Association of genetic scores related to insulin resistance with neurological outcomes in ancestrally diverse cohorts from the Trans-Omics for Precision Medicine (TOPMed) program

doi:10.1038/s42003-025-08674-9

Association of genetic scores related to insulin resistance with neurological outcomes in ancestrally diverse cohorts from the Trans-Omics for Precision Medicine (TOPMed) program

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Producción científica: Article › revisión exhaustiva

Resumen

To better characterize the potential biological mechanisms underlying insulin resistance (IR) and dementia, we derive cross-population and population specific polygenic scores [PSs] for fasting insulin and IR-related partitioned PSs [pPSs]. We conduct a cross-sectional study of the associations of these genetic scores with neurological outcomes in >17k participants (36% men, mean age 55 yrs) from the Trans-Omics for Precision Medicine (TOPMed) program (50% Non-Hispanic White, 23% Black/African American, 21% Hispanic/Latino American, and 4% Asian American). We report significant negative associations (P < 0.002) of the cross-population (P = 1.3 × 10^-5) and European (P_EA = 3.0 × 10^-8) fasting insulin PSs with total cranial volume, and of a metabolic syndrome European PS with general cognitive function (B_EA = -0.13, P_EA = 0.0002) and lateral ventricular volume (B_EA = 0.09, P_EA = 0.002). We identify suggestive negative associations (P < 0.007) of metabolic syndrome and obesity pPSs with general cognitive function, and of lipodystrophy pPSs with total cranial volume. A higher genetic predisposition to IR is associated with lower brain size, and a genetic predisposition to specific IR-related type 2 diabetes subtypes, such as metabolic syndrome and mechanisms of IR mediated through obesity and lipodystrophy, is potentially involved in cognitive decline.

Idioma original	English
Número de artículo	1352
Publicación	Communications Biology
Volumen	8
N.º	1
DOI	https://doi.org/10.1038/s42003-025-08674-9
Estado	Published - dic 2025

Nota bibliográfica

Publisher Copyright:
© The Author(s) 2025.

