Association Between Sex Hormones Levels and Cancer Prevalence in Women：A Study On NHANES 2007-2020

doi:10.21203/rs.3.rs-4849044/v1

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Association Between Sex Hormones Levels and Cancer Prevalence in Women：A Study On NHANES 2007-2020

https://doi.org/10.21203/rs.3.rs-4849044/v1

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Background: Estradiol and testosterone are the two most important sex hormones in the human body. Previous studies have shown that these two sex hormones have a certain relationship with the prevalence of cancer.

Methods: Subject data were analyzed using logistic regression analysis, interaction analysis, RCS, and mediation effects. Measures in the demographic characteristics data were described as mean (SD) to describe the distribution, and t-tests were used to determine differences between groups. Count data were expressed as N (%), and the chi-square test was used to determine differences between groups.

Results: The results of multifactorial logistic regression analysis showed a negative correlation between testosterone levels and cancer prevalence, and a negative correlation between estradiol levels and breast cancer prevalence. Cancer prevalence and breast cancer prevalence were both lower in people with high estradiol and testosterone levels. There was a significant non-linear relationship between estradiol and cancer prevalence (P=0.03), while there was a significant linear relationship between testosterone and cancer prevalence (P=0.27). None of the results of the median analysis showed a significant linear relationship between testosterone and cancer incidence (P>0.05). Association between testosterone and cancer risk mediated by PIR (IE=-6.46e-04, P=0.54;DE=-8.18e-02, P<0.01). Association between testosterone and cancer risk mediated by Energy (IE=-5.45e-04, P=0.89;DE=-8.08e-02, P<0.01).

Conclusion: Serum testosterone levels were more significantly and negatively correlated with cancer prevalence in women. Serum estradiol levels were more significantly and negatively correlated with breast cancer prevalence. There was a significant nonlinear relationship between estradiol and cancer prevalence.

Biological sciences/Cancer

Health sciences/Diseases

Health sciences/Endocrinology

Health sciences/Medical research

hormones

estradiol

testosterone

cancer

breast

Estrogens are cholesterol-derived steroid hormones that are synthesized in the female ovaries and act throughout the body ^[1]. Estrogen is an extremely important sex hormone in the female body, responsible for the development of the female reproductive system and regulating the female menstrual cycle. There are three main forms of estrogen: estrone, estradiol, and estriol. Of these, estradiol is the predominant form in action ^[2]. The level of estrogen in the human body is closely related to the physiological functions of the human body and is closely linked to the development and of numerous diseases ^[3]. Estrogen has been recognized as an important influencing factor in the occurrence, development, treatment, and prognosis of lung cancer ^[4]. It had been shown that estradiol was involved in regulating the tumor microenvironment in thyroid cancer, effectively interfering with the biological behavior of cancer cells ^[5]. Estradiol effectively regulated the antioxidant pathway in vivo through the nuclear factor-like receptor signaling pathway and exerted certain anticancer effects by scavenging reactive oxygen species in vivo ^[6]. Estradiol derivatives had also been used as a novel anticancer drug in the treatment of osteosarcoma in adolescents with effects on tumor development and angiogenesis ^[7]. In addition, estradiol has a regulatory effect on aspects of food metabolism and energy expenditure ^[8]. As a major female sex hormone, estrogen plays an extremely important role in women's physical and mental health ^[9]. Although estrogen is a female sex hormone, estrogen also has an influential effect on male physiologic function ^[10].

Testosterone is another important sex hormone that plays an important role in male sexual development and maintenance of male characteristics. In addition to this, testosterone has multiple target organs of action and has a regulatory effect on the function of several systems and organs of the body ^[11]. Circulating testosterone is converted to two important metabolites in peripheral tissue species: dihydrotestosterone and estradiol ^[12]. Serum testosterone levels are closely related to the occurrence, treatment, and prognosis of many cancers ^[11,13]. In a study on testosterone and sex hormone-binding globulin and 19 cancers, it was shown that serum testosterone levels were associated with cancer prevalence in either male or female populations ^[14]. In another study of serum testosterone levels and sex hormone binding proteins and mortality in men, it was shown that serum testosterone and sex hormone binding proteins were negatively correlated with mortality in men ^[15]. In addition, data suggest that the incidence of breast cancer may also be influenced by androgen levels ^[16]. Serum testosterone levels decline with age. Therefore, symptoms that arise with declining serum testosterone levels can be treated with replacement therapy using testosterone and its derivatives ^[17]. Testosterone is a drug approved by the U.S. Food and Drug Administration (FDA) for the treatment of male hypogonadism ^[18]. The only indication for testosterone in the treatment of women is low libido accompanied by distress in postmenopausal women ^[19]. Over the last 80 years of using testosterone as an alternative therapy, testosterone sales have been increasing ^[20]. In today's increasingly serious aging, testosterone plays an important role in the physical and mental health of both men and women ^[21,22].

Recent research data indicate that breast cancer is now one of the most common cancers worldwide ^{[23, 24]}. Breast cancer is approximately 100 times more common in women than in men ^[25]. Estrogen-positive breast cancer is one of the most common breast cancers and is the leading cause of death in women of reproductive age ^{[26, 27]}. Estrogen plays a crucial role in the development of breast cancer, and it can promote the metastasis of cancer cells by recruiting activated neutrophils ^[28]. Besides estrogen, there are numerous other factors that trigger the development of breast cancer, such as alcohol consumption and physical activity ^[29]. There have been many studies on the relationship between estradiol or testosterone on cancer incidence, but there is a lack of research on the effect of a mixture of the two sex hormones on cancer incidence in women. Estradiol and testosterone are important sex hormones, and they are closely related to the occurrence, development, and prognosis of cancer. Here, we performed a statistical analysis based on population data from the National Health and Nutrition Examination Survey (NHANES) database from 2007–2020. We analyzed the mixed effects of estradiol and testosterone on the incidence of pan-cancer cancers in women, focusing on their association with breast cancer incidence. This study hopes to provide new insights into cancer prevention around the world, identify potential opportunities for intervention, and enhance the quality of life of cancer patients.

2.1 Study population

We analyzed consecutive 2007–2020 NHANES database information. NHANES is a stratified, multistage periodic survey conducted by the National Center for Health Statistics (NCHS) using a nationally representative sample of the United States. All NHANES protocols were approved by the NCHS Ethics Review Board and written informed consent was obtained from the participants. By randomly selecting participants, annual home interviews, physical examinations, and finally a complex statistical process. Of all participants (n = 14409) in these NHANES, incomplete cancer information (n = 4201), male population (n = 4943), incomplete demographics information (n = 517), incomplete smokingbehavior,alcohol consumption and energy intake information (n = 1082) and incomplete dietary information (n = 134) was excluded from analysis. A total of 3532 subjects were included in the final statistical analysis.

