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Variant of the lactase LCT gene explains association between milk intake and incident type 2 diabetes

Nature Metabolism volume 6, pages 169–186 (2024)Cite this article

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Abstract

Cow’s milk is frequently included in the human diet, but the relationship between milk intake and type 2 diabetes (T2D) remains controversial. Here, using data from the Hispanic Community Health Study/Study of Latinos, we show that in both sexes, higher milk intake is associated with lower risk of T2D in lactase non-persistent (LNP) individuals (determined by a variant of the lactase LCT gene, single nucleotide polymorphism rs4988235) but not in lactase persistent individuals. We validate this finding in the UK Biobank. Further analyses reveal that among LNP individuals, higher milk intake is associated with alterations in gut microbiota (for example, enriched Bifidobacterium and reduced Prevotella) and circulating metabolites (for example, increased indolepropionate and reduced branched-chain amino acid metabolites). Many of these metabolites are related to the identified milk-associated bacteria and partially mediate the association between milk intake and T2D in LNP individuals. Our study demonstrates a protective association between milk intake and T2D among LNP individuals and a potential involvement of gut microbiota and blood metabolites in this association.

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Fig. 1: An overview of study design and main analyses in the HCHS/SOL.
Fig. 2: Milk intake, LCT genotype and incident T2D.
Fig. 3: Milk intake, LCT genotype, gut microbial species and T2D-related metabolic traits.
Fig. 4: Milk intake, LCT genotype, circulating metabolites and risk of T2D.
Fig. 5: LCT genotype-specific milk-associated metabolites explain the milk–LCT interaction on risk of T2D.
Fig. 6: Relationship between LCT genotype-specific milk-associated gut bacterial species and metabolites.

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Data availability

The genetics data of HCHS/SOL used in this paper are archived at the NIH repositories dbGap (accession number phs000810.v1.p1) and BioLINCC (accession number HLB01141418a), and gut microbiome sequence data in this study are deposited in QIITA (study ID 11666). HCHS/SOL has established a process for the scientific community to apply for access to participant data and materials, with such requests reviewed by the project’s Steering Committee. These policies are described at https://sites.cscc.unc.edu/hchs/ (accessed December 2022). Original data from the UKB and the ARIC used in the present study are available through appropriate application as instructed. Details on data application from the UKB and the ARIC can be found at https://www.ukbiobank.ac.uk/ and https://aric.cscc.unc.edu/aric9/, respectively. The summary statistics on the relationship between gut microbiome and blood metabolites from SCAPIS are publicly available at https://gutsyatlas.serve.scilifelab.se/. Source data are provided with this paper.

Code availability

We used R 4.2 (https://www.r-project.org/), kinship2 v.1.1.3 (https://cran.r-project.org/web/packages/kinship2/), optmatch v.0.10.7 (https://cran.r-project.org/web/packages/optmatch/index.html), SHAPEIT2 v.2.r644 (https://mathgen.stats.ox.ac.uk/genetics_software/shapeit/shapeit.html), IMPUTE2 v.2.3.0 (https://mathgen.stats.ox.ac.uk/impute/impute_v2.html), SHOGUN v.1 (https://github.com/knights-lab/SHOGUN/), ancombc v1.4.0 (https://github.com/FrederickHuangLin/ANCOMBC/), metacoder v.0.3.5 (https://github.com/grunwaldlab/metacoder/), mediation v.4.5.0 (https://cran.r-project.org/web/packages/mediation/), survey v.4.1.1 (https://cran.r-project.org/web/packages/survey/), ppcor v.1.1 (https://cran.r-project.org/web/packages/ppcor/), TwoSampleMR v.0.5.7 (https://mrcieu.github.io/TwoSampleMR/) and mr.raps v.0.4.1 (https://github.com/qingyuanzhao/mr.raps/). No custom codes or functions were generated in this study.

