Incident prolonged QT interval in midlife and late-life cognitive performance.

BACKGROUND: Measures of cardiac ventricular electrophysiology have been associated with cognitive performance in cross-sectional studies. We sought to evaluate the association of worsening ventricular repolarization in midlife, as measured by incident prolonged QT interval, with cognitive decline in late life.
METHODS: Midlife QT interval was assessed by electrocardiography during three study visits from 1965/68 to 1971/74 in a cohort of Japanese American men aged 46-68 at Exam 1 from the Honolulu Heart Study. We defined incident prolonged QT as the QT interval in the upper quartile at Exam 2 or 3 after QT interval in lower three quartiles at Exam 1. Cognitive performance was assessed at least once using the Cognitive Abilities Screening Instrument (CASI), scored using item response theory (CASI-IRT), during four subsequent visits from 1991/93 to 1999/2000 among 2,511 of the 4,737 men in the Honolulu-Asia Aging Study otherwise eligible for inclusion in analyses. We used marginal structural modeling to determine the association of incident prolonged QT with cognitive decline, using weighting to account for confounding and attrition.
RESULTS: Incident prolonged QT interval in midlife was not associated with late-life CASI-IRT at cognitive baseline (estimated difference in CASI-IRT: 0.04; 95% CI: -0.28, 0.35; p = 0.81), or change in CASI-IRT over time (estimated difference in annual change in CASI-IRT: -0.002; 95%CI: -0.013, 0.010; p = 0.79). Findings were consistent across sensitivity analyses.
CONCLUSIONS: Although many midlife cardiovascular risk factors and cardiac structure and function measures are associated with late-life cognitive decline, incident prolonged QT interval in midlife was not associated with late-life cognitive performance or cognitive decline.

Introduction

Dementia is the sixth-leading cause of death and a major leading cause of disability in the United States. [1, 2] It is the only disease among the top 10 causes of death that has no effective treatment or prevention.[3] Over the past 20 years, research has linked several cardiovascular risk factors (CVRF) to higher risks of dementia, Alzheimer’s disease, and vascular dementia.[4-7] For many CVRF, the risk for later cognitive decline depends on the timing of risk factor evaluation. Numerous studies indicate that dementia and cognitive dysfunction are more strongly associated with CVRF measured in midlife[6, 8, 9] than with CVRF assessed later in life.[10-13]

Better understanding of cardiac function in midlife may offer novel insights for dementia prevention. The QT interval on electrocardiogram represents the length of time required for the process of ventricular depolarization and repolarization, with longer QT interval indicating slower repolarization. Although clinically relevant prolonged QT interval is a known risk factor for major cardiovascular events like stroke,[14, 15] few studies have examined the potential link between ventricular repolarization and cognitive outcomes. All studies focused on cross-sectional associations with mild cognitive impairment (MCI) or overt dementia.[16-19] For example, QT dispersion, a measure of short-term variability in QT interval, was associated with worse cognitive performance in patients with MCI in small cross-sectional studies comparing individuals with normal cognition, MCI, and dementia.[16, 18] Conversely, QT interval was not cross-sectionally associated with cognitive performance in 839 very-old adults from the Chicago Health and Aging Project (mean age of 81years).[20]

We are unaware of any longitudinal studies of the association between prolonged QT interval and cognition. Despite the evidence that midlife CVRFs are often most relevant to late-life cognitive health, the longitudinal association of ventricular repolarization at midlife and late-life cognitive decline has not yet been considered. Therefore, we aimed to examine whether prolonged QT interval in midlife was predictive of late-life cognitive decline over 25 years of follow-up.

Material and methods

Participants

The Honolulu-Asia Aging Study (HAAS) began in 1991 and has been described in detail previously.[21] HAAS extends the Honolulu Heart Program (HHP), a prospective study of heart disease and stroke in Japanese-American men born between 1900 and 1919, who lived on Oahu in 1965. Exams 1 (1965/68), 2 (1967/70), and 3 (1971/74) were conducted in midlife as a part of the original HHP study. Beginning twenty years after Exam 3, Exams 4 (1991/93), 5 (1994/96), 6 (1997/99), and 7 (1999/2000) were conducted in late life as part of HAAS. Of the original HHP cohort of 8,006 men, 2,511were enrolled in HAAS and assessed longitudinally for cognitive performance beginning at Exam 4 in 1991/93 (Fig 1). HAAS was approved by the Kuakini Medical Center Institutional Review Board and by the Human Research committees, and participants were informed about the study and signed informed consent forms.

Fig 1

Flowchart of the study participants from the Honolulu-Asia Aging study.

QT interval measurements, adjustment for ventricular rate, and identification of incident prolonged QT interval

The QT interval, which is comprised of the QRS complex, the ST segment, and T wave, is a widely-used measure of ventricular repolarization.[22] We used a correction formula that considers QT interval variation by ventricular rate.[23, 24] Specifically, we used ECG data from Exam 1 to estimate the coefficient ß1 in the following linear regression model: E(QT interval) = ß0 + ß1(RR interval), where the RR interval is the average time elapsed in seconds between ventricular beats (60 / ventricular rate). We then used the estimated coefficient (ß1 = 0.158) to calculate a ventricular rate-corrected QT interval: QTadj = QT + 0.158(1-RR). We also explored linear regression models in which the ß1 coefficient was adjusted for age or allowed to vary with age, but found these enhancements did not meaningfully change the coefficient.

