Abstract
Evidence suggests a strong association between prostate diseases, lower urinary tract symptoms (LUTS), and pro-inflammatory diets. Our study was conducted to assess the relationship between the Dietary Inflammatory Index (DII) and prostate diseases, LUTS using the 2003 to 2008 U.S. National Health and Nutrition Examination Survey (NHANES) database. After the chi-square test to investigate whether demographic data and prostate diseases, LUTS were correlated, for positive results, we performed weighted multivariable logistic regression models analysis. In addition, we performed nonlinear tests using restricted cubic spline (RCS) and assessed the stability between different subgroups by subgroup and interaction analyses. The study included 30,619 subjects. After adjusting the regression model for fully confounding variables, DII was only correlated with benign prostatic hyperplasia (BPH) (OR = 1.074, 95% CI = 1.016–1.136; P = .012). And, the RCS relationship between DII and BPH was positively correlated (nonlinear: P = .830). We did not find statistically significant interactions in all subgroups. At the same time, we did not find any correlation between DII and other prostate diseases and LUTS. Pro-inflammatory diets are associated with an increased risk of BPH. Dietary modifications to reduce the intake of pro-inflammatory nutrients can be helpful in mitigating the development of BPH.
Keywords: benign prostatic hyperplasia, dietary inflammatory index, lower urinary tract symptoms, National Health and Nutrition Examination Survey, pro-inflammatory diet, prostate diseases
1. Introduction
Prostate diseases include prostatitis, benign prostatic hyperplasia (BPH), and prostate cancer (PCa). Pro-inflammatory markers, as an active substance that can activate the body’s immune system, are closely associated with the development of prostate diseases. Relevant studies have shown that prostate cancer antigen 3, a long-stranded noncoding Ribonucleic Acid in urine sediment, has a correlation with prostate cancer higher than the ratio of free Prostate-Specific Antigen and total Prostate-Specific Antigen.[1] In addition, The systemic immune-inflammation index SII, a comprehensive pro-inflammatory marker, was significantly and positively associated with prostate cancer development.[2] In addition to blood and urine markers of inflammation, dietary factors are also correlated with prostate disease. Relevant studies have shown that dietary factors are closely related to prostate diseases. Excessive intake of pro-inflammatory fats is a risk factor for prostatitis and leads to proliferation and differentiation of basal cells in prostate tissue, accelerating the development of intraepithelial tumors in the prostate.[3] Similarly, the lower urinary tract symptoms (LUTS) brought about by the disease, such as frequent urination, incomplete urination, nocturia, and waiting for urination, have a great impact on the quality of life of patients.[4] In order to evaluate the relationship between dietary structure and inflammation, the Shivappa team developed a dietary score: the Dietary Inflammation Index (DII). Higher DII scores represent a stronger pro-inflammatory effect of ingested food. DII as an indicator to explore diet-mediated changes in inflammation has been validated by a variety of inflammatory biomarkers since it was proposed in 2009.[5]
The National Health and Nutrition Examination Survey (NHANES) database assesses the health and nutritional status of the U.S. population by sampling a representative sample. It has been recognized by a growing number of researchers, especially in the direction of nutritional surveys.[6,7]
Because there are no studies of pro-inflammatory diets and prostate diseases, and LUTS, the NHANES database was used in this study to explore the correlation between pro-inflammatory diets and the risk of developing prostate diseases, and LUTS. This will provide a new basis for dietary prevention of prostate diseases and related LUTS.
2. Methods
2.1. Ethical approval and informed consent
This was a study based on the NHANES database. The NHANES study was thoroughly vetted and approved by the National Center for Health Statistics (NCHS) Research Ethics Review Board, and comprehensive information about NHANES can be found at: https://www.cdc.gov/nchs/nhanes/about_nhanes.htm.[8] In addition, all individuals participating in the survey voluntarily provided informed consent (https://www.cdc.gov/nchs/nhanes/irba98.htm). Because all NHANES data were rigorously de-identified to ensure anonymity of the data, no other rights or interests of the subjects were compromised by this study, so no additional Institutional Review Board approval or signed informed consent was required.
2.2. Source of data
Inclusion criteria for this study: respondents included in the NHANES database from 2003 to 2008. Exclusion criteria: missing data or conflicting responses on prostate diseases; missing data on DII; missing data on LUTS; missing basic data; and missing data on covariates.
2.3. Diagnosis of prostate diseases
The diagnosis of prostatitis is based on the question “{Do you/does SP} have an infection or inflammation of the prostate gland at the present time?,” in the NHANES questionnaire, or “What did your prostate biopsy show? Your biopsy showed an inflammation of your prostate gland,” which was defined as prostatitis if the participant answered yes. The diagnosis of BPH was based on the question in the questionnaire “Was it a benign enlargement - that is, not cancerous, also called benign prostatic hypertrophy?.” If the participant answered “Yes,” it was defined as BPH. Similarly, prostate cancer was diagnosed based on the question in the questionnaire “{Have you/Has SP} ever been told by a doctor or health professional that {you/he} had prostate cancer?.” The diagnosis of non-prostate disease was based on the NHANES questionnaire question “Have you ever been told by a doctor or health professional that you have any disease of the prostate? (This includes an enlarged prostate.) .” If the participant answered No, the participant was defined as not having prostate disease.
2.4. Diagnosis of LUTS
Diagnosis of urinary hesitancy was defined by answering Yes to the question “ Do you usually have trouble starting to urinate (pass water)?” in the questionnaire. The diagnosis of incomplete emptying is based on the questionnaire “ Does your bladder feel empty after urinating (expelling water)?” Answer No to determine this. The question in the questionnaire“ During the past 12 months, {have you/has SP} leaked or lost control of even a small amount of urine with an urge or pressure to urinate and {you/he/she} couldn’t get to the toilet fast enough?,” and if the subject answered yes, it was defined as urge urinary incontinence. Similarly, we asked the patient by the question“ During the past 30 days, how many times per night did {you/SP} most typically get up to urinate, from the time {you/s/he} went to bed at night until the time {you/he/she} got up in the morning.” about nocturia. If the patient answered 2 or more times, he/she was defined as nocturia.“ How often {do you/does SP} have urinary leakage?” was used by us to define urinary frequency. Finally, subjects were defined as having clinical LUTS if they experienced ≥2 of these symptoms.
