Abstract
Objective
We aim to evaluate the association of composite dietary antioxidant index (CDAI) and Benign Prostatic Hyperplasia (BPH) in a large population in the United States using a cross-sectional design.
Methods
Data was retrieved from the National Health and Nutrition Examination Survey (NHANES) 2003–2008 and 2013–2020 datasets. Univariate and multivariate logistic regression were performed to explore the association between CDAI and BPH. The restricted cubic spline (RCS) model was also conducted to investigate the potential linear relationship. Sub-group analyses were also conducted.
Results
Totally, this study included 13,419 participants with 1085 BPH patients among them. The higher continuous CDAI value was significantly related to higher BPH risk (OR = 1.05, 95%CI: 1.02, 1.08). Besides, individuals with the highest quartile (Q4) CDAI possessed 1.87 times risk of BPH than the lowest quartile (Q1, OR = 1.87, 95%CI: 1.41, 2.50). The RCS curve also showed a positive linear dose-response relationship between CDAI and BPH (cut-off = -0.64). The P for interaction in any subgroup was > 0.05, indicating that the main outcome was not affected by other covariates. The limitation of this study was the lack of data on the relationship between CDAI and the severity of BPH symptoms.
Conclusions
This study reveals that an elevated CDAI may be associated with a linear higher risk of BPH. We do not recommend intentional or excessive antioxidant diet to prevent BPH based on the current results.
Keywords: CDAI, Antioxidant, BPH, Risk, Dose-response
Introduction
Benign prostatic hyperplasia (BPH) is an age-related disease bothering middle-aged and elderly men worldwide. BPH is usually under-diagnosed with a micronodular hyperplasia at first, however, when macroscopic nodular enlargement occurs, patients will suffer from lower urinary tract symptoms (LUTS) and may eventually have serious complications such as urinary retention or renal failure [1]. Although the underlying mechanisms of BPH is still not well-acknowledged, several studies over the past three decades have provided some insights on the etiology of BPH, such as aging, obesity, environmental and oxidative stress, systematic inflammation, elevated androgen, and multiple growth factors [2]. Among these risk factors, diet may play a crucial role in the occurrence and progression of BPH. As reviewed by Giorgio et al., a Mediterranean diet containing mainly vegetables, fruits, nuts, fish and lower red meat intake, may have a beneficial role on BPH symptoms [3]. Generally, we deem that a Western diet will perhaps lead to BPH, however, an epidemiology study reported that regular alcohol consumption may reduce the risk of symptomatic BPH [4]. An animal study reported that selenium could provide a beneficial and protective therapeutic option against oxidative stress in BPH model [5]. Observational studies on the association of a single dietary factor with BPH can not avoid the weakness of unobserved confounding, low causality and bias like misclassifcation. Moreover, several antioxidants, including curcumin, vitamins (C, E, D) and multivitamins, phytoestrogens, saw palmetto, cranberries and walnuts have been investigated in controlled trials in human prostate health [6]. However, the evidence appears inadequate to recommend their daily dietary use. Thus, we aimed to investigate the value of an aggregative dietary indicator in BPH etiology.
Composite Dietary Antioxidant Index (CDAI) is a quantitative tool for assessing diet antioxidant potential based on a combination of dietary zinc, selenium, vitamin A, vitamin C, vitamin E, carotenoid [7]. By employing the CDAI, we can investigate the cumulative effect of antioxidant in several physiological and pathological situations more comprehensively than a specific dietary antioxidant [8]. Recently, it was reported that a higher CDAI (higher antioxidant potential) may be beneficial for reducing the risk of colorectal cancer and lung cancer in the general population but has no significant relation with the risk of cervical cancer [9–11]. Whereas other studies focused on the CDAI and mortality. The results also remain inconsistent: one indicated that the CDAI was not associated with the risk of cardiovascular death [12], however the other pointed out that a high CDAI was linked to decreased all-cause and cardiovascular mortality [13]. Up to now, no published articles have reported the relationship between the CDAI and the risk of BPH.
