Introduction

Nonalcoholic fatty liver disease (NAFLD), known as metabolic dysfunction-associated steatotic liver disease (MASLD) [1], affects approximately one in four adults worldwide [2]. And because of its high prevalence, it is currently the liver-related disease with the greatest increase in mortality worldwide [3]. It is related to a higher risk of complications such as end-stage liver disease, hepatocellular carcinoma and cirrhosis in patients with NAFLD [4]. At the same time, mental disorders also carry a high risk of mortality and may worsen over time and are estimated to account for nearly 21.2–32.4% of years lived with disability globally [5]. Related studies have shown that, globally, the greatest risk factor for years lived with disability is neurological and mental health disorders [6]. Therefore, the prevention and treatment of neuropsychiatric disorders and NAFLD are substantial problems warranting attention in clinical work.

Related studies have also suggested that NAFLD can directly affect brain structure through the liver-brain axis [7, 8]. NAFLD has a common pathogenesis with neurological and psychiatric diseases, which are jointly caused by inflammation and oxidative stress pathways [9, 10]. Hence, it is necessary to recognize the interaction between NAFLD and neuropsychiatric disorders. Researchers have discovered a potential connection between them in recent years. In terms of central nervous system disorders, dementia is a neurodegenerative disorder of cognitive dysfunction [11], and Alzheimer's disease is the most common form of dementia [12]. NAFLD contains toxic components that cannot be cleared by the liver, and these may further affect brain function [13]. Several studies have already associated NAFLD with cognitive dysfunction [14, 15]. In terms of mental disorders, a 3-year prospective study hinted that metabolic liver disease including NAFLD is more prevalent in schizophrenia patients [16]; a retrospective cross-sectional study showed that NAFLD patients often have depression and anxiety disorders [17]. However, these associations between NAFLD and neuropsychiatric disorders may in part be affected by confounding or selection bias inherent in conventional observational researches. Furthermore, whether NAFLD is causal or reverse causal in neuropsychiatric disorders remains controversial. Therefore, any potential causal relationship still requires further investigation.

In this context, Mendelian randomization (MR) is a statistical method for assessing exposure factors' causal relationship with disease outcomes [18]. Because genetic variation is completely random during gamete formation and genotypes do not change with disease progression, in MR analysis, the measure of genetic variation, such as a single nucleotide polymorphism (SNP) can be used as an instrument of exposure (IV) or a modifiable risk factor for diseases. Since genetic variation is relatively less susceptible to measurement errors and biases, causal inferences about exposure-outcomes can be strengthened. As a result, causal relationships are explored extensively by MR. Here, we selected seven mental disorder-related diseases and six central nervous system dysfunction-related diseases, performing Two-sample MR to comprehensively assess the association of the risk of NAFLD with those diseases in discovery stage. Multivariate MR was followed immediately to adjust for the potential effects of body mass index (BMI) and waist circumference on the causal relationships. Finally, we performed replication analyses and combined the findings from the above MR studies. In order to examine the inverse effect of NAFLD on neuropsychiatric disorders, we performed a reverse MR analysis (Fig. 1). This study was performed in accordance with the STROBE-MR guidelines (Additional file 1: Table S1).

Fig. 1
figure 1

Design of the bidirectional two-sample Mendelian randomization study. The three core assumptions were as follows: (MRI) relevance assumption; (MRII) independence assumption; and (MRIII) exclusion restriction. PTSD, posttraumatic stress disorder; OCD, obsessive–compulsive disorder; MDD, Major Depressive Disorder; ALS, amyotrophic lateral sclerosis

