India, kidney bean ( 20 Rajma), chickpea, legumes, wheat, corn, rice and several spices such as turmeric, chili, cumin, mustard, coriander etc. were used as daily foods . When the raw values of per capita daily consumptions of the identified twelve key food components were plotted against COVID-19-associated deaths to create a column chart, a distinct food habit of Indians was found which may be associated with low death rate in Indian populations ( 21 Fig. 2 and Supplementary Table I). Supplementary Figure:
Differences between Indian and western food and spices. The figure is developed based on Supplementary Table 1, several web portals, following literature
Dietary habits in Indian and western populations. Per capita daily consumption of 12 key foods and nutrients (variables) along with the population density and death rates (person/100,000) in India and three western countries (Also shown in
Supplementary Table 1
Cytokine storm and complement related pathways are upregulated in western and Indian severe COVID-19 samples, respectively: Two pathway databases showed upregulation of interferon (IFN) (type I and II), tumour necrosis factor (TNF), cytokine, chemokine and NOD-like receptor signalling pathways in severe COVID-19 patients from Spain and Greece ( Supplementary Table IIE and G). The USA samples displayed over expression of the VEGFA-VEGFR2 signaling pathway ( Supplementary Table IIF). The up regulated DEG of combined western populations was associated with IFN, TNF, cytokine, chemokine, VEGFA-VEGFR2 and NOD-like receptor signalling pathways ( Supplementary Table IIH). The upregulated genes of combined western countries were also associated with lipopolysaccharide (LPS) and IFN-beta responses ( Supplementary Table IIE- H). In contrast, the cell cycle and vitamin D metabolism related pathways were over represented in north Indian severe and south Indian asymptomatic cases ( Supplementary Table IIA and C). South Indian severe COVID-19 samples showed upregulation of complement and coagulation cascades ( Supplementary Table IIB). The combined Indian severe cases showed similar results to those found for cases from south India ( Supplementary Table IIB and D). Supplementary Table IIA:
Gene set enrichment analysis of asymptomatic South Indian samples
Supplementary Table IIB:
Gene set enrichment analysis of severe South Indian samples
Supplementary Table IIC:
Gene set enrichment analysis of severe north Indian samples
Supplementary Table IID:
Gene set enrichment analysis of combined severe North and South Indian samples
Supplementary Table IIE:
Gene set enrichment analysis of severe Greece samples
Supplementary Table IIF:
Gene set enrichment analysis of severe USA samples
Supplementary Table IIG:
Gene set enrichment analysis of severe Spain samples
Supplementary Table IIH:
Gene set enrichment analysis of severe all western (combined) samples
Atorvastatin, lipopolysaccharide (LPS), and interferon (IFN)-β responsive genes are differentially upregulated in Indian and western populations: In Using Drug perturbations from GEO up analysis, it was found that the cholesterol lowering drug atorvastatin ranked at 3 rd position among severe COVID-19 cases in the USA, 2 nd amond severe Indian and 3 rd asymptomatic Indian cases. Atorvastatin was not enriched in any other western country ( Supplementary Table IIA- H). Therefore, atorvastatin itself, or metabolites or nutrients that act as atorvastatin may have a role in regulating the severity of COVID-19. In contrast, LPS and IFN-β-1a were enriched in all samples except in severe Indian cases. The ranks of LPS and INF-β in asymptomatic Indian cases and in severe USA cases were lower as compared to other severe cases from western countries ( Supplementary Table IIA- H).
