Dynamic associations between glucose and ecological momentary cognition in Type 1 Diabetes

Nenhuma Miniatura disponível
Citações na Scopus
0
Tipo de produção
article
Data de publicação
2024
Título da Revista
ISSN da Revista
Título do Volume
Editora
NATURE PORTFOLIO
Autores
HAWKS, Z. W.
BECK, E. D.
JUNG, L.
SLIWINSKI, M. J.
WEINSTOCK, R. S.
GRINSPOON, E.
XU, I.
STRONG, R. W.
SINGH, S.
Citação
NPJ DIGITAL MEDICINE, v.7, n.1, article ID 59, 13p, 2024
Projetos de Pesquisa
Unidades Organizacionais
Fascículo
Resumo
Type 1 diabetes (T1D) is a chronic condition characterized by glucose fluctuations. Laboratory studies suggest that cognition is reduced when glucose is very low (hypoglycemia) and very high (hyperglycemia). Until recently, technological limitations prevented researchers from understanding how naturally-occurring glucose fluctuations impact cognitive fluctuations. This study leveraged advances in continuous glucose monitoring (CGM) and cognitive ecological momentary assessment (EMA) to characterize dynamic, within-person associations between glucose and cognition in naturalistic environments. Using CGM and EMA, we obtained intensive longitudinal measurements of glucose and cognition (processing speed, sustained attention) in 200 adults with T1D. First, we used hierarchical Bayesian modeling to estimate dynamic, within-person associations between glucose and cognition. Consistent with laboratory studies, we hypothesized that cognitive performance would be reduced at low and high glucose, reflecting cognitive vulnerability to glucose fluctuations. Second, we used data-driven lasso regression to identify clinical characteristics that predicted individual differences in cognitive vulnerability to glucose fluctuations. Large glucose fluctuations were associated with slower and less accurate processing speed, although slight glucose elevations (relative to person-level means) were associated with faster processing speed. Glucose fluctuations were not related to sustained attention. Seven clinical characteristics predicted individual differences in cognitive vulnerability to glucose fluctuations: age, time in hypoglycemia, lifetime severe hypoglycemic events, microvascular complications, glucose variability, fatigue, and neck circumference. Results establish the impact of glucose on processing speed in naturalistic environments, suggest that minimizing glucose fluctuations is important for optimizing processing speed, and identify several clinical characteristics that may exacerbate cognitive vulnerability to glucose fluctuations.
Palavras-chave
Referências
  1. Agiostratidou G, 2017, DIABETES CARE, V40, P1622, DOI 10.2337/dc17-1624
  2. Allen KV, 2015, DIABETES CARE, V38, P1108, DOI 10.2337/dc14-1657
  3. Arnold KD., 2020, Advancing Educational Research with Emerging Technology, P124, DOI 10.4018/978-1-7998-1173-2.CH007
  4. Axelsson J, 2008, CHRONOBIOL INT, V25, P297, DOI 10.1080/07420520802107031
  5. Bando S, 2015, ARTIF LIFE ROBOT, V20, P28, DOI 10.1007/s10015-014-0191-8
  6. Battelino T, 2023, LANCET DIABETES ENDO, V11, P42, DOI 10.1016/S2213-8587(22)00319-9
  7. Brands AMA, 2005, DIABETES CARE, V28, P726, DOI 10.2337/diacare.28.3.726
  8. Brands AMA, 2006, DIABETES, V55, P1800, DOI 10.2337/db05-1226
  9. Brose A, 2014, EMOTION, V14, P1, DOI 10.1037/a0035210
  10. Bubu OM, 2020, SLEEP MED REV, V50, DOI 10.1016/j.smrv.2019.101250