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Methods

Data sources

The data source for our analysis was primarily European populations, which avoided potential bias due to population stratification (Additional file 1: Table S2). Because the data used in the study were published, no ethical approval or informed consent was required. In discovery phase, genetic associations for NAFLD were examined using GWAS, involving 8,434 cases and 770,180 controls [19]. Obtaining a replication phase with data from several leading European tertiary liver centers, which involved 1,483 NAFLD patients and 17,781 control individuals [20]. Schizophrenia is a highly heritable disease with GWAS analysis of 33,640 patients and 43,456 control individuals from the Psychiatric Genomics Consortium (PGC) [21]. Data on bipolar disorder, characterized by severe swings in mood and behavior, were obtained from a meta-analysis of 41,917 patients and 371,549 control individuals [22]. Posttraumatic stress disorder is a common disease in a stressful state after exposure to extreme, life-threatening trauma, and the GWAS data used for its analysis came from the PGC analysis of 23,212 patients and 151,447 control individuals [23]. GWAS data for Major Depressive Disorder (MDD) were also derived from the UK Biobank and PGC (excluding 23andme) analysis of 170,756 patients and 329,443 control individuals [24]. In the International Parkinson's Disease Genomics Consortium, we retrieved a GWAS date included 33,674 patients and 449,056 control individuals [25]. Genetic variants in Alzheimer's disease (AD) were acquired from a large GWAS included 39,106 patients of European ancestry and 46,828 control individuals of European ancestry [26]. Neurodegenerative diseases such as amyotrophic lateral sclerosis can be fatal, and GWAS data were obtained by analyzing 27,205 patients and 110,881 control individuals [27]. The data we selected for epilepsy included 4,382 cases and 453,928 controls [28]. Data on migraine used for analysis included 1,488 cases and 454,860 controls [29]. The study provided us with analytical data on insomnia, encompassing 345,022 cases and 108,357 controls [30]. Mania is clinically manifested by increased speech, disturbed thinking, and elevated mood [31]. Genetic associations for anxiety disorders and mania were obtained by analyzing 29,129 patients and 6,455 control individuals and 7,988 cases and 18,667 controls from UK Biobank (http://www.nealelab.is/uk-biobank), respectively. Obsessive–compulsive disorder is characterized by repetitive behaviors that individuals feel compelled to engage in due to unwanted personal thoughts [32], the GWAS from the FinnGen Biobank were analyzed, including 1,059 patients and 198,110 control individuals (https://r9.finngen.fi/).

Selection of instrumental variables

In order to satisfy the three core MR assumptions, we performed strict filtering steps to control the quality of genetic IVs. 1) We extracted SNPs strongly associated with neuropsychiatric disorders from published GWAS databases and took P < 5 × 10–8 as the initial filter criteria. However, except for AD, schizophrenia, bipolar disorder, MDD, amyotrophic lateral sclerosis, Parkinson's disease and insomnia, SNPs were absent or too few with P values less than 5 × 10–8. Therefore, to select eligible instrumental variables, we expanded the threshold to < 5 × 10–6. SNPs strongly associated with NAFLD for reverse MR analysis are displayed in Additional file 1: Table S3 and S4. 2) SNPs in linkage disequilibrium (LD) were excluded from exposure experiments to ensure the instruments were independent (r2 < 0.001, aggregation window = 10,000 kb). 3) To maintain consistency of the SNPs serving as IVs across the different analyses, we only used variants available for all traits examined and did not replace missing variants with proxies. 4) The F statistic was applied to quantify the intensity of the genetic tool, whose calculation of (beta/se)2 greater than 10 was regarded sufficient [33]. Finally, exposure-associated SNPs were extracted from each result.

MR analysis

In the current study, we applied inverse variance weighting (IVW), the weighted median method and the MR‒Egger method for analysis, and the multiplicative random-effects IVW method was the primary method, followed by sensitivity analysis. In IVW, the Wald estimates associated with the individual SNPs are combined to estimate the causal relationship of exposure on the outcome. It is a weighted regression that transforms the MR estimates into SNP-outcome effects on SNP-exposure effects [34]. MR‒Egger is found on the InSIDE assumption, and the outcome coefficients are linearly weighted regressions on the exposure coefficients, which can also detect small study biases [35]. We can calculate the weighted median estimate of Wald ratio by using the weighted median method that does not require the InSIDE assumption. The analysis of invalid instrumental variables provides 50% of the information, but it can also provide the outcome of causal effect detection [36]. If the results of the IVW method are significant (P < 0.05), even if the results of other methods are not significant, on the basis of no pleiotropic effects and heterogeneity, as long as there are same trends in the beta values of other methods, it is also acceptable and considered a positive result [37]. Multivariable MR (MVMR) can discern the effects of various interrelated factors, suggesting that these factors operate in concert. Consequently, each association identified by MVMR is regarded as a "direct" or "independent" causal link [38]. Thus, we conducted MVMR analysis to adjust for the genetic association of the instruments with BMI and waist circumference.