2.2 Socio-demographic characteristics, lifestyles and long-term conditions

Sociodemographic characteristics include age, sex (male or female), race (Non-Hispanic White, Non-Hispanic Black, Mexican American, Other Hispanic, Other Race), Education level (Less than high school, High school ,More than high school), marital status (Married / living with partner, Widowed / Divorced / Separated, Never married) family property income ratio (PIR).

Smoking behavior was classified: nexer (smoked less than 100 cigarettes in life), former (smoked more than 100 cigarettes in life and smoke not at all now), now (smoked more than 100 cigarettes in life and smoke some days or every day). Drinking grades were categorized into five groups: 1.never (drinked less than 1 drinks for females ,2 drinks for males in the past months), 2.former (drinked more than 1 drinks for females), 3mild (≥ 1 drinks per day for females), 4.moderate (≥ 2 drinks per day for females), 5.heavy (≥ 3 drinks per day for females).

The clinical diagnostic criteria for hypertension is ≥ 140 / 90 mmHg. Average blood pressure was calculated by the following protocol: 1.The diastolic reading with zero was not used to calculate the diastolic average; 2.If all diastolic reading were zero, then the average would be zero; 3. If only one blood pressure reading was obtained, that reading was the average; 4. If there was more than one blood pressure reading, the first reading was always exclude from the average.The diagnostic criteria for diabetes were: 1.Doctor told you have diabetes; 2.Glycohemoglobin HbA1c (%) > 6.5; 3.Fasting glucose (mmol / L) ≥ 7.0; 4. Random blood glucose (mmol / L) ≥ 11.1; 5. Two-hour OGTT blood glucose (mmol / L) ≥ 11.1; 6. Use of diabetes medication or insulin.

2.3 Statistical analysis

A variety of statistical methods were used in this study. Logistic regression analyses were individually modeled and adjusted for the two sex hormones. Weighted quartiles (WQS) and qgcomp analyses were performed to estimate the mixed effect of all two sex hormones. For random missing samples, we used multiple interpolation to fill in. For non-random missing data, we excluded them to maximize the representativeness of the sample. The filtered data were analyzed using R (v.4.2.1). Measures in the demographic characteristics data were described as mean (SD) to describe the distribution, and t-tests were used to determine differences between groups. Count data were expressed as N (%), and the chi-square test was used to determine differences between groups. The female sex hormone levels were categorized into high and low levels according to the subject's work characteristic curve (ROC) and the cut-off values were taken as 18.3 pg / mL for estradiol and 18.28 ng / mL for testosterone. The ROC curve is an intuitive method that combines sensitivity and specificity, compares model performance and is unaffected by category imbalance. The classification effect is optimized by adjusting the critical values of the metrics. It is plotted in terms of True Positive Rate (TPR) and False Positive Rate (FPR) as coordinates indicating the sensitivity and specificity of the model. The Jordon index is a measure of the performance of the ROC curve, and its maximization corresponds to the best classification results. Because both sex hormones were heavily right skewed, we performed In transformations to improve the normal distribution when including these 2 sex hormones as continuous variables in the analysis. In addition, we performed several sensitivity analyses, hierarchically adjusting for age, race, education level, marital status, PIR, BMI, waist circumference, smoking, alcohol consumption, hypertension, diabetes mellitus, and energy intake levels to ensure the reliability of the study results.

In the regression analysis, we first used multivariate logistic regression. Because NHANES employs a complex multistage sampling design, we calculated weights and performed survey-weighted multivariate logistic regressions to assess the association between the two sex hormones and pan- and breast cancer in women. After that, we also segmented the regression by categorizing the two sex hormone levels into Q1, Q2, Q3, and Q4 based on interquartile spacing. The association between the two sex hormones and pan-cancer and breast cancer was assessed by comparing sex hormone levels in the second, third, and fourth quartiles with those in the first quartile. In addition, we further fitted the model with several subgroup analyses and cross-validation to obtain a more accurate target population.

In addition, we used three times restricted cubic spline (RCS) to explore the nonlinear relationship between sex hormones and cancer prevalence and further grouped the variables according to the presence of interaction effects. In this analysis, we adjusted for all 12 covariates. For the subgroups with interactions in the interaction analysis, we further explored the mediating effects. Mediation analyses were conducted to explore whether the effect of sex hormones on cancer was mediated through an interaction variable, and Bootstrapping samples of 1,000 were taken to determine the mediating effect. In this analysis, we similarly adjusted for all 10 covariates.

All analyses were performed using R software (4.2.1). All statistical analyses were performed using the software packages base, qWQS, qgcomp, stats, survey. P-values less than 0.05 were considered statistically significant.

3.1 Baseline characteristics

We went through a series of screening process and finally 3532 female subjects were included in the study. The screening process was shown in Fig. 1. The general characteristics of the study population were shown in Table 1. We included a total of 3532 female subjects, the number of cancer was 366, which was 10.36% of the total study population, and the number of breast cancer was 97, with a prevalence of 2.75%.

Table.1. Baseline characteristics of the study population.

Characteristics	Cancer		P-value
	no	yes
Total	3166(89.64)	366(10.36)
Age ~ years	47.10(0.41)	61.52(0.82)	< 0.01
Race~%			< 0.01
Non-Hispanic White	1216(66.49)	247(86.19)
Non-Hispanic Black	651(11.49)	31( 3.24)
Mexican American	514(8.54)	32(2.94)
Other Hispanic	375(5.90)	33(2.81)
Other Race	410(7.59)	23(4.82)
Education level~%			0.06
Less than High School	599(12.33)	53( 8.22)
High school	691(21.07)	68(17.71)
More than High School	1876(66.60)	245(74.07)
Marital Status~%			< 0.01
Married/ living with partner	1769(61.51)	186(56.06)
Widowed/Divorced/Separated	793(21.49)	155(37.38)
Never married	604(17.00)	25( 6.56)
Family PIR~%	2.94(0.08)	3.01(0.13)	0.59
BMI ~ kg/m2	29.73(0.24)	29.09(0.36)	0.09
Waist ~ cm	98.42(0.50)	99.01(0.72)	0.45
Smoking behavior~%			0.01
never	2095(63.40)	200(53.29)
former	544(19.85)	107(29.78)
now	527(16.76)	59(16.93)
Alcohol consumption~%			< 0.01
never	671(15.67)	49( 8.68)
former	441(12.16)	85(19.84)
mild	906(30.88)	123(37.85)
moderate	650(23.90)	70(23.42)
heavy	498(17.39)	39(10.21)
Energy ~ kcal	1796.03(11.16)	1762.33(40.67)	0.44
Diabetes~%			0.97
yes	560(14.34)	80(14.41)
no	2606(85.66)	286(85.59)
Hypertension~%			< 0.01
no	1874(63.81)	151(45.40)
yes	1292(36.19)	215(54.60)
Estradiol ~ pg/ml	57.80(1.34)	28.47(4.16)	< 0.01
Testosterone ~ pg/ml	24.81(0.90)	19.10(0.72)	< 0.01

