Metabolic-Associated Fatty Liver Disease and Cognitive Performance in Type 2 Diabetes: Basal Data from the Phytate, Neurodegeneration and Diabetes (PHYND) Study
Abstract
:1. Introduction
2. Materials and Methods
2.1. Subjects and Design
2.2. Data Recollection
2.2.1. Clinical and Anthropometric Data
2.2.2. Laboratory Data
2.2.3. Diagnostic Assessment
2.2.4. Liver Fibrosis and Steatosis Risk Assessment
2.3. Defining Cognitive Performance as an Outcome Variable
2.4. Statistical Analysis
3. Results
3.1. Baseline Patient Characteristics
3.2. Laboratory Analysis Parameters
3.3. Fibrosis Score and Hepatoestatosis Index among MoCA Groups
3.4. Fibrosis Score and MoCA Subscales
3.5. Optimal Cut-off of Age and FIB-4 Values Associated with MCI or Dementia (vs. no-CI) in Patients with T2DM
3.6. Risk Factors Associated with MCI or Dementia
4. Discussion
5. Conclusions
Author Contributions
Funding
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
- Jeeyavudeen, M.S.; Khan, S.K.; Fouda, S.; Pappachan, J.M. Management of metabolic-associated fatty liver disease: The diabetology perspective. World J. Gastroenterol. 2023, 29, 126–143. [Google Scholar] [CrossRef] [PubMed]
- Danaei, G.; Lu, Y.; Singh, G.M.; Carnahan, E.; Stevens, G.A.; Cowan, M.J.; Farzadfar, F.; Lin, J.K.; Finucane, M.M.; Rao, M.; et al. Cardiovascular disease, chronic kidney disease, and diabetes mortality burden of cardiometabolic risk factors from 1980 to 2010: A comparative risk assessment. Lancet Diabetes Endocrinol. 2014, 2, 634–647. [Google Scholar] [CrossRef]
- Chan, K.E.; Koh, T.J.L.; Tang, A.S.P.; Quek, J.; Yong, J.N.; Tay, P.; Tan, D.J.H.; Lim, W.H.; Lin, S.Y.; Huang, D.; et al. Global Prevalence and Clinical Characteristics of Metabolic-Associated Fatty Liver Disease: A Meta-Analysis and Systematic Review of 10 739 607 Individuals. J. Clin. Endocrinol. Metab. 2022, 107, 2691–2700. [Google Scholar] [CrossRef] [PubMed]
- GBD 2016 Dementia Collaborators Global, Regional, and National Burden of Alzheimer’s Disease and Other Dementias, 1990–2016: A Systematic Analysis for the Global Burden of Disease Study 2016. Lancet Neurol. 2019, 18, 88–106. [CrossRef]
- Livingston, G.; Huntley, J.; Sommerlad, A.; Ames, D.; Ballard, C.; Banerjee, S.; Brayne, C.; Burns, A.; Cohen-Mansfield, J.; Cooper, C.; et al. Dementia Prevention, Intervention, and Care: 2020 Report of the Lancet Commission. Lancet 2020, 396, 413–446. [Google Scholar] [CrossRef]
- Albai, O.; Frandes, M.; Timar, R.; Roman, D.; Timar, B. Risk Factors for Developing Dementia in Type 2 Diabetes Mellitus Patients with Mild Cognitive Impairment. Dis. Treat. 2019, 15, 167–175. [Google Scholar] [CrossRef]
- George, E.S.; Sood, S.; Daly, R.M.; Tan, S.-Y. Is there an association between non-alcoholic fatty liver disease and cognitive function? A systematic review. BMC Geriatr. 2022, 22, 47. [Google Scholar] [CrossRef]
- Kang, S.; Kim, E.; Cho, H.; Kim, D.J.; Kim, H.C.; Jung, S.J. Associations between non-alcoholic fatty liver disease and cognitive impairment and the effect modification of inflammation. Sci. Rep. 2022, 12, 12614. [Google Scholar] [CrossRef]
- Mohamed-Mohamed, H.; García-Morales, V.; Lara, E.M.S.; González-Acedo, A.; Pardo-Moreno, T.; Tovar-Gálvez, M.I.; Melguizo-Rodríguez, L.; Ramos-Rodríguez, J.J. Physiological Mechanisms Inherent to Diabetes Involved in the Development of Dementia: Alzheimer’s Disease. Neurol. Int. 2023, 15, 1253–1272. [Google Scholar] [CrossRef]
- Ninomiya, T. Epidemiological Evidence of the Relationship between Diabetes and Dementia. Adv. Exp. Med. Biol. 2019, 1128, 13–25. [Google Scholar] [CrossRef]
- Bello-Chavolla, O.Y.; Antonio-Villa, N.E.; Vargas-Vázquez, A.; Ávila-Funes, J.A.; Aguilar-Salinas, C.A. Pathophysiological Mechanisms Linking Type 2 Diabetes and Dementia: Review of Evidence from Clinical, Translational and Epidemiological Research. Curr. Diabetes Rev. 2019, 15, 456–470. [Google Scholar] [CrossRef] [PubMed]
- Bharadwaj, P.; Wijesekara, N.; Liyanapathirana, M.; Newsholme, P.; Ittner, L.; Fraser, P.; Verdile, G. The Link between Type 2 Diabetes and Neurodegeneration: Roles for Amyloid-β, Amylin, and Tau Proteins. J. Alzheimers Dis. 2017, 59, 421–432. [Google Scholar] [CrossRef]
- ElSayed, N.A.; Aleppo, G.; Aroda, V.R.; Bannuru, R.R.; Brown, F.M.; Bruemmer, D.; Collins, B.S.; Hilliard, M.E.; Isaacs, D.; Johnson, E.L.; et al. 13. Older Adults: Standards of Care in Diabetes-2023. Diabetes Care 2023, 46, S216–S229. [Google Scholar] [CrossRef]
- National Institute on Aging. Assessing Cognitive Impairment in Older Patients: A Quick Guide for Health Care Professionals. Available online: https://www.nia.nih.gov/health/health-care-professionals-information/assessing-cognitive-impairment-older-patients (accessed on 2 January 2024).
