It is relatively well established that over a lifetime women have a higher probability of developing Alzheimer’s disease than men. The reason is still largely unknown. Chene et al. [2] have summarized the problem as follows:

Among other potential risk factors, a link with gender has been particularly contentious.

They listed and discussed possible factors that might explain why women have higher risk, such as smaller brain size, sex hormones, and cognitive reserve. They failed to mention that women on average are shorter, lighter in weight, and have less potential for maximum oxygen uptake [1] [3] [4]. Women are proportionately smaller than men. So, brain size might or might not explain why women have a higher risk for Alzheimer’s disease.

Women on average live about 10 percent longer than men. Vina et al. [5] present evidence that sex hormones, especially estrogens, reduce oxidative stress and account for women living longer than men. Brain cells with relatively lower turnover are subject to DNA damage with age [6]. It seems logical that female sex hormones that reduce oxidative stress might decrease the risk for Alzheimer’s disease for women instead of increasing risk. Living longer, however, provides more opportunity for Alzheimer’s disease to develop.

Finally, cognitive reserve is associated with education and use of brain in work [7]. Gender differences may only be associated with college education differences between men and women.

Thus, why women seem to have higher risk for Alzheimer’s disease, is confusing, unknown, and contentious. Based on the many possibilities, there appears to be a need to better explain the effects of gender on Alzheimer’s disease risk.

Gregory [1] presented a mathematical model that estimates the risk of developing Alzheimer’s disease as a function of age. This model is based on the theory that waste products are flushed from the brain during slow-wave sleep. Unflushed amyloid β accumulates in the front executive part of the brain that also provides control over slow-wave sleep [8]. As people age, amyloid β accumulation weakens the control over slow-wave sleep causing less slow-wave sleep and less flushing.

Gregory [1] related slow-wave sleep to maximum oxygen uptake (VO_2max) that on average decreases with age [3] [4]. The mathematical model for VO_2max is the same for both men and women except for a gender factor currently estimated to be 10.5 ml∙kg⁻¹∙min⁻¹ for men and 3.5 ml∙kg⁻¹∙min⁻¹ for women. The current Alzheimer’s model calibration is based on a mixed population of 50 - 50 men and women using an average gender factor of 7.0.

The gender effect on the prediction of Alzheimer’s disease in the existing model is a consequence of using VO_2max to estimate the amount of daily slow- wave sleep [1]. For details on the prediction of slow-wave sleep, see Appendix B in Gregory [1]. The following equation is used in this process along with other equations described in his Appendix B [1]:

VO 2 max = 107.4 R F ( 1 − A 120 ) + G (1)

where VO_2max = maximum oxygen uptake (ml∙kg⁻¹∙min⁻¹).

RF = relative fitness (number between 0 and 1).

A = age (years).

G = gender factor (males: 10.5; females: 3.5) (ml∙kg⁻¹∙min⁻¹).

For details on the various relative fitness values and use of this equation to predict risks of fatal heart disease, probability of living, and time awake after sleep onset, see Gregory [9]. The relative fitness variable allows the model to consider fitness, a lifestyle choice, in predicting the risk of developing Alzheimer’s disease as age increases. A value of 0.45 was used for relative fitness for a general population (1/4 endurance-trained (RF = 0.67) and 3/4 sedentary (RF = 0.38)) [9]. This value was used in the Alzheimer’s model for a general population. Estimated risk for Alzheimer’s disease is high for sedentary lifestyles compared to a physically active lifestyle as reported in the literature [10] [11]. The model seems to be on the right track in terms of physical lifestyles.

The gender factor allows the model to adjust predictions based on sex. When the model is run for a gender factor of 3.5 ml∙kg⁻¹∙min⁻¹ for women and 10.5 ml∙kg⁻¹∙min⁻¹ for men, it overpredicts the risk for women and underpredict the risk for men compared to the data from Lautenschlager et al. [12]. This built-in gender adjustment, nevertheless, relates back to a physical property of maximum oxygen uptake. The model appears to be missing additional gender factors.