As our exposure of interest, we identified incident prolonged QT interval at Exam 2 or 3, rather than prevalent prolonged QT interval at Exam 1. Incident prolonged QT interval represents a change from faster to slower ventricular repolarization over the course of a few years, suggesting a person may be transitioning away from their normal ventricular function. Also, identifying incident prolonged QT interval at Exam 2 or 3 as our exposure of interest allowed us to use variables measured at Exam 1 to control for confounding via inverse probability weighting in the statistical analysis (see below). To identify participants who experienced incident prolonged QT interval, we first identified the 75th percentile of rate-corrected QT interval at Exam 1, which was 407 milliseconds. Next, we excluded participants whose rate-corrected QT interval was above this value at Exam 1, considering them to have prevalent prolonged QT interval at Exam 1, and thus be ineligible for developing incident prolonged QT interval. Among the remaining participants, we then identified those whose rate-corrected QT interval was above 407 milliseconds at Exam 2 or 3 as having incident prolonged QT interval (Fig 1).

Cognitive Abilities Screening Instrument (CASI)

CASI is a 40-item test of global cognitive function with scores ranging from 0 to 100. Higher scores indicate better cognitive performance.[25, 26] Inventory items were designed to be comparable to the Hasegawa Dementia Screening Scale,[27] the Mini Mental State Examination (MMSE),[28] and the Modified MMSE.[29] One additional item indexing judgment ability was also included in the inventory. To ensure equivalence across English and Japanese languages, the CASI was developed in parallel in English and Japanese at three workshops in which items were scrutinized for cultural equivalence, back-translated, and pilot-tested. CASI scores obtained in HAAS and in a purely Japanese sample were analyzed using item-response theory to further validate the cross-cultural sensitivity of CASI; no evidence of salient differential item functioning due to language of testing was found.[25] For analysis, the CASI was scored using item response theory, to measure change over time on a consistent metric;[30] the mean CASI-IRT score across Exams 4–7 was approximately normal, with mean 0 and standard deviation of 1.

Statistical analysis

We estimated the association of incident prolonged QT interval at Exam 2 or 3 with trajectories of cognitive function from Exam 4 to Exam 7 using a marginal structural model.[31] This model uses weighting to address potential bias due to confounding and additionally implements weighting to address the potential for informative missingness related to loss to follow-up.[31, 32] To address confounding, we reweight persons in the observed sample using inverse probability of exposure weights (IPEW) to eliminate associations between potential confounders and incident prolonged QT. Thus, under standard assumptions, IPEW addresses confounding by making the exposure statistically independent of the confounders. Similarly, to reduce bias due to selective attrition, we reweight persons in the observed sample who are similar to those who drop out of the sample using inverse probability of attrition weights (IPAW). Thus, under standard assumptions, IPAW addresses informative censoring by making attrition independent of predictors of attrition, including ascertained exposure and outcome.

To establish our sample for analysis of midlife incident prolonged QT interval in relation late life to cognitive decline, we first excluded participants who did not have an electrocardiogram (ECG) at Exam 1 (n = 570), had prevalent prolonged QT interval at Exam 1 (n = 1,844), were lost to follow-up after Exam 1 (n = 317), or did not have ECG at Exam 2 or 3 (n = 538), leaving 4,737 participants (Fig 1). Data from these 4,737 persons were used to derive IPEW to account for confounding and IPAW to account for attrition from Exam 2 to Exam 4. After calculating those weights, we then further excluded participants who died or were lost to follow-up between Exam 2 and Exam 4 (n = 2,208) or had no CASI data at Exam 4 (n = 18), leaving 2,511 participants who completed CASI at Exam 4. Data from these 2,511 persons were used to derive IPAW to account for attrition from Exam 4 to Exam 7 (Fig 1).

We estimated stabilized IPEW using logistic regression models on a dataset including one line for each participant at risk of experiencing incident prolonged QT interval at either Exam 2 or Exam 3. Covariates included in the denominator model were potential confounders of the association between prolonged QT interval and cognition (S1 Table). Covariates included in all numerator models included the subset of baseline or time-invariant covariates included in the denominator model. Stabilized IPEW were derived for each participant at each time point using standard formulae, and IPEW at each visit for each participant were then multiplied together to provide a final stabilized IPEW for each participant.[31, 32]

Similarly, we estimated stabilized IPAW to address attrition from Exam 2 through Exam 7 using logistic regression models. Because cognitive data from CASI (our outcome measure) was available only from Exam 4 onward, we modeled attrition from Exam 2 to 4 and from Exam 4 to 7 separately. Furthermore, given the expectation of two separate attrition processes—attrition due to death and attrition due to non-death drop-out—we also used separate models to account for these two processes, with models for attrition due to non-death drop out conditional on remaining alive.[33, 34]. Details of variables included in each model are available in S1 Table. As with the IPEW models, time-invariant and baseline covariates were included in the numerator models used to derive stabilized IPAW.