Sensitivity analysis

MR‒Egger regression was performed to assess whether there was a potential pleiotropic effect, and the intercept item can assess the horizontal pleiotropic effect of its direction; P < 0.05 indicates that there is a potential horizontal pleiotropic effect [39]. The MR pleiotropic residuals and outliers (MR-PRESSO) global test can also be used for horizontal pleiotropic detection. P > 0.05 indicates that there is no level pleiotropy, and pleiotropy refers to genetic variation, which may impact the causal effects [40]. The MR-PRESSO outlier test corrects for pleiotropy by removing outliers, while the outlier test involves at least 50% valid variables, has balanced pleiotropy, and depends on instrument exposure and multiple effects [41]. Heterogeneity was analyzed by applying Rucker's Q test (MR‒Egger regression) and Cochran's Q test (MR-IVW), mainly Cochran's Q test, with P < 0.05. Moreover, I2 was used to quantify heterogeneity (take 0 when I2 is negative). Multiplicity or other causes of heterogeneity were mainly explored with the Q test [42]. Then, the MR-robust adjusted spectral score (MR-RAPS) method was applied to further verify the causality (estimated causal effect) and detect the stability of the analysis, that is, the presence of pleiotropic effects (estimated overdispersion variance trend toward 0 or a P value > 0.05 indicates the absence of horizontal pleiotropic effects) [43]. MR-RAPS can provide unbiased, as well as reliable results, even with weak instrumental variables [44]. What's more, to ensure that the results were robust and consistent, we used leave-one-out analysis [34]. Applying IVW (fixed-effects) to further verify the solidity of the results. In the visual analysis, heterogeneity can be represented by scatter plots, and horizontal pleiotropic effects can be represented by forest plots and funnel plots. Finally, the fixed-effect meta-analysis was performed to assess the integrated causality between NAFLD and neuropsychiatric disorders from MR results of the discovery and replication phases [45, 46]. We chose the results of the meta-analysis as the final causality [47].

Statistical analysis

The association between genetically predicted neuropsychiatric disorders and NAFLD was signaled as the odds ratio (OR) and its 95% confidence interval (95%CI). MR analyses were conducted using R (version 4.3.1) and the "TwoSampleMR", "MR-PRESSO", “MVMR”and "MR. RAPS" software packages. Bonferroni correction was applying to adjust the data because of multiple comparisons. When the observed P < 4.17 × 10–3 (0.05/12), associations were deemed statistically significant evidence of causality. P values between 4.17 × 10–3 and 0.05 were considered suggestive evidence of potential causality.

Result

Instrumental variable selection

In the discovery stage of mental disorders, we extracted 9 SNPs for anxiety disorders, 75 for schizophrenia, 17 for posttraumatic stress disorder, 10 SNPs for obsessive–compulsive disorder, 50 for MDD, 44 SNPs for bipolar disorder, and 13 for mania. For central nervous system disorders, 50 SNPs for AD, 23 SNPs for Parkinson's disease, 11 for epilepsy, 11 SNPs for amyotrophic lateral sclerosis, 14 for migraine and 43 for insomnia were extracted. There was no weak instrumental variable based on the F-statistics, which were all greater than 10. In the replication phase, the same manipulation was performed and sufficiently powerful instrumental variables were extracted. Next, so as to analyze the inverse effect of NAFLD (exposure) on neuropsychiatric disorders (outcome), we included 4 SNPs with a median F value of 59.450 (discovery) and 4 SNPs with a median F value of 49.071 (replication), indicating sufficient heritability. Detailed information on the instrumental variables, such as beta, SE, and P values, can be found in Additional file 1: Tables S3 and S4.