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Acknowledgements

The present work is supported by R01-DK119268 (to Q.Q.) and R01-DK126698 (to Q.Q.) from the National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK), and R01-MD011389 (to R.K., R.D.B. and R.C.K.) from the National Institute on Minority Health and Health Disparities. B.Y. is supported by R01-HL141824 and R01-HL142003 from the National Heart, Lung and Blood Institute (NHLBI). J.-Y.M. is supported by BioData Catalyst Fellowship. J.L. is supported by R00-DK122128 from the NIDDK. G.H. is partially supported by R01-DK132011 from the NIDDK and U54GM104940 from the National Institute of General Medical Sciences. K.L. is supported by an AHA postdoctoral fellowship award (23POST1020455). Other funding sources for this study include UM1-HG008898 from the National Human Genome Research Institute; R01-HL060712, R01-HL140976 and R01-HL136266 from the NHLBI; and R01-DK120870 and the New York Regional Center for Diabetes Translation Research (P30-DK111022) from the NIDDK. Support for metabolomics data was graciously provided by the JLH Foundation (Houston, Texas, to B.Y.).

The HCHS/SOL is a collaborative study supported by contracts from the NHLBI to the University of North Carolina (HHSN268201300001I/N01-HC-65233), University of Miami (HHSN268201300004I/N01-HC-65234), Albert Einstein College of Medicine (HHSN268201300002I/N01-HC-65235), University of Illinois at Chicago (HHSN268201300003I/N01-HC-65236 Northwestern University) and San Diego State University (HHSN268201300005I/N01-HC-65237). The following institutes/centres/offices have contributed to the HCHS/SOL through a transfer of funds to the NHLBI: National Institute on Minority Health and Health Disparities, National Institute on Deafness and Other Communication Disorders, National Institute of Dental and Craniofacial Research, NIDDK, National Institute of Neurological Disorders and Stroke and National Institutes of Health (NIH) Institution-Office of Dietary Supplements. We thank the staff and participants of HCHS/SOL for their important contributions. A complete list of staff and investigators is available on the study website at http://www.cscc.unc.edu/hchs/.

The ARIC study has been funded in whole or in part with Federal funds from the NHLBI, NIH, Department of Health and Human Services (contract numbers HHSN268201700001I, HHSN268201700002I, HHSN268201700003I, HHSN268201700004I and HHSN268201700005I), R01-HL087641, R01-HL059367 and R01-HL086694; National Human Genome Research Institute contract number U01HG004402; and NIH contract number HHSN268200625226C. We thank the staff and participants of the ARIC study for their important contributions. Infrastructure was partly supported by grant number UL1RR025005, a component of the NIH and NIH Roadmap for Medical Research. Metabolomics measurements were sponsored by the National Human Genome Research Institute (3U01HG004402-02S1). This research is part of our Diet and Cardiometabolic Health Collection. The funding agencies have no role in the data analyses and results interpretation.

Author information

Authors and Affiliations

  1. Department of Epidemiology and Population Health, Albert Einstein College of Medicine, Bronx, NY, USA

    Kai Luo, Guo-Chong Chen, Yanbo Zhang, Jee-Young Moon, Jiaqian Xing, Brandilyn A. Peters, Zheng Wang, Tao Wang, Carmen R. Isasi, Robert D. Burk, Robert C. Kaplan & Qibin Qi

  2. Department of Nutrition and Food Hygiene, MOE Key Laboratory of Geriatric Diseases and Immunology, School of Public Health, Suzhou Medical College of Soochow University, Suzhou, China

