Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Article
  • Published:

Urban living in healthy Tanzanians is associated with an inflammatory status driven by dietary and metabolic changes

Abstract

Sub-Saharan Africa currently experiences an unprecedented wave of urbanization, which has important consequences for health and disease patterns. This study aimed to investigate and integrate the immune and metabolic consequences of rural or urban lifestyles and the role of nutritional changes associated with urban living. In a cohort of 323 healthy Tanzanians, urban as compared to rural living was associated with a pro-inflammatory immune phenotype, both at the transcript and protein levels. We identified different food-derived and endogenous circulating metabolites accounting for these differences. Serum from urban dwellers induced reprogramming of innate immune cells with higher tumor necrosis factor production upon microbial re-stimulation in an in vitro model of trained immunity. These data demonstrate important shifts toward an inflammatory phenotype associated with an urban lifestyle and provide new insights into the underlying dietary and metabolic factors, which may affect disease epidemiology in sub-Sahara African countries.

This is a preview of subscription content, access via your institution

Access options

Rent or buy this article

Prices vary by article type

from$1.95

to$39.95

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: Schematic depiction of the study population and distribution.
Fig. 2: Associations of blood transcriptomes, ex vivo cytokine immune responses and plasma cytokines with urban or rural living.
Fig. 3: Impact of age and sex on cytokine production capacity.
Fig. 4: Differences in plasma metabolite abundance in rural- versus urban-living individuals.
Fig. 5: Relation of plasma metabolites with ex vivo cytokine production capacity.
Fig. 6: Impact of annual seasonality on blood transcriptome and cytokine immune responses.
Fig. 7: Association of urban individuals’ food-derived metabolome on cytokine immune responses and blood transcriptome.

Similar content being viewed by others

Data availability

Data that support the findings of this study are available from the corresponding author upon request. Sequence data have been deposited at the European Genome–phenome Archive, which is hosted by the EBI and the CRG, under accession number EGAS00001004284. In addition to the deposition of the raw sequencing data on the European Genome–phenome Archive, we provide an interactive platform for data inspection and analysis via FASTGenomics (https://beta.fastgenomics.org/p/Temba_300FG_NatureImmun). In this platform, we provide processed count tables of the datasets generated in this study as well as key analytical results and the code written to analyze the respective data. Metabolomics data have been deposited to the EMBL-EBI MetaboLights database65 with study identifier MTBLS2267. Code analysis scripts are available at: https://github.com/schultzelab/Temba-Kullaya-Pecht-et-al.- and https://beta.fastgenomics.org/p/Temba_300FG_NatureImmun. Publicly available databases used for this study include NCBI’s Gene Expression Omnibus G3 under accession code GSE110749 and differential gene analysis results from the study of Arango et al. (Biochem. Pharmacol., https://doi.org/10.1016/j.bcp.2012.09.005, 2012). Other databases are KEGG (https://www.genome.jp/kegg/), HMDB (https://www.hmdb.ca/) and ChEBI (https://www.ebi.ac.uk/chebi/). All other data are available in the main text, supplementary materials and auxiliary supplemental tables.

References

  1. De Brauw, A., Mueller, V. & Lee, H. L. The role of rural–urban migration in the structural transformation of sub-Saharan Africa. World Dev. 63, 33–42 (2014).

    Google Scholar 

  2. Beaglehole, R. et al. Priority actions for the non-communicable disease crisis. Lancet 377, 1438–1447 (2011).

    PubMed  Google Scholar 

  3. Unwin, N. et al. Rural to urban migration and changes in cardiovascular risk factors in Tanzania: a prospective cohort study. BMC Public Health 10, 272 (2010).

    PubMed  PubMed Central  Google Scholar 

  4. Popkin, B. M. The nutrition transition: an overview of world patterns of change. Nutr. Rev. 62, S140–S143 (2004).

    PubMed  Google Scholar 

  5. Abrahams, Z., McHiza, Z. & Steyn, N. P. Diet and mortality rates in sub-Saharan Africa: stages in the nutrition transition. BMC Public Health 11, 801 (2011).

    PubMed  PubMed Central  Google Scholar 

  6. Thorburn, A. N., Macia, L. & Mackay, C. R. Diet, metabolites, and ‘western-lifestyle’ inflammatory diseases. Immunity 40, 833–842 (2014).

