Achieved educational attainment, inherited genetic endowment for education, and obesity

Configural path of obesity: linkage of physical activity and lifestyle based on fuzzy-set qualitative comparative analysis

Obesity plays a significant role in the burden of various health conditions, it is not only a global health issue but challenges the national public health system. Some regions of China still face a high prevalence of obesity, and it is broadly recognized that physical activities interact with lifestyle in different pathways would affect obesity. We aim to capture different configurational paths that lead to obesity, using the fuzzy set Qualitative Comparative Analysis. Eight obesity-related variables were involved, and data were collected between January 1, 2021, and January 31, 2022. The study shows six configurational paths result in obesity, in which the necessary condition is "educational status," and core conditions of "the time of exercise" and "weekly sitting time*sleeping time less than 6 h*second hand smoking exposure on average of 4-6 days per week *keep excising on average of 4 times per week* exercise intensity on the shortness of breath, markedly increased heart rate, heavy sweating" play an important role in the obesity outcome, and the solution exhibits acceptable consistency is 0.50. The six configurational paths solution consistency is 0.76, and the solution coverage is 0.31. Besides the necessary condition and core factors that play(s) an important role in the development of obesity, we have to consider the multiple factors of physical activity and lifestyle have a cross-cutting effect on obesity. This can offer a better understanding of the mechanisms that cause obesity by identifying and characterizing the regional population, which would help develop an effective protective measure for obesity.

Risk factors affecting polygenic score performance across diverse cohorts

Apart from ancestry, personal or environmental covariates may contribute to differences in polygenic score (PGS) performance. We analyzed the effects of covariate stratification and interaction on body mass index (BMI) PGS (PGSBMI) across four cohorts of European (N = 491,111) and African (N = 21,612) ancestry. Stratifying on binary covariates and quintiles for continuous covariates, 18/62 covariates had significant and replicable R2 differences among strata. Covariates with the largest differences included age, sex, blood lipids, physical activity, and alcohol consumption, with R2 being nearly double between best- and worst-performing quintiles for certain covariates. Twenty-eight covariates had significant PGSBMI-covariate interaction effects, modifying PGSBMI effects by nearly 20% per standard deviation change. We observed overlap between covariates that had significant R2 differences among strata and interaction effects - across all covariates, their main effects on BMI were correlated with their maximum R2 differences and interaction effects (0.56 and 0.58, respectively), suggesting high-PGSBMI individuals have highest R2 and increase in PGS effect. Using quantile regression, we show the effect of PGSBMI increases as BMI itself increases, and that these differences in effects are directly related to differences in R2 when stratifying by different covariates. Given significant and replicable evidence for context-specific PGSBMI performance and effects, we investigated ways to increase model performance taking into account nonlinear effects. Machine learning models (neural networks) increased relative model R2 (mean 23%) across datasets. Finally, creating PGSBMI directly from GxAge genome-wide association studies effects increased relative R2 by 7.8%. These results demonstrate that certain covariates, especially those most associated with BMI, significantly affect both PGSBMI performance and effects across diverse cohorts and ancestries, and we provide avenues to improve model performance that consider these effects.

Causal association between mental disorders and cerebrovascular diseases: Evidence from Mendelian randomization study

BACKGROUND AND OBJECTIVE

Observational studies have suggested that mental disorders and cerebrovascular diseases (CVDs) may be risk factors for each other, but genetic evidence of a causal relationship is still lacking. We used Mendelian randomization (MR) studies to explore the causal relationship between mental disorders and CVDs from the genetic perspective.

METHODS

To investigate the causal association between major depressive disorder (MDD), anxiety, attention deficit/hyperactivity disorder (ADHD), bipolar disorder and schizophrenia five kinds of mental disorders and CVDs using two-sample two-way MR analysis based on publicly available genome-wide association study (GWAS) data. We used as instrumental variables (IVs) single-nucleotide polymorphisms (SNPs) that were strongly associated with mental disorders and CVDs. IVW method was used as the main analysis method, and MR-IVW, MR-Egger methods, MR-PRESSO test, leave-one-out analysis and funnel plot were used for sensitivity analysis. We further conducted a meta-analysis to summarize the currently available MR analyses.

