Longitudinal metabolite and protein trajectories prior to diabetes mellitus diagnosis in Danish blood donors: a nested case-control study.
Molecular biomarkers
Multi-omics
Polygenic risk scores
Temporality
Time-to-event prediction
Type 1 diabetes mellitus
Type 2 diabetes mellitus
Journal
Diabetologia
ISSN: 1432-0428
Titre abrégé: Diabetologia
Pays: Germany
ID NLM: 0006777
Informations de publication
Date de publication:
30 Jul 2024
30 Jul 2024
Historique:
received:
15
12
2023
accepted:
03
06
2024
medline:
30
7
2024
pubmed:
30
7
2024
entrez:
30
7
2024
Statut:
aheadofprint
Résumé
Metabolic risk factors and plasma biomarkers for diabetes have previously been shown to change prior to a clinical diabetes diagnosis. However, these markers only cover a small subset of molecular biomarkers linked to the disease. In this study, we aimed to profile a more comprehensive set of molecular biomarkers and explore their temporal association with incident diabetes. We performed a targeted analysis of 54 proteins and 171 metabolites and lipoprotein particles measured in three sequential samples spanning up to 11 years of follow-up in 324 individuals with incident diabetes and 359 individuals without diabetes in the Danish Blood Donor Study (DBDS) matched for sex and birth year distribution. We used linear mixed-effects models to identify temporal changes before a diabetes diagnosis, either for any incident diabetes diagnosis or for type 1 and type 2 diabetes mellitus diagnoses specifically. We further performed linear and non-linear feature selection, adding 28 polygenic risk scores to the biomarker pool. We tested the time-to-event prediction gain of the biomarkers with the highest variable importance, compared with selected clinical covariates and plasma glucose. We identified two proteins and 16 metabolites and lipoprotein particles whose levels changed temporally before diabetes diagnosis and for which the estimated marginal means were significant after FDR adjustment. Sixteen of these have not previously been described. Additionally, 75 biomarkers were consistently higher or lower in the years before a diabetes diagnosis. We identified a single temporal biomarker for type 1 diabetes, IL-17A/F, a cytokine that is associated with multiple other autoimmune diseases. Inclusion of 12 biomarkers improved the 10-year prediction of a diabetes diagnosis (i.e. the area under the receiver operating curve increased from 0.79 to 0.84), compared with clinical information and plasma glucose alone. Systemic molecular changes manifest in plasma several years before a diabetes diagnosis. A particular subset of biomarkers shows distinct, time-dependent patterns, offering potential as predictive markers for diabetes onset. Notably, these biomarkers show shared and distinct patterns between type 1 diabetes and type 2 diabetes. After independent replication, our findings may be used to develop new clinical prediction models.
Identifiants
pubmed: 39078488
doi: 10.1007/s00125-024-06231-3
pii: 10.1007/s00125-024-06231-3
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Subventions
Organisme : Novo Nordisk Fonden
ID : NNF14CC0001
Organisme : Novo Nordisk Fonden
ID : NNF17OC0027594
Organisme : Novo Nordisk Fonden
ID : NNF17OC0027812
Organisme : Novo Nordisk Fonden
ID : NNF17OC0027864
Informations de copyright
© 2024. The Author(s).