Palmitic acid (PA) and CO 2 responsive genes upregulated in western severe COVID cases and zinc, iron and carbohydrate responsive genes are enriched in Indian patients: The HMDB-based metabolite analysis showed that carbon dioxide (CO 2), hexadecanoyl-CoA, dermatan sulfate, arachidonic acid and palmitic acid (PA) were differentially enriched for individual western country samples ( Supplementary Table IIE- G). However, for combined western samples, CO 2, hexadecanoyl-CoA and PA were enriched ( Supplementary Table IIH). In contrast, upregulation of zinc and glucose responsive genes ( Supplementary Table IIB) was observed in severe south Indian samples and upregulation of iron, sodium, ammonia, folic acid, riboflavin etc. responsive genes in asymptomatic south Indian samples ( Supplementary Table IIA). Importantly, the combined severe Indian samples gave a result similar to the south Indian samples. We found that zinc, iron and glucose responsive genes were over represented ( Supplementary Table IIB and D). Based on the enrichment ranks, our results indicated that blood iron levels might be associated with COVID-19 severity . Taken together, there were distinct metabolic processes and metabolites governing the severity of COVID-19 between western and Indian populations and they could potentially be linked to the dietary habits of these populations. 22 PA and CO 2 responsive genes were associated with increased sphingolipid metabolism and PPAR signaling: In a separate Enrichr analysis, we found that the CO 2 responsive genes were associated with the TCA cycle, sphingolipid metabolism, proximal tubule transport and O 2/CO 2 exchange in erythrocyte pathways in western samples. Upregulation of these genes was associated with chronic obstructive pulmonary disease (COPD)-like conditions and metabolic acidosis. In addition, we found curcumin and iron having some association with CO 2 ( Supplementary Table IIIA and B). On the other hand, a separate nutrigenomics analysis of the CO 2 responsive genes showed that curcumin negatively regulated CO 2 production ( Supplementary Table IIIA and C). Conversely, the PA responsive genes were associated with fatty acid beta-oxidation, PPAR signalling and sphingolipid metabolism. Furthermore, the PA responsive genes also had indicative association with hypertension and obesity like comorbid conditions in COVID-19 ( Supplementary Table IIIA and D). Supplementary Table IIIA:
HMDB-based enriched genes for CO
2, hexadecanoyl-CoA and palmitic acid responses from combined all severe Western samples Supplementary Table IIIB:
Gene set enrichment of CO2 responsive genes from severe western samples (HMDB-based enriched) by Enrichr
Supplementary Table IIIC:
Nutrigenomics (blood transcriptome) analysis of CO
2 responsive genes from severe western samples (HMDB-based enriched) using NutriGenomeDB Supplementary Table IIID:
Gene set enrichment of palmitic acid responsive genes from severe western samples (HMDB-based enriched) by Enrichr
Supplementary Table IIIE:
Nutrigenomics (blood transcriptome) analysis of all Indian and western samples
Curcumin determines COVID-19 severity: In NutriGenome DB analyses, three datasets were found to be related to blood based gene expression in response to Lactobacillus rhamnosus, curcumin and docosahexaenoic acid. Based on NOG calculation, the number of response genes (RG) for these three nutrients were higher in severe cases as compared to asymptomatic COVID-19 in south Indian samples. The number of RGs was higher in severe western samples than in severe Indian cases ( Fig. 3A and Supplementary Table IIIE). The NES analysis yielded positive scores for curcumin, which was higher in cases of asymptomatic Indian samples as compared to severe cases from both north and south India. The NES of curcumin RGs was negative for all western samples. Importantly, we found that the negative NES of curcumin RGs of all severe western samples was lower after treatment with curcumin for 4 h than 18 h ( Fig. 3B and Supplementary Table IIIE). Fig. 3:
Blood nutrigenomics profiles of samples from western and Indian populations. (
A) Number of L. rhamnosus, curcumin, and DHA responsive genes increased with increased disease severity in India and the USA and Greece, but not in Spain. ( B) The NES-based analysis shows that the curcumin response score is positive and highest in samples from asymptomatic cases in India compared to the severe cases in India. In contrast, the NES is negative for curcumin in all samples in western countries (Black arrow). NES, net enrichment score. Diets and nutrients correlated with pathways and metabolites related to COVID-19 severity: A linear correlation was observed between diet or nutrients and molecular mechanisms of COVID-19 severity. The cytokine storm, intussusceptive angiogenesis and respiratory acidosis related pathways, which were exclusively upregulated in severe western samples, were positively associated with PA and CO 2 but were negatively correlated with curcumin. The sources of the PA and CO 2 were red meat, processed food and dairy products. PA also increased the expression of ACE2 leading to more severe COVID-19. The low iron and zinc levels in red meat and dairy products of western diets had the probability of increasing the severity and death from COVID-19. In contrast, south Indian Idli and other food components are high in iron and zinc content. Curcumin also reduces respiratory acidosis and blood glucose levels ( Fig. 4) 18 , 22 , . Therefore, regular intake of an Indian diet rich in zinc, iron, curcumin, fibre, catechins and EGCG have the potential to reduce the severity and death due to COVID-19. However, consumption of regular western diet, mainly red meat, processed food, dairy products, coffee and alcohol could activate the pathways and factors associated with COVID-19 severity, which may therefore contribute to the increased deaths observed in western countries. 23–75 Fig. 4:
Key dietary and nutrient interactions with COVID-19 pathways at molecular level that determines COVID-19 severity and fatality rates in Indian and Western populations. The figure is developed based on our results, available literature
18 , 22–75
.↑ and ▲ indicate upregulation or increase, ← is activation, and Tdenotes inhibition.
