| Literature DB >> 32542536 |
Jeffrey Hoek1, Sam Schoenmakers1, Esther B Baart1, Maria P H Koster1, Sten P Willemsen1,2, Eva S van Marion3, Eric A P Steegers1, Joop S E Laven3, Régine P M Steegers-Theunissen4.
Abstract
Inadequate nutrition and lifestyle behaviors, particularly during the periconception period, are associated with a negative impact on embryonic and subsequent fetal development. We investigated the associations between parental nutritional and lifestyle factors and pre-implantation embryo development. A total of 113 women and 41 partners, with a corresponding 490 embryos, who underwent intracytoplasmic sperm injection (ICSI) treatment subscribed to the mHealth coaching platform "Smarter Pregnancy." At baseline, nutrition and lifestyle behaviors (intake of fruits, vegetables, folic acid, and smoking and alcohol use) were identified and risk scores were calculated. A lower risk score represents healthier behavior. As outcome measure, a time-lapse morphokinetic selection algorithm (KIDScore) was used to rank pre-implantation embryo quality on a scale from 1 (poor) to 5 (good) after being cultured in the Embryoscope™ time-lapse incubator until embryonic day 3. To study the association between the nutritional and lifestyle risk scores and the KIDScore in men and women, we used a proportional odds model. In women, the dietary risk score (DRS), a combination of the risk score of fruits, vegetables, and folic acid, was negatively associated with the KIDScore (OR 0.86 (95% CI 0.76 to 0.98), p = 0.02). This could mainly be attributed to an inadequate vegetable intake (OR 0.76 (95% CI 0.59 to 0.96), p = 0.02). In men, smoking was negatively associated with the KIDscore (OR 0.53 (95% CI 0.33 to 0.85), p < 0.01). We conclude that inadequate periconceptional maternal vegetable intake and paternal smoking significantly reduce the implantation potential of embryos after ICSI treatment. Identifying modifiable lifestyle risk factors can contribute to directed, personalized, and individual recommendations that can potentially increase the chance of a healthy pregnancy.Entities:
Keywords: Assisted reproductive technology; Embryo development; Lifestyle; Nutrition; Smoking; Vegetables
Year: 2020 PMID: 32542536 PMCID: PMC7522074 DOI: 10.1007/s43032-020-00220-8
Source DB: PubMed Journal: Reprod Sci ISSN: 1933-7191 Impact factor: 3.060
Fig. 1Embryo development from 1 cell to 8 cells (embryonic day 1 till day 3) as seen from the EmbryoScope™ with the corresponding time points used for the KIDScore
Baseline characteristics of women and men in the entire study population
| Women ( | Missing | Men ( | Missing | |
|---|---|---|---|---|
| Age, years | 32.4 (29.1–35.1) | 0 | 34.0 (29.5–41.5) | 0 |
| Ethnicity | ||||
| Western | 95 (84%) | 0 | 41 (100%) | |
| Non-Western | 18 (16%) | 0 (0%) | ||
| Education | ||||
| Low | 8 (7%) | 0 | 3 (7%) | |
| Middle | 47 (42%) | 14 (34%) | ||
| High | 58 (51%) | 24 (59%) | ||
| BMI (measured), kg/m2 | 23.2 (21.7–26.0) | 4 | 24.0 (22.6–27.5) | 0 |
| BMI category | ||||
| < 25 | 66 (59%) | 1 | 24 (59%) | 0 |
| ≥ 25 | 46 (41%) | 17 (41%) | ||
| Diagnosis category: | 0 | 0 | ||
| Only male factor | 92 (81%) | 32 (78%) | ||
| Of which surgically retrieved sperm | 51 | 17 | ||
| Only female factor | 2 (2%) | 1 (2%) | ||
| Anovulation | 2 | 1 | ||
| Tubal pathology | 0 | 0 | ||
| Both male and female | 17 (15%) | 7 (18%) | ||
| Of which surgically retrieved sperm | 6 | 3 | ||
| Anovulation | 16 | 6 | ||
| Tubal pathology | 1 | 1 | ||
| Unexplained | 2 (2%) | 1 (2%) | ||
| Time between activation Smarter Pregnancy and oocyte retrieval, days | 49 (35–126) | 0 | 45 (34.5–97) | 0 |