Références
NCD Risk Factor Collaboration (2016) Worldwide trends in diabetes since 1980: a pooled analysis of 751 population-based studies with 4.4 million participants. Lancet 387(10027):1513–1530. https://doi.org/10.1016/S0140-6736(16)00618-8
doi: 10.1016/S0140-6736(16)00618-8
United Kingdom Prospective Diabetes Study Group (1995) United Kingdom Prospective Diabetes Study (UKPDS) 13: relative efficacy of randomly allocated diet, sulphonylurea, insulin, or metformin in patients with newly diagnosed non-insulin dependent diabetes followed for three years. BMJ 310(6972):83–88. https://doi.org/10.1136/bmj.310.6972.83
doi: 10.1136/bmj.310.6972.83
Arcaro G, Cretti A, Balzano S et al (2002) Insulin causes endothelial dysfunction in humans. Circulation 105(5):576–582. https://doi.org/10.1161/hc0502.103333
doi: 10.1161/hc0502.103333
pubmed: 11827922
Kolberg JA, Jørgensen T, Gerwien RW et al (2009) Development of a type 2 diabetes risk model from a panel of serum biomarkers from the Inter99 cohort. Diabetes Care 32(7):1207–1212. https://doi.org/10.2337/DC08-1935
doi: 10.2337/DC08-1935
pubmed: 19564473
pmcid: 2699726
Thorand B, Zierer A, Büyüközkan M et al (2021) A panel of 6 biomarkers significantly improves the prediction of type 2 diabetes in the MONICA/KORA study population. J Clin Endocrinol Metab 106(4):e1647–e1659. https://doi.org/10.1210/clinem/dgaa953
doi: 10.1210/clinem/dgaa953
pubmed: 33382400
Herder C, Kowall B, Tabak AG, Rathmann W (2014) The potential of novel biomarkers to improve risk prediction of type 2 diabetes. Diabetologia 57(1):16–29. https://doi.org/10.1007/s00125-013-3061-3
doi: 10.1007/s00125-013-3061-3
pubmed: 24078135
Abbasi A, Sahlqvist A-S, Lotta L et al (2016) A systematic review of biomarkers and risk of incident type 2 diabetes: an overview of epidemiological, prediction and aetiological research literature. PLoS One 11(10):e0163721. https://doi.org/10.1371/journal.pone.0163721
doi: 10.1371/journal.pone.0163721
pubmed: 27788146
pmcid: 5082867
Strawbridge RJ, van Zuydam NR (2018) Shared genetic contribution of type 2 diabetes and cardiovascular disease: implications for prognosis and treatment. Curr Diab Rep 18(8):59. https://doi.org/10.1007/s11892-018-1021-5
doi: 10.1007/s11892-018-1021-5
pubmed: 29938349
pmcid: 6015804
Hulsegge G, Spijkerman A, Schouw YTVD et al (2017) Trajectories of metabolic risk factors and biochemical markers prior to the onset of type 2 diabetes: the population-based longitudinal Doetinchem study. Nat Publ Group 7:270. https://doi.org/10.1038/nutd.2017.23
doi: 10.1038/nutd.2017.23
Færch K, Witte DR, Tabák AG et al (2013) Trajectories of cardiometabolic risk factors before diagnosis of three subtypes of type 2 diabetes: a post-hoc analysis of the longitudinal Whitehall II cohort study. Lancet Diabetes Endocrinol 1(1):43–51. https://doi.org/10.1016/S2213-8587(13)70008-1
doi: 10.1016/S2213-8587(13)70008-1
pubmed: 24622266
Vistisen D, Witte DR, Tabák AG et al (2014) Patterns of obesity development before the diagnosis of type 2 diabetes: the Whitehall II cohort study. PLoS Med 11(2):e1001602. https://doi.org/10.1371/journal.pmed.1001602
doi: 10.1371/journal.pmed.1001602
pubmed: 24523667
pmcid: 3921118
Nano J, Dhana K, Asllanaj E et al (2020) Trajectories of BMI before diagnosis of type 2 diabetes: the Rotterdam study. Obesity 28(6):1149–1156. https://doi.org/10.1002/OBY.22802
