Retinoblastoma Protein

We assessed pesticide residues in fruits and vegetables using data from US Department of Agriculture’s Pesticide Data Program, a national program started in 1991 that annually tests agricultural commodities in the USA for the presence of ~ 450 different pesticide residues.²² To best represent the pesticide residues in the food supply, the Pesticide Data Program collects samples from 10 or more participating States comprising 50% of the nation’s population. Before testing, the produce is either washed or peeled to mimic consumer practices, allowing for realistic estimates of exposure. To determine the average pesticide residue status of fruits and vegetables, we developed the PRBS using the Pesticide Data Program annual reports corresponding to the periods in which the diet history of the participants was captured by the FFQ.²² Briefly, we defined PRBS^{13 (link)} according to three contamination measures from the Pesticide Data Program: (1) the percentage of samples tested with any detectable pesticides; (2) the percentage of samples tested with pesticides exceeding tolerance levels; and (3) the percentage of samples with three or more individual detectable pesticides. We ranked the 36 FVs included in the FFQ according to each of the three contamination measures, divided them into tertiles for each of these three measures, and assigned each food a score of 0, 1, and 2 corresponding to the bottom, middle, and top tertile, respectively. The final PRBS for each food was the sum of tertile scores across the three PDP contamination measures (Supplementary Table 1). We classified foods with a PRBS ≥ 4 as high pesticide residue foods and those with a PRBS < 4 as low pesticide residue foods.^{13 (link)} To derive a PRBS specific to a class of pesticides, we used a similar algorithm (i.e., three contamination measures) but restricted Pesticide Data Program data to organophosphates and pyrethroids only for calculating organophosphate-PRBS and pyrethroid-PRBS, respectively. In sensitivity analyses, we also considered an alternate measure, PRBS-weighted fruit and vegetable intake, calculated as the product of each food’s PRBS score (on a scale of 0 to 6) and its intake frequency.

Diet was assessed before initiation of ART using a self-administered, previously validated food frequency questionnaire.^{29 (link)} Women reported how often they typically consumed specified amounts of each food, beverage, and supplement over the past year. Two data-derived dietary pattern scores, the prudent and Western pattern,^{30 (link)} were used to summarize overall food choices.
We used the annual reports from the US Department of Agriculture Pesticide Data Program (PDP) to classify FVs according to their mean pesticide residue status in the US food supply.³¹ Details of the PRBS methods have been described elsewhere.^{25 (link),27 (link),32 (link)} We considered 3 measures of contamination from the PDP to classify FVs: (1) the percentage of samples tested with any detectable pesticides, (2) the percentage of samples tested with pesticides exceeding the tolerance level, and (3) the percentage of samples with 3 or more individual detectable pesticides. The pesticide residue data in FVs were averaged by annual PDP reports from 2006 through 2015, corresponding to the periods when the diet history of the participants was captured by the food frequency questionnaire.
Next, we categorized foods according to tertiles for each of the 3 measurements of contamination and assigned a score of 0 to FVs in the bottom tertile, 1 to FVs in the middle tertile, and 2 for FVs in the top tertile. The PRBS for each food was the sum of scores across the 3 PDP contamination measures. We considered FVs with a PRBS of 4 or greater on a scale of 0 to 6 to be high–pesticide residue foods while FVs with a PRBS of less than 4 to be low–pesticide residue foods. Based on these criteria, 14 FVs were categorized as high pesticide residue and 22 as low pesticide residue (Table 1).
Statistical Analysis Women were classified according to quartiles of total FV intake, high–pesticide residue FV intake, and low–pesticide residue FV intake. We conducted Kruskal-Wallis tests (for continuous variables) and Fisher exact tests (for categorical variables) to compare baseline characteristics across quartiles of FV intake. To evaluate the relationship of FV intake with ART outcomes, we used cluster-weighted generalized estimating equations to account for within-person correlations in the presence of nonignorable cluster size.^{33 (link)} Each observation was weighted inversely to the number of cycles they contributed to the analysis. We evaluated ART outcomes per initiated cycle to estimate effects relevant in practice and mirror intention-to-treat analyses for studies of ART.^{34 (link),35 (link)} However, in a post-hoc analysis, we evaluated the association of FV intake with risk of pregnancy loss only among cycles in which implantation was achieved.^{34 (link)} Population marginal means were used to present population averages adjusted for the covariates at their average levels for continuous variables and weighted average levels of categorical variables in the model.³⁶ Tests for linear trend were performed using the median intake of FVs in each quartile as a continuous variable.
Confounding was evaluated using directed acyclic graphs based on prior knowledge.
Specifically, variables previously reported to be associated with live birth/pregnancy loss as well as associated with FV intake were considered as potential confounders.^{37 (link)–40 (link)} In addition, we included dietary pattern scores to distinguish relations between FV intake from those of overall food choices. The final multivariable models were adjusted for age (years), BMI, smoking status (current/former vs never), race (white vs nonwhite), supplemental folate intake (micrograms per day), organic FV consumption frequency (<3 vs ≥3 times/wk), residential pesticide exposure history (yes vs no), prudent and Western dietary patterns, total energy intake (kilocalories per day), and infertility diagnosis (male factor vs female factor vs unexplained). The model for high–pesticide residue FV intake was additionally adjusted for low-pesticide FV intake and vice versa because they may confound each other. To minimize residual confounding, we performed separate sensitivity analyses restricting to women younger than 40 years, women without a history of miscarriage, autologous cycles, and cycles initiated within 1 year of food frequency questionnaire completion. We also estimated the effect of substituting 1 serving/d of low–pesticide residue FVs for high–pesticide residue FVs on clinical outcomes.^{41 (link)} All statistical analyses were performed in SAS, version 9.4 (SAS Institute). P values were 2 sided. Findings were considered statistically significant when P < .05.