Despite limited previous studies in this field of research demonstrated that a single antioxidant like higher selenium or zinc intake was beneficial to BPH, the size of participants was relatively low [14, 15]. What’s more, there is a lack of evidence on totally dietary antioxidant potential and the risk of BPH [16]. In this scenario, we aim to evaluate the association of combined dietary antioxidant potential and the risk of BPH in a large population in the United States, by uni-variate/multi-variate and different subgroup analyses via a huge cross-sectional database.
Methods
Study population
The current study is retrospective and the design of this study is cross-sectional.
The National Center for Health Statics (NCHS) carried out an open-access, nationally representative survey in the United States, which was known as the National Health and Nutrition Examination Survey (NHANES, www.cdc.gov/nchs/nhanes/). The NCHS ethics review board have given permission to the questionnaires/laboratory tests in each study protocol and informed consents from all the participants were obtained prior to the interview.
Initially, 75,579 participants were screened in the NHANES 2003–2008 and 2013–2020 datasets. The datasets were directly downloaded from the aforementioned NHANES website. Finally, this study included 13,419 adult men for data analysis. The exclusion criteria were: (1). female (n = 38260); (2). Age < 18 years old (n = 15519); (3). CDAI data not available (n = 5423) (4). Missing or N/A of BPH data (n = 2958), as shown in Fig. 1.
Fig. 1.
Flaw chart of the participant collection from the NHANES 2003–2008 and 2013–2020. NHANES National Health and Nutrition Examination Survey
This study is a secondary analysis of public available datasets without personally identifiable information, so the Institutional Review Board is not applicable.
Assessment of BPH and CDAI
The outcome of this study is the diagnosis of BPH. BPH was defined in the International Classification of Disease, Tenth Revision, Clinical Modification (ICD-10-CM) in the NHANES 2013–2020 datasets. The survey participants filled in the ICD-10-CM code N40 were diagnosed as BPH. Whereas in the NHANES 2003–2008 datasets, BPH was told to the participants by doctors as a benign enlargement in the interview question KIQ140. We found the missing data of BPH in the NHANES 2009–2012 datasets. Thus we can not investigate the uninterrupted data from 2003 to 2020.
CDAI was calculated by a well-established method containing six food-sourced antioxidants (zinc, selenium, vitamin A, vitamin C, vitamin E and carotenoids) derived from nonconsecutive two-day 24-hour dietary recall. The NHANES interviewers from Mobile Examination Center carried out the survey on the first day and re-interviewed on telephone after a week. To ensure the stability of results, only the participants with two-day record of CDAI were enrolled. The final analytic CDAI value was the average of the two-day CDAI value. Under the guidance of US Department of Agriculture, the dosage, frequency and duration of antioxidants intake was determined based on the questionnaire interview. Briefly, CDAI was the summation of each antioxidant intake minus sex-specific mean value which was divided by standard deviation. CDAI=∑ (each intake - mean value) / SD (n = 6). Afterwards, the CDAI value was quartered into 4 parts, from the lowest (quartile 1, Q1) to the highest (Q4).
Assessment of covariates
Demographic covariates including age (years), ethnicity (Mexican American/ Non-Hispanic White/ Non-Hispanic Black/ other Hispanic/ other race - including Multi-Racial), education (less than high school/ high school or equivalent/ more than high school), ratio of family income to poverty (PIR) were extracted or calculated from self-reported questionnaires. Individual behavioral covariates contained body mass index (BMI: weight in kilograms divided by height in meters squared), smoking status (never/ former/ current), alcohol user (never/ former/ mild/ heavy). Past medical history covariates included whether the participants had hypertension (systolic blood pressure > 140mmHg or diastolic blood pressure > 90mmHg), diabetes mellitus (DM) or cardiovascular disease (CVD) history. The details concerned were demonstrated in the www.cdc.gov/nchs/nhanes/.