    CAS  PubMed  Google Scholar 

  7. Christ, A. et al. Western diet triggers NLRP3-dependent innate immune reprogramming. Cell 172, 162–175 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  8. Bickler, S. W. et al. Urbanization in sub-Saharan Africa: declining rates of chronic and recurrent infection and their possible role in the origins of non-communicable diseases. World J. Surg. 42, 1617–1628 (2018).

    PubMed  PubMed Central  Google Scholar 

  9. Kodaman, N. et al. Cardiovascular disease risk factors in Ghana during the rural-to-urban transition: a cross-sectional study. PLoS ONE 11, e0162753 (2016).

    PubMed  PubMed Central  Google Scholar 

  10. Kann, P. H. et al. Alterations of cortisol homeostasis may link changes of the sociocultural environment to an increased diabetes and metabolic risk in developing countries: a prospective diagnostic study performed in cooperation with the Ovahimba people of the Kunene region/northwestern. J. Clin. Endocr. Metab. 100, E482–E486 (2015).

    CAS  PubMed  Google Scholar 

  11. Mbow, M. et al. Changes in immunological profile as a function of urbanization and lifestyle. Immunology 143, 569–577 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  12. Gruebner, O. et al. Cities and mental health. Dtsch Arztebl. Int. 114, 121–127 (2017).

    PubMed  PubMed Central  Google Scholar 

  13. Misra, A. & Ganda, O. P. Migration and its impact on adiposity and type 2 diabetes. Nutrition 23, 696–708 (2007).

    PubMed  Google Scholar 

  14. Ter Horst, R. et al. Host and environmental factors influencing individual human cytokine responses. Cell 167, 1111–1124 (2016).

    PubMed  PubMed Central  Google Scholar 

  15. Piasecka, B. et al. Distinctive roles of age, sex, and genetics in shaping transcriptional variation of human immune responses to microbial challenges. Proc. Natl Acad. Sci. USA 115, E488–E497 (2018).

    CAS  PubMed  Google Scholar 

  16. Baylis, D., Bartlett, D. B., Patel, H. P. & Roberts, H. C. Understanding how we age: insights into inflammaging. Longev. Healthspan 2, 8 (2013).

    PubMed  PubMed Central  Google Scholar 

  17. Fuhrer, T., Heer, D., Begemann, B. & Zamboni, N. High-throughput, accurate mass metabolome profiling of cellular extracts by flow injection-time-of-flight mass spectrometry. Anal. Chem. 83, 7074–7080 (2011).

    CAS  PubMed  Google Scholar 

  18. Hostetler, G. L., Ralston, R. A. & Schwartz, S. J. Flavones: food sources, bioavailability, metabolism, and bioactivity. Adv. Nutr. 8, 423–435 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  19. Ratter, J. M., Tack, C. J., Netea, M. G. & Stienstra, R. Environmental signals influencing myeloid cell metabolism and function in diabetes. Trends Endocrinol. Metab. 29, 468–480 (2018).

    CAS  PubMed  Google Scholar 

  20. Barbie, D. A. et al. Systematic RNA interference reveals that oncogenic KRAS-driven cancers require TBK1. Nature 462, 108–112 (2009).

    CAS  PubMed  PubMed Central  Google Scholar 

  21. Lee, J. H. et al. Anti-inflammatory mechanisms of apigenin: inhibition of cyclooxygenase-2 expression, adhesion of monocytes to human umbilical vein endothelial cells, and expression of cellular adhesion molecules. Arch. Pharm. Res. 30, 1318–1327 (2007).

    CAS  PubMed  Google Scholar 

  22. Ren, K., Jiang, T., Zhou, H. F., Liang, Y. & Zhao, G. J. Apigenin retards atherogenesis by promoting ABCA1-mediated cholesterol efflux and suppressing inflammation. Cell. Physiol. Biochem. 47, 2170–2184 (2018).

    CAS  PubMed  Google Scholar 

  23. Wang, J. et al. Anti-inflammatory effects of apigenin in lipopolysaccharide-induced inflammatory in acute lung injury by suppressing COX-2 and NF-kB pathway. Inflammation 37, 2085–2090 (2014).