| Vegetable risk score | ||||
| 0 (≥ 200 g/day) | 31 (28%) | 3 | 14 (34%) | 0 |
| 1.5 (150–199 g/day) | 22 (20%) | 8 (20%) | ||
| 3 (< 150 g/day) | 57 (52%) | 19 (46%) | ||
| Fruit risk score | ||||
| 0 (≥ 2 pieces/day) | 46 (42%) | 4 | 18 (44%) | 0 |
| 1.5 (1,5–1.9 pieces/day) | 13 (12%) | 8 (20%) | ||
| 3 (< 1.5 pieces/day) | 50 (46%) | 15 (36%) | ||
| Folic acid risk score | ||||
| 0 (0.4 mg/day) | 111 (98%) | 0 | n/a | |
| 3 (no usage) | 2 (2%) | |||
| Smoking risk score | ||||
| 0 (no smoking) | 107 (98%) | 4 | 38 (93%) | 0 |
| 1 (1–5 cigarettes/day) | 0 (0%) | 0 (0%) | ||
| 3 (6–14 cigarettes/day) | 2 (2%) | 2 (5%) | ||
| 6 (≥ 15 cigarettes/day) | 0 (0%) | 1 (2%) | ||
| Alcohol risk score | ||||
| 0 (no alcohol) | 67 (61%) | 4 | 13 (32%) | 0 |
| 1.5 (1–2 units/day) | 39 (36%) | 24 (59%) | ||
| 3 (≥ 2 units/day) | 3 (3%) | 4 (9%) | ||
Data are presented as medians (IQR) or number of subjects (%). IQR, interquartile range; BMI, body mass index
Fig. 2Flowchart of inclusions and exclusions of the study population
Effect estimates and odds ratios from the proportional odds model for the nutrition and lifestyle risk scores on the KIDScore for (a) total study population of women, (b) overweight women only, and (c) total study population of males
| Effect estimate | Odds ratio (95% CI) | Effect estimate | Odds ratio (95% CI) | |||
|---|---|---|---|---|---|---|
| Total study population of women | Crude | Adjusted* | ||||
| Total risk score | − 0.13 | 0.88 (0.78 to 1.00) | 0.049 | − 0.13 | 0.88 (0.78 to 1.00) | 0.047 |
| Dietary risk score | − 0.15 | 0.86 (0.76 to 0.98) | 0.025 | − 0.15 | 0.86 (0.76 to 0.98) | 0.024 |
| Lifestyle risk score | 0.15 | 1.16 (0.78 to 1.71) | 0.47 | 0.07 | 1.08 (0.76 to 1.53) | 0.42 |
| Vegetable intake | − 0.28 | 0.76 (0.59 to 0.96) | 0.03 | − 0.28 | 0.76 (0.59 to 0.96) | 0.02 |
| Fruit intake | − 0.14 | 0.87 (0.68 to 1.11) | 0.25 | − 0.16 | 0.86 (0.70 to 1.05) | 0.24 |
| FA supplement use | n/a | n/a | ||||
| Alcohol use | 0.15 | 1.16 (0.78 to 1.71) | 0.62 | 0.07 | 1.08 (0.76 to 1.53) | 0.68 |
| Smoking | n/a | n/a | ||||
| Overweight women only | Crude | Adjusted* | ||||
| Total risk score | − 0.29 | 0.75 (0.57 to 0.98) | 0.04 | − 0.30 | 0.74 (0.56 to 0.98) | 0.04 |
| Dietary risk score | − 0.31 | 0.73 (0.56 to 0.97) | 0.029 | − 0.31 | 0.74 (0.55 to 0.98) | 0.033 |
| Lifestyle risk score | 0.06 | 1.06 (0.52 to 2.19) | 0.87 | 0.02 | 1.01 (0.47 to 2.17) | 0.97 |
| Vegetable intake | − 0.55 | 0.58 (0.37 to 0.91) | 0.02 | − 0.63 | 0.53 (0.32 to 0.87) | 0.01 |
| Fruit intake | − 0.18 | 0.84 (0.56 to 1.24) | 0.37 | − 0.18 | 0.84 (0.56 to 1.26) | 0.39 |
| FA supplement use | n/a | n/a | ||||
| Alcohol use | 0.06 | 1.06 (0.52 to 2.19) | 0.87 | 0.01 | 1.01 (0.47 to 2.17) | 0.97 |
| Smoking | n/a | n/a | ||||
| Total study population of males | Crude | Adjusted# | ||||
| Total risk score | − 0.13 | 0.88 (0.73 to 1.06) | 0.18 | − 0.11 | 0.90 (0.74 to 1.09) | 0.28 |
| Dietary risk score | − 0.08 | 0.92 (0.74 to 1.15) | 0.45 | − 0.03 | 0.97 (0.76 to 1.24) | 0.83 |
| Lifestyle risk score | − 0.23 | 0.80 (0.57 to 1.11) | 0.18 | − 0.27 | 0.76 (0.55 to 1.06) | 0.11 |
| Vegetable intake | − 0.21 | 0.81 (0.58 to 1.13) | 0.22 | − 0.13 | 0.88 (0.57 to 1.37) | 0.57 |
| Fruit intake | 0.02 | 1.02 (0.74 to 1.42) | 0.90 | 0.06 | 1.06 (0.76 to 1.48) | 0.75 |
| Alcohol use | 0.14 | 1.15 (0.72 to 1.83) | 0.57 | 0.08 | 1.08 (0.67 to 1.73) | 0.75 |
| Smoking | − 0.63 | 0.54 (0.34 to 0.85) | <0.01 | − 0.63 | 0.53 (0.33 to 0.85) | <0.01 |
Crude model: no adjustments made
Adjusted model*: model 1 + adjusted for maternal age
Adjusted model#: model 1 + adjusted for maternal age and the corresponding risk score from the women