doi: 10.1002/OBY.22802
pubmed: 32379398
Tu Z-Z, Yuan Y, Xia P-F et al (2022) Trajectories of metabolic risk factors during the development of type 2 diabetes in Chinese adults. Diabetes Metab 48:101348. https://doi.org/10.1016/j.diabet.2022.101348
doi: 10.1016/j.diabet.2022.101348
pubmed: 35452819
Tabák AG, Jokela M, Akbaraly TN, Brunner EJ, Kivimäki M, Witte DR (2009) Trajectories of glycaemia, insulin sensitivity, and insulin secretion before diagnosis of type 2 diabetes: an analysis from the Whitehall II study. Lancet 373(9682):2215–2221. https://doi.org/10.1016/S0140-6736(09)60619-X
doi: 10.1016/S0140-6736(09)60619-X
pubmed: 19515410
pmcid: 2726723
Shoelson SE, Lee J, Goldfine AB (2006) Inflammation and insulin resistance. J Clin Invest 116(7):1793. https://doi.org/10.1172/JCI29069
doi: 10.1172/JCI29069
pubmed: 16823477
pmcid: 1483173
Marques-Vidal P, Schmid R, Bochud M et al (2012) Adipocytokines, hepatic and inflammatory biomarkers and incidence of type 2 diabetes. The CoLaus study. PLoS One 7(12):e51768. https://doi.org/10.1371/journal.pone.0051768
doi: 10.1371/journal.pone.0051768
pubmed: 23251619
pmcid: 3520903
Irvine WJ, McCallum CJ, Gray RS, Duncan LJ (1977) Clinical and pathogenic significance of pancreatic-islet-cell antibodies in diabetics treated with oral hypoglycaemic agents. Lancet 309(8020):1025–1027. https://doi.org/10.1016/s0140-6736(77)91258-2
doi: 10.1016/s0140-6736(77)91258-2
Subauste A, Gianani R, Chang AM et al (2014) Islet autoimmunity identifies a unique pattern of impaired pancreatic beta-cell function, markedly reduced pancreatic beta cell mass and insulin resistance in clinically diagnosed type 2 diabetes. PLoS One 9(9):e106537. https://doi.org/10.1371/journal.pone.0106537
doi: 10.1371/journal.pone.0106537
pubmed: 25226365
pmcid: 4165581
de Candia P, Prattichizzo F, Garavelli S et al (2019) Type 2 diabetes: how much of an autoimmune disease? Front Endocrinol 10:451. https://doi.org/10.3389/fendo.2019.00451
doi: 10.3389/fendo.2019.00451
Carstensen B, Rønn PF, Jørgensen ME (2020) Prevalence, incidence and mortality of type 1 and type 2 diabetes in Denmark 1996–2016. BMJ Open Diabetes Res Care 8(1):e001071. https://doi.org/10.1136/BMJDRC-2019-001071
doi: 10.1136/BMJDRC-2019-001071
pubmed: 32475839
pmcid: 7265004
Hansen TF, Banasik K, Erikstrup C et al (2019) DBDS Genomic Cohort, a prospective and comprehensive resource for integrative and temporal analysis of genetic, environmental and lifestyle factors affecting health of blood donors. BMJ Open 9(6):e028401. https://doi.org/10.1136/BMJOPEN-2018-028401
doi: 10.1136/BMJOPEN-2018-028401
pubmed: 31182452
pmcid: 6561431
Burgdorf KS, Simonsen J, Sundby A et al (2017) Socio-demographic characteristics of Danish blood donors. PLOS ONE 12(2):e0169112. https://doi.org/10.1371/journal.pone.0169112
doi: 10.1371/journal.pone.0169112
pubmed: 28182624
pmcid: 5300150
Privé F, Arbel J, Vilhjálmsson BJ (2020) LDpred2: better, faster, stronger. Bioinformatics 36(22–23):5424–5431. https://doi.org/10.1093/bioinformatics/btaa1029
doi: 10.1093/bioinformatics/btaa1029
pmcid: 8016455
van Buuren S (2016) Multiple imputation of discrete and continuous data by fully conditional specification. Stat Methods Med Res 16(3):219–242. https://doi.org/10.1177/0962280206074463