The objective of the current work is 1) to reassess the values used for male and female gender factors, 2) add gender related variables that change the risk of developing Alzheimer’s disease based on published odds values reported in the literature, such as smoking and cognitive reserve, and 3) add a variable that adjusts the risk for developing Alzheimer’s disease associated with hormonal changes of men and women as they progress through life (birth to death). A final objective is to add variables to the model to consider new medical understanding and treatments implemented after 1996 when Lautenschlager et al. [12] published their Alzheimer’s disease risks as a function of age.

Numerous references associate heart disease and Alzheimer’s disease. Both seem to be related to saturated-fat intake and high levels of cholesterol [19] [20]. Management of diet and cholesterol-lowering treatments are important. Morris et al. [21] summarized the value of these two actions as follows:

…studies have found that high-fat/high-cholesterol diets increased, and cholesterol-lowering drugs decreased amyloid β peptide deposition and Alzheimer disease-related abnormalities.

As mentioned earlier in this paper, the newer study reported by Chene et al. [2] has much less risk for Alzheimer’s disease with age than the older study reported by Lautenschlager et al. [12]. Chene et al. [2] computed the risk for Alzheimer’s disease with a process they defined as competing mortality. While their description of the process lacks detail, they considered the effect of early death from various causes on their final estimates of developing dementia and Alzheimer’s disease, the major component of dementia. While their methodology is different from Lautenschlager et al. [12], the major differences between the two datasets appear to be due to improved medical knowledge and new treatments— not methodology.

A column was added to the Excel program to allow 0.0 or 1.0 as input at each year of age that statins are used. When statins or low-saturated fat diets are used, a 1.0 is entered. When statins are not used, a 0.0 is entered. The column was programed to produce a relative risk or odds ratio of 0.35 for each year statins are used based on the work of Wolozin et al. [20]. An entry of 0.0 produces an odds value equal to 1.0. The user also enters the percent of the population that manages their fat intake either through reducing saturated fat or taking cholesterol-lowering medicine.

Data from the US Department of Health and Human Services Center for Disease Control and Prevention (Kuklina et al. [19] was obtained from their Figure 3 on the total percentage of people who met the guidelines for low saturated fat intake. Forty-two percent met the guidelines from 2001 through 2010. The prior period, 1988-1994, was only down by one percent at 41 percent. This left 58 percent with high fat and probably high cholesterol—a need for cholesterol-lowering medication. The same report gave the percentage of people using cholesterol- lowering medications as 39 percent. This resulted in 67 percent of the people needing cholesterol treatment receiving treatment.

The Alzheimer’s model was then used to generate the lifetime risk (age 95) of developing Alzheimer’s disease as shown in Table 2, specifically row for 67 percent receiving treatment. The smoking percentage for men was also reduced from 40 to 20 based on the data in the paper by Chene et al. [2]. Predictions for men varied with the value of male gender used in calibration: hence three columns of predictions are shown for men. Predictions for women did not vary with the calibration for men. Values for men over predicted the measured risk from Chene et al. [2]. Values for women were almost within the confidence range of measured values for the age 65 analysis (Table 3). In general, the model calibrated for 67 percent receiving treatment over predicted measured values. Chene et al. [2] also presented risk for dementia, which include the Alzheimer’s disease risk.

Another way to consider the data reported by Kuklina et al. [19] is that 75 percent of the population in the US did not meet the guidelines for low saturated-fat intake in 1976-1980. Also, cholesterol-lowering drugs were just beginning to be used at this time. These condition probably closely reflect the condition in the population study reported by Lautenschlager et al. [12].

How people manage their fat intake certainly seems to affect risk in developing Alzheimer’s disease. Morris et al., [21] conclude the following:

High intake of unsaturated, unhydrogenated fats may be protective against Alzheimer disease, whereas intake of saturated or trans-unsaturated (hydrogenated) fats may increase risk.

