Skip to main content
NIHPA Author Manuscripts logoLink to NIHPA Author Manuscripts
. Author manuscript; available in PMC: 2025 Mar 1.
Published in final edited form as: J Hypertens. 2023 Nov 22;42(3):484–489. doi: 10.1097/HJH.0000000000003620

FOXO3 longevity genotype attenuates the impact of hypertension on cerebral microinfarct risk

Kazuma Nakagawa 1,2,3, Randi Chen 4, G Webster Ross 5,6,7,8, Timothy A Donlon 9,10, Richard C Allsopp 11, D Craig Willcox 12,13, Brian J Morris 14,15,16, Bradley J Willcox 17,18,*, Kamal H Masaki 19,20,*
PMCID: PMC10873049  NIHMSID: NIHMS1943296  PMID: 38009316

Abstract

Objective:

The G-allele of FOXO3 SNP rs2802292, which is associated with human resilience and longevity, has been shown to attenuate the impact of hypertension on the risk of intracerebral haemorrhage. We sought to determine whether the FOXO3 G-allele similarly attenuates the impact of hypertension on the risk of cerebral microinfarcts (CMI).

Methods:

From a prospective population-based cohort of American men of Japanese ancestry from the Kuakini Honolulu Heart Program (KHHP) and Kuakini Honolulu-Asia Aging Study (KHAAS) that had brain autopsy data, age-adjusted prevalence of any CMI on brain autopsy was assessed. Logistic regression models, adjusted for age at death, cardiovascular risk factors, FOXO3 and APOE-ε4 genotypes, were utilized to determine the predictors of any CMI. Interaction of FOXO3 genotype and hypertension was analyzed.

Results:

Among 809 men with complete data, 511 (63.2%) participants had evidence of CMI. A full multivariable model demonstrated that body mass index (OR:1.07, 95% CI=1.01–1.14, P = 0.015) was the only predictor of CMI, while hypertension was a borderline predictor (OR:1.44, 95% CI=1.00–2.08, = 0.052). However, a significant interaction between FOXO3 G-allele carriage and hypertension was observed (P = 0.020). In the stratified analyses, among the participants without the longevity-associated FOXO3 G-allele, hypertension was a strong predictor of CMI (OR:2.25, 95% CI=1.34–3.77, P = 0.002), while among those with the longevity-associated FOXO3 G-allele, hypertension was not a predictor of CMI (OR:0.88, 95% CI: 0.51–1.54, P = 0.66).

Conclusions:

The longevity-associated FOXO3 G-allele mitigates the impact of hypertension on the risk of CMI.

Keywords: FOXO3, stroke, hypertension

INTRODUCTION

Cerebral microinfarcts (CMIs) are microscopic vascular lesions of the brain that are often “invisible” to the conventional structural neuroimaging studies. CMIs are detected mainly by histologic examination [1] with a mean diameter of approximately 0.2 mm [2] and characterized as regions of cellular death or tissue necrosis [35]. Prevalence of one or more detectable CMIs ranges from 16% to 46% among elderly populations who died from all causes [1], and CMI severity is associated with the development of dementia [6,7]. Although CMIs share similar histopathological characteristics of macroscopic infarcts, the pathophysiology of CMIs is not understood completely. Epidemiological studies have identified hypertension as an important risk factor for CMI [8,9]. How other genetic or environmental factors modulate the association between hypertension and CMI, however, has not been adequately studied.

Minor alleles of multiple single nucleotide polymorphisms (SNPs) located in the forkhead winged-helix box O type 3 gene (FOXO3) – particularly the G allele of SNP rs2802292 – have been strongly associated with human longevity [1012]. In the Kuakini Honolulu Heart Program (KHHP) cohort, carriers of the longevity-associated FOXO3 G-allele lived longer than subjects who were homozygous for the major (T) allele [10]. We subsequently showed that G-allele carriage was associated with protection against coronary artery disease (CAD)-associated mortality [13]. Another study also demonstrated the protective effect of FOXO3 G-allele against heart disease among older Japanese men [14]. Although the exact mechanism by which FOXO3 genotype is associated with healthy aging and increased lifespan remains to be fully clarified, it has been postulated that the longevity-associated FOXO3 G-allele may serve as a “resilience” gene by mitigating the adverse effects of chronic cardiometabolic stress on intracellular processes, thereby reducing the risk of life-threatening cardiovascular events [12,15].

