Summary of results
We presented a sex-specific scoring system (SRSS-CNMCI) for predicting the risk of conversion to MCI within 12 years in cognitively normal adults aged 61 to 90 years. Our scoring system not only estimated the absolute risk of conversion but also assessed the risk grade, that is, whether the conversion risk of the participants is high or low, which will provide the most intuitive understanding of the risk. In SRSS-CNMCI, there were differences in MCI conversion risk between men and women, indicating that research on sex-specific models is indeed a direction worthy of further exploration. This also indicates that specific monitoring and treatment plans should be implemented for men and women.
Previous studies have found that there are significant gender differences in the incidence and progression of AD and MCI , primarily in the following aspects. First, in terms of brain structure, Pfefferbaum et al.  found that in the study of patients with MCI and AD, women exhibited a faster decrease in brain volume than men, while men themselves had higher brain reserves, meaning that compared to women with AD, men with AD had the same nerve pathological changes, had a stronger ability to resist the disease, resisted the clinical symptoms of the disease and exhibited reduced incidence of disease. Second, in terms of hormones, studies of the effects of sex hormones on brain neurons found that sex hormones play a role in the entire life cycle of a person. Sex hormone levels and sexual genetic differences determine nerve regeneration in the brain, highlight form, facilitate axon guidance for the two-way aspect of the development of vessels and nerves, and the differences between men and women are the most notable features of sex hormones in the body type and have different expression levels [30-32]. Third, in terms of genetics, among AD patients, the number of women carrying the APOE4 genotype is much higher than that of men, and women carrying one APOE4 allele have a 4-fold higher risk of developing the disease, while men with the same genotype show only a slight increase in prevalence . Fourth, in terms of social life, Wookyoo et al.  found that highly educated AD patients suffered far less damage in the structural connections of the brain than the general population. According to history, men are far more likely than women to obtain higher education and higher vocational positions, which may mean that men have stronger cognitive reserve than women, thus having stronger resistance to brain pathological attacks.
Therefore, we hypothesized that the development of SRSS-CNMCI from different gender perspectives will improve the prediction accuracy of the scoring system. From the baseline characteristic table (eTable 1) of this study and the prediction accuracy results of SRSS-CNMCI (Figure 3 (a) and (b)), it was indeed observed that there are many differences between men and women, which further strengthened the validity of our hypothesis.
Referring to past research and clinical significance, we purposely incorporated clinical risk factors that are readily and routinely accessible in clinical trials and primary care. Our study only included data on demographic characteristics, genetics, cognitive tests, vital signs, and medical history and did not take into account neuroimaging or Cerebral Spinal Fluid (CSF) biomarkers. At present, most neuroimaging indexes included in the prediction model were the volume, surface area and thickness of a certain area of interest in the brain, such as middle temporal cortical thickness, hippocampal subcortical volume and right amygdala surface area [8, 9, 35], which lack relatively strong specificity in relationship with MCI, so we did not include neuroimaging data in this study. For biomarkers with high specificity, due to incomplete data records in ADNI, biomarker information with sufficient sample size meeting the inclusion criteria of this study could not be found, so it was not considered in this study. In the female multivariable Cox proportional hazards regression model, APOE ε4 was included, even though there was no significance in the model, because APOE ε4 is the gene with the strongest impact on the risk of late-onset Alzheimer’s disease , and the final multivariable models were significant (P<0.001). The male multivariable Cox proportional hazards regression model yielded the same result. Clinical significance, previous studies, univariate analysis and multivariate analysis were integrated into the consideration of risk factors in this study. The difference in FHD was statistically significant only between men and women and had no effect on the conversion of MCI, which may be due to the large extent of recall bias and inaccuracy in the collection of this information. Therefore, FHD as a risk factor is not convincing enough to be considered in subsequent studies.
First, previous studies on risk scoring and prediction models related to AD or MCI [8, 36, 37] rarely consider sex-specific modeling to explore whether there are different prediction results and performance between men and women. The SRSS-CNMCI developed in this study demonstrated that there are differences in risk prediction between men and women, which cannot be ignored and is the basis for improving the accuracy of prediction across genders. Second, most previous studies only considered whether the end point was converted or whether the disease was present but ignored the influence of time on the predicted results and did not include the follow-up time as an outcome indicator . In this study, we comprehensively evaluated the performance of the scoring system , estimated discrimination to evaluate the ability of the scoring system to distinguish the unconverted from the converted, and estimated calibration to evaluate the performance of the consistency between the predicted value and the actual value. Third, some studies have shown that if the scoring system can be validated in a new independent sample, the results of the study provide a good basis for early prevention and screening in the future , so we ran an external validation in the independent cohort HABS. The key risk factors for the two databases were consistent and comparable, so it is reliable to use this external database for validation (eTable 6). The scoring system showed good performance in goodness of fit and calibration, indicating that our scoring system has strong credibility in predictive ability. Forth, some studies have found that ROC analysis is useful to identify the optimal concentration threshold of CSF biomarkers , therefore, we tried to use ROC analysis to determine the threshold of high and low risk predicted by the scoring system, showing that the threshold value is good for risk prediction (C statistic 0.881 in females and C statistic 0.873 in males), indicating that the threshold value is reliable.
First, the risk factors included in the model were not comprehensive. Our goal was to develop a simple and accurate predictive tool. If the most common and easily accessible clinical indicators, such as body mass index (BMI) and daily activities (e.g., exercise frequency and reading), can be incorporated to predict the risk of MCI conversion, they will be of greater value for early prevention. However, there is almost no record of height data in the ADNI database, which cannot be converted into BMI, furthermore, while data related to daily activities cannot be obtained from the ADNI database, some common variables mentioned above were not included in the scoring system. Second, the sample size we used for modeling was not very large. Although we used the world’s largest AD database (ADNI), we included only small sample sizes for modeling. In the future, we will continue to enrich the sample size and further improve the prediction effect of SRSS-CNMCI. Third, the proportion of white people in the samples collected from ADNI and HABS was greater than 90%, and the population in the study was single. Even though the performance of external verification was good, SRSS-CNMCI still lacks the credibility to be promoted to other groups.