Discovery results

Genetics predict neuropsychiatric disorders on NAFLD

A potential positive causal effect of MDD on NAFLD was demonstrated (OR = 1.210, 95% CI = 1.031–1.420, P value = 0.020) (Table 1). the "leave one out" analysis (Additional file 2: Supplementary Figure S5), the IVW fixed-effects model (OR = 1.210, 95% CI = 1.020–1.436) (Additional file 1: Table S11) and the effect size of MR.RAPS, also suggested the same results. The MR‒Egger regression test (intercept P = 0.214) and the MR-PRESSO global test (P value = 0.696) did not reveal pleiotropy. Meanwhile, no unusual outliers were detected. Cochran's Q test (P value = 0.716) revealed no evidence of heterogeneity (Additional file 1: Table S5).

Table 1 The causal effects of NAFLD with the risk of neuropsychiatric disorders in discovery stage
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In addition, we found that insomnia potentially elevated the risk of NAFLD (OR = 1.865, 95% CI = 1.044–3.333, P value = 0.035) (Table 1), the IVW fixed-effects model (OR = 1.865, 95% CI = 1.090–3.192) (Additional file 1: Table S11) also indicated the same trend. Additionally, sensitivity analyses revealed no evidence of pleiotropy (intercepts P = 0.085) or heterogeneity (P value = 0.212).

Genetics predict NAFLD on neuropsychiatric disorders

In reverse MR, the random-effects IVW model showed potential evidence that NAFLD increased the risk of anxiety disorders (OR = 1.024, 95% CI = 1.001–1.046, P value = 0.034) (Table 1). Several other methods, including the "leave one out" analysis (Additional file 2: Supplementary Figure S14), the IVW fixed-effects model (OR = 1.024, 95% CI = 1.006–1.042) (Additional file 1: Table S11) and the effect size of MR.RAPS, also suggested the same causal trend. The MR‒Egger regression test did not indicate horizontal pleiotropic effects (intercept P = 0.857), the MR-PRESSO global test (P value = 0.421) also did not suggest pleiotropic results, and no abnormal outliers were detected. At the same time, MR-RAPS (overdispersion variance P value = 0.704) also indicated that no pleiotropy was present. Cochran's Q test (P value = 0.219) showed no heterogeneity (Additional file 1: Table S6).

Potential negative associations were found between NAFLD and PTSD (OR = 0.943, 95% CI = 0.892–0.996, P value = 0.036) (Table 1). Effect size values for several other methods, including MR.RAPS, also suggested the same causal trend. Sensitivity analyses also failed to observe evidence of any level of pleiotropy (intercepts P = 0.690) or heterogeneity (P value = 0.782).

Some degree of heterogeneity was present (P value = 0.024) in the causal effect of NAFLD on AD. However, there were no pleiotropy and outliers (Additional file 1: Table S6). Therefore, we believed that this result was feasible.

Given the frequent occurrence of obesity in patients with NAFLD, after adjusting for genetically predicted BMI and waist circumference, we performed MVMR for anxiety disorders, schizophrenia, MDD, bipolar disorder, AD and insomnia. Our results showed that MMD remained a potential risk factor for NAFLD after adjusting for genetically predicted BMI (OR = 1.334, 95% CI = 1.054–1.687, P value = 0.016), which confirmed the robustness of the results. However, once we accounted for waist circumference, the causal connection between MDD and NAFLD disappeared, indicating that this relationship might be influenced by waist circumference. Additionally, there was no significant link between insomnia and NAFLD, suggesting that BMI and waist circumference might influence this association. For the other diseases, no connections were observed either. (Additional file 1: Table S7).