Stabilized weights for each process were derived for each participant at each time point, and were then multiplied together to provide a final set of stabilized IPAW or IPEW weights, with unique weights assigned to each participant at each time point. Finally, our stabilized IPEW and IPAW were multiplied together to create a unique weight for each participant contributing to the final analysis at each time point from Exams 4 to 7. Extreme weights were truncated at the 99th and 1st percentiles for use in primary analyses, as use of truncation often provides a good balance between bias and efficiency.[35]

For our primary analysis (Model 1), we estimated the association of incident prolonged QT interval on cognitive trajectories using weighted linear regression models estimated using generalized estimating equations with an independence covariance matrix.[36] We adjusted this analysis for covariates included in the models to estimate the numerators of the stabilized IPAW or IPEW, as well as important predictors of cognition: baseline age, time in study, an age by time in study interaction, generation, the presence of any APOEε 4 alleles, education, occupation (Exam 1), height (Exam 2), chest depth (Exam 1), alcohol use (Exam 1), physical activity level (Exam 1), and history of hypertension (Exam 2). In secondary analyses, we considered the sensitivity of our findings to our modeling choices. Specifically, we compared our primary analyses to analyses using non-truncated stabilized weights (Model 2), or only stabilized IPEW weights (Model 3), complete omission of weighting, i.e., adjustment only for baseline/time-invariant covariates (Model 4), and omission of multiple imputation, i.e., analyzing only participants who had complete data on all covariates (Model 5).

Missing data in covariates used to create the weights requires either censoring of persons at the time of first missing data or implementation of methods to address missingness. Given that many participants were missing data on at least one of the covariates included in models used to create our weights, we implemented multiple imputation by chained equations (MICE) to address missing covariate data.[37] We imputed five replicate datasets after a burn-in of 10 iterations. Derivation of the weights and estimation of the MSM occurred separately within each of our five imputed datasets. Reported findings combined estimates from each of the five imputed datasets using standard methods.[38]

Results

Of the 4,737 participants at Exam 2 (midlife), 2,511 had follow-up at Exam 4 (late life), when cognitive performance was first evaluated (Fig 1). These 2,511 were our analytic sample, weighted to represent the 4,737 who met our eligibility criteria. Their mean IRT-CASI score at Exam 4 was 0.4±0.9. Their mean corrected QT interval had been 395.1±19.3 milliseconds (range 318–475) at Exam 2, and 1,076 participants (42.9%) had incident prolonged QT interval at either Exam 2 or 3. Participant characteristics at Exam 2 by incident prolonged QT interval at Exam 2 or 3 are shown in Table 1. The mean of the final stabilized weights applied to the Visit 4 data from the 2,511 participants in our analytic sample was 0.99 (range: 0.46, 1.86). Additional details about the weights are provided in the Supplemental Appendix (S2, S3, and S4 Tables).

Table 1

Participant characteristics at Exam 2, stratified by incident prolonged QT interval at midlife (Exam 2 or 3).

	Total (n = 2,511)	Prolonged QT (n = 1,076)	No Prolonged QT (n = 1,435)	p-valuea
Age (years), mean (SD)	54.5 (4.4)	54.7 (4.6)	54.3 (4.4)	0.053
Generation, n (%)				0.691
Issei	149 (6%)	68 (6%)	81 (6%)
Kibei	226 (9%)	93 (9%)	133 (9%)
Nisei	2,136 (85%)	915 (85%)	1,221 (85%)
Education, n (%)				0.217
None or primary	449 (18%)	210 (20%)	239 (17%)
Intermediate	682 (27%)	295 (27%)	387 (27%)
High School	821 (33%)	332 (31%)	489 (34%)
Technical School	278 (11%)	104 (10%)	174 (12%)
University	281 (11%)	135 (13%)	146 (10%)
Clerical, sales, professional or managerial jobb, n (%)	787 (31%)	368 (34%)	419 (29%)	0.007
Hypertension diagnosis, n (%)	225 (9%)	107 (10%)	118 (8%)	0.135
Alcohol (ounces/ month), mean (SD)	11.9 (20.3)	12.0 (19.4)	11.8 (20.9)	0.797
Height (cm), mean (SD)	164 (6)	164 (6)	163 (6)	0.001
Chest depth (cm)b, mean (SD)	19.2 (1.8)	19.3 (1.9)	19.1 (1.8)	0.123
Physical Activity Index (midlife)b, mean (SD)	32.9 (4.7)	32.8 (4.6)	33.0 (4.8)	0.169
Presence of at least one APOE-4 allele, n (%)	462 (19%)	218 (21%)	244 (18%)	0.038

a T-tests for continuous variables, chi-squared for categorical variables, and Wilcoxon’s rank sum test for education.