More recent studies using the KHHP cohort demonstrated that the longevity-associated FOXO3 G-allele attenuates the deleterious impact of chronic hypertension on the long-term risk of spontaneous intracerebral haemorrhage (ICH) by possibly providing cerebrovascular resilience and protection against the adverse effects of chronic hypertension [16], and that FOXO3 G-allele carriage was associated with protection against Alzheimer’s disease in men with late-life hypertension [17]. Although FOXO3 has never been identified as a stroke-specific gene, its encoded protein, the transcription factor FOXO3, may nevertheless protect against CMIs by activating intracellular pathways in vessel walls so as to reduce the cumulative burden of hypertension on the cerebral vasculature. Assessing gene-environment interaction can improve understanding of how a known genotype (i.e., FOXO3 genotype) may modulate the end-organ manifestation of chronic cardiovascular diseases [18]. To date, the impact of the longevity-associated FOXO3 G-allele on hypertension-related risk of CMI has not been studied. Therefore, we sought to assess whether FOXO3 G-allele carriage protects against CMIs by mitigating the deleterious impact of hypertension on cerebral arterioles.

METHODS

Study Design and Subjects

The KHHP is a prospective population-based study of cardiovascular disease among American men of Japanese ancestry living in Hawaii. Starting in the mid-1960s, the KHHP began following 8,006 men of Japanese ancestry living on the island of Oahu for the development of CAD and stroke [1921]. Participants were identified using World War II Selective Service Registration files. They were 45 to 68 years old at baseline examination after recruitment between 1965 and 1968. They have been followed since then with periodic examinations, and continuous hospital surveillance for selected morbidity and all mortality through December 2000. Details of the selection process for the cohort have been published [21]. Morbidity and mortality have been assessed since the beginning of the study by monitoring hospital discharge records, death certificates, and local obituaries. Data collection is believed to be complete for all-cause mortality. Attrition in this cohort was very small – at the fourth examination only five men were lost to follow-up. The Kuakini Honolulu-Asia Aging Study (KHAAS) was a continuation of the KHHP that started in 1991 with the fourth examination of the KHHP and focused on cognitive disorders and dementia. All KHAAS participants were eligible for inclusion in the autopsy sample. Consent for each autopsy was provided by a next-of-kin family member or a legally authorized representative. Procedures performed were in accord with institutional guidelines and were approved by the Institutional Review Board of Kuakini Medical Center. Written informed consent was also obtained at all examination cycles. The study adhered to the observational cohort guideline.

Data Collection

Data on cardiovascular risk factors were obtained at the KHAAS baseline examination (examination 4 in 1991–1993). Hypertension at baseline was defined by systolic/diastolic blood pressure (SBP/DBP) of ≥140/90 mmHg or the self-reported use of anti-hypertensive medications. Body mass index (BMI) was defined as weight in kilograms divided by height in meters squared. Diabetes was defined by modified American Diabetes Association (ADA) criteria, as fasting glucose/two-hour oral glucose tolerance test result of ≥126/200 mg/dl or reported use of insulin or oral hypoglycemic medications. Smoking was defined as pack-years by self-report. Physical activity index (PAI) was quantified as metabolic output during a typical 24-hour period by multiplying a weighting factor by the number of hours spent in 5 activity levels (no activity=1.0, sedentary=1.1, slight=1.5, moderate=2.4, and heavy=5.0) [22]. Serum cholesterol and high-density lipoprotein (HDL) were measured in fasting blood samples. Alcohol intake was measured by self-report as ounces per month. Cognitive function was measured by the 100-point Cognitive Abilities Screening Instrument (CASI) [23]. Age at death was used for the analyses. Controlled hypertension was defined as a diagnosis of baseline hypertension on Examination 4 and a measurement of SBP ≤ 140 mmHg and DBP ≤ 90 mmHg at the follow up (Examination 5: 1994–1996).

Genotyping

Genotyping of FOXO3 and apolipoprotein E (APOE) variants was performed on blood samples that had been frozen at –70ºC. For men who participated in Examination 4 (1991–1993), DNA for genotyping was obtained from the blood sample buffy coat [24]. After DNA isolation, PCR was used for amplification of a suitable region of each gene using a combination of QIAmp cell-free DNA isolation followed by REPLI-g Single-Cell WGA & WTA amplification (QIAGEN Sciences, Germantown, MD, USA). Genotyping was performed using TaqMan on an Applied Biosystems QuantStudio 12K Flex system (ThermoFisher Scientific, Waltham, MA, USA).

