ORIGINAL ARTICLE: GYNECOLOGY Time-to-conception and clinical pregnancy rate with a myo-inositol, probiotics, and micronutrient supplement: secondary outcomes of the NiPPeR randomized trial Shiao-Yng Chan, Ph.D.,a,b Sheila J. Barton, Ph.D.,c See Ling Loy, Ph.D.,b,d,e Hsin Fang Chang, M.Sc.,a Philip Titcombe, Ph.D.,c Jui-Tsung Wong, M.Sc.,b Marilou Ebreo, M.R.C.O.G.,a Judith Ong, M.R.C.O.G.,a Karen ML. Tan, Ph.D.,b Heidi Nield, BSc.,c Sarah El-Heis, M.R.C.P., D.M.,c Timothy Kenealy, Ph.D.,f Yap-Seng Chong,M.D.,a,b Philip N. Baker, Ph.D.,gWayne S. Cutfield,M.D.,f KeithM. Godfrey, Ph.D.,c,h and the NiPPeR Study Group# a Department of Obstetrics and Gynaecology, Yong Loo Lin School of Medicine, National University of Singapore and National University Health System, Singapore; b Singapore Institute for Clinical Sciences, Agency for Science, Technology and Research (A*STAR), Singapore, Singapore; c Medical Research Council Lifecourse Epidemiology Centre, University of Southampton, Southampton, United Kingdom; d Department of Reproductive Medicine, KK Women’s and Children’s Hospital, Singapore, Singapore; e Duke-NUS Medical School, Singapore, Singapore; f Liggins Institute, University of Auckland, Auckland, New Zealand; g College of Life Sciences, University of Leicester, Leicester, United Kingdom; and h National Institute for Health Research Southampton Biomedical Research Centre, University of Southampton, Southampton and University Hospital Southampton National Health Service Foundation Trust, Southampton, United Kingdom. Objective: To determine whether a combined myo-inositol, probiotics and micronutrient nutritional supplement impacts time-to- natural-conception and clinical pregnancy rates. Design: Secondary outcomes of a double-blind randomized controlled trial. Setting: Community recruitment. Patients: Women aged 18 to 38 years planning to conceive in the United Kingdom, Singapore, and New Zealand, excluding those with diabetes mellitus or receiving fertility treatment. Intervention: A standard (control) supplement (folic acid, iron, calcium, iodine, b-carotene), compared with an intervention addition- ally containing myo-inositol, probiotics, and other micronutrients (vitamins B2, B6, B12, D, zinc). Main Outcome Measures: Number of days between randomization and estimated date of natural conception of a clinical pregnancy, as well as cumulative pregnancy rates at 3, 6, and 12 months. Received May 23, 2022; revised January 11, 2023; accepted January 30, 2023; published online February 6, 2023. #Members of the NiPPeR Study Group authors for the Medline citation are listed in the Acknowledgments. Public good funding for this investigator-led study is through the UK Medical Research Council (as part of an MRC award to the MRC Lifecourse Epidemi- ology Unit (MC_UU_12011/4)); the Singapore National Research Foundation, National Medical Research Council (NMRC, NMRC/TCR/012-NUHS/2014); the National University of Singapore (NUS) and the Agency of Science, Technology and Research (as part of the Growth, Development andMetabolism Programme of the Singapore Institute for Clinical Sciences (SICS) (H17/01/a0/005); and as part of Gravida, a New Zealand Government Centre of Research Excellence. Funding for provision of the intervention and control drinks and to cover aspects of thefieldwork for the study has been provided by Soci�et�e Des Produits Nestl�e S.A. under a Research Agreement with the University of Southampton, Auckland UniServices Ltd, SICS, National Uni- versity Hospital Singapore PTE Ltd and NUS. K.M.G. is supported by the National Institute for Health Research (NIHR Senior Investigator (NF-SI-0515-10042), NIHR Southampton 1000DaysPlus Global Nutrition Research Group (17/63/154) and NIHR Southampton Biomedical Research Cen- ter (IS-BRC-1215-20004)), British Heart Foundation (RG/15/17/3174), and the European Union (Erasmusþ Programme ImpENSA 598488-EPP-1-2018-1-- DE-EPPKA2-CBHE-JP). S.Y.C. is supported by a Singapore NMRC Clinician Scientist Award (NMRC/CSA-INV/0010/2016; MOH-CSAINV19nov-0002). The funders had no role in the data collection and analysis, and the decision to submit for publication. W.S.C. and K.M.G. contributed equally to this research work. K.G., Y.S.C., W.C., and S.C. report grants from Soci�et�e Des Produits Nestl�e S.A. during the conduct of the study and are co-inventors on patent filings by Nestl�e S.A. relating to the NiPPeR intervention or its components. K.G., S.B., Y.S.C., W.C., and S.C. are part of an academic consortium that has received grants from Nestl�e S.A. and Benevolent AI Bio Ltd outside the submitted work. All other authors declare no competing interests. For the purpose of Open Access, the author has applied a Creative Commons Attribution (CC BY) licence to any Author Accepted Manuscript version arising from this submission. Correspondence: Shiao-Yng Chan, Ph.D., Department of Obstetrics andGynaecology, Yong Loo Lin School ofMedicine, National University of Singapore, 1E Kent Ridge Road, Level 12 NUHS Tower Block, Singapore, 119228 (E-mail: obgchan@nus.edu.sg). Fertility and Sterility® Vol. 119, No. 6, June 2023 0015-0282 Copyright ©2023 The Authors. Published by Elsevier Inc. on behalf of the American Society for Reproductive Medicine. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). https://doi.org/10.1016/j.fertnstert.2023.01.047 VOL. 119 NO. 6 / JUNE 2023 1031 mailto:obgchan@nus.edu.sg http://creativecommons.org/licenses/by-nc-nd/4.0/ https://doi.org/10.1016/j.fertnstert.2023.01.047 http://crossmark.crossref.org/dialog/?doi=10.1016/j.fertnstert.2023.01.047&domain=pdf ORIGINAL ARTICLE: GYNECOLOGY Results: Of 1729 women randomized, 1437 (83%; intervention, n¼736; control, n¼701) provided data. Kaplan-Meier curves of conception were similar between intervention and control groups; the time at which 20% achieved natural conception was 90.5 days (95% confidence interval: 80.7, 103.5) in the intervention group compared with 92.0 days (76.0, 105.1) in the control group. Cox's proportional hazard ratios (HRs) comparing intervention against control for cumulative achievement of pregnancy (adjusted for site, ethnicity, age, body mass index, and gravidity) were similar at 3, 6, and 12 months. Among both study groups combined, overall time-to-conception lengthened with higher preconception body mass index, and was longer in non-White than in White women. Among women who were overweight the intervention shortened time-to-conception compared with control regardless of ethnicity (12-month HR¼1.47 [1.07, 2.02], P¼ .016; 20% conceived by 84.5 vs. 117.0 days) and improved it to that comparable to nonoverweight/nonobese women (20% conceived by 82.1 days). In contrast, among women with obesity, time-to-conception was lengthened with intervention compared with control (12-month HR¼0.69 [0.47, 1.00]; P¼ .053; 20% conceived by 132.7 vs. 108.5 days); an effect predominantly observed in non-White women with obesity. Conclusions: Time-to-natural-conception and clinical pregnancy rates within a year were overall similar in women receiving the inter- vention supplement compared with control. Overweight women had a longer time-to-conception but there was suggestion that the supplement may shorten their time-to-conception to that comparable to the nonoverweight/nonobese women. Further studies are required to confirm this. Clinical Trial Registration Number: clinicaltrials.gov (NCT02509988) (Fertil Steril� 2023;119:1031-42. �2023 by American Society for Reproductive Medicine.) El resumen está disponible en Español al final del artículo. Key Words: preconception, nutritional supplement, fertility, fecundability, NiPPeR trial F ertility rates are declining globally, especially in high- income settings, with a concurrent increase inwomen re- porting subfertility (1). Rising obesity rates accompanied by metabolic dysregulation and upward trends in diets rich in fat and sugar, but low inminerals and vitamins (2–4), are likely contributors. Increasing glycemia associates with reduced fecundability, even across the normal glycemic range (5). Additionally, several micronutrient insufficiencies have been linked with altered ovarian function and subfertility (6). Thus, a