b Evaluated at Exam 1

Prolonged QT interval in midlife (Exam 2 or 3) was not associated with IRT-CASI score at Exam 4 (20 years after Exam 3) or with subsequent decline in IRT-CASI score over subsequent exams spanning the next 10 years (Table 2). In our primary analysis (Model 1), assuming reference level for all covariates, participants without midlife prolonged QT interval declined on IRT-CASI score by an average of 0.09 points per year (95% CI: 0.08 to 0.09 points per year). Participants with midlife prolonged QT interval were not significantly different in the magnitude of subsequent decline in IRT-CASI score over time (estimated difference of -0.002 points of decline per year; 95% CI: -0.013 to 0.010 points; P = 0.79). In a series of sensitivity analyses in which we applied different modeling strategies (Table 2, Models 2–5), we obtained results very similar to those from our primary analysis.

Table 2

Association of elevated QT interval in midlife with item response theory-adjusted Cognitive Abilities Screening Instrument (IRT-CASI) score later in life.

Model and parameter	Estimated IRT-CASI score	95% CI	P value
Model 1
Study time (years)	-0.09	(-0.09, -0.08)	<0.0001
Elevated QT	0.04	(-0.28, 0.35)	0.81
Elevated QT × study time	-0.002	(-0.013, 0.010)	0.79
Model 2
Study time, y	-0.10	(-0.11, -0.09)	<0.0001
Elevated QT	-0.06	(-0.46, 0.34)	0.76
Elevated QT × study time	0.003	(-0.013, 0.018)	0.71
Model 3
Study time, y	-0.07	(-0.08, -0.06)	<0.0001
Elevated QT	0.06	(-0.25, 0.39)	0.68
Elevated QT × study time	-0.003	(-0.015, 0.009)	0.62
Model 4
Study time, y	-0.07	(-0.08, -0.06)	<0.0001
Elevated QT	0.05	(-0.27, 0.36)	0.77
Elevated QT × study time	-0.002	(-0.014, 0.009)	0.69
Model 5
Study time, y	-0.07	(-0.08, -0.06)	<0.0001
Elevated QT	0.04	(-0.26, 0.34)	0.78
Elevated QT × study time	-0.002	(-0.014, 0.010)	0.72

Model 1: Inverse probability of exposure weighting (IPEW) and inverse probability of attrition weighting (IPAW), with weights truncated at 1st and 99th percentiles, with multiple imputation.

Model 2: IPEW and IPAW, with weights not truncated, with multiple imputation.

Model 3: IPEW (no IPAW), with weights not truncated, with multiple imputation.

Model 4: Unweighted, with multiple imputation.

Model 5: Unweighted, without multiple imputation.

All models additionally adjusted for generation, alcohol use, physical activity level, education, occupation, and chest depth at Visit 1; age, height, and hypertension at Visit 2; and the presence of any APO-E4 alleles at visit 4.

The reference person was, at exam 2, 55 years old, 164 cm tall, and without a hypertension diagnosis. At exam 1 he was Nisei, with a primary education or less, had a chest depth of 19 cm, did not have a Clerical, sales, professional or managerial job, did not drink, and had a Physical Activity Index of 33. He also had no APOE-4 alleles.

Discussion

Using marginal structural models to reduce bias from confounding and participant attrition, we found that midlife QT interval was not associated with late-life CASI score approximately 25 years later, nor with cognitive decline in CASI over time in a large sample of Japanese American men from the HAAS. We confirmed this result in sensitivity analyses.

Certain aspects of left ventricular (LV) dimensions and ejection fraction have previously been associated with cognitive impairment and decline.[17, 39] Increased LV dimensions in 211 men who were 68 years old at baseline were associated with higher risk of cognitive decline 14 years later.[17] Similarly, in 1,114 participants of the Framingham Heart Study Offspring Cohort, LV ejection fraction, an indicator of cardiac dysfunction, was associated with worse performance on neuropsychological tests related to visuospatial memory, object recognition, and executive function.[39] In addition, patients with severe LV dysfunction showed improved performance on executive and visuospatial function tests three to six months after cardiac resynchronization therapy.[40, 41] Alterations in LV structure and function may be related to cognitive impairment through either brain hypoperfusion. An additional mechanism could be the presence of ventricular arrhythmias and the generation of thromboembolism, which could lead to cerebral infarcts and transient hypoperfusion.[42]

However, the association between ventricular arrhythmias and cognitive performance has been far less investigated. Silent myocardial ischemia and repeated ventricular premature beats were more frequent in patients with MCI and Alzheimer’s disease than in participants with normal cognition.[19] In a same sample of 33 patients with Alzheimer’s disease, 39 with MCI, and 29 controls, QT dispersion was associated with worse performance in the MMSE.[18] In a large sample of patients with normal LV ejection fraction, Coppola et al. found lower QT interval in participants with normal cognition (n = 224) compared to patients with MCI (n = 77) and Alzheimer’s disease (n = 77). These findings came from small cross-sectional studies with patients with cognitive complaints.