- Yaffe, K.; Falvey, C.; Hamilton, N.; Schwartz, A.V.; Simonsick, E.M.; Satterfield, S.; Cauley, J.A.; Rosano, C.; Launer, L.J.; Strotmeyer, E.S.; et al. Diabetes, Glucose Control, and 9-Year Cognitive Decline among Older Adults without Dementia. Arch. Neurol. 2012, 69, 1170–1175. [Google Scholar] [CrossRef] [PubMed]
- Kjærgaard, K.; Mikkelsen, A.C.D.; Wernberg, C.W.; Grønkjær, L.L.; Eriksen, P.L.; Damholdt, M.F.; Mookerjee, R.P.; Vilstrup, H.; Lauridsen, M.M.; Thomsen, K.L. Cognitive Dysfunction in Non-Alcoholic Fatty Liver Disease—Current Knowledge, Mechanisms and Perspectives. J. Clin. Med. 2021, 10, 673. [Google Scholar] [CrossRef] [PubMed]
- Yki-Järvinen, H. Non-Alcoholic Fatty Liver Disease as a Cause and a Consequence of Metabolic Syndrome. Lancet Diabetes Endocrinol. 2014, 2, 901–910. [Google Scholar] [CrossRef]
- Elahy, M.; Jackaman, C.; Mamo, J.C.; Lam, V.; Dhaliwal, S.S.; Giles, C.; Nelson, D.; Takechi, R. Blood–brain barrier dysfunction developed during normal aging is associated with inflammation and loss of tight junctions but not with leukocyte recruitment. Immun. Ageing 2015, 12, 2. [Google Scholar] [CrossRef]
- Wang, H.; Chen, F.; Du, Y.-F.; Long, Y.; Reed, M.N.; Hu, M.; Suppiramaniam, V.; Hong, H.; Tang, S.-S. Targeted Inhibition of RAGE Reduces Amyloid-β Influx across the Blood-Brain Barrier and Improves Cognitive Deficits in Db/Db Mice. Neuropharmacology 2018, 131, 143–153. [Google Scholar] [CrossRef]
- Wang, Y.-R.; Wang, Q.-H.; Zhang, T.; Liu, Y.-H.; Yao, X.-Q.; Zeng, F.; Li, J.; Zhou, F.-Y.; Wang, L.; Yan, J.-C.; et al. Associations between Hepatic Functions and Plasma Amyloid-Beta Levels—Implications for the Capacity of Liver in Peripheral Amyloid-Beta Clearance. Mol. Neurobiol. 2016, 54, 2338–2344. [Google Scholar] [CrossRef] [PubMed]
- Lin, Y.-K.; Cai, X.-R.; Chen, J.-Z.; Hong, H.-J.; Tu, K.; Chen, Y.-L.; Du, Q. Non-alcoholic fatty liver disease causally affects the brain cortical structure:A Mendelian randomization study. Front. Neurosci. 2024, 17, 1305624. [Google Scholar] [CrossRef]
- Craft, S.; Watson, G.S. Insulin and Neurodegenerative Disease: Shared and Specific Mechanisms. Lancet Neurol. 2004, 3, 169–178. [Google Scholar] [CrossRef]
- Medina-Julio, D.; Ramírez-Mejía, M.M.; Cordova-Gallardo, J.; Peniche-Luna, E.; Cantú-Brito, C.; Mendez-Sanchez, N. From Liver to Brain: How MAFLD/MASLD Impacts Cognitive Function. Med. Sci. Monit. 2024, 30, e943417-13. [Google Scholar] [CrossRef]
- Cheon, S.Y.; Song, J. Novel Insights into Non-Alcoholic Fatty Liver Disease and Dementia: Insulin Resistance, Hyperammonemia, Gut Dysbiosis, Vascular Impairment, and Inflammation. Cell Biosci. 2022, 12, 99. [Google Scholar] [CrossRef] [PubMed]
- Pujol, A.; Sanchis, P.; Tamayo, M.I.; Nicolau, J.; Grases, F.; Espino, A.; Estremera, A.; Rigo, E.; Amengual, G.J.; Rodríguez, M.; et al. Oral Phytate Supplementation on the Progression of Mild Cognitive Impairment, Brain Iron Deposition and Diabetic Retinopathy in Patients with Type 2 Diabetes: A Concept Paper for a Randomized Double Blind Placebo Controlled Trial (the PHYND Trial). Front. Endocrinol. 2024, 15, 1332237. [Google Scholar] [CrossRef]
- Milani, S.A.; Marsiske, M.; Cottler, L.B.; Chen, X.; Striley, C.W. Optimal Cutoffs for the Montreal Cognitive Assessment Vary by Race and Ethnicity. Alzheimers Dement. 2018, 10, 773–781. [Google Scholar] [CrossRef] [PubMed]