nutritional supplement aimed at improving metabolic health and micronutrient status could potentially enhance reproductive potential in young women (7, 8). A particular nutrient of interest is inositol, an endogenously produced 6-carbon polyol, also derived from the diet (9). Myo- inositol, the most abundant inositol (10), is a component of phospholipids governing membrane functions such as calcium fluxes and second messenger signaling of hormones, including insulin and gonadotropins (11, 12). In ameta-analysis of trials in anovulatory polycystic ovary syndrome (PCOS) (13), inositol supplementation improved ovulation rate (relative risk: 2.3), menstrual cycle regularity (relative risk: 3.2), and glycemia/insu- lin parameters, but there was no impact on pregnancy rates. However, inositol’s efficacy in promoting spontaneous concep- tion in the general population is unclear (14). Optimizing micronutrient status may directly improve both ovarian function and oocyte quality, and indirectly pro- mote fertility through the promotion of insulin sensitivity and glucose metabolism, which suppresses hyperinsulinemia and hyperandrogenism that impair ovarian function (15). Vitamin D insufficiency is implicated in insulin resistance, metabolic syndrome, and PCOS, as well as associates with poor assisted reproductive technology (ART) outcomes and miscarriage (16, 17). Vitamins B6 and B12 are involved in homocysteine metabolism and DNA synthesis, vital for oocyte maturation, and insufficiencymay compromise fertility (18, 19). In animal models, zinc insufficiency is linked to a low sex drive, anov- ulation, and irregular menstrual cycles, but its role in human reproduction remains inconclusive (20). 1032 Positive effects of probiotics on fertility have been reported (21, 22). Gut microbiome dysbiosis is associated with PCOS in rodents (23, 24) and can trigger chronic inflammatory re- sponses that exacerbate insulin resistance (25), resulting in hy- perinsulinemia that interferes with follicular development and impairs oocyte function (26, 27). A meta-analysis of studies of probiotic supplements containing lactobacillus and bifidobac- teria showed improved metabolic, hormonal, and inflamma- tory profiles in women with PCOS (28). Furthermore, oral Lactobacillus rhamnosus promotes a vaginal microbiome that favors sperm function (29, 30). Previously, a trial of a multivitamin supplement without myo-inositol or probiotics reported a 5% shortening in time- to-conception (31). Now the Nutritional Intervention Precon- ception and During Pregnancy to Maintain Healthy Glucose Metabolism and Offspring Health (NiPPeR) trial (32) provides an opportunity to assess, as a secondary outcome, the influ- ence of a combined myo-inositol, probiotic, and micronu- trient supplement in further improving fertility in women planning to conceive, recruited outside the assisted reproduc- tion setting. With the postulation of additive or synergistic effects between the supplement components, we hypothe- sized that women in the intervention arm would have a shorter time to spontaneous conception and a higher clinical pregnancy rate than those in the control arm. Materials and Methods The NiPPeR study protocol was approved by the research ethics committees at 3 study sites: Southampton (United Kingdom), Auckland (New Zealand), and Singapore (33). All participants provided written informed consent. Study Design and Participants Between August 2015 and May 2017, NiPPeR recruited from the community women aged 18 to 38 years who were plan- ning to conceive (33). Women were excluded if they had any type of diabetes mellitus, a known serious allergy, were VOL. 119 NO. 6 / JUNE 2023 Fertility and Sterility® pregnant/lactating, or in the past month had received oral, implanted, or intrauterine contraception, metformin, sys- temic steroids, anti-convulsant medication or treatment for HIV or Hepatitis B/C. Women taking clomiphene citrate or letrozole within the previous 3 months or undergoing ART were excluded for this substudy. Women were randomly assigned to the control or interven- tion groups (1:1 ratio) through a study database, with stratifica- tion by site and ethnicity. Participants, study teams and health care staff were blinded to the trial-group assignments until data- base lock of the primary outcome of gestational glycemia at 28 weeks gestation, which showed no difference (33). ^Fertility outcomes were prespecified at the outset in our internal proto- col, however, we mistakenly omitted this information from the orig- inal trial registration in July 2015, and it was added in September 2018 (before completion of the 1 year follow-up allowed to achieve conception for all participants). Formulations and Procedures The control and intervention formulations were packaged as a powder in sachets to be mixed with water (200 mL) and consumed twice daily; folic acid (400 mg/d), iron (12 mg/d), calcium (150 mg/d), iodine (150 mg/d), and b-carotene (720 mg/d) were common to both arms, with the intervention addi- tionally containing myo-inositol (4 g/d; i.e., 2 g twice daily), vitamin D (10 mg/d), riboflavin (1.8 mg/d), vitamin B6 (2.6 mg/d), vitamin B12 (5.2 mg/d), zinc (10 mg/d) and probiotics (Lactobacillus rhamnosus NCC4007 [CGMCC 1.3724; LPR] and Bifidobacterium animalis sp. lactis NCC2818 [CNCM I-3446; Bl818]) (33). Investigational products were blinded with ‘‘nonspeaking’’ three-character length alphanumeric codes (2 for each study arm to minimize the risk of inadver- tent unblinding) and had similar sensory characteristics. They had comparable rates of perceived minor side-effects (8.3% control, 7.5% intervention). Adherence was determined by sachet counting. Women were advised not to consume other supplements throughout the trial period. At enrolment, sociodemographic characteristics, menstrual, obstetric and health histories, lifestyle habits and psychological stress measures were collected via interviewer-administered questionnaires. Weight and height were measured to derive body mass index (BMI). A 75-g oral glucose tolerance test was conducted providing fasting and 2-hour plasma glucose and in- sulin levels alongside glycated hemoglobin (HbA1c) (measured by a single standardized laboratory at each site per the Royal College of Pathologists of Australasia Quality Assurance Pro- gram). Insulin, antim€ullerian hormone (AMH) (Roche Diagnos- tics immunoassay), and C-reactive protein (CRP; MALDI-TOF, Bevital Platform G) were each batch-analyzed by one common laboratory. Women with newly diagnosed diabetes mellitus at recruitment were excluded from the analysis. The homeostasis model assessment for insulin resistance (HOMA2-IR) (34) and Matsuda index measure of insulin sensitivity (35) were calcu- lated. PCOS-like phenotype was defined by an AMH >3.2 ng/ mL (36) accompanied by self-reported irregular menstruation (variation of>5 days inmenstrual cycle length in last 6months) with an average cycle length of R35 days (37). Participants with a positive urinary pregnancy test by the second preconception study visit (PCV2; mostly 23–30 days [range 21–42] after randomization, 7.3% and 7.6% in the con- trol and intervention arms, respectively) (Fig. 1) were excluded from the main analysis as we had postulated a priori that conception occurring within this duration may not be VOL. 119 NO. 6 / JUNE 2023 influenced by the intervention. At PCV2, HbA1c and CRP were measured again. With a positive pregnancy test, women were scheduled for an ultrasound scan at 6 to 8 weeks of amenorrhea, at which time information on menstrual and contraceptive histories over the3monthsbefore conceptionwere collectedagain. Trial partic- ipation ended after one year for those who had not conceived. Outcomes Assessment of fertility rate was a prespecified secondary out- come.