doi: 10.1177/0962280206074463
Porta M, Curletto G, Cipullo D et al (2014) Estimating the delay between onset and diagnosis of type 2 diabetes from the time course of retinopathy prevalence. Diabetes Care 37(6):1668–1674. https://doi.org/10.2337/dc13-2101
doi: 10.2337/dc13-2101
pubmed: 24705614
Gallagher EJ, Le Roith D, Bloomgarden Z (2009) Review of hemoglobin A
doi: 10.1111/j.1753-0407.2009.00009.x
pubmed: 20923515
Waugh NR, Shyangdan D, Taylor-Phillips S, Suri G, Hall B (2013) Screening for type 2 diabetes: a short report for the National Screening Committee. Health Technol Assess 17(35):1–90. https://doi.org/10.3310/hta17350
doi: 10.3310/hta17350
pubmed: 23972041
pmcid: 4780946
Aiello LP, Avery RL, Arrigg PG et al (1994) Vascular endothelial growth factor in ocular fluid of patients with diabetic retinopathy and other retinal disorders. N Engl J Med 331(22):1480–1487. https://doi.org/10.1056/NEJM199412013312203
doi: 10.1056/NEJM199412013312203
pubmed: 7526212
Kowalczuk L, Touchard E, Omri S et al (2011) Placental growth factor contributes to micro-vascular abnormalization and blood–retinal barrier breakdown in diabetic retinopathy. PLoS One 6(3):e17462. https://doi.org/10.1371/journal.pone.0017462
doi: 10.1371/journal.pone.0017462
pubmed: 21408222
pmcid: 3049767
Grant M, Russell B, Fitzgerald C, Merimee TJ (1986) Insulin-like growth factors in vitreous: studies in control and diabetic subjects with neovascularization. Diabetes 35(4):416–420. https://doi.org/10.2337/diab.35.4.416
doi: 10.2337/diab.35.4.416
pubmed: 2420665
Kumar PA, Brosius FC, Menon RK (2011) The glomerular podocyte as a target of growth hormone action: implications for the pathogenesis of diabetic nephropathy. Curr Diabetes Rev 7(1):50–55. https://doi.org/10.2174/157339911794273900
doi: 10.2174/157339911794273900
pubmed: 21067510
pmcid: 4007067
Nandy D, Mukhopadhyay D, Basu A (2010) Both vascular endothelial growth factor and soluble Flt-1 are increased in type 2 diabetes but not in impaired fasting glucose. J Investig Med 58(6):804–806. https://doi.org/10.231/JIM.0b013e3181e96203
Yao Y, Du J, Li R et al (2019) Association between ICAM-1 level and diabetic retinopathy: a review and meta-analysis. Postgrad Med J 95(1121):162–168. https://doi.org/10.1136/POSTGRADMEDJ-2018-136102
doi: 10.1136/POSTGRADMEDJ-2018-136102
pubmed: 31109934
Chow FY, Nikolic-Paterson DJ, Ozols E, Atkins RC, Tesch GH (2005) Intercellular adhesion molecule-1 deficiency is protective against nephropathy in type 2 diabetic db/db mice. J Am Soc Nephrol 16(6):1711–1722. https://doi.org/10.1681/ASN.2004070612
doi: 10.1681/ASN.2004070612
pubmed: 15857924
Jiang L, Hu X, Feng Y et al (2024) Reduction of renal interstitial fibrosis by targeting Tie2 in vascular endothelial cells. Pediatr Res 95(4):959–965. https://doi.org/10.1038/s41390-023-02893-8
doi: 10.1038/s41390-023-02893-8
pubmed: 38012310
Campochiaro PA, Peters KG (2016) Targeting Tie2 for treatment of diabetic retinopathy and diabetic macular edema. Curr Diab Rep 16(12):126. https://doi.org/10.1007/s11892-016-0816-5
doi: 10.1007/s11892-016-0816-5
pubmed: 27778249
Tapp RJ, Tikellis G, Wong TY et al (2008) Longitudinal association of glucose metabolism with retinopathy: results from the Australian Diabetes Obesity and Lifestyle (AusDiab) study. Diabetes Care 31(7):1349–1354. https://doi.org/10.2337/dc07-1707