3.2 Relationship between sex hormone levels and cancer prevalence

The results of the multifactorial logistic regression analysis between sex hormone levels and cancer prevalence were shown in Table 2. Model 1 was adjusted for age and race. The results showed a negative correlation between testosterone levels and cancer prevalence (OR: 0.66, 95% CI: 0.48–0.90), and the difference was statistically significant (P = 0.01). There was a negative correlation between estradiol levels and breast cancer prevalence (OR: 0.24, 95% CI: 0.08–0.71), and the difference was statistically significant (P = 0.01). Model 2 was adjusted for age, race, marital status, education level, BMI, PIR, and waist circumference according to model two. The results showed a negative correlation between testosterone levels and cancer prevalence (OR: 0.64, 95% CI: 0.46–0.89), and the difference was statistically significant (P = 0.01). There was a negative correlation between estradiol levels and breast cancer prevalence (OR: 0.24, 95% CI: 0.08–0.73), and the difference was statistically significant (P = 0.01). Model 3 was adjusted for age, race, marital status, education level, BMI, PIR, waist, smoking, alcohol consumption, hypertension, diabetes, and energy intake. The results showed a negative correlation between testosterone levels and cancer prevalence (OR: 0.61, 95% CI: 0.43–0.88), and the difference was statistically significant (P = 0.01). There was a negative correlation between estradiol levels and breast cancer prevalence (OR: 0.25, 95% CI: 0.08–0.80), and the difference was statistically significant (P = 0.03). Trends in the distribution of cancer prevalence among different sex hormone levels are shown in Fig. 2. Both cancer prevalence and breast cancer prevalence were lower in populations with high levels of estradiol and testosterone.

Table.2. Results of multifactorial regression analysis of sex hormone levels and cancer prevalence and breast cancer prevalence.

Outcomes	Sex hormone	Model	OR(95%CI)	p value
Pan-cancer	Estradiol	Model 1	0.71(0.45,1.14)	0.15
		Model 2	0.72(0.45,1.18)	0.18
		Model 3	0.72(0.42,1.22)	0.19
	Testosterone	Model 1	0.66(0.48,0.90)	0.01
		Model 2	0.64(0.46,0.89)	0.01
		Model 3	0.61(0.43,0.88)	0.01
Breast cancer	Estradiol	Model 1	0.24(0.08,0.71)	0.01
		Model 2	0.24(0.08,0.73)	0.01
		Model 3	0.25(0.08,0.80)	0.03
	Testosterone	Model 1	0.70(0.40,1.24)	0.21
		Model 2	0.69(0.39,1.25)	0.21
		Model 3	0.70(0.37,1.33)	0.24

* Model 1 was adjusted for age and race. Model 2 was adjusted for age, race, marital status, education levels, waist, BMI, PIR, Model 3 was adjusted for age, race, marital status, education levels, waist, BMI, PIR, smoking status, alcohol consumption level, hypertension, and diabetes, energy intake.

3.3 Segmented regression

The results of the segmented regression were shown in Table 3. Testosterone at the third and fourth levels was consistently significantly associated with cancer prevalence in all three models before and after adjustment. In the final adjusted model, the third level of testosterone was able to reduce the prevalence of cancer by 48% (OR: 0.52, 95% CI: 0.28–0.94), and the fourth level of testosterone was able to reduce the prevalence of cancer by 45% (OR: 0.55, 95% CI: 0.32–0.94). Estradiol at the third level was consistently and significantly associated with developing breast cancer. In the final adjusted model, the third level of estradiol was able to reduce the prevalence of breast cancer by 90% (OR:0.10, 95% CI: 0.02–0.52).

Table.3. Results of segmented regression analysis of models adjusted for different variables.

			Quartile 1	Quartile 2		Quartile 3		Quartile 4
Group	Sex hormone	Model		OR(95%CI)	p	OR(95%CI)	p	OR(95%CI)	p
Pan-cancer	Estradiol	Model 1	Ref	0.83(0.64,1.07)	0.14	0.72(0.45,1.15)	0.16	0.70(0.36,1.36)	0.28
		Model 2	Ref	0.84(0.62,1.12)	0.22	0.74(0.45,1.22)	0.21	0.73(0.36,1.46)	0.35
		Model 3	Ref	0.82(0.56,1.18)	0.22	0.72(0.41,1.27)	0.21	0.72(0.32,1.62)	0.36
	Testosterone	Model 1	Ref	0.77(0.55,1.06)	0.10	0.55(0.35,0.88)	0.01	0.60(0.38,0.95)	0.03
		Model 2	Ref	0.75(0.53,1.06)	0.10	0.54(0.33,0.87)	0.01	0.58(0.36,0.93)	0.03
		Model 3	Ref	0.74(0.48,1.12)	0.13	0.52(0.28,0.94)	0.04	0.55(0.32,0.94)	0.03
Breast cancer	Estradiol	Model 1	Ref	0.90(0.45,1.77)	0.74	0.10(0.02,0.42)	< 0.01	0.43(0.12,1.64)	0.21
		Model 2	Ref	0.80(0.40,1.61)	0.51	0.09(0.02,0.40)	< 0.01	0.42(0.11,1.66)	0.20
		Model 3	Ref	0.82(0.35,1.91)	0.59	0.10(0.02,0.52)	0.01	0.43(0.09,2.03)	0.23
	Testosterone	Model 1	Ref	1.00(0.52,1.93)	1.00	0.75(0.36,1.54)	0.41	0.67(0.36,1.25)	0.19
		Model 2	Ref	0.96(0.50,1.87)	0.91	0.72(0.34,1.55)	0.38	0.64(0.33,1.24)	0.17
		Model 3	Ref	1.02(0.48,2.17)	0.96	0.74(0.30,1.83)	0.45	0.66(0.31,1.40)	0.23

3.4 Subgroup analysis

The results of the subgroup analysis are shown in Tables 4 and 5. There was an interaction of PIR and energy intake with testosterone levels. Among people at higher income levels, those with high testosterone levels had a lower risk of cancer than those with low testosterone levels (OR: 0.48, 95% CI: 0.29–0.81). Among those with high energy intake, those with high testosterone levels had a lower risk of cancer than those with low testosterone levels (OR: 0.41, 95% CI: 0.24–0.71).