Change in fat intake since the study used for calibration probably should be considered in the Fat Treatment column. In 2007-2010, 17 percent had lowered their saturated-fat intake plus 39 percent were on medications to lower cholesterol. The sum of 17 and 39 percent divided by the 75 percent needing to reduce fat resulted in 56/75 or 74.7 percent achieved treatment for cholesterol. The predicted risk from the Alzheimer’s model for a 75 percent achieving treatment is shown in the row for 75 percent treatment.

The prediction for women is close to the measured value for age 65 analysis (2.8 percent error). The predicted value of 13.5 percent for men for a male gender calibration of 10.5 is almost within the measured range for the age 65 analysis (Table 3).

There appears to be considerable scatter in the reported usage of cholesterol-lowering medications. Most values are below the 39 percent used above. In contrast, Franco [22] showed that cholesterol-lowering drug usage had increased from 23.4 percent of people 65 years and older to 46.8 percent by 2012. The sum of 17 for saturated fat reduction and 46.8 is 63.8 which results in 85.1 percent having treatment to lower saturated fat and cholesterol by 2012. The predictions in the row for 85 percent treatment is also shown in Table 2.

The predictions for both male gender factors of 10.5 ml∙kg⁻¹∙min⁻¹ and 10.0 ml∙kg⁻¹∙min⁻¹ were within the measured confidence range for the age 65 analysis. The predicted value for women is a close match to the measured value for age 45 analysis and within the range for the age 65 analysis.

There is uncertainty about the percentage of the people in the Framingham Heart Study in terms of unsaturated fat intake and usage of cholesterol-lowering medicines. The age 65 analysis started with older people who were less likely to have used cholesterol-lowering medications compared to the age 45 analysis. The measured data seem to reflect this change in usage of cholesterol-lowering drugs and use of better diets. The model seems to have done well in predicting the difference in age analysis and has done extremely well in predicting the reduction in risk for Alzheimer’s disease from the older data set reported by Lautenschlager et al. [12]. For comparison, the predicted data for men and women with no treatment for high-fat diets for conditions in people living before 1996 [12] are shown in the bottom line in bold.

Finally, the model was run for 100 percent of the population managing their fat either through diet or cholesterol-lowering drugs. From this upper limit, it appears that further large reductions from cholesterol-lowering drugs are not very likely. A new drug development that reduces the odds below 0.35 is needs but probably unlikely.

These results favor the use of 10.5 for the male gender factor as was originally used in the Gregory model [1].

Based on the expanded calibration for the Alzheimer’s disease model presented by Gregory [1], it appears that several factors affect the gender difference in risk for Alzheimer’s disease. Smoking, cognitive reserve, and sex hormones components were added to the model. It appears that the gender factor from the VO_2max model and the sex hormone calibration are the two most important determinates of the gender differences. Smoking and cognitive reserve differences in population studies have only a minor effect.

The gender factors for men and women were reanalyzed. There was little room statistically to deviate from the original gender factor of 3.5 ml∙kg⁻¹∙min⁻¹ for women. When the gender factor of males was reanalyzed, it was found that a 9.5 ml∙kg⁻¹∙min⁻¹ value produced the best R² value in the VO_2max data. However, the difference between 10.5 ml∙kg⁻¹∙min⁻¹ and 9.5 ml∙kg⁻¹∙min⁻¹ was small. It now appears that the 10.5 ml∙kg⁻¹∙min⁻¹ value for the male gender is the best value to use.

This paper started with the concern that the Gregory Alzheimer’s disease model overpredicted risks for women. After adding components for sex hormones, smoking, and cognitive reserve, the model did well matching measured results reported by Lautenschlageret al. [12] for both women and men. The model did well in matching the results form Chene et al. [2] for women after adding a component to consider unsaturated fat diets and cholesterol medication, especially for women. The model slightly overpredicted the risks for men in this newer data set.

Why did this overprediction for men but not for women occur? The answer appears to be associated with another medication primarily used by men starting in the late 1990s—Viagra. Fang et al. [23] reported that an ED medication, Viagra (Sildenafil), reduced Alzheimer’s development in men by 69 percent over a six-year period. They report a hazard ratio of 0.31, which was used in the expanded Gregory Alzheimer’s model. While females had some benefit, their results were weaker. Females typically take a lower dosage and only a small percentage of women are prescribed this medication relative to men.