2.5. Calculation of the DII
DII is an indicator of inflammatory potential established by assessing the relationship between diet and pro-inflammatory factors (IL-1β, IL-6, TNF-α, and CRP), diet and anti-inflammatory factors (IL-4, IL-10).[5] It takes into account all the nutrients that regulate inflammation and reflects the inflammatory tendencies of dietary patterns more accurately than individual nutrients.[9] The NHANES database provides subjects with 2 24-hour dietary recall profiles with 28 parameters that can be used to calculate DII scores. The steps of calculating DII were as follows[10]: first, the exposure Z-score of individual dietary components was calculated, Z-score = (average daily intake of individual dietary components - mean global dietary component intake)/standard deviation of global dietary component intake; then, the exposure Z-score was centered and processed so as to be uniformly distributed, and the obtained exposure Z-score was converted to a percentile value of n; the third step was to calculate the exposure Z-score for individual dietary components of DII = nxb (the corresponding inflammation score for each food component); finally, the DII of all dietary components was summed to obtain the overall DII of the subjects.
2.6. Statistical analysis
In this study, subjects who participated in the DII survey were divided into 2 groups (with DII data at 0 as the cutoff) and demographic data were analyzed using the chi-square test. These 2 groups were the anti-inflammatory group G1 (<0) and the pro-inflammatory group G2 (>0), with the G1 group as the reference. Subsequently, we calculated the odds ratio (OR) and 95% confidence intervals (95% CI) using Logistic regression to assess the association between DII and positive results (BPH, Incomplete Emptying). And, Logistic regression analysis was performed using DII as continuous and categorical variables respectively. Model 1 corrected for socio-demographic characteristics including Age, Race, Body Mass Index (BMI), Education Attainment, and Poverty Level. Model 2 corrected for lifestyle habits The alcohol quality-related (ALQ), and Smoking on the basis of Model 1. In this study, the restricted cubic spline (RCS) test was used to determine whether there was a quantitative relationship between DII and a positive result (BPH). A smoothed curve-fit plot was created using the 5 nodes of the DII level distribution (5th, 27.5th, 50th, 72.5th, and 95th percentile) based on all covariates included in Model 2. Logistic regression models were used for interaction and subgroup analyses based on Age, Race, Alq, BMI, Smoke, Education Attainment, and Poverty Level. Statistical performance estimation was not performed in advance because the sample size of this study relied exclusively on the NHANES database.
This study was statistically analyzed using R open source software version 4.3.3 and Free Statistics software version 1.9.[11] P-value < .05 difference is statistically significant.
3. Results
3.1. Population studied
The data used in this study were obtained from the NHANES 2003 to 2008 dataset. This dataset contained information on 30,619 subjects, and we subsequently excluded those with missing or contradictory data on prostate disease, missing data on DII, missing data on LUTS, missing underlying data, and missing data on covariates. The final study of prostate diseases included 2629 subjects with prostatitis, 3059 subjects with BPH, 2761 subjects with PCa, and 2621 subjects with non-prostate diseases. To prevent interference between prostate diseases, we uniformly set non-prostate disease subjects as the control group. Similarly, the study of LUTS included 3584 subjects with urinary hesitancy, 3569 subjects with incomplete emptying, 3605 subjects with urge urinary incontinence, 2445 subjects with nocturia, and 2442 subjects with urinary frequency, and Clinical LUTS subjects 3615. The specific exclusion and inclusion process (Fig. 1).
Figure 1.
The flow chart of participant selection. DII = the dietary inflammation index.
3.2. Demographic characteristics
The results of the chi-square test showed that DII was significantly correlated with BPH and incomplete emptying only (P < .01). Among the 3059 patients with complete DII and BPH included in this study, 438 (14.32%) were diagnosed with BPH. These patients were mostly aged 70 to 79 (136, 31.05%), with the majority being Non-Hispanic White (323, 73.74%). 27.85% of them had an education level of Some College or AA degree. At baseline, most patients with BPH have a history of smoking and occasionally drink alcohol. Similarly, among the 3569 eligible patients, 470 had clear symptoms of incomplete emptying (13.17%), with a higher proportion aged between 60 and 69 (124, 26.38%). The majority of these patients were Non-Hispanic White (228, 48.51%), and 27.74% had an education level of <9th grade. At baseline, the majority of patients with incomplete emptying symptoms had a history of smoking and would have consumed alcohol occasionally. Detailed data on demographic characteristics of prostate diseases (PCa, Prostatitis, BPH) are shown in Table 1. Detailed data on LUTS (urinary hesitancy, incomplete emptying, nocturia, urge urinary incontinence, urinary frequency, clinical LUTS) detailed data on demographic characteristics are shown in Table 2.
Table 1.
Sample demographics by pro-inflammatory diet (prostate diseases).