Outcome measures

The current analysis was based on participants with autopsy and genotyping data. Microscopic neuropathologic assessments were performed by 1 of 3 senior neuropathologists who had trained together for standardization. The neuropathologist who carried out the assessment was blind to the clinical features. Methodology and definitions of cerebrovascular lesions have been described in previously published studies [7,25,26]. The brain autopsy data were coded in two distinct periods (1992–2003 and 2003–2012) using two different coding forms. To standardize both batches of data, brain locations that were used commonly in both periods were included for the study, namely bilateral (right/left) sections from each of the following areas: caudate, putamen, globus pallidus, thalamus, frontal lobe, temporal lobe, parietal lobe, occipital lobe, hippocampus, and brainstem. CMI was defined as presence of microinfarct, microlacune, and other focal ischemic changes. A dichotomized variable, defined as presence of any CMI regardless of the number of lesions, was defined as the primary outcome for the analysis.

Statistical Analysis

General linear model (GLM) was used to compare the baseline variables between subjects with CMI and without any CMI. Logistic regression models were used to assess the association of hypertension with prevalence of any CMI. Odds ratios (OR) and 95% confidence intervals (CI) were estimated for the whole cohort and then stratified by FOXO3 genotype (G-allele carriage versus TT genotype) adjusting for age at death, and baseline variables at exam 4 (1991–1993), namely: BMI, fasting glucose, type 2 diabetes, total cholesterol, HDL, smoking, PAI, alcohol consumption, history of prevalent clinical stroke, and APOE ε4 genotype. The interaction term of hypertension with FOXO3 G-allele in a multivariate full logistic model was tested for any genetic modulating effect to the present. Levels of P <0.05 were considered statistically significant. All statistical analyses were performed using the Statistical Analysis System (SAS) version 9.4 (Cary, North Carolina, USA).

RESULTS

Among a total of 8,006 participants, 809 men with complete brain autopsy and information from KHHP examination 4 on hypertension and FOXO3 genotype were included in the analysis. Overall, 551 (63.2%) participants had evidence of any CMI. Characteristics of those with and without any CMI are shown in Table 1. Those with any CMI were significantly more likely to have baseline hypertension, higher BMI, higher fasting glucose, and prevalent clinical stroke. Furthermore, there was no difference in the proportion of controlled hypertension between those with and without CMI (36.8% vs. 29.6% respectively, P = 0.13). Prevalence of APOE ε4 allele and total cholesterol levels was slightly higher in those with any CMI but did not reach statistical significance.

TABLE 1.

Baseline characteristics of participants with and without any cerebral microinfarcts (CMI)

No CMI Any CMI P *
n 258 (%) 551 (%)
Hypertension 177 (68.6) 423 (76.8) 0.013
Age at death (years) 88.7 ± 6.2 88.9 ± 5.7 0.73
BMI (kg/m2) 23.1 ± 3.07 23.8 ± 3.22 0.008
Fasting glucose (mg/dL) 108.4 ± 21.2 113.0 ± 27.9 0.021
Diabetes 63 (24.3) 153 (27.8) 0.31
APOE ε4 genotype 40 (15. 6) 114 (20.7) 0.082
Total cholesterol (mg/dL) 187.3 ± 32.0 192.0 ± 33.2 0.063
HDL cholesterol (mg/dL) 51.3 ± 13.1 51.3 ± 13.2 0.97
Smoking (pack-years) 24. 5 ± 31.9 25.5 ± 35.5 0.70
Physical activity index 30. 9 ± 4.97 30.6 ± 4.28 0.40
Alcohol intake (oz/month) 17.8 ± 40.3 14.6 ± 26.2 0.19
Prevalent clinical stroke 6 (2.3) 33 (6.0) 0.023
CASI at baseline 82.2 ± 16.1 81.7 ± 18.0 0.72
FOXO3 G-allele carriage 125 (48. 5) 259 (47.0) 0.70
*

Statistically significant P values are shown in bold.

Abbreviations: CMI, cerebral microinfarct; Hypertension = SBP ≥ 140 mmHg and/or DBP ≥ 90 mmHg or use of anti-hypertensive medications at baseline (Examination 4); BMI, body mass index; Diabetes = Type 2 diabetes by modified ADA criteria, with and without use of medications; APOE, apolipoprotein E; HDL, high-density lipoprotein; FOXO3, forkhead winged-helix box O type 3; CASI, Cognitive Abilities Screening Instrument. Data are n (%) and mean ± SD.

Multivariable models were created (Table 2) to assess the predictors of any CMI. In models 1 and 2, hypertension was a significant predictor of CMI. However, in the full covariate adjusted multivariable model, only BMI (OR: 1.07, 95% CI: 1.01–1.14, P = 0.015) was a predictor of CMI, while hypertension was only a borderline predictor (OR: 1.44, 95% CI: 1.00–2.08, P = 0.052). The interaction effect of the FOXO3 G-allele with hypertension on any CMI was estimated by adding the interaction term FOXO3 x hypertension to model 4. Doing so resulted in a significant interaction being seen (P = 0.020). The significant interaction indicated that FOXO3 genotype modulated the effect of hypertension on CMI.