Table.4. Results of a subgroup analysis of the association between estradiol and the prevalence of pan-cancer.

Subgroup Variable	Cancer ~ Estradiol
	OR(95%CI)	P value	P interaction
Age			0.88
<65	0.44(0.27,0.70)	< 0.01
≥65	0.51(0.12,2.25)	0.33
Race			0.40
Non-Hispanic White	0.74(0.41,1.32)	0.27
Non-Hispanic Black	0.61(0.08, 4.55)	0.53
Mexican American	1.48(0.06,34.43)	0.64
Other Hispanic	0.62(0.23,1.62)	0.29
Other Race	0.29(0.07, 1.13)	0.07
Education level			0.31
Less than high school	1.03(0.34,3.12)	0.95
High school	1.02(0.27,3.81)	0.97
More than high school	0.66(0.36,1.22)	0.16
Marital Status			0.73
Married/ living with partner	0.72(0.37,1.41)	0.3
Widowed/Divorced/Separated	0.73(0.35,1.50)	0.35
Never married	1.04(0.39,2.78)	0.94
Family PIR			0.11
<2.1	0.91(0.49,1.70)	0.75
≥ 2.1	0.62(0.29,1.34)	0.19
BMI			0.56
<25	0.95(0.47,1.92)	0.87
≥25	0.60(0.33,1.09)	0.08
Waist			0.84
<97	0.81(0.37,1.79)	0.57
≥97	0.60(0.33,1.07)	0.08
Smoking behavior			0.47
never	0.74(0.41,1.33)	0.28
former	0.90(0.32, 2.52)	0.82
now	0.38(0.21, 0.68)	< 0.01
Alcohol consumption			0.56
never	0.58(0.07, 5.07)	0.59
former	1.20(0.37,3.93)	0.74
mild	0.44(0.17, 1.12)	0.08
moderate	1.03(0.47, 2.29)	0.93
heavy	0.53(0.19, 1.51)	0.21
Energy intake			0.06
<1700	0.88(0.38,2.06)	0.75
≥1700	0.60(0.31,1.20)	0.13
Hypertension			0.54
yes	0.77(0.36,1.67)	0.47
no	0.73(0.33,1.61)	0.39
Diabetes			0.75
yes	0.53(0.14,1.92)	0.29
no	0.75(0.45,1.23)	0.22

*Model was adjusted for age, race, marital status, education levels, waist, BMI, PIR, smoking status, alcohol consumption level, hypertension, and diabetes, energy intake.

Table.5. Results of a subgroup analysis of the association between testosterone and the prevalence of pan-cancer.

Subgroup Variable	Cancer ~ Testosterone
	OR(95%CI)	P value	P interaction
Age			0.16
<65	0.45(0.26,0.79)	0.01
≥65	0.79(0.46,1.37)	0.36
Race
Non-Hispanic White	0.58(0.39,0.87)	0.01	0.51
Non-Hispanic Black	0.61(0.21, 1.81)	0.28
Mexican American	1.04(0.10,10.57)	0.94
Other Hispanic	1.02(0.43,2.43)	0.95
Other Race	0.47(0.12, 1.90)	0.26
Education level
Less than high school	0.67(0.36,1.26)	0.19	0.69
High school	0.87(0.39,1.94)	0.71
More than high school	0.56(0.39,0.83)	0.01
Marital Status			0.76
Married/ living with partner	0.58(0.32,1.03)	0.06
Widowed/Divorced/Separated	0.66(0.44,1.00)	0.05
Never married	1.01(0.27,3.82)	0.98
Family PIR			0.03
<2.1	0.98(0.62,1.55)	0.93
≥ 2.1	0.48(0.29,0.81)	0.01
BMI			0.77
<25	0.63(0.34, 1.19)	0.14
≥25	0.59(0.40,0.88)	0.01
Waist			0.48
<97	0.54(0.30,0.96)	0.04
≥97	0.66(0.42,1.01)	0.06
Smoking behavior			0.90
never	0.59(0.35,1.00)	0.05
former	0.66(0.30, 1.44)	0.26
now	0.54(0.28, 1.06)	0.07
Alcohol consumption			0.48
never	0.72(0.37, 1.39)	0.3
former	0.58(0.30,1.11)	0.09
mild	0.82(0.48, 1.41)	0.44
moderate	0.41(0.19, 0.86)	0.02
heavy	0.46(0.15, 1.47)	0.17
Energy intake			< 0.01
<1700	0.86(0.63,1.18)	0.31
≥1700	0.41(0.24,0.71)	0.01
Hypertension			0.18
yes	0.73(0.55,0.98)	0.04
no	0.51(0.28,0.95)	0.04
Diabetes			0.27
yes	0.84(0.44,1.61)	0.56
no	0.58(0.39,0.86)	0.01

* Model was adjusted for age, race, marital status, education levels, waist, BMI, PIR, smoking status, alcohol consumption level, hypertension, and diabetes, energy intake.

3.5 Analysis of nonlinear relationships

The results of the nonlinear analysis between sex hormone levels and cancer prevalence are shown in Fig. 3. After adjusting for all covariates, we observed a significant nonlinear relationship between estradiol and cancer prevalence (P = 0.03) (Fig. 3.A). And there was a significant linear relationship between testosterone and cancer prevalence (P = 0.27) (Fig. 3.B).

3.5 Analysis of mediating effects

Since no subgroups showed significant interactions in the subgroup analysis of estradiol with cancer prevalence. And in the subgroup analysis of testosterone and cancer prevalence, both PIR and energy showed a significant interaction. Therefore, we further analyzed the mediating role exhibited by both in the middle of T and cancer disease. The results of the neutral linear analysis were shown in Tables 6 and 7. The results showed that PIR, as a continuous variable, had a significant effect on cancer prevalence (OR: 0.91, 95%CI: 0.86-1.00, P = 0.04). When testosterone was used as a categorical variable, testosterone in the fourth quartile was negatively correlated with the level of PIR compared to the first quartile (β: -0.18, 95%CI: -0.36-0.01, P = 0.04). After being divided into four groups according to the mediating variables PIR and Energy levels, the results of the nonlinear relationship analysis were shown in Fig. 4. There was no significant linear relationship between testosterone and cancer prevalence at any of the four levels (P > 0.05).