- American Diabetes Association. Diagnosis and Classification of Diabetes Mellitus. Diabetes Care 2014, 37 (Suppl. S1), S81–S90. [Google Scholar] [CrossRef]
- Chobanian, A.V.; Bakris, G.L.; Black, H.R.; Cushman, W.C.; Green, L.A.; Izzo, J.L.; Jones, D.W.; Materson, B.J.; Oparil, S.; Wright, J.T.; et al. Seventh Report of the Joint National Committee on Prevention, Detection, Evaluation, and Treatment of High Blood Pressure. Hypertension 2003, 42, 1206–1252. [Google Scholar] [CrossRef] [PubMed]
- National Cholesterol Education Program (NCEP) Expert Panel on Detection, Evaluation, and Treatment of High Blood Cholesterol in Adults (Adult Treatment Panel III). Third Report of the National Cholesterol Education Program (NCEP) Expert Panel on Detection, Evaluation, and Treatment of High Blood Cholesterol in Adults (Adult Treatment Panel III) Final Report. Circulation 2002, 106, 3143–3421. [Google Scholar] [CrossRef]
- Levey, A.S.; Eckardt, K.-U.; Tsukamoto, Y.; Levin, A.; Coresh, J.; Rossert, J.; Zeeuw, D.D.; Hostetter, T.H.; Lameire, N.; Eknoyan, G. Definition and classification of chronic kidney disease: A position statement from Kidney Disease: Improving Global Outcomes (KDIGO). Kidney Int. 2005, 67, 2089–2100. [Google Scholar] [CrossRef]
- Solomon, S.D.; Chew, E.; Duh, E.J.; Sobrin, L.; Sun, J.K.; VanderBeek, B.L.; Wykoff, C.C.; Gardner, T.W. Diabetic Retinopathy: A Position Statement by the American Diabetes Association. Diabetes Care 2017, 40, 412–418. [Google Scholar] [CrossRef]
- ElSayed, N.A.; Aleppo, G.; Aroda, V.R.; Bannuru, R.R.; Brown, F.M.; Bruemmer, D.; Collins, B.S.; Cusi, K.; Hilliard, M.E.; Isaacs, D.; et al. Addendum. 4. Comprehensive Medical Evaluation and Assessment of Comorbidities: Standards of Care in Diabetes-2023. Diabetes Care 2023;46(Suppl. 1):S49–S67. Diabetes Care 2023, 46, 1718–1720. [Google Scholar] [CrossRef] [PubMed]
- McPherson, S.; Hardy, T.; Dufour, J.-F.; Petta, S.; Romero-Gomez, M.; Allison, M.; Oliveira, C.P.; Francque, S.; Van Gaal, L.; Schattenberg, J.M.; et al. Age as a Confounding Factor for the Accurate Non-Invasive Diagnosis of Advanced NAFLD Fibrosis. Am. J. Gastroenterol. 2017, 112, 740–751. [Google Scholar] [CrossRef]
- Chalasani, N.; Younossi, Z.; Lavine, J.E.; Diehl, A.M.; Brunt, E.M.; Cusi, K.; Charlton, M.; Sanyal, A.J. The Diagnosis and Management of Non-Alcoholic Fatty Liver Disease: Practice Guideline by the American Association for the Study of Liver Diseases, American College of Gastroenterology, and the American Gastroenterological Association. Hepatology 2012, 55, 2005–2023. [Google Scholar] [CrossRef]
- Lee, J.-H.; Kim, D.; Kim, H.J.; Lee, C.-H.; Yang, J.I.; Kim, W.; Kim, Y.J.; Yoon, J.-H.; Cho, S.-H.; Sung, M.-W.; et al. Hepatic steatosis index: A simple screening tool reflecting nonalcoholic fatty liver disease. Dig. Liver Dis. 2010, 42, 503–508. [Google Scholar] [CrossRef]
- Scharre, D.W.; Chang, S.-I.; Murden, R.A.; Lamb, J.; Beversdorf, D.Q.; Kataki, M.; Nagaraja, H.N.; Bornstein, R.A. Self-Administered Gerocognitive Examination (SAGE): A Brief Cognitive Assessment Instrument for Mild Cognitive Impairment (MCI) and Early Dementia. Alzheimer Dis. Assoc. Disord. 2010, 24, 64–71. [Google Scholar] [CrossRef] [PubMed]
- Seo, S.W.; Gottesman, R.F.; Clark, J.M.; Hernaez, R.; Chang, Y.; Kim, C.; Ha, K.H.; Guallar, E.; Lazo, M. Nonalcoholic Fatty Liver Disease Is Associated with Cognitive Function in Adults. Neurology 2016, 86, 1136–1142. [Google Scholar] [CrossRef]