doi: 10.2337/dc07-1707
pubmed: 18411241
pmcid: 2453668
Plantinga LC, Crews DC, Coresh J et al (2010) Prevalence of chronic kidney disease in US adults with undiagnosed diabetes or prediabetes. Clin J Am Soc Nephrol 5(4):673–682. https://doi.org/10.2215/CJN.07891109
doi: 10.2215/CJN.07891109
pubmed: 20338960
pmcid: 2849697
US Preventive Services Task Force (2021) Screening for prediabetes and type 2 diabetes: US Preventive Services Task Force recommendation statement. JAMA 326(8):736–743. https://doi.org/10.1001/jama.2021.12531
doi: 10.1001/jama.2021.12531
Takashina C, Tsujino I, Watanabe T et al (2016) Associations among the plasma amino acid profile, obesity, and glucose metabolism in Japanese adults with normal glucose tolerance. Nutr Metab 13(1):5. https://doi.org/10.1186/s12986-015-0059-5
doi: 10.1186/s12986-015-0059-5
Winocour PH, Ishola M, Durrington PN, Anderson DC (1986) Lipoprotein abnormalities in insulin-dependent diabetes mellitus. Lancet 327(8491):1176–1178. https://doi.org/10.1016/S0140-6736(86)91159-1
doi: 10.1016/S0140-6736(86)91159-1
Ahola-Olli AV, Mustelin L, Kalimeri M et al (2019) Circulating metabolites and the risk of type 2 diabetes: a prospective study of 11,896 young adults from four Finnish cohorts. Diabetologia 62(12):2298–2309. https://doi.org/10.1007/s00125-019-05001-w
doi: 10.1007/s00125-019-05001-w
pubmed: 31584131
pmcid: 6861432
Festa A, Williams K, Hanley AJG et al (2005) Nuclear magnetic resonance lipoprotein abnormalities in prediabetic subjects in the Insulin Resistance Atherosclerosis Study. Circulation 111(25):3465–3472. https://doi.org/10.1161/CIRCULATIONAHA.104.512079
doi: 10.1161/CIRCULATIONAHA.104.512079
pubmed: 15983261
Sokooti S, Flores-Guerrero JL, Kieneker LM et al (2021) HDL particle subspecies and their association with incident type 2 diabetes: the PREVEND study. J Clin Endocrinol Metab 106(6):1761–1772. https://doi.org/10.1210/clinem/dgab075
doi: 10.1210/clinem/dgab075
pubmed: 33567068
pmcid: 8118359
Gorst C, Kwok CS, Aslam S et al (2015) Long-term glycemic variability and risk of adverse outcomes: a systematic review and meta-analysis. Diabetes Care 38(12):2354–2369. https://doi.org/10.2337/dc15-1188
doi: 10.2337/dc15-1188
pubmed: 26604281
Chen J, Yi Q, Wang Y et al (2022) Long-term glycemic variability and risk of adverse health outcomes in patients with diabetes: a systematic review and meta-analysis of cohort studies. Diabetes Res Clin Pract 192:110085. https://doi.org/10.1016/j.diabres.2022.110085
doi: 10.1016/j.diabres.2022.110085
pubmed: 36126799
Qiu A-W, Cao X, Zhang W-W, Liu Q-H (2021) IL-17A is involved in diabetic inflammatory pathogenesis by its receptor IL-17RA. Exp Biol Med 246(1):57–65. https://doi.org/10.1177/1535370220956943
doi: 10.1177/1535370220956943
Crawford MP, Sinha S, Renavikar PS, Borcherding N, Karandikar NJ (2020) CD4 T cell-intrinsic role for the T helper 17 signature cytokine IL-17: effector resistance to immune suppression. Proc Natl Acad Sci USA 117(32):19408–19414. https://doi.org/10.1073/pnas.2005010117
doi: 10.1073/pnas.2005010117
pubmed: 32719138
pmcid: 7430972
Brodersen T, Rostgaard K, Lau CJ et al (2023) The healthy donor effect and survey participation, becoming a donor and donor career. Transfusion (Paris) 63(1):143–155. https://doi.org/10.1111/trf.17190
doi: 10.1111/trf.17190