Table.6 Association between Log-T levels and PIR and Energy.

Variable		Model1		Model2		Model3
Variable		OR(95%CI)	P-value	ORβ(95%CI)	P-value	ORβ(95%CI)	P-value
PIR	Continuous	0.95(0.86,1.04)	0.24	0.91(0.83,1.00)	0.05	0.91(0.83,1.00)	0.04
	Q1	Reference
	Q2	0.86(0.55,1.36)	0.51	0.79(0.49,1.25)	0.29	0.81(0.49,1.33)	0.36
	Q3	0.83(0.51,1.35)	0.44	0.73(0.44,1.20)	0.19	0.75(0.44,1.29)	0.26
	Q4	0.81(0.54,1.21)	0.28	0.68(0.46,1.01)	0.06	0.68(0.44,1.06)	0.08
Log-Energy	Continuous	1.13(0.70,1.85)	0.60	1.09(0.65,1.84)	0.72	1.04(0.61,1.80)	0.87
	Q1	Reference
	Q2	1.20(0.82,1.75)	0.33	1.20(0.82,1.76)	0.33	1.19(0.79,1.79)	0.35
	Q3	1.01(0.65,1.59)	0.95	1.00(0.63,1.59)	0.99	1.00(0.60,1.65)	0.99
	Q4	1.13(0.71,1.81)	0.58	1.10(0.68,1.78)	0.68	1.04(0.62,1.76)	0.85

Table.7 Association between PIR and Energy levels and cancer risk.

Outcomes		Model1		Model2		Model3
Outcomes		β(95%CI)	P-value	β(95%CI)	P-value	β(95%CI)	P-value
PIR	Continuous	0.01(-0.11,0.13)	0.86	-0.02(-0.12, 0.09)	0.77	-0.02(-0.12, 0.07)	0.59
	Q1	Reference
	Q2	0.03(-0.17, 0.23)	0.77	-0.01(-0.22, 0.20)	0.92	-0.01(-0.21, 0.19)	0.90
	Q3	0.12(-0.04, 0.28)	0.15	0.11(-0.07, 0.28)	0.23	0.07(-0.11, 0.26)	0.40
	Q4	-0.18(-0.36,-0.01)	0.04	-0.17(-0.34, 0.00)	0.05	-0.17(-0.35, 0.01)	0.06
Log-Energy	Continuous	-0.01(-0.05, 0.02)	0.41	-0.02(-0.05, 0.02)	0.36	-0.02(-0.06, 0.02)	0.23
	Q1	Reference
	Q2	0.02(-0.03, 0.07)	0.39	0.02(-0.03, 0.06)	0.48	0.01(-0.04, 0.07)	0.55
	Q3	0.04(-0.01, 0.08)	0.09	0.03(-0.01, 0.08)	0.11	0.03(-0.02, 0.08)	0.22
	Q4	-0.02(-0.08, 0.04)	0.51	-0.02(-0.08, 0.04)	0.49	-0.03(-0.10, 0.04)	0.36

The results of the mediation analysis were shown in Fig. 5. After adjusting for all covariates, we did not find a significant mediating effect of the two between testosterone levels and cancer prevalence. Association between testosterone and cancer risk mediated by PIR (IE=-6.46e-04, P = 0.54;DE=-8.18e-02, P < 0.01). Association between testosterone and cancer risk mediated by Energy (IE=-5.45e-04, P = 0.89;DE=-8.08e-02, P < 0.01).

In our study, a correlation was presented between estradiol and testosterone and cancer prevalence. Serum testosterone levels were negatively associated with the prevalence of pan-cancer in women. Serum estradiol levels were negatively associated with the prevalence of breast cancer. In addition, there was a negative correlation between the mixed effects of the two sex hormones and the prevalence of both pan-cancer and breast cancer in women. Among these, testosterone had a more pronounced negative association weight for pan-cancer in women, and estradiol had a more pronounced negative association weight for breast cancer. As the two most important sex hormones in the body, estradiol and testosterone, their potential link to cancer is well worth exploring. Clarifying the association between sex hormones and cancer prevalence and their pathways of action is important for human cancer research.

More and more evidence shows that metabolic disorders are not only an important marker of cancer occurrence, but also a potential mechanism of tumor occurrence ^[30]. Important advances have been made in therapeutic modalities developed for tumor metabolism ^[31]. Among the many metabolic dysregulations, dysregulation of lipid metabolism is one of the most prominent metabolic alterations in carcinogenesis, where the energy generated by lipid metabolism is utilised by tumour cells for proliferation, survival, invasion and other activities ^[32,33]. Lipids play an important role as signalling molecules in many cellular activities and are essential for the regulation of cellular homeostasis ^[34]. Cholesterol is an important lipid analogue and an essential substrate for the synthesis of steroid hormones such as oestradiol and testosterone ^[1,12,34]. Studies have shown that sex hormones can regulate the process of cellular lipid metabolism by controlling the expression of transcribed genes, thus affecting the physiological functions of the organism ^[35]. Estradiol is a sex hormone that is closely linked to the female menstrual cycle, and cyclical changes in estradiol levels in women lead to cyclical changes in the body's metabolic state. When this change in the metabolic state of the body mutates, the metabolic state of the cells in the body also changes. Altered cellular metabolic states are closely related to cellular functions ^[36,37]. When the metabolic state of many cells in the body is altered, the cells become dysfunctional and the body undergoes metabolic dysregulation. This metabolic dysregulation is crucial in the development of cancer.

Lipid metabolism is valuable for the diagnosis, treatment, and prognosis of multiple subtypes of breast cancer, and promotes individualised treatment of breast cancer patients ^[38,39]. A study showed that NDRG 1 modifies the metabolic fate of breast cancer cells by regulating lipid metabolism, thereby modulating the invasiveness of breast cancer cells ^[40]. In a study on retinoic acid receptor responder 2 (RARRES2) and triple-negative breast cancer, RARRES2 was found to regulate the brain metastatic pathway in triple-negative breast cancer by modulating lipid metabolism through the phosphatase and tensin homologue (PTEN)-mammalian target of rapamycin (mTOR)-sterol regulatory element binding protein 1 (SREBP 1) signalling pathway ^[41]. In a review on lipid metabolism and the breast cancer tumour microenvironment, the authors elucidated the pathways by which dysregulated lipid metabolism causes breast carcinogenesis. However, this subtle difference between different subtypes of breast cancer has extremely complex mechanisms ^[42]. Lipid metabolism not only influences the development of breast cancer, but also has an important impact on breast cancer treatment, prognosis, and drug resistance. In recent studies, altered lipid metabolism has been found in the treatment of drug-resistant breast cancer, which may be an effective anticancer therapy targeting altered lipid metabolic status ^[43]. Regulation of lipid metabolism is an important physiological function of estrogen in the body ^[44]. As the menstrual cycle changes in women of childbearing age, so do the levels of sex hormones in the body, which leads to changes in lipid metabolism in the body. However, the occurrence, development, invasion and prognosis of breast cancer are closely related to lipid metabolism. This suggests that an in-depth study of the potential link between oestrogen, lipid metabolism and breast cancer is of great significance.

