| Literature DB >> 32595918 |
Zhao-Min Liu1, Bailing Chen2, Shuyi Li1, Guoyi Li1, Di Zhang1, Suzanne C Ho3, Yu-Ming Chen1, Jing Ma1, Huang Qi1, Wen-Hua Ling1.
Abstract
BACKGROUND: Human studies have demonstrated the beneficial effects of soy or isoflavones on bone metabolism. However, conflicting data remain. Equol is the intestinal metabolite of the isoflavone daidzein. The health benefits of soy are more pronounced in equol producers than those not producing equol. This 6-month randomized controlled trial aimed to examine the effect of whole soy (soy flour) and purified daidzein on bone turnover markers (BTMs) in Chinese postmenopausal women who are equol producers.Entities:
Keywords: bone turnover markers; daidzein; inflammatory markers; postmenopausal women; whole soy
Year: 2020 PMID: 32595918 PMCID: PMC7303504 DOI: 10.1177/2042018820920555
Source DB: PubMed Journal: Ther Adv Endocrinol Metab ISSN: 2042-0188 Impact factor: 3.565
Baseline characteristics of 270 Chinese postmenopausal women by the three study groups.
| Whole soy group | Daidzein group | Placebo group | ||
|---|---|---|---|---|
| Age (years) | 57.6 ± 5.3 | 57.7 ± 5.0 | 58.5 ± 4.7 | 0.464 |
| Housewife (%) | 37 (13.7%) | 46 (17.0%) | 54 (20.0%) | 0.128 |
| Education beyond university (%) | 19 (7.0%) | 11 (4.1%) | 15 (5.6%) | 0.782 |
| Body weight (kg) | 56.5 ± 7.3 | 56.5 ± 9.2 | 57.6 ± 9.0 | 0.615 |
| Body weight change at 6 months (kg) | 0.2 ± 1.8 | 0.5 ± 3.3 | −0.2 ± 1.5 | 0.100 |
| Body fatness (%) | 30.5 ± 5.9 | 30.6 ± 6.6 | 30.6 ± 6.4 | 0.992 |
| Ambulatory blood pressure (24 h) | 134.4 ± 16.1 | 134.0 ± 14.5 | 132.8 ± 13.6 | 0.762 |
| DBP (mmHg) | 81.0 ± 7.1 | 80.6 ± 8.9 | 79.2 ± 8.1 | 0.288 |
| BS total PA (MET-min/day) | 1251.3 ± 726.4 | 1218.5 ± 625.1 | 1134.2 ± 534.7 | 0.450 |
| Sports frequency (time/week) | 3.67 ± 1.29 | 3.36 ± 1.20 | 3.45 ± 1.22 | 0.325 |
| Years after menopause | 9.0 ± 6.4 | 8.9 ± 5.7 | 9.1 ± 5.2 | 0.977 |
| Natural menopause (%) | 76 (86.4%) | 78 (87.6%) | 76 (84.4%) | 0.981 |
| Null parity (%) | 15 (16.7%) | 13 (14.4%) | 9 (10.0%) | 0.667 |
| Null gravidity (%) | 13 (14.6%) | 12 (13.5%) | 9 (10.0%) | 0.627 |
| Never breastfed (%) | 43 (56.6%) | 47 (61.8%) | 44 (55.0%) | 0.666 |
| Use of HRT ever (%) | 9 (3.3%) | 13 (4.8%) | 19 (7.0%) | 0.112 |
| Duration of HRT for ever-HRT users (months) | 42.9 ± 34.8 | 23.7 ± 27.6 | 29.5 ± 31.9 | 0.368 |
| Use of contraceptives ever (%) | 46 (17.0%) | 39 (14.4%) | 45 (16.7%) | 0.528 |
| Medical history (%) | ||||
| Type 2 diabetes | 5 (5.6%) | 2 (3.4%) | 6 (2.2%) | 0.471 |
| Hypertension | 24 (27.0%) | 29 (32.6%) | 24 (26.7%) | 0.728 |
| Hyperlipidemia | 35 (39.3%) | 31 (34.8%) | 37 (41.1%) | 0.726 |
| Passive smoking (%) | 13 (4.8%) | 12 (4.4%) | 15 (5.6%) | 0.814 |
| Regular alcohol drinking (%) | 7 (2.6%) | 8 (3.0%) | 10 (3.7%) | 0.738 |
| Regular coffee drinking (%) | 34 (12.6%) | 37 (13.7%) | 32 (11.9%) | 0.742 |
| Regular tea drinking (%) | 73 (32.7%) | 73 (32.7%) | 77 (34.5%) | 0.662 |
Data are presented as mean ± SD for continuous variables with comparison by one-way analysis of variance or number (%) for categorical variables, with comparison by Chi-square test. Regular drinking means habitual drinking of alcohol, tea, or coffee more than once per week. METs are multiples of resting metabolic rates and a MET-min is computed by multiplying the MET score of an activity by the minutes performed.
BS, baseline; DBP, diastolic blood pressure; HRT, hormone replacement treatment; PA, physical activity; SBP, systolic blood pressure; SD, standard deviation.
