Objective: Sex differences have been observed in many aspects of schizophrenia, including cognitive deficits. Despite extensive research into the relationship between metabolic factors and cognitive deficits in schizophrenia, few studies have explored the potential sex difference in their association. Methods: We recruited 358 schizophrenia patients and 231 healthy controls. The participants underwent measurements of body mass index (BMI), waist circumference, blood pressure, triglycerides, high-density lipoprotein cholesterol, and fasting blood glucose. Metabolic risk factors included abdominal obesity, hypertension, hyperglycemia, and dyslipidemia. A collection of these metabolic risk factors has been defined as metabolic syndrome. These diagnoses were based on the criteria of the National Cholesterol Education Program’s Adult Treatment Panel III. Cognitive performance was measured using the Repeatable Battery for the Assessment of Neuropsychological Status (RBANS). A descriptive analysis, difference analysis, and linear regression model were used to identify the metabolic risk factors for cognitive function in schizophrenia. Results: Our findings revealed sex differences in the rate of abdominal obesity and hypertension in schizophrenic patients. Additionally, we observed sex differences in the association between metabolic risk factors and cognitive impairment in schizophrenia. Specifically, hyperglycemia was associated with the immediate memory index score of RBANS in male patients, while dyslipidemia was associated with language, attention, delayed memory index scores, and RBANS total score in female patients. Conclusion Our results suggest that sex should be considered when evaluating the impact of metabolic disorders on the cognitive function of schizophrenic patients. Moreover, our study identifies hyperglycemia and dyslipidemia as potential targets for precise treatment by sex stratification, which could benefit the improvement of cognitive impairment in schizophrenic patients.

Pancreatic cancer often has an insidious onset and difficulties in treatment， with various limitations in early diagnosis and treatment. This article reviews the application of Mendelian randomization （MR） in exploring the risk factors for pancreatic cancer， with a special focus on the causal relationships of factors such as gut microbiota， lifestyle， and metabolic diseases. Leveraging data from large-scale genome-wide association studies （GWAS）， MR analysis has revealed several biomarkers associated with the risk of pancreatic cancer. The two-sample MR approach is commonly used in current research， including the methods such as Inverse Variance Weighted， Weighted Median， and MR-Egger， which helps to explain the causal network of the disease from a genetic perspective. While MR strategy provides a new perspective for understanding the etiology of pancreatic cancer， caution is still needed in data synthesis， selection of instrumental variables， and pleiotropy assessment. The use of emerging analytical models such as BWMR， CAUSE， and MVMR offers new possibilities for the comprehensive evaluation of multiple risk factors and their interaction. In the future， with the combination of these methods and the ever-increasing genetic epidemiological data， MR analysis is expected to provide more solid evidence for identifying potential therapeutic targets for pancreatic cancer and formulating prevention strategies.