Low serum iron and zinc levels are associated with increased severity and death rates in COVID-19
( 22 Fig. 4). Zinc is used for treatment of COVID-19 . Dairy products are low in iron contents and alcohol consumption decreases plasma zinc levels. Notably, 23 Idli (zinc 23.4 mg/g, iron 46.4 mg/g, 3-4% fibre) contains higher amount of zinc and iron than meat and its zinc content is twice the amount available from vitamin tablets containing zinc which were consumed (10 mg), commonly during the COVID-19 pandemic. Similarly, rice, legumes, wheat, chickpeas and 18 Rajma, which are daily used ingredients in north Indian diet , are rich in vitamins, minerals, zinc and iron. Hence, Indian foods, in contrast to the western diet are able to maintain high blood zinc and iron levels, which can lower the COVID-19 severity and death rates in India ( 21 Fig. 4) 18 , . 22–75
Low plasma HDL-C and high triglyceride levels increase COVID-19 severity
. High alcohol consumption in western countries, increases plasma triglyceride and atorvastatin, a triglyceride lowering medicine and increases HDL-C in the blood is enriched in the Indian samples. Atorvastatin reduces COVID-19 severity, contributes to shortening hospitalization and reduction in COVID-19 mortality 24 . Catechins present in tea are the natural substitutes of statin. Catechin and EGCG in tea block SARS-CoV-2 Spike RBD and ACE2 interactions and prevent initiation of SARS-CoV-2 infection. India is the largest tea consumer ( 25 Supplementary Table I), where >64 per cent of Indians drink tea . Furthermore, curcumin, consumed in India, enhances the permeability and lipid-lowering effect of EGCG. In contrast, caffeine in coffee reduces statin function, decreases zinc levels, and also inhibits iron absorption 26 ( 27 Fig. 4). Coffee is the main source of caffeine and is consumed in large quantities per capita in western countries, whereas the consumption is negligible in India ( Supplementary Table 1). Therefore, while coffee consumption contribute to COVID-19 severity in western countries; high consumption of tea is potentially associated with less severe form of COVID-19 and lower death rates in India.
Low zinc and high PA-containing western food induces pro-inflammatory activity of PPAR signalling and enhances SARS-CoV-2 pathogenesis by activating pro-inflammatory cytokines, chemokines, NF-κB and ACE2
. Western foods also contain high amounts of sphingolipids which activate the SphK1/S1P/S1PR (S1P) hyperinflammatory response pathway and increased COVID-19 severity 28 ( 29 Fig. 4). We found that PA and sphingolipids, the two key metabolites of western foods, were associated with the activation of COVID-19 severity pathways and higher death rates in western countries ( Fig. 4) 18 , . 22–75
Meat, fish, eggs, cheese and alcohol induce hypercapnia and respiratory acidosis
. Furthermore, high animal fat and protein diets, which are low in fibres, are known to cause gut microbiota dysbiosis leading to increased CO 30 2 and hypercapnia and LPS-induced increased blood glucose levels . Both the hypercapnia and increased blood glucose levels are associated with COVID-19 severity ( 31 Fig. 4) 18 , . CO 22–75 2 RGs are highly enriched in patients with severe COVID-19 in western countries, but not in India. Therefore, the western diet might be associated with hypercapnia and increased blood glucose levels contributing to increase the severity and death during COVID-19 in western populations.