Research on ventricular repolarization has been even more sparse. The only study so far that had investigated the association between QT interval and cognitive performance using community-dwelling older adults was cross-sectional and included mostly very old participants (mean = 81, range of 76–85 years).[20] In that study, as in ours, repolarization measurements were not related to cognitive performance.[20] Although additional confirmation is required, our study suggests that subtle changes in the natural rhythm of the heart in midlife are unlikely to affect cognition decades later.

Our study should also be examined in light of its limitations. We did not have information on drugs that can affect the QT interval [43]. However, the low prevalence of use of drugs that may cause QT prolongation in previous studies (2–3%) suggest that our inability to consider drug use will not be a large source of bias.[44] Although we included several factors that could increase the chance of QT interval prolongation, information was missing on other clinical conditions that could influence QT interval (e.g. bundle branch block, hypokalemia and hypocalcemia, endocrine disorders). In addition, the HAAS is a cohort of Japanese American men, and our findings may not hold for other ethnicities and women. Techniques for measuring QT interval have improved since 1965–1971, so it is possible that modern measurements would be more predictive. We also cannot account for any incident prolonged QT interval between Exam 3 and the cognitive measurements. Moreover, we could not examine the association of QT interval with specific cognitive domains since we did not have a complete neuropsychological examination. Although QT interval was not associated with cognitive decline evaluated by CASI, it could be associated with vascular dementia or with cognitive decline in specific domains, such as executive function.

The strengths of this study include a large sample of participants with ECG data and cognitive evaluation over time. In addition, the causal modeling approach used in this study is a strength. MSM with IPEW and IPAW enables robust estimation of the association in contexts where we are concerned about bias due to confounding and attrition. Additionally, we examined for the first time the association of midlife incident prolonged QT interval with later cognitive performance. Since neuropathological lesions associated with dementia may start even two decades before the clinical symptoms of dementia, midlife risk factors are likely most relevant to later cognition.[45] Finally, we present here results from a community-dwelling cohort of adults; thus our results should be generalizable to similar community-dwelling populations.

In conclusion, in a prospective study of midlife ventricular function and cognition in Japanese-American men, we did not find an association of prolonged QT interval in midlife with cognitive performance or decline after 25 years of follow-up. However, future longitudinal studies with different ethnicities and women are important to confirm our findings.

Variables included in the denominator of the inverse probability weight models.

(DOCX)

Click here for additional data file.

Visit-specific estimated inverse probability of attrition and inverse probability of exposure weights.

(DOCX)

Click here for additional data file.

Mean of final non-truncated weights applied in estimation of the MSM at each visit with cognitive data.

(DOCX)

Click here for additional data file.

Weighted demographics demonstrating inverse probability weights recover distribution of characteristics at baseline (Exam 2).

(DOCX)

Click here for additional data file.

25 Oct 2019

PONE-D-19-23773

Incident prolonged QT interval in midlife and late-life cognitive performance

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Dear Dr. Suemoto,

Thank you for submitting your manuscript to PLOS ONE. After careful consideration by 3 Reviewers and an Academic Editor, all of the critiques of all three Reviewers must be addressed in detail in a revision to determine publication status. If you are prepared to undertake the work required, I would be pleased to reconsider my decision, but revision of the original submission without directly addressing the critiques of the three Reviewers does not guarantee acceptance for publication in PLOS ONE. If the authors do not feel that the queries can be addressed, please consider submitting to another publication medium. A revised submission will be sent out for re-review. The authors are urged to have the manuscript given a hard copyedit for syntax and grammar.

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Reviewer #1: In the manuscript by Suemoto1 et al., it was shown that “Incident prolonged QT interval in midlife and late-life cognitive performance." In this study, the authors tried to show that the time rate of blood pressure variation was a risk factor of developing brain edema. This study is interesting. However, critical flaws are pointed below.

Major comments

#1; Patients with the use of hypertensive therapy.

How many subjects were treated with antihypertensive agent. Several agents might be associated with the study results. Several studies showed that hypertensive status has negative impact on blood brain barrier (BBB) permeability resulting in BBB breakdown. In this point, cerebral autoregulation may be disrupted in the very elderly hypertensive patients. Long acting CCB, ACEI and ARB which does not decrease cerebral blood flow (CBF) are suggested to be appropriate in BP control with high risk at stroke, whereas diuretic which does decrease CBF is not. From these points, it is possible to take the possibility into account that the antihypertensive agents could be affected by the class of antihypertensives agents with cognitive function. How would be the results if the impact of antihypertensive agent class were taken into account in the regression model?

#2: It would be helpful if there was information other than antihypertensive medication (e.g. use of statin, hypoglycemic agent).

#3: Antihypertensive agents after baseline

This question may be similar to that in #1. Althoguh antihypertensive agents that could be used in the baseline would be limited, it would be helpful if there was information about the contents of antihypertensive medication after baseline. These agents change during the follow up might affect the results.

#4: Short QT

Recent notion is not only long QT but also short QT also associated with adverse cardiovascular events. Thus, short QT should be taken into account.