    Guo-Chong Chen

  3. Department of Pediatrics, Albert Einstein College of Medicine, Bronx, NY, USA

    Mykhaylo Usyk & Robert D. Burk

  4. Department of Microbiology and Immunology, Albert Einstein College of Medicine, Bronx, NY, USA

    Mykhaylo Usyk & Robert D. Burk

  5. Chronic Disease Epidemiology Laboratory, Pennington Biomedical Research Center, Baton Rouge, LA, USA

    Gang Hu

  6. Division of Preventive Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA

    Jun Li

  7. Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, USA

    Jun Li & Qibin Qi

  8. Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA

    Elizabeth Selvin & Casey M. Rebholz

  9. Human Genetics Center, University of Texas Health Science Center at Houston, Houston, TX, USA

    Bing Yu & Eric Boerwinkle

  10. Center for Microbiome Innovation, University of California, San Diego, La Jolla, CA, USA

    Rob Knight

  11. Department of Bioengineering, University of California, San Diego, La Jolla, CA, USA

    Rob Knight

  12. Department of Pediatrics, University of California, San Diego, La Jolla, CA, USA

    Rob Knight

  13. Department of Computer Science and Engineering, University of California, San Diego, La Jolla, CA, USA

    Rob Knight

  14. Department of Obstetrics and Gynecology and Women’s Health, Albert Einstein College of Medicine, Bronx, NY, USA

    Robert D. Burk

  15. Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA, USA

    Robert C. Kaplan

Authors
  1. Kai Luo
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  18. Robert D. Burk
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  19. Robert C. Kaplan
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  20. Qibin Qi
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Contributions

K.L. and Q.Q. conceptualized the study. K.L. performed most of the data analysis and drafted the paper. Q.Q. critically revised the paper. G.-C.C., Y.Z., J.-Y.M., J.X., B.A.P., M.U., Z.W., J.L., G.H. and T.W. contributed to data analysis and bioinformatic analysis. E.S., C.M.R., C.R.I., B.Y., R.K., E.B., R.D.B., R.C.K. and Q.Q. contributed to acquisition of the data and funding. All authors interpreted the results and reviewed and edited the paper. R.D.B., R.C.K. and Q.Q. supervised the study.

Corresponding author

Correspondence to Qibin Qi.

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Competing interests

The authors declare no competing interests.

Peer review

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Nature Metabolism thanks the anonymous reviewers for their contribution to the peer review of this work. Primary Handling Editor: Ashley Castellanos-Jankiewicz, in collaboration with the Nature Metabolism team.

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Extended data

Extended Data Fig. 1 Genotype frequency of the LCT-rs4988235 in 1000 Genomes Project Phase 3.

(a) frequencies of genotypes by subpopulations; (b) annotations of subpopulations. Data were accessed on March 27, 2023 through http://useast.ensembl.org/Homo_sapiens/Variation/Population?r=2:135850576-135851576;v=rs4988235;vdb=variation;vf=184227258#population_freq_SAS.

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Extended Data Fig. 2 Abundances of milk intake-associated gut bacterial species across milk intake levels by LCT genotype.

Abundances of milk intake-associated gut bacterial species across milk intake levels (<0.5 serving/day [n = 141 in AA/AG group, and 331 in GG group], 0.5 ~ 1.0 serving/day [n = 210 in AA/AG group and 419 in GG group], > 1 serving/day [n = 286 in AA/AG group and 380 in GG group]) by LCT genotype groups. Data are the least-squares means of centered log-ratio transferred abundances (shown as points) and the corresponding 95% confidence interval (shown as error bars) adjusted for baseline age, sex, study field center, Hispanic/Latino background, physical activity, and education, annual household income, smoking, alcohol consumption, US born status, use of probiotics or antibiotics, prevalent status of hypertension, dyslipedemia, and diabetes at the time of fecal samples collection. P-interaction (Pint) was derived from the test of the product of LCT genotype and milk intake, and P-trend was derived from linear regression with categorical milk intake modeled as an ordinal variable. All statistical tests shown in this figure were two-sided.

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Extended Data Fig. 3 Glycoside hydrolases (GH) family enzymes distribution patterns of identified milk-associated Bifidobacterium species.

Data in the heatmap are abundances of GH enzymes in species, calculated as the ratios of total counts of contained enzymes with GH family to the number of reference genomes (that is, the numbers following the species in the right row annotations). Values < 1 (toward white color) indicate that less than one copy per genome of the corresponding GH family for the examined species, whereas values > 1 refer that more than one copy per reference genome was detected. Annotations of GH family number are from the carbohydrate-active enzymes (CAZy enzyme) database (http://www.cazy.org/Glycoside-Hydrolases.html). The reference genomes of assessed Bifidobacterium species were accessed through the Genome Taxonomy Database (GTDB, release 89; https://gtdb.ecogenomic.org/). Of the originally identified 7 milk-associated Bifidobacterium spp., data of B.reuteri were not available. Annotations of enzyme classes shown in the figure were summarized according to the description on the CAZypedia.org website (https://www.cazypedia.org/index.php/Main_Page).