Replication results

In reverse MR, the random-effects IVW model showed significant evidence that NAFLD increased the risk of anxiety disorders (OR = 1.015, 95% CI = 1.010–1.021, P value = 9.24 × 10–9) (Table 2). The "leave one out" analysis (Additional file 2: Supplementary Figure S40) and fixed-effects model (Additional file 1: Table S11) demonstrated the robustness of the results (OR = 1.015, 95% CI = 1.007–1.024). Furthermore, sensitivity analyses also didn’t observe evidence of any level of pleiotropy (intercepts P = 0.415) or heterogeneity (P value = 0.763).

Table 2 The causal effects of NAFLD with the risk of neuropsychiatric disorders in replication stage
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Some degree of heterogeneity was present (P value = 0.013) in the causal effect of NAFLD on MDD. There was also some degree of heterogeneity in the causal effect of NAFLD on Bipolar Disorder and insomnia, as indicated by a P-values of 0.017 and 0.002. However, there were no pleiotropy and outliers (Additional file 1: Table S9). Hence, we considered this outcome to be achievable.

Combined results from the meta-analysis

Pooled analysis suggested that MDD was the potential risk factor for NAFLD (OR = 1.233, 95% CI = 1.063–1.430, P value = 0.006) (Fig. 2 and Additional file 1: Table S10). The fixed-effect model of MDD suggested the same results (OR = 1.241, 95% CI = 1.064–1.447, P value = 0.006). Meta-analysis also showed significant evidence that NAFLD increased the risk of anxiety disorders (OR = 1.016, 95% CI = 1.010–1.021, P value < 0.0001) (Fig. 3), and fixed-effects model (Additional file 1: Table S11) demonstrated the robustness of the results (OR = 1.017, 95% CI = 1.009–1.024, P value < 0.0001).

Fig. 2
figure 2

The effects of neuropsychiatric disorders on NAFLD were estimated by Meta-analysis using IVW. OR, odds ratio; CI, confidence interval; IVW, inverse-variance weighted method; PTSD, posttraumatic stress disorder; OCD, obsessive–compulsive disorder; MDD, Major Depressive Disorder; ALS, amyotrophic lateral sclerosis

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Fig. 3
figure 3

The effects of NAFLD on neuropsychiatric disorders were estimated by Meta-analysis using IVW. OR, odds ratio; CI, confidence interval; IVW, inverse-variance weighted method; PTSD, posttraumatic stress disorder; OCD, obsessive–compulsive disorder; MDD, Major Depressive Disorder; ALS, amyotrophic lateral sclerosis

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In addition, there was no causal connection between NAFLD and other neuropsychiatric disorders (all P values > 0.05), nor was any level of pleiotropy or heterogeneity observed (Additional file 1: Tables S5, S6, S8, S9). The visual analyses of the discovery (Additional file 2: Supplementary Figures S1-S26) and replication (Additional file 2: Supplementary Figures S27-S52) stages can be found in Supplementary materials.

Discussion

This bidirectional Mendelian randomization study leveraged genetic data from a large number of published GWASs to explore a bidirectional hypothetical causal association between NAFLD and neuropsychiatric disorders. From a genetic perspective, our results revealed that MDD was the potential risk factor for NAFLD, and NAFLD also increased significantly the risk of anxiety disorders.

Anxiety disorders are common in Western populations [48]. A study comparing 19,871 NAFLD patients with 19,871 matched control individuals found that new-onset anxiety disorders was linked to NAFLD even after multiple complications were controlled [48]. In a cross-sectional study conducted in the United States, depression and anxiety symptoms were evaluated using scale scores in 567 patients diagnosed with NAFLD through biopsy [49], 45% and 25% of the patients had subclinical and clinical anxiety symptoms, respectively, indicating that anxiety disorders is one of the common symptoms of NAFLD and that it is related to advanced NAFLD. On the other hand, to some extent, NAFLD may progress to nonalcoholic steatohepatitis (NASH) [50]. A smaller study involving 36 NASH patients and 36 control individuals without liver disease after cholecystectomy revealed that NASH increased the risk of anxiety disorders than the control group. It was also found that anxiety disorders was related to more serious changes in liver histological characteristics [51]. This is consistent with the evidence that NAFLD increased significantly the risk of anxiety disorders observed in our MR study.