As another important sex hormone in women, the effect of testosterone on lipid metabolism cannot be ignored. Studies have shown that the effects of testosterone on the cardiovascular system in men are realised through the regulation of lipid metabolism ^[45]. In an animal study on prenatal androgen exposure and ovarian lipid metabolism, it was found that cholesterol metabolism and steroidogenesis were specifically altered in animals with prenatal hyperandrogenic sessions ^[46]. Testosterone levels in women are closely related to their physical functioning. The only indication for testosterone in the treatment of women is low libido accompanied by distress in postmenopausal women ^[19]. Studies have shown that free testosterone levels in women are closely associated with ovarian function and metabolic disorders in women ^[47]. The current clinical use of testosterone in the treatment of invasive breast cancer in women has also achieved good efficacy ^[48]. Testosterone levels in women have also shown significant correlations with depression, cognitive function, and cardiovascular system diseases in women ^[49–51]. In addition, testosterone and estrogen have important effects on glucose metabolism regulation ^[52–55]. Dysregulation of glucose metabolism is another important hallmark of cancer and is closely related to the nasty transformation of cancer cells ^[56,57]. In our study, multifactorial logistic regression analyses revealed that testosterone levels were negatively associated with the prevalence of pan-cancer and oestradiol levels were negatively associated with the prevalence of breast cancer in women. The potential relationship behind this correlation needs to be explored by further experiments, which is of great significance for the discovery of therapeutic targets and the investigation of disease mechanisms.

In this cross-sectional study of a nationally representative sample from the United States, there was a negative correlation between testosterone levels and the prevalence of pan-cancer in women, and a negative correlation between oestradiol levels and the prevalence of breast cancer. In addition, analyses of sex hormone mixtures had shown a negative correlation between the mixtures and the prevalence of pan-cancer and breast cancer in women. The findings of this study have important implications for the prevention and treatment of pan-cancer in women, especially for female breast cancer. By gaining a deeper understanding of the relationship between sex hormones and female cancers, public health and clinical staff may be able to designate more effective female cancer prevention measures and treatment protocols. Ultimately, it addresses the needs of women with cancer and improves women's physical and mental health.

Conflicts of interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Author Contributions

Feng Zhao:Writing-Original Draft; Hao Sun: Methodology, Data Analysis; All authors contributed to the article and approved the submitted version. The authors declare no conflict of interest.

Acknowledgments

We thank all the participants and staff of NHANES 2007-2020 for their valuable contributions.

Compliance with ethical standards

The NHANES survey was approved by the National Center for Health Statistics Institutional Review Board. The study reported in this manuscript was exempt from ethical committee approval because it was based on publicly available data.

No funding is available for this study. The data sets used in the study were all from National Health and Nutrition Examination Survey (NHANES) datebase.Not applicable.

DATA AVAILABILITY

The datasets generated and/or analysed during the current study are available in the National Health and Nutrition Examination Survey (NHANES) repository. https://www.cdc.gov/nchs/nhanes/index.htm.