| Variable | Prostate cancer | Prostatitis | Benign prostatic hyperplasia | |||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Total (n = 2761) | Yes (n = 140) | No (n = 2621) | P | Total (n = 2629) | Yes (n = 8) | No (n = 2621) | P | Total (n = 3059) | Yes (n = 438) | No (n = 2621) | P | |
| Age group, n (%) | ||||||||||||
| 40 to 49 yr | 844 (30.57) | 0 (0.00) | 844 (32.20) | <.001 | 844 (32.1) | 0 (0.00) | 844 (32.20) | <.001 | 869 (28.41) | 25 (5.71) | 844 (32.20) | <.001 |
| 50 to 59 yr | 662 (23.98) | 4 (2.86) | 658 (25.10) | 658 (25.03) | 0 (0.00) | 658 (25.10) | 717 (23.44) | 59 (13.47) | 658 (25.10) | |||
| 60 to 69 yr | 631 (22.85) | 34 (24.29) | 597 (22.78) | 599 (22.78) | 2 (25.00) | 597 (22.78) | 723 (23.64) | 126 (28.77) | 597 (22.78) | |||
| 70 to 79 yr | 429 (15.54) | 59 (42.14) | 370 (14.12) | 375 (14.26) | 5 (62.50) | 370 (14.12) | 506 (16.54) | 136 (31.05) | 370 (14.12) | |||
| 80+ yr | 195 (7.06) | 43 (30.71) | 152 (5.80) | 153 (5.82) | 1 (12.50) | 152 (5.80) | 244 (7.98) | 92 (21.00) | 152 (5.80) | |||
| Race, n (%) | ||||||||||||
| Mexican American | 484 (17.53) | 4 (2.86) | 480 (18.31) | <.001 | 482 (18.33) | 2 (25.00) | 480 (18.31) | .846 | 524 (17.13) | 44 (10.05) | 480 (18.31) | <.001 |
| Non-Hispanic Black | 538 (19.49) | 32 (22.86) | 506 (19.31) | 508 (19.32) | 2 (25.00) | 506 (19.31) | 554 (18.11) | 48 (10.96) | 506 (19.31) | |||
| Non-Hispanic White | 1514 (54.84) | 96 (68.57) | 1418 (54.10) | 1422 (54.09) | 4 (50.00) | 1418 (54.10) | 1741 (56.91) | 323 (73.74) | 1418 (54.10) | |||
| Other Hispanic | 141 (5.11) | 4 (2.86) | 137 (5.23) | 137 (5.21) | 0 (0.00) | 137 (5.23) | 155 (5.07) | 18 (4.11) | 137 (5.23) | |||
| Other race/including multiracia | 84 (3.04) | 4 (2.86) | 80 (3.05) | 80 (3.04) | 0 (0.00) | 80 (3.05) | 85 (2.78) | 5 (1.14) | 80 (3.05) | |||
| Alq group, n (%) | ||||||||||||
| Nondrinker | 504 (18.25) | 32 (22.86) | 472 (18.01) | .198 | 473 (17.99) | 1 (12.50) | 472 (18.01) | .810 | 553 (18.08) | 81 (18.49) | 472 (18.01) | .125 |
| 1 to 5 drinks/mo | 1432 (51.87) | 66 (47.14) | 1366 (52.12) | 1370 (52.11) | 4 (50.00) | 1366 (52.12) | 1595 (52.14) | 229 (52.28) | 1366 (52.12) | |||
| 5 to 10 drinks/mo | 222 (8.04) | 6 (4.29) | 216 (8.24) | 217 (8.25) | 1 (12.50) | 216 (8.24) | 239 (7.81) | 23 (5.25) | 216 (8.24) | |||
| 10+ drinks/mo | 602 (21.8) | 36 (25.71) | 566 (21.59) | 568 (21.61) | 2 (25.00) | 566 (21.59) | 670 (21.9) | 104 (23.74) | 566 (21.59) | |||
| Wait | 1 (0.04) | 0 (0.00) | 1 (0.04) | 1 (0.04) | 0 (0.00) | 1 (0.04) | 2 (0.07) | 1 (0.23) | 1 (0.04) | |||
| Pro-inflammatory diet, n(%) | ||||||||||||
| G1 | 865 (31.33) | 42 (30.00) | 823 (31.40) | .728 | 824 (31.34) | 1 (12.50) | 823 (31.40) | .442 | 998 (32.63) | 175 (39.95) | 823 (31.40) | <.001 |
| G2 | 1896 (68.67) | 98 (70.00) | 1798 (68.60) | 1805 (68.66) | 7 (87.50) | 1798 (68.60) | 2061 (67.37) | 263 (60.05) | 1798 (68.60) | |||
| BMI group, n (%) | ||||||||||||
| Underweight (<18.5) | 33 (1.2) | 3 (2.14) | 30 (1.14) | .255 | 30 (1.14) | 0 (0.00) | 30 (1.14) | .835 | 34 (1.11) | 4 (0.91) | 30 (1.14) | .856 |
| Normal (18.5 to < 25) | 619 (22.42) | 35 (25.00) | 584 (22.28) | 586 (22.29) | 2 (25.00) | 584 (22.28) | 683 (22.33) | 99 (22.60) | 584 (22.28) | |||
| Overweight (25 to < 30) | 1188 (43.03) | 65 (46.43) | 1123 (42.85) | 1127 (42.87) | 4 (50.00) | 1123 (42.85) | 1303 (42.6) | 180 (41.10) | 1123 (42.85) | |||
| Obese (30 or greater) | 921 (33.36) | 37 (26.43) | 884 (33.73) | 886 (33.7) | 2 (25.00) | 884 (33.73) | 1039 (33.97) | 155 (35.39) | 884 (33.73) | |||
| Smoke group, n (%) | ||||||||||||
| Never smoker | 1065 (38.57) | 51 (36.43) | 1014 (38.69) | <.001 | 1018 (38.72) | 4 (50.00) | 1014 (38.69) | .825 | 1139 (37.23) | 125 (28.54) | 1014 (38.69) | <.001 |
| Former smoker | 1031 (37.34) | 77 (55.00) | 954 (36.40) | 957 (36.4) | 3 (37.50) | 954 (36.40) | 1218 (39.82) | 264 (60.27) | 954 (36.40) | |||
| Current smoker | 665 (24.09) | 12 (8.57) | 653 (24.91) | 654 (24.88) | 1 (12.50) | 653 (24.91) | 702 (22.95) | 49 (11.19) | 653 (24.91) | |||
| Education attainment, n (%) | ||||||||||||
| <9th grade | 431 (15.61) | 18 (12.86) | 413 (15.76) | .279 | 416 (15.82) | 3 (37.50) | 413 (15.76) | .026 | 468 (15.3) | 55 (12.56) | 413 (15.76) | <.001 |
| 9th to 11th grade | 400 (14.49) | 22 (15.71) | 378 (14.42) | 381 (14.49) | 3 (37.50) | 378 (14.42) | 419 (13.7) | 41 (9.36) | 378 (14.42) | |||
| High school grad/GED | 685 (24.81) | 27 (19.29) | 658 (25.10) | 660 (25.1) | 2 (25.00) | 658 (25.10) | 759 (24.81) | 101 (23.06) | 658 (25.10) | |||
| College graduate or above | 573 (20.75) | 37 (26.43) | 536 (20.45) | 536 (20.39) | 0 (0.00) | 536 (20.45) | 655 (21.41) | 119 (27.17) | 536 (20.45) | |||
| Some college or AA degree | 672 (24.34) | 36 (25.71) | 636 (24.27) | 636 (24.19) | 0 (0.00) | 636 (24.27) | 758 (24.78) | 122 (27.85) | 636 (24.27) | |||
| Poverty level, n (%) | ||||||||||||
| 0 to 4.98 | 2158 (78.16) | 114 (81.43) | 2044 (77.99) | .337 | 2052 (78.05) | 8 (100.00) | 2044 (77.99) | .283 | 2370 (77.48) | 326 (74.43) | 2044 (77.99) | .099 |
| 5+ | 603 (21.84) | 26 (18.57) | 577 (22.01) | 577 (21.95) | 0 (0.00) | 577 (22.01) | 689 (22.52) | 112 (25.57) | 577 (22.01) | |||
The bold values indicate statistical significance.