Curcumin, the active compound of turmeric is a prophylactic agent
and treatment with curcumin reduces the severity and mortality from COVID-19 9 . Curcumin increases serum zinc levels 32 and it blocks the Spike RBD interaction with ACE2, decreases cholesterol and triglyceride levels, and inhibits hypercapnia, IFN, TNF, chemokine, cytokine, VEGFA-VEGFR2-mediated intussusceptive angiogenesis and NOD-like receptor signaling pathways, which are associated with cytokine storm leading to severity and deaths from COVID-19 33 34 , ( 35 Fig. 4).
We found that all these pathways were exclusively upregulated in western but not in Indian samples (
Supplementary Table II). Further, we identified curcumin to be inversely associated with COVID-19 severity ( Fig. 3 and Supplementary Table IIIE). Curcumin is the active compound of turmeric and turmeric is regularly consumed (>2 g/day/capita) spice/condiment in India, but not in western countries ( Fig. 2 and Supplementary Table I). Therefore, daily intake of turmeric in India maintains high concentration of body curcumin that inhibits almost all molecular mechanisms associated with SARS-CoV-2 infection and COVID-19 severity, leading to less severe disease outcome and lower death rates in India as compared to western countries ( Fig. 4) 18 , 22 , . 23–75
Although our findings are significant from the nutrigenomics point of view, there are some limitations. Our study does not represent a precise case-control study where specific foods were used as treatment. Rather, we considered population-specific dietary habits and transcriptomes of patients. We also did not consider factors such as major clinical determinants of health outcomes, co-morbid conditions, age, gender, vaccination status, nutrition index, food habit diversity, smoking status and other biological and socio-economic factors. Our sample sizes were small and hence establishing statistically significant correlation is not possible.
In conclusion, our results suggested that Indian dietary habits and food ingredients could possibly be associated with reduced severity and death rates from COVID-19 in India. While the western diet and food components seemed to contribute to severity of COVID-19, Indian dietary habits and food ingredients might play a role in reduction of severity of COVID-19 disease. Regular consumption of plant-based foods,
Idli, whole grains, legume, vegetables, tea and turmeric (curcumin) diets were probably the key elements behind reduced severity and lower death rates from COVID-19 in India, despite the much higher population density in the country as compared to western countries. However, additional large scale and intervention trial are required for drawing definitive inference in this direction.
: Authors acknowledge the Omics Science Network (RECOM) and CNPq for their support. AAA acknowledges the Taif University Researchers Supporting Program and KJA would like to acknowledge the support from Deanship of Scientific Research, Taif University, Saudi Arabia. Acknowledgment
: None. Financial support & sponsorship : None. Conflicts of Interest References
1. Fathi M, Vakili K, Sayehmiri F, Mohamadkhani A, Hajiesmaeili M, Rezaei-Tavirani M, et al. The prognostic value of comorbidity for the severity of COVID-19:A systematic review and meta-analysis study. PLoS One 2021;16:e0246190.
2. Dieter C, Brondani LA, Leitão CB, Gerchman F, Lemos NE, Crispim D. Genetic polymorphisms associated with susceptibility to COVID-19 disease and severity:A systematic review and meta-analysis. PLoS One 2022;17:e0270627.
3. Kim H, Rebholz CM, Hegde S, LaFiura C, Raghavan M, Lloyd JF, et al. Plant-based diets, pescatarian diets and COVID-19 severity:A population-based case-control study in six countries. BMJ Nutr Prev Health 2021;4:257–66.
4. El Khoury CN, Julien SG. Inverse association between the Mediterranean diet and COVID-19 risk in Lebanon:A case-control study. Front Nutr 2021;8:707359.