- Weinstein, A.A.; de Avila, L.; Paik, J.; Golabi, P.; Escheik, C.; Gerber, L.; Younossi, Z.M. Cognitive Performance in Individuals with Non-Alcoholic Fatty Liver Disease and/or Type 2 Diabetes Mellitus. Psychosomatics 2018, 59, 567–574. [Google Scholar] [CrossRef] [PubMed]
- Weinstein, G.; Davis-Plourde, K.; Himali, J.J.; Zelber-Sagi, S.; Beiser, A.S.; Seshadri, S. Non-alcoholic fatty liver disease, liver fibrosis score and cognitive function in middle-aged adults: The Framingham Study. Liver Int. 2019, 39, 1713–1721. [Google Scholar] [CrossRef]
- Williamson, R.M.; Price, J.F.; Glancy, S.; Perry, E.; Nee, L.D.; Hayes, P.C.; Frier, B.M.; Van Look, L.A.F.; Johnston, G.I.; Reynolds, R.M.; et al. Prevalence of and Risk Factors for Hepatic Steatosis and Nonalcoholic Fatty Liver Disease in People with Type 2 Diabetes: The Edinburgh Type 2 Diabetes Study. Diabetes Care 2011, 34, 1139–1144. [Google Scholar] [CrossRef]
- Berg, E.v.D.; Dekker, J.M.; Nijpels, G.; Kessels, R.P.; Kappelle, L.J.; de Haan, E.H.; Heine, R.J.; Stehouwer, C.D.; Biessels, G.J. Cognitive Functioning in Elderly Persons with Type 2 Diabetes and Metabolic Syndrome: The Hoorn Study. Dement. Geriatr. Cogn. Disord. 2008, 26, 261–269. [Google Scholar] [CrossRef]
- Takahashi, A.; Kono, S.; Wada, A.; Oshima, S.; Abe, K.; Imaizumi, H.; Fujita, M.; Hayashi, M.; Okai, K.; Miura, I.; et al. Reduced Brain Activity in Female Patients with Non-Alcoholic Fatty Liver Disease as Measured by near-Infrared Spectroscopy. PLoS ONE 2017, 12, e0174169. [Google Scholar] [CrossRef] [PubMed]
- Tarter, R.E.; Hegedus, A.M.; Van Thiel, D.H.; Schade, R.R.; Gavaler, J.S.; Starzl, T.E. Nonalcoholic Cirrhosis Associated with Neuropsychological Dysfunction in the Absence of Overt Evidence of Hepatic Encephalopathy. Gastroenterology 1984, 86, 1421–1427. [Google Scholar] [CrossRef] [PubMed]
- You, Y.; Liu, Z.; Chen, Y.; Xu, Y.; Qin, J.; Guo, S.; Huang, J.; Tao, J. The Prevalence of Mild Cognitive Impairment in Type 2 Diabetes Mellitus Patients: A Systematic Review and Meta-Analysis. Acta Diabetol. 2021, 58, 671–685. [Google Scholar] [CrossRef]
- Lopez-De-Andres, A.; Jimenez-Garcia, R.; Zamorano-Leon, J.J.; Omaña-Palanco, R.; Carabantes-Alarcon, D.; Hernández-Barrera, V.; De Miguel-Diez, J.; Cuadrado-Corrales, N. Prevalence of Dementia among Patients Hospitalized with Type 2 Diabetes Mellitus in Spain, 2011–2020: Sex-Related Disparities and Impact of the COVID-19 Pandemic. Int. J. Environ. Res. Public Health 2023, 20, 4923. [Google Scholar] [CrossRef]
- Zuñiga, A.M.O.; Simó, R.; Rodriguez-Gómez, O.; Hernández, C.; Rodrigo, A.; Jamilis, L.; Campo, L.; Alegret, M.; Boada, M.; Ciudin, A. Clinical Applicability of the Specific Risk Score of Dementia in Type 2 Diabetes in the Identification of Patients with Early Cognitive Impairment: Results of the MOPEAD Study in Spain. J. Clin. Med. 2020, 9, 2726. [Google Scholar] [CrossRef]
- Brito, D.V.C.; Esteves, F.; Rajado, A.T.; Silva, N.; ALFA score Consortium; Andrade, R.; Apolónio, J.; Calado, S.; Faleiro, L.; Matos, C.; et al. Assessing cognitive decline in the aging brain: Lessons from rodent and human studies. npj Aging 2023, 9, 23. [Google Scholar] [CrossRef] [PubMed]
- Anstey, K.J.; Cherbuin, N.; Budge, M.; Young, J. Body mass index in midlife and late-life as a risk factor for dementia: A meta-analysis of prospective studies. Obes. Rev. 2011, 12, e426–e437. [Google Scholar] [CrossRef]