Statistical analysis
Weighted analyses and stratification were performed according to the guideline of NCHS (https://wwwn.cdc.gov/nchs/nhanes/tutorials/). The baseline clinical characteristics were acquired at first. Continuous variables (age, BMI, CDAI) were described as weighted means and standard deviation (SD), while categorical variables (ethnicity, education, PIR, smoke, alcohol user, hypertension, DM, CVD) were expressed as proportions [17]. We utilized t-test to compare continuous variables in different groups and applied chi-square test to compare categorical variables among groups.
Subsequently, we explore the relationship between CDAI and BPH by univariate and multivariate logistic regression. The odds ratios (ORs) and 95% confidence interval (CI) were calculated as effect index. By adjusting confounding factors in 3 different models, we can reach more reliable conclusions in the multivariate logistic regression. In addition, we fully adjusted the most comprehensive covariates in the model 3. When the CDAI value was divided into quartiles, the lowest quartile (Q1) was treated as reference. Fully-adjusted subgroup analyses were also performed to find potential bias. A P for interaction > 0.05 in the subgroup analysis indicated conformity in this subgroup.
After judging a linear relationship was suitable, we carried out the restricted cubic spline (RCS) curve with three knots (5th, 50th and 75th), the dose-response relationship between CDAI and BPH was assessed eventually. We selected the median CDAI value (-0.64) as the cut-off value.
The R software (Version 4.3.1) was used to conduct all the statistical analyses mentioned above. A two-sided P value < 0.05 meant statistically significant.
Results
Baseline characteristics of the participants
Table 1 lists the demographic, behavioral and past medical history factors of the.
Table 1.
Baseline characteristics of 13,419 participants from the NAHENS 2003–2008 and 2013–2020
| variable | total | Q1 | Q2 | Q3 | Q4 | P value |
|---|---|---|---|---|---|---|
| n = 3357 | n = 3353 | n = 3354 | n = 3355 | |||
| age | 49.06(0.29) | 48.73(0.53) | 50.28(0.40) | 49.45(0.55) | 47.89(0.44) | < 0.0001 |
| poverty | 3.20(0.04) | 2.75(0.06) | 3.13(0.05) | 3.36(0.04) | 3.42(0.06) | < 0.0001 |
| BMI | 29.15(0.11) | 29.24(0.14) | 29.44(0.18) | 29.23(0.18) | 28.77(0.16) | 0.01 |
| ethnicity | < 0.0001 | |||||
| Mexican American | 1870( 8.39) | 466(8.74) | 472(8.26) | 481(8.52) | 451(8.12) | |
| Non-Hispanic White | 5849(67.47) | 1259(61.79) | 1501(66.83) | 1555(70.04) | 1534(69.82) | |
| Non-Hispanic Black | 2993(10.46) | 993(15.76) | 709(10.84) | 675( 8.60) | 616( 7.94) | |
| Other Hispanic | 1117( 5.46) | 312(5.84) | 281(5.29) | 258(5.18) | 266(5.59) | |
| Other Race - Including Multi-Racial | 1590( 8.22) | 327(7.87) | 390(8.77) | 385(7.66) | 488(8.54) | |
| edu | < 0.0001 | |||||
| less than high school | 2962(13.66) | 1087(21.73) | 752(13.59) | 630(12.68) | 493( 9.02) | |
| high school | 3283(25.28) | 898(30.95) | 850(27.85) | 770(23.13) | 765(21.55) | |
| more than high school | 6991(60.38) | 1312(47.32) | 1704(58.57) | 1912(64.19) | 2063(69.43) | |