5. de Faria Coelho-Ravagnani C, Corgosinho FC, Sanches FFZ, Prado CMM, Laviano A, Mota JF. Dietary recommendations during the COVID-19 pandemic. Nutr Rev 2021;79:382–93.
6. Butler MJ, Barrientos RM. The impact of nutrition on COVID-19 susceptibility and long-term consequences. Brain Behav Immun 2020;87:53–4.
7. Manne BK, Denorme F, Middleton EA, Portier I, Rowley JW, Stubben C, et al. Platelet gene expression and function in patients with COVID-19. Blood 2020;136:1317–29.
8. Barh D, Tiwari S, Andrade BS, Weener ME, Góes-Neto A, Azevedo V, et al. A novel multi-omics-based highly accurate prediction of symptoms, comorbid conditions, and possible long-term complications of COVID-19. Mol Omics 2021;17:317–37.
9. Barh D, Tiwari S, Weener ME, Azevedo V, Góes-Neto A, Gromiha MM, et al. Multi-omics-based identification of SARS-CoV-2 infection biology and candidate drugs against COVID-19. Comput Biol Med 2020;126:104051.
10. Barh D, Aljabali AA, Tambuwala MM, Tiwari S, Serrano-Aroca Á, Alzahrani KJ, et al. Predicting COVID-19-comorbidity pathway crosstalk-based targets and drugs:Towards personalized COVID-19 management. Biomedicines 2021;9:556.
11. Wargodsky R, Dela Cruz P, LaFleur J, Yamane D, Kim JS, Benjenk I, et al. RNA sequencing in COVID-19 patients identifies neutrophil activation biomarkers as a promising diagnostic platform for infections. PLoS One 2022;17:e0261679.
12. Thair SA, He YD, Hasin-Brumshtein Y, Sakaram S, Pandya R, Toh J, et al. Transcriptomic similarities and differences in host response between SARS-CoV-2 and other viral infections. iScience 2021;24:101947.
13. Utrero-Rico A, González-Cuadrado C, Chivite-Lacaba M, Cabrera-Marante O, Laguna-Goya R, Almendro-Vazquez P, et al. Alterations in circulating monocytes predict COVID-19 severity and include chromatin modifications still detectable six months after recovery. Biomedicines 2021;9 1253.
14. Law CW, Chen Y, Shi W, Smyth GK. voom:Precision weights unlock linear model analysis tools for RNA-seq read counts. Genome Biol 2014;15:R29.
15. Kaur S, Singh A, Kaur J, Verma N, Pandey AK, Das S, et al. Upregulation of cytokine signalling in platelets increases risk of thrombophilia in severe COVID-19 patients. Blood Cells Mol Dis 2022;94:102653.
16. Xie Z, Bailey A, Kuleshov MV, Clarke DJB, Evangelista JE, Jenkins SL, et al. Gene set knowledge discovery with enrichr. Curr Protoc 2021;1:e90.
17. Martín-Hernández R, Reglero G, Ordovás JM, Dávalos A. NutriGenomeDB:A nutrigenomics exploratory and analytical platform. Database (Oxford) 2019;2019:baz097.
18. Reddy NR, Sathe SK, Pierson MD, Salunkhe DK. Idli, an Indian fermented food:A review. J Food Qual 1982;5:89–101.
19. Devarajan A, Mohanmarugaraja MK. A comprehensive review on rasam:A South Indian traditional functional food. Pharmacogn Rev 2017;11:73–82.
20. Prasad VG, Reddy N, Francis A, Nayak PG, Kishore A, Nandakumar K, et al. Sambar, an Indian dish prevents the development of dimethyl hydrazine-induced colon cancer:A preclinical study. Pharmacogn Mag 2016;12 (Suppl 4) S441–5.
21. Guha M, Banerjee H, Mitra P, Das M. The demographic diversity of food intake and prevalence of kidney stone diseases in the Indian continent. Foods 2019;8:37.