- Puzianowska-Kuznicka, M.; Kuryłowicz, A.; Walkiewicz, D.; Borkowska, J.; Owczarz, M.; Olszanecka-Glinianowicz, M.; Wieczorowska-Tobis, K.; Skalska, A.; Szybalska, A.; Mossakowska, M. Obesity Paradox in Caucasian Seniors: Results of the PolSenior Study. J. Nutr. Health Aging 2019, 23, 796–804. [Google Scholar] [CrossRef]
- Benito-León, J.; Mitchell, A.J.; Hernández-Gallego, J.; Bermejo-Pareja, F. Obesity and impaired cognitive functioning in the elderly: A population-based cross-sectional study (NEDICES). Eur. J. Neurol. 2013, 20, 899-e77. [Google Scholar] [CrossRef]
- Gottesman, R.F.; Rawlings, A.M.; Sharrett, A.R.; Albert, M.; Alonso, A.; Bandeen-Roche, K.; Coker, L.H.; Coresh, J.; Couper, D.J.; Griswold, M.E.; et al. Impact of Differential Attrition on the Association of Education with Cognitive Change Over 20 Years of Follow-up: The ARIC Neurocognitive Study. Am. J. Epidemiol. 2014, 179, 956–966. [Google Scholar] [CrossRef]
- Elias, P.K.; Elias, M.F.; D’Agostino, R.B.; Cupples, L.A.; Wilson, P.W.; Silbershatz, H.; Wolf, P.A. NIDDM and Blood Pressure as Risk Factors for Poor Cognitive Performance. The Framingham Study. Diabetes Care 1997, 20, 1388–1395. [Google Scholar] [CrossRef] [PubMed]
- Legdeur, N.; van der Lee, S.J.; de Wilde, M.; van der Lei, J.; Muller, M.; Maier, A.B.; Visser, P.J. The association of vascular disorders with incident dementia in different age groups. Alzheimer’s Res. Ther. 2019, 11, 47. [Google Scholar] [CrossRef]
- Patrono, C. The Multifaceted Clinical Readouts of Platelet Inhibition by Low-Dose Aspirin. J. Am. Coll. Cardiol. 2015, 66, 74–85. [Google Scholar] [CrossRef]
- Li, H.; Li, W.; Zhang, X.; Ma, X.-C.; Zhang, R.-W. Aspirin Use on Incident Dementia and Mild Cognitive Decline: A Systematic Review and Meta-Analysis. Front. Aging Neurosci. 2021, 12, 578071. [Google Scholar] [CrossRef] [PubMed]
- Hussain, S.; Singh, A.; Rahman, S.O.; Habib, A.; Najmi, A.K. Calcium channel blocker use reduces incident dementia risk in elderly hypertensive patients: A meta-analysis of prospective studies. Neurosci. Lett. 2018, 671, 120–127. [Google Scholar] [CrossRef]
- Lovell, M.A.; Abner, E.; Kryscio, R.; Xu, L.; Fister, S.X.; Lynn, B.C. Calcium Channel Blockers, Progression to Dementia, and Effects on Amyloid Beta Peptide Production. Oxid. Med. Cell. Longev. 2015, 2015, 787805. [Google Scholar] [CrossRef] [PubMed]
- Xie, Z.; Tong, S.; Chu, X.; Feng, T.; Geng, M. Chronic Kidney Disease and Cognitive Impairment: The Kidney-Brain Axis. Kidney Dis. 2022, 8, 275–285. [Google Scholar] [CrossRef]
- Hooper, S.R.; Gerson, A.C.; Butler, R.W.; Gipson, D.S.; Mendley, S.R.; Lande, M.B.; Shinnar, S.; Wentz, A.; Matheson, M.; Cox, C.; et al. Neurocognitive Functioning of Children and Adolescents with Mild-to-Moderate Chronic Kidney Disease. Clin. J. Am. Soc. Nephrol. 2011, 6, 1824–1830. [Google Scholar] [CrossRef]
- Tamura, M.K.; Muntner, P.; Wadley, V.; Cushman, M.; Zakai, N.A.; Bradbury, B.D.; Kissela, B.; Unverzagt, F.; Howard, G.; Warnock, D.; et al. Albuminuria, Kidney Function, and the Incidence of Cognitive Impairment Among Adults in the United States. Am. J. Kidney Dis. 2011, 58, 756–763. [Google Scholar] [CrossRef]
- Pinto-Sietsma, S.-J.; Janssen, W.M.; Hillege, H.L.; Navis, G.; DE Zeeuw, D.; DE Jong, P.E. Urinary Albumin Excretion Is Associated with Renal Functional Abnormalities in a Nondiabetic Population. J. Am. Soc. Nephrol. 2000, 11, 1882–1888. [Google Scholar] [CrossRef]