| alcohol user | < 0.0001 | |||||
| never | 1087( 7.25) | 324(9.28) | 274(7.10) | 254(8.94) | 235(6.36) | |
| former | 1782(10.86) | 544(14.48) | 452(11.80) | 441(12.05) | 345( 9.37) | |
| mild | 5278(41.99) | 1127(37.66) | 1324(46.43) | 1428(45.89) | 1399(49.55) | |
| heavy | 3947(32.46) | 1012(38.58) | 956(34.66) | 920(33.12) | 1059(34.72) | |
| CVD | 0.003 | |||||
| no | 11,378(88.46) | 2740(86.69) | 2812(87.34) | 2845(88.65) | 2981(90.57) | |
| yes | 2041(11.54) | 617(13.31) | 541(12.66) | 509(11.35) | 374( 9.43) | |
| smoke | < 0.0001 | |||||
| never | 6212(48.63) | 1373(43.58) | 1527(46.36) | 1599(50.42) | 1713(52.69) | |
| former | 4358(31.70) | 1072(29.75) | 1156(32.91) | 1061(30.99) | 1069(32.83) | |
| current | 2840(19.64) | 909(26.67) | 666(20.74) | 693(18.59) | 572(14.48) | |
| hypertension | < 0.001 | |||||
| no | 7344(59.16) | 1736(57.62) | 1776(56.32) | 1836(58.35) | 1996(63.52) | |
| yes | 6075(40.84) | 1621(42.38) | 1577(43.68) | 1518(41.65) | 1359(36.48) | |
| DM | < 0.0001 | |||||
| no | 10,550(84.04) | 2542(83.22) | 2581(81.20) | 2643(83.94) | 2784(87.21) | |
| yes | 2869(15.96) | 815(16.78) | 772(18.80) | 711(16.06) | 571(12.79) | |
| BPH | 0.04 | |||||
| no | 12,334(93.27) | 3105(94.87) | 3053(92.54) | 3076(93.45) | 3100(92.51) | |
| yes | 1085( 6.73) | 252(5.13) | 300(7.46) | 278(6.55) | 255(7.49) |
BMI: body mass index; BPH: benign prostatic hyperplasia; CVD: cardiovascular disease; DM: diabetes mellitus
The values are means and standard deviation (SD); number and proportion
The P values were calculated from the chi-square test to test the difference among CDAI Q1-Q4
total 13419 participants screened from the NHANES 2003–2008 and 2013–2020 datasets by quartile of the CDAI. Among them, there were 1085 participants diagnosed with BPH. The range and median of CDAI value from the lowest to the highest was Q1: [-8.344,-2.716] -3.911, Q2: (-2.716,-0.712] -1.706, Q3: (-0.712,1.78] 0.382, Q4 (1.78,48.46] 4.028, respectively. As shown in Table 1, we can express that the including variates have significant differences between different groups (P < 0.05). Generally, non-hispanic white participants and men with over high school education level were in the majority. Participants who have a lower dietary potential (lower CDAI value) are more likely to suffer from hypertension, DM and CVD.
Association of BPH and CDAI
Table 2 shows the results of univariate logistic regression analyses of each variate between BPH and non-BPH. According to the non-adjusted model, older/ higher income/ higher BMI participants may probably be attacked by BPH (P < 0.05). Diabetes, hypertension and CVD were also associated with the risk of BPH (P < 0.0001). Former smoker and former/heavy alcohol user are expected to sick with BPH (P < 0.001).
Table 2.
Univariate logistic regression analyses between the covariates and BPH
| character | OR (95%CI) | P value |
|---|---|---|
| age | 1.08(1.08,1.09) | < 0.0001 |
| poverty | 1.11(1.04,1.19) | 0.002 |
| BMI | 1.01(1.00,1.03) | 0.04 |
| ethnicity | ||
| Mexican American | 1 (reference) | |
| Non-Hispanic White | 2.41(1.70,3.42) | < 0.0001 |
| Non-Hispanic Black | 1.11(0.76,1.63) | 0.59 |