22. Zhao K, Huang J, Dai D, Feng Y, Liu L, Nie S. Serum iron level as a potential predictor of coronavirus disease 2019 severity and mortality:A retrospective study. Open Forum Infect Dis 2020;7:ofaa250.
23. Pal A, Squitti R, Picozza M, Pawar A, Rongioletti M, Dutta AK, et al. Zinc and COVID-19:Basis of current clinical trials. Biol Trace Elem Res 2021;199:2882–92.
24. Masana L, Correig E, Ibarretxe D, Anoro E, Arroyo JA, Jericó C, et al. Low HDL and high triglycerides predict COVID-19 severity. Sci Rep 2021;11 7217.
25. Cho DH, Choi J, Gwon JG. Atorvastatin reduces the severity of COVID-19:A nationwide, total population-based, case-control study. COVID 2022;2:398–406.
26. Board T. Executive summary of study on domestic consumption of tea in India 2018 Available from:
accessed on May 30, 2022.
27. Mascitelli L, Pezzetta F, Sullivan JL. Inhibition of iron absorption by coffee and the reduced risk of type 2 diabetes mellitus. Arch Intern Med 2007;167:204–5.
28. Korbecki J, Bajdak-Rusinek K. The effect of palmitic acid on inflammatory response in macrophages:An overview of molecular mechanisms. Inflamm Res 2019;68:915–32.
29. Khan SA, Goliwas KF, Deshane JS. Sphingolipids in lung pathology in the coronavirus disease era:A review of sphingolipid involvement in the pathogenesis of lung damage. Front Physiol 2021;12:760638.
30. Carnauba RA, Baptistella AB, Paschoal V, Hübscher GH. Diet-induced low-grade metabolic acidosis and clinical outcomes:A review. Nutrients 2017;9:538.
31. Fan Y, Pedersen O. Gut microbiota in human metabolic health and disease. Nat Rev Microbiol 2021;19:55–71.
32. Pawar KS, Mastud RN, Pawar SK, Pawar SS, Bhoite RR, Bhoite RR, et al. Oral curcumin with piperine as adjuvant therapy for the treatment of COVID-19:A randomized clinical trial. Front Pharmacol 2021;12:669362.
33. Safarian H, Parizadeh SMR, Saberi-Karimain M, Darroudi S, Javandoost A, Mohammadi F, et al. The effect of curcumin on serum copper and zinc and Zn/Cu ratio in individuals with metabolic syndrome:A double-blind clinical trial. J Diet (Suppl)2019;16:625–34.
34. Rattis BAC, Ramos SG, Celes MRN. Curcumin as a potential treatment for COVID-19. Front Pharmacol 2021;12:675287.
35. Fuloria S, Mehta J, Chandel A, Sekar M, Rani NNIM, Begum MY, et al. A comprehensive review on the therapeutic potential of
Linn. in relation to its major active constituent curcumin. Front Pharmacol 2022;13:820806.
36. Ghanei E, Baghani M, Moravvej H, Talebi A, Bahmanjahromi A, Abdollahimajd F. Low serum levels of zinc and 25-hydroxyvitmain D as potential risk factors for COVID-19 susceptibility:A pilot case-control study. Eur J Clin Nutr 2022;76:1297–302.
37. Jothimani D, Kailasam E, Danielraj S, Nallathambi B, Ramachandran H, Sekar P, et al. COVID-19:Poor outcomes in patients with zinc deficiency. Int J Infect Dis 2020;100:343–9.
38. Williams P. Section 2:Key nutrients delivered by red meat in the diet. Nutr Dietet 2007;64 (Suppl 4) S113–9.
39. Ghosh D, Chattopadhyay P. Preparation of idli batter, its properties and nutritional improvement during fermentation. J Food Sci Technol 2011;48:610–5.
40. Jalili M. Chemical composition and sensory characteristics of Feta cheese fortified with iron and ascorbic acid. Dairy Sci Technol 2016;96:579–89.