- Roehlen, N.; Crouchet, E.; Baumert, T.F. Liver Fibrosis: Mechanistic Concepts and Therapeutic Perspectives. Cells 2020, 9, 875. [Google Scholar] [CrossRef] [PubMed]
All Patients | Normal Cognitive Function | Mild Cognitive Impairment | Dementia | p-Value | ||
---|---|---|---|---|---|---|
(n = 219) | (n = 64) | (n = 118) | (n = 36) | |||
Anthropometric Measures | ||||||
Age (years) | 70.6 ± 6.2 | 67.0 ± 5.2 | 71.4 ± 6.2 | 73.9 ± 4.7 | <0.001 | |
Gender (male) | 118 (54.1%) | 35 (54.7%) | 65 (55.1%) | 18 (50.0%) | 0.861 | |
BMI (kg/m2) | 31.0 ± 5.9 | 31.6 ± 6.7 | 30.9 ± 5.6 | 30.4 ± 5.1 | 0.558 | |
Waist circumference (cm) | 110 ± 16 | 112 ± 17 | 109 ± 15 | 107 ± 13 | 0.502 | |
Systolic blood pressure (mmHg) | 144 ± 19 | 139 ± 18 | 145 ± 20 | 144 ± 20 | 0.162 | |
Diastolic blood pressure (mmHg) | 79 ± 11 | 78 ± 12 | 80 ± 11 | 77 ± 10 | 0.448 | |
Pulse pressure (mmHg) | 79 ± 10 | 78.6 ± 7.8 | 79 ± 10 | 80 ± 13 | 0.946 | |
Comorbidities, Toxics, and Clinical History | ||||||
Time from diagnosis DM2 | ||||||
Less than 5 years | 8 (3.7%) | 3 (4.7%) | 4 (3.4%) | 1 (2.8%) | 0.794 | |
Between 5 and 10 years | 14 (6.4%) | 6 (9.4%) | 6 (5.1%) | 2 (5.6%) | ||
More than 10 years | 196 (89.9%) | 55 (85.9%) | 108 (91.5%) | 33 (91.7%) | ||
Diabetic retinopathy | 18 (8.3%) | 3 (4.7%) | 12 (10.2%) | 3 (8.3%) | 0.439 | |
Diabetic nephropathy | 38 (17.4%) | 6 (9.4%) | 23 (19.5%) | 9 (25.0%) | 0.097 | |
Intermittent claudication | 3 (1.4%) | 2 (3.1%) | 1 (0.8%) | 0 (0.0%) | 0.335 | |
Cerebrovascular accident | 10 (4.6%) | 4 (6.3%) | 6 (5.1%) | 0 (0.0%) | 0.330 | |
Polyneuropathy | 19 (8.7%) | 7 (10.9%) | 11 (9.3%) | 1 (2.8%) | 0.359 | |
Diabetic foot | 5 (2.3%) | 0 (0.0%) | 4 (3.4%) | 1 (2.8%) | 0.337 | |
Intermittent claudication | 4 (1.8%) | 1 (1.6%) | 3 (2.5%) | 0 (0.0%) | 0.598 | |
Atherosclerosis | 200 (91.7%) | 60 (93.8%) | 105 (89.0%) | 35 (97.2%) | 0.288 | |
Hypertension | 189 (86.7%) | 54 (84.4%) | 103 (87.3%) | 32 (88.9%) | 0.785 | |
Smoking | 17 (7.8%) | 7 (10.9%) | 9 (7.6%) | 1 (2.8%) | 0.584 | |
Alcohol | 12 (5.5%) | 4 (6.3%) | 6 (5.1%) | 2 (5.6%) | 0.637 | |
Prior cardiovascular disease | 1 (0.5%) | 1 (1.6%) | 0 (0.0%) | 0 (0.0%) | 0.072 | |
Chronic kidney disease (CKD) | 41 (18.8%) | 8 (12.5%) | 20 (16.9%) | 13 (36.1%) | 0.011 | |
2 (eGFR 89–60 mL/min/1.73 m2) | 87 (39.9%) | 26 (40.6%) | 48 (40.7%) | 13 (36.1%) | ||
3a (eGFR 59–45 mL/min/1.73 m2) | 14 (6.4%) | 3 (4.7%) | 9 (7.6%) | 2 (5.6%) | ||
3b (eGFR 44–30 mL/min/1.73 m2) | 20 (9.2%) | 4 (6.3%) | 9 (7.6%) | 7 (19.4%) | ||
4 (eGFR 29–15 mL/min/1.73 m2) | 0 (0.0%) | 4 (3.4%) | 2 (5.6%) | 6 (2.8%) | ||
Medication Use | ||||||
Angiotensin-converting enzyme inhibitors/angiotensin II receptor-blocking agents | 177 (81.2%) | 54 (84.4%) | 96 (81.4%) | 27 (75.0%) | 0.514 | |
Beta-blockers | 81 (37.2%) | 17 (26.6%) | 46 (39.0%) | 18 (50.0%) | 0.055 | |
Calcium antagonists | 61 (28.0%) | 13 (20.3%) | 32 (27.1%) | 16 (44.4%) | 0.034 | |
Statins | 187 (85.8%) | 53 (82.8%) | 102 (86.4%) | 32 (88.9%) | 0.674 | |
25-hydroxyvitamin D | 90 (41.3%) | 20 (31.3%) | 53 (44.9%) | 17 (47.2%) | 0.148 | |
Calcium supplements | 15 (6.9%) | 2 (3.1%) | 10 (8.5%) | 3 (8.3%) | 0.369 | |
Antiplatelets | 90 (41.3%) | 13 (20.3%) | 59 (50.0%) | 18 (50.0%) | <0.001 | |
Insulin | 98 (45.0%) | 33 (51.6%) | 47 (39.8%) | 18 (50.0%) | 0.253 | |
Oral antidiabetic medication | 211 (96.8%) | 64 (100%) | 114 (96.6%) | 33 (91.7%) | 0.075 | |
Uric medication (allopurinol or febuxostat)) | 35 (16.1%) | 8 (12.5%) | 20 (16.9%) | 7 (19.4%) | 0.614 | |
