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The findings of this study suggest that coffee intake and alcohol consumption have a significant causal effect on AGA, as indicated by the IVW analysis. The IVW odds ratio (OR) per one standard deviation (SD) increase for coffee intake was 35.914 (95% confidence interval [CI], 2.522-511.354) with a P-value of 0.008, while for alcohol consumption it was 24.098 (95% CI, 1.291-449.698) with a P-value of 0.033. The MR-Egger intercept and Cochran's Q tests did not provide evidence of directional pleiotropy or heterogeneity for these associations. Leave-one-out analysis and scatter plot analysis did not identify any significant outliers. The weighted median and MR-Egger methods also supported the positive association, although with insignificant p-values. BMI showed a borderline significant causal effect on AGA, with an IVW OR per one SD increase of 2.097 (95% CI, 0.999-4.402) and a P-value of 0.050. However, the MR-Egger regression and weighted median methods did not yield statistically significant results. Genetically predicted waist circumference was not found to be associated with AGA risk. Other lifestyle factors, including tea intake, smoking, insomnia, television watching, computer use, and driving time, were not significantly associated with AGA. For AA, insomnia was found to have a causal effect, with an IVW OR per one SD increase of 10.301 (95% CI, 1.273-83.339) and a P-value of 0.029. No significant associations were observed between other lifestyle factors and AA.

This study utilized 40, 41, 35, 30, 23, 113, 83, 7, 458, and 374 single nucleotide polymorphisms (SNPs) from the genome-wide association study (GWAS) database as instrumental variables (IVs) for coffee intake, tea intake, alcohol consumption, smoking, insomnia, television watching, computer use, driving time, BMI, and waist circumference, respectively. After clumping and harmonizing (excluding variants with palindromic intermediate allele frequencies), 35, 33, 28, 20, 24, 96, 70, 5, 346, and 299 variants, respectively, remained for further analysis. Details of the SNPs used as instrumental variables were displayed in Supplementary file 1: Supplementary tables (Table S1-S10).

Figure 2 displays scatterplots of the associations of each genetic variant plotted against their association with the corresponding outcomes for all traits that had significant associations (false discovery rate, q<0.05). Forest plots evaluate the risk effect of each SNP on AGA alone (Figure 3). Leave-one-out analysis is performed to sequentially remove one SNP and evaluate the overall effect of the remaining SNPs on AGA. If the result remains unchanged, it indicates that the result is stable (Figure 3).

3.1 AGA The IVW analysis revealed an increased risk of genetically predicted coffee intake and alcohol consumption on AGA: coffee intake [IVW OR per one SD increase: 35.914 (95% CI, 2.522-511.354), P = 0.008], alcohol consumption [IVW OR per one SD increase: 24.098 (95% CI, 1.291-449.698), P = 0.033). The MR-Egger intercept revealed no evidence of directional pleiotropy (coffee intake: P = 0.0773, alcohol consumption: P = 0.795), and Cochran's Q showed no evidence for heterogeneity (coffee intake: P = 0.884, alcohol consumption: P = 0.970) (Table 3).

Further leave-one-out analysis and scatter plot analysis did not detect significant outliers (Figure 2). Weighted median and MR-Egger methods were directionally consistent, and there was no evidence of pleiotropy or heterogeneity, although the p-values were not significant (Table 2). According to the paper's description, these results could be considered positive [19].

The causal effect of BMI on the risk of AGA was borderline statistically significant [IVW OR per one SD increase: 2.097 (95% CI, 0.999-4.402), P = 0.050). Similar risk estimates were obtained using the MR-Egger regression [OR per one SD increase: 4.274 (95% CI, 0.597-30.599), P = 0.149) and weighted median [OR per one SD increase: 1.876 (95% CI, 0.464-7.578), P = 0.377), although the association was not statistically significant. Additionally, no directional pleiotropy (MR-Egger intercept = , P = 0.444) or heterogeneity (Q = 340.291, P = 0.561) was observed. Therefore, this MR analysis indicates that BMI may have a potential causal effect on AGA, but the evidence is weak. However, genetically predicted waist circumference was not associated with AGA risk (IVW P = 0.145).

Furthermore, other genetically predicted lifestyle factors, including tea intake (IVW P = 0.376), smoking (IVW P = 0.765), insomnia (IVW P = 0.105), television watching (IVW P = 0.058), computer use (IVW P = 0.366), and driving time (IVW P = 0.496), were not significantly associated with AGA (Table 3).

3.2 AA IVW methods demonstrated a causal effect of insomnia on AA [IVW OR per one SD increase: 10.301 (95% CI, 1.273-83.339), P = 0.029]. The weighted median and MR Egger methods showed consistent effects (Table 2). In the sensitivity analysis, no heterogeneity (Q = 20.254, P = 0.627) or horizontal pleiotropy (MR-Egger intercept = 0.006, P = 0.881) was observed. There were no significant associations between other lifestyle factors and AA, including coffee intake (IVW P = 0.531), tea intake (IVW P = 0.755), alcohol consumption (IVW P = 0.601), smoking (IVW P = 0.994), television watching (IVW P = 0.819), computer use (IVW P = 0.431), driving time (IVW P = 0.064), BMI (IVW P = 0.517), and waist circumference (IVW P = 0.229) (Table 3)