#5: Incidence of Alzheimer disease, vascular dementia or total dementia

I would be interesting if the relationship between QT length and incidence of Alzheimer disease, vascular dementia or total dementia.

Reviewer #2: Paper bySuemoto et all addresses interesting question whether prolonged QT might be associated with decline in cognitive function.

The paper is very clearly written and I do not have any objection to statistical analysis.

However, there are several points which I should raise.

• QT interval is a dynamic parameter affected by several factors, which were not reported or considered in statistical analysis. It should be therefore at least mentioned in limitations of the study the possible effect of presence of bundle branch block, hypokalemia and hypocalcemia, presence of heart failure, ischemia, cerebrovascular disease, endocrine disorders etc.

• There is huge attrition during follow-up. Again, it is usual and inevitable in this kind of study. But the possibility is that it could affect results.

• As QT interval duration affects many drug classes, the estimate that use of this medication is only 2 to 3 % might be underestimated, mainly in older age. Please comment.

• The cut-off value of 370 ms might be too low to be associated with any outcome. Have you considered to use more strict cut-off value, e.g. >400 ms, >440 ms?

Reviewer #3: Dear Colleague

Thank you for this article, which is relevant, and interesting.

The methodology is particularly developed, with particular attention to missing data.

The limits of methodology (and multiple imputation) are detailed in the discussion.

I have some minor comments to submit to the authors:

- Why did not you compared the characteristics of the population according to the extension of the QT in Table 1? I particularly wonder about the difference that there could be concerning the profession (clerical, sales, professional or managerial job) in the 2 groups, and how the authors explain this difference. I think this is a point worth discussing.

- A difference may also be present regarding the ApoE-4 allele (?)

- There are 2 errors in the Total column of Table 1: the total number of "jobs" (708 + 419 = 1127 and not 787), the total number of "hypertension" (435 + 551 = 986 and not 225); the rates of these two results are also incorrect.

Among the limitations, I would add that CASI estimates a cognitive decline (the main cause of which is known to be Alzheimer's disease); perhaps the prolonged QT interval is not a good indicator for cognitive declines in a broad sense, but could be a good indicator of vascular dementia (this is more a perspective than a limit to your work in fact).

Data are not available, but the authors have specified how to access them.

Thank you again for this work,

With kind regards,

Michaël Rochoy, MD, PhD

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Reviewer #2: Yes: Jitka Seidlerová

Reviewer #3: Yes: Michaël Rochoy

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21 Jan 2020

Dear Professor Stephen Ginsberg,

We would like to thank you for the opportunity to resubmit the revised version of our manuscript entitled “Incident prolonged QT interval in midlife and late-life cognitive performance” (PONE-D-19-23773) for consideration for publication in PLOS ONE. We are thankful to the referees and the Editor for their thoughtful suggestions that helped to strengthen our manuscript. We have addressed all the reviewers’ concerns, and provided a detail response below.

Please do not hesitate to contact me if you have any further questions.

We look forward to hearing back from you soon.

Best regards,

Claudia K. Suemoto

Reviewer #1

In the manuscript by Suemoto et al., it was shown that “Incident prolonged QT interval in midlife and late-life cognitive performance." In this study, the authors tried to show that the time rate of blood pressure variation was a risk factor of developing brain edema. This study is interesting. However, critical flaws are pointed below.

We investigated the association between prolonged QT interval on cognitive performance, rather than blood pressure variation on brain edema. Therefore, for comments 1 to 3, we did our best to clarify any aspect of the comments that could be related to the association between prolonged QT interval in midlife and cognitive performance in late-life.

Major comments:

1. Patients with the use of hypertensive therapy. How many subjects were treated with antihypertensive agent. Several agents might be associated with the study results. Several studies showed that hypertensive status has negative impact on blood brain barrier (BBB) permeability resulting in BBB breakdown. In this point, cerebral autoregulation may be disrupted in the very elderly hypertensive patients. Long acting CCB, ACEI and ARB which does not decrease cerebral blood flow (CBF) are suggested to be appropriate in BP control with high risk at stroke, whereas diuretic which does decrease CBF is not. From these points, it is possible to take the possibility into account that the antihypertensive agents could be affected by the class of antihypertensives agents with cognitive function. How would be the results if the impact of antihypertensive agent class were taken into account in the regression model?

The role of antihypertensive drugs was not the focus of our study. However, as shown in S1 Table, use of antihypertensive drugs was included in the inverse probability weighting models for having prolonged QT interval as a time-dependent variable, since some of these drugs could influence QT interval duration. Unfortunately, information on specific classes of antihypertensive drugs is not available in the Honolulu-Asia Aging Study.

2. It would be helpful if there was information other than antihypertensive medication (e.g. use of statin, hypoglycemic agent).

Similarly, we included the use of cholesterol-lowering drugs and hypoglycemic agents as time-dependent variables in the inverse-probability weighting models for having prolonged QT interval as shown in S1 Table.