TABLE 2.

Logistic regression models for any cerebral microinfarcts (CMI)

Model 1
OR (95% CI)
Model 2
OR (95% CI)
Model 3
OR (95% CI)
Model 4
OR (95% CI)
Hypertension 1.51 (1.09, 2.10) * 1.45 (1.03, 2.05) * 1.40 (0.99, 1.98) 1.44 (1.00, 2.08)
Age at death 1.00 (0.98, 1.03) 1.01 (0.99, 1.04) 1.01 (0.98, 1.04) 1.01 (0.99, 1.04)
BMI (kg/m2) 1.05 (1.00, 1.11) * 1.06 (1.01, 1.11) * 1.07 (1.01, 1.13) *
Fasting glucose (mg/dL) 1.01 (1.00, 1.02) 1.01 (1.00, 1.02) 1.01 (1.00, 1.02)
Diabetes 0.86 (0.56, 1.31) 0.85 (0.55, 1.30) 0.84 (0.54, 1.33)
APOE ε4 genotype 1.44 (0.95, 2.19) 1.45 (0.93, 2.25)
Total cholesterol (mg/dL) 1.00 (1.00, 1.01)
HDL cholesterol (mg/dL) 1.01 (0.99, 1.02)
Smoking (pack-years) 1.00 (1.00, 1.01)
Physical activity index 0.98 (0.94, 1.01)
Alcohol intake (oz/month) 1.00 (0.99, 1.00)
Prevalent clinical stroke 2.32 (0.82, 6.55)
CASI at baseline 1.01 (0.99, 1.02)
FOXO3 G-allele carriage 0.92 (0.66, 1.27)
*

Statistically significant P values are shown in bold.

Abbreviations: Hypertension = SBP ≥ 140 mmHg and/or DBP ≥ 90 mmHg or use of anti-hypertensive medications; BMI, body mass index; Diabetes = Type 2 diabetes by modified ADA criteria, with and without use of medications; APOE, apolipoprotein E; HDL, high-density lipoprotein; FOXO3, forkhead winged-helix box O type 3. *P <0.05.

In the stratified analyses, among the participants without the longevity-associated FOXO3 G-allele (i.e., those who had the TT genotype), hypertension was a strong predictor of CMI (OR: 2.25, 95% CI: 1.34–3.77, P = 0.002), while among those with the longevity-associated FOXO3 G-allele, hypertension was not a predictor of CMI (OR: 0.88, 95% CI: 0.51–1.54, P = 0.660) (Table 3).

TABLE 3.

Logistic regression models for any cerebral microinfarcts (CMI), stratified by FOXO3 genotype

FOXO3 TT (n=425) FOXO3 G-allele (n=384)
OR (95% CI) P OR (95% CI) P
Hypertension 2.30 (1.37, 3.87) 0.002 0.89 (0.51, 1.56) 0.69
Age at death 1.01 (0.97, 1.06) 0.53 1.02 (0.98, 1.06) 0.41
BMI (kg/m2) 1.04 (0.96, 1.13) 0.30 1.10 (1.01, 1.20) 0.029
Fasting glucose (mg/dL) 1.01 (1.00, 1.02) 0.27 1.01 (1.00, 1.03) 0.08
Diabetes mellitus 0.98 (0.51, 1.88) 0.94 0.62 (0.32, 1.22) 0.17
APOE ε4 genotype 1.74 (0.90, 3.35) 0.10 1.17 (0.63, 2.17) 0.63
Total cholesterol (mg/dL) 1.00 (1.00, 1.01) 0.35 1.0 (1.00, 1.01) 0.25
HDL cholesterol (mg/dL) 1.02 (1.00, 1.04) 0.056 0.99 (0.98, 1.01) 0.53
Smoking (pack-years) 0.99 (0.99, 1.00) 0.13 1.01 (1.00, 1.01) 0.20
Physical activity index 0.94 (0.89, 0.99) 0.030 1.01 (0.96, 1.06) 0.81
Alcohol intake (oz/month) 1.00 (0.99, 1.01) 0.87 0.99 (0.99, 1.00) 0.13
Prevalent clinical stroke 5.34 (1.11, 25.7) 0.037 1.21 (0.26, 5.74) 0.81
CASI at baseline 1.02 (1.00, 1.04) 0.058 0.99 (0.97, 1.01) 0.44

DISCUSSION

Among subjects homozygous or heterozygous for the longevity-associated G-allele of FOXO3 SNP rs2802292 (i.e., who were G-allele carriers), the impact of hypertension on the risk of any CMI found during brain autopsy was attenuated compared with those lacking the longevity-associated FOXO3 genotype (i.e., were TT). This is the first reported evidence indicating that FOXO3 longevity associated variants also provide resilience to CMI. We speculate that longevity associated FOXO3 variants exert an effect on cerebrovascular resilience so as to protect against the adverse effects of chronic hypertension (Figure 1). FOXO3 SNP rs2802292 G-allele carriage-related resilience to chronic cardiovascular stress was shown previously in KHHP subjects having a cardiometabolic disease (CMD) [15] and against intracerebral haemorrhage [27].