^ Spontaneously conceived clinical pregnancy was the event of interest, defined as ultrasonographic evidence of a viable intrauterine pregnancy with a fetal pole and cardiac activity detected after 6 weeks amenorrhea, including multi- ple pregnancies. Time-to-conception of a clinical pregnancy was computed as the interval between the date of randomization at preconcep- tion and the estimated date of conception (EDC; 38 weeks before the expected date of delivery) using an algorithm (38). In 406 conceptions (66%), EDC was computed from the first day of the last menstrual period (LMP), as these women had self- reported regular cycles and were certain of their LMP date, also considering their usual cycle length (averaged over last 3 months before conception) assuming ovulation occurs 14 days before each anticipated menstruation. In the remaining 209 cases (34%), EDC was based on the first ultrasound scan (using crown-rump length measurement of a viable fetus (39) in 203 cases, and biparietal diameter/head circumference in 6 cases) since there was >7 days discrepancy between LMP and scan dates, uncertainty of LMP, cycle irregularity or recent stopping of hormonal contraception (within 3 months). An indicative time at which 20% of women achieved conception (predicted a priori to be in the middle of the steepest section of slope of conception rate) in each group is presented. Clinical pregnancy rates were defined as proportions of women achieving a clinical pregnancy by natural conception within 3, 6, and 12months af- ter randomization, and live birth rates were compared between the study groups. Statistical Analysis All randomized participants remaining in the study at PCV2 were included in the analyses (Fig. 1). Time-to-conception for each group was estimated with the Kaplan-Meier method, with statistical significance assessed by log rank testing. Censoring was applied when a woman had not conceived af- ter a year, was lost to follow-up, reported no longer trying to conceive, withdrew voluntarily or for a medical reason, sub- sequently initiated fertility or ART treatment including clomi- phene citrate or letrozole or metformin, miscarried before a clinical pregnancy could be established or had an ectopic pregnancy (Fig. 1). Cox proportional hazards modeling estimated the hazard ratio (HR with 95% confidence intervals [CI]) between control and intervention groups for achievement of a clinical 1033 FIGURE 1 CONSORT: Flow of participants assigned to the control and intervention groups. *Our initial target was 1800 recruits to have 600 pregnancies to study the primary outcome of gestational glycemia; in the event pregnancy rates were higher and recruitment was stopped at 1729 women as the projected number of pregnancies would exceed our target. &DM diagnosed by fasting glycemiaR7.0 mmol/L or 2-hour glycemiaR11.1mmol/L in a prepregnancy 75-g oral glucose tolerance test (51) at recruitment. ART¼ assisted reproductive technology, PCV2¼ preconception visit 2 (mostly 23–30 days [range 21–42] after randomization), DM ¼ diabetes mellitus. Chan. Nutritional supplement and conception. Fertil Steril 2023. 1034 VOL. 119 NO. 6 / JUNE 2023 ORIGINAL ARTICLE: GYNECOLOGY Fertility and Sterility® pregnancy daily/monthly or a live birth. The HR of clinical pregnancy achievement was first calculated from a multivar- iate Coxmodel that included the intervention as the exposure, adjusted for the stratification factors of site and ethnicity (basic model), followed by a fully-adjusted model including clinically important prognostic factors based on literature (age/BMI as continuous variables, gravidity as a dichotomous variable). Possible model fit improvement was examined by the addition of further covariates including cycle regularity and glycemia. Sensitivity analyses were conducted excluding those with a possible infertility issue unlikely to be addressed by maternal nutritional interventions, as reflected by subse- quent ART post-recruitment or failure to conceive after a year. An additional per protocol analysis was performed including only participants with >80% adherence, and another including those who conceived before PCV2 but us- ing assumed EDC because of missing LMP and scan data. To examine whether the intervention had differential ef- fects in subpopulations, prespecified subgroup analyses by metabolic, menstrual, and gravidity status were conducted. Additionally, interaction terms (intervention characteristic) were introduced into the basic models to uncover differential effects in subpopulations stratified by ethnicity, age, BMI (nonoverweight/nonobese, overweight, obese using ethnic- specific [Asian vs. non-Asian] thresholds (40)], household income, cardiometabolic health status (fasting/2-hour glyce- mia, HbA1c, HOMA2-IR, CRP), gravidity, menstrual cycle regularity, PCOS-like features, different ovarian reserve (AMH concentrations), and psychological stress (Table 1 and Supplementary Fig. 1, available online, for categoriza- tions). Analyses were also performed to examine the influence of these factors on overall time-to-conception in the com- bined control/intervention group. Where intervention effects were found, exploratory analyses (with emphasis on effect sizes and 95% CI) were conducted to investigate the potential underlying mechanisms, including changes in cycle regular- ity, HbA1c, and CRP with the intervention. Analyses were performed using Stata 15 software (Stata- Corp. 2017. Stata Statistical Software: Release 15. College Station, TX:StataCorp LP). Statistical significance was considered when 2-tailed probability was <0.05. The power calculation for the primary outcome of gestational glycemia dictated a sample of 300 in each study arm to achieve 80% po- wer (with a¼0.017) to detect pre-defined clinically appre- ciable group differences in fasting, 1-hour and 2-hour glucose concentrations in a 28-week oral glucose tolerance test (33). Based on previous studies we had initially antici- pated that 1800 would need to be recruited preconception to have 600 established pregnancies to study, but actual conception rates were higher and recruitment was stopped at 1729 when projected conceptions were estimated to exceed 600. There was no interim analysis during the trial. RESULTS Of 1729 women randomized, 1437 (intervention, n ¼ 736 [85% of randomized]; control n ¼ 701 [82% of randomized]) fulfilled the criteria for inclusion into this substudy (Fig. 1). At baseline, sociodemographic characteristics, gynecological VOL. 119 NO. 6 / JUNE 2023 history, lifestyle, and metabolic health parameters were similar between the 2 study groups (Table 1). Prepregnancy supplement adherence was similar in control and intervention groups, with 73.3% having >80% adherence, 19.8% at 60% to 80% adherence, 6.1% at 30% to 60% adherence and <1% with <30% adherence or missing data. Overall time-to-conception was not different between the 2 study groups (Fig. 2). The time taken for 20% of women to achieve a natural conception was similar in the intervention and control groups (90.5 [95% CI, 80.7–103.5] vs. 92 [76.0– 105.1] days). HRs comparing control against intervention groups for achievement of a clinical pregnancy at 3, 6, and 12 months indicated no differences between study groups, either with adjustment for site and ethnicity only, or with further adjustment for age, BMI, and gravidity. Addition of further covariates did not improve model fit. Findings were robust to sensitivity analyses: excluding those with a possible fertility issue not rectifiable by nutritional supplementation (n ¼ 82 who subsequently received fertility treatment, n ¼ 371 who did not conceive at 1 year; Supplementary Table 1, avail- able online), per protocol analyses including only those with adherence >80% (n ¼ 1054; HR: 0.99 [0.84–1.18]; P¼ .946), as well as analyses additionally including those who conceived before PCV2 (n ¼ 1566; HR: 1.00 [0.86–1.15]; P¼ .980), showed similar results. Live birth rates were also not different between groups (HR: 0.94 [0.79–1.11]; P¼ .46). Further analyses combining the control and intervention groups confirmed that participant characteristics associated with time-to-conception were as expected based on literature. Increased maternal age, high BMI, low household income, increased psychological stress, menstrual irregularity, no pre- vious pregnancy, high fasting/2-hour glycemia, HbA1c, HOMA2-IR, and CRP characteristics were all associated with longer time-to-conception (Supplementary Fig. 1). Women in Singapore (20% conceived by 151.0 [95% CI: 115.5– 174.0] days) conceived slower than women in