| Other Hispanic | 1.07(0.70,1.63) | 0.76 |
| Other Race - Including Multi-Racial | 0.85(0.48,1.49) | 0.56 |
| edu | ||
| less than high school | 1 (reference) | |
| high school | 1.07(0.77,1.47) | 0.69 |
| more than high school | 1.14(0.84,1.56) | 0.39 |
| alcohol.user | ||
| never | 1 (reference) | |
| former | 2.55(1.66,3.92) | < 0.0001 |
| mild | 1.43(0.90,2.30) | 0.13 |
| heavy | 0.44(0.26,0.72) | 0.001 |
| CVD | ||
| no | 1 (reference) | |
| yes | 3.24(2.55,4.12) | < 0.0001 |
| smoke | ||
| never | 1 (reference) | |
| former | 2.30(1.87,2.83) | < 0.0001 |
| current | 0.58(0.39, 0.86) | 0.01 |
| hypertension | ||
| no | 1 (reference) | |
| yes | 2.53(2.05,3.13) | < 0.0001 |
| DM | ||
| no | 1 (reference) | |
| yes | 2.16(1.71,2.73) | < 0.0001 |
OR: odds ratio; BMI: body mass index; BPH: benign prostatic hyperplasia; CVD: cardiovascular disease; DM: diabetes mellitus
After adjusted by some variates, CDAI was regarded as a continuous variable as well as a categorical variable in the multi-variate logistic regression analyses. Elevated CDAI value could increase the risk of BPH in the model adjusted by age, ethnicity and education (Table 3, OR: 1.04 95%CI: 1.01, 1.07; P = 0.003) and the fully adjusted model (Model 3, OR: 1.05; 95%CI: 1.02, 1.08; P = 0.002). Furthermore, when the CDAI was considered as a categorical variable, we reached the results that participants with the highest CDAI (Q4) possessed lower incidence of BPH compared with the lowest CDAI quartile (Q1) in the crude model (OR: 1.50 95%CI: 1.13, 1.99; P = 0.01), the model adjusted by age, ethnicity and education (Model 2, OR: 1.84 95%CI: 1.38, 2.45; P < 0.001) and the model adjusted by all covariates (Model 3, OR: 1.87; 95%CI: 1.41, 2.50; P < 0.001).
Table 3.
Univariate and multivariate logistic regression analyses between the CDAI and BPH
| Model 1 | Model 2 | Model 3 | ||||
|---|---|---|---|---|---|---|
| OR (95%CI) | P value | OR (95%CI) | P value | OR (95%CI) | P value | |
| CDAI continuous | 1.02(0.99,1.04) | 0.17 | 1.04(1.01,1.07) | 0.003 | 1.05(1.02,1.08) | 0.002 |
| CDAI quartile | ||||||
| Q1 | 1 (reference) | 1 (reference) | 1 (reference) | |||
| Q2 | 1.49(1.11,2.00) | 0.01 | 1.48(1.10,1.98) | 0.01 | 1.49(1.09,2.06) | 0.01 |
| Q3 | 1.29(0.96,1.74) | 0.09 | 1.35(1.01,1.81) | 0.04 | 1.36(1.00,1.86) | 0.05 |
| Q4 | 1.50(1.13,1.99) | 0.01 | 1.84(1.38,2.45) | < 0.0001 | 1.87(1.41,2.50) | < 0.0001 |
| 0.03 | < 0.001 | < 0.001 |
model1 was adjusted by none
model2 was adjusted by ‘age’,‘ethnicity’,‘edu’
model3 was adjusted by ‘age’, ‘ethnicity’, ‘edu’, ‘BMI’, ‘poverty’, ‘alcohol user’, ‘smoke’, ‘hyper
tension’, ‘DM’ and ‘CVD’
Dose-response linear relationship analysis
Three dose knots at 5% (dose1), 50% (dose2) and 75% (dose3) in the dose range were generated before dose-response evaluation via the restricted cubic spline method [18]. Then we calculated whether there was a linear or non-linear relationship between CDAI and BPH. The P_nonlinear = 0.1933, therefore, we chose a linear model. As shown in the dose-response RCS plot (Fig. 2), the risk of BPH keep increasing after the CDAI increasing (cut-off value = median − 0.64).
Fig. 2.