3. Antihypertensive agents after baseline.

This question may be similar to that in #1. Althoguh antihypertensive agents that could be used in the baseline would be limited, it would be helpful if there was information about the contents of antihypertensive medication after baseline. These agents change during the follow up might affect the results.

We agree with the reviewer that specific class of antihypertensive drugs could influence QT interval (e.g. angiotensin-converting enzyme inhibitors, angiotensin receptor blockers, beta-blockers, calcium channel blockers). Unfortunately, we do not have the information on the class of antihypertensive medication during the follow-up. Also, the percentage of participants who had hypertension at baseline was small (9%); therefore, this sample is not well-suited for exploring the role of antihypertensive medication classes in the relationship of prolonged QT interval with cognitive decline.

4. Short QT

Recent notion is not only long QT but also short QT also associated with adverse cardiovascular events. Thus, short QT should be taken into account.

Short QT syndrome is a very rare genetic syndrome, which is usually defined as QTc ≤330 ms or QTc interval <360 ms. On the other hand, prolonged QT interval is more common, and it has been consistently associated with stroke, myocardial infarction, and cardiovascular mortality. 1,2 Therefore, the aim of our study is to investigate the association of prolonged QT interval with cognitive performance since prolonged QT is more frequent than short QT in the general population.

5. Incidence of Alzheimer disease, vascular dementia or total dementia

I would be interesting if the relationship between QT length and incidence of Alzheimer disease, vascular dementia or total dementia.

We agree with the reviewer, but this is beyond the scope of this paper. We have added this to the discussion:

“Although QT interval was not associated with cognitive decline evaluated by CASI, it could be associated with vascular dementia or with cognitive decline in specific domains, such as executive function.” (Page 17, Lines 6-8)

Reviewer #2

Paper by Suemoto et all addresses interesting question whether prolonged QT might be associated with decline in cognitive function.

The paper is very clearly written and I do not have any objection to statistical analysis.

Thank you for your comments that helped strengthen our manuscript.

However, there are several points which I should raise.

1. QT interval is a dynamic parameter affected by several factors, which were not reported or considered in statistical analysis. It should be therefore at least mentioned in limitations of the study the possible effect of presence of bundle branch block, hypokalemia and hypocalcemia, presence of heart failure, ischemia, cerebrovascular disease, endocrine disorders etc.

Although we included several factors that could influence QT interval duration, like stroke and myocardial ischemia (S1 Table), we agree with the reviewer that some factors were still missing in the IPEW. We included the lack of information on these factors among our study limitations:

“Although we included several factors that could increase the chance of QT interval prolongation, information was missing on other clinical conditions that could influence QT interval (e.g. bundle branch block, hypokalemia and hypocalcemia, endocrine disorders).” (Page 16, Lines 9-12)

2. There is huge attrition during follow-up. Again, it is usual and inevitable in this kind of study. But the possibility is that it could affect results.

The Honolulu-Asia Aging Study has a long follow-up since individuals were followed from midlife until late life. Therefore, attrition rates are expected to be high. As we too were concerned about the potential for selection bias, we accounted for potentially informative attrition using inverse probability of attrition weights (IPAW). However, we note that this method will only fully mitigate any selection bias present if the assumptions of the method are met; thus, selection bias may not be fully mitigated using this method. We state this limitation in our manuscript:

“Attrition during the follow-up was high in the HAAS. We account for this important problem using IPAW as described. However, some bias due to informative attrition may still be present.” (Page 16, Lines 4-6)

3. As QT interval duration affects many drug classes, the estimate that use of this medication is only 2 to 3 % might be underestimated, mainly in older age. Please comment.

The exact incidence of drug induced QT interval prolongation is unknown, but it is expected to be low.3 In one population-based study showed that about 2–3% of total drug prescriptions may cause unintended QT prolongation in UK and Italy.4

We discuss the lack of information on drug induced QT prolongation among our study limitations:

“We did not have information on drugs that can affect the QT interval. However, the low prevalence of use of drugs that may cause QT prolongation in previous studies (2-3%) suggest that our inability to consider drug use will not be a large source of bias.” (Page 16, Lines 6-9)

4. The cut-off value of 370 ms might be too low to be associated with any outcome. Have you considered to use more strict cut-off value, e.g. >400 ms, >440 ms?

In fact, we used a more strict cutoff value as suggested by the reviewer. The cutoff of 407 milliseconds was chosen, since it was the 75th percentile of rate-corrected QT interval at Exam 1. Participants were considered to have incident prolonged QT interval if their rate-corrected QT interval was above 407 milliseconds at Exam 2 or 3.

Reviewer #3

Dear Colleague

Thank you for this article, which is relevant, and interesting.

The methodology is particularly developed, with particular attention to missing data.

The limits of methodology (and multiple imputation) are detailed in the discussion.

Thank you Dr. Rochoy for your kind comments, which help us to improve our manuscript.

I have some minor comments to submit to the authors:

1. Why did not you compared the characteristics of the population according to the extension of the QT in Table 1? I particularly wonder about the difference that there could be concerning the profession (clerical, sales, professional or managerial job) in the 2 groups, and how the authors explain this difference. I think this is a point worth discussing.