the United Kingdom (74.0 [58.0–84.5] days; P¼ .005) or New Zealand (80.5 [69.8–99.5] days; P¼ .015). In the United Kingdom and New Zealand, White women (20% conceived by 73.0 [64.0–82.1] days) conceived more quickly than non-White women (102.4 [71.0–136.0] days; P¼ .003). As Singapore had an ethnic mix comprising only Asians, women were analyzed separately; compared with Chinese women (103.6 [79.9–131.5] days), South Asian women were not signifi- cantly different (185.0 days; P¼ .274) but Malay women took longer to conceive (284.5 days; P< .0001). Prespecified subgroup analyses examined for differential effects of the intervention according to baseline characteristics. Intervention showed similar effects as control on time-to- conception in different ethnic, age, gravidity, income, psycho- logical stress, metabolic health, and menstrual regularity groups, except for BMI categories; the intervention effect differed in overweight vs. nonoverweight/nonobese women (P-interaction ¼ 0.014). Among obese women, intervention demonstrated a further interaction with White/non-White cat- egories, and differed to its effect in nonoverweight/nonobese women (P-interaction ¼ 0.049). Among nonoverweight/nonobese women, time-to- conception was similar in the control and intervention groups 1035 TABLE 1 Baseline characteristics of preconception women by nutritional supplement allocation Characteristics Control (n[701) Intervention (n[736) Sociodemographic Study site UK 174 (24.8%) 195 (26.5%) Singapore 279 (39.8%) 292 (39.7%) New Zealand 248 (35.4%) 249 (33.8%) Age, (y) mean (SD) 30.70 (3.56) 30.64 (3.69) Ethnicity White 321 (45.8%) 346 (47.0%) Chinese 193 (27.5%) 213 (28.9%) South Asian 46 (6.6%) 47 (6.4%) Malay 72 (10.3%) 67 (9.1%) Other 69 (9.8%) 63 (8.6%) Household incomea Low income 59 (9.0%) 59 (8.6%) Middle income 291 (44.2%) 296 (43.4%) High income 308 (46.8%) 327 (48.0%) Gynecological Gravidity Never pregnant 378 (53.9%) 376 (51.1%) Pregnant before 323 (46.1%) 360 (48.9%) Parity Nulliparous 492 (70.2%) 487 (66.2%) Parous 209 (29.8%) 249 (33.8%) Cycle length, (d) mean (SD) 30.70 (6.27) 30.44 (5.9) Cycle regularity Regular 450 (65.0%) 466 (64.5%) Irregularb 242 (35.0%) 257 (35.5%) Possible PCOSc Not PCOS-like 633 (93.1%) 675 (94.0%) PCOS-like 47 (6.9%) 43 (6.0%) AMH concentrations (ng/mL) median (IQR) 2.7 (1.7, 4.5) 3.1 (1.7, 4.7) Lifestyle Alcohol intake (per week) None 212 (30.2%) 227 (30.8%) >0 and%2.5 units 260 (37.1%) 265 (36.0%) >2.5 units 229 (32.7%) 244 (33.2%) Smoking status Never 558 (79.8%) 565 (77.0%) Previous 100 (14.3%) 114 (15.6%) Active 41 (5.9%) 54 (7.4%) Instances of moderate/ vigorous physical activity in past 7 days Number of instances 3 (2, 5) 3 (1, 5) Psychological Stress and Pressured None 170 (24.2%) 168 (22.8%) Slightly 339 (48.4%) 379 (51.5%) Moderately to extremely 192 (27.4%) 189 (25.7%) Metabolic health BMI, kg/m2 median (IQR) 24.1 (21.3, 28.8) 23.9 (21.3, 28.4) Fasting glucose, mmol/L median (IQR) 4.96 (4.63, 5.18) 4.85 (4.63, 5.18) Chan. Nutritional supplement and conception. Fertil Steril 2023. TABLE 1 Continued. Characteristics Control (n[701) Intervention (n[736) 2-hour post glucosee, mmol/L median (IQR) 5.62 (4.63, 6.82) 5.62 (4.63, 6.60) HbA1c, mmol/mol median (IQR) 34 (32, 36) 34 (31, 36) HOMA2-IR median (IQR) 0.96 (0.67, 1.54) 0.95 (0.63, 1.40) Matsuda index median (IQR) 4.38 (2.74, 6.42) 4.62 (3.00, 6.59) C-reactive protein, mg/mL median (IQR) 0.71 (0.24, 2.28) 0.67 (0.24, 1.93) Data presented as number (%) unless otherwise stated. Sample sizes do not always equal to 701 for control group and 736 for intervention group because of missing values. AMH ¼ antim€ullerian hormone, BMI ¼ body mass index (calculated as weight in kilograms divided by height in meters squared), HbA1c¼ glycated hemoglobin, HOMA2-IR¼ updated homeostasis model assessment for insulin resistance, IQR ¼ interquartile range, PCOS ¼ polycystic ovary syndrome, Matsuda index ¼ marker of insulin sensitivity, SD ¼ standard deviation. a Low: 1st-3rd decile, Middle: 4th-7th decile, High: 8th-10th decile b Self-reported menstrual cycle lengths that varied by more than 5 days in past 6 months. c PCOS-like defined as thosewith AMH>3.2 ng/ml and self-reportedmenstrual irregularities (defined as a variation of >5 days in menstrual cycle length in last 6 months and an average cycle length of greater than 35 days). d Responses to the question ‘‘In general, howmuch stress or pressure have you experience in your daily living in the last 4 weeks?’’ based on the 12-Item Short-Form Health Survey (SF- 12v2) and is a good measure of mental health functioning (52). e As evaluated in a 75g oral glucose tolerance test. Chan. Nutritional supplement and conception. Fertil Steril 2023. 1036 ORIGINAL ARTICLE: GYNECOLOGY (Fig. 3A). Compared with control, the intervention shortened time-to-conception among overweight women toward that of nonoverweight/nonobese women (Fig. 3A). This effect was observed in White and non-White women (Fig. 3B). However, women with obesity showed longer time-to-conception with intervention than control (Fig. 3A). This effect was only observed in non-White women (Fig. 3C). Within the obese category, baseline characteristics of the control and interven- tion groups were similar (Supplementary Table 2, available online), so our results cannot be attributed to imbalances in these potential confounders. Furthermore, among non-White women with obesity additional adjustment for household income and psychological stress did not alter the intervention effect (HR: 0.35; P¼ .003). Exploratory analyses suggest that a potential underlying mechanism for shortening time-to-conception could be suppres- sion of inflammation by the intervention during the preconcep- tion period; this is supported by a lower CRP trend seen in the overweight intervention group compared with overweight con- trols (mean difference -0.12 [-0.26, 0.01] SDs accounting for baseline CRP values, site and ethnicity, P¼ .078), but no study arm difference was observed among the obese group (CRP -0.032 [-0.14, 0.07] SDs; P¼ .55; with no effect of White ethnicity within this model, P¼ .914). There were no changes in menstrual cycle regularity or preconception HbA1c with the intervention in women with overweight or obesity. DISCUSSION This randomized controlled trial demonstrated that a com- bined myo-inositol, probiotic, and micronutrient supplement VOL. 119 NO. 6 / JUNE 2023 FIGURE 2 Time-to-conception and clinical pregnancy rates in control and intervention groups. Kaplan-Meier plot with over-time-censoring of withdrawn cases, including initiation of fertility treatment and very early pregnancy losses. Hazard ratios by Cox proportional hazards modeling between the intervention and the control groups yadjusted for the stratification factors of site and ethnicity (5 groups), and ^further adjusted for age, body mass index, and gravidity. CI ¼ confidence interval, HR ¼ hazard ratio, m ¼ number of months after randomization. Chan. Nutritional supplement and conception. Fertil Steril 2023. Fertility and Sterility® in women planning conception did not change the overall time-to-natural-conception or clinical pregnancy rates up to one year, nor the live birth rates. However, the intervention shortened time-to-conception among overweight women. The chance of conceiving in the overweight intervention group was 1.47 times that of the overweight control group by the end of a year, becoming equivalent to that of nonover- weight/nonobese women. This effect may be mediated by an improved regulation of inflammatory factors, represented by a lower CRP level. Among women with obesity the interven- tion lengthened the time-to-conception, an effect confined to non-White women with obesity, where the chance of conceiving in the intervention group was lowered to less than half (2/5th) of that of the control group by one year. The overall longer time-to-conception in older, obese, and metabolically less healthy women provides internal vali- dation that our trial population behaved as expected based on current understanding. This suggests that recruitment and eligibility methods did not result in a population unrepresen- tative of women planning conception. A