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Polymorphisms in LMNA and near a SERPINA gene cluster are associated with cognitive function in older people.

ABSTRACT: A recent genome-wide association (GWA) study of late-onset Alzheimer's disease (LOAD) identified 15 novel single nucleotide polymorphisms (SNPs) independent of ApoE. We hypothesised that variants associated with LOAD are also associated with poor cognitive function in elderly populations. We measured additive associations between the five most strongly associated LOAD SNPs and grouped Mini Mental State Examination (MMSE) scores. Variants were genotyped in respondents (mean age 79 years) from the Oxford Healthy Ageing project (OHAP) and other sites of the MRC Cognitive Function and Ageing Study (MRC-CFAS). In adjusted ordinal logistic models, two variants were associated with poorer cognitive function: rs11622883 (OR=1.14, 95% CI: 1.01-1.28, p=0.040) and rs505058 (OR=1.29, 95% CI: 1.02-1.64, p=0.036). These SNPs are close to a SERPINA gene cluster and within LMNA, respectively. The mechanisms underlying the associations with cognitive impairment and LOAD require further elucidation, but both genes are interesting candidates for involvement in age-related cognitive impairment.

SUBMITTER: Cluett C

PROVIDER: S-EPMC2975102 | biostudies-literature | 2010 Sep

REPOSITORIES: biostudies-literature

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Polymorphisms in LMNA and near a SERPINA gene cluster are associated with cognitive function in older people.

Cluett Christie C Brayne Carol C Clarke Robert R Evans Grimley G Matthews Fiona F Rubinsztein David C DC Huppert Felicia F Llewellyn David J DJ Rice Neil N Henley William W Frayling Timothy M TM Murray Anna A Melzer David D

Neurobiology of aging 20081009 9

A recent genome-wide association (GWA) study of late-onset Alzheimer's disease (LOAD) identified 15 novel single nucleotide polymorphisms (SNPs) independent of ApoE. We hypothesised that variants associated with LOAD are also associated with poor cognitive function in elderly populations. We measured additive associations between the five most strongly associated LOAD SNPs and grouped Mini Mental State Examination (MMSE) scores. Variants were genotyped in respondents (mean age 79 years) from the ...[more]

PMID: 18848371

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Project description:Background: The development of evidence-based interventions for delaying or preventing cognitive impairment is an important challenge. Most previous studies using self-report questionnaires face problems with reliability and consistency due to recall bias or misclassification among older people. Therefore, objective measurement of lifestyle components is needed to confirm the relationships between lifestyle factors and cognitive function. Aims: The current study examined the relationship between lifestyle factors collected with wearable sensors and cognitive function among community-dwelling older people using machine learning. Methods: In total, 855 participants (mean age: 73.8 years) wore a wristband sensor for 7.8 days on average every 3 months. Various lifestyle parameters were measured, including walking steps, conversation time, total sleep time (TST), sleep efficiency, time awake after sleep onset, awakening count, napping time, and heart rate. Random forest (RF) regression analysis was used to examine the relationships between total daily sensing data and Mini-Mental State Examination (MMSE) scores. Confounding factor analysis was conducted with models that were adjusted and unadjusted for demographic and vascular risk factors, and selected variables were assessed as risk and protective factors using partial dependence plots (PDPs). Results: Lifestyle data were collected for 31.3 ± 7.1 days per year using wristband sensors. RF regression analysis adjusted for age, gender, and education levels selected four variables, including number of walking steps, conversation time, TST, and heart rate. Moreover, walking steps, conversation time, and heart rate remained after RF regression analysis adjusted for demographic and vascular risk factors. Number of walking steps, conversation time, and heart rate were categorized as protective factors, whereas TST was categorized as a risk factor for cognitive function. Although PDPs of number of walking steps and heart rate revealed continuously increased MMSE scores, those of conversation time and TST and revealed that the tendency in the graph was reversed at the boundary of a particular threshold (321.1 min for conversation time, 434.1 min for TST). Conclusions: Lifestyle factors, such as physical activity, sleep, and social activity appear to be associated with cognitive function among older people. Physical activity and appropriate durations of sleep and conversation are important for cognitive function.

Project description:ObjectiveTo analyze the relationship between plasma metal elements, ApoE gene polymorphisms and the interaction between the two and impaired cognitive function in elderly population.MethodA stratified sample was drawn according to the age of the study population, and 911 subjects were included. Baseline information and health indicators were obtained, and cognitive function status was assessed by health examination, a general questionnaire and Mini-Mental Status Examination. Plasma metal elements were measured, and SNP typing was performed. Binary logistic regression was used to analyze the factors influencing cognitive function status and the association between the SNP genetic pattern of the ApoE gene and cognitive function.ResultsThe differences in gene frequencies and genotype frequencies of the ApoE rs7412 and rs7259620 genotype frequencies were statistically different between the cognitive impairment group and the control group (P < 0.05). statistically differences were found for the codominant model in rs7412-TT compared with the CC genotype (OR = 3.112 (1.159-8.359), P = 0.024) and rs7259620-AA compared with the GG genotype (OR = 1.588 (1.007-2.504), P = 0.047). Statistically differences were found in the recessive models rs7412-TT compared with (CC + CT) (OR = 2.979 (1.112-7.978), P = 0.030), rs7259620-AA compared with (GG + GA), and rs405509-GG compared with (TT + TG) (OR = 1.548(1.022-2.344), P = 0.039) all of which increased the risk of developing cognitive impairment. The differences in plasma Fe, Cu, and Rb concentrations between the case and control groups were significant (P < 0.05). The regression results showed that the plasma Cd concentrations in the Q1 range was a protective factor for cognitive function compared with Q4 (0.510 (0.291-0.892), P = 0.018). Furthermore, there was a multiplicative interaction between the codominant and recessive models for the Q2 concentrations of Cd and the rs7259620 loci, and the difference was significant, indicating increased risk of developing cognitive impairment (codominant model OR = 3.577 (1.496-8.555), P = 0.004, recessive model OR = 3.505 (1.479-8.307), P = 0.004). There was also a multiplicative interaction between Cd and the recessive model at the rs405509 loci, and the difference was significant, indicating increased risk of developing cognitive impairment (OR = 3.169 (1.400-7.175), P = 0.006).ConclusionThe ApoE rs7412, rs7259620 and rs405509 loci were associated with cognitive impairment in the elderly population, and there was an interaction between plasma metalloid Cd and the rs7259620 and rs405509 loci that increased the risk of cognitive impairment in the elderly population.

Dataset Information

Polymorphisms in LMNA and near a SERPINA gene cluster are associated with cognitive function in older people.

Publications

Polymorphisms in LMNA and near a SERPINA gene cluster are associated with cognitive function in older people.

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