Financiación

We gratefully acknowledge the studies and participants who provided biological samples and data for TOPMed. We acknowledge the TOPMed Program. The TOPMed Banner is provided as a Supplementary file (Supplementary Data 3). We acknowledge the TOPMed Diabetes and the TOPMed Neurocognitive working groups. A list of members with their affiliations for each working group is provided as a Supplementary file (Supplementary Data 4). We acknowledge the Type 2 Diabetes Global Genetics Initiative (T2DGGI) consortium. Membership of the T2DGGI consortium with affiliations is available at: https://www.diagram-consortium.org/T2DGGI.html [diagram-consortium.org] and provided in the Supplement. We acknowledge the Meta-Analyses of Glucose and Insulin-related traits Consortium (MAGIC). More information about the Consortium can be found at: https://magicinvestigators.org/index.html. A list of authors with their affiliations who contributed to the MAGIC FI meta-analysis is provided in the Supplement. Molecular data for the Trans-Omics in Precision Medicine (TOPMed) program was supported by the National Heart, Lung, and Blood Institute (NHLBI). Study-specific omics support information is provided in the Supplement. Core support, including centralized genomic read mapping and genotype calling, along with variant quality metrics and filtering, was provided by the TOPMed Informatics Research Center (3R01HL-117626-02S1; contract HHSN268201800002I). Core support, including phenotype harmonization, data management, sample-identity QC, and general program coordination, was provided by the TOPMed Data Coordinating Center (R01HL-120393; U01HL-120393; contract HHSN268201800001I). This work is funded through NIA R00 AG066849 (PI C.S.). J.C.F. is funded through NHLBI K24 HL157960. X.J. is funded through KL2TR002646, R21 AG075791. Y.Z. is funded through NHLBI R01HL151855 (PI J.B.M.). M.S.U. is funded through a Doris Duke Foundation grant 2022063. M.F. is funded through U01 AG058589. K.A. Gonzalez is funded through an NSF GRFP fellowship (2021-2024). A.B. is funded through R01 AG054076. C.S.D. is funded through R01 AG054076, P30 AG072972. C.L.S. is funded through R01 AG059727, R01 AG082360, U01 NS125513, P30 AG066546. S.S. is funded through R01 HL105756, R01 AG033193, P30 AG066546, RF1 AG059421, R01 AG054076, R01 AG049607. We gratefully acknowledge the studies and participants who provided biological samples and data for TOPMed. We acknowledge the TOPMed Program. The TOPMed Banner is provided as a Supplementary file (Supplementary Data ) . We acknowledge the TOPMed Diabetes and the TOPMed Neurocognitive working groups. A list of members with their affiliations for each working group is provided as a Supplementary file (Supplementary Data ). We acknowledge the Type 2 Diabetes Global Genetics Initiative (T2DGGI) consortium. Membership of the T2DGGI consortium with affiliations is available at: https://www.diagram-consortium.org/T2DGGI.html [diagram-consortium.org] and provided in the Supplement. We acknowledge the Meta-Analyses of Glucose and Insulin-related traits Consortium (MAGIC). More information about the Consortium can be found at: https://magicinvestigators.org/index.html . A list of authors with their affiliations who contributed to the MAGIC FI meta-analysis is provided in the Supplement. Molecular data for the Trans-Omics in Precision Medicine (TOPMed) program was supported by the National Heart, Lung, and Blood Institute (NHLBI). Study-specific omics support information is provided in the Supplement. Core support, including centralized genomic read mapping and genotype calling, along with variant quality metrics and filtering, was provided by the TOPMed Informatics Research Center (3R01HL-117626-02S1; contract HHSN268201800002I). Core support, including phenotype harmonization, data management, sample-identity QC, and general program coordination, was provided by the TOPMed Data Coordinating Center (R01HL-120393; U01HL-120393; contract HHSN268201800001I). This work is funded through NIA R00 AG066849 (PI C.S.). J.C.F. is funded through NHLBI K24 HL157960. X.J. is funded through KL2TR002646, R21 AG075791. Y.Z. is funded through NHLBI R01HL151855 (PI J.B.M.). M.S.U. is funded through a Doris Duke Foundation grant 2022063. M.F. is funded through U01 AG058589. K.A. Gonzalez is funded through an NSF GRFP fellowship (2021-2024). A.B. is funded through R01 AG054076. C.S.D. is funded through R01 AG054076, P30 AG072972. C.L.S. is funded through R01 AG059727, R01 AG082360, U01 NS125513, P30 AG066546. S.S. is funded through R01 HL105756, R01 AG033193, P30 AG066546, RF1 AG059421, R01 AG054076, R01 AG049607.

Financiadores	Número del financiador
T2DGGI
National Heart, Lung, and Blood Institute (NHLBI)
National Institute on Aging	KL2TR002646, K24 HL157960, R21 AG075791, R00 AG066849, R01HL151855
TOPMed Informatics Research Center	U01HL-120393, HHSN268201800001I, HHSN268201800002I, R01HL-120393, 3R01HL-117626-02S1
National Science Foundation Arctic Social Science Program	R01 AG049607, R01 AG059727, U01 NS125513, R01 AG054076, R01 AG033193, P30 AG072972, R01 AG082360, P30 AG066546, RF1 AG059421, 2021-2024, R01 HL105756
Doris Duke Charitable Foundation	2022063, U01 AG058589

ODS de las Naciones Unidas

Este resultado contribuye a los siguientes Objetivos de Desarrollo Sostenible

Good health and well being

ASJC Scopus subject areas

Medicine (miscellaneous)
General Biochemistry, Genetics and Molecular Biology
General Agricultural and Biological Sciences

