Bisherige Ergebnisse der LIFE Adult-Studie – Highlights in Publikationen
1. Loeffler M, Engel C, Ahnert P et al. The LIFE-Adult-Study: objectives and design of a population-based cohort study with 10,000 deeply phenotyped adults in Germany. BMC Public Health 2015;15:691.
2. Enzenbach C, Wicklein B, Wirkner K, Loeffler M. Evaluating selection bias in a population-based cohort study with low baseline participation: the LIFE-Adult-Study. BMC Med Res Methodol 2019;19:135.
3. Kharabian Masouleh S, Arelin K, Horstmann A et al. Higher body mass index in older adults is associated with lower gray matter volume: implications for memory performance. Neurobiol Aging 2016;40:1–10.
4. Schaare HL, Kharabian Masouleh S, Beyer F et al. Association of peripheral blood pressure with gray matter volume in 19- to 40- year-old adults. Neurology 2019;92:e758–73.
5. Beyer F, Kharabian Masouleh S, Kratzsch J et al. A metabolic obesity profile is associated with decreased gray matter volume in cognitively healthy older adults. Front Aging Neurosci 2019; 11:202.
6. Zhang R, Beyer F, Lampe L et al. White matter microstructural variability mediates the relation between obesity and cognition in healthy adults. Neuroimage 2018;172:239–49.
7. Beyer F, Garcia-Garcia I, Heinrich M et al. Neuroanatomical correlates of food addiction symptoms and body mass index in the general population. Hum Brain Mapp 2019;40:2747–58.
8. Beyer F, Kharabian Masouleh S, Huntenburg JM et al. Higher body mass index is associated with reduced posterior default mode connectivity in older adults. Hum Brain Mapp 2017;38:3502–15.
9. Beyer F, Zhang R, Scholz M et al. Higher BMI, but not obesityrelated genetic polymorphisms, correlates with lower structural connectivity of the reward network in a population-based study. Int J Obes (Lond) 2021;45:491–501.
10. Kharabian Masouleh S, Beyer F, Lampe L et al. Gray matter structural networks are associated with cardiovascular risk factors in healthy older adults. J Cereb Blood Flow Metab 2018;38: 360–72.
11. Thomas K, Beyer F, Lewe G et al. Higher body mass index is linked to altered hypothalamic microstructure. Sci Rep 2019;9: 17373.
12. Lampe L, Zhang R, Beyer F et al. Visceral obesity relates to deep white matter hyperintensities via inflammation. Ann Neurol 2019;85:194–203.
13. Zsido RG, Heinrich M, Slavich GM et al. Association of estradiol and visceral fat with structural brain networks and memory performance in adults. JAMA Netw Open 2019;2:e196126.
14. Luck T, Then FS, Schroeter ML et al. Prevalence of DSM-5 mild neurocognitive disorder in dementia-free older adults: results of the population-based LIFE-Adult-Study. Am J Geriatr Psychiatry 2017;25:328–39.
15. Luck T, Roehr S, Rodriguez FS et al. Memory-related subjective cognitive symptoms in the adult population: prevalence and associated factors: results of the LIFE-Adult-Study. BMC Psychol 2018;6:23.
16. Luck T, Pabst A, Rodriguez FS et al. Age-, sex-, and educationspecific norms for an extended CERAD Neuropsychological Assessment Battery: results from the population-based LIFEAdult-Study. Neuropsychology 2018;32:461–75.
17. Luck T, Then FS, Luppa M et al. Association of the apolipoprotein E genotype with memory performance and executive functioning in cognitively intact elderly. Neuropsychology 2015;29: 382–87.
18. Kuehnapfel A, Ahnert P, Loeffler M, Broda A, Scholz M. Reliability of 3D laser-based anthropometry and comparison with classical anthropometry. Sci Rep 2016;6:26672.