RESULTS

The results of forward MR study showed that there was a significant causal relationship between ADHD and AS (any stroke) (p(AS) = 0.001, OR (95%CI) =1.118 (1.047-1.195)), any ischemic stroke (AIS) (p(AIS) = 0.004, OR (95%CI) =1.118(1.035-1.206)) and large artery stroke (LAS) (p(LAS) = 0.026, OR (95%CI): 1.206(1.023-1.422)). No heterogeneity, pleiotropy and outliers were found in sensitivity analysis. The reverse MR study showed that IA (intracranial aneurysm) (p(IA) = 0.033, OR (95%CI) = 1.123(1.009-1.249)) and UIA (unruptured intracranial aneurysm) (p(UIA) = 0.015, OR (95%CI) =1.040(1.008-1.074)) were risk factors for schizophrenia. Sensitivity analysis showed no pleiotropy, but there was heterogeneity. After excluding outliers, MR analysis showed that IA and UIA were still risk factors for schizophrenia. Our meta-analyses found statistical significance in causal relationships between ADHD and LAS (OR (95%CI) =1.18 (1.06-1.32), p = 0.003), IA and schizophrenia (OR (95%CI) =1.05 (1.02-1.08), p = 0.002) and UIA and schizophrenia (OR (95%CI) =1.03 (1.01-1.06), p = 0.010).

CONCLUSION

The MR study and meta-analysis suggest that genetically predicted ADHD is a risk factor for LAS, and IA and UIA increase the risk of schizophrenia. The result has implications for the development of feasible prevention strategies in the future.

Poor sleep quality and overweight/obesity in healthcare professionals: a cross-sectional study

Objective

This study aimed to analyze the relationship between the sleep quality of healthcare professionals and the incidence of overweight and obesity, exploring the potential impact of sleep quality on the onset of overweight and obesity in order to provide a scientific basis for formulating effective health intervention measures.

Methods

A convenience sampling method was used to conduct a survey on the sleep characteristics and obesity status among healthcare professionals at Peking Union Medical College Hospital and Tianjin Dongli District Traditional Chinese Medicine Hospital. The survey was conducted via online questionnaires, which included demographic data, the Pittsburgh Sleep Quality Index (PSQI), height, weight, and related sleep, exercise, and dietary habits. Univariate and multivariate logistic regression analyses were applied to study the relationship between sleep quality and overweight/obesity among healthcare professionals.

Results

A total of 402 questionnaires were distributed, with a 100% retrieval rate, yielding 402 valid questionnaires. The average body mass index of the 402 participants was 23.22 ± 3.87 kg/m^2. Among them, 144 cases were overweight or obese, accounting for 35.8% (144/402) of the total. The prevalence of poor sleep quality among healthcare professionals was 27.4% (110/402), with an average PSQI score of 8.37 ± 3.624. The rate of poor sleep quality was significantly higher in the overweight and obese group compared to the normal weight group (36.1% vs. 22.5%, p = 0.003). The multivariate analysis indicated that gender, marital status, lower education level, sleep duration (odds ratio [OR] =1.411, 95% confidence interval [CI] 1.043-1.910, p = 0.026), and sleep disturbances (OR = 1.574, 95%CI 1.123-2.206, p = 0.008) were significant risk factors for overweight and obesity among healthcare professionals.

Conclusion

Overweight or obese healthcare professionals had poorer sleep quality compared to those with a normal weight. Sleep duration and sleep disorders were identified as independent risk factors for overweight or obesity in healthcare professionals. Increasing sleep duration and improving sleep disorders may play a positive role in controlling overweight and obesity among healthcare professionals.

Associations of education attainment with gestational diabetes mellitus and the mediating effects of obesity: A Mendelian randomization study

We aim to assess the causal association between educational attainment and gestational diabetes mellitus, and the mediating effect of obesity on this association. We estimated the causal effects of educational attainment on gestational diabetes mellitus using European ancestry genome-wide association study summary data with two-sample univariate Mendelian randomization (UVMR) approach. Two-stage Mendelian randomization analysis was performed to assess the potential mediating role of obesity traits in this association and to calculate the mediating proportion. UVMR analysis demonstrated that higher educational attainment was associated with a reduced risk of GDM (OR 0.76, 95% CI 0.67-0.86; p < 0.01). EA has also been associated with decreased obesity in women. Mediation Mendelian randomization results indicated that body mass index (BMI) was the most significant mediating factor in the relationship between educational attainment and GDM, accounting for 42.52% (95% CI 37.75-55.44%) of the effect, followed by waist-to-hip ratio (WHR) at 34.35% (95% CI 29.82-46.41%), body fat percentage at 28.95% (95% CI 35.99-46.81%), and WHR adjusted for BMI (WHRadjBMI) at 12.51% (95% CI 36.2-58.5%). educational attainment exerts a potential causal protective effect against gestational diabetes mellitus, and obesity-related risk factors play a mediating role. Attention should be paid to the educational attainment of women, and obese women with lower educational attainment may represent a higher risk group for GDM than those with higher educational attainment.