novel finding is the variation in time-to-conception between ethnic groups, with VOL. 119 NO. 6 / JUNE 2023 a shorter time-to-conception in White than in non-White women and in Chinese than in Malay women among Asian ethnicities. This accords with an observational study in Singapore (41) which showed lower conception rates inMalay than in Chinese women (39% vs. 46%) over one year. Most non-White participants were recruited in Singapore, and in agreement with our findings, the 2017 Global Burden of Dis- ease Study also reported a lower total fertility rate in Singapore than that in the United Kingdom and New Zealand (42). Ethnic differences may be explained by genetic varia- tions, diet, culture, unmeasured lifestyle, and sexual practices, which remain poorly characterized. Nonetheless, these factors could not be addressed by the intervention because there were no clear ethnic differences in the overall response to the NiPPeR supplement, accepting that there were relatively modest numbers of participants in each non-White ethnicity. Not finding an improved conception rate nor changes in cycle regularity with an intervention containing myo-inositol contrasts with smaller trials in women with PCOS or fertility issues (13, 43). Possible reasons for these discrepancies include the generally healthier population in our trial, with 1037 FIGURE 3 A B C Effect of intervention stratified by body mass index (BMI) groupings (defined using ethnic-specific thresholds for overweight and obesity: BMIR23 to<27$5 andR27$5 kg/m2, respectively, for Asians including Chinese, Indians, Pakistani, Bangladeshi, Malay, mixed Asian; BMIR25 to<30 and R30 kg/m2, respectively, for non-Asians including White Caucasian, Polynesian, Black, mixed Asian-non-Asian) (40). Hazard ratios by Cox proportional hazards modeling between the intervention and the control groups in (A) all 3 BMI categories stratified by intervention group yadjusted for the stratification factors of site and White/non-White; (B) overweight women stratified by ethnicity and intervention group; (C) obese women stratified by ethnicity and intervention group. BMI ¼ body mass index. CI ¼ confidence interval, HR ¼ hazard ratio, non-overwt/ obese ¼ nonoverweight/nonobese, NSDNM ¼ not significantly different from the null model, TTC ¼ time-to-conception. Chan. Nutritional supplement and conception. Fertil Steril 2023. 1038 VOL. 119 NO. 6 / JUNE 2023 ORIGINAL ARTICLE: GYNECOLOGY Fertility and Sterility® only 6.4% having PCOS-like features, the more diverse ethnic mix, and the possibility of potential interactions with other intervention components. The PCOS-like subgroup was, how- ever, underpowered for separate assessment of intervention efficacy. Our findings also differ from the multivitamin (without myo-inositol or probiotics) trial conducted in Hungary that reported a marginally shorter time-to- conception with supplementation (31), which could also be because of population differences. Young women at our respective study sites are known to show low to moderate prevalence of micronutrient insufficiencies (44–47). With randomization, baseline micronutrient levels are expected to be similar between groups; however, we cannot discount the possibility that anticipated increases in the intervention group may not have risen to a level capable of improving fertility or reducing miscarriage (48). Promisingly, our trial suggests that a nutritional supple- ment might optimize time-to-conception among overweight women to that of nonoverweight/nonobese women, with similar effects in White and non-White women; caution is nonetheless needed in drawing a definitive conclusion given that this is a secondary trial outcome with more limited statis- tical power in this subgroup. Exploratory analyses suggest a generally less proinflammatory environment, represented by lower plasma CRP approximately a month after starting the intervention, could be a possible underlying mechanism. Lower CRP has previously been associated with better fertility (49), with improved ovulatory function and endometrial receptivity, hence the chances of conception and successful implantation. Similar to our findings, differential metabolic responses to myo-inositol supplements in the morbidly obese compared with the less obese/overweight have previously been reported among women experiencing oligomenorrhoea and PCOS, with benefit reported only in the latter group and not the former (50), suggesting that supplementation effect is influ- enced by the degree of BMI elevation. As expected, our study found higher baseline CRP, HbA1c, and HOMA2-IR with increasing BMI categories (Supplementary Table 3, available online). We speculate that metabolic dysregulation in women with obesity could be too extreme to be amenable to sufficient improvement with a nutritional supplement alone. Further, the lengthened time-to-conception in non-White women with obesity was not accompanied by detrimental changes in menstrual cycle regularity, inflammation, or HbA1c, nor explained by variations in household income or psychological stress. We previously reported that the NiPPeR intervention slightly increased post-prandial glycemia at 28 weeks of gestation in women with a high preconception BMI (33), and higher glycemia could also be occurring preconception. Given that the non-White obese subgroup started off with the highest HbA1c at baseline, partly reflecting increased gly- cemia over the previous 3 months that may impair fertility, just a further slight increase in preconception glycemia could result in discernable deterioration in fertility that may be less obvious in White women with obesity who started with a lower baseline HbA1c. However, our trial design did not allow us to examine this postulation further because HbA1c was the only available marker of glycemia measured after VOL. 119 NO. 6 / JUNE 2023 intervention and preconception, and its assessment a month after intervention would not be long enough to permit discernment of an intervention effect on glycemia. Strengths of our study are its multi-centered, multi- ethnic, double-blind nature that decrease bias and improve generalizability, the relatively large sample size for a community-recruited preconception trial as opposed to a sub- fertile population seeking assisted conception, and the over 70% follow-up rate at 1 year. Randomization resulting in similar baseline characteristics in the 2 study groups would have largely mitigated the effects of any unmeasured con- founding. By design, participants were confined to those planning conception, which would be the main scenario where a woman could choose to commence a nutritional sup- plement, so the lack of application to unplanned pregnancies is not relevant. Recruited women were mostly healthy, highly educated with high socioeconomic status, so the efficacy of a nutritional supplement in more deprived, less healthy women, who may arguably derive more benefit, remains uncertain. However, our study showed no differential effects of the inter- vention in different household income groups. Further limita- tions were predominantly because of fecundability being a secondary outcome, and concerns regarding subject burden, so participants were not clinically assessed for PCOS using in- ternational consensus criteria, other causes of female/male subfertility, nor time trying to conceive before trial participa- tion, but these would likely be balanced between randomized groups. The possibility that antagonistic effects between com- ponents of the intervention have masked the potential bene- ficial effects of some of its ingredients requires further examination. CONCLUSIONS Overall, compared with a standard micronutrient supplement, women taking the combined myo-inositol, probiotics, and micronutrient supplement showed similar time-to-natural- conception and clinical pregnancy rates within a year. Although supplementation may have the potential to shorten time-to-conception in overweight women to that comparable with nonoverweight/nonobese women, it may exacerbate the already suboptimal fertility in women with obesity, particu- larly those of non-White ethnicity. Further investigation is required to confirm