Linear restricted cubic spline (RCS) plot between CDAI and BPH. Cut-off value of CDAI = -0.64. CDAI: combined dietary antioxidant index; OR: odds ratio. The red line indicates the dose-response relationship between CDAI and BPH and the pink area represents the 95% confidence interval
Subgroup analyses
The results of subgroup analyses were shown in Table 4. For the subgroup stratified by age, a significant positive correction between increased CDAI and BPH was detected in participants ≥ 60 years old (OR: 1.042; 95%CI: 1.004, 1.080; P = 0.029), but not in the group less than 60 years old (OR: 1.051; 95%CI: 1.000, 1.104; P = 0.051). As for the subgroup stratified by smoke or alcohol, significant relationship was found in former smoke, as well as in the mild alcohol user (both P < 0.05). As for the subgroup stratified by hypertension, significant relationship was found in both groups (OR: 1.048; 95%CI: 1.002, 1.095; P = 0.040; OR: 1.042; 95%CI: 1.004, 1.082; P = 0.030, respectively). Besides, the association between the risk of BPH and CDAI was not statistically significant in some categories including individuals with diabetes or CVD (P > 0.05). In addition, we carried out the interaction analysis between CDAI and above subgroup variables. The results revealed that any subgroup may not affect the relationship between CDAI and incidence of BPH (any P for the interaction > 0.05).
Table 4.
Subgroup analyses between the association of CDAI and BPH
| Subgroups | OR (95%CI) | P value | P for interaction |
|---|---|---|---|
| Age | 0.783 | ||
| < 60 | 1.051(1.000,1.104) | 0.051 | |
| >=60 | 1.042(1.004,1.080) | 0.029 | |
| alcohol user | 0.086 | ||
| never | 1.066(0.954, 1.191) | 0.255 | |
| former | 1.042(0.986,1.100) | 0.139 | |
| mild | 1.062(1.020,1.105) | 0.004 | |
| heavy | 0.983(0.922,1.049) | 0.603 | |
| smoke | 0.965 | ||
| never | 1.038(0.989,1.089) | 0.128 | |
| former | 1.060(1.021,1.101) | 0.003 | |
| current | 1.027(0.979, 1.078) | 0.275 | |
| BMI | 0.896 | ||
| < 25 | 1.033(0.971, 1.100) | 0.299 | |
| 25–30 | 1.042(0.996,1.089) | 0.074 | |
| >=30 | 1.050(1.011,1.090) | 0.012 | |
| hypertension | 0.707 | ||
| no | 1.048(1.002,1.095) | 0.040 | |
| yes | 1.042(1.004,1.082) | 0.030 | |
| DM | 0.59 | ||
| no | 1.048(1.014,1.083) | 0.006 | |
| yes | 1.046(0.989,1.106) | 0.114 | |
| CVD | 0.062 | ||
| no | 1.058(1.022,1.096) | 0.002 | |
| yes | 0.994(0.937,1.054) | 0.838 |
OR: odds ratio; BMI: body mass index; BPH: benign prostatic hyperplasia; CVD: cardiovascular disease; DM: diabetes mellitus
The CDAI was seen as a continuous variable. A P for interaction > 0.05 indicated that the subgroup was meaningless
Discussions
As far as we are aware, this is the first cross-sectional study to explore whether the CDAI was associated with BPH prevalence in the US men. The higher continuous CDAI value was significantly related to higher BPH risk (OR = 1.05, 95%CI: 1.02, 1.08). However, the association was slight, we can only draw the conclusion that antioxidant diet may not be deliberately recommended to adult men to prevent BPH. Besides, individuals with the highest quartile (Q4) CDAI possessed 1.87 times risk of BPH than the lowest quartile (Q1, OR = 1.87, 95%CI: 1.41, 2.50). The RCS curve also showed a positive linear dose-response relationship between CDAI and BPH. The cut-off value of the RCS curve was − 0.64, therefore we only recommend antioxidant non-excessive intake to prevent BPH. Generally, our results demonstrate that the more antioxidant intake may unfavorably affect BPH and even prompt BPH.