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10.1038/s42003-025-08674-9

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title = "Association of genetic scores related to insulin resistance with neurological outcomes in ancestrally diverse cohorts from the Trans-Omics for Precision Medicine (TOPMed) program",

abstract = "To better characterize the potential biological mechanisms underlying insulin resistance (IR) and dementia, we derive cross-population and population specific polygenic scores [PSs] for fasting insulin and IR-related partitioned PSs [pPSs]. We conduct a cross-sectional study of the associations of these genetic scores with neurological outcomes in >17k participants (36\% men, mean age 55 yrs) from the Trans-Omics for Precision Medicine (TOPMed) program (50\% Non-Hispanic White, 23\% Black/African American, 21\% Hispanic/Latino American, and 4\% Asian American). We report significant negative associations (P < 0.002) of the cross-population (P = 1.3 × 10-5) and European (PEA = 3.0 × 10-8) fasting insulin PSs with total cranial volume, and of a metabolic syndrome European PS with general cognitive function (BEA = -0.13, PEA = 0.0002) and lateral ventricular volume (BEA = 0.09, PEA = 0.002). We identify suggestive negative associations (P < 0.007) of metabolic syndrome and obesity pPSs with general cognitive function, and of lipodystrophy pPSs with total cranial volume. A higher genetic predisposition to IR is associated with lower brain size, and a genetic predisposition to specific IR-related type 2 diabetes subtypes, such as metabolic syndrome and mechanisms of IR mediated through obesity and lipodystrophy, is potentially involved in cognitive decline.",

author = "Chlo{\'e} Sarnowski and Yixin Zhang and Farah Ammous and Shade, \{Lincoln M.P.\} and Daniel DiCorpo and Xueqiu Jian and Arnett, \{Donna K.\} and Austin, \{Thomas R.\} and Alexa Beiser and Bis, \{Joshua C.\} and John Blangero and Eric Boerwinkle and Jan Bressler and Curran, \{Joanne E.\} and DeCarli, \{Charles S.\} and Harsha Doddapaneni and Jos{\'e}e Dupuis and Fardo, \{David W.\} and Florez, \{Jose C.\} and Stacey Gabriel and Gibbs, \{Richard A.\} and Glahn, \{David C.\} and Namrata Gupta and Gonz{\'a}lez, \{Hector M.\} and Gonz{\'a}lez, \{Kevin A.\} and Konstantinos Hatzikotoulas and Hayden, \{Kathleen M.\} and Heckbert, \{Susan R.\} and Bertha Hidalgo and Alicia Huerta-Chagoya and Hughes, \{Timothy M.\} and Kardia, \{Sharon L.R.\} and Kooperberg, \{Charles L.\} and Launer, \{Lenore J.\} and Longstreth, \{W. T.\} and Ravi Mandla and Mathias, \{Rasika A.\} and Morris, \{Andrew P.\} and Mosley, \{Thomas H.\} and Nasrallah, \{Ilya M.\} and Paul Nyquist and Psaty, \{Bruce M.\} and Qibin Qi and Raffield, \{Laura M.\} and Rayner, \{Nigel W.\} and Reiner, \{Alexander P.\} and Satizabal, \{Claudia L.\} and Elizabeth Selvin and Sevilla-Gonzalez, \{Magdalena D.R.\} and Smith, \{Albert V.\}",

note = "Publisher Copyright: {\textcopyright} The Author(s) 2025.",

year = "2025",

month = dec,

doi = "10.1038/s42003-025-08674-9",

language = "English",

volume = "8",

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T1 - Association of genetic scores related to insulin resistance with neurological outcomes in ancestrally diverse cohorts from the Trans-Omics for Precision Medicine (TOPMed) program

AU - Sarnowski, Chloé

AU - Zhang, Yixin

AU - Ammous, Farah

AU - Shade, Lincoln M.P.

AU - DiCorpo, Daniel

AU - Jian, Xueqiu

AU - Arnett, Donna K.

AU - Austin, Thomas R.

AU - Beiser, Alexa

AU - Bis, Joshua C.

AU - Blangero, John

AU - Boerwinkle, Eric

AU - Bressler, Jan

AU - Curran, Joanne E.

AU - DeCarli, Charles S.

AU - Doddapaneni, Harsha

AU - Dupuis, Josée

AU - Fardo, David W.

AU - Florez, Jose C.

AU - Gabriel, Stacey

AU - Gibbs, Richard A.

AU - Glahn, David C.

AU - Gupta, Namrata

AU - González, Hector M.

AU - González, Kevin A.

AU - Hatzikotoulas, Konstantinos

AU - Hayden, Kathleen M.

AU - Heckbert, Susan R.