Dietary intakes at baseline and 6 months after treatment among the three study groups.
| Whole soy group | Daidzein group | Placebo group | ||
|---|---|---|---|---|
| Dietary intake at baseline | ||||
| Energy (kcal/day) | 2048.3 ± 543.2 | 2090.4 ± 655.3 | 1983.5 ± 431.6 | 0.341 |
| Protein (g/day) | 88.9 ± 22.7 | 90.1 ± 29.4 | 86.1 ± 20.6 | 0.373 |
| Fat (g/day) | 64.9 ± 21.8 | 69.0 ± 22.6 | 63.4 ± 37.1 | 0.386 |
| Isoflavones (mg/day) | 14.6 ± 10.1 | 14.2 ± 8.5 | 14.5 ± 9.5 | 0.852 |
| Refined grains (g/day) | 413.0 ± 163.4 | 452.93 ± 141.8 | 433.1 ± 141.5 | 0.224 |
| Whole grains (g/day) | 103.6 ± 160.0 | 104.9 ± 123.7 | 94.7 ± 135.3 | 0.869 |
| Fruits (g/day) | 299.0 ± 203.5 | 281.0 ± 174.6 | 282.1 ± 170.6 | 0.765 |
| Dark green vegetables (g/day) | 109.7 ± 84.4 | 112.3 ± 70.7 | 107.5 ± 96.5 | 0.930 |
| Legumes (g/day) | 17.2 ± 32.8 | 15.0 ± 29.4 | 15.3 ± 26.9 | 0.863 |
| Red/processed meat (g/day) | 66.9 ± 50.4 | 76.3 ± 53.4 | 65.2 ± 41.6 | 0.261 |
| Fish (g/day) | 43.1 ± 40.6 | 54.8 ± 42.8 | 56.2 ± 42.3 | 0.492 |
| Dietary intake at 6 months (not including supplements) | ||||
| Energy (kcal/day) | 1956.2 ± 475.65 | 2006.9 ± 560.6 | 1938.7 ± 391.1 | 0.443 |
| Protein (g/day) | 82.6 ± 21.9 | 80.8 ± 27.5 | 76.5 ± 18.4 | 0.368 |
| Fat (g/day) | 61.5 ± 19.8 | 63.6 ± 15.8 | 60.4 ± 23.9 | 0.325 |
| Isoflavones (mg/day) | 8.6 ± 6.6 | 8.3 ± 7.5 | 7.9 ± 7.2 | 0.714 |
Data are presented as mean ± SD for continuous variables and compared by one-way analysis of variance among the three groups. The dietary survey was conducted by food frequency questionnaires and 3-day dietary records at baseline and after treatment. Dietary nutrient intakes were calculated based on the China Food Composition Tables 2002 and 2004.[36]
SD, standard deviation.
The effect of whole soy and purified daidzein on bone turnover markers among the three study groups.
| Whole soy group | Daidzein group | Placebo group | ||
|---|---|---|---|---|
| Bone resorption marker | ||||
| β-CTX (ng/ml) | ||||
| Baseline | 0.359 ± 0.125 | 0.341 ± 0.139 | 0.350 ± 0.104 | 0.632 |
| 6 months | 0.330 ± 0.122 | 0.318 ± 0.117 | 0.345 ± 0.105 | 0.319 |
| Change | −0.029 ± 0.100 | −0.022 ± 0.079 | −0.006 ± 0.089 | 0.209 |
| % change | −5.169 ± 27.171 | −3.140 ± 20.756 | 1.232 ± 25.995 | 0.217 |
| Bone formation markers | ||||
| PINP (ng/ml) | ||||
| Baseline | 54.389 ± 18.176 | 51.42 ± 17.425 | 52.021 ± 14.078 | 0.458 |
| 6 months | 51.935 ± 17.154 | 49.39 ± 16.451 | 50.157 ± 13.141 | 0.544 |
| Change | −2.453 ± 13.763 | −2.032 ± 11.094 | −1.864 ± 10.264 | 0.943 |
| % change | −2.131 ± 22.324 | −0.831 ± 23.320 | −1.794 ± 19.381 | 0.920 |
| BALP (μg/l) | ||||
| Baseline | 15.689 ± 4.923 | 19.46 ± 32.729 | 16.124 ± 4.478 | 0.362 |
| 6 months | 15.299 ± 4.527 | 18.40 ± 31.315 | 16.103 ± 4.855 | 0.507 |
| Change | −0.390 ± 2.747 | −1.052 ± 2.905 | −0.020 ± 3.002 | 0.060 |
| % change | −0.985 ± 15.623 | −3.633 ± 13.845 | 1.010 ± 20.947 | 0.201 |
| OST (ng/ml) | ||||
| Baseline | 18.807 ± 4.967 | 19.261 ± 7.824 | 18.517 ± 4.622 | 0.709 |
| 6 months | 18.208 ± 5.257 | 18.067 ± 7.137 | 18.075 ± 4.388 | 0.983 |
| Change | −0.599 ± 3.079 | −1.195 ± 3.185 | −0.442 ± 2.842 | 0.228 |
| % change | −2.099 ± 16.529 | −1.459 ± 16.758 | −1.279 ± 14.741 | 0.476 |
| 25(OH)D3 (ng/ml) | ||||
| Baseline | 24.799 ± 8.010 | 24.628 ± 7.741 | 25.110 ± 8.237 | 0.922 |
| 6 months | 21.912 ± 7.377 | 22.331 ± 6.760 | 22.431 ± 7.433 | 0.878 |
| Change | −2.887 ± 5.459 | −2.298 ± 4.044 | −2.679 ± 4.288 | 0.684[ |
| % change | −9.394 ± 21.788 | −7.350 ± 18.170 | −8.651 ± 20.249 | 0.795 |
| Coupling index ( | ||||
| Baseline | −0.027 ± 0.955 | −0.060 ± 0.989 | 0.086 ± 1.060 | 0.601 |
| 6 months | −0.145 ± 0.895 | −0.071 ± 0.952 | 0.217 ± 1.114 | 0.038 |
| Change | −0.125 ± 0.966 | −0.015 ± 1.052 | 0.142 ± | 0.201 |
| % change | −11.088 ± 74.144 | 3.006 ± 138.742 | 8.437 ± 75.483 | 0.406 |
p value.