19. Loffler-Wirth H, Willscher E, Ahnert P et al. Novel anthropometry based on 3D-bodyscans applied to a large population based cohort. PLoS One 2016;11:e0159887.
20. Frenzel A, Binder H, Walter N, Wirkner K, Loeffler M, Loeffler-Wirth H. The aging human body shape. NPJ Aging Mech Dis 2020;6:5.
21. Kuehnapfel A, Ahnert P, Loeffler M, Scholz M. Body surface assessment with 3D laser-based anthropometry: reliability, validation, and improvement of empirical surface formulae. Eur J Appl Physiol 2017;117:371–80.
22. Wang M, Elze T, Li D et al. Age, ocular magnification, and circumpapillary retinal nerve fiber layer thickness. J Biomed Opt 2017;22:1–19.
23. Li D, Rauscher FG, Choi EY et al. Sex-specific differences in circumpapillary retinal nerve fiber layer thickness. Ophthalmology 2020;127:357–68.
24. Baniasadi N, Rauscher FG, Li D et al. Norms of interocular circumpapillary retinal nerve fiber layer thickness differences at 768 retinal locations. Trans Vis Sci Tech 2020;9:23.
25. Rauscher FG, Wang M, Francke M et al. Renal function and lipid metabolism are major predictors of circumpapillary retinal nerve fiber layer thickness-the LIFE-Adult Study. BMC Med 2021;19:202.
26. Berger T, Fuchs M, Dippold S et al. Standardization and feasibility of voice range profile measurements in epidemiological studies. J Voice 2022;36:142 e9–20.
27. Berg M, Fuchs M, Wirkner K, Loeffler M, Engel C, Berger T. The speaking voice in the general population: normative data and associations to sociodemographic and lifestyle factors. J Voice 2017;31:257.e13–24.
28. Jost L, Fuchs M, Loeffler M et al. Associations of sex hormones and anthropometry with the speaking voice profile in the adult general population. J Voice 2018;32:261–72.
29. Wilkinson MD, Dumontier M, Aalbersberg IJ et al. The FAIR Guiding Principles for scientific data management and stewardship. Sci Data 2016;3:160018. International Journal of Epidemiology, 2022, Vol. 00, No. 00 13 Downloaded from https://academic.oup.com/ije/advance-article/doi/10.1093/ije/dyac114/6594394 by Universitaetsbibliothek user on 30 May 2022
30. Dogan-Sander E, Willenberg A, Batmaz I et al. Association of serum 25-hydroxyvitamin D concentrations with sleep phenotypes in a German community sample. PLoS One 2019;14:e0219318.
31. Spada J, Sander C, Burkhardt R et al. Genetic association of objective sleep phenotypes with a functional polymorphism in the neuropeptide S receptor gene. PLoS One 2014;9:e98789.
32. Jawinski P, Sander C, Mauche N et al. Brain arousal regulation in carriers of bipolar disorder risk alleles. Neuropsychobiology 2015;72:65–73.
33. Jawinski P, Tegelkamp S, Sander C et al. Time to wake up: No impact of COMT Val158Met gene variation on circadian preferences, arousal regulation and sleep. Chronobiol Int 2016;33: 893–905.
34. Jawinski P, Kirsten H, Sander C et al. Human brain arousal in the resting state: a genome-wide association study. Mol Psychiatry 2019;24:1599–609.
35. Spada J, Scholz M, Kirsten H et al. Genome-wide association analysis of actigraphic sleep phenotypes in the LIFE Adult Study. J Sleep Res 2016;25:690–701.
36. Pott J, Burkhardt R, Beutner F et al. Genome-wide meta-analysis identifies novel loci of plaque burden in carotid artery. Atherosclerosis 2017;259:32–40.
37. Buchmann N, Scholz M, Lill CM et al. Association between lipoprotein(a) level and type 2 diabetes: no evidence for a causal role of lipoprotein(a) and insulin. Acta Diabetol 2017;54:1031–38.
