Background: Insulin resistance (IR) is a major risk factor for Alzheimer’s disease (AD) and is primarily driven by obesity, another AD risk factor. The biological mechanism by which IR predisposes to AD is unknown. Genetic clustering analyses of type 2 diabetes (T2D) loci can help characterize which mechanism of impaired insulin action may be involved in AD and related traits.
Method: We constructed five genetic clusters related to IR (Obesity, Lipodystrophy, Liver/Lipid, ALP [alkaline phosphatase] negative, and Hyper-Insulin Secretion) based on a recent clustering analysis of T2D loci (PMID: 36538063). We evaluated the association of each cluster with the Homeostatic Model Assessment for Insulin Resistance (HOMA-IR) and neurological traits (AD, dementia, general cognitive function, and four brain MRI volumes) in >38k participants (36% men, mean age 55yrs (14.5)) with diverse race or ethnicity, from the Trans-Omics for Precision Medicine (TOPMed) Program. We conducted both pooled and race/ethnicity-stratified association analyses (European-50%, African-American-22.5%, and Hispanic/Latino-21%). We used logistic or linear mixed-effect models, adjusted for age, sex, study, and the first 11 genetic principal components. We accounted for relatedness and allowed for heterogeneous variances across studies.
Result: We confirmed the association of each IR genetic cluster with HOMA-IR in the pooled analysis (2.6E-66≤P≤2E-4) as well as in group-stratified analyses (6.3E-39≤P≤0.05), except for the Hyper-Insulin Secretion cluster in African-Americans (P=0.30). The genetic clusters were not significantly associated with neurological outcomes after adjusting for multiple testing (P=0.05/Ngroups/Nclusters/Noutcomes=0.05/4/5/7=3.6×10-4). We observed nominal associations (P<0.05) for two genetic clusters. The Lipodystrophy cluster was negatively associated with intracranial volume in the pooled analysis (beta=-0.51, P=0.003) as well as in European (beta=-0.47, P=0.02) and Hispanic/Latino (beta=-1.23, P=0.01) participants. The Hyper-Insulin Secretion cluster was negatively associated with hippocampal volume in Hispanic/Latino participants (beta=-0.005, P=0.03), and positively associated with AD and dementia (OR=1.02 [1.001-1.038], P=0.03) in European participants.
Conclusion: Our findings are consistent with the literature suggesting that IR may be associated with lower brain volumes and increased AD risk. Our analyses shed light on two biological mechanisms of impaired insulin action potentially involved in AD and related traits, with genetic associations that may differ by race or ethnicity.
Funding: R00AG066849

Objective:
To better characterize genetic variations underlying neurological traits by leveraging whole-genome sequencing (WGS) data in participants of diverse ancestry from the Trans-Omics for Precision Medicine (TOPMed) program.

Background:
Genome-wide association studies (GWAS), conducted mainly in European (EA) participants, have identified common genetic variants with modest effect sizes for brain volumes, accepted endophenotypes of vascular brain injury, and few genetic loci for insulin resistance (IR), a major risk factor for stroke and dementia.

Design/Methods:
1. We performed WGS association analyses of hippocampal (HV), total brain, lateral ventricular (LVV), and intracranial (ICV) volumes in ~8k participants (62% EA, 21% African-American (AA), 16% Hispanic/Latino (HA)).
2. We derived three ancestry-specific polygenic scores (PSEA, PSAA, PSHA) based on UK Biobank reference panels and fasting insulin GWAS summary statistics, adjusted for body mass index. We generated a multi-ancestry PS (Multi-PS) by fitting a linear combination of the standardized PSEA, PSAA, and PSHA that most accurately predicted IR in a validation set of ~17k participants without diabetes (34% EA, 28% AA, 38% HA). We evaluated the association of the PSs with neurological traits in a testing set of ~14k participants (66% EA, 22% AA, 11% HA).
Mixed-effect models were used for all analyses, adjusted for age, sex, study, and principal components, and accounting for relatedness and trait variance variability. Brain volume analyses were adjusted for ICV and excluded participants with dementia or stroke.

Results:
We identified novel significant hits (P<5×10-8) at 2q22 & 5q14 for LVV and at 1q32 & 13q14 for HV. The top 13q14 variant was common in AA (13%), less frequent in HA (1.4%), and rare in EA (0.1%), and had a consistent effect size across population groups (-0.27 to -0.34). The Multi-PS was strongly associated with IR (proportion of variance explained: 12%). The PSEA was significantly (P<0.002) associated with ICV (P=7×10-7), and suggestively with LVV (P=0.004).

Conclusions:
By leveraging TOPMed multi-ancestry and WGS data, we identified new loci underlying brain volumes, including ancestry-specific associations, and confirmed the association of IR with brain volumes.

