Visual memory and alcohol use in a middle-aged birth cohort

doi:10.21203/rs.3.rs-1984142/v1

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Visual memory and alcohol use in a middle-aged birth cohort

https://doi.org/10.21203/rs.3.rs-1984142/v1

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published 13 Mar, 2024

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Purpose: Light and moderate alcohol use has been reported to be associated with both impaired and enhanced cognition. The purpose of this study was to explore whether reported alcohol use was associated with reduced visual memory in males and females in a large middle-aged birth cohort population.

Methods: The study population was from the Northern Finland Birth Cohort 1966 (NFBC1966). The Paired Associate Learning (PAL) test was conducted at the age of 46 years. Reported alcohol use was measured as gram per day use of total alcohol, beer, wine, and spirits, converted into 100% alcohol, and as frequency and amount of use of beer, wine, and spirits. Data on alcohol use was available at age 31 years and at age 46 years. Visual memory was assessed using two outcome variables of the PAL test: the Total errors adjusted score (TEAS) and First Trial Memory Score (FTMS). Both PAL TEA and PAL FTMS were used as continuous variables. The continuous variables were normalised using z-scores. Linear regression analysis was done to measure the associations. Adjusted models included education, marital status, diet, and physical activity. All analyses were done separately in males and in females.

Results: Total alcohol consumption (grams per day) was 17.7 at age 31 years and 13.1 at age 46 years in males. The respective figures for females were 7.2 and 5.1. Grams per day use of total alcohol, beer, wine, and spirits, and frequency and amount of use of beer and wine were not associated with visual memory in males or females in cross-sectional or longitudinal settings. Using six or more servings of spirits was associated with worse visual memory in males in both cross-sectional and longitudinal settings. The frequency of use of wine in males was associated with better visual memory in cross-sectional settings.

Conclusion: Most participants reported light drinking. The reported gram per day alcohol use was not associated with reduced visual memory in males or females, with the exception of spirits in males. The study suggests a lack of a linear association between drinking and visual memory in the middle-aged population.

Alcohol use

visual memory

cognition

middle age

birth cohort

In general population studies, both alcohol use disorder (AUD) and heavy drinking have been found to be associated with cognitive impairment, including visual memory deficits [1,2,3,4,5]. Heavy episodic drinking has been found to be associated with impaired visual memory in both male and female adolescents [6]. Light and moderate drinking, on the other hand, have not been found to be associated with cognitive impairment. On the contrary, recent studies have found that light and moderate drinking are associated with cognitive enhancement [7,8,9].

Cross-sectional studies mostly suggest that moderate to heavy drinking is associated with cognitive decline [10,11,12,13] and mild to moderate drinking is either associated with cognitive enhancement [13,14,15,16,17] or is not associated with cognitive performance [18,19]. A dose-response positive association between amount of alcohol use and cognition has been found in females [20].

Longitudinal studies among the general population mostly suggest a positive correlation between light to moderate drinking and cognitive function [2, 21,22,23,24]. Some longitudinal studies, however, suggest no association, whether positive or negative [25,26,27,28]. Mild to moderate alcohol use, compared to former alcohol use and lifelong abstinence, has been found to be associated with better cognition [23].

There are not many longitudinal studies investigating the cognitive impact of alcohol use and any change in alcohol use in middle-aged people. In this longitudinal study, we focused on the associations between alcohol use and visual memory and new learning in the Finnish middle-aged birth cohort population.

The main aim of this study was to explore the association of alcohol use with visual memory and new learning.

The specific research aims were to study the following:

The association of grams per day alcohol use at 46 years of age with visual memory at 46 years of age (Cross-sectional study).

The association of grams per day alcohol use at 31 years with visual memory at 46 years of age (Longitudinal study 1).

The association of changes in grams per day alcohol use from 31 to 46 years of age with visual memory at 46 years of age (Longitudinal study 2).

The association of different types of alcohol use (beer, wine, and spirits) with visual memory.

2.1 Study population:

The study population of the present study was selected from the participants of the Northern Finland Birth Cohort 1966 (NFBC1966) [29]. A total of 12,055 mothers with expected delivery dates between 1 January and 31 December 1966 from Oulu and Lapland (the two northernmost provinces of Finland) had been recruited for the study. Originally 12,231 males and females whose expected year of birth was 1966 were included in this study. The follow-ups were conducted at 1, 14, 31, and 46 years of age [30]. Data from the 31-year and 46-year follow-ups were used in the present study.