41. Ordak M, Bulska E, Jablonka-Salach K, Luciuk A, Maj-Żurawska M, Matsumoto H, et al. Effect of disturbances of zinc and copper on the physical and mental health status of patients with alcohol dependence. Biol Trace Elem Res 2018;183:9–15.
42. Wallace TC, Murray R, Zelman KM. The nutritional value and health benefits of chickpeas and hummus. Nutrients 2016;8:766.
43. Jan S, Rather IA, Sofi PA, Wani MA, Sheikh FA, Bhat MA, et al. Characterization of common bean (
L.). germplasm for morphological and seed nutrient traits from Western Himalayas. Legum Sci 2021;3:e86.
44. Crouse JR, Grundy SM. Effects of alcohol on plasma lipoproteins and cholesterol and triglyceride metabolism in man. J Lipid Res 1984;25:486–96.
45. Branchi A, Fiorenza AM, Torri A, Muzio F, Berra C, Colombo E, et al. Atorvastatin increases HDL cholesterol in hypercholesterolemic patients. Evidence of a relationship with baseline HDL cholesterol. Nutr Metab Cardiovasc Dis 2002;12:24–8.
46. Senanayake SPJN. Green tea extract:Chemistry, antioxidant properties and food applications –A review. J Funct Food 2013;5:1529–41.
47. Liu J, Bodnar BH, Meng F, Khan AI, Wang X, Saribas S, et al. Epigallocatechin gallate from green tea effectively blocks infection of SARS-CoV-2 and new variants by inhibiting spike binding to ACE2 receptor. Cell Biosci 2021;11:168.
48. Varun TC, Kerutagi MG, Kunnal LB, Basavaraja H, Ashalatha KV, Dodamani MT. Consumption pattern of coffee and tea in Karnataka. Karnataka J Agric Sci 2009;22:824–7.
49. Pandit AP, Joshi SR, Dalal PS, Patole VC. Curcumin as a permeability enhancer enhanced the antihyperlipidemic activity of dietary green tea extract. BMC Complement Altern Med 2019;19:129.
50. Ye Y, Abu Said GH, Lin Y, Manickavasagam S, Hughes MG, McAdoo DJ, et al. Caffeinated coffee blunts the myocardial protective effects of statins against ischemia-reperfusion injury in the rat. Cardiovasc Drugs Ther 2008;22:275–82.
51. Rossowska MJ, Nakamoto T. Caffeine decreases zinc and metallothionein levels in heart of newborn and adult rats. Pediatr Res 1992;32:330–2.
52. Reyes CM, Cornelis MC. Caffeine in the diet:Country-level consumption and guidelines. Nutrients 2018;10 1772.
53. Shen H, Oesterling E, Stromberg A, Toborek M, MacDonald R, Hennig B. Zinc deficiency induces vascular pro-inflammatory parameters associated with NF-kappaB and PPAR signaling. J Am Coll Nutr 2008;27:577–87.
54. Joshi C, Jadeja V, Zhou H. Molecular mechanisms of palmitic acid augmentation in COVID-19 pathologies. Int J Mol Sci 2021;22 7127.
55. Vesper H, Schmelz EM, Nikolova-Karakashian MN, Dillehay DL, Lynch DV, Merrill AH Jr. Sphingolipids in food and the emerging importance of sphingolipids to nutrition. J Nutr 1999;129:1239–50.
56. McGowan EM, Haddadi N, Nassif NT, Lin Y. Targeting the SphK-S1P-SIPR pathway as a potential therapeutic approach for COVID-19. Int J Mol Sci 2020;21 7189.
57. Saavedra-Romero R, Paz F, Litell JM, Weinkauf J, Benson CC, Tindell L, et al. Treatment of severe hypercapnic respiratory failure caused by SARS-CoV-2 lung injury with ECCO
R using the hemolung respiratory assist system. Case Rep Crit Care 2021;2021:9958343.
58. Chen J, Wu C, Wang X, Yu J, Sun Z. The impact of COVID-19 on blood glucose:A systematic review and meta-analysis. Front Endocrinol (Lausanne) 2020;11:574541.