Ramesh, S. S.; Christopher, R.; Indira Devi, B.; Bhat, D. I., The vascular protective role of oestradiol: a focus on postmenopausal oestradiol deficiency and aneurysmal subarachnoid haemorrhage. Biological Reviews 2019, 94 (6), 1897-1917.
Ceccarelli, I.; Bioletti, L.; Peparini, S.; Solomita, E.; Ricci, C.; Casini, I.; Miceli, E.; Aloisi, A. M., Estrogens and phytoestrogens in body functions. Neuroscience & Biobehavioral Reviews 2022, 132, 648-663.
Waifalkar, P. P.; Noh, D.; Derashri, P.; Barage, S.; Oh, E., Role of Estradiol Hormone in Human Life and Electrochemical Aptasensing of 17β-Estradiol: A Review. Biosensors 2022, 12 (12).
Hsu, L.-H.; Chu, N.-M.; Kao, S.-H., Estrogen, Estrogen Receptor and Lung Cancer. International Journal of Molecular Sciences 2017, 18 (8).
Hima, S.; Sreeja, S., Modulatory role of 17β‐estradiol in the tumor microenvironment of thyroid cancer. IUBMB Life 2015, 68 (2), 85-96.
Solar Fernandez, V.; Cipolletti, M.; Ascenzi, P.; Marino, M.; Fiocchetti, M., Neuroglobin As Key Mediator in the 17β-Estradiol-Induced Antioxidant Cell Response to Oxidative Stress. Antioxidants & Redox Signaling 2020, 32 (4), 217-227.
Gorska, M.; Kuban-Jankowska, A.; Zmijewski, M.; Gorzynik, M.; Szkatula, M.; Wozniak, M., Neuronal Nitric Oxide Synthase Induction in the Antitumorigenic and Neurotoxic Effects of 2-Methoxyestradiol. Molecules 2014, 19 (9), 13267-13281.
Vigil, P.; Meléndez, J.; Petkovic, G.; Del Río, J. P., The importance of estradiol for body weight regulation in women. Frontiers in Endocrinology 2022, 13.
Rieder, J. K.; Darabos, K.; Weierich, M. R., Estradiol and Women’s Health: Considering the Role of Estradiol as a Marker in Behavioral Medicine. International Journal of Behavioral Medicine 2020, 27 (3), 294-304.
Appiah, D.; Luitel, S.; Nwabuo, C. C.; Ebong, I.; Winters, S. J., Low endogenous estradiol levels are associated with elevated risk of cardiovascular disease mortality in young and middle-aged men in the United States. Atherosclerosis 2022, 361, 34-40.
Chang, J.; Wu, Y.; Zhou, S.; Tian, Y.; Wang, Y.; Tian, J.; Song, W.; Dong, Y.; Li, J.; Zhao, Z.; Che, G., Genetically predicted testosterone and cancers risk in men: a two-sample Mendelian randomization study. Journal of Translational Medicine 2022, 20 (1).
Stephens-Shields, A. J.; Snyder, P. J.; Ellenberg, S. S.; Taylor, L.; Bhasin, S., Relation of Testosterone, Dihydrotestosterone, and Estradiol With Changes in Outcomes Measures in the Testosterone Trials. The Journal of Clinical Endocrinology & Metabolism 2022, 107 (5), 1257-1269.
Ørsted, D. D.; Nordestgaard, B. G.; Bojesen, S. E., Plasma testosterone in the general population, cancer prognosis and cancer risk: a prospective cohort study. Annals of Oncology 2014, 25 (3), 712-718.
Watts, E. L.; Perez‐Cornago, A.; Knuppel, A.; Tsilidis, K. K.; Key, T. J.; Travis, R. C., Prospective analyses of testosterone and sex hormone‐binding globulin with the risk of 19 types of cancer in men and postmenopausal women in UK Biobank. International Journal of Cancer 2021, 149 (3), 573-584.
Yeap, B. B.; Marriott, R. J.; Antonio, L.; Chan, Y. X.; Raj, S.; Dwivedi, G.; Reid, C. M.; Anawalt, B. D.; Bhasin, S.; Dobs, A. S.; Hankey, G. J.; Matsumoto, A. M.; Norman, P. E.; O’Neill, T. W.; Ohlsson, C.; Orwoll, E. S.; Vanderschueren, D.; Wittert, G. A.; Wu, F. C. W.; Murray, K., Serum Testosterone is Inversely and Sex Hormone-binding Globulin is Directly Associated with All-cause Mortality in Men. The Journal of Clinical Endocrinology & Metabolism 2021, 106 (2), e625-e637.
Eisenberg, M. L.; Li, S.; Betts, P.; Herder, D.; Lamb, D. J.; Lipshultz, L. I., Testosterone therapy and cancer risk. BJU International 2014, 115 (2), 317-321.
Ahmad, A.; Walsh, T. J.; Shores, M. M.; Krakauer, C. A.; Forsberg, C. W.; Fox, A. E.; Moore, K. P.; Korpak, A.; Heckbert, S. R.; Zeliadt, S. B.; Kinsey, C. E.; Thompson, M. L.; Smith, N. L.; Matsumoto, A. M., Testosterone treatment and the risk of aggressive prostate cancer in men with low testosterone levels. Plos One 2018, 13 (6).
Rodrigues dos Santos, M.; Bhasin, S., Benefits and Risks of Testosterone Treatment in Men with Age-Related Decline in Testosterone. Annual Review of Medicine 2021, 72 (1), 75-91.
Martínez-García, A.; Davis, S. R., Testosterone use in postmenopausal women. Climacteric 2020, 24 (1), 46-50.
David J, H.;MB, B.; FRACP.; FAHMS., Testosterone and Male Aging Faltering Hope for Rejuvenation. JAMA February 21, 2017 Volume 317, Number 7 699.
Butler, E. N.; Zhou, C. K.; Curry, M.; McMenamin, Ú.; Cardwell, C.; Bradley, M. C.; Graubard, B. I.; Cook, M. B., Testosterone therapy and cancer risks among men in the SEER-Medicare linked database. British Journal of Cancer 2022, 128 (1), 48-56.
Ide, H., The impact of testosterone in men’s health. Endocrine Journal 2023, 70 (7), 655-662.
Yang, P.; Feng, X.; Li, J.; Zhang, T.; Sheng, C.; Zhang, L.; Hua, J.; Wei, W.; Ding, N.; He, J.; Zhang, Y.; Wang, J.; Zhou, H., Ionizing radiation downregulates estradiol synthesis via endoplasmic reticulum stress and inhibits the proliferation of estrogen receptor-positive breast cancer cells. Cell Death & Disease 2021, 12 (11).
Harbeck, N.; Gnant, M., Breast cancer. The Lancet 2017, 389 (10074), 1134-1150.
Olver, I. N., Prevention of breast cancer. Medical Journal of Australia 2016, 205 (10), 475-479.
Pacheco-Velázquez, S. C.; Ortega-Mejía, I. I.; Vargas-Navarro, J. L.; Padilla-Flores, J. A.; Robledo-Cadena, D. X.; Tapia-Martínez, G.; Peñalosa-Castro, I.; Aguilar-Ponce, J. L.; Granados-Rivas, J. C.; Moreno-Sánchez, R.; Rodríguez-Enríquez, S., 17-β Estradiol up-regulates energy metabolic pathways, cellular proliferation and tumor invasiveness in ER+ breast cancer spheroids. Frontiers in Oncology 2022, 12.
KAREN, L.; MAUGHAN.; MARK, A. L.; PETER, S.H., Treatment of Breast Cancer. American Family Physician June 1, 2010, Volume 81, Number 11.
Vazquez Rodriguez, G.; Abrahamsson, A.; Jensen, L. D. E.; Dabrosin, C., Estradiol Promotes Breast Cancer Cell Migration via Recruitment and Activation of Neutrophils. Cancer Immunology Research 2017, 5 (3), 234-247.
Rainey, L.; Eriksson, M.; Trinh, T.; Czene, K.; Broeders, M. J. M.; van der Waal, D.; Hall, P., The impact of alcohol consumption and physical activity on breast cancer: The role of breast cancer risk. International Journal of Cancer 2020, 147 (4), 931-939.