these effects and better understand the underlying mechanisms. Acknowledgments: The NiPPeR Study Group authors for the Medline citation comprises: Ben Albert (b.albert@ auckland.ac.nz), Shirong Cai (obgcais@nus.edu.sg), Philip C Calder (P.C.Calder@soton.ac.uk), Ryan Carvalho (Ryan. Carvalho@nestle.com), Julie Ann Guiao Castro (julie_ castro@nuhs.edu.sg), Mary Cavanagh (m.cavanagh@ auckland.ac.nz), Jerry KY Chan (jerry.chan.k.y@singhealth. com.sg), Mei Ling Chang (changmeiling86@gmail.com), Claudia Chi (drclaudiachi@gmail.com), Caroline E Childs (C. E.Childs@soton.ac.uk), Mei Kit Choh (choh_mei_kit@sics- a-star.edu.sg), Mary FF Chong (mary_chong@nus.edu.sg), Anne HY Chu (anne.chu.hy@gmail.com), Cathryn Conlon (C.Conlon@massey.ac.nz), Cyrus Cooper (cc@mrc.soton.ac. uk), Paula Costello (pc@mrc.soton.ac.uk), Vanessa Cox 1039 mailto:b.albert@auckland.ac.nz mailto:b.albert@auckland.ac.nz mailto:obgcais@nus.edu.sg mailto:P.C.Calder@soton.ac.uk mailto:Ryan.Carvalho@nestle.com mailto:Ryan.Carvalho@nestle.com mailto:julie_castro@nuhs.edu.sg mailto:julie_castro@nuhs.edu.sg mailto:m.cavanagh@auckland.ac.nz mailto:m.cavanagh@auckland.ac.nz mailto:jerry.chan.k.y@singhealth.com.sg mailto:jerry.chan.k.y@singhealth.com.sg mailto:changmeiling86@gmail.com mailto:drclaudiachi@gmail.com mailto:C.E.Childs@soton.ac.uk mailto:C.E.Childs@soton.ac.uk mailto:choh_mei_kit@sics-a-star.edu.sg mailto:choh_mei_kit@sics-a-star.edu.sg mailto:mary_chong@nus.edu.sg mailto:anne.chu.hy@gmail.com mailto:C.Conlon@massey.ac.nz mailto:cc@mrc.soton.ac.uk mailto:cc@mrc.soton.ac.uk mailto:pc@mrc.soton.ac.uk ORIGINAL ARTICLE: GYNECOLOGY (vac@mrc.soton.ac.uk), Sevasti Galani (s.galani@soton.ac.uk), Judith Hammond (j.hammond@auckland.ac.nz), Nicholas C Harvey (nch@mrc.soton.ac.uk), Richard Holt (R.I.G.Holt@ soton.ac.uk), Hazel M Inskip (hmi@mrc.soton.ac.uk), Mrunalini Jagtap (mrunalini_jagtap@sics.a-star.edu.sg), Gene Jeon (Gene. Jeon@middlemore.co.nz), Neerja Karnani (neerja_karnani@ sics.a-star.edu.sg), Chiara Nembrini (Chiara.Nembrini@rdls. nestle.com), Karen A. Lillycrop (K.A.Lillycrop@soton.ac.uk), Falk M€uller-Riemenschneider (falk.m-r@nus.edu.sg), Padmap- riya Natarajan (obgnp@nus.edu.sg), Sharon Ng (sharon.ng. kmu@gmail.com), Adaikalavan Ramasamy (adai@gis.a-star. edu.sg), Elizabeth Tham (elizabeth_tham@nuhs.edu.sg), Mya Thway Tint (Mya_Thway_Tint@sics.a-star.edu.sg), Justin M O’Sullivan (justin.osullivan@auckland.ac.nz), Gernalia Satiane- gara (gernalia_satianegara@sics.a-star.edu.sg), Lynette PC Shek (lynette_shek@nuhs.edu.sg), Irma Silva-Zolezzi (Irma. SilvaZolezzi@nestle.com), Wendy Sim (sin_nie_sim@nuhs. edu.sg), Shu E Soh (shu_e_soh@nuhs.edu.sg), Vicky Tay (Vicky_tay@sics.a-star.edu.sg), Rachel Taylor (Rachel.taylor@ otago.ac.nz), Salika Theodosia (t.salika@ucl.ac.uk), Clare Wall (c.wall@auckland.ac.nz), Gladys Woon (gladys_woon@nuhs. edu.sg), Mark Vickers (m.vickers@auckland.ac.nz), Wei Ying (weiying11099@hotmail.com). We thank the participants and their families for their enthu- siastic involvement in the study; the study research staff and hospital clinical staff at participating centers, and operational support staff for their contributions to the trial; and themembers of the Independent Data Monitoring and Safety Committee for their invaluable contributions and for overseeing the conduct of the trial. REFERENCES 1. Baird DT, Collins J, Egozcue J, Evers LH, Gianaroli L, Leridon H, et al. Fertility and ageing. Hum Reprod Update 2005;11:261–76. 2. Bentov Y. "AWestern diet side story": the effects of transitioning to aWest- ern-type diet on fertility. Endocrinology 2014;155:2341–2. 3. Chavarro JE, Rich-Edwards JW, Rosner BA, Willett WC. Diet and lifestyle in the prevention of ovulatory disorder infertility. Obstet Gynecol 2007;110: 1050–8. 4. Fontana R, Della Torre S. The deep correlation between energy metabolism and reproduction: a view on the effects of nutrition for women fertility. Nu- trients 2016;8:87. 5. Loy SL, Ku CW, Lai AEQ, Choo XH, Ho AHM, Cheung YB, et al. Plasma gly- cemic measures and fecundability in a Singapore preconception cohort study. Fertil Steril 2021;115:138–47. 6. Cetin I, Berti C, Calabrese S. Role of micronutrients in the periconceptional period. Hum Reprod Update 2010;16:80–95. 7. Gaskins AJ, Chavarro JE. Diet and fertility: a review. Am J Obstet Gynecol 2018;218:379–89. 8. Schaefer E, Nock D. The impact of preconceptional multiple-micronutrient supplementation on female fertility. Clin Med Insights Womens Health 2019;12:1179562X19843868. 9. Holub BJ. Metabolism and function of myo-inositol and inositol phospho- lipids. Annu Rev Nutr 1986;6:563–97. 10. Bizzarri M, Fuso A, Dinicola S, Cucina A, Bevilacqua A. Pharmacodynamics and pharmacokinetics of inositol(s) in health and disease. Expert Opin Drug Metab Toxicol 2016;12:1181–96. 11. Milewska EM, Czyzyk A, Meczekalski B, Genazzani AD. Inositol and human reproduction. From cellular metabolism to clinical use. Gynecol Endocrinol 2016;32:690–5. 1040 12. Watkins OC, Yong HEJ, Sharma N, Chan SY. A review of the role of inositols in conditions of insulin dysregulation and in uncomplicated and pathological pregnancy. Crit Rev Food Sci Nutr 2020:1–49. 13. Pundir J, Psaroudakis D, Savnur P, Bhide P, Sabatini L, Teede H, et al. Inositol treatment of anovulation in womenwith polycystic ovary syndrome: a meta- analysis of randomised trials. BJOG 2018;125:299–308. 14. Carlomagno G, Unfer V. Inositol safety: clinical evidences. Eur RevMed Phar- macol Sci 2011;15:931–6. 15. Awlaqi AA, Alkhayat K, Hammadeh ME. Metabolic syndrome and infertility in women. IJWHR 2016;4:89–95. 16. Pludowski P, Holick MF, Pilz S, Wagner CL, Hollis BW, Grant WB, et al. Vitamin D effects on musculoskeletal health, immunity, autoimmunity, car- diovascular disease, cancer, fertility, pregnancy, dementia and mortality-a review of recent evidence. Autoimmun Rev 2013;12:976–89. 17. Tamblyn JA, Pilarski NSP, Markland AD, Marson EJ, Devall A, Hewison M, et al. Vitamin D and miscarriage: a systematic review and meta-analysis. Fer- til Steril 2022;118:111–22. 18. Refsum H. Folate, vitamin B12 and homocysteine in relation to birth defects and pregnancy outcome. Br J Nutr 2001;85(Suppl 2):S109–13. 19. Thaver D, Saeed MA, Bhutta ZA. Pyridoxine (vitamin B6) supplementation in pregnancy. Cochrane Database Syst Rev 2006:CD000179. 20. Ebisch IM, Thomas CM, Peters WH, Braat DD, Steegers-Theunissen RP. The importance of folate, zinc and antioxidants in the pathogenesis and preven- tion of subfertility. Hum Reprod Update 2007;13:163–74. 21. Corbett GA, Crosby DA, McAuliffe FM. Probiotic therapy in couples with infertility: a systematic review. Eur J Obstet Gynecol Reprod Biol 2021; 256:95–100. 22. Lopez-Moreno A, Aguilera M. Probiotics dietary supplementation for modu- lating endocrine and fertility microbiota dysbiosis. Nutrients 2020;12. 23. Guo Y, Qi Y, Yang X, Zhao L, Wen S, Liu Y, et al. Association between poly- cystic ovary syndrome and gut microbiota. PLoS One 2016;11:e0153196. 24. Moreno-Indias I, Sanchez-Alcoholado L, Sanchez-Garrido MA, Martin- Nunez GM, Perez-Jimenez F, Tena-Sempere M, et al. Neonatal androgen exposure causes persistent gut microbiota dysbiosis related to metabolic dis- ease in adult female rats. Endocrinology 2016;157:4888–98. 25. Tremellen K, Pearce K. Dysbiosis of Gut microbiota (DOGMA)–a novel theory for the development of polycystic ovarian syndrome. Med Hypotheses 2012; 79:104–12. 26. Lindheim L, Bashir M, Munzker J, Trummer C, Zachhuber V, Leber B, et al. Alterations in Gut microbiome composition and barrier function are associ- ated with reproductive and metabolic defects in women with polycystic ovary syndrome (PCOS): a pilot study. PLoS One 2017;12:e0168390. 27. Torres PJ, Siakowska M, Banaszewska B, Pawelczyk L, Duleba AJ, Kelley ST, et al. Gut microbial diversity in women with polycystic ovary syndrome corre- lates with hyperandrogenism. J Clin Endocrinol Metab 2018;103:1502–11. 28. Cozzolino M, Vitagliano A, Pellegrini L, Chiurazzi M, Andriasani A, Ambrosini G, et al. Therapy with probiotics and synbiotics for polycystic ovarian syndrome: a systematic review and meta-analysis. Eur J Nutr 2020;59:2841–56. 