Graphical Abstract/Figure 1.

Graphical Abstract/Figure 1.

Conceptual framework illustrating the protective effect of the resilience allele on the cerebral vessels compared to the common allele with existing chronic hypertension. In subjects having the common FOXO3 genotype (TT), hypertension increased the risk of cerebral microinfarct (CMI). In subjects with a longevity-associated FOXO3 genotype (G-allele carriers), the deleterious impact of hypertension was attenuated and helped preserve brain health.

The precise pathophysiology of CMI is not entirely understood but the pathological appearance of CMI shares similar characteristics as the macroscopic ischaemic infarctions present, and thus the mechanism is speculated to be related to ischemia. When CMIs are identified in early stages, they show the predicted acute ischemic appearance of red neurons (if cortical) and sometimes with vacuolization from cytotoxic edema. In subacute stages, the lesions show an influx of macrophages followed by surrounding gemistocytic astrocytosis at about 10 days. Chronic lesions show cavitation with surrounding fibrillary gliosis [1]. The presence of cerebral vessel pathologies such as atherosclerosis, arteriosclerosis and/or amyloid angiopathy is independently associated with CMI [28].

Although hypertension is a well-established risk factor for stroke and cerebrovascular disease, the exact mechanism whereby hypertension contributes to the formation of CMI is uncertain. It is speculated that chronic hypertension increases mechanical stress on the endothelium, leading to degenerative changes in the penetrating arterioles, such as fibrinoid necrosis and deposition of plasma proteins, including fibrin, in the arteriolar wall. Such endothelial damage may lead to altered blood cell-endothelial interaction, microthrombus formation, and ultimately microscopic ischemic lesions [29]. Furthermore, chronic hypertension can accelerate the arteriosclerotic process and increase the likelihood of cerebral large artery and small artery stenosis [29], which may further worsen cerebral perfusion and increases the risk of CMI when a significant decline in blood pressure occurs [30].

Since the risk of stroke is proportional to the blood pressure level [31], one would assume that there may be a direct relationship between the severity of small vessel disease pathologies and blood pressure level. However, some studies have found fibrinoid necrosis of the vessel walls in those with only mild or benign hypertension, suggesting that other factors may modulate the susceptibility of cerebral vessel injuries in response to chronically elevated blood pressure [32].

It has been proposed that the FOXO3 transcription factor may protect blood vessels by effects on pathways that result in inhibition of vascular smooth muscle cell proliferation and neointimal hyperplasia [33], and thereby provide protection from vascular aging processes [12]. This may be particularly important during events of acute stress. Activation of FOXO3 transcription in human embryonic stem cells resulted in reinforcement of human vascular cell homeostasis, delayed aging, and increased resistance to oxidative injury compared with wild-type cells [34]. Loss-of-function studies have shown that FOXO3 expression helps to maintain homeostasis of a diverse array of vascular cell types [35,36]. We therefore speculate that elevation in intracellular FOXO3 levels, as occurs for G-allele carriers [37], may protect against progressive injuries to the cerebral arterioles of individuals with chronic hypertension, thereby protecting them from CMI. Future studies are needed to further assess how FOXO3 and its encoded protein may impact small vessel wall injuries of cerebral arterioles in response to chronic hypertension.

We acknowledge that our study has some limitations. Our study’s main dichotomized outcome variable was a presence or absence of one or more CMIs, and the total number of CMIs present in the collected specimen were not measured. Therefore, we were unable to assess the impact of the FOXO3 G-allele on the severity of the underlying cerebrovascular disease. Our study population of American men of Japanese ancestry living on Oahu in Hawaii limits generalizability of the study findings to other ethnic groups and to women. The study did not include women, the reason being that in 1965–1968 when recruitment took place, heart disease was uncommon in middle-aged women, and particularly in American women of Japanese ancestry. However, our study also has many strengths, including a large overall sample size, its longitudinal prospective study design, and the very long follow-up period which was accompanied by multiple periodic examinations involving collection of a wide array of key clinical information. Although the cohort only included men of Japanese descent, the population is unique in that it is genetically more homogenous than other racial populations, and that the latter and other similar Japanese populations have not been studied extensively. We had, moreover, a considerable amount of data on other cardiovascular risk factors, allowing us to adjust for these factors to minimize confounding. Our surveillance system for incident stroke was thorough, facilitated by ours being an island population, and follow up was meticulous.