Data are presented as mean ± SD and compared by one-way analysis of variance. Variables with significant heterogeneity in variances which cannot be adjusted by data transformation were compared by Kruskal–Wallis non-parametric analysis. Coupling index was calculated as normalizing resorption (CTX) and formation marker (PINP) as Z scores and expressed as the ratio of the two markers.
β-CTX, beta C-terminal telopeptide of type I collagen; BALP, bone alkaline phosphatase; OST, N-mid osteocalcin; P1NP, N-terminal propeptides of type I procollagen; SD, standard deviation; 25(OH)D3, 25-hydroxyvitamin D.
The effect of whole soy and purified daidzein on inflammatory markers among the three study groups.
| Whole soy group | Daidzein group | Placebo group | ||
|---|---|---|---|---|
| Inflammatory markers | ||||
| IL-6 (pg/ml) | ||||
| Baseline | 1.704 ± 0.894 | 1.687 ± 1.306 | 1.987 ± 2.046 | 0.330 |
| 6 months | 1.657 ± 1.009 | 1.712 ± 1.142 | 2.077 ± 1.944 | 0.108 |
| Change | −0.023 ± 1.249 | 0.025 ± 1.550 | 0.090 ± 2.128 | 0.904 |
| % change | 20.026 ± 102.278 | 17.239 ± 79.761 | 34.077 ± 140.971 | 0.562 |
| TNF-α (pg/ml) | ||||
| Baseline | 5.710 ± 2.114 | 6.304 ± 5.079 | 5.427 ± 1.887 | 0.212 |
| 6 months | 5.738 ± 2.497 | 6.426 ± 5.293 | 5.370 ± 1.669 | 0.381[ |
| Change | 0.027 ± 1.913 | 0.122 ± 1.304 | −0.057 ± 1.326 | 0.748 |
| % change | 3.298 ± 30.868 | 3.309 ± 23.273 | 9.489 ± 87.589 | 0.694 |
| Hcy (μmol/l) | ||||
| Baseline | 9.700 ± 2.071 | 9.624 ± 2.348 | 9.836 ± 2.259 | 0.818 |
| 6 months | 9.457 ± 2.062 | 9.360 ± 2.183 | 9.841 ± 2.387 | 0.316 |
| Change | −0.244 ± 1.597 | −0.264 ± 1.008 | 0.005 ± 1.283 | 0.638[ |
| % change | −1.654 ± 14.649 | −2.026 ± 11.726 | 0.820 ± 13.757 | 0.315 |
| TFR (g/l) | ||||
| Baseline | 2.601 ± 0.394 | 2.811 ± 2.170 | 2.579 ± 0.297 | 0.529[ |
| 6 months | 2.555 ± 0.344 | 2.739 ± 1.929 | 2.562 ± 0.311 | 0.492[ |
| Change | −0.046 ± 0.263 | −0.072 ± 0.335 | −0.017 ± 0.216 | 0.415 |
| % change | −1.051 ± 9.607 | −1.503 ± 8.734 | −0.406 ± 7.877 | 0.947[ |
| Hs-CRP (mg/l) | ||||
| Baseline | 1.747 ± 2.041 | 1.260 ± 1.155 | 1.686 ± 2.267 | 0.946[ |
| 6 months | 1.411 ± 1.963 | 2.011 ± 3.262 | 1.648 ± 2.734 | 0.327 |
| Change | −0.330 ± 1.801 | 0.402 ± 2.643 | −0.05 ± 3.192 | 0.022[ |
p values.
Data are presented as mean ± SD and compared by one-way analysis of variance. Variables with significant heterogeneity in variances which cannot be adjusted by data transformation were compared by Kruskal–Wallis non-parametric analysis.
Hcy, homocysteine; Hs-CRP, high-sensitivity C-reactive protein; IL-6, interleukin-6; SD, standard deviation; TFR: transferrin; TNF-α: tumor necrosis factor alpha.