Gyamfi, J.; Kim, J.; Choi, J., Cancer as a Metabolic Disorder. Int J Mol Sci 2022, 23 (3).
Luengo, A.; Gui, D. Y.; Vander Heiden, M. G., Targeting Metabolism for Cancer Therapy. Cell Chemical Biology 2017, 24 (9), 1161-1180.
Wang, W.; Bai, L.; Li, W.; Cui, J., The Lipid Metabolic Landscape of Cancers and New Therapeutic Perspectives. Frontiers in Oncology 2020, 10.
Liu, H.; Zhang, Z.; Song, L.; Gao, J.; Liu, Y., Lipid metabolism of cancer stem cells (Review). Oncology Letters 2022, 23 (4).
Bian, X.; Liu, R.; Meng, Y.; Xing, D.; Xu, D.; Lu, Z., Lipid metabolism and cancer. J Exp Med 2021, 218 (1).
Raut, S.; Kumar, A. V.; Deshpande, S.; Khambata, K.; Balasinor, N. H., Sex hormones regulate lipid metabolism in adult Sertoli cells: A genome-wide study of estrogen and androgen receptor binding sites. The Journal of Steroid Biochemistry and Molecular Biology 2021, 211.
Giuliani, C., The Flavonoid Quercetin Induces AP-1 Activation in FRTL-5 Thyroid Cells. Antioxidants (Basel) 2019, 8 (5).
Ahmed, D.; Al-Daraawi, M.; Cassol, E., Innate sensing and cellular metabolism: role in fine tuning antiviral immune responses. Journal of Leukocyte Biology 2023, 113 (2), 164-190.
Shen, L.; Huang, H.; Li, J.; Chen, W.; Yao, Y.; Hu, J.; Zhou, J.; Huang, F.; Ni, C., Exploration of prognosis and immunometabolism landscapes in ER+ breast cancer based on a novel lipid metabolism-related signature. Frontiers in Immunology 2023, 14.
Chang, X.; Xing, P., Identification of a novel lipid metabolism-related gene signature within the tumour immune microenvironment for breast cancer. Lipids in Health and Disease 2022, 21 (1).
Sevinsky, C. J.; Khan, F.; Kokabee, L.; Darehshouri, A.; Maddipati, K. R.; Conklin, D. S., NDRG1 regulates neutral lipid metabolism in breast cancer cells. Breast Cancer Research 2018, 20 (1).
Shubhra, Q. T. H., RARRES2’s impact on lipid metabolism in triple-negative breast cancer: a pathway to brain metastasis. Military Medical Research 2023, 10 (1).
Zipinotti dos Santos, D.; de Souza, J. C.; Pimenta, T. M.; da Silva Martins, B.; Junior, R. S. R.; Butzene, S. M. S.; Tessarolo, N. G.; Cilas, P. M. L.; Silva, I. V.; Rangel, L. B. A., The impact of lipid metabolism on breast cancer: a review about its role in tumorigenesis and immune escape. Cell Communication and Signaling 2023, 21 (1).
Xiao, Q.; Xia, M.; Tang, W.; Zhao, H.; Chen, Y.; Zhong, J., The lipid metabolism remodeling: A hurdle in breast cancer therapy. Cancer Letters 2023.
Al-Shami, K.; Awadi, S.; Khamees, A. a.; Alsheikh, A. M.; Al-Sharif, S.; Ala’ Bereshy, R.; Al-Eitan, S. F.; Banikhaled, S. H.; Al-Qudimat, A. R.; Al-Zoubi, R. M.; Al Zoubi, M. S., Estrogens and the risk of breast cancer: A narrative review of literature. Heliyon 2023, 9 (9).
Khripun, S.; Vorobyev, S.; Khripun, M.; Kogan, A.; Demidova, I., Effect of testosterone on lipid metabolism and endothelium in men with type 2 diabetes mellitus and hypogonadism. ESC Congress 2020 – The Digital Experience 29 August – 1 September 2020.
Abruzzese, G. A.; Heber, M. F.; Ferreira, S. R.; Ferrer, M. J.; Motta, A. B., Prenatal androgen exposure affects ovarian lipid metabolism and steroid biosynthesis in rats. Journal of Endocrinology 2020, 247 (3), 239-250.
Antonio, L.; Pauwels, S.; Laurent, M. R.; Vanschoubroeck, D.; Jans, I.; Billen, J.; Claessens, F.; Decallonne, B.; De Neubourg, D.; Vermeersch, P.; Vanderschueren, D., Free Testosterone Reflects Metabolic as well as Ovarian Disturbances in Subfertile Oligomenorrheic Women. International Journal of Endocrinology 2018, 2018, 1-8.
Donovitz, G. S., A Personal Prospective on Testosterone Therapy in Women—What We Know in 2022. Journal of Personalized Medicine 2022, 12 (8).
Giuliani, C., The Flavonoid Quercetin Induces AP-1 Activation in FRTL-5 Thyroid Cells. Antioxidants (Basel) 2019, 8 (5).
Szadkowska, I.; Guligowska, A.; Jegier, A.; Pawlikowski, M.; Pisarek, H.; Winczyk, K.; Kostka, T., Serum testosterone level correlates with left ventricular hypertrophy in older women. Frontiers in Endocrinology 2023, 13.
Sukhapure, M.; Eggleston, K.; Douglas, K.; Fenton, A.; Frampton, C.; Porter, R. J., Free testosterone is related to aspects of cognitive function in women with and without polycystic ovary syndrome. Archives of Women's Mental Health 2021, 25 (1), 87-94.
Leutner, M.; Matzhold, C.; Bellach, L.; Wohlschläger-Krenn, E.; Winker, R.; Nistler, S.; Endler, G.; Thurner, S.; Klimek, P.; Kautzky-Willer, A., Increase in testosterone levels is related to a lower risk of conversion of prediabetes to manifest diabetes in prediabetic males. Wiener klinische Wochenschrift 2021, 134 (1-2), 1-6.
Jerald, B., The many faces of testosterone. Clinical Interventions in Aging 2007:2(4) 567–576.
Maria, A., Estrogens and the regulation of glucose metabolism. World J Diabetes 2021; 12(10): 1622-1654.
Luo, M.; Zeng, Q.; Jiang, K.; Zhao, Y.; Long, Z.; Du, Y.; Wang, K.; He, G., Estrogen deficiency exacerbates learning and memory deficits associated with glucose metabolism disorder in APP/PS1 double transgenic female mice. Genes & Diseases 2022, 9 (5), 1315-1331.
Peng, Y.; Yang, H.; Li, S., The role of glycometabolic plasticity in cancer. Pathology - Research and Practice 2021, 226.
Xin, X.; Cheng, C.; Bei-yu, C.; Hong-shan, L.; Hua-jie, T.; Xin, W.; Zi-ming, A.; Qin-mei, S.; Yi-yang, H.; Qin, F., Caffeine and EGCG Alleviate High-Trans Fatty Acid and High-Carbohydrate Diet-Induced NASH in Mice: Commonality and Specificity. Frontiers in Nutrition 2021, 8.

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Association Between Sex Hormones Levels and Cancer Prevalence in Women：A Study On NHANES 2007-2020

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Abstract

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1. Introduction

2. Methods

2.1 Study population

2.2 Socio-demographic characteristics, lifestyles and long-term conditions

2.3 Statistical analysis

3. Results

3.1 Baseline characteristics

3.2 Relationship between sex hormone levels and cancer prevalence

3.3 Segmented regression

3.4 Subgroup analysis

3.5 Analysis of nonlinear relationships

3.5 Analysis of mediating effects

4 Discussion

5 Conclusion

Declarations

References

Additional Declarations

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