29. Reid G, Beuerman D, Heinemann C, Bruce AW. Probiotic Lactobacillus dose required to restore andmaintain a normal vaginal flora. FEMS Immunol Med Microbiol 2001;32:37–41. 30. Younis N, Mahasneh A. Probiotics and the envisaged role in treating human infertility. Middle East Fertil Soc J 2020;25:33. 31. Czeizel AE, M�etneki J, Dud�as I. The effect of preconceptional multivitamin supplementation on fertility. Int J Vitam Nutr Res 1996;66:55–8. 32. Godfrey KM, Cutfield W, Chan SY, Baker PN, Chong YS, NiPPeR Study Group. Nutritional Intervention Preconception and During Pregnancy to Maintain Healthy Glucose Metabolism and Offspring Health ("NiPPeR"): study protocol for a randomised controlled trial. Trials 2017;18:131. 33. Godfrey KM, Barton SJ, El-Heis S, Kenealy T, Nield H, Baker PN, et al. Myo- Inositol, probiotics, and micronutrient supplementation from preconception for glycemia in pregnancy: NiPPeR international multicenter double-blind randomized controlled trial. Diabetes Care 2021;44:1091–9. 34. Levy JC, Matthews DR, Hermans MP. Correct homeostasis model assess- ment (HOMA) evaluation uses the computer program. Diabetes Care 1998;21:2191–2. VOL. 119 NO. 6 / JUNE 2023 mailto:vac@mrc.soton.ac.uk mailto:s.galani@soton.ac.uk mailto:j.hammond@auckland.ac.nz mailto:nch@mrc.soton.ac.uk mailto:R.I.G.Holt@soton.ac.uk mailto:R.I.G.Holt@soton.ac.uk mailto:hmi@mrc.soton.ac.uk mailto:mrunalini_jagtap@sics.a-star.edu.sg mailto:Gene.Jeon@middlemore.co.nz mailto:Gene.Jeon@middlemore.co.nz mailto:neerja_karnani@sics.a-star.edu.sg mailto:neerja_karnani@sics.a-star.edu.sg mailto:Chiara.Nembrini@rdls.nestle.com mailto:Chiara.Nembrini@rdls.nestle.com mailto:K.A.Lillycrop@soton.ac.uk mailto:falk.m-r@nus.edu.sg mailto:obgnp@nus.edu.sg mailto:sharon.ng.kmu@gmail.com mailto:sharon.ng.kmu@gmail.com mailto:adai@gis.a-star.edu.sg mailto:adai@gis.a-star.edu.sg mailto:elizabeth_tham@nuhs.edu.sg mailto:Mya_Thway_Tint@sics.a-star.edu.sg mailto:justin.osullivan@auckland.ac.nz mailto:gernalia_satianegara@sics.a-star.edu.sg mailto:lynette_shek@nuhs.edu.sg https://Irma.SilvaZolezzi@nestle.com https://Irma.SilvaZolezzi@nestle.com 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http://refhub.elsevier.com/S0015-0282(23)00128-0/sref12 http://refhub.elsevier.com/S0015-0282(23)00128-0/sref12 http://refhub.elsevier.com/S0015-0282(23)00128-0/sref13 http://refhub.elsevier.com/S0015-0282(23)00128-0/sref13 http://refhub.elsevier.com/S0015-0282(23)00128-0/sref13 http://refhub.elsevier.com/S0015-0282(23)00128-0/sref14 http://refhub.elsevier.com/S0015-0282(23)00128-0/sref14 http://refhub.elsevier.com/S0015-0282(23)00128-0/sref15 http://refhub.elsevier.com/S0015-0282(23)00128-0/sref15 http://refhub.elsevier.com/S0015-0282(23)00128-0/sref16 http://refhub.elsevier.com/S0015-0282(23)00128-0/sref16 http://refhub.elsevier.com/S0015-0282(23)00128-0/sref16 http://refhub.elsevier.com/S0015-0282(23)00128-0/sref16 http://refhub.elsevier.com/S0015-0282(23)00128-0/sref17 http://refhub.elsevier.com/S0015-0282(23)00128-0/sref17 http://refhub.elsevier.com/S0015-0282(23)00128-0/sref17 http://refhub.elsevier.com/S0015-0282(23)00128-0/sref18 http://refhub.elsevier.com/S0015-0282(23)00128-0/sref18 http://refhub.elsevier.com/S0015-0282(23)00128-0/sref19 http://refhub.elsevier.com/S0015-0282(23)00128-0/sref19 http://refhub.elsevier.com/S0015-0282(23)00128-0/sref20 http://refhub.elsevier.com/S0015-0282(23)00128-0/sref20 http://refhub.elsevier.com/S0015-0282(23)00128-0/sref20 http://refhub.elsevier.com/S0015-0282(23)00128-0/sref21 http://refhub.elsevier.com/S0015-0282(23)00128-0/sref21 http://refhub.elsevier.com/S0015-0282(23)00128-0/sref21 http://refhub.elsevier.com/S0015-0282(23)00128-0/sref22 http://refhub.elsevier.com/S0015-0282(23)00128-0/sref22 http://refhub.elsevier.com/S0015-0282(23)00128-0/sref23 http://refhub.elsevier.com/S0015-0282(23)00128-0/sref23 http://refhub.elsevier.com/S0015-0282(23)00128-0/sref24 http://refhub.elsevier.com/S0015-0282(23)00128-0/sref24 http://refhub.elsevier.com/S0015-0282(23)00128-0/sref24 http://refhub.elsevier.com/S0015-0282(23)00128-0/sref24 http://refhub.elsevier.com/S0015-0282(23)00128-0/sref25 http://refhub.elsevier.com/S0015-0282(23)00128-0/sref25 http://refhub.elsevier.com/S0015-0282(23)00128-0/sref25 http://refhub.elsevier.com/S0015-0282(23)00128-0/sref26 http://refhub.elsevier.com/S0015-0282(23)00128-0/sref26 http://refhub.elsevier.com/S0015-0282(23)00128-0/sref26 http://refhub.elsevier.com/S0015-0282(23)00128-0/sref26 http://refhub.elsevier.com/S0015-0282(23)00128-0/sref27 http://refhub.elsevier.com/S0015-0282(23)00128-0/sref27 http://refhub.elsevier.com/S0015-0282(23)00128-0/sref27 http://refhub.elsevier.com/S0015-0282(23)00128-0/sref28 http://refhub.elsevier.com/S0015-0282(23)00128-0/sref28 http://refhub.elsevier.com/S0015-0282(23)00128-0/sref28 http://refhub.elsevier.com/S0015-0282(23)00128-0/sref28 http://refhub.elsevier.com/S0015-0282(23)00128-0/sref29 http://refhub.elsevier.com/S0015-0282(23)00128-0/sref29 http://refhub.elsevier.com/S0015-0282(23)00128-0/sref29 http://refhub.elsevier.com/S0015-0282(23)00128-0/sref30 http://refhub.elsevier.com/S0015-0282(23)00128-0/sref30 http://refhub.elsevier.com/S0015-0282(23)00128-0/sref31 http://refhub.elsevier.com/S0015-0282(23)00128-0/sref31 http://refhub.elsevier.com/S0015-0282(23)00128-0/sref31 http://refhub.elsevier.com/S0015-0282(23)00128-0/sref31 http://refhub.elsevier.com/S0015-0282(23)00128-0/sref32 http://refhub.elsevier.com/S0015-0282(23)00128-0/sref32 http://refhub.elsevier.com/S0015-0282(23)00128-0/sref32 http://refhub.elsevier.com/S0015-0282(23)00128-0/sref32 http://refhub.elsevier.com/S0015-0282(23)00128-0/sref33 http://refhub.elsevier.com/S0015-0282(23)00128-0/sref33 http://refhub.elsevier.com/S0015-0282(23)00128-0/sref33 http://refhub.elsevier.com/S0015-0282(23)00128-0/sref33 http://refhub.elsevier.com/S0015-0282(23)00128-0/sref34 http://refhub.elsevier.com/S0015-0282(23)00128-0/sref34 http://refhub.elsevier.com/S0015-0282(23)00128-0/sref34 Fertility and Sterility® 35. DeFronzo RA, Matsuda M. Reduced time points to calculate the composite index. Diabetes Care 2010;33:e93. 36. Dietz de Loos A, Hund M, Buck K, Meun C, Sillman J, Laven JSE. Antimuller- ian hormone to determine polycystic ovarian morphology. Fertil Steril 2021; 116:1149–57. 37. Sahmay S, Aydin Y, Oncul M, Senturk LM. Diagnosis of polycystic ovary syn- drome: AMH in combination with clinical symptoms. J Assist Reprod Genet 2014;31:213–20. 38. Pike KC, Crozier SR, Lucas JS, Inskip HM, Robinson S. SouthamptonWomen's Survey Study G, et al. Patterns of fetal and infant growth are related to atopy and wheezing disorders at age 3 years. Thorax 2010;65:1099–106. 39. Hadlock FP, Shah YP, Kanon DJ, Lindsey JV. Fetal crown-rump length: reeval- uation of relation to menstrual age (5-18 weeks) with high-resolution real- time US. Radiology 1992;182:501–5. 40. WHO Expert Consultation. Appropriate body-mass index for Asian popula- tions and its implications for policy and intervention strategies. Lancet (Lon- don, England) 2004;363:157–63. 41. Loo EXL, Soh SE, Loy SL, Ng S, Tint MT, Chan SY, et al. Cohort profile: Singapore Preconception Study of Long-TermMaternal and Child Outcomes (S-PRESTO). Eur J Epidemiol 2021;36:129–42. 42. Population GBD, Fertility C. Population and fertility by age and sex for 195 countries and territories, 1950-2017: a systematic analysis for the Global Burden of Disease Study 2017. Lancet 2018;392:1995–2051. 43. Unfer V, Facchinetti F, Orr�u B, Giordani B, Nestler J. Myo-inositol effects in women with PCOS: a meta-analysis of randomized controlled trials. Endocr Connect 2017;6:647–58. 44. Cooper C, Harvey NC, Bishop NJ, Kennedy S, Papageorghiou AT, Schoenmakers I, et al. Maternal gestational vitamin D supplementation and offspring bone health (MAVIDOS): a multicentre, double-blind, rando- VOL. 119 NO. 6 / JUNE 2023 mised placebo-controlled trial. Lancet Diabetes Endocrinol 2016;4:393– 402. 45. Lai JS, Mohamad Ayob MN, Cai S, Quah PL, Gluckman PD, Shek LP, et al. Maternal plasma vitamin B12 concentrations during pregnancy and infant cognitive outcomes at 2 years of age. Br J Nutr 2019;121:1303–12. 