Risk factors affecting polygenic score performance across diverse cohorts

Apart from ancestry, personal or environmental covariates may contribute to differences in polygenic score (PGS) performance. We analyzed effects of covariate stratification and interaction on body mass index (BMI) PGS (PGSBMI) across four cohorts of European (N=491,111) and African (N=21,612) ancestry. Stratifying on binary covariates and quintiles for continuous covariates, 18/62 covariates had significant and replicable R2 differences among strata. Covariates with the largest differences included age, sex, blood lipids, physical activity, and alcohol consumption, with R2 being nearly double between best and worst performing quintiles for certain covariates. 28 covariates had significant PGSBMI-covariate interaction effects, modifying PGSBMI effects by nearly 20% per standard deviation change. We observed overlap between covariates that had significant R2 differences among strata and interaction effects - across all covariates, their main effects on BMI were correlated with their maximum R2 differences and interaction effects (0.56 and 0.58, respectively), suggesting high-PGSBMI individuals have highest R2 and increase in PGS effect. Using quantile regression, we show the effect of PGSBMI increases as BMI itself increases, and that these differences in effects are directly related to differences in R2 when stratifying by different covariates. Given significant and replicable evidence for context-specific PGSBMI performance and effects, we investigated ways to increase model performance taking into account non-linear effects. Machine learning models (neural networks) increased relative model R2 (mean 23%) across datasets. Finally, creating PGSBMI directly from GxAge GWAS effects increased relative R2 by 7.8%. These results demonstrate that certain covariates, especially those most associated with BMI, significantly affect both PGSBMI performance and effects across diverse cohorts and ancestries, and we provide avenues to improve model performance that consider these effects.

Four modifiable factors that mediate the effect of educational time on major depressive disorder risk: A network Mendelian randomization study

BACKGROUND

Major depressive disorder (MDD) is a mental illness, which is a notable public health problem that aggravates the global economic burden. This study aimed to investigate the causal relationship between education and MDD risk and the contributions of effects mediated by four modifiable factors.

MATERIALS AND METHODS

Instrumental variables were screened from several large-scale genome-wide association study (GWAS) data (years of schooling with 766,345 participants, MDD with 59,851 cases and 113,154 controls, neuroticism with 329,821 individuals, smoking behavior with 195,068 cases and 164,638 controls, body mass index [BMI] with 336,107 individuals, and household income with 397,751 individuals). The data were used to evaluate the association of the four modifiable factors (neuroticism, smoking behavior, BMI, and household income) that mediate the effect of education on MDD risk via Mendelian randomization (MR) analysis.

RESULTS

Each standard deviation increase in years of schooling could reduce the risk for MDD by 30.70%. Higher neuroticism and BMI were associated with a higher risk of MDD. Non-smoking status and increased household income were protective factors for MDD. Notably, the mediator neuroticism, BMI, smoking behavior, and household income explained 52.92%, 15.54%, 31.86%, and 81.30% of the effect of years of schooling on MDD risk, respectively.

CONCLUSIONS

Longer years of schooling have a protective effect on MDD risk. Reasonable interventions to reduce neuroticism, BMI, smoking, and increasing household income are beneficial for MDD prevention. Our work provides new ideas for the development of prevention strategies for MDD.

Modifiable risk factors that mediate the effect of educational attainment on the risk of stroke: a network Mendelian randomization study

BACKGROUND

Stroke is a common cerebrovascular disease with great danger to public health. Educational inequality is a universal issue that influences populations' stroke risk. This study aimed to investigate the causal relationship between education and stroke risk and the contributions of effects mediated by four modifiable factors.