    CAS  PubMed  Google Scholar 

  24. Marton, A. et al. Anti-inflammatory effects of inosine in human monocytes, neutrophils and epithelial cells in vitro. Int. J. Mol. Med. 8, 617–621 (2001).

    CAS  PubMed  Google Scholar 

  25. Wang, Q. et al. Pyruvate protects against experimental stroke via an anti-inflammatory mechanism. Neurobiol. Dis. 36, 223–231 (2009).

    CAS  PubMed  PubMed Central  Google Scholar 

  26. Constantin, G., Laudanna, C., Baron, P. & Berton, G. Sulfatides trigger cytokine gene expression and secretion in human monocytes. FEBS Lett. 350, 66–70 (1994).

    CAS  PubMed  Google Scholar 

  27. Horii, Y. et al. Leukotriene B4 receptor 1 exacerbates inflammation following myocardial infarction. FASEB J. 34, 8749–8763 (2020).

    CAS  PubMed  Google Scholar 

  28. Basu, S. Bioactive eicosanoids: role of prostaglandin F and F2-isoprostanes in inflammation and oxidative stress related pathology. Mol. Cells 30, 383–391 (2010).

    CAS  PubMed  Google Scholar 

  29. Branco, A., Yoshikawa, F. S. Y., Pietrobon, A. J. & Sato, M. N. Role of histamine in modulating the immune response and inflammation. Mediators Inflamm. 2018, 9524075 (2018).

    PubMed  PubMed Central  Google Scholar 

  30. Netea, M. G., Quintin, J. & van der Meer, J. W. Trained immunity: a memory for innate host defense. Cell Host Microbe 9, 355–361 (2011).

    CAS  PubMed  Google Scholar 

  31. Li, Y. et al. Inter-individual variability and genetic influences on cytokine responses to bacteria and fungi. Nat. Med. 22, 952–960 (2016); erratum 22, 1192 (2016).

  32. Bekkering, S. et al. Oxidized low-density lipoprotein induces long-term proinflammatory cytokine production and foam cell formation via epigenetic reprogramming of monocytes. Arterioscler. Thromb. Vasc. Biol. 34, 1731–1738 (2014).

    CAS  PubMed  Google Scholar 

  33. Sadler, A. J. & Williams, B. R. Interferon-inducible antiviral effectors. Nat. Rev. Immunol. 8, 559–568 (2008).

    CAS  PubMed  PubMed Central  Google Scholar 

  34. Minton, K. Viperin breaks viral chains. Nat. Rev. Immunol. 18, 480–481 (2018).

    CAS  PubMed  Google Scholar 

  35. Napier, B. A. et al. Western diet regulates immune status and the response to LPS-driven sepsis independent of diet-associated microbiome. Proc. Natl Acad. Sci. USA 116, 3688–3694 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  36. Strandberg, L. et al. Mice chronically fed high-fat diet have increased mortality and disturbed immune response in sepsis. PLoS ONE https://doi.org/10.1371/journal.pone.0007605 (2009).

  37. Christ, A., Lauterbach, M. & Latz, E. Western diet and the immune system: an inflammatory connection. Immunity 51, 794–811 (2019).

    CAS  PubMed  Google Scholar 

  38. Leentjens, J. et al. Trained innate immunity as a novel mechanism linking infection and the development of atherosclerosis. Circ. Res. 122, 664–669 (2018).

    CAS  PubMed  Google Scholar 

  39. García-Lafuente, A., Guillamón, E., Villares, A., Rostagno, M. A. & Martínez, J. A. Flavonoids as anti-inflammatory agents: implications in cancer and cardiovascular disease. Inflamm. Res. 58, 537–552 (2009).

    PubMed  Google Scholar 

  40. Zhang, X., Wang, G., Gurley, E. C. & Zhou, H. Flavonoid apigenin inhibits lipopolysaccharide-induced inflammatory response through multiple mechanisms in macrophages. PLoS ONE 9, e107072 (2014).

    PubMed  PubMed Central  Google Scholar 

  41. Hemler, E. C. & Hu, F. B. Plant-based diets for cardiovascular disease prevention: all plant foods are not created equal. Curr. Atheroscler. Rep. 21, 18 (2019).