Alq = the alcohol quality-related, BMI = body mass index, GED = general educational development.
Table 2.
Sample demographics by pro-inflammatory diet (lower urinary tract symptoms).
| Urinary hesitancy | Incomplete emptying | Nocturia | ||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Variables | Total (n = 3584) | Yes (n = 362) | No (n = 3222) | P | Total (n = 3569) | Yes (n = 470) | No (n = 3099) | P | Total (n = 2445) | Yes (n = 877) | No (n = 1568) | P |
| Age group, n (%) | ||||||||||||
| 40 to 49 yr | 896 (25.00) | 33 (9.12) | 863 (26.78) | <.001 | 894 (25.05) | 86 (18.30) | 808 (26.07) | .001 | 607 (24.83) | 122 (13.91) | 485 (30.93) | <.001 |
| 50 to 59 yr | 778 (21.71) | 67 (18.51) | 711 (22.07) | 774 (21.69) | 96 (20.43) | 678 (21.88) | 562 (22.99) | 151 (17.22) | 411 (26.21) | |||
| 60 to 69 yr | 874 (24.39) | 103 (28.45) | 771 (23.93) | 867 (24.29) | 124 (26.38) | 743 (23.98) | 601 (24.58) | 252 (28.73) | 349 (22.26) | |||
| 70 to 79 yr | 684 (19.08) | 93 (25.69) | 591 (18.34) | 682 (19.11) | 108 (22.98) | 574 (18.52) | 444 (18.16) | 220 (25.09) | 224 (14.29) | |||
| 80+ yr | 352 (9.82) | 66 (18.23) | 286 (8.88) | 352 (9.86) | 56 (11.91) | 296 (9.55) | 231 (9.45) | 132 (15.05) | 99 (6.31) | |||
| Race, n (%) | ||||||||||||
| Mexican American | 579 (16.16) | 51 (14.09) | 528 (16.39) | .024 | 575 (16.11) | 116 (24.68) | 459 (14.81) | <.001 | 350 (14.31) | 124 (14.14) | 226 (14.41) | .002 |
| Non-Hispanic Black | 667 (18.61) | 61 (16.85) | 606 (18.81) | 662 (18.55) | 92 (19.57) | 570 (18.39) | 485 (19.84) | 210 (23.95) | 275 (17.54) | |||
| Non-Hispanic White | 2071 (57.78) | 234 (64.64) | 1837 (57.01) | 2068 (57.94) | 228 (48.51) | 1840 (59.37) | 1405 (57.46) | 483 (55.07) | 922 (58.80) | |||
| Other Hispanic | 172 (4.80) | 13 (3.59) | 159 (4.93) | 170 (4.76) | 22 (4.68) | 148 (4.78) | 144 (5.89) | 43 (4.90) | 101 (6.44) | |||
| Other race/Including multi-racia | 95 (2.65) | 3 (0.83) | 92 (2.86) | 94 (2.63) | 12 (2.55) | 82 (2.65) | 61 (2.49) | 17 (1.94) | 44 (2.81) | |||
| Alq group, n (%) | ||||||||||||
| Nondrinker | 677 (18.89) | 76 (20.99) | 601 (18.65) | .418 | 671 (18.80) | 92 (19.57) | 579 (18.68) | .203 | 461 (18.85) | 185 (21.09) | 276 (17.60) | .055 |
| 1 to 5 drinks/mo | 1850 (51.62) | 184 (50.83) | 1666 (51.71) | 1848 (51.78) | 261 (55.53) | 1587 (51.21) | 1240 (50.72) | 446 (50.86) | 794 (50.64) | |||
| 5 to 10 drinks/mo | 277 (7.73) | 20 (5.52) | 257 (7.98) | 275 (7.71) | 28 (5.96) | 247 (7.97) | 199 (8.14) | 56 (6.39) | 143 (9.12) | |||
| 10 + drinks/mo | 778 (21.71) | 82 (22.65) | 696 (21.60) | 773 (21.66) | 89 (18.94) | 684 (22.07) | 543 (22.21) | 189 (21.55) | 354 (22.58) | |||
| Wait | 2 (0.06) | 0 (0.00) | 2 (0.06) | 2 (0.06) | 0 (0.00) | 2 (0.06) | 2 (0.08) | 1 (0.11) | 1 (0.06) | |||
| Pro-inflammatory diet, n (%) | ||||||||||||
| G1 | 1175 (32.78) | 107 (29.56) | 1068 (33.15) | .168 | 1172 (32.84) | 132 (28.09) | 1040 (33.56) | .019 | 832 (34.03) | 298 (33.98) | 534 (34.06) | .969 |
| G2 | 2409 (67.22) | 255 (70.44) | 2154 (66.85) | 2397 (67.16) | 338 (71.91) | 2059 (66.44) | 1613 (65.97) | 579 (66.02) | 1034 (65.94) | |||
| BMI group, n (%) | ||||||||||||
| Underweight (<18.5) | 40 (1.12) | 9 (2.49) | 31 (0.96) | .007 | 40 (1.12) | 4 (0.85) | 36 (1.16) | .756 | 32 (1.31) | 14 (1.60) | 18 (1.15) | .123 |
| Normal (18.5 to < 25) | 801 (22.35) | 96 (26.52) | 705 (21.88) | 796 (22.30) | 99 (21.06) | 697 (22.49) | 525 (21.47) | 195 (22.23) | 330 (21.05) | |||
| Overweight (25 to < 30) | 1521 (42.44) | 149 (41.16) | 1372 (42.58) | 1518 (42.53) | 199 (42.34) | 1319 (42.56) | 1025 (41.92) | 341 (38.88) | 684 (43.62) | |||