In conclusion, the present study found that FOXO3 longevity genotype attenuates the impact of hypertension on the risk of CMI in American men of Japanese ancestry living on the island of Oahu in Hawaii. Future studies should attempt to replicate these findings in other large populations, including in other ethnic groups, and in women.

Acknowledgements

The authors thank all study participants and their families for their cooperation, and the Hawaii State Department of Health for its help, Ms. Ayako Elliott and Ms. Eva Ardo for assistance with genotyping, and Ms. Hiromi Nakada and Ms. Ka-on Fong for monitoring the vital status of KHHP participants.

Funding

Research reported in this publication was supported by the Kuakini Medical Center, NIH (contract N01-AG-4-2149, Grants 5 U01 AG019349-05, 5R01AG027060 [Kuakini Hawaii Lifespan Study], 5R01AG038707 [Kuakini Hawaii Healthspan Study], 1P20GM125526-01A1 [Kuakini NIH Center of Biomedical Research Excellence for Clinical and Translational Research on Aging]), and contract N01-HC-05102 from the National Heart, Lung, and Blood Institute.

Footnotes

Conflict of Interest

None.

Contributor Information

Kazuma Nakagawa, NIH Center for Biomedical Research Excellence on Aging, Department of Research, Kuakini Medical Center, Honolulu, HI 96817, USA; Neuroscience Institute, The Queen’s Medical Center, Honolulu, HI 96813, USA; Department of Medicine, John A. Burns School of Medicine, University of Hawaii, Honolulu, HI 96813, USA.

Randi Chen, NIH Center for Biomedical Research Excellence on Aging, Department of Research, Kuakini Medical Center, Honolulu, HI 96817, USA.

G. Webster Ross, Department of Medicine, John A. Burns School of Medicine, University of Hawaii, Honolulu, HI 96813, USA; Pacific Health Research and Education Institute, Honolulu, HI 96819, USA; Veterans Affairs Pacific Islands Health Care Systems, Honolulu, HI 96819; Department of Geriatric Medicine, John A. Burns School of Medicine, University of Hawaii, Honolulu, HI 96813, USA.

Timothy A. Donlon, NIH Center for Biomedical Research Excellence on Aging, Department of Research, Kuakini Medical Center, Honolulu, HI 96817, USA; Department of Cell and Molecular Biology, John A. Burns School of Medicine, University of Hawaii, Honolulu, HI 96813, USA.

Richard C. Allsopp, Institute for Biogenesis Research, University of Hawaii, Honolulu, HI, USA.

D. Craig Willcox, NIH Center for Biomedical Research Excellence on Aging, Department of Research, Kuakini Medical Center, Honolulu, HI 96817, USA; Department of Human Welfare, Okinawa International University, Ginowan, Okinawa 901-2701, Japan.

Brian J. Morris, NIH Center for Biomedical Research Excellence on Aging, Department of Research, Kuakini Medical Center, Honolulu, HI 96817, USA; Department of Geriatric Medicine, John A. Burns School of Medicine, University of Hawaii, Honolulu, HI 96813, USA; School of Medical Sciences, University of Sydney, Sydney, New South Wales 2006, Australia.

Bradley J. Willcox, NIH Center for Biomedical Research Excellence on Aging, Department of Research, Kuakini Medical Center, Honolulu, HI 96817, USA; Department of Geriatric Medicine, John A. Burns School of Medicine, University of Hawaii, Honolulu, HI 96813, USA.

Kamal H. Masaki, NIH Center for Biomedical Research Excellence on Aging, Department of Research, Kuakini Medical Center, Honolulu, HI 96817, USA; Department of Geriatric Medicine, John A. Burns School of Medicine, University of Hawaii, Honolulu, HI 96813, USA.