Thiazides | 65 (29.8%) | 19 (29.7%) | 36 (30.5%) | 10 (27.8%) | 0.925 | |
Furosemide | 28 (12.8%) | 10 (15.6%) | 15 (12.7%) | 3 (8.3%) | 0.577 | |
Metformin | 166 (76.1%) | 55 (85.9%) | 85 (72.0%) | 26 (72.2%) | 0.092 | |
Semaglutide | 50 (22.9%) | 19 (29.7%) | 27 (22.9%) | 4 (11.1%) | 0.105 | |
Liraglutide | 14 (6.5%) | 6 (9.4%) | 6 (5.1%) | 2 (5.6%) | 0.524 | |
Dulaglutide | 36 (16.8%) | 8 (12.5%) | 21 (17.8%) | 7 (7 19.4%) | 0.573 | |
Dapaglifozine | 35 (16.1%) | 8 (12.5%) | 20 (16.9%) | 7 (19.4%) | 0.614 | |
Empaglifozine | 65 (29.8%) | 19 (29.7%) | 36 (30.5%) | 10 (27.8%) | 0.925 | |
Pioglitazone | 15 (6.9%) | 2 (3.1%) | 10 (8.5%) | 3 (8.3%) | 0.369 |
All Patients (n = 219) | Normal Cognitive Function (n = 64) | Mild Cognitive Impairment (n = 118) | Dementia (n = 36) | p-Value for Trend | |
---|---|---|---|---|---|
Glomerular filter MDR4IDMS (mL/min/sup) | 74 ± 20 | 76 ± 20 | 75 ± 20 | 66 ± 22 | 0.013 |
Glomerular filter CKD-EPI (mL/min/1.73 m2) | 74 ± 20 | 77 ± 18 | 75 ± 20 | 66 ± 22 | 0.004 |
Leukocytes (×109/L) | 7.7 ± 2.2 | 7.7 ± 1.9 | 7.6 ± 2.4 | 7.7 ± 2.3 | 0.954 |
Hemoglobin (g/dL) | 14.0 ± 1.6 | 14.4 ± 1.5 | 14.1 ± 1.7 | 13.3 ± 1.6 | 0.001 |
Glucose (mg/dL) | 144 ± 50 | 146 ± 45 | 141 ± 46 | 154 ± 69 | 0.440 |
Urea (mg/dL) | 44 ± 19 | 43 ± 18 | 42 ± 17 | 52 ± 24 | 0.035 |
Creatinine (mg/dL) | 0.96 ± 0.35 | 0.92 ± 0.28 | 0.95 ± 0.37 | 1.07 ± 0.39 | 0.042 |
Urate (mg/dL) | 5.3 ± 1.7 | 5.6 ± 1.8 | 5.2 ± 1.7 | 5.2 ± 1.7 | 0.328 |
Sodium (mEq/L) | 140.3 ± 2.1 | 140.4 ± 2.1 | 140.4 ± 2.1 | 139.9 ± 2.1 | 0.230 |
Potassium (mEq/L) | 4.62 ± 0.48 | 4.59 ± 0.44 | 4.61 ± 0.45 | 4.73 ± 0.62 | 0.160 |
Chloride (mEq/L) | 103.9 ± 3.0 | 104.0 ± 2.8 | 103.9 ± 2.9 | 103.7 ± 3.7 | 0.684 |
Calcium (mg/dL) | 9.48 ± 0.44 | 9.47 ± 0.42 | 9.52 ± 0.43 | 9.37 ± 0.46 | 0.214 |
Phosphate (mg/dL) | 3.48 ± 0.62 | 3.57 ± 0.39 | 3.41 ± 0.64 | 3.53 ± 0.86 | 0.887 |
Total cholesterol (mg/dL) | 154 ± 39 | 153 ± 34 | 153 ± 41 | 159 ± 41 | 0.491 |
HDL cholesterol (mg/dL) | 43 ± 11 | 43 ± 8 | 43 ± 11 | 43 ± 13 | 0.783 |
LDL cholesterol (mg/dL) | 80 ± 30 | 80 ± 27 | 81 ± 33 | 82 ± 28 | 0.770 |
Triglycerides (mg/dL) | 151 ± 85 | 154 ± 88 | 149 ± 88 | 153 ± 67 | 1.000 |
Albumin (g/dL) | 4.38 ± 0.38 | 4.47 ± 0.37 | 4.37 ± 0.38 | 4.25 ± 0.39 | 0.004 |
Phosphatase alkaline (U/L) | 76 ± 30 | 75 ± 28 | 75 ± 27 | 80 ± 44 | 0.401 |
HbA1c_NGSPDCCT (%) | 7.4 ± 1.3 | 7.5 ± 1.3 | 7.3 ± 1.1 | 7.7 ± 1.8 | 0.488 |
25-hydroxyvitamin D3 (ng/mL) | 26 ± 15 | 27 ± 13 | 25 ± 14 | 28 ± 19 | 0.692 |
Creatinine urine (mg/dL) | 77 ± 43 | 72 ± 34 | 76 ± 42 | 87 ± 59 | 0.179 |
Urinary albumin/creatinine ratio (mg/g) | 60 ± 146 | 27 ± 32 | 54 ± 95 | 132 ± 299 | 0.020 |
13.8 [8.5–34.6] | 13.1 [10.2–33.2] | 14.2 [8.2–78.3] | 13.7 [7.4–42.5] | ||
Microalbuminurie (mg/dL) | 4.7 ± 14.7 | 2.5 ± 3.3 | 5.3 ± 18.9 | 6.8 ± 12.1 | 0.280 |
Parathyroid hormone (pg/dL) | 147.9 ± 88.0 | 133.1 ± 66.5 | 149.9 ± 104.7 | 163.7 ± 17.9 | 0.652 |
Fibrinogen (mg/dL) | 481.9 ± 84.4 | 497.4 ± 73.7 | 475.1 ± 91.9 | 474.6 ± 93.1 | 0.648 |
Transferrin (mg/dL) | 287.2 ± 47.4 | 295.4 ± 48.3 | 283.8 ± 46.3 | 281.2 ± 49.6 | 0.310 |
C-Reactive protein (mg/dL) | 0.88 ± 5.37 | 0.46 ± 0.39 | 1.24 ± 7.28 | 0.48 ± 0.44 | 0.978 |
Platelets (109/L) | 231.6 ± 92.8 | 233.5 ± 63.5 | 233.0 ± 110.1 | 223.7 ± 74.3 | 0.607 |
AST (UI/L) | 20.1 ± 9.8 | 20.0 ± 6.9 | 20.0 ± 10.7 | 20.2 ± 11.3 | 0.935 |
ALT (UI/L) | 21.1 ± 11.2 | 22.8 ± 11.7 | 20.2 ± 9.6 | 20.8 ± 14.7 | 0.439 |