Information about alcohol consumption, background factors, and lifestyle were gathered using a postal questionnaire in 1997 when the participants were 31 years old. The survey was repeated, and the Paired Associative Learning (PAL) test was conducted, at follow-up in 2012–2014 when the participants were 46 years old.

From the original study population, 5,585 participants conducted the PAL test at the age of 46 years (Fig. 1).

The cross-sectional study exploring the association between alcohol use at the age of 46 years and cognition at the age of 46 years included 4,417 participants, after excluding those with missing information on alcohol consumption (N = 1,040) and covariates (N = 128) at the age of 46 years (Fig. 1).

Longitudinal study 1 exploring the association between alcohol use at the age of 31 years and cognition at the age of 46 years included 4,819 participants after excluding those with missing information on alcohol consumption (N = 692) and covariates (N = 74) at the age of 31 years (Fig. 1).

Longitudinal study 2 exploring the association between change in alcohol use between the ages of 31 years and 46 years and cognition at the age of 46 years included 3,957 participants after excluding those with missing information on alcohol consumption (N = 1,581) at the age 31 and 46, and covariates (N = 47) at the age of 31 years.

2.2 Alcohol drinking measures:

Alcohol use was measured by questionnaire at the ages of 31 years and 46 years. The items included questions on the frequency and amount of use of different types of alcohol (mild drinks or beer, wine, and spirits).

The frequency of alcohol use was measured on a 10-point scale (1 drinking never and 10 drinking daily). Quantities consumed on a typical occasion were asked about separately for all three beverage types: mild drinks in the number of 0.33 l bottles; wine in the number of glasses (= 12 cl) or 0.75 l bottles; and spirits in the number of 4 cl shots or 0.5 l bottles. The mild drink quantity was measured on a 9-point scale (1 drinking none and 9 drinking 15 bottles or more), and for wines and spirits, the typical quantity was measured similarly.

Gram per day alcohol use by the participants was assessed by forming the following variables: Intake of total alcohol in grams of 100% alcohol per day, intake of beer in grams per day, intake of wine in grams per day, and intake of spirit in grams per day. We assumed one bottle of mild drink to contain one standard unit of alcohol (= 12 g of ethanol), wine to contain 13% alcohol, and spirits to contain 38% alcohol. From the questionnaire answers, daily consumption of ethanol was calculated by multiplying the typical frequency of use by quantity at both 31 years and 46 years.

For the frequency of drinking light drinks, wine, or spirits, the cut-off was weekly drinking (drinking less frequently than once a week vs. drinking weekly or more frequently). The cut-off point for the amount usually used when drinking for light drinks was 6 bottles (under 6 bottles vs. 6 bottles or more), and for wines, one bottle (under one bottle of wine vs. a bottle of wine or more), and for spirits six drinks (under six drinks vs. six drinks or more).

2.3 Visual memory:

The Paired Associative Learning (PAL) task from the Cambridge Neuropsychological Test Automated Battery (CANTAB) was used to assess visual memory. PAL has been found to differentiate between Alzheimer's disease and mild cognitive impairment [31]. PAL has been successfully used in different cultures and educations in both longitudinal and cross-sectional studies [32]. This study used the PAL test in particular because it can reliably assess the type and degree of functional loss and the specificity of age-related cognitive decline [33].

In the PAL test, we assessed visual memory using the primary outcome variables, ‘total errors adjusted score’ and ‘first trial memory score’. Total Errors (Adjusted) (TEA) reflects how quickly the participant learns when they have multiple attempts at each problem, while First Trial Memory Score (FTMS) reflects how many patterns the participant correctly places on the first attempt at each problem.

Both PAL TEA and PAL FTMS were used as continuous variables.

2.4 Covariates:

Covariates were selected from the 31-year follow-up data. We used educational level, marital status, diet, and physical activity as covariates.

Educational level is associated with cognitive functioning [34]. The question addressing the education of the NFBC1966 participants was: ‘What is your basic education?’ (Less than 9 years of basic school, 9 years of basic school, matriculation examination). In the analysis, we combined ‘less than 9 years of basic school’ and ‘9 years of basic school’ as ‘No matriculation examination’.

The question addressing the marital status of the NFBC1966 participants was: ‘What is your relationship status?’ (Married since, cohabiting since, single, legal separation or divorced since, widowed since). In the analysis, we combined 'married’ and ‘cohabiting’ as ‘In a relationship’, and ‘single’, ‘legal separation or divorced’ and ‘widowed’ as ‘Not in a relationship’.