  11. Cameron FJ, 2019, LANCET CHILD ADOLESC, V3, P427, DOI 10.1016/S2352-4642(19)30055-0
  12. Caporale M, 2021, SLEEP BREATH, V25, P29, DOI 10.1007/s11325-020-02084-3
  13. Carr ALJ, 2022, DIABETOLOGIA, V65, P1854, DOI 10.1007/s00125-022-05778-3
  14. Cerino ES, 2021, FRONT DIGIT HEALTH, V3, DOI 10.3389/fdgth.2021.758031
  15. CHATTERJEE S, 1990, DECISION SCI, V21, P241, DOI 10.1111/j.1540-5915.1990.tb00327.x
  16. Chaytor NS, 2021, CLIN NEUROPSYCHOL, V35, P148, DOI 10.1080/13854046.2020.1811893
  17. Chaytor NS, 2019, J DIABETES COMPLICAT, V33, P91, DOI 10.1016/j.jdiacomp.2018.04.003
  18. Chung F, 2008, ANESTHESIOLOGY, V108, P812, DOI 10.1097/ALN.0b013e31816d83e4
  19. Core RT., 2022, R: A language and environment for statistical computing
  20. Cox DJ, 2005, DIABETES CARE, V28, P71, DOI 10.2337/diacare.28.1.71
  21. D'Ardenne K, 2020, FRONT PSYCHOL, V11, DOI 10.3389/fpsyg.2020.554127
  22. Dancey DR, 2003, CHEST, V123, P1544, DOI 10.1378/chest.123.5.1544
  23. Eelco V, 2020, NEUROBIOL DIS, V134, DOI 10.1016/j.nbd.2019.104608
  24. Elbalshy M, 2022, DIABETIC MED, V39, DOI 10.1111/dme.14854
  25. ElSayed NA, 2023, DIABETES CARE, V46, pS1, DOI [10.2337/dc23-Sint, 10.2337/dc23-SINT]
  26. Emmert-Streib F, 2019, MACH LEARN KNOW EXTR, V1, P359, DOI 10.3390/make1010021
  27. Esterman M, 2013, CEREB CORTEX, V23, P2712, DOI 10.1093/cercor/bhs261
  28. Ewing FME, 1998, PHYSIOL BEHAV, V64, P653, DOI 10.1016/S0031-9384(98)00120-6
  29. Fortenbaugh FC, 2015, PSYCHOL SCI, V26, P1497, DOI 10.1177/0956797615594896
  30. Friedman J, 2010, J STAT SOFTW, V33, P1, DOI 10.18637/jss.v033.i01
  31. Gelman A., 1992, Stat. Sci, V7, P457, DOI [DOI 10.1214/SS/1177011136, 10.1214/SS/1177011136]
  32. Germine L, 2021, NEUROPSYCHOPHARMACOL, V46, P209, DOI 10.1038/s41386-020-0757-1
  33. Goodrich B., 2024, rstanarm: {Bayesian} applied regression modeling via {Stan}
  34. Gosselin D, 2017, FRONT PSYCHOL, V8, DOI 10.3389/fpsyg.2017.01607
  35. Hartshorne JK, 2015, PSYCHOL SCI, V26, P433, DOI 10.1177/0956797614567339
  36. Hawks ZW, 2023, BIOL PSYCHIAT-COGN N, V8, P841, DOI 10.1016/j.bpsc.2022.12.002
  37. He ZH, 2004, ENDOCRIN METAB CLIN, V33, P215, DOI 10.1016/j.ecl.2003.12.003
  38. Hingorjo MR, 2012, J PAK MED ASSOC, V62, P36
  39. Holt RIG, 2021, DIABETES CARE, V44, P2589, DOI 10.2337/dci21-0043
  40. Hudson AN, 2020, NEUROPSYCHOPHARMACOL, V45, P21, DOI 10.1038/s41386-019-0432-6
  41. Jin CY, 2022, HELIYON, V8, DOI 10.1016/j.heliyon.2022.e10073
  42. Jun JE, 2019, DIABETES-METAB RES, V35, DOI 10.1002/dmrr.3092
  43. Kawaguchi Y, 2011, OBESITY, V19, P276, DOI 10.1038/oby.2010.170
  44. Kay Matthew, 2023, Zenodo, DOI 10.5281/ZENODO.1308151
  45. Kern W, 2001, NEUROENDOCRINOLOGY, V74, P270, DOI 10.1159/000054694
  46. Koa TB, 2021, J SLEEP RES, V30, DOI 10.1111/jsr.13252
  47. Kovatchev BP, 2015, DIABETES TECHNOL THE, V17, P766, DOI 10.1089/dia.2015.0276
  48. Kruschke JK, 2018, ADV METH PRACT PSYCH, V1, P270, DOI 10.1177/2515245918771304
  49. Kumar Naveen, 2018, Indian J Endocrinol Metab, V22, P780, DOI 10.4103/ijem.IJEM_58_18
  50. Law Charity W, 2020, F1000Res, V9, P1444, DOI 10.12688/f1000research.27893.1
  51. Makowski D., 2019, J OPEN SOURCE SOFTW, V4, P1541, DOI [DOI 10.21105/JOSS.01541, 10.21105/joss.01541]
  52. Mascarenhas Fonseca Luciana, 2023, JMIR Diabetes, V8, pe39750, DOI 10.2196/39750