| Obese (30 or greater) | 1222 (34.10) | 108 (29.83) | 1114 (34.57) | 1215 (34.04) | 168 (35.74) | 1047 (33.79) | 863 (35.30) | 327 (37.29) | 536 (34.18) | |||
| Smoke group, n (%) | ||||||||||||
| Never smoker | 1320 (36.83) | 99 (27.35) | 1221 (37.90) | <.001 | 1314 (36.82) | 145 (30.85) | 1169 (37.72) | .016 | 919 (37.59) | 278 (31.70) | 641 (40.88) | <.001 |
| Former smoker | 1502 (41.91) | 185 (51.10) | 1317 (40.88) | 1500 (42.03) | 217 (46.17) | 1283 (41.40) | 1016 (41.55) | 421 (48.00) | 595 (37.95) | |||
| Current smoker | 762 (21.26) | 78 (21.55) | 684 (21.23) | 755 (21.15) | 108 (22.98) | 647 (20.88) | 510 (20.86) | 178 (20.30) | 332 (21.17) | |||
| Education attainment, n (%) | ||||||||||||
| <9th grade | 556 (15.51) | 77 (21.27) | 479 (14.87) | .012 | 552 (15.47) | 121 (25.74) | 431 (13.91) | <.001 | 363 (14.85) | 154 (17.56) | 209 (13.33) | <.001 |
| 9th to 11th grade | 498 (13.90) | 56 (15.47) | 442 (13.72) | 494 (13.84) | 75 (15.96) | 419 (13.52) | 362 (14.81) | 167 (19.04) | 195 (12.44) | |||
| High school grad/GED | 861 (24.02) | 73 (20.17) | 788 (24.46) | 859 (24.07) | 104 (22.13) | 755 (24.36) | 590 (24.13) | 205 (23.38) | 385 (24.55) | |||
| College graduate or above | 781 (21.79) | 72 (19.89) | 709 (22.00) | 780 (21.85) | 75 (15.96) | 705 (22.75) | 536 (21.92) | 163 (18.59) | 373 (23.79) | |||
| Some college or AA degree | 888 (24.78) | 84 (23.20) | 804 (24.95) | 884 (24.77) | 95 (20.21) | 789 (25.46) | 594 (24.29) | 188 (21.44) | 406 (25.89) | |||
| Poverty level, n (%) | ||||||||||||
| 0 to 4.98 | 2774 (77.40) | 291 (80.39) | 2483 (77.06) | .152 | 2760 (77.33) | 399 (84.89) | 2361 (76.19) | <.001 | 1864 (76.24) | 725 (82.67) | 1139 (72.64) | <.001 |
| 5+ | 810 (22.60) | 71 (19.61) | 739 (22.94) | 809 (22.67) | 71 (15.11) | 738 (23.81) | 581 (23.76) | 152 (17.33) | 429 (27.36) | |||
| Age group, n (%) | ||||||||||||
| 40 to 49 yr | 898 (24.91) | 98 (13.67) | 800 (27.70) | <.001 | 607 (24.86) | 65 (14.29) | 542 (27.28) | <.001 | 898 (24.84) | 81 (11.25) | 817 (28.22) | <.001 |
| 50 to 59 yr | 782 (21.69) | 121 (16.88) | 661 (22.89) | 562 (23.01) | 95 (20.88) | 467 (23.50) | 783 (21.66) | 125 (17.36) | 658 (22.73) | |||
| 60 to 69 yr | 875 (24.27) | 205 (28.59) | 670 (23.20) | 600 (24.57) | 122 (26.81) | 478 (24.06) | 878 (24.29) | 201 (27.92) | 677 (23.39) | |||
| 70 to 79 yr | 691 (19.17) | 191 (26.64) | 500 (17.31) | 441 (18.06) | 115 (25.27) | 326 (16.41) | 694 (19.20) | 198 (27.50) | 496 (17.13) | |||
| 80+ yr | 359 (9.96) | 102 (14.23) | 257 (8.90) | 232 (9.50) | 58 (12.75) | 174 (8.76) | 362 (10.01) | 115 (15.97) | 247 (8.53) | |||
| Race, n (%) | ||||||||||||
| Mexican American | 584 (16.20) | 112 (15.62) | 472 (16.34) | .003 | 349 (14.29) | 48 (10.55) | 301 (15.15) | .008 | 586 (16.21) | 101 (14.03) | 485 (16.75) | .022 |
| Non-Hispanic Black | 673 (18.67) | 167 (23.29) | 506 (17.52) | 485 (19.86) | 99 (21.76) | 386 (19.43) | 674 (18.64) | 158 (21.94) | 516 (17.82) | |||
| Non-Hispanic White | 2084 (57.81) | 399 (55.65) | 1685 (58.34) | 1403 (57.45) | 278 (61.10) | 1125 (56.62) | 2088 (57.76) | 420 (58.33) | 1668 (57.62) | |||
| Other Hispanic | 172 (4.77) | 27 (3.77) | 145 (5.02) | 144 (5.90) | 16 (3.52) | 128 (6.44) | 172 (4.76) | 27 (3.75) | 145 (5.01) | |||
| Other race/Including multiracia | 92 (2.55) | 12 (1.67) | 80 (2.77) | 61 (2.50) | 14 (3.08) | 47 (2.37) | 95 (2.63) | 14 (1.94) | 81 (2.80) | |||
| Alq group, n (%) | ||||||||||||