Diet might be associated with cognitive functioning [35,36,37]. Diet was categorised as: ‘Healthy (Consuming vegetables, roots, and salad 3 times per week or more)’ and ‘Unhealthy’ (Consuming vegetables, roots, and salad 2 times per week or less)’.

Physical activity might [38,39,40,41] or might not [42,43] be associated with cognition. Physical activity was categorised as: ‘Active (1 hour or more brisk physical activity at a time causing at least some breathlessness and sweating)’ and ‘Inactive (Less than 1 hour of brisk physical activity at a time causing at least some breathlessness and sweating)’.

2.5 Statistical analysis:

Statistical analysis was performed using R version 1.1.456. We evaluated the association between cognition and alcohol use using two different cognition variables: PAL FTMS and PAL TEA. The association between the PAL tests was analysed using linear regression, and a β with 95% CI was reported. We considered < 0.01 p-value as statistically significant, as there were so many comparisons.

All continuous variables were normalised using z-scores. We assessed crude models and models adjusted with education, relationship status, diet, and physical activity. All analyses were conducted separately in males and females, as alcohol consumption differs between the two sexes [44] and performance in visual memory tests, including the PAL test, has also been reported to be different in males and females [45,46].

2.6 Attrition analysis:

To evaluate the representativeness of our study population, an attrition analysis was performed while studying the associations with alcohol use in the longitudinal study 1 dataset.

3.1 Background factors:

About two-thirds of the participants in all three datasets were female. One-third of the males and half of the females had basic education above matriculation (12 years or more). About four-fifths of the participants were married or cohabitating. Half of the males and two-thirds of the females ate a healthy diet in both the cross-section at age 31 dataset and the change in alcohol use dataset. In the cross-section at age 46 dataset, three-fifths of males and four-fifths of females ate a healthy diet. One-third of males and four-fifths of females took active exercise in the age 31 and the change in alcohol use dataset. One-third of males and females took active exercise at the age of 46 (Table 1).

3.2 Alcohol consumption and cognitive measures:

In males, total alcohol consumption was 18 grams per day in the cross-sectional dataset, 13 in the longitudinal 1 and 4 in the longitudinal 2 dataset. In females, total alcohol consumption was 7 grams per day in the cross-sectional dataset, 5 in the longitudinal 1 and 2 in the longitudinal 2 dataset (Table 2).

The mean PAL TEA score was 14 in males and 12 in females in all datasets. The mean PAL FTMS score was 19 in males and 20 in females in all datasets (Table 2).

3.3 Association of grams per day total alcohol use with visual memory:

No statistically significant associations were found in males or females, between gram per day total alcohol use in the cross-sectional, longitudinal 1 or longitudinal 2 settings (Table 3).

3.4 Association of gram per day beer, wine, and spirits use with visual memory:

No statistically significant associations were found in males or females, between gram per day beer, wine, and spirits use in the cross-sectional or longitudinal 1 setting (Supplementary tables 1–3).

3.5 Association of frequency of beer, wine, and spirit use with visual memory in the cross-sectional and longitudinal 1 datasets:

The association of frequency of use of beer, wine, and spirits in males and females in the cross-sectional and longitudinal 1 datasets are shown in Supplementary Tables 4–6.

Males using wine once a week or more at 46 years had better first trial visual memory scores at 46 years (cross-sectional) than males using wine less than once a week (Supplementary Table 5).

3.6 Association of amount of beer, wine, and spirit use with visual memory in the cross-sectional and longitudinal 1 datasets:

The association of amount of beer, wine, and spirits in males and females in the cross-sectional and longitudinal 1 datasets are shown in Supplementary Tables 7–9.

Males using six or more servings of spirits made more errors at 46 years (cross-sectional) than males using fewer than six servings (Supplementary Table 9).

3.7 Attrition analysis:

For longitudinal study-1, alcohol use data at age 31 years were available for 2,744 male and 3,369 female participants. At the age of 46 years, 19.9% of males and 18.3% of females had dropped out. The dropped-out males used significantly more alcohol than those who participated at the age of 46 years (Supplementary Table 10).

4.1 Summary of main findings:

Most participants reported light consumption of alcohol. The main finding of our birth-cohort-based longitudinal population study revealed that neither cross-sectional nor longitudinal light use of total alcohol, beer, and wine was associated with decreased visual memory in mid-life when controlled for education, marital status, diet, and physical activity.