59. Yu L, Fan Y, Ye G, Li J, Feng X, Lin K, et al. Curcumin inhibits apoptosis and brain edema induced by hypoxia-hypercapnia brain damage in rat models. Am J Med Sci 2015;349:521–5.
60. Manik M, Singh RK. Role of toll-like receptors in modulation of cytokine storm signaling in SARS-CoV-2-induced COVID-19. J Med Virol 2022;94:869–77.
61. Meini S, Giani T, Tascini C. Intussusceptive angiogenesis in Covid-19:Hypothesis on the significance and focus on the possible role of FGF2. Mol Biol Rep 2020;47:8301–4.
62. de Almeida JC, Perassolo MS, Camargo JL, Bragagnolo N, Gross JL. Fatty acid composition and cholesterol content of beef and chicken cuts from southern Brazil. Br J Pharml Sci 2016;42:109–17.
63. Murru E, Manca C, Carta G, Banni S. Impact of dietary palmitic acid on lipid metabolism. Front Nutr 2022;9:861664.
64. Oliveira R, Faria M, Silva R, Bezerra L, Carvalho G, Pinheiro A, et al. Fatty acid profile of milk and cheese from dairy cows supplemented a diet with palm kernel cake. Molecules 2015;20:15434–48.
65. Lamminpaa A, Vilska J. Acid-base balance in alcohol users seen in an emergency room. Vet Hum Toxicol 1991;33:482–5.
66. Shanmugarajan D, Prabitha P, Kumar BRP, Suresh B. Curcumin to inhibit binding of spike glycoprotein to ACE2 receptors:Computational modelling, simulations, and ADMET studies to explore curcuminoids against novel SARS-CoV-2 targets. RSC Adv 2020;10:31385–99.
67. Lee J, Im YH, Jung HH, Kim JH, Park JO, Kim K, et al. Curcumin inhibits interferon-alpha induced NF-kappaB and COX-2 in human A549 non-small cell lung cancer cells. Biochem Biophys Res Commun 2005;334:313–8.
68. Wang SL, Li Y, Wen Y, Chen YF, Na LX, Li ST, et al. Curcumin, a potential inhibitor of up-regulation of TNF-alpha and IL-6 induced by palmitate in 3T3-L1 adipocytes through NF-kAPPAB and JNK pathway. Biomed Environ Sci 2009;22:32–9.
69. Fu Z, Chen X, Guan S, Yan Y, Lin H, Hua ZC. Curcumin inhibits angiogenesis and improves defective hematopoiesis induced by tumor-derived VEGF in tumor model through modulating VEGF-VEGFR
signaling pathway. Oncotarget 2015;6:19469–82.
70. Kong F, Ye B, Cao J, Cai X, Lin L, Huang S, et al. Curcumin represses NLRP3 inflammasome activation via TLR4/MyD88/NF-κB and P2X7R signaling in PMA-induced macrophages. Front Pharmacol 2016;7:369.
71. Hunter PM, Hegele RA. Functional foods and dietary supplements for the management of dyslipidaemia. Nat Rev Endocrinol 2017;13:278–88.
72. Ohgitani E, Shin-Ya M, Ichitani M, Kobayashi M, Takihara T, Kawamoto M, et al. Significant inactivation of SARS-CoV-2
by a green tea catechin, a catechin-derivative, and black tea galloylated theaflavins. Molecules 2021;26 3572.
73. Joshi-Barve S, Barve SS, Amancherla K, Gobejishvili L, Hill D, Cave M, et al. Palmitic acid induces production of proinflammatory cytokine interleukin-8 from hepatocytes. Hepatology 2007;46:823–30.
74. Nag A, Banerjee R, Paul S, Kundu R. Curcumin inhibits spike protein of new SARS-CoV-2 variant of concern (VOC) omicron, an
study. Comput Biol Med 2022;146:105552.
75. Liu Z, Ying Y. The inhibitory effect of curcumin on virus-induced cytokine storm and its potential use in the associated severe pneumonia. Front Cell Dev Biol 2020;8:479.