MATERIALS AND METHODS

Public large-scale genome-wide association study (GWAS) summary data associated with educational attainment, hypertensive diseases, body mass index (BMI), smoking behavior, time spent on watching the television (TV), and stroke were obtained from European ancestry. The data were used to investigate the causal relationship among educational attainment, hypertensive disease, BMI, smoking, watching TV, and stroke risk. Inverse variance weighted (IVW) method was used as a primary algorithm for estimating causal direction and effect size in univariable and multivariable Mendelian randomization (MR) analyses.

RESULTS

Higher educational attainment was a causal protective factor, while hypertensive diseases, higher BMI, smoking, and longer time spent on watching the TV were all causal risk factors for the risk of stroke. Hypertensive disease, BMI, smoking, and watching TV were all mediators for linking the causal relationship between educational attainment and stroke risk. Hypertensive disease, BMI, smoking, and watching TV explained 47.35%, 24.74%, 15.72%, and 2.29% of the variance in educational attainment's effect on stroke risk, respectively. The explained proportion reached 69.32% after integrating the four factors.

CONCLUSIONS

These findings support the causal effect of educational attainment on the risk of stroke, with a substantial proportion mediated by modifiable risk factors. Interventions on these modifiable factors would lead to substantial reductions in stroke cases attributable to educational inequality.

Lifestyle choices mediate the association between educational attainment and BMI in older adults in China: A cross-sectional study

As the Chinese population ages, unhealthfully high body mass index (BMI) levels in older adults are becoming a public health concern as an unhealthfully high BMI is an ill-being condition and can contribute to the risk of disease. Education and lifestyle choices affect BMI; however, the evidence on the relationships and interactions among these factors remains unclear. This study aimed to investigate the mediating effect of lifestyle choices on educational attainment and BMI among older adults in China. Using the Chinese Family Panel Studies (CFPS) 2018 panel data, this study integrated personal- and family-level economic data libraries, including 7,359 adults aged ≥60 years. Lifestyle parameters included smoking amount and screen time. Height and weight values were used to calculate BMI. The chi-square test, binary logistic regression analysis, stepwise regression analysis, and bootstrapping mediating effect tests were used for data analysis. Single-factor chi-square test revealed differences in BMI levels among groups defined by sex, age, residence, marital status, per capita annual household income, education years, and lifestyle choices. Binary logistic regression showed that age, residence, education years, smoking amount, and screen time influenced BMI. Stepwise regression results showed that education years, smoking amount, and screen time were associated with BMI (t = 3.907, -4.902, 7.491, P < 0.001). The lifestyle variables had partial mediating effects on BMI. The mediating effect of lifestyle on BMI was 0.009, while smoking amount was 0.003, and screen time was 0.006. Unhealthfully high BMI levels are increasing among older adults in China and are affected by many factors. Lifestyle factors and educational attainment can interact, affecting BMI. Interventions should consider lifestyle factors and education attainment to help maintain healthy BMI and reduce unhealthfully high BMI incidence.

Intergenerational transmission of body mass index and associations with educational attainment

Background

Individual differences in educational attainment (EA) and physical health, as indexed by body mass index (BMI), are correlated within persons and across generations. The present aim was to assess these associations while controlling for parental transmission.

Methods

We analyzed BMI and EA obtained for 8,866 families from the Netherlands. Data were available for 19,132 persons, including 6,901 parents (mean age 54) and 12,234 of their adult offspring (mean age 32). We employed structural equation modeling to simultaneously model the direct and indirect transmission of BMI and EA from parents to offspring, spousal correlations, and the residual within-person BMI-EA association and tested for gender differences in the transmission parameters.

Results

We found moderate intergeneration transmission for BMI (standardized beta ~ .20) and EA (~ .22), and substantial spousal correlations for BMI (.23) and EA (.51). Cross-trait parent to offspring transmission was weak. The strength of transmission was largely independent of parent or offspring gender. Negative within person EA-BMI correlations were observed for all family members (fathers, -0.102; mothers, -0.147; sons, -0.154; daughters, -0.173). About 60% of the EA-BMI correlation in offspring persisted after taking into account the intergeneration transmission.

Conclusions

The intergenerational transmission for BMI and EA is mainly predictive within traits. Significant spousal and within person correlations in the parental generation are responsible for the effect of parental EA on offspring BMI. Offspring EA and BMI are further correlated beyond parental influences.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12889-022-13270-1.