| Nondrinker | 683 (18.95) | 132 (18.41) | 551 (19.08) | .304 | 459 (18.80) | 97 (21.32) | 362 (18.22) | .029 | 688 (19.03) | 146 (20.28) | 542 (18.72) | .074 |
| 1 to 5 drinks/mo | 1860 (51.60) | 389 (54.25) | 1471 (50.93) | 1240 (50.78) | 246 (54.07) | 994 (50.03) | 1865 (51.59) | 389 (54.03) | 1476 (50.98) | |||
| 5 to 10 drinks/mo | 278 (7.71) | 44 (6.14) | 234 (8.10) | 199 (8.15) | 25 (5.49) | 174 (8.76) | 278 (7.69) | 40 (5.56) | 238 (8.22) | |||
| 10+ drinks/mo | 782 (21.69) | 152 (21.20) | 630 (21.81) | 542 (22.19) | 87 (19.12) | 455 (22.90) | 782 (21.63) | 145 (20.14) | 637 (22.00) | |||
| Wait | 2 (0.06) | 0 (0.00) | 2 (0.07) | 2 (0.08) | 0 (0.00) | 2 (0.10) | 2 (0.06) | 0 (0.00) | 2 (0.07) | |||
| Pro-inflammatory diet, n (%) | ||||||||||||
| G1 | 1180 (32.73) | 241 (33.61) | 939 (32.51) | .575 | 831 (34.03) | 159 (34.95) | 672 (33.82) | .648 | 1183 (32.72) | 218 (30.28) | 965 (33.33) | .118 |
| G2 | 2425 (67.27) | 476 (66.39) | 1949 (67.49) | 1611 (65.97) | 296 (65.05) | 1315 (66.18) | 2432 (67.28) | 502 (69.72) | 1930 (66.67) | |||
| BMI group, n (%) | ||||||||||||
| Underweight (<18.5) | 39 (1.08) | 15 (2.09) | 24 (0.83) | <.001 | 32 (1.31) | 4 (0.88) | 28 (1.41) | .074 | 40 (1.11) | 12 (1.67) | 28 (0.97) | .002 |
| Normal (18.5 to < 25) | 816 (22.64) | 143 (19.94) | 673 (23.30) | 526 (21.54) | 95 (20.88) | 431 (21.69) | 818 (22.63) | 149 (20.69) | 669 (23.11) | |||
| Overweight (25 to < 30) | 1524 (42.27) | 261 (36.40) | 1263 (43.73) | 1024 (41.93) | 173 (38.02) | 851 (42.83) | 1528 (42.27) | 276 (38.33) | 1252 (43.25) | |||
| Obese (30 or greater) | 1226 (34.01) | 298 (41.56) | 928 (32.13) | 860 (35.22) | 183 (40.22) | 677 (34.07) | 1229 (34.00) | 283 (39.31) | 946 (32.68) | |||
| Smoke group, n(%) | ||||||||||||
| Never smoker | 1329 (36.87) | 225 (31.38) | 1104 (38.23) | <.001 | 917 (37.55) | 154 (33.85) | 763 (38.40) | .094 | 1333 (36.87) | 223 (30.97) | 1110 (38.34) | <.001 |
| Former smoker | 1513 (41.97) | 341 (47.56) | 1172 (40.58) | 1015 (41.56) | 209 (45.93) | 806 (40.56) | 1517 (41.96) | 347 (48.19) | 1170 (40.41) | |||
| Current smoker | 763 (21.17) | 151 (21.06) | 612 (21.19) | 510 (20.88) | 92 (20.22) | 418 (21.04) | 765 (21.16) | 150 (20.83) | 615 (21.24) | |||
| Education attainment, n (%) | ||||||||||||
| <9th grade | 572 (15.87) | 127 (17.71) | 445 (15.41) | .197 | 360 (14.74) | 65 (14.29) | 295 (14.85) | .706 | 574 (15.88) | 131 (18.19) | 443 (15.30) | .004 |
| 9th to 11th grade | 497 (13.79) | 112 (15.62) | 385 (13.33) | 361 (14.78) | 75 (16.48) | 286 (14.39) | 500 (13.83) | 124 (17.22) | 376 (12.99) | |||
| High school grad/GED | 867 (24.05) | 167 (23.29) | 700 (24.24) | 590 (24.16) | 103 (22.64) | 487 (24.51) | 868 (24.01) | 168 (23.33) | 700 (24.18) | |||
| College graduate or above | 778 (21.58) | 143 (19.94) | 635 (21.99) | 537 (21.99) | 105 (23.08) | 432 (21.74) | 782 (21.63) | 139 (19.31) | 643 (22.21) | |||
| Some college or AA degree | 891 (24.72) | 168 (23.43) | 723 (25.03) | 594 (24.32) | 107 (23.52) | 487 (24.51) | 891 (24.65) | 158 (21.94) | 733 (25.32) | |||
| Poverty level, n (%) | ||||||||||||
| 0 to 4.98 | 2796 (77.56) | 575 (80.20) | 2221 (76.90) | .059 | 1862 (76.25) | 348 (76.48) | 1514 (76.20) | .896 | 2804 (77.57) | 588 (81.67) | 2216 (76.55) | .003 |
| 5+ | 809 (22.44) | 142 (19.80) | 667 (23.10) | 580 (23.75) | 107 (23.52) | 473 (23.80) | 811 (22.43) | 132 (18.33) | 679 (23.45) | |||
The bold values indicate statistical significance.
Alq = the alcohol quality-related, BMI = body mass index, GED = general educational development, LUTS = lower urinary tract symptoms.