Disclosure and Study Support:
None.
NINDS F30NS124136, NIA T32AG57461, K99AG066849, U01AG058589, NHLBI R01s HL131136, HL105756

Insulin resistance (IR) is a major risk factor for Alzheimer’s Disease (AD) and has been associated with cognitive impairment, dementia, and neurodegeneration. Few genetic loci have been uncovered by genome-wide association studies (GWAS) of IR, limiting the proportion of variance explained (PVE) of IR genetic instruments. We constructed polygenic scores (PSs) for IR based on whole-genome sequencing (WGS) data from the Trans-Omics for Precision Medicine (TOPMed). We derived ancestry-specific PSs using PRS-CSx based on ancestry-specific UK Biobank reference panels and fasting insulin (FI) GWAS summary statistics adjusted for body mass index (BMI). We generated a multi-ancestry PSFI (Multi-PSFI) by fitting a linear combination of the standardized ancestry-specific PSFIs that most accurately predicted HOMA-IR in Nvalidation~17k participants without diabetes (34% European-EA, 28% African-AA, 38% Hispanic-HA), and evaluated its association with AD, dementia, general cognitive function (GENCOG), and four brain volumes in Ntesting~14k participants (66% EA, 22% AA, 11% HA). Logistic or linear mixed-effect models were adjusted for age, sex, study, 11 genetic principal components, and accounted for relatedness using a genetic relationship matrix. IR, GENCOG, and brain volumes analyses were additionally adjusted for BMI, education, and intracranial volume respectively. Statistically significant associations were defined by P<0.05/Ntraits/NPSs=0.05/7/4=0.002. Multi-PSFI was strongly associated with HOMA-IR (PVEJoint=12%). No significant or suggestive association was detected for the Multi-PSFI, the AA-PSFI, or the HA-PSFI with the neurological outcomes. EA-PSFI was significantly associated with intracranial volume (P=7E-07), and suggestively with lateral ventricular (P=0.004) and total brain volumes (P=0.05). By leveraging multi-ancestry and WGS data, we increased the IR PSs PVE and confirmed the association of IR with brain volumes. Funded by NIA K99AG066849.

Background: Insulin resistance (IR) is a major risk factor for Alzheimer’s Disease (AD) and has been associated with cognitive impairment, dementia, and neurodegeneration. Though the association between IR and AD has been explored genetically, the proportion of variance explained (PVE) by the IR genetic instruments has been limited, due to few genetic loci identified in genome-wide association studies (GWAS) of IR.
Methods: We constructed polygenic scores (PSs) for fasting insulin (FI) based on the Trans-Omics for Precision Medicine (TOPMed) Freeze 9 whole-genome sequencing (WGS) data. We used PRS-CSx (Ruan et al., Nat Genet. 2022) to generate ancestry-specific PSs (European, African, and Hispanic/Latino), with weights derived based on ancestry-specific UK Biobank reference panels and FI GWAS summary statistics adjusted for body mass index (BMI) (Chen et al., Nat Genet., 2021). We used MetaSubtract (Nolte et al., Eur J Hum Genet. 2017) to remove the effect of TOPMed studies from the FI meta-analyses. We generated a multi-ancestry PSFI by fitting a linear combination of the standardized ancestry-specific PSs that most accurately predicted HOMA-IR in five TOPMed cohorts (validation set, N~17k participants [34% European, 28% African, 38% Hispanic] without diabetes), adjusting for BMI. We then evaluated the association of the multi-ancestry PSFI with HOMA-IR, AD, dementia, general cognitive function, and four brain volumes in eight TOPMed cohorts (testing set, ~14k participants [66% European, 22% African, 11% Hispanic]). Association analyses were performed using logistic or linear mixed-effect models adjusted for age, sex, study, 11 genetic principal components, and accounting for relatedness using a genetic relationship matrix. General cognitive function and brain volumes analyses were additionally adjusted for education and intracranial volume (ICV) respectively. We used a threshold of P<0.05/Ntraits/Ntests=0.05/7/4=0.002 to define an association as significant.
Results: The multi-ancestry PSFI was strongly associated with HOMA-IR (PVEJoint=12%). No significant or suggestive association was detected for the multi-ancestry, the African, or the Hispanic PSFI with any of the neurological outcomes. The European PSFI was significantly associated with ICV (P=7E-07), and suggestively associated with lateral ventricular (P=0.004) and total brain volumes (P=0.05).
Conclusion: By leveraging multi-ancestry and WGS data, we increased the PVE of the genetic instruments for IR and confirmed the association of IR with brain volumes. The identified European-specific associations require further investigation.
Funding: NIH K99AG066849-02