Previous studies looked for the effect of different kinds of foods on BPH and LUTS. For example, Bravi et al. reported that a diet rich in cereals and poor in vegetables increased the risk of BPH in Italian men [19]. A study reported that antioxidants like vitamin A, C,E and selenium were not associated with the prostate specific antigen (PSA) level [20]. Different dietary status meant less intake of a kind of food and unavoidable more consumption of other kinds of nutrition sources, which may account for the heterogeneity among previous nutritional studies. BPH is a multi-pathogenic factors disease. Aging, hormone, inflammation and oxidative stress may took part in the process of symptomatic BPH [21]. The mechanism studies of antioxidants and prostate health are still inadequate. A possible biological process was that dietary antioxidants might mediate reactive oxygen species (ROS) balance then affect androgen, which was a key hormone in the progression of BPH [22–25]. One of the first line approaches to enhancing antioxidant capacity is to use antioxidants supplementation. The recommendation dose of antioxidant intake is still inconclusive. A judicious dose of antioxidants may help to protect cells from oxidative stress, but we don’t think that the more antioxidant intake, the better outcome. Researchers have reported that excessive supplement of vitamin A precursor could increase lung cancer risk [26]. Another study reported that long-term and high dose vitamin A would aggravate the condition of dyslipidemia patients [27]. Recently, it was reported that plasma Vitamin D and selenium level could not show a correlation between the biochemical recurrence of prostate cancer [28]. Another review also pointed out that despite the plethora of trials about supplements and PSA, the evidence supporting the efficacy of most dietary factors were inadequate to recommend [6]. To our knowledge, there is no published articles investigating the relationship between antioxidant index and BPH. Compared with previous studies on a single antioxidant, our current study has the novelty of evaluating the joint effect of 6 common dietary antioxidants [14, 15]. A case-control study revealed that vitamin E was not related to the risk of BPH [29]. A meta-analysis showed that the serum zinc level in BPH patients were significantly higher than normal controls, which indicated that excessive zinc intake may pose worse effect on BPH [30]. The CDAI contains zinc level, thus we put forward a hypothesis that a higher CDAI may correlated with higher zinc level, resulting in higher prevalence of BPH.
We utilized the US survey data and appropriate sample weighting to generalize the results to the US population. The novelty of this study is the report of a linear dose-response association between CDAI and BPH. Despite of the strengthens, there are still a number of limitations. First, the CDAI value was calculated from a two-day telephone interview inside of face-to-face interview or a recorded diary, which may cause recall bias to some extent. And the two-day dietary may not represent the total dietary pattern. Second, even though the BPH was diagnosed by doctors, we lack the data of International Prognostic Scoring System (IPSS) score. Hence, we failed to investigate the relationship of CDAI and the different severity of BPH symptoms. Third, although we included 13,419 participants in which 1085 diagnosed with BPH, the results indicated that the higher CDAI (higher antioxidant potential) might increased the risk of BPH in the US men. This is probably inconsistent with cognition. However, we checked the data and logistic regression carefully and the RCS curve plot also supported this results. We believe that our report may decrease the publication bias on only the “positive results”. Fourth, The limitations also contain the selection bias and lack of randomization because of the retrospective cross-sectional design. Further large prospective cohort studies in diverse populations and meta-analysis about CDAI and BPH are urgently needed.
Conclusions
In conclusion, this study reveals that an elevated CDAI may be associated with a higher risk of BPH, and there is perhaps a linear-relationship between them. As a convenient marker of dietary antioxidant potential, CDAI may be useful in disease etiology after more extensive validation. However, we do not suggest intentional antioxidant diet to prevent BPH based on the current results. The CDAI with more robust methods like blood antioxidant levels are recommended in future studies. Large prospective cohort studies or randomized controlled trials in different regions or countries should be performed to verify the findings of this paper.
Author contributions
X.H. and D.L. wrote the main manuscript text and X.H. prepared figures. All authors reviewed the manuscript.
Funding
No funding.
Data availability
All data can be publicly accessed from the National Health and Nutrition Examination Survey (NHANES, www.cdc.gov/nchs/nhanes/).
Declarations
Ethics approval and consent to participate
This study used publicly available data and did not require ethical approval and consent to participate.
Consent for publication
Not applicable.
Competing interests
The authors declare no competing interests.
Footnotes
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Data Availability Statement
All data can be publicly accessed from the National Health and Nutrition Examination Survey (NHANES, www.cdc.gov/nchs/nhanes/).