REFERENCES

  • 1.Smith EE, Schneider JA, Wardlaw JM, Greenberg SM. Cerebral microinfarcts: the invisible lesions. Lancet Neurol 2012; 11:272–282. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 2.Okamoto Y, Ihara M, Fujita Y, Ito H, Takahashi R, Tomimoto H. Cortical microinfarcts in Alzheimer’s disease and subcortical vascular dementia. Neuroreport 2009; 20:990–996. [DOI] [PubMed] [Google Scholar]
  • 3.Hachinski V, Iadecola C, Petersen RC, Breteler MM, Nyenhuis DL, Black SE, et al. National Institute of Neurological Disorders and Stroke-Canadian Stroke Network vascular cognitive impairment harmonization standards. Stroke 2006; 37:2220–2241. [DOI] [PubMed] [Google Scholar]
  • 4.Vinters HV, Ellis WG, Zarow C, Zaias BW, Jagust WJ, Mack WJ, et al. Neuropathologic substrates of ischemic vascular dementia. J Neuropathol Exp Neurol 2000; 59:931–945. [DOI] [PubMed] [Google Scholar]
  • 5.Sonnen JA, Larson EB, Crane PK, Haneuse S, Li G, Schellenberg GD, et al. Pathological correlates of dementia in a longitudinal, population-based sample of aging. Ann Neurol 2007; 62:406–413. [DOI] [PubMed] [Google Scholar]
  • 6.Matthews FE, Brayne C, Lowe J, McKeith I, Wharton SB, Ince P. Epidemiological pathology of dementia: attributable-risks at death in the Medical Research Council Cognitive Function and Ageing Study. PLoS Med 2009; 6:e1000180. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 7.White L Brain lesions at autopsy in older Japanese-American men as related to cognitive impairment and dementia in the final years of life: a summary report from the Honolulu-Asia aging study. J Alzheimers Dis 2009; 18:713–725. [DOI] [PubMed] [Google Scholar]
  • 8.Troncoso JC, Zonderman AB, Resnick SM, Crain B, Pletnikova O, O’Brien RJ. Effect of infarcts on dementia in the Baltimore longitudinal study of aging. Ann Neurol 2008; 64:168–176. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 9.Wang LY, Larson EB, Sonnen JA, Shofer JB, McCormick W, Bowen JD, et al. Blood pressure and brain injury in older adults: findings from a community-based autopsy study. J Am Geriatr Soc 2009; 57:1975–1981. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 10.Willcox BJ, Donlon TA, He Q, Chen R, Grove JS, Yano K, et al. FOXO3A genotype is strongly associated with human longevity. Proc Natl Acad Sci USA 2008; 105:13987–13992. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 11.Broer L, Buchman AS, Deelen J, Evans DS, Faul JD, Lunetta KL, et al. GWAS of longevity in CHARGE consortium confirms APOE and FOXO3 candidacy. J Gerontol A Biol Sci Med Sci 2015; 70:110–118. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 12.Morris BJ, Willcox DC, Donlon TA, Willcox BJ. FOXO3: A major gene for human longevity--A mini-review. Gerontology 2015; 61:515–525. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 13.Willcox BJ, Tranah GJ, Chen R, Morris BJ, Masaki KH, He Q, et al. The FoxO3 gene and cause-specific mortality. Aging Cell 2016; 15:617–624. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 14.Klinpudtan N, Allsopp RC, Kabayama M, Godai K, Gondo Y, Masui Y, et al. The association between longevity-associated FOXO3 allele and heart disease in septuagenarians and octogenarians: The SONIC study. J Gerontol A Biol Sci Med Sci 2022; 77:1542–1548. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 15.Chen R, Morris BJ, Donlon TA, Masaki KH, Willcox DC, Davy PMC, et al. FOXO3 longevity genotype mitigates the increased mortality risk in men with a cardiometabolic disease. Aging (Albany NY) 2020; 12:23509–23524. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 16.Nakagawa K, Chen R, Greenberg SM, Ross GW, Willcox BJ, Donlon TA, et al. Forkhead box O3 longevity genotype may attenuate the impact of hypertension on risk of intracerebral haemorrhage. J Hypertens 2022; 40:2230–2235. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 17.Chen R, Morris BJ, Donlon TA, Ross GW, Kallianpur KJ, Allsopp RC, et al. Incidence of Alzheimer’s disease in men with late-life hypertension is ameliorated by FOXO3 longevity genotype. J Alzheimers Dis 2023; 95:79–91. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 18.Ottman R Gene-environment interaction: definitions and study designs. Prev Med 1996; 25:764–770. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 19.Kagan A, Harris BR, Winkelstein W Jr., Johnson KG, Kato H, Syme SL, et al. Epidemiologic studies of coronary heart disease and stroke in Japanese men living in Japan, Hawaii and California: demographic, physical, dietary and biochemical characteristics. J Chronic Dis 1974; 27:345–364. [DOI] [PubMed] [Google Scholar]