Red cell distribution width (%) | 14.0 ± 1.4 | 14.1 ± 1.5 | 14.0 ± 1.4 | 13.9 ± 1.2 | 0.435 |
Lymphocytes (%) | 30.4 ± 9.1 | 30.0 ± 8.7 | 30.3 ± 9.0 | 31.5 ± 10.3 | 0.430 |
Mean cell volume (fL) | 90.7 ± 7.3 | 91.0 ± 7.0 | 90.9 ± 7.2 | 89.8 ± 8.2 | 0.431 |
White blood cell count (1000 cells/UL) | 7.7 ± 2.2 | 7.7 ± 1.9 | 7.6 ± 2.4 | 7.7 ± 2.3 | 0.954 |
Crude β Coefficient | 95% CI for Crude β | p-Value | Adjusted β Coefficient * | 95% CI for Adj. β | p-Value | |
---|---|---|---|---|---|---|
Visuospatial/executive function | −0.103 | (−0.236–0.031) | 0.130 | −0.066 | (−0.204–−0.970) | 0.343 |
Naming | −0.082 | (−0.215–0.052) | 0.229 | −0.120 | (−0.255–−0.375) | 0.080 |
Attention | −0.033 | (−0.167–0.101) | 0.633 | −0.059 | (−0.201–−0.943) | 0.415 |
Language fluency | −0.214 | (−0.345–−0.083) | 0.001 | −0.174 | (−0.318–0.973) | 0.018 |
Abstraction | −0.115 | (−0.248–0.018) | 0.090 | −0.133 | (−0.267–−0.063) | 0.054 |
Memory | −0.066 | (−0.200–0.067) | 0.329 | −0.011 | (−0.149–−0.278) | 0.879 |
Orientation | 0.055 | (−0.079–0.189) | 0.416 | 0.054 | (−0.079–2.230) | 0.423 |
Total MOCA | −0.151 | (−0.283–−0.018) | 0.026 | −0.154 | (−0.299–0.234) | 0.038 |
Univariate Analysis | Multivariate Model 1 | Multivariate Model 2 | |||||||
---|---|---|---|---|---|---|---|---|---|
Crude OR | (95% C.I. for Crude OR) | p-Value | Adj OR | (95% C.I. for Adj. OR) | p-Value | Adj OR | (95% C.I.f or Adj. OR) | p-Value | |
Age > 70.5 years | 4.102 | (2.160–7.791) | <0.001 | 2.850 | (1.378–5.894) | 0.005 | 3.328 | (1.675–6.612) | 0.001 |
Gender (female) | 1.016 | (0.758–1.362) | 0.915 | 1.094 | (0.549–2.178) | 0.798 | |||
CKD (yes vs. no) | 1.909 | (0.828–4.399) | 0.129 | 1.135 | (0.427–3.019) | 0.800 | |||
Coronary disease (yes vs. no) | 2.293 | (1.003–5.240) | 0.049 | 1.214 | (0.401–3.673) | 0.731 | |||
Betablockers (yes vs. no) | 1.966 | (1.036–3.731) | 0.039 | 1.386 | (0.647–2.970) | 0.402 | |||
Antiaggregants (yes vs. no) | 3.923 | (1.976–7.790) | 0.000 | 3.920 | (1.751–8.775) | 0.001 | 3.859 | (1.885–7.901) | <0.001 |
Calcium antagonists (yes vs. no) | 1.776 | (0.884–3.570) | 0.107 | 1.535 | (0.689–3.420) | 0.295 | |||
FIB-4 > 1.54 | 2.942 | (1.479–5.852) | 0.002 | 2.365 | (1.102–5.077) | 0.027 | 2.195 | (1.040–4.631) | 0.039 |
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Pujol, A.; Sanchis, P.; Tamayo, M.I.; Godoy, S.; Calvó, P.; Olmos, A.; Andrés, P.; Speranskaya, A.; Espino, A.; Estremera, A.; et al. Metabolic-Associated Fatty Liver Disease and Cognitive Performance in Type 2 Diabetes: Basal Data from the Phytate, Neurodegeneration and Diabetes (PHYND) Study. Biomedicines 2024, 12, 1993. https://doi.org/10.3390/biomedicines12091993
Pujol A, Sanchis P, Tamayo MI, Godoy S, Calvó P, Olmos A, Andrés P, Speranskaya A, Espino A, Estremera A, et al. Metabolic-Associated Fatty Liver Disease and Cognitive Performance in Type 2 Diabetes: Basal Data from the Phytate, Neurodegeneration and Diabetes (PHYND) Study. Biomedicines. 2024; 12(9):1993. https://doi.org/10.3390/biomedicines12091993
Chicago/Turabian StylePujol, Antelm, Pilar Sanchis, María I. Tamayo, Samantha Godoy, Paula Calvó, Asier Olmos, Pilar Andrés, Aleksandra Speranskaya, Ana Espino, Ana Estremera, and et al. 2024. "Metabolic-Associated Fatty Liver Disease and Cognitive Performance in Type 2 Diabetes: Basal Data from the Phytate, Neurodegeneration and Diabetes (PHYND) Study" Biomedicines 12, no. 9: 1993. https://doi.org/10.3390/biomedicines12091993