The percentage of men who took Viagra from 2000 to 2015 is unknown. The number is thought to be somewhat limited because of newness and cost. An assumed value of 10 percent was tried. The predicted results are shown in bold in Table 2 for 67, 75, and 85 percent of fat and cholesterol management. Even this small percent usage reduced the predicted risk for men to values close to measurements in Table 3.

Predictions for 10 percent usage in men were in line with measured values in Table 3 from Chene et al. [2]. Predictions for 20 percent usage in men are also shown in Table 2 and are a close match to the measured values in Table 3. While the actual population usage of Viagra in the data from Chene et al. [2] is unknown, it is logical that Viagra usage is the reason that the measured results for men in Table 3 were below the initial predictions without considering Viagra.

Adding Viagra as a treatment reduced the risk for men and raised the ratios for risk for women over risk for men. The ratio is approximately 1.77 for 10 percent Viagra and 1.97 for 20 percent. The ratios for the measured values are 1.82 for the 65-age group and 1.89 for the 45-age group. These measured ratios are approximately midway between the 10 and 20 percent usage values. While the model does not predict the cause of Alzheimer’s disease, it appears to correctly model the dynamics of the disease progression for both genders.

It appears that most of the gender factors have been included in the expanded model. There are probably gender differences associated with other risk factors, such as sleep apnea and obesity. Data to enable a reasonable calibration for these considerations were not found. Part of the problem, for example is that definitions of the number used to define sleep apnea vary. These two variables seem to be related and affect other health issues that often cause death before Alzheimer’s disease has time to develop. Obviously, more research with precise measurements is needed.

Gregory [1] reported a mathematical model programmed in Excel to predict the risks of developing Alzheimer’s disease and Chronic Traumatic Encephalopathy, CTE, as a function of age and other variables. This model considered gender through a gender factor of 10.5 ml∙kg⁻¹∙min⁻¹ for males and 3.5 ml∙kg⁻¹∙min⁻¹ for females. The initial model overpredicted the risks for women and underpredicted risks for men. Most of this problem was resolved by adding a sex hormone factor that varied with age. These calibration values were obtained by first obtaining values for estradiol for women from the literature as a function of age and dividing all values by the maximum value. This process provided a woman’s relative hormone values (0 to 1) as a function of age. The process was repeated for male testosterone to obtain a man’s relative hormone values. The average of these to relative values provided the sex hormone calibration values. Minor adjustments to predictions of risks associated with gender were made by considering gender differences in smoking history and cognitive reserve (education) history.

The expanded model performed well in estimating risks associated with gender compared to measured gender specific results reported by Lautenschlager et al. [12]. Values for R² comparing measured to predicted were 0.99 and were highly significant (p = 0.001).

A more recent data set (Chene et al. [2]) was used to consider the use of unsaturated fat diets and cholesterol reducing drugs for both men and women to reduce risk of developing Alzheimer’s disease. Viagra for men to reduce risk of developing Alzheimer’s disease was also considered. Final predictions for lifetime risk of developing Alzheimer’s disease closely matched measured values and were certainly within the 95 percent confidence range of measured values for both genders.

While initially, it was thought that the male gender factor of 10.5 ml∙kg⁻¹∙min⁻¹ might need to be adjusted, it now appears that the 10.5 ml∙kg⁻¹∙min⁻¹ for men and 3.5 ml∙kg⁻¹∙min⁻¹ for women are acceptable calibration values. These values worked well for both the older data set [12] and a newer data set [2] published about two decades later.

Finally, the expanded Gregory Alzheimer’s model provides the same predictions for a 50 - 50 mix of males and females as the original model with the added feature that it now considers use of unsaturated fat diets, cholesterol reducing medications, and Viagra for men.

The author declares no conflicts of interest regarding the publication of this paper.

Gregory, J.M. (2022) Alzheimer’s Disease: An Analysis of Gender Effects. Journal of Behavioral and Brain Science, 12, 455-473. https://doi.org/10.4236/jbbs.2022.1210026