  • 20.Kagan A, Popper J, Reed DM, MacLean CJ, Grove JS. Trends in stroke incidence and mortality in Hawaiian Japanese men. Stroke 1994; 25:1170–1175. [DOI] [PubMed] [Google Scholar]
  • 21.Worth RM, Kagan A. Ascertainment of men of Japanese ancestry in Hawaii through World War II Selective Service registration. J Chronic Dis 1970; 23:389–397. [DOI] [PubMed] [Google Scholar]
  • 22.Abbott RD, Rodriguez BL, Burchfiel CM, Curb JD. Physical activity in older middle-aged men and reduced risk of stroke: the Honolulu Heart Program. Am J Epidemiol 1994; 139:881–893. [DOI] [PubMed] [Google Scholar]
  • 23.Launer LJ, Masaki K, Petrovitch H, Foley D, Havlik RJ. The association between midlife blood pressure levels and late-life cognitive function. The Honolulu-Asia Aging Study. JAMA 1995; 274:1846–1851. [PubMed] [Google Scholar]
  • 24.Bellus GA, Hefferon TW, Ortiz de Luna RI, Hecht JT, Horton WA, Machado M, et al. Achondroplasia is defined by recurrent G380R mutations of FGFR3. Am J Hum Genet 1995; 56:368–373. [PMC free article] [PubMed] [Google Scholar]
  • 25.Petrovitch H, Ross GW, Steinhorn SC, Abbott RD, Markesbery W, Davis D, et al. AD lesions and infarcts in demented and non-demented Japanese-American men. Ann Neurol 2005; 57:98–103. [DOI] [PubMed] [Google Scholar]
  • 26.Launer LJ, Hughes TM, White LR. Microinfarcts, brain atrophy, and cognitive function: the Honolulu Asia Aging Study Autopsy Study. Ann Neurol 2011; 70:774–780. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 27.Nakagawa K, Chen R, Greenberg SM, Ross GW, Willcox BJ, Donlon TA, et al. Forkhead box O3 longevity genotype may attenuate the impact of hypertension on risk of intracerebral haemorrhage. J Hypertens 2023; 40:2230–2235. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 28.Arvanitakis Z, Capuano AW, Leurgans SE, Buchman AS, Bennett DA, Schneider JA. The Relationship of Cerebral Vessel Pathology to Brain Microinfarcts. Brain Pathol 2017; 27:77–85. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 29.Johansson BB. Hypertension mechanisms causing stroke. Clin Exp Pharmacol Physiol 1999; 26:563–565. [DOI] [PubMed] [Google Scholar]
  • 30.Graff-Radford J, Raman MR, Rabinstein AA, Przybelski SA, Lesnick TG, Boeve BF, et al. Association Between Microinfarcts and Blood Pressure Trajectories. JAMA Neurol 2018; 75:212–218. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 31.Rodgers A, MacMahon S, Gamble G, Slattery J, Sandercock P, Warlow C. Blood pressure and risk of stroke in patients with cerebrovascular disease. The United Kingdom Transient Ischaemic Attack Collaborative Group. BMJ 1996; 313:147. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 32.Rosenblum WI. Fibrinoid necrosis of small brain arteries and arterioles and miliary aneurysms as causes of hypertensive hemorrhage: a critical reappraisal. Acta Neuropathol 2008; 116:361–369. [DOI] [PubMed] [Google Scholar]
  • 33.Abid MR, Yano K, Guo S, Patel VI, Shrikhande G, Spokes KC, et al. Forkhead transcription factors inhibit vascular smooth muscle cell proliferation and neointimal hyperplasia. J Biol Chem 2005; 280:29864–29873. [DOI] [PubMed] [Google Scholar]
  • 34.Yan P, Li Q, Wang L, Lu P, Suzuki K, Liu Z, et al. FOXO3-Engineered Human ESC-Derived Vascular Cells Promote Vascular Protection and Regeneration. Cell Stem Cell 2019; 24:447–461 e448. [DOI] [PubMed] [Google Scholar]
  • 35.Deng L, Huang L, Sun Y, Heath JM, Wu H, Chen Y. Inhibition of FOXO1/3 promotes vascular calcification. Arterioscler Thromb Vasc Biol 2015; 35:175–183. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 36.Zhang H, Zhao Z, Pang X, Yang J, Yu H, Zhang Y, et al. MiR-34a/sirtuin-1/foxo3a is involved in genistein protecting against ox-LDL-induced oxidative damage in HUVECs. Toxicol Lett 2017; 277:115–122. [DOI] [PubMed] [Google Scholar]
  • 37.Donlon TA, Morris BJ, Chen R, Masaki KH, Allsopp RC, Willcox DC, et al. FOXO3 longevity interactome on chromosome 6. Aging Cell 2017; 16:1016–1025. [DOI] [PMC free article] [PubMed] [Google Scholar]

RESOURCES