    PubMed  Google Scholar 

  42. Minutoli, L. et al. The disaccharide trehalose inhibits proinflammatory phenotype activation in macrophages and prevents mortality in experimental septic shock. Shock 27, 91–96 (2007).

    CAS  PubMed  Google Scholar 

  43. Collins, J. et al. Dietary trehalose enhances virulence of epidemic Clostridium difficile. Nature 553, 291–294 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  44. Choy, K. W., Murugan, D. D., Leong, X.-F., Abas, R. & Alias, A. Flavonoids as natural anti-inflammatory agents targeting nuclear factor-kappa B (NFκB) signalling in cardiovascular diseases: a mini review. Front. Pharmacol. 10, 1295 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  45. Dopico, X. C. et al. Widespread seasonal gene expression reveals annual differences in human immunity and physiology. Nat. Commun. 6, 7000 (2015).

    CAS  PubMed  Google Scholar 

  46. Oosting, M. et al. Functional and genomic architecture of Borrelia burgdorferi-induced cytokine responses in humans. Cell Host Microbe 20, 822–833 (2016).

    CAS  PubMed  Google Scholar 

  47. Koeken, V. A. et al. BCG vaccination in humans inhibits systemic inflammation in a sex-dependent manner. J. Clin. Invest. https://doi.org/10.1172/JCI133935 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  48. Klein, S. L. & Flanagan, K. L. Sex differences in immune responses. Nat. Rev. Immunol. 16, 626–638 (2016).

    CAS  PubMed  Google Scholar 

  49. Onyango, E. M. & Onyango, B. M. The rise of noncommunicable diseases in Kenya: an examination of the time trends and contribution of the changes in diet and physical inactivity. J. Epidemiol. Glob. Health 8, 1–7 (2018).

    PubMed  PubMed Central  Google Scholar 

  50. Tibshirani, R. Estimating transformations for regression via additivity and variance stabilization. J. Am. Stat. Assoc. 83, 394–405 (1988).

    Google Scholar 

  51. Anders, S. & Huber, W. Differential expression analysis for sequence count data. Nat. Preced. https://doi.org/10.1038/npre.2010.4282.2 (2010).

  52. Leek, J. T., Johnson, W. E., Parker, H. S., Jaffe, A. E. & Storey, J. D. The sva package for removing batch effects and other unwanted variation in high-throughput experiments. Bioinformatics 28, 882–883 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  53. Ritchie, M. E. et al. limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res. 43, e47 (2015).

    PubMed  PubMed Central  Google Scholar 

  54. Yu, G., Wang, L.-G., Han, Y. & He, Q.-Y. clusterProfiler: an R package for comparing biological themes among gene clusters. Omics 16, 284–287 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  55. Benjamini, Y. & Hochberg, Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing. J. R. Stat. Soc. B 57, 289–300 (1995).

    Google Scholar 

  56. Subramanian, A. et al. Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc. Natl Acad. Sci. USA 102, 15545–15550 (2005).

    CAS  PubMed  PubMed Central  Google Scholar 

  57. Arango, D. et al. Apigenin induces DNA damage through the PKC δ-dependent activation of ATM and H2AX causing down-regulation of genes involved in cell cycle control and DNA repair. Biochem. Pharmacol. 84, 1571–1580 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  58. Hänzelmann, S., Castelo, R. & Guinney, J. GSVA: gene set variation analysis for microarray and RNA-seq data. BMC Bioinformatics 14, 7 (2013).

    PubMed  PubMed Central  Google Scholar 

  59. Chong, J., Wishart, D. S. & Xia, J. Using Metaboanalyst 4.0 for comprehensive and integrative metabolomics data analysis. Curr. Protoc. Bioinformatics 68, e86 (2019).

    PubMed  Google Scholar 

  60. Repnik, U., Knezevic, M. & Jeras, M. Simple and cost-effective isolation of monocytes from buffy coats. J. Immunol. Methods 278, 283–292 (2003).