MDD is a common recurrent mental illness that leads to decreased quality of life and increased mortality in the relevant population [52]. After adjusting for genetically predicted BMI, our findings indicated that MDD continued to be a potential risk factor for NAFLD. However, after adjusting for waist circumference, the association disappeared. This suggested that multiple factors impact the relationship between them, and future research should further explore the offset effects of these factors. In patients with depression, biological disturbances related to obesity are linked to a prolonged course of the disease and negative reactions to conventional antidepressant therapies [53]. Meantime, many studies have found a high prevalence of MDD in patients with NAFLD [54, 55]. A study found that NAFLD patients with MDD had more severe histological liver steatosis and higher NAFLD activity scores compared with NAFLD patients without MDD [56]. Pathologically, MDD is associated with a severe hepatocellular balloon [49], which is one of the characteristics of NAFLD [57]. In another aspect, the rising levels of inflammatory markers in depressed patients, such as intercytotin-6, C-reactive protein and tumor necrosis factor α [58], provide another reasonable explanation for the association with NAFLD. Meta-analyzing the results of nine longitudinal studies showed that depression was an independent risk factor for diabetes [59]. Moreover, depression triggers activity in the HPA axis and the sympathetic nervous system, which leads to the secretion of the stress hormone cortisol. Elevated cortisol levels promote the breakdown of fats and the release of free fatty acids into the bloodstream. Concurrently, cortisol suppresses insulin production and reduces the body's sensitivity to insulin [60]. Meantime, insulin resistance is tightly related to NAFLD. Insulin resistance may also lead to damage to the mitochondrial fatty acid β-oxidation pathway, increasing free fatty acids and other lipids in the body. This may lead to endothelial dysfunction, resulting in a proinflammatory state in the body [61], and increasing the incidence of NAFLD.

Our MR results found no causal relationship between NAFLD and AD, which goes against the previous studies. However, these studies were basically cross-sectional studies, and the longitudinal associations between them should still be supported by further research data. At the same time, there are also many studies showing that there is no significant direct correlation between NAFLD and AD [62, 63]. As mentioned above, differences in the mean ages of the study population and the cognitive level tests used are also responsible. And the severity of liver fibrosis with disease progression (rather than NAFLD itself) may be associated with cognitive dysfunction [64, 65]. Numerous studies also emphasize the influence of fibrosis severity on the development of diseases such as cardiovascular dysfunction [66] and stroke [67]).

The results of this study contradict previous observational studies that reported a link between schizophrenia and NAFLD. Similarly, it has been reported that there may be a causal link between bipolar disorder and NAFLD [68], but this is not consistent with our results. The results may be affected by the confusion or selection bias inherent in traditional observational studies, such as differences in diet structure, lifestyle and genetic susceptibility of different populations. The connection between NAFLD and these factors may be caused by lots of factors rather than simply the disease itself. For example, a research explained that the use of atypical antipsychotic drugs for the treatment of schizophrenia and other diseases will produce many metabolic and endocrine-related side effects, like insulin resistance, dyslipidemia and systemic inflammation, which will increased the risk of NAFLD [69]. And antidepressants have been shown to be associated with hyperglycemia [70]. As a study showed that MDD was not associated with an increased risk of any cardiovascular disease, and the use of antidepressants increased the risk of multiple cardiovascular diseases [71]. In young male patients, different drug doses and combinations are also risk factors for NAFLD [72]. Although glucose homeostasis is altered before the onset of schizophrenia [73], these only suggest that both have a similar pathogenesis. The possible potential causal relationships between them still need to be further explored.