AU - Hidalgo, Bertha

AU - Huerta-Chagoya, Alicia

AU - Hughes, Timothy M.

AU - Kardia, Sharon L.R.

AU - Kooperberg, Charles L.

AU - Launer, Lenore J.

AU - Longstreth, W. T.

AU - Mandla, Ravi

AU - Mathias, Rasika A.

AU - Morris, Andrew P.

AU - Mosley, Thomas H.

AU - Nasrallah, Ilya M.

AU - Nyquist, Paul

AU - Psaty, Bruce M.

AU - Qi, Qibin

AU - Raffield, Laura M.

AU - Rayner, Nigel W.

AU - Reiner, Alexander P.

AU - Satizabal, Claudia L.

AU - Selvin, Elizabeth

AU - Sevilla-Gonzalez, Magdalena D.R.

AU - Smith, Albert V.

PY - 2025/12

Y1 - 2025/12

N2 - To better characterize the potential biological mechanisms underlying insulin resistance (IR) and dementia, we derive cross-population and population specific polygenic scores [PSs] for fasting insulin and IR-related partitioned PSs [pPSs]. We conduct a cross-sectional study of the associations of these genetic scores with neurological outcomes in >17k participants (36% men, mean age 55 yrs) from the Trans-Omics for Precision Medicine (TOPMed) program (50% Non-Hispanic White, 23% Black/African American, 21% Hispanic/Latino American, and 4% Asian American). We report significant negative associations (P < 0.002) of the cross-population (P = 1.3 × 10-5) and European (PEA = 3.0 × 10-8) fasting insulin PSs with total cranial volume, and of a metabolic syndrome European PS with general cognitive function (BEA = -0.13, PEA = 0.0002) and lateral ventricular volume (BEA = 0.09, PEA = 0.002). We identify suggestive negative associations (P < 0.007) of metabolic syndrome and obesity pPSs with general cognitive function, and of lipodystrophy pPSs with total cranial volume. A higher genetic predisposition to IR is associated with lower brain size, and a genetic predisposition to specific IR-related type 2 diabetes subtypes, such as metabolic syndrome and mechanisms of IR mediated through obesity and lipodystrophy, is potentially involved in cognitive decline.

AB - To better characterize the potential biological mechanisms underlying insulin resistance (IR) and dementia, we derive cross-population and population specific polygenic scores [PSs] for fasting insulin and IR-related partitioned PSs [pPSs]. We conduct a cross-sectional study of the associations of these genetic scores with neurological outcomes in >17k participants (36% men, mean age 55 yrs) from the Trans-Omics for Precision Medicine (TOPMed) program (50% Non-Hispanic White, 23% Black/African American, 21% Hispanic/Latino American, and 4% Asian American). We report significant negative associations (P < 0.002) of the cross-population (P = 1.3 × 10-5) and European (PEA = 3.0 × 10-8) fasting insulin PSs with total cranial volume, and of a metabolic syndrome European PS with general cognitive function (BEA = -0.13, PEA = 0.0002) and lateral ventricular volume (BEA = 0.09, PEA = 0.002). We identify suggestive negative associations (P < 0.007) of metabolic syndrome and obesity pPSs with general cognitive function, and of lipodystrophy pPSs with total cranial volume. A higher genetic predisposition to IR is associated with lower brain size, and a genetic predisposition to specific IR-related type 2 diabetes subtypes, such as metabolic syndrome and mechanisms of IR mediated through obesity and lipodystrophy, is potentially involved in cognitive decline.

UR - https://www.scopus.com/pages/publications/105016909601

UR - https://www.scopus.com/pages/publications/105016909601#tab=citedBy

U2 - 10.1038/s42003-025-08674-9

DO - 10.1038/s42003-025-08674-9

M3 - Article

C2 - 40993182

AN - SCOPUS:105016909601

VL - 8

JO - Communications Biology

JF - Communications Biology

IS - 1

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ER -

Association of genetic scores related to insulin resistance with neurological outcomes in ancestrally diverse cohorts from the Trans-Omics for Precision Medicine (TOPMed) program

Resumen

Nota bibliográfica

Financiación

ODS de las Naciones Unidas

ASJC Scopus subject areas

Acceder al documento

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