  53. McAulay V, 2006, J CLIN PSYCHOPHARM, V26, P143, DOI 10.1097/01.jcp.0000203202.41947.6d
  54. McAulay V, 2001, DIABETES CARE, V24, P1745, DOI 10.2337/diacare.24.10.1745
  55. McCrimmon RJ, 2021, DIABETOLOGIA, V64, P971, DOI 10.1007/s00125-020-05369-0
  56. McCrimmon RJ, 2012, LANCET, V379, P2291, DOI 10.1016/S0140-6736(12)60360-2
  57. McNeilly AD, 2018, DIABETOLOGIA, V61, P743, DOI 10.1007/s00125-018-4548-8
  58. Albuquerque PM, 2023, ACTA NEUROL BELG, V123, P1421, DOI 10.1007/s13760-023-02250-w
  59. NESSELROADE JR, 1991, VISIONS OF AESTHETICS, THE ENVIRONMENT & DEVELOPMENT : THE LEGACY OF JOACHIM F WOHLWILL, P213
  60. Nevo-Shenker M, 2021, HORM RES PAEDIAT, V94, P115, DOI 10.1159/000517352
  61. Nicosia J, 2023, BEHAV RES METHODS, V55, P2800, DOI 10.3758/s13428-022-01925-1
  62. Ozougwu J.C., 2013, J. Physiol. Pathophysiol, V4, P46, DOI [10.5897/jpap2013.0001, 10.5897/JPAP2013.0001, DOI 10.5897/JPAP2013.0001]
  63. Passell E, 2021, BEHAV RES METHODS, V53, P2544, DOI 10.3758/s13428-021-01597-3
  64. PASTORE RE, 1974, PSYCHOL BULL, V81, P945, DOI 10.1037/h0037357
  65. Perfect MM, 2012, SLEEP, V35, P81, DOI 10.5665/sleep.1590
  66. Pyatak EA, 2023, DIABETES CARE, V46, P1345, DOI 10.2337/dc22-2008
  67. PYLYSHYN Z W, 1988, Spatial Vision, V3, P179, DOI 10.1163/156856888X00122
  68. Rodríguez-Fernández JM, 2017, J GERIATR PSYCH NEUR, V30, P67, DOI 10.1177/0891988716686832
  69. Russell SJ, 2016, DIABETES CARE, V39, P1161, DOI 10.2337/dc15-2449
  70. Schächinger H, 2003, PHARMACOL BIOCHEM BE, V75, P915, DOI 10.1016/S0091-3057(03)00167-9
  71. Schwartz MW, 2023, DIABETES CARE, V46, P237, DOI 10.2337/dc22-1445
  72. Seaquist ER, 2022, DIABETES CARE, V45, P2799, DOI 10.2337/dc22-1242
  73. Segev N, 2021, J CLIN MED, V10, DOI 10.3390/jcm10091893
  74. SERVICE FJ, 1970, DIABETES, V19, P644, DOI 10.2337/diab.19.9.644
  75. Shah VN, 2018, DIABETES TECHNOL THE, V20, P428, DOI 10.1089/dia.2018.0143
  76. Shalimova A, 2019, J CLIN ENDOCR METAB, V104, P2239, DOI 10.1210/jc.2018-01315
  77. Sherr JL, 2022, DIABETES CARE, V45, P3058, DOI 10.2337/dci22-0018
  78. Simon N, 2011, J STAT SOFTW, V39, P1
  79. Singh S, 2023, J MED INTERNET RES, V25, DOI 10.2196/45028
  80. Sliwinski M., 2010, INDIVIDUAL PATHWAYS, P37, DOI 10.1037/12140-003
  81. Sliwinski MJ, 2006, PSYCHOL AGING, V21, P545, DOI 10.1037/0882-7974.21.3.545
  82. Sliwinski MJ, 2018, ASSESSMENT, V25, P14, DOI 10.1177/1073191116643164
  83. Sommerfield AJ, 2003, NEUROPSYCHOLOGY, V17, P125, DOI 10.1037/0894-4105.17.1.125
  84. Treviño M, 2021, COGN RES, V6, DOI 10.1186/s41235-021-00313-1
  85. Van Belle TL, 2011, PHYSIOL REV, V91, P79, DOI 10.1152/physrev.00003.2010
  86. Vanek J, 2020, SLEEP MED, V72, P50, DOI 10.1016/j.sleep.2020.03.017
  87. Vasudevan S, 2022, NPJ DIGIT MED, V5, DOI 10.1038/s41746-022-00583-z
  88. Vehtari A., 2021, Bayesian Analysis, V16, P667, DOI 10.1214/20-BA1221
  89. Wechsler D., 2002, WPPSI-III administration and scoring manual
  90. West NA, 2017, J AM GERIATR SOC, V65, P1282, DOI 10.1111/jgs.14786
  91. West RK, 2016, ALZHEIMERS DEMENT, V12, P925, DOI 10.1016/j.jalz.2016.03.017
  92. Wickham H., 2019, J OPEN SOURCE SOFTW, V4, P1686, DOI [DOI 10.21105/JOSS.01686, 10.21105/joss.01686]
  93. Wright RJ, 2009, DIABETES CARE, V32, P1503, DOI 10.2337/dc09-0212
  94. Wrighten SA, 2009, BBA-MOL BASIS DIS, V1792, P444, DOI 10.1016/j.bbadis.2008.10.013