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Extended Data Fig. 4 Relationship between milk associated bacteria, metabolic traits, and incident T2D.

The top heatmap was retrieved from Fig. 3 in the main text, where bacteria species highlighted in red had positive associations with milk intake, whereas those highlighted in grey were inversely associated with milk intake. Data are partial spearman correlation coefficients adjusted for baseline age, sex, field center, Hispanic/Latino background, physical activity, education, annual household income, smoking, alcohol consumption, US nativity, use of probiotics or antibiotics, prevalent status of hypertension, dyslipidemia, and T2D at the time of fecal sample collection. The bottom forest plot shows the associations of milk-associated bacteria with incident T2D among an interim subsample of HCHS/SOL participants that included 1311 individuals who were free of T2D at the time of stool sample collection but completed the 3rd follow-up visit. Of these 1,311 individuals, 139 incident T2D cases were identified at the 3rd visit. Data are relative risks (RRs, points) and 95% confidence intervals (shown as error bars) estimated from Poisson regression models adjusted for age, sex, filed center, Hispanic/Latino background, US nativity, education, annual household income, smoking, drinking, physical activity, AHEI-2010, use of probiotics and antibiotics at the time of stool sample collection. FDR-q: false discovery rate corrected P-value.

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Extended Data Fig. 5 Associations of milk-associated metabolites with incident T2D in HCHS/SOL and in ARIC.

Results of 42 metabolites in both cohorts were shown. Estimates (relative risk: RR) in HCHS/SOL were derived from survey Poisson regression models adjusted for baseline age, sex, field center, Hispanic/Latino background, education, annual household income, smoking, alcohol consumption, physical activity, prevalent status of hypertension and dyslipidemia. Estimates (harzard risk:HR) in ARIC were derived from Cox proportional hazard regression models adjusted for baseline age, sex, race/ethnicity, smoking, alcohol consumption, education, prevalent status of hypertension and dyslipidemia. Metabolite levels were inverse-normal transformed. Data are effect estimates (shown as points) and 95% confidence intervals (shown as error bars). All statistical tests shown in this figure are two-sided.

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Extended Data Fig. 6 Correlation between GG specific milk-associated bacteria spp. and metabolites.

Data in the left panel were partial spearman correlation adjusted for age, sex, place of birth, study site (Uppsala or Malmö), microbial DNA extraction plate, and metabolomics delivery batch from the Swedish CArdioPulmonary bioImage Study (SCAPIS, n = 8,583) (https://gutsyatlas.serve.scilifelab.se/), while data in the right panel were partial spearman correlation adjusted for baseline age, sex, field center, Hispanic/Latino background, physical activity, education, annual household income, smoking, alcohol consumption, US nativity, use of probiotics or antibiotics, and status of hypertension, dyslipidemia, and T2D at time of fecal sample collection in the present study (HCHS/SOL, n = 804). In SCAPIS, only partial correlations with false discovery rate corrected p-value: FDR-q < 0.05 were available in the summary statistics. Cells with black bold border refer to significant results in both SCAPIS and the present study.

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Extended Data Fig. 7 A summary of key findings on the altered gut microbiota species, circulating metabolites, and metabolic traits associated with milk intake in lactase non-persistent individuals (LNP: LCT GG group).

Created with BioRender.com.

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Supplementary Tables 1–6.

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Luo, K., Chen, GC., Zhang, Y. et al. Variant of the lactase LCT gene explains association between milk intake and incident type 2 diabetes. Nat Metab 6, 169–186 (2024). https://doi.org/10.1038/s42255-023-00961-1

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