46. Ong YL, Quah PL, Tint MT, Aris IM, Chen LW, van Dam RM, et al. The asso- ciation of maternal vitamin D status with infant birth outcomes, postnatal growth and adiposity in the first 2 years of life in a multi-ethnic Asian pop- ulation: the Growing Up in Singapore Towards healthy Outcomes (GUSTO) cohort study. Br J Nutr 2016;116:621–31. 47. El-Heis S, Crozier SR, Robinson SM, Harvey NC, Cooper C, Inskip HM, et al. Higher maternal serum concentrations of nicotinamide and related metab- olites in late pregnancy are associated with a lower risk of offspring atopic eczema at age 12 months. Clin Exp Allergy 2016;46:1337–43. 48. Cavoretto PI, Vigan�o P. Time to implement vitamin D assessment and sup- plementation into routine obstetric practice? Fertil Steril 2022;118:123–4. 49. Radin RG, Sjaarda LA, Silver RM, Nobles CJ, Mumford SL, Perkins NJ, et al. C- Reactive protein in relation to fecundability and anovulation among eume- norrheic women. Fertil Steril 2018;109:232–9, e1. 50. Gerli S, Papaleo E, Ferrari A, Di Renzo GC. Randomized, double blind pla- cebo-controlled trial: effects of myo-inositol on ovarian function and meta- bolic factors in women with PCOS. Eur Rev Med Pharmacol Sci 2007;11: 347–54. 51. American Diabetes A. 2. Classification and diagnosis of diabetes: standards of medical care in diabetes-2019. Diabetes Care 2019;42:S13–28. 52. Gill SC, Butterworth P, Rodgers B, Mackinnon A. Validity of the mental health component scale of the 12-item Short-Form Health Survey (MCS- 12) as measure of common mental disorders in the general population. Psy- chiatry Res 2007;152:63–71. 1041 http://refhub.elsevier.com/S0015-0282(23)00128-0/sref35 http://refhub.elsevier.com/S0015-0282(23)00128-0/sref35 http://refhub.elsevier.com/S0015-0282(23)00128-0/sref36 http://refhub.elsevier.com/S0015-0282(23)00128-0/sref36 http://refhub.elsevier.com/S0015-0282(23)00128-0/sref36 http://refhub.elsevier.com/S0015-0282(23)00128-0/sref37 http://refhub.elsevier.com/S0015-0282(23)00128-0/sref37 http://refhub.elsevier.com/S0015-0282(23)00128-0/sref37 http://refhub.elsevier.com/S0015-0282(23)00128-0/sref38 http://refhub.elsevier.com/S0015-0282(23)00128-0/sref38 http://refhub.elsevier.com/S0015-0282(23)00128-0/sref38 http://refhub.elsevier.com/S0015-0282(23)00128-0/sref39 http://refhub.elsevier.com/S0015-0282(23)00128-0/sref39 http://refhub.elsevier.com/S0015-0282(23)00128-0/sref39 http://refhub.elsevier.com/S0015-0282(23)00128-0/sref51 http://refhub.elsevier.com/S0015-0282(23)00128-0/sref51 http://refhub.elsevier.com/S0015-0282(23)00128-0/sref51 http://refhub.elsevier.com/S0015-0282(23)00128-0/sref41 http://refhub.elsevier.com/S0015-0282(23)00128-0/sref41 http://refhub.elsevier.com/S0015-0282(23)00128-0/sref41 http://refhub.elsevier.com/S0015-0282(23)00128-0/sref42 http://refhub.elsevier.com/S0015-0282(23)00128-0/sref42 http://refhub.elsevier.com/S0015-0282(23)00128-0/sref42 http://refhub.elsevier.com/S0015-0282(23)00128-0/sref43 http://refhub.elsevier.com/S0015-0282(23)00128-0/sref43 http://refhub.elsevier.com/S0015-0282(23)00128-0/sref43 http://refhub.elsevier.com/S0015-0282(23)00128-0/sref43 http://refhub.elsevier.com/S0015-0282(23)00128-0/sref40 http://refhub.elsevier.com/S0015-0282(23)00128-0/sref40 http://refhub.elsevier.com/S0015-0282(23)00128-0/sref40 http://refhub.elsevier.com/S0015-0282(23)00128-0/sref40 http://refhub.elsevier.com/S0015-0282(23)00128-0/sref40 http://refhub.elsevier.com/S0015-0282(23)00128-0/sref44 http://refhub.elsevier.com/S0015-0282(23)00128-0/sref44 http://refhub.elsevier.com/S0015-0282(23)00128-0/sref44 http://refhub.elsevier.com/S0015-0282(23)00128-0/sref45 http://refhub.elsevier.com/S0015-0282(23)00128-0/sref45 http://refhub.elsevier.com/S0015-0282(23)00128-0/sref45 http://refhub.elsevier.com/S0015-0282(23)00128-0/sref45 http://refhub.elsevier.com/S0015-0282(23)00128-0/sref45 http://refhub.elsevier.com/S0015-0282(23)00128-0/sref46 http://refhub.elsevier.com/S0015-0282(23)00128-0/sref46 http://refhub.elsevier.com/S0015-0282(23)00128-0/sref46 http://refhub.elsevier.com/S0015-0282(23)00128-0/sref46 http://refhub.elsevier.com/S0015-0282(23)00128-0/sref47 http://refhub.elsevier.com/S0015-0282(23)00128-0/sref47 http://refhub.elsevier.com/S0015-0282(23)00128-0/sref47 http://refhub.elsevier.com/S0015-0282(23)00128-0/sref48 http://refhub.elsevier.com/S0015-0282(23)00128-0/sref48 http://refhub.elsevier.com/S0015-0282(23)00128-0/sref48 http://refhub.elsevier.com/S0015-0282(23)00128-0/sref49 http://refhub.elsevier.com/S0015-0282(23)00128-0/sref49 http://refhub.elsevier.com/S0015-0282(23)00128-0/sref49 http://refhub.elsevier.com/S0015-0282(23)00128-0/sref49 http://refhub.elsevier.com/S0015-0282(23)00128-0/sref50 http://refhub.elsevier.com/S0015-0282(23)00128-0/sref50 http://refhub.elsevier.com/S0015-0282(23)00128-0/sref52 http://refhub.elsevier.com/S0015-0282(23)00128-0/sref52 http://refhub.elsevier.com/S0015-0282(23)00128-0/sref52 http://refhub.elsevier.com/S0015-0282(23)00128-0/sref52 ORIGINAL ARTICLE: GYNECOLOGY Tiempo de concepci�on y tasa de gestaci�on clínica con un suplemento de mioinositol, probi�oticos, y micronutrientes: resultados secun- darios del ensayo aleatorizado NiPPeR Objetivo: Determinar si un suplemento nutricional combinado de mioinositol, probi�oticos y micronutrientes impacta en el tiempo de concepci�on natural y tasas de gestaci�on clínica. Dise~no: Resultados secundarios de un ensayo controlado aleatorizado doble ciego. Lugar: Reclutamiento comunitario. Pacientes: Mujeres de 18 a 38 a~nos planificando concebir en el Reino Unido, Singapur, y Nueva Zelanda, excluyendo aquellas con diabetes mellitus o que recibieron tratamiento de fertilidad. Intervenci�on: Un suplemento est�andar (control) (�acido f�olico, hierro, calcio, yodo, b-caroteno), comparado con una intervenci�on adi- cional conteniendo mioinositol, probi�oticos, y otros micronutrientes (vitaminas B2, B6, B12, D, zinc). Medidas de resultado principal: N�umero de días entre la aleatorizaci�on y la fecha estimada de concepci�on natural de un embarazo clínico, así como tambi�en tasas de embarazo acumulativas a 3, 6, y 12 meses. Resultados: De 1729 mujeres aleatorizadas, 1437 (83%; intervenci�on, n¼736; control, n¼701) proporcionaron datos. Las curvas de concepci�on de Kaplan-Meier fueron similares entre los grupos de intervenci�on y control; el tiempo en el que el 20% logr�o la concepci�on natural fue 90.5 días (intervalo de confianza 95%: 80.7, 103.5) en el grupo de intervenci�on comparado con 92.0 días (76.0, 105.1) en el grupo control. Las razones de riesgo proporcional de Cox (HRs) comparando intervenci�on contra control para el logro acumulativo de embarazo (ajustado por lugar, etnia, edad, índice de masa corporal, y paridad) fueron similares a los 3, 6, y 12meses. Entre ambos grupos de estudio combinados, el tiempo total para la concepci�on se alarg�o conmayor índice de masa corporal preconcepcional, y fue mayor en mujeres no blancas que en blancas. Entre las mujeres con sobrepeso la intervenci�on acort�o el tiempo a la concepci�on comparado con los controles independientemente de la etnia (12 meses HR ¼ 1.47 [1.07, 2.02], P¼.016; 20% concebido en 84.5 vs 117.0 días) y lo mejor�o comparable a mujeres sin sobrepeso/ no obesas (20% concebido en 82.1 días). En cambio, entre mujeres con obesidad, el tiempo a la concepci�on se alarg�o con intervenci�on en comparaci�on con controles (12 meses HR ¼ 0.69 [0.47, 1.00], P¼.053; 20% concebido en 132.7 vs 108.5 días); un efecto predominantemente observado en mujeres no blancas con obesidad. Conclusiones: El tiempo de concepci�on natural y las tasas de gestaci�on clínica dentro del a~no fueron en general similares en mujeres recibiendo el suplemento de intervenci�on comparado con el control. Las mujeres con sobrepeso tuvieron un tiempo m�as largo para la concepci�on pero hubo cierta tendencia a que el suplemento pudiese acortar el tiempo de concepci�on, comparable al de las mujeres sin sobrepeso/ no obesas. Se requieren m�as estudios para confirmar esto. 1042 VOL. 119 NO. 6 / JUNE 2023 Time-to-conception and clinical pregnancy rate with a myo-inositol, probiotics, and micronutrient supplement: secondary out ... Materials and Methods Study Design and Participants Formulations and Procedures Outcomes Statistical Analysis Results Discussion Conclusions Acknowledgments References