4.2 Comparison with other studies:

In general population studies, AUD has been found to be associated with visual memory deficits [1,3]. A positive association between moderate to heavy drinking and cognition has also been found [47]. Heavy drinking has also been found to be associated with visual memory [2] and reaction time [4]. Heavy episodic drinking has also been found to be associated with poor visual memory in adolescents [6]. In the present study, we found that use of six or more servings of spirits was associated with impaired visual memory in males, but that was the only finding that showed a negative association between drinking and cognition. This may be connected to that fact that our measures mostly reflected light alcohol use.

There are not many longitudinal studies investigating the cognitive impact of alcohol use and change in alcohol use in middle-aged people. In a study by Yan and colleagues no significant association of mild to moderate alcohol use with cognition was found in both the baseline and follow-up after one year [48]. A prospective cohort study of the middle-aged to elderly population with 9 years of mean follow-up revealed that mild to moderate alcohol consumption was associated with better cognitive functioning [49]. In another longitudinal study, an increase in alcohol use from the baseline to follow-up after 11 years was found to be associated with better cognition in females [24]. A fourth study by Arntzen et al. reported that light to moderate wine use at the baseline was associated with better cognition at follow-up after 7 years [50]. Our current study did not find any association between the baseline use of alcohol and better – or worse – cognition at follow-up.

As in our study, some other longitudinal studies, however, suggest no association, whether positive or negative [25,26,27,28]. One recent study also did not find any significant association of mild to moderate alcohol use with cognition both at the baseline and at follow-up after one year [48].

Earlier cross-sectional studies suggested that mild to moderate drinking was either associated with cognitive enhancement [13,14,15,16,17] or was not associated with cognition at all [18,19]. The study findings in our cross-sectional dataset at age 46 were similar to those studies.

Hop-Derived Iso-α-Acid, a chemical component found in dark beer, has been associated with better visual memory in animal model studies [51]. Resveratrol, a polyphenol compound found in red wine, has been associated with better cognitive performance in a double-blind, placebo-controlled, crossover study in humans [52]. This might explain our findings suggesting an association of frequency of wine consumption with better visual memory in males. Activation of pro-inflammatory cytokines, free-radical damage, and thiamine deficiency are the major causes of ethanol-induced neurotoxicity [53].

The number of drinks per week has been found to be associated with cognitive impairment [54]. However, one cross-sectional cohort study revealed that moderate, regular drinking was associated with better cognition than non-drinking and less-frequent drinking [14]. Another cohort study revealed that irregular drinking and abstinence were associated with cognitive decline compared to regular drinking [55].

A dose-response positive association between alcohol use and cognition has been found among female current drinkers compared to former heavy drinkers and abstainers [20]. An increase in alcohol use from the baseline to follow-up after 11 years has been found to be associated with better cognition in females [24]. Light to moderate wine use at the baseline has been found to be associated with better cognition at follow-up after 7 years [50].

4.3 Strengths:

Different patterns of alcohol use in both cross-sectional and longitudinal settings of a large cohort population are the novel aspects of the current study. The age group of the participants was middle age, which has not been studied thoroughly previously. We were able to use several covariates in this study. Gram per day use of total alcohol, beers, wine, and spirits and frequencies and amounts of beers, wine and spirits were used. We were able to study the association between visual memory and reported alcohol use in both cross-sectional and longitudinal settings. The longitudinal setting included using both alcohol use at age 31 years and change in alcohol use between ages 31 and 46 years as exposures.

4.4 Limitations:

We could not categorise alcohol use into mild, moderate, and heavy; neither did we exclude abstinence. It is also important to note that our results do not represent cognition in general, but only one very limited aspect of this wide phenomenon. As only one cognitive measure was applied, we cannot draw conclusions on general cognitive performance. No measure of cognition or visual memory was assessed at the baseline. Without such a baseline control, it is difficult to interpret the findings and the direction of the effect. Heavy drinking with smoking has been associated with worse cognition than only heavy drinking [56,57]. We did not use smoking as a possible covariate. We also did not use occupation as a possible covariate even though type of occupation has been found to be associated with cognitive function in later life [58]. However, we used education as a possible covariate, and educational largely correlates with earning [59]. Depression is associated with impairment of memory [60] and executive functioning [61]. In the current study, however, we did not use depressive symptoms as a possible confounder because a recent study found no association between depressive symptoms and visual memory in the same population [62]. Possible changes in marital status, diet, and exercise over time might also make it difficult to predict the true effects of covariants in this longitudinal study. We could not present interaction analyses by sex to show whether any findings were indeed different for men and women, or whether the differences in the observed sex-specific associations between alcohol use and cognition stemmed from differences in statistical power. The high attrition level is another limitation of our study.

Our study suggests a lack of a linear association between drinking and visual memory. The reported gram per day alcohol use was not associated with reduced visual memory in male and female members of this birth cohort population, controlled for education, marital status, diet, and physical activity.

Acknowledgements:

We thank all cohort members and researchers who participated in the 31 years study. We also acknowledge the work of the NFBC project center, funding bodies, and proof-reader of the manuscript.

Author contributions:

Each author has made substantial contributions to the conception or design of the work; the acquisition, analysis, and interpretation of data; has drafted the work and substantively revised it; has approved the submitted version (and any substantially modified version that involves the author's contribution to the study); has agreed both to be personally accountable for the author's own contributions and to ensure that questions related to the accuracy or integrity of any part of the work, even ones in which the author was not personally involved, are appropriately investigated, resolved, and the resolution documented in the literature.

Competing interests:

The authors declare no competing interests.

Ethical approval:

The study protocol of the NFBC 1966 was approved by The Ethics Committee of the Faculty of Medicine of the University of Oulu. Data protection of the NFBC 1966 was approved by the Finnish Privacy Protection Agency and the Ministry of Social Affairs and Health. Each follow-up study of the NFBC1966 has been evaluated by the regional ethical committee of the Northern Ostrobothnia Hospital District (EETTMK 94/11, 17.09.2012) (Nordström et al., 2021).

Informed consent statement:

In the NFBC1966 written informed consent was obtained from all participants. The study subjects were also informed that they could withdraw their consent at any time, at which point any samples or data stored from them would be destroyed.

Consent to publish statement:

From all the NFBC1966 participants, written informed consent for the publication of the study results without disclosing their identities was sought.

Data availability statement:

NFBC data is available from the University of Oulu, Infrastructure for Population Studies. Permission to use the data can be applied for research purposes via electronic material request portal. In the use of data, we follow the EU general data protection regulation (679/2016) and Finnish Data Protection Act. The use of personal data is based on cohort participant’s written informed consent at his/her latest follow-up study, which may cause limitations to its use. Please, contact NFBC project center ([email protected]) and visit the cohort website for more information.

The datasets used and/or analysed during the current study available from the corresponding author on reasonable request.

Role of the funding source:

The NFBC1966 received financial support from University of Oulu Grant no. 24000692, Oulu University Hospital Grant no. 24301140, ERDF European Regional Development Fund Grant no. 539/2010 A31592. The principal author was supported by the H2020 Marie Skłodowska-Curie Actions co-funding of regional, national, and international programmes (COFUND) (grant agreement no. 713606) for his doctoral programme (MSC-COFUND, I4FUTURE). The principal author was also supported by the Iso-Mällinen Foundation (Iso-Mällisen Säätiö) through a medical grant of EUR 4000 for the year 2019 (grant no. 0400 584622).

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Table 1 Background factors for male and female participants in the cross-sectional, longitudinal 1, and longitudinal 2 datasets.

Parameters	Cross-sectional		Longitudinal 1		Longitudinal 2
Parameters	Male	Female	Male	Female	Male	Female
N	1981	2436	2066	2753	1758	2199
Education
No matriculation (%)	1269 (64.1)	1072 (44.0)	1350 (65.3)	1247 (45.3)	1130 (64.3)	984 (44.7)
Matriculation (%)	712 (35.9)	1364 (56.0)	716 (34.7)	1506 (54.7)	628 (35.7)	1215 (55.3)
Marital status
Married / Cohabitating (%)	1614 (81.5)	1907 (78.3)	1492 (72.2)	2169 (78.8)	1278 (72.7)	1718 (78.1)
Single (%)	367 (18.5)	529 (21.7)	574 (27.8)	584 (21.2)	480 (27.3)	481 (21.9)
Diet
Healthy (%)	1267 (64.0)	1959 (80.4)	1030 (49.9)	1869 (67.9)	903 (51.4)	1506 (68.5)
Unhealthy (%)	714 (36.0)	477 (19.6)	1036 (50.1)	884 (32.1)	855 (48.6)	693 (31.5)
Physical activity
Active (%)	653 (33.0)	735 (30.2)	670 (32.4)	597 (21.7)	577 (32.8)	469 (21.3)
Inactive (%)	1328 (67.0)	1701 (69.8)	1396 (67.6)	2156 (78.3)	1181 (67.2)	1730 (78.7)
Cross-sectional = Alcohol use data at the age of 46 and PAL test data at the age of 46. Longitudinal 1 = Alcohol use data at the age of 31 and PAL test data at the age of 46. Longitudinal 2 = Change in alcohol use data from the age of 31 to 46 and visual memory data at the age of 46.

Table 2 Alcohol consumption and visual memory for male and female participants in the cross-sectional, longitudinal 1, and longitudinal 2 datasets.

Parameters	Cross-sectional		Longitudinal 1		Longitudinal 2
Parameters	Male	Female	Male	Female	Male	Female
N	2066	2753	1981	2436	1758	2199
Total alcohol consumption (g/d)	13.07 (17.72)	5.06 (9.15)	17.71 (24.53)	7.19 (11.47)	-3.78 (21.10)	-2.15 (11.28)
PAL TEA (mean (SD))	14.40 (14.30)	11.88 (10.66)	14.24 (13.97)	11.69 (10.11)	14.20 (14.10)	11.75 (10.28)
PAL FTMS (mean (SD))	19.03 (3.25)	19.53 (3.28)	19.08 (3.27)	19.55 (3.22)	19.08 (3.26)	19.54 (3.23)
Cross-sectional = Alcohol use data at the age of 46 and PAL test data at the age of 46. Longitudinal 1 = Alcohol use data at the age of 31 and PAL test data at the age of 46. Longitudinal 2 = Change in alcohol use data from the age of 31 to 46 and PAL test data at the age of 46. ‘-’ symbol before total alcohol use (g/d) in longitudinal 2 setting denotes ‘Reduction’.

Table 3 Association of alcohol use in grams of alcohol per day with visual memory for male and female participants in the cross-sectional, longitudinal 1, and longitudinal 2 datasets.

Parameters	PAL TEA					PAL FTMS
Parameters	Crude		Adjusted		Crude		Adjusted
	Beta (95 % CI)	p	Beta (95 % CI)	p	Beta (95 % CI)	p	Beta (95 % CI)	p
Males
Cross-sectional	-0.01 (-0.05-0.04)	0.78	-0.02 (-0.06-0.03)	0.479	0 0.01 (-0.03-0.05)	0.671	0.02 (-0.02-0.05)	0.43
Longitud inal 1	-0.02 (-0.06–0.02)	0.371	-0.04 (-0.08–0.01)	0.098	0 (-0.03–0.04)	0.842	0.02 (-0.02–0.06)	0.33
Longitud inal 2	-0.02 (-0.07–0.03)	0.348	-0.02 (-0.07–0.03)	0.349	0.03 (-0.01–0.08)	0.149	0.03 (-0.01–0.08)	0.141
Females
Cross-sectional	-0.01 (-0.07-0.05)	0,674	-0.01 (-0.07-0.05)	0,805	0.06 (-0.02-0.13)	0.143	0.05 (-0.02-0.13)	0.16
Longitud inal 1	-0.03 (-0.09-0.02)	0.253	-0.02 (-0.07-0.04)	0.546	0.04 (-0.03-0.1)	0.272	0.02 (-0.04-0.09)	0.472
Longitud inal 2	0.04 (-0.02–0.1)	0.245	0.03 (-0.03–0.08)	0.396	0 (-0.07–0.07)	0.966	0.01 (-0.06–0.08)	0.819
Linear regression analysis. All continuous variables used in these models were normalised using z-scores. Adjusted models included education, marital status, diet, and physical activity. Cross-sectional = Alcohol use data at the age of 46 and PAL test data at the age of 46. Longitudinal 1 = Alcohol use data at the age of 31 and PAL test data at the age of 46. Longitudinal 2 = Change in alcohol use data from the age of 31 to 46 and PAL test data at the age of 46.

No competing interests reported.

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Visual memory and alcohol use in a middle-aged birth cohort

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Abstract

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1. Introduction

2. Materials And Methods

2.1 Study population:

2.2 Alcohol drinking measures:

2.3 Visual memory:

2.4 Covariates:

2.5 Statistical analysis:

2.6 Attrition analysis:

3 Results

3.1 Background factors:

3.2 Alcohol consumption and cognitive measures:

3.3 Association of grams per day total alcohol use with visual memory:

3.4 Association of gram per day beer, wine, and spirits use with visual memory:

3.7 Attrition analysis:

4 Discussion

4.1 Summary of main findings:

4.2 Comparison with other studies:

4.3 Strengths:

4.4 Limitations:

5 Conclusions

Declarations

References

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