38. Davies G, Lam M, Harris SE et al. Study of 300,486 individuals identifies 148 independent genetic loci influencing general cognitive function. Nat Commun 2018;9:2098.
39. Franceschini N, Giambartolomei C, de Vries PS et al.; MEGASTROKE Consortium. GWAS and colocalization analyses implicate carotid intima-media thickness and carotid plaque loci in cardiovascular outcomes. Nat Commun 2018;9:5141.
40. Vojinovic D, Adams HH, Jian X et al. Genome-wide association study of 23,500 individuals identifies 7 loci associated with brain ventricular volume. Nat Commun 2018;9:3945.
41. Walker CJ, Oakes CC, Genutis LK et al. Genome-wide association study identifies an acute myeloid leukemia susceptibility locus near BICRA. Leukemia 2019;33:771–75.
42. Wuttke M, Li Y, Li M et al.; V. A. Million Veteran Program. A catalog of genetic loci associated with kidney function from analyses of a million individuals. Nat Genet 2019;51:957–72.
43. Pott J, Bae YJ, Horn K et al. Genetic association study of eight steroid hormones and implications for sexual dimorphism of coronary artery disease. J Clin Endocrinol Metab 2019;104: 5008–23.
44. Pott J, Schlegel V, Teren A et al. Genetic regulation of PCSK9 (Proprotein Convertase Subtilisin/Kexin Type 9) plasma levels and its impact on atherosclerotic vascular disease phenotypes. Circ Genom Precis Med 2018;11:e001992.
45. Pott J, Beutner F, Horn K et al. Genome-wide analysis of carotid plaque burden suggests a role of IL5 in men. PLoS One 2020;15: e0233728.
46. Schmidt M, Hopp L, Arakelyan A et al. The human blood transcriptome in a large population cohort and its relation to aging and health. Front Big Data 2020;3:548873.
47. Vosa U, Claringbould A, Westra HJ et al.; i2QTL Consortium. Large-scale cis- and trans-eQTL analyses identify thousands of genetic loci and polygenic scores that regulate blood gene expression. Nat Genet 2021;53:1300–10.
48. Dittrich J, Adam M, Maas H et al. Targeted on-line SPE-LC-MS/ MS assay for the quantitation of 12 apolipoproteins from human blood. Proteomics 2018;18:1700279.
49. Reinicke M, Schroter J, Muller-Klieser D, Helmschrodt C, Ceglarek U. Free oxysterols and bile acids including conjugates: simultaneous quantification in human plasma and cerebrospinal fluid by liquid chromatography-tandem mass spectrometry. Anal Chim Acta 2018;1037:245–55.
50. Bae YJ, Zeidler R, Baber R et al. Reference intervals of nine steroid hormones over the life-span analyzed by LC-MS/MS: effect of age, gender, puberty, and oral contraceptives. J Steroid Biochem Mol Biol 2019;193:105409.
51. Melzer S, Zachariae S, Bocsi J, Engel C, Löffler M, Tarnok A. Reference intervals for leukocyte subsets in adults: results from a population-based study using 10-color flow cytometry. Cytometry B Cytometry 2015;88:270–81.
52. Wichmann G, Gaede C, Melzer S et al. Discrimination of head and neck squamous cell carcinoma patients and healthy adults by 10-color flow cytometry: development of a score based on leukocyte subsets. Cancers (Basel) 2019;11:814.
53. Zeynalova S, Bucksch K, Scholz M et al. Monocyte subtype counts are associated with 10-year cardiovascular disease risk as determined by the Framingham Risk Score among subjects of the LIFE-Adult study. PLoS One 2021;16:e0247480.
54. Bocsi J, Melzer S, Dahnert I, Tarnok A. OMIP-023: 10-color, 13 antibody panel for in-depth phenotyping of human peripheral blood leukocytes. Cytometry A 2014;85:781–84.