    CAS  PubMed  Google Scholar 

  61. Bekkering, S. et al. In vitro experimental model of trained innate immunity in human primary monocytes. Clin. Vaccine Immunol. 23, 926–933 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  62. Shah, T. S. et al. optiCall: a robust genotype-calling algorithm for rare, low-frequency and common variants. Bioinformatics 28, 1598–1603 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  63. Deelen, P. et al. Genotype harmonizer: automatic strand alignment and format conversion for genotype data integration. BMC Res. Notes 7, 901 (2014).

    PubMed  PubMed Central  Google Scholar 

  64. Das, S. et al. Next-generation genotype imputation service and methods. Nat. Genet. 48, 1284–1287 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  65. Haug, K. et al. MetaboLights: a resource evolving in response to the needs of its scientific community. Nucleic Acids Res. 48, D440–D444 (2020).

    CAS  PubMed  Google Scholar 

Download references

Acknowledgements

The authors thank all volunteers in the Human Functional Genomics in Healthy Tanzanian Individuals study for their participation. We thank J. Njau, J. Kwayu and E. Kimaro for help in sample collection; H. Lemmers and H. Toenhake-Dijkstra for help in laboratory analysis; Y. Li for help in statistics; and M. Miclaus for help with metabolome data processing. We also thank M. Kraut and K. Händler for their great contribution to RNA sequencing. This study was funded by the following grants: the European Union’s Horizon 2020 Research and Innovation Program under the ERA-Net Cofund action no. 727565; the Joint Programming Initiative, A Healthy Diet for a Healthy Life (JPI-HDHL; project 529051018) awarded to M.G.N., Q.d.M., A.V. and J.L.S.; ZonMw (the Netherlands Organisation for Health Research and Development) awarded to M.G.N., Q.dM. and A.V.; Radboud Revolving Research Funds (3R-Fund) awarded to G.S.T.; Spinoza Prize (NWO SPI94-212) and ERC Advanced grant (no. 833247) awarded to M.G.N.; and the Deutsche Forschungsgemeinschaft (German Research Foundation) under Germany’s Excellence Strategy (EXC2151) 390873048 awarded to M.G.N. and J.L.S.

Author information

Authors and Affiliations

Authors

Contributions

Q.d.M., A.V., M.G.N., L.A.B.J., G.K., J.L.S. and B.T.M. contributed to the conceptualization, study design and data interpretation and led the project; G.S.T., V.K., B.T.M. and F.L. contributed to participant recruitment, data collection and laboratory analyses; G.S.T. designed and performed functional validation experiments and analysis of immunological and metabolome data; T.P., T.U., A.C.A. and J.L.S. contributed to RNA-seq analysis and analytical integration with metabolome data and interpretation; C.K.B. and V.K. contributed to genetics analysis and interpretation; G.S.T., T.P. and Q.d.M. wrote the original draft of the manuscript; and G.S.T., T.P., V.K., C.K.B., B.T.M., A.C.A., T.U., G.K., F.L., V.K., L.A.B.J., J.L.S., A.V., M.G.N. and Q.d.M. contributed to writing and editing the manuscript.

Corresponding authors

Correspondence to Mihai G. Netea or Quirijn de Mast.

Ethics declarations

Competing interests

The authors declare no competing interests.

Additional information

Peer reviewer information Nature Immunology thanks the anonymous reviewers for their contribution to the peer review of this work. Peer reviewer reports are available. Zoltan Fehervari was the primary editor on this article and managed its editorial process and peer review in collaboration with the rest of the editorial team.

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended data

Extended Data Fig. 1 Schematic depiction of the study parameters.

Schematic depiction of the study recruitment procedure. b, collected samples for blood transcriptome measured in unstimulated blood and circulating inflammatory mediators and metabolome were measured in EDTA plasma. c, Cytokine production capacity of the circulating immune cells in the ex vivo whole blood stimulation experimental setup.

Supplementary information

Supplementary Information

Supplementary Figs. 1–7 and Tables 7 and 16.

Reporting Summary

Peer Review Information

Supplementary Tables

Supplementary Tables 1–6 and 8–15.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Temba, G.S., Kullaya, V., Pecht, T. et al. Urban living in healthy Tanzanians is associated with an inflammatory status driven by dietary and metabolic changes. Nat Immunol 22, 287–300 (2021). https://doi.org/10.1038/s41590-021-00867-8

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41590-021-00867-8

This article is cited by

Search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing