Tuesday, August 23, 2022

Abstinence from alcohol appears to be associated with an increased risk for all-cause dementia

The relationship between alcohol use and dementia in adults aged over 60 years: A combined analysis of prospective, individual-participant data from 15 international studies. Louise Mewton et al. Addiction, August 22 2022. https://doi.org/10.1111/add.16035


Aim: To synthesise international findings on the alcohol-dementia relationship, including representation from low- and middle-income countries.

Methods: Individual participant data meta-analysis of 15 prospective epidemiological cohort studies from countries situated in six continents. Cox regression investigated the dementia risk associated with alcohol use in older adults aged over 60 years. Additional analyses assessed the alcohol-dementia relationship in the sample stratified by sex and by continent. Participants included 24,478 community dwelling individuals without a history of dementia at baseline and at least one follow-up dementia assessment. The main outcome measure was all-cause dementia as determined by clinical interview.

Results: At baseline, the mean age across studies was 71.8 (standard deviation 7.5, range 60-102 years), 14,260 (58.3%) were female, and 13,269 (54.2%) were current drinkers. During 151,636 person-years of follow-up, there were 2,124 incident cases of dementia (14.0 per 1,000 person-years). When compared with abstainers, the risk for dementia was lower in occasional (hazard ratio [HR]: 0.78; 95% confidence interval [CI]: 0.68-0.89), light-moderate (HR: 0.78; 95% CI: 0.70-0.87) and moderate-heavy drinkers (HR: 0.62; 95% CI: 0.51-0.77). There was no evidence of differences between lifetime abstainers and former drinkers in terms of dementia risk (HR: 0.98; 95% CI: 0.81-1.18). In dose-response analyses, moderate drinking up to 40g/day was associated with a lower risk of dementia when compared with lifetime abstaining. Among current drinkers, there was no consistent evidence for differences in terms of dementia risk. Results were similar when the sample was stratified by sex. When analysed at the continent level, there was considerable heterogeneity in the alcohol-dementia relationship.

Conclusions: Abstinence from alcohol appears to be associated with an increased risk for all-cause dementia. Among current drinkers, there appears to be no consistent evidence to suggest that the amount of alcohol consumed in later life is associated with dementia risk.

On average, refugees assimilate both culturally and economically; however, while refugees assigned to more hostile regions converge to local culture more quickly, they do not exhibit faster economic assimilation

Scared Straight? Threat and Assimilation of Refugees in Germany. Philipp Jaschke, Sulin Sardoschau & Marco Tabellini. NBER Working Paper 30381. Aug 2022. DOI 10.3386/w30381

Abstract: This paper studies the effects of threat on convergence to local culture and economic assimilation of refugees, exploiting plausibly exogenous variation in their allocation across German regions between 2013 and 2016. We combine novel survey data on cultural preferences and economic outcomes of refugees with corresponding information on locals, and construct a threat index that integrates contemporaneous and historical variables. On average, refugees assimilate both culturally and economically. However, while refugees assigned to more hostile regions converge to local culture more quickly, they do not exhibit faster economic assimilation. Our evidence suggests that refugees exert more assimilation effort in response to local threat, but do not integrate faster because of higher discrimination in more hostile regions.

Failed replication: The “brain drain” effect of Ward et al. (2017), the purported effect on cognitive performance due to the mere presence of one's smartphone

Reexamining the “brain drain” effect: A replication of Ward et al. (2017). Ana C.Ruiz Pardo, John Paul Minda. Acta Psychologica, Volume 230, October 2022, 103717. https://doi.org/10.1016/j.actpsy.2022.103717

Abstract: The present study was a pre-registered direct replication of Ward et al.'s (2017) second experiment (OSF pre-registration found at: https://osf.io/5fq4r). This replication assigned both smartphone location (on desk, in pocket/bag, or outside of the testing room) and smartphone power (on, or off) for a total of six conditions. Participants completed an automated operation span (OSpan) task, a Cue-Dependent Go/No-Go task, and the smartphone attachment and dependency inventory. It was hypothesized that performance on an attention-demanding task (i.e., the OSpan task) would be worse for those in closer proximity to their smartphone (on desk) and that those with greater smartphone attachment and dependency would have a larger “brain drain” effect. Using the same tasks and conditions as in Ward et al.'s (2017) second experiment, the present study found that the “brain drain” effect did not replicate: there was no difference between smartphone location conditions on performance on either the o-span task or the go/no-go task. These findings demonstrate that the mere presence of one's smartphone may not be enough to affect cognitive performance. Understanding these effects is crucial in a time where smartphones are a basic necessity.

Keywords: Smartphone presenceAttentionResponse inhibitionSmartphone dependency

4. Discussion

Smartphones provide an easy and effective method of communicating with the world right at our fingertips. The rising prevalence of smartphones (Pew Research Center, 2019) has prompted research including possible behavioural addictions (WHO, 2015) and how these might affect cognitive abilities. Although there are many benefits to using a smartphone in terms of communication, the present study investigated how smartphones affect performance on cognitively demanding tasks. This was done by reexamining the “brain drain” effect (i.e., those who were in closer proximity to their smartphone performed worse on a cognitively demanding task, which is moderated by smartphone reliance) found by Ward et al.'s (2017) second experiment. The three main hypotheses (i.e., location effect, power effect, and moderation effect) from Ward et al. (2017) were evaluated in the present study.

4.1. The OSpan Task and the Cue-Dependent Go/No-Go task

There were no significant main or interaction effects of smartphone location on performance on OSpan absolute score. There was a significant main effect of cue type and an interaction effect of cue type and smartphone location on omission errors in the Cue-Dependent Go/No-Go task (Bezdjian et al., 2009). However, this effect was explored with tests of simple main effects and found no significant effect of smartphone location for either cue type. Overall, the present study did replicate Ward et al.'s null effect on the Cue-Dependent Go/No-Go task performance. More notably, however, the present study's findings failed to replicate Ward et al.'s main effect concerning performance on the OSpan task (Unsworth et al., 2005). Therefore, the “brain drain” effect was not replicated in the present study. The smartphone power effect hypothesis was supported: there was no significant difference between power conditions (i.e., powered ON vs. OFF) on performance for both tasks. This was a replication of Ward et al.'s findings.

4.2. Factor analysis of the smartphone attachment and dependency inventory

Findings from a principal components analysis on the smartphone attachment and dependency inventory (Ward et al., 2017) partially supported the two-factor findings from Ward et al. (i.e., smartphone dependence and emotional attachment), but also added a third factor: smartphone distractibility.

4.3. Moderation analysis on OSpan Score

Finally, the moderation effect did not replicate: smartphone dependency, emotional attachment, and distractibility were not significant moderators of OSpan performance. In contrast with Ward and colleagues, emotional attachment showed a trend for those in the desk condition, where higher emotional attachment predicted lower OSpan performance. It should be noted that this analysis was completed as a pre-registered analysis and was exploratory in nature. Overall, the present study demonstrated that the “brain drain” effect may not be a replicable effect of smartphone presence on cognition. Possible reasons for this are given.

4.4. Failure to replicate the “brain drain” effect

A stark difference in performance was observed between the present study's OSpan performance and in Ward et al.'s (2017) second experiment. This was one of the critical results in Ward et al., because they described the OSpan as a difficult working memory task intended to be sensitive to a decrease in cognitive capacity. They argued that this difficulty difference was the reason why they found an effect on OSpan performance but not on the Cue-Dependent Go/No-Go (Bezdjian et al., 2009) performance, and indeed this was the locus of the “brain drain” effect. However, participants in our study did not find the OSpan as challenging and the presence of their own smartphone on the desk did not seem to interfere with their performance on the task. Not only was the mean-difference in OSpan performance for the present study much smaller than for Ward et al. but also, the average performance between the present study and Ward et al. implies that participants in the present study did not find the OSpan task as challenging as in Ward et al.'s study. This difference was also seen when compared to Ward et al.'s first experiment, where average OSpan performance was lower than a score of 34. These differences may explain why participants in our experiment did not experience a “brain drain” in their performance: the task did not diminish participant’s available cognitive capacity. In fact, the present study showed participants with perfect performance on both the math and letter recall components and, consequently, there was a possible ceiling effect. This defeated the purpose of the OSpan as a more difficult cognitive task. Therefore, to determine the underlying mechanisms behind smartphones' impact on cognition, future work should use reliable and normed cognitive tasks. The Cambridge Brain Sciences (CBS; Hampshire et al., 2012) test battery, for example, evaluates a broad range of cognitive abilities such as selective attention, response inhibition, reasoning, and working memory. These short cognitive tests have been used across different populations (Wild et al., 2018) to test people across three main components (i.e., short-term memory, reasoning, and verbal ability) with varying difficulty levels. Therefore, using this test battery could examine how smartphone presence affects an overview of cognitive aspects and could explain why the present study did not replicate the “brain drain” effect.

Another limitation to consider in the present study was the measure for smartphone reliance. In order to directly compare the present study to Ward et al.'s second experiment, the smartphone attachment and dependency inventory (Ward et al., 2017) was used to measure participant’s smartphone attachment and dependency (i.e., reliance). However, current research typically uses additional measures to measure things such as nomophobia (i.e., the fear of being without one's phone or the internet; (Yildirim & Correia, 2015) and smartphone involvement (Walsh et al., 2010). Although the use of the smartphone attachment and dependency inventory (Ward et al., 2017) allowed the present study to directly compare findings to Ward et al.'s second experiment, measuring smartphone reliance based on only one scale limited the present study. Therefore, future research should expand on other measures of smartphone reliance.

Additionally, it should be noted that the present study focused on a North American population to compare directly to Ward et al.'s original study. However, as smartphone prevalence emerges globally and differently across countries (Silver, 2019), future research should consider comparing different countries' smartphone use.

In a large Dutch twin sample, we found that genetic variance accounted for 74% and 77% of whether people were pescatarian or vegetarian, respectively; the remaining variance was accounted for by non-shared environmental influences

The heritability of pescetarianism and vegetarianism. Laura W. Wesseldijk et al. Food Quality and Preference, August 23 2022, 104705. https://doi.org/10.1016/j.foodqual.2022.104705


• Pescetarianism is 74% heritable and vegetarianism 77%.

• Genetic influences account for 70-80% of individual differences in abstinence from eating beef, pork, poultry, fish and shellfish.

• Individuals did not eat pork mostly because of health reasons, poultry, fish and shellfish because of dislike, and beef because of beliefs.

• Regardless of the different reasons for abstinence, heritability estimates were of a similar large magnitude.

Abstract: Genetic factors have a substantial influence on individuals' food preferences, but less is known about their influence on abstinence from eating meat and fish. Here we looked at the influence genetics may have on pescetarianism (not eating meat but eating fish) and vegetarianism (not eating meat and fish) in a Dutch twin sample (N = 8,196). We also examined genetic and environmental influences on abstinence from eating beef, pork, poultry, fish or shellfish separately and explored the reasons individuals gave for not eating these types of meat and fish (e.g., disliking, health concerns or beliefs). Abstinence from eating various meats or (shell)fish varied from 5.3% for beef to 46% for shellfish, and 3.7% did not eat meat (1.9% was pescatarian and 1.8% vegetarian). The prevalence of all abstinences was higher in women than men. Genetic factors accounted for 74% and 77% of variation in pescetarianism and vegetarianism, respectively, with the remaining variance accounted for by non-shared environmental influences. Heritability for abstinence from eating beef, pork, poultry, fish or shellfish ranged from 70 to 80%. Abstention from pork was mostly due to health concerns, abstention from poultry, fish and shellfish because of dislike, and abstention from beef because of beliefs (i.e., religion or convictions). Most pescatarians and vegetarians reported beliefs as one of their reason for abstinence (∼75%). Overall, regardless of the fact that different reasons seem to play a role in pescetarianism, vegetarianism and abstinence from eating different meats and fish, genetic factors undergirded all with a similar large magnitude.


See also Where the Rubber Meats the Road: Relationships between Vegetarianism and Socio-political Attitudes and Voting Behavior. John B. Nezlek & Catherine A. Forestell. Jul 15 2019. Ecology of Food and Nutrition, Volume 58, 2019 - Issue 6, pp 548.559. https://www.bipartisanalliance.com/2020/01/possible-genetic-predisposition-to.html

4. Discussion

In a large Dutch twin sample, we found that genetic variance accounted for 74% and 77% of whether people were pescatarian or vegetarian, respectively. The remaining variance was accounted for by non-shared environmental influences. Hence, we detected no effect of the common environment, which should include social transmission from parents and other forms of environmental exposures shared by twins. These results are in line with findings from the recent Finnish study that found vegetarianism/veganism to be 76% heritable (Çınar et al., 2021). We replicated the heritability estimate in a sample of a similar age (Finnish sample M = 29.51 years, SD = 7.84, Dutch sample M = 35.25 years, SD = 15.26), though with a much lower prevalence of vegetarianism. This is, however, roughly in line with country-specific prevalence for vegetarianism (approximately 4-5% in the Netherlands and 11% in Finland) (Motrøen, 2020van Rossum et al., 2020). Furthermore, we found the degree of genetic influences on abstaining from beef, pork, poultry, fish and shellfish to all be roughly around 70 to 80%. Notably, common environment factors were strongest for abstention from pork (28%), though the influence was non-significant with an alpha of .01 (corrected for multiple testing). In a previous study, the heritability of food preference was explored in the same sample (Vink et al., 2020). The broad sense heritability for liking of meat and fish, measured on a continuous scale, varied between 41% and 60%. This suggests that genetic factors play a larger role in abstinence from eating meat and fish than in the liking of these foods.

Pork was the only type of meat that individuals in our sample abstained from mostly because of health concerns. Beliefs, which was often selected as a reason not to eat particular types of meat (especially for beef and not eating meat), could reflect religion or conviction (in the survey the example given was veganism). We recommend future studies to differentiate the reason ‘beliefs’ and ask in more depth about the specific beliefs or convictions, like type of religion, mitigation, climate change or animal welfare. No information on religion was obtained in the current study. However, information obtained for 79.2% of the sample from one or more other surveys showed that 22% of the sample reported having no religious affiliation, 77.5% reported a religious affiliation at one point in their lives and 0.5% indicated some belief but no affiliation with a specific religion. The vast majority of those ever affiliated with a religion indicated to be Christian or to be raised as Christian and only a small minority indicated affiliations with religions that forbid the consumption of pork, beef, or both. It is therefore unlikely that beliefs reflect the influence of religion. Interestingly, people mostly abstained from poultry, fish and shellfish due to dislike. Some individuals reported 'other reasons' for their abstinence from eating meat or fish. We can only speculate about what those other reasons could be. Given that concerns about animal welfare and environmental sustainability were not among the response options, these 'other reasons' could have reflected such concerns. Additionally, some people might not eat certain types of meat or fish because they are too expensive (like shellfish), although the prevalence of being pescatarian and vegetarian was higher among higher educated participants (with probably higher incomes) than moderate or lower educated participants. Overall, regardless of the fact that different reasons seem to motivate abstention from beef, poultry, pork, fish or shellfish, genetic factors undergirded abstinence from eating all types of meat and fish with a similar magnitude (around 70-80%).

This study has limitations. We made use of self-reports. Whereas participants in the earlier Finnish sample answered yes or no to being vegetarian or vegan (Çınar et al., 2021), participants in the current study were designated as pescatarian or vegetarian based on reporting abstaining from eating specific types of meat and fish. It is possible that, in the Finnish sample, being vegetarian was interpreted as abstaining from eating meat, but not fish, or as abstaining only from certain types of meat. Conversely, some participants in the current study could abstain from beef, pork or poultry, but eat a different type of meat. Further, standard assumptions of classical twin modeling apply here (Verweij, Mosing, Zietsch, & Medland, 2012). Inferences are limited to the population which was approached for the study. The factors underlying vegetarianism in other populations might be different, especially for populations from different cultures, with different religions and/or a higher prevalence of vegetarianism. Lastly, we were initially also interested in investigating environmental and genetic influences on abstaining from all types of meats, fish and dairy (i.e., being vegan). However, our sample included only 20 vegans. Similarly, the majority of pescatarians and vegetarians were female and the data therefore did not allow to investigate sex differences in the genetic architecture. Larger sample sizes are needed to examine genetic influences on veganism, differences between males and females in the heritability of food abstinence, and genetic correlations between abstinence from different types of food. Genetic correlations (with either twin studies or molecular genetic approaches) between vegetarianism and other variables (e.g., education or personality traits) could shed light on the mechanisms underlying dietary choices. We would expect that abstaining for reasons like beliefs with respect to sustainability and animal welfare are more related to genetic factors for personality, while abstaining for reasons like dislike or allergy are more related to genetic factors for more biological processes (for example, genes that code for taste receptors). For this second scenario, we expect that molecular genetic studies will identify genetic variants that shed more light on the underlying biological mechanisms (e.g., genes related to taste, as has been found for coriander abstinence (Eriksson et al., 2012), versus genes related to personality).

To conclude, we confirmed that genetic factors play a large role in individual's choice to be pescatarian, vegetarian or to not eat meat (74-80%) in a Dutch population. Genetic influences on abstaining from beef, pork, poultry, fish and shellfish ranged from 70 to 80%, regardless of the fact that different reasons seem to motivate abstinence from these different types of food. Future research should further investigate genetic correlations and genetic influences on associations between vegetarianism and other psychological traits.