Schizophrenia and AD, similar in many clinical and pathophysiological features, may accelerate brain aging by promoting neuroinflammation [74] and show similarities in white matter damage patterns [75]. Therefore, the causal link between them and NAFLD may also share similarities, which is consistent with our findings.

In discovery stage, we found that insomnia was a risk factor for NAFLD, while the results of the meta-analysis indicated no causal relationship between them. In the MVMR analysis, there was still no significant association between them. Therefore, the connection between them should still be further evaluated. Insomnia is usually defined by asking "Do you have trouble falling asleep or staying asleep?" [76] Multiple studies indicated that short sleep duration may cause an increase in insulin resistance [77], a significant decrease in antioxidant activity and significantly higher levels of lipid peroxidation [78]. Furthermore, the concentrations of gamma glutamyl transpeptidase and 8-hydroxydeoxyguanosine are often elevated in the serum of NAFLD patients, suggesting that oxidative stress may have occurred [79]. However, a study found that the incidence of insomnia did not seem to be significantly different in NAFLD patients compared to the general population, and the severity of inflammation-related histology may not play a critical role in the pathogenesis of insomnia in NAFLD patients [80]. In conclusion, there may be no direct causal relationship between insomnia and NAFLD, and the association between them may result from a combination of multiple factors.

A cross-sectional study suggested that there was no significant relationship between NAFLD and migraine [81], which is consistent with our results. Although a study has found that there may be a causal relationship [82], we believe that the sample size used is too small and the appropriate control cases are not used, so the causal relationship between them still needs further studies to verify.

Alternatively, socioeconomic status consists mainly of income and education level, the genetic liability of which may play a role in the outcome of mental and physical health. Intelligence can be seen as an individual's innate mental capacity, whereas education reflects the process of socialization from birth through adulthood, with income being an outcome of this process [83]. A study showed that higher income levels are effective in reducing the incidence of psychiatric disorders, and that a higher genetic liability for education may increase the occurrence of mental health problems independently of salary change [84]. On the other hand, higher education can reduce the occurrence of NAFLD, and the income level is independent of it [85]. Hence, the link between NAFLD and neuropsychiatric disorders within socioeconomic contexts requires deeper investigation, and upcoming public health initiatives could gain from the careful utilization of research on social environments and genetics.

The main strength of our study was that the MR design avoids the confusion or selection bias common in traditional observational studies and supports a robust causal relationships between exposure and results. Besides, our study used aggregate data from large genetic databases, which made our research assumptions more accurate. The use of multiple sensitivity analyses combined with "leave one out" analysis enabled the testing of the stability of key results and provided additional reliable evidence. In addition, this was a MVMR and bidirectional causal-association study that expands the evidence on the relationships between neuropsychiatric disorders and NAFLD. Finally, this analysis had guiding recommendations for future studies on MASLD.

However, our research also had some limitations. First, the population studied was exclusively of European descent; therefore, further studies should be encouraged to verify these results in other populations. Second, although the sensitivity analysis did not reveal any evidence of multiple effects that may affect the results, the potential for horizontal pleiotropic effects couldn’t be completely excluded. Third, we chose some disease-related instruments with a relatively loose threshold of 5 × 10–6, which was considered to be a reasonable threshold [86, 87], but it will affect the statistical efficacy of causal estimation to some extent. Furthermore, since some databases are not publicly available, databases such as MMD and insomnia were not the largest used in our study. Moreover, given that the data analysis should be all European populations, our database for schizophrenia analysis was not the largest. Finally, in studies, exposure and outcome were not only likely to have potentially duplicate participants, but it was also difficult to predict the extent to which the sample overlap. However, in this study, the use of sufficiently powerful tool variables (F statistics > 10) could minimize the potential deviation caused by sample overlap [33].

In summary, our study implied causal relationships of NAFLD with MDD and anxiety disorders. For other related diseases, more genetic instruments and larger GWAS data are needed to verify the results. On the other hand, our study further verified the bidirectional causality between NAFLD and neuropsychiatric disorders.