We added P values to Table 1 for univariate tests of differences in characteristics across QT interval groups. Participants with prolonged QT interval were significantly more likely to have clerical, sales, professional, or managerial jobs (34% vs 29%; P = 0.007). We accounted for profession in our analysis as a potential confounder by including it in the inverse probability weight models (see Table S1). Limited evidence in the literature suggests that mental stress (such as might be encountered in some professions) affects QT interval (for example, Andrássy G, et al., Ann Noninvasive Electrocardiol, 2007).5 We chose not to discuss this point in the Discussion because it was peripheral to the purpose of our analysis.

2. A difference may also be present regarding the ApoE-4 allele (?)

The presence of at least one APOE-4 allele was significantly higher among participants who had prolonged QT interval (21% vs 18%; P = 0.038). We accounted for APOE-4 allele in our analysis as a potential confounder by including it in the inverse probability weight models (see Table S1).

3. There are 2 errors in the Total column of Table 1: the total number of "jobs" (708 + 419 = 1127 and not 787), the total number of "hypertension" (435 + 551 = 986 and not 225); the rates of these two results are also incorrect.

We corrected the numbers in Table 1.

4. Among the limitations, I would add that CASI estimates a cognitive decline (the main cause of which is known to be Alzheimer's disease); perhaps the prolonged QT interval is not a good indicator for cognitive declines in a broad sense, but could be a good indicator of vascular dementia (this is more a perspective than a limit to your work in fact).

Thank you for your suggestion. We discuss this interesting perspective in the new version of the manuscript:

“Although QT interval was not associated with cognitive decline evaluated by CASI, it could be associated with vascular dementia or with cognitive decline in specific domains, such as executive function.” (Page 16, Lines 18-20)

5. Data are not available, but the authors have specified how to access them.

Yes, data access statement is still valid.

References

1. O'Neal WT, Efird JT, Kamel H, Nazarian S, Alonso A, Heckbert SR, Longstreth WT, Jr., Soliman EZ. The association of the QT interval with atrial fibrillation and stroke: the Multi-Ethnic Study of Atherosclerosis. Clin Res Cardiol 2015;104(9):743-50.

2. Ishikawa J, Ishikawa S, Kario K. Prolonged corrected QT interval is predictive of future stroke events even in subjects without ECG-diagnosed left ventricular hypertrophy. Hypertension 2015;65(3):554-60.

3. Yap YG, Camm AJ. Drug induced QT prolongation and torsades de pointes. Heart (British Cardiac Society) 2003;89(11):1363-1372.

4. De Ponti F, Poluzzi E, Montanaro N, Ferguson J. QTc and psychotropic drugs. Lancet. Vol. 356. England, 2000;75-6.

5. Andrassy G, Szabo A, Ferencz G, Trummer Z, Simon E, Tahy A. Mental stress may induce QT-interval prolongation and T-wave notching. Ann Noninvasive Electrocardiol 2007;12(3):251-9.

Submitted filename: Rebuttal letter_final.docx

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10 Feb 2020

Incident prolonged QT interval in midlife and late-life cognitive performance

PONE-D-19-23773R1

Dear Dr. Suemoto,

We are pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it complies with all outstanding technical requirements.

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With kind regards,

Stephen D. Ginsberg, Ph.D.

Section Editor

PLOS ONE

Additional Editor Comments:

1. Please fix the typo as pointed out by Reviewer #1. "First sentence on third paragraph in P16, The phrase "We did not have information" was duplicated."

2. Please fix the terminology as pointed out by Reviewer #2. "Please use same terminology for ECG (EKG vs ECG). In limitations of the study there is text in bold font "We did not have information" which is duplicate."

Comments to the Author

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Reviewer #1: All comments have been addressed

Reviewer #2: All comments have been addressed

Reviewer #3: All comments have been addressed

**********

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Reviewer #1: Yes

Reviewer #2: Yes

Reviewer #3: Yes

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Reviewer #1: Yes

Reviewer #2: Yes

Reviewer #3: Yes

**********

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Reviewer #1: Yes

Reviewer #2: Yes

Reviewer #3: Yes

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Reviewer #1: Yes

Reviewer #2: Yes

Reviewer #3: Yes

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Reviewer #1: The manuscript is well revised.

Minor comment

First sentence on third paragraph in P16, The phrase "We did not have information" was duplicated.

Reviewer #2: The authors addressed all my comments. I have only minor comments. Please use same terminology for ECG (EKG vs ECG). In limitations of the study there is text in bold font "We did not have information" which is duplicate.

Reviewer #3: Thanks to the authors for their clear and detailed answers. For me, this manuscript is now acceptable for publication.

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Reviewer #1: Yes: Michiaki Nagai

Reviewer #2: No

Reviewer #3: Yes: Michaël Rochoy

11 Feb 2020

PONE-D-19-23773R1

Incident prolonged QT interval in midlife and late-life cognitive performance

Dear Dr. Suemoto:

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