3.3. Association of DII with prostate diseases (positive results)
The results of the chi-square test showed that DII was only correlated with BPH (P < .05). Further multifactorial Logistic regression analysis of DII and BPH was performed and the results are shown in Table 3. In the unadjusted model, when DII was analyzed as a continuous variable, there was a significant independent positive correlation between DII and BPH (OR = 1.099, 95% CI = 1.046–1.156; P < .001); the risk of BPH increased as the DII dichotomy increased, with a higher OR in the pro-inflammatory group than in the anti-inflammatory group (OR = 1.454, 95% CI = 1.180–1.790; P < .001). After adjusting for demographic characteristics variables (including Age, Race, BMI, Education Attainment, and Poverty Level) in Model 1, the positive association between DII and BPH when used as a continuous variable did not change (OR = 1.078, 95% CI = 1.020–1.140; P = .008), and the difference remained statistically significant; the risk of developing BPH increased with increasing DII dichotomy, with a higher OR in the pro-inflammatory group than in the anti-inflammatory group (OR = 1.343, 95% CI = 1.069–1.687; P = .011). In model 2, after adjusting for demographic characteristics and ALQ and Smoking, the positive association between the continuous variable DII and BPH remained (OR = 1.074, 95% CI = 1.016–1.136; P = .012); as the dichotomy of DII increased, the risk of BPH increased, with a higher OR for the pro-inflammatory than the anti-inflammatory group (OR = 1.327, 95 percent CI 1.055–1.669; P = .016). The RCS relationship between DII and BPH is shown in Figure 2. When all confounding covariates were considered, there was a positive association between DII and the risk of developing BPH (nonlinear: P = .830).
Table 3.
Association of DII and benign prostatic hyperplasia.
| Model | Pro-inflammatory diet (continuous variables) | Pro-inflammatory diet (categorical variables) | |||
|---|---|---|---|---|---|
| Total (n = 3059) OR (95% CI) | P | G1 (n = 998) OR (95% CI) | G2 (n = 2061) OR (95% CI) | P | |
| Unadjusted model | 1.099 (1.046, 1.156) | <.001 | 1.0 (Ref) | 1.454 (1.180, 1.790) | <.001 |
| Model 1 | 1.078 (1.020, 1.140) | .008 | 1.0 (Ref) | 1.343 (1.069, 1.687) | .011 |
| Model 2 | 1.074 (1.016, 1.136) | .012 | 1.0 (Ref) | 1.327 (1.055, 1.669) | .016 |
The bold values indicate statistical significance.
Figure 2.
The restricted cubic spline between pro-inflammatory diet and benign prostatic hyperplasia. DII = the dietary inflammation index.
3.4. Association of DII with LUTS (positive results)
The results of the chi-square test showed that there was a correlation between DII and Incomplete Emptying (P < .019). Further multifactorial Logistic regression analysis of DII and Incomplete Emptying was performed and the results are shown in Table 4. In the unadjusted model, when DII was analyzed as a continuous variable, there was a negative association between DII and symptoms of Incomplete Emptying (OR = 0.934, 95% CI = 0.888–0.982; P = .007); the risk of Incomplete Emptying was reduced as the dichotomous level of DII increased in the OR was lower in the pro-inflammatory group than in the anti-inflammatory group (OR = 0.773, 95% CI = 0.624–0.958; P = .019). Such a correlation disappeared after adjusting for demographic characteristics variables in model 1, demographic characteristics and ALQ and Smoking in model 2 (P = .258).
Table 4.
Association of DII and incomplete emptying.
| Model | Pro-inflammatory diet (Continuous variables) | Pro-inflammatory diet (Categorical variables) | |||
|---|---|---|---|---|---|
| Total (n = 3569) OR (95% CI) | P | G1 (n = 1172) OR (95% CI) | G2 (n = 2397) OR (95% CI) | P | |
| Unadjusted model | 0.934 (0.888, 0.982) | .007 | 1.0 (Ref) | 0.773 (0.624, 0.958) | .019 |
| Model 1 | – | .285 | 1.0 (Ref) | – | .258 |
| Model 2 | |||||
The bold values indicate statistical significance.
3.5. Subgroup analysis and interaction of positive results
In order to determine whether the relationship between DII and BPH (positive results) was consistent across subgroups, we performed stratification and interaction analyses. The subgroups were set up to incorporate variables with P values <.05 in the univariate analyses of Table 1 that could have caused confounding, as shown in Figure 3. Age 70 to 79 (OR = 1.60, 95% CI = 1.02–2.49; P = .039), Non-Hispanic White race (OR = 1.33, 95% CI = 1.01–1.73; P = .04), BMI belonging to 18.5–25 (OR = 1.70, 95% CI = 1.02–2.81; P = .04), Education belongs to College Graduate or above (OR = 1.61, 95% CI = 1.01–2.56; P = .044), Poverty Level belongs to 5 + (OR = 1.59, 95% CI = 1.01–2.50; P = .047), Nondrinker (OR = 1.98, 95% CI = 1.12–3.51; P = .019) group, DII was associated with the occurrence of BPH. And, we found no statistically significant interactions in all subgroups.
Figure 3.
Subgroup analysis, interaction between pro-inflammatory diet and benign prostatic hyperplasia. Alq = the alcohol use, BMI = body mass index, GED = general educational development, OR = odds ratio.
4. Discussions
Due to the progressive, long-term nature of prostate disease after its onset, dietary and lifestyle modifications, in addition to medication, have received increasing attention from researchers.[12] In particular, the study of the relationship between pro-inflammatory diets and prostate disease is a hot topic.[13] This study is the first to investigate the correlation between a pro-inflammatory diet and prostate disease, LUTS by assessing the DII score, which comprehensively calculates the level of inflammation in an individual. In order to minimize the interference of various confounding factors, we excluded some subjects who answered the questions contradictorily, and we also uniformly set non-prostate disease subjects as the control group for the prostate disease study.
According to previous studies, high-fat intake has been correlated with prostate disease.[14] DII is calculated from nutrients such as fat and protein, and increased fat intake is 1 of the most important factors contributing to elevated DII. Prior to the study, we hypothesized that DII was likely to be correlated with BPH, Prostatitis, PCa, and LUTS symptoms. However, our study did not find any statistical significance between DII and PCa and Prostatitis (P > .05). At the same time, our study showed that there was a difference in the level of dietary inflammation between subjects with BPH and subjects without prostate disease. Patients with BPH had higher DII scores compared with non-prostate disease subjects. After adjusting for potential confounders, DII remained positively associated with the risk of developing BPH, with a 7.4% increase in the risk of developing BPH for each unit increase in DII (OR = 1.074, 95% CI = 1.016–1.136; P = .012), and this association remained stable in subgroup analyses, with no interaction observed. This study suggests that patients with BPH tend to be associated with a higher intake of pro-inflammatory nutrients.
Related studies have found that pro-inflammatory diets such as high-fat increase low-grade inflammatory factors (monocyte chemoattractant protein-1/MCP-1 levels) that contribute to 1 of the pathologic mechanisms that lead to the development of BPH. Cystic dilatation of the prostate gland occurs when the inflammatory state of the organism is increased. This finding can well explain our study.[14] In addition, Eswar Shankar et al showed that, pro-inflammatory diet such as high-fat causes a increase in the levels of pro-inflammatory cytokines and gene products through activation of 2 signaling pathways: the Signal Transducer and Activator of Transcription (STAT)-3 and Nuclear Factor-kappa B (NF-κB). Both these pathways function as transcription factors required for regulating genes involved in proliferation. By modulating these signaling pathways, a pro-inflammatory diet can increase the risk of BPH to some extent.[15] Our study suggests that dietary modifications to reduce the intake of pro-inflammatory nutrients can be helpful in controlling the development of BPH.
Some studies have shown that the pro-inflammatory diet, represented by the high-fat diet, significantly increases inflammation in the mouse prostate.[16] This is not consistent with our study (P = .442), and we believe that it is possible that the small number of subjects in the prostatitis-positive group resulted in some bias in the results. It is worth mentioning that we also did not find a significant correlation between a pro-inflammatory diet and PCa (P = .728), despite the fact that we took into account the interference of multiple factors. This is not consistent with the study of Kulkarni P et al, who found that a pro-inflammatory diet increased ataxin levels in the adipose tissue, causing increased production of lysophosphatidic acid. this compound has been shown to create a pro-inflammatory environment that induces PCa development and progression.[17] We believe that perhaps the treatment of prostate cancer has reduced the impact of the pro-inflammatory diet to some extent, and we need further research and discussion on the exact reasons for this.
Considering that there is a certain threshold for the diagnosis of the disease, not all patients have the opportunity to be diagnosed by a physician. In order to study in more depth the effect of a pro-inflammatory diet on patients with prostate diseases, we further analyzed the relationship between DII and LUTS. We found that DII was not statistically significant with urinary hesitancy, nocturia, urge urinary incontinence, urinary frequency, and clinical LUTS (P > .05). And, DII was correlated with Incomplete Emptying symptoms (OR = 0.934, 95% CI = 0.888–0.982; P = .007). However, after adjusting for covariates, such a correlation disappeared (P = .285). This study showed that there was no significant correlation between patients’ LUTS and pro-inflammatory nutrient intake.
Although BPH refers to changes in the size of the prostate, rather than the symptoms it may cause, these symptoms are often referred to as LUTS. However, these symptoms have been shown to correlate with the progression of BPH.[18] There is a relative lack of research on the relationship between pro-inflammatory diets and LUTS. Xinyang Liao et al[19] also conducted a study on pro-inflammatory diets and clinical LUTS using the NHANES database. When used as a continuous variable, they did not find that DII had an association with clinical LUTS, which is to some extent similar to our findings. Unlike their study, ours used data from more cycles and excluded more interferences. Our study suggests that dietary modifications do not directly impact single or clinical multiple LUTS. However, patients with BPH are often associated with a higher intake of pro-inflammatory nutrients. Reducing the intake of pro-inflammatory nutrients may be helpful for BPH-induced LUTS. The exact mechanism still needs further experimental evidence.
We have to admit that our study has some limitations. Our study was conducted based on the NHANES database, and the number of cases of certain groups of subjects is skewed, which may lead to biased results. In addition, prostate diseases, especially prostate cancer, have a strong genetic correlation,[20] and we believe that the applicability of the results of our study to other races requires further discussion.
5. Conclusions
Pro-inflammatory diets are associated with an increased risk of BPH. Dietary modifications to reduce the intake of pro-inflammatory nutrients can be helpful in mitigating the development of BPH, especially in the nondrinker, Non-Hispanic White, age 70 to 79 category. At the same time, the pro-inflammatory diet was not significantly correlated with PCa, Prostatitis, and LUTS.
Author contributions
Conceptualization: Zhengping Yang, Jibao He, Anjie Hong, Li Zhang, Haoyu Zhao, Chongrui Wei, Zhijie Zhang.
Data curation: Chongrui Wei, XueYan Niu.
Formal analysis: Chongrui Wei, Zhijie Zhang.
Investigation: Zhengping Yang, Haoyu Zhao.
Methodology: Chongrui Wei.
Project administration: Zhengping Yang, Haoyu Zhao.
Resources: Li Zhang, Zhijie Zhang.
Supervision: Li Zhang, Zhijie Zhang.
Validation: Zhengping Yang, Haoyu Zhao.
Visualization: Zhengping Yang, Haoyu Zhao.
Writing – original draft: Zhengping Yang, Anjie Hong, Haoyu Zhao, XueYan Niu.
Writing – review & editing: Zhengping Yang, Jibao He, Anjie Hong, Li Zhang, XueYan Niu, Zhijie Zhang.
Abbreviations:
- ALQ
- alcohol quality-related
- BMI
- body mass index
- BPH
- benign prostatic hyperplasia
- DII
- dietary inflammatory index
- GED
- general educational development
- LUTS
- lower urinary tract symptoms
- NCHS
- national center for health statistics
- NHANES
- national health and nutrition examination survey
- OR
- odds ratio
- PCA
- prostate cancer
- RCS
- restricted cubic spline
The authors have no funding and conflicts of interest to disclose.
The datasets generated during and/or analyzed during the current study are publicly available.
How to cite this article: Yang Z, He J, Hong A, Zhang L, Zhao H, Wei C, Niu X, Zhang Z. Pro-inflammatory diet and risk of prostate diseases, lower urinary tract symptoms: A cross-sectional study from the National Health and Nutrition Examination Survey (NHANES) 2003 to 2008. Medicine 2024;103:48(e40685).
Contributor Information
Zhengping Yang, Email: jorya127@126.com.
Jibao He, Email: lyghjb